From 8a62b6fe3932c4a8d31cd30f2016da39cf0c76e5 Mon Sep 17 00:00:00 2001 From: baiyfbupt Date: Thu, 9 Jan 2020 14:39:58 +0800 Subject: [PATCH] Deployed ab60054 with MkDocs version: 1.0.4 --- 404.html | 2 +- algo/algo/index.html | 2 +- api/analysis_api/index.html | 16 +- api/api_guide/index.html | 4 +- api/nas_api/index.html | 315 ++++-------------- api/prune_api/index.html | 14 +- api/quantization_api/index.html | 8 +- api/single_distiller_api/index.html | 2 +- index.html | 4 +- model_zoo/index.html | 2 +- search.html | 2 +- search/search_index.json | 2 +- {api/search_space => search_space}/index.html | 186 +++++------ sitemap.xml | 4 +- sitemap.xml.gz | Bin 204 -> 208 bytes table_latency/index.html | 6 +- tutorials/demo_guide/index.html | 2 +- tutorials/distillation_demo/index.html | 2 +- tutorials/nas_demo/index.html | 4 +- tutorials/pruning_demo/index.html | 2 +- tutorials/quant_aware_demo/index.html | 4 +- tutorials/quant_embedding_demo/index.html | 10 +- tutorials/quant_post_demo/index.html | 8 +- tutorials/sensitivity_demo/index.html | 2 +- 24 files changed, 207 insertions(+), 396 deletions(-) rename {api/search_space => search_space}/index.html (78%) diff --git a/404.html b/404.html index cfc7a564..72a38f6e 100644 --- a/404.html +++ b/404.html @@ -104,7 +104,7 @@
  • - 搜索空间 + 搜索空间
  • diff --git a/algo/algo/index.html b/algo/algo/index.html index 253c5976..c713696d 100644 --- a/algo/algo/index.html +++ b/algo/algo/index.html @@ -111,7 +111,7 @@
  • - 搜索空间 + 搜索空间
  • diff --git a/api/analysis_api/index.html b/api/analysis_api/index.html index e091b995..c6061be5 100644 --- a/api/analysis_api/index.html +++ b/api/analysis_api/index.html @@ -123,7 +123,7 @@
  • - 搜索空间 + 搜索空间
  • @@ -178,7 +178,7 @@

    FLOPs#

    -
    paddleslim.analysis.flops(program, detail=False) 源代码
    +
    paddleslim.analysis.flops(program, detail=False) [源代码]

    获得指定网络的浮点运算次数(FLOPs)。

    @@ -314,8 +314,12 @@

    model_size#

    -

    paddleslim.analysis.model_size(program) 源代码

    +
    +
    paddleslim.analysis.model_size(program) [源代码]
    +

    获得指定网络的参数数量。

    +
    +

    参数：

    • program(paddle.fluid.Program) - 待分析的目标网络。更多关于Program的介绍请参考：Program概念介绍。
      • @@ -419,7 +423,7 @@

      TableLatencyEvaluator#

      -
      paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") 源代码
      +
      paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") [源代码]

      基于硬件延时表的模型延时评估器。

      @@ -427,7 +431,7 @@

      参数：

      • -

        table_file(str) - 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：PaddleSlim硬件延时评估表格式

        +

        table_file(str) - 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：PaddleSlim硬件延时评估表格式

      • delimiter(str) - 硬件延时评估表中，操作信息之前所使用的分割符，默认为英文字符逗号。

        @@ -438,7 +442,7 @@
      • Evaluator - 硬件延时评估器的实例。
      -
      paddleslim.analysis.TableLatencyEvaluator.latency(graph) 源代码
      +
      paddleslim.analysis.TableLatencyEvaluator.latency(graph) [源代码]

      获得指定网络的预估延时。

      diff --git a/api/api_guide/index.html b/api/api_guide/index.html index d9e1a088..5361af30 100644 --- a/api/api_guide/index.html +++ b/api/api_guide/index.html @@ -111,7 +111,7 @@
    • - 搜索空间 + 搜索空间
    • @@ -187,7 +187,7 @@

      小模型结构搜索#

      diff --git a/api/nas_api/index.html b/api/nas_api/index.html index 3d391585..a61ebf31 100644 --- a/api/nas_api/index.html +++ b/api/nas_api/index.html @@ -110,22 +110,17 @@ SA搜索
    • - 搜索空间 + 搜索空间
    • @@ -178,24 +173,35 @@
      -

      paddleslim.nas API文档#

      -

      SANAS API文档#

      -

      class SANAS#

      -

      SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。

      -
      -
      -

      paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server)

      -
      -

      参数： -- configs(list): 搜索空间配置列表，格式是[(key, {input_size, output_size, block_num, block_mask})]或者[(key)]（MobileNetV2、MobilenetV1和ResNet的搜索空间使用和原本网络结构相同的搜索空间，所以仅需指定key即可）, input_size 和output_size表示输入和输出的特征图的大小，block_num是指搜索网络中的block数量，block_mask是一组由0和1组成的列表，0代表不进行下采样的block，1代表下采样的block。 更多paddleslim提供的搜索空间配置可以参考。 -- server_addr(tuple): SANAS的地址，包括server的ip地址和端口号，如果ip地址为None或者为""的话则默认使用本机ip。默认：（"", 8881）。 -- init_temperature(float): 基于模拟退火进行搜索的初始温度。默认：100。 -- reduce_rate(float): 基于模拟退火进行搜索的衰减率。默认：0.85。 -- search_steps(int): 搜索过程迭代的次数。默认：300。 -- save_checkpoint(str|None): 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：./nas_checkpoint。 -- load_checkpoint(str|None): 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。 -- is_server(bool): 当前实例是否要启动一个server。默认：True。

      -

      返回： +

      搜索空间参数的配置#

      +

      通过参数配置搜索空间。更多搜索空间的使用可以参考search_space

      +

      参数：

      +
        +
      • input_size(int|None)：- input_size表示输入feature map的大小。
        • +
      • output_size(int|None)：- output_size表示输出feature map的大小。
        • +
      • block_num(int|None)：- block_num表示搜索空间中block的数量。
        • +
      • block_mask(list|None)：- block_mask是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了block_mask，则主要以block_mask为主要配置，input_size，output_size和block_num三种配置是无效的。
        • +
      +

      Note:
      +1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。
      +2. input_size和output_size用来计算整个模型结构中reduction block数量。

      +

      SANAS#

      +
      +
      paddleslim.nas.SANAS(configs, server_addr=("", 8881), init_temperature=100, reduce_rate=0.85, search_steps=300, save_checkpoint='./nas_checkpoint', load_checkpoint=None, is_server=True)源代码
      +
      SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。
      +
      +

      参数：

      +
        +
      • configs(list) - 搜索空间配置列表，格式是[(key, {input_size, output_size, block_num, block_mask})]或者[(key)]（MobileNetV2、MobilenetV1和ResNet的搜索空间使用和原本网络结构相同的搜索空间，所以仅需指定key即可）, input_size 和output_size表示输入和输出的特征图的大小，block_num是指搜索网络中的block数量，block_mask是一组由0和1组成的列表，0代表不进行下采样的block，1代表下采样的block。 更多paddleslim提供的搜索空间配置可以参考。
        • +
      • server_addr(tuple) - SANAS的地址，包括server的ip地址和端口号，如果ip地址为None或者为""的话则默认使用本机ip。默认：（"", 8881）。
        • +
      • init_temperature(float) - 基于模拟退火进行搜索的初始温度。默认：100。
        • +
      • reduce_rate(float) - 基于模拟退火进行搜索的衰减率。默认：0.85。
        • +
      • search_steps(int) - 搜索过程迭代的次数。默认：300。
        • +
      • save_checkpoint(str|None) - 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：./nas_checkpoint。
        • +
      • load_checkpoint(str|None) - 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。
        • +
      • is_server(bool) - 当前实例是否要启动一个server。默认：True。
        • +
      +

      返回： 一个SANAS类的实例

      示例代码： 
      1
@@ -205,15 +211,18 @@
 sanas = SANAS(config=config)

      -
      -
      -

      tokens2arch(tokens) -通过一组token得到实际的模型结构，一般用来把搜索到最优的token转换为模型结构用来做最后的训练。

      -
      -

      参数： -- tokens(list): 一组token。

      -

      返回 -返回一个模型结构实例。

      +
      +
      paddlesim.nas.SANAS.tokens2arch(tokens)
      +
      通过一组token得到实际的模型结构，一般用来把搜索到最优的token转换为模型结构用来做最后的训练。
      +
      +

      Note:
      +tokens是一个列表，token映射到搜索空间转换成相应的网络结构，一组token对应唯一的一个网络结构。

      +

      参数：

      +
        +
      • tokens(list): - 一组token。
        • +
      +

      返回： +根据传入的token得到一个模型结构实例。

      示例代码： 
      1
 2
@@ -228,12 +237,11 @@
     input = output

      -
      -
      -

      next_archs(): -获取下一组模型结构。

      -
      -

      返回 +

      +
      paddleslim.nas.SANAS.next_archs()
      +
      获取下一组模型结构。
      +
      +

      返回： 返回模型结构实例的列表，形式为list。

      示例代码： 
      1
@@ -249,211 +257,22 @@
     input = output

      -
      -
      -

      reward(score): -把当前模型结构的得分情况回传。

      -
      -

      参数： -score: 当前模型的得分，分数越大越好。

      -

      返回 +

      +
      paddleslim.nas.SANAS.reward(score)
      +
      把当前模型结构的得分情况回传。
      +
      +

      参数：

      +
        +
      • score: - 当前模型的得分，分数越大越好。
        • +
      +

      返回： 模型结构更新成功或者失败，成功则返回True，失败则返回False。

      -

      代码示例 -
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      import numpy as np
-import paddle
-import paddle.fluid as fluid
-from paddleslim.nas import SANAS
-from paddleslim.analysis import flops
-
-max_flops = 321208544
-batch_size = 256
-
-# 搜索空间配置
-config=[('MobileNetV2Space')] 
-
-# 实例化SANAS
-sa_nas = SANAS(config, server_addr=("", 8887), init_temperature=10.24, reduce_rate=0.85, search_steps=100, is_server=True)
-
-for step in range(100):
-    archs = sa_nas.next_archs()
-    train_program = fluid.Program()
-    test_program = fluid.Program()
-    startup_program = fluid.Program()
-    ### 构造训练program
-    with fluid.program_guard(train_program, startup_program):
-        image = fluid.data(name='image', shape=[None, 3, 32, 32], dtype='float32')
-        label = fluid.data(name='label', shape=[None, 1], dtype='int64')
-
-        for arch in archs:
-            output = arch(image)
-        out = fluid.layers.fc(output, size=10, act="softmax") 
-        softmax_out = fluid.layers.softmax(input=out, use_cudnn=False)
-        cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
-        avg_cost = fluid.layers.mean(cost)
-        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
-
-        ### 构造测试program
-        test_program = train_program.clone(for_test=True)
-        ### 定义优化器
-        sgd = fluid.optimizer.SGD(learning_rate=1e-3)
-        sgd.minimize(avg_cost)
-
-
-    ### 增加限制条件，如果没有则进行无限制搜索
-    if flops(train_program) > max_flops:
-        continue
-
-    ### 定义代码是在cpu上运行
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(startup_program)
-
-    ### 定义训练输入数据
-    train_reader = paddle.batch(
-        paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
-        batch_size=batch_size,
-        drop_last=True)
-
-    ### 定义预测输入数据
-    test_reader = paddle.batch(
-        paddle.dataset.cifar.test10(cycle=False),
-        batch_size=batch_size,
-        drop_last=False)
-    train_feeder = fluid.DataFeeder([image, label], place, program=train_program)
-    test_feeder = fluid.DataFeeder([image, label], place, program=test_program)
-
-
-    ### 开始训练，每个搜索结果训练5个epoch
-    for epoch_id in range(5):
-        for batch_id, data in enumerate(train_reader()):
-            fetches = [avg_cost.name]
-            outs = exe.run(train_program,
-                           feed=train_feeder.feed(data),
-                           fetch_list=fetches)[0]
-            if batch_id % 10 == 0:
-                print('TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}'.format(step, epoch_id, batch_id, outs[0]))
-
-    ### 开始预测，得到最终的测试结果作为score回传给sa_nas
-    reward = []
-    for batch_id, data in enumerate(test_reader()):
-        test_fetches = [
-            avg_cost.name, acc_top1.name
-        ]
-        batch_reward = exe.run(test_program,
-                               feed=test_feeder.feed(data),
-                               fetch_list=test_fetches)
-        reward_avg = np.mean(np.array(batch_reward), axis=1)
-        reward.append(reward_avg)
-
-        print('TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}'.
-            format(step, batch_id, batch_reward[0],batch_reward[1]))
-
-    finally_reward = np.mean(np.array(reward), axis=0)
-    print(
-        'FINAL TEST: avg_cost: {}, acc_top1: {}'.format(
-            finally_reward[0], finally_reward[1]))
-
-    ### 回传score
-    sa_nas.reward(float(finally_reward[1]))
-
      -

      +
      +
      paddleslim.nas.SANAS.current_info()
      +
      返回当前token和搜索过程中最好的token和reward。
      +
      +

      返回： +搜索过程中最好的token，reward和当前训练的token，形式为dict。

      @@ -461,7 +280,7 @@ diff --git a/api/prune_api/index.html b/api/prune_api/index.html index bf6e077d..a7da8018 100644 --- a/api/prune_api/index.html +++ b/api/prune_api/index.html @@ -129,7 +129,7 @@
    • - 搜索空间 + 搜索空间
    • @@ -184,7 +184,7 @@

      Pruner#

      -
      paddleslim.prune.Pruner(criterion="l1_norm")源代码
      +
      paddleslim.prune.Pruner(criterion="l1_norm")[源代码]

      对卷积网络的通道进行一次剪裁。剪裁一个卷积层的通道，是指剪裁该卷积层输出的通道。卷积层的权重形状为[output_channel, input_channel, kernel_size, kernel_size]，通过剪裁该权重的第一纬度达到剪裁输出通道数的目的。

      @@ -202,7 +202,7 @@
      -
      paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False)源代码
      +
      paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False)[源代码]

      对目标网络的一组卷积层的权重进行裁剪。

      @@ -404,7 +404,7 @@

      sensitivity#

      -
      paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) 源代码
      +
      paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) [源代码]

      计算网络中每个卷积层的敏感度。每个卷积层的敏感度信息统计方法为：依次剪掉当前卷积层不同比例的输出通道数，在测试集上计算剪裁后的精度损失。得到敏感度信息后，可以通过观察或其它方式确定每层卷积的剪裁率。

      @@ -653,7 +653,7 @@

      merge_sensitive#

      -
      paddleslim.prune.merge_sensitive(sensitivities)源代码
      +
      paddleslim.prune.merge_sensitive(sensitivities)[源代码]

      合并多个敏感度信息。

      @@ -690,7 +690,7 @@

      示例：

      load_sensitivities#

      -
      paddleslim.prune.load_sensitivities(sensitivities_file)源代码
      +
      paddleslim.prune.load_sensitivities(sensitivities_file)[源代码]

      从文件中加载敏感度信息。

      @@ -706,7 +706,7 @@

      示例：

      get_ratios_by_loss#

      -
      paddleslim.prune.get_ratios_by_loss(sensitivities, loss)源代码
      +
      paddleslim.prune.get_ratios_by_loss(sensitivities, loss)[源代码]

      根据敏感度和精度损失阈值计算出一组剪切率。对于参数w, 其剪裁率为使精度损失低于loss的最大剪裁率。

      diff --git a/api/quantization_api/index.html b/api/quantization_api/index.html index 68bc1b5b..2a30f9c7 100644 --- a/api/quantization_api/index.html +++ b/api/quantization_api/index.html @@ -129,7 +129,7 @@
    • - 搜索空间 + 搜索空间
    • @@ -254,13 +254,13 @@

    注意事项

    -
    • 此接口会改变program结构，并且可能增加一些persistable的变量，所以加载模型参数时请注意和相应的program对应。
    • 此接口底层经历了fluid.Program-> fluid.framework.IrGraph->fluid.Program的转变，在fluid.framework.IrGraph中没有Parameter的概念，Variable只有persistable和not persistable的区别，所以在保存和加载参数时，请使用fluid.io.save_persistables和fluid.io.load_persistables接口。
    • 由于此接口会根据program的结构和量化配置来对program添加op，所以Paddle中一些通过fuse op来加速训练的策略不能使用。已知以下策略在使用量化时必须设为False： fuse_all_reduce_ops, sync_batch_norm。
    • 如果传入的program中存在和任何op都没有连接的Variable，则会在量化的过程中被优化掉。
    +

    convert#

    paddleslim.quant.convert(program, place, config, scope=None, save_int8=False)[源代码]
    @@ -283,8 +283,8 @@

    注意事项

    -

    因为该接口会对op和Variable做相应的删除和修改，所以此接口只能在训练完成之后调用。如果想转化训练的中间模型，可加载相应的参数之后再使用此接口。

    +

    代码示例

     1
  2
@@ -388,8 +388,8 @@
 
    无。
    
 
    
 
    注意事项
    
-
    
 
    因为该接口会收集校正数据的所有的激活值，所以使用的校正图片不能太多。'KL'散度的计算也比较耗时。
    
+

    代码示例

    注： 此示例不能直接运行，因为需要加载${model_dir}下的模型，所以不能直接运行。

    diff --git a/api/single_distiller_api/index.html b/api/single_distiller_api/index.html index 6f9f1bc4..ae39f9a6 100644 --- a/api/single_distiller_api/index.html +++ b/api/single_distiller_api/index.html @@ -129,7 +129,7 @@
  • - 搜索空间 + 搜索空间
  • diff --git a/index.html b/index.html index 5e3556fe..afeca7bf 100644 --- a/index.html +++ b/index.html @@ -129,7 +129,7 @@
  • - 搜索空间 + 搜索空间
  • @@ -293,5 +293,5 @@ diff --git a/model_zoo/index.html b/model_zoo/index.html index ac9b9522..c7bc0541 100644 --- a/model_zoo/index.html +++ b/model_zoo/index.html @@ -151,7 +151,7 @@
  • - 搜索空间 + 搜索空间
  • diff --git a/search.html b/search.html index 20a681e3..3241d6e5 100644 --- a/search.html +++ b/search.html @@ -104,7 +104,7 @@
  • - 搜索空间 + 搜索空间
  • diff --git a/search/search_index.json b/search/search_index.json index 9a739a75..15e0c2ac 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"PaddleSlim # PaddleSlim\u662fPaddlePaddle\u6846\u67b6\u7684\u4e00\u4e2a\u5b50\u6a21\u5757\uff0c\u4e3b\u8981\u7528\u4e8e\u538b\u7f29\u56fe\u50cf\u9886\u57df\u6a21\u578b\u3002\u5728PaddleSlim\u4e2d\uff0c\u4e0d\u4ec5\u5b9e\u73b0\u4e86\u76ee\u524d\u4e3b\u6d41\u7684\u7f51\u7edc\u526a\u679d\u3001\u91cf\u5316\u3001\u84b8\u998f\u4e09\u79cd\u538b\u7f29\u7b56\u7565\uff0c\u8fd8\u5b9e\u73b0\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u548c\u5c0f\u6a21\u578b\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u529f\u80fd\u3002\u5728\u540e\u7eed\u7248\u672c\u4e2d\uff0c\u4f1a\u6dfb\u52a0\u66f4\u591a\u7684\u538b\u7f29\u7b56\u7565\uff0c\u4ee5\u53ca\u5b8c\u5584\u5bf9NLP\u9886\u57df\u6a21\u578b\u7684\u652f\u6301\u3002 \u529f\u80fd # \u6a21\u578b\u526a\u88c1 \u652f\u6301\u901a\u9053\u5747\u5300\u6a21\u578b\u526a\u88c1\uff08uniform pruning) \u57fa\u4e8e\u654f\u611f\u5ea6\u7684\u6a21\u578b\u526a\u88c1 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u81ea\u52a8\u6a21\u578b\u526a\u88c1\u4e09\u79cd\u65b9\u5f0f \u91cf\u5316\u8bad\u7ec3 \u5728\u7ebf\u91cf\u5316\u8bad\u7ec3\uff08training aware\uff09 \u79bb\u7ebf\u91cf\u5316\uff08post training\uff09 \u652f\u6301\u5bf9\u6743\u91cd\u5168\u5c40\u91cf\u5316\u548cChannel-Wise\u91cf\u5316 \u84b8\u998f \u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301\u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301 FLOPS / \u786c\u4ef6\u5ef6\u65f6\u7ea6\u675f \u652f\u6301\u591a\u5e73\u53f0\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30 \u5b89\u88c5 # \u5b89\u88c5PaddleSlim\u524d\uff0c\u8bf7\u786e\u8ba4\u5df2\u6b63\u786e\u5b89\u88c5Paddle1.6\u7248\u672c\u6216\u66f4\u65b0\u7248\u672c\u3002Paddle\u5b89\u88c5\u8bf7\u53c2\u8003\uff1a Paddle\u5b89\u88c5\u6559\u7a0b \u3002 \u5b89\u88c5develop\u7248\u672c 1 2 3 git clone https : // github . com / PaddlePaddle / PaddleSlim . git cd PaddleSlim python setup . py install \u5b89\u88c5\u5b98\u65b9\u53d1\u5e03\u7684\u6700\u65b0\u7248\u672c 1 pip install paddleslim - i https : // pypi . org / simple \u5b89\u88c5\u5386\u53f2\u7248\u672c \u8bf7\u70b9\u51fb pypi.org \u67e5\u770b\u53ef\u5b89\u88c5\u5386\u53f2\u7248\u672c\u3002 \u4f7f\u7528 # API\u6587\u6863 \uff1aAPI\u4f7f\u7528\u4ecb\u7ecd\uff0c\u5305\u62ec \u84b8\u998f \u3001 \u526a\u88c1 \u3001 \u91cf\u5316 \u548c \u6a21\u578b\u7ed3\u6784\u641c\u7d22 \u3002 \u793a\u4f8b \uff1a\u57fa\u4e8emnist\u548ccifar10\u7b49\u7b80\u5355\u5206\u7c7b\u4efb\u52a1\u7684\u6a21\u578b\u538b\u7f29\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8be5\u90e8\u5206\u5feb\u901f\u4f53\u9a8c\u548c\u4e86\u89e3PaddleSlim\u7684\u529f\u80fd\u3002 \u5b9e\u8df5\u6559\u7a0b \uff1a\u7ecf\u5178\u6a21\u578b\u7684\u5206\u6790\u548c\u538b\u7f29\u5b9e\u9a8c\u6559\u7a0b\u3002 \u6a21\u578b\u5e93 \uff1a\u7ecf\u8fc7\u538b\u7f29\u7684\u5206\u7c7b\u3001\u68c0\u6d4b\u3001\u8bed\u4e49\u5206\u5272\u6a21\u578b\uff0c\u5305\u62ec\u6743\u91cd\u6587\u4ef6\u3001\u7f51\u7edc\u7ed3\u6784\u6587\u4ef6\u548c\u6027\u80fd\u6570\u636e\u3002 Paddle\u68c0\u6d4b\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u68c0\u6d4b\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 Paddle\u5206\u5272\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u5206\u5272\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 PaddleLite \uff1a\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u9884\u6d4b\u5e93PaddleLite\u90e8\u7f72PaddleSlim\u4ea7\u51fa\u7684\u6a21\u578b\u3002 \u8d21\u732e\u4e0e\u53cd\u9988 #","title":"Home"},{"location":"#paddleslim","text":"PaddleSlim\u662fPaddlePaddle\u6846\u67b6\u7684\u4e00\u4e2a\u5b50\u6a21\u5757\uff0c\u4e3b\u8981\u7528\u4e8e\u538b\u7f29\u56fe\u50cf\u9886\u57df\u6a21\u578b\u3002\u5728PaddleSlim\u4e2d\uff0c\u4e0d\u4ec5\u5b9e\u73b0\u4e86\u76ee\u524d\u4e3b\u6d41\u7684\u7f51\u7edc\u526a\u679d\u3001\u91cf\u5316\u3001\u84b8\u998f\u4e09\u79cd\u538b\u7f29\u7b56\u7565\uff0c\u8fd8\u5b9e\u73b0\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u548c\u5c0f\u6a21\u578b\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u529f\u80fd\u3002\u5728\u540e\u7eed\u7248\u672c\u4e2d\uff0c\u4f1a\u6dfb\u52a0\u66f4\u591a\u7684\u538b\u7f29\u7b56\u7565\uff0c\u4ee5\u53ca\u5b8c\u5584\u5bf9NLP\u9886\u57df\u6a21\u578b\u7684\u652f\u6301\u3002","title":"PaddleSlim"},{"location":"#_1","text":"\u6a21\u578b\u526a\u88c1 \u652f\u6301\u901a\u9053\u5747\u5300\u6a21\u578b\u526a\u88c1\uff08uniform pruning) \u57fa\u4e8e\u654f\u611f\u5ea6\u7684\u6a21\u578b\u526a\u88c1 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u81ea\u52a8\u6a21\u578b\u526a\u88c1\u4e09\u79cd\u65b9\u5f0f \u91cf\u5316\u8bad\u7ec3 \u5728\u7ebf\u91cf\u5316\u8bad\u7ec3\uff08training aware\uff09 \u79bb\u7ebf\u91cf\u5316\uff08post training\uff09 \u652f\u6301\u5bf9\u6743\u91cd\u5168\u5c40\u91cf\u5316\u548cChannel-Wise\u91cf\u5316 \u84b8\u998f \u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301\u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301 FLOPS / \u786c\u4ef6\u5ef6\u65f6\u7ea6\u675f \u652f\u6301\u591a\u5e73\u53f0\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30","title":"\u529f\u80fd"},{"location":"#_2","text":"\u5b89\u88c5PaddleSlim\u524d\uff0c\u8bf7\u786e\u8ba4\u5df2\u6b63\u786e\u5b89\u88c5Paddle1.6\u7248\u672c\u6216\u66f4\u65b0\u7248\u672c\u3002Paddle\u5b89\u88c5\u8bf7\u53c2\u8003\uff1a Paddle\u5b89\u88c5\u6559\u7a0b \u3002 \u5b89\u88c5develop\u7248\u672c 1 2 3 git clone https : // github . com / PaddlePaddle / PaddleSlim . git cd PaddleSlim python setup . py install \u5b89\u88c5\u5b98\u65b9\u53d1\u5e03\u7684\u6700\u65b0\u7248\u672c 1 pip install paddleslim - i https : // pypi . org / simple \u5b89\u88c5\u5386\u53f2\u7248\u672c \u8bf7\u70b9\u51fb pypi.org \u67e5\u770b\u53ef\u5b89\u88c5\u5386\u53f2\u7248\u672c\u3002","title":"\u5b89\u88c5"},{"location":"#_3","text":"API\u6587\u6863 \uff1aAPI\u4f7f\u7528\u4ecb\u7ecd\uff0c\u5305\u62ec \u84b8\u998f \u3001 \u526a\u88c1 \u3001 \u91cf\u5316 \u548c \u6a21\u578b\u7ed3\u6784\u641c\u7d22 \u3002 \u793a\u4f8b \uff1a\u57fa\u4e8emnist\u548ccifar10\u7b49\u7b80\u5355\u5206\u7c7b\u4efb\u52a1\u7684\u6a21\u578b\u538b\u7f29\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8be5\u90e8\u5206\u5feb\u901f\u4f53\u9a8c\u548c\u4e86\u89e3PaddleSlim\u7684\u529f\u80fd\u3002 \u5b9e\u8df5\u6559\u7a0b \uff1a\u7ecf\u5178\u6a21\u578b\u7684\u5206\u6790\u548c\u538b\u7f29\u5b9e\u9a8c\u6559\u7a0b\u3002 \u6a21\u578b\u5e93 \uff1a\u7ecf\u8fc7\u538b\u7f29\u7684\u5206\u7c7b\u3001\u68c0\u6d4b\u3001\u8bed\u4e49\u5206\u5272\u6a21\u578b\uff0c\u5305\u62ec\u6743\u91cd\u6587\u4ef6\u3001\u7f51\u7edc\u7ed3\u6784\u6587\u4ef6\u548c\u6027\u80fd\u6570\u636e\u3002 Paddle\u68c0\u6d4b\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u68c0\u6d4b\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 Paddle\u5206\u5272\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u5206\u5272\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 PaddleLite \uff1a\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u9884\u6d4b\u5e93PaddleLite\u90e8\u7f72PaddleSlim\u4ea7\u51fa\u7684\u6a21\u578b\u3002","title":"\u4f7f\u7528"},{"location":"#_4","text":"","title":"\u8d21\u732e\u4e0e\u53cd\u9988"},{"location":"model_zoo/","text":"1. \u56fe\u50cf\u5206\u7c7b # \u6570\u636e\u96c6\uff1aImageNet1000\u7c7b 1.1 \u91cf\u5316 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 FP32 baseline 70.99%/89.68% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 FP32 baseline 72.15%/90.65% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 FP32 baseline 76.50%/93.00% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 1.2 \u526a\u88c1 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d MobileNetV1 baseline 70.99%/89.68% 17 1.11 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 uniform -50% 69.4%/88.66% (-1.59%/-1.02%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -30% 70.4%/89.3% (-0.59%/-0.38%) 12 0.74 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -50% 69.8% / 88.9% (-1.19%/-0.78%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 baseline 72.15%/90.65% 15 0.59 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 uniform -50% 65.79%/86.11% (-6.35%/-4.47%) 11 0.296 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 baseline 72.15%/90.65% 84 7.36 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 uniform -50% 70.99%/89.95% (-1.36%/-0.87%) 41 3.67 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 auto -55.05% 70.24%/89.63% (-2.04%/-1.06%) 33 3.31 \u4e0b\u8f7d\u94fe\u63a5 1.3 \u84b8\u998f # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 student 70.99%/89.68% 17 \u4e0b\u8f7d\u94fe\u63a5 ResNet50_vd teacher 79.12%/94.44% 99 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 ResNet50_vd 1 distill 72.77%/90.68% (+1.78%/+1.00%) 17 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 student 72.15%/90.65% 15 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 ResNet50_vd distill 74.28%/91.53% (+2.13%/+0.88%) 15 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 student 76.50%/93.00% 99 \u4e0b\u8f7d\u94fe\u63a5 ResNet101 teacher 77.56%/93.64% 173 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 ResNet101 distill 77.29%/93.65% (+0.79%/+0.65%) 99 \u4e0b\u8f7d\u94fe\u63a5 Note [1] \uff1a\u5e26_vd\u540e\u7f00\u4ee3\u8868\u8be5\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u4e86Mixup\uff0cMixup\u76f8\u5173\u4ecb\u7ecd\u53c2\u8003 mixup: Beyond Empirical Risk Minimization 2. \u76ee\u6807\u68c0\u6d4b # 2.1 \u91cf\u5316 # \u6570\u636e\u96c6\uff1a COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 FP32 baseline COCO 8 29.3 29.3 27.1 xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain FP32 baseline COCO 8 41.4 xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 \u6570\u636e\u96c6\uff1aWIDER-FACE \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Image/GPU \u8f93\u5165\u5c3a\u5bf8 Easy/Medium/Hard \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d BlazeFace FP32 baseline 8 640 0.915/0.892/0.797 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite FP32 baseline 8 640 0.909/0.885/0.781 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS FP32 baseline 8 640 0.837/0.807/0.658 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 2.2 \u526a\u88c1 # \u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef(MB) GFLOPs (608*608) \u4e0b\u8f7d MobileNet-V1-YOLOv3 baseline Pascal VOC 8 76.2 76.7 75.3 94 40.49 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -52.88% Pascal VOC 8 77.6 (+1.4) 77.7 (1.0) 75.5 (+0.2) 31 19.08 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 baseline COCO 8 29.3 29.3 27.0 95 41.35 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -51.77% COCO 8 26.0 (-3.3) 25.1 (-4.2) 22.6 (-4.4) 32 19.94 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 baseline COCO 8 39.1 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -9.37% COCO 8 39.3 (+0.2) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -24.68% COCO 8 37.3 (-1.8) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain baseline COCO 8 41.4 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -9.37% COCO 8 40.5 (-0.9) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -24.68% COCO 8 37.8 (-3.3) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 2.3 \u84b8\u998f # \u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 student Pascal VOC 8 76.2 76.7 75.3 94 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher Pascal VOC 8 82.6 81.9 80.1 162 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill Pascal VOC 8 79.0 (+2.8) 78.2 (+1.5) 75.5 (+0.2) 94 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 student COCO 8 29.3 29.3 27.0 95 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher COCO 8 36.2 34.3 31.4 163 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill COCO 8 31.4 (+2.1) 30.0 (+0.7) 27.1 (+0.1) 95 \u4e0b\u8f7d\u94fe\u63a5 3. \u56fe\u50cf\u5206\u5272 # \u6570\u636e\u96c6\uff1aCityscapes 3.1 \u91cf\u5316 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d DeepLabv3+/MobileNetv1 FP32 baseline 63.26 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 FP32 baseline 69.81 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 3.2 \u526a\u88c1 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d fast-scnn baseline 69.64 11 14.41 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn uniform -17.07% 69.58 (-0.06) 8.5 11.95 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn sensitive -47.60% 66.68 (-2.96) 5.7 7.55 \u4e0b\u8f7d\u94fe\u63a5","title":"\u6a21\u578b\u5e93"},{"location":"model_zoo/#1","text":"\u6570\u636e\u96c6\uff1aImageNet1000\u7c7b","title":"1. \u56fe\u50cf\u5206\u7c7b"},{"location":"model_zoo/#11","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 FP32 baseline 70.99%/89.68% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 FP32 baseline 72.15%/90.65% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 FP32 baseline 76.50%/93.00% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5","title":"1.1 \u91cf\u5316"},{"location":"model_zoo/#12","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d MobileNetV1 baseline 70.99%/89.68% 17 1.11 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 uniform -50% 69.4%/88.66% (-1.59%/-1.02%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -30% 70.4%/89.3% (-0.59%/-0.38%) 12 0.74 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -50% 69.8% / 88.9% (-1.19%/-0.78%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 baseline 72.15%/90.65% 15 0.59 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 uniform -50% 65.79%/86.11% (-6.35%/-4.47%) 11 0.296 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 baseline 72.15%/90.65% 84 7.36 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 uniform -50% 70.99%/89.95% (-1.36%/-0.87%) 41 3.67 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 auto -55.05% 70.24%/89.63% (-2.04%/-1.06%) 33 3.31 \u4e0b\u8f7d\u94fe\u63a5","title":"1.2 \u526a\u88c1"},{"location":"model_zoo/#13","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 student 70.99%/89.68% 17 \u4e0b\u8f7d\u94fe\u63a5 ResNet50_vd teacher 79.12%/94.44% 99 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 ResNet50_vd 1 distill 72.77%/90.68% (+1.78%/+1.00%) 17 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 student 72.15%/90.65% 15 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 ResNet50_vd distill 74.28%/91.53% (+2.13%/+0.88%) 15 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 student 76.50%/93.00% 99 \u4e0b\u8f7d\u94fe\u63a5 ResNet101 teacher 77.56%/93.64% 173 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 ResNet101 distill 77.29%/93.65% (+0.79%/+0.65%) 99 \u4e0b\u8f7d\u94fe\u63a5 Note [1] \uff1a\u5e26_vd\u540e\u7f00\u4ee3\u8868\u8be5\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u4e86Mixup\uff0cMixup\u76f8\u5173\u4ecb\u7ecd\u53c2\u8003 mixup: Beyond Empirical Risk Minimization","title":"1.3 \u84b8\u998f"},{"location":"model_zoo/#2","text":"","title":"2. \u76ee\u6807\u68c0\u6d4b"},{"location":"model_zoo/#21","text":"\u6570\u636e\u96c6\uff1a COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 FP32 baseline COCO 8 29.3 29.3 27.1 xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain FP32 baseline COCO 8 41.4 xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 \u6570\u636e\u96c6\uff1aWIDER-FACE \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Image/GPU \u8f93\u5165\u5c3a\u5bf8 Easy/Medium/Hard \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d BlazeFace FP32 baseline 8 640 0.915/0.892/0.797 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite FP32 baseline 8 640 0.909/0.885/0.781 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS FP32 baseline 8 640 0.837/0.807/0.658 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5","title":"2.1 \u91cf\u5316"},{"location":"model_zoo/#22","text":"\u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef(MB) GFLOPs (608*608) \u4e0b\u8f7d MobileNet-V1-YOLOv3 baseline Pascal VOC 8 76.2 76.7 75.3 94 40.49 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -52.88% Pascal VOC 8 77.6 (+1.4) 77.7 (1.0) 75.5 (+0.2) 31 19.08 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 baseline COCO 8 29.3 29.3 27.0 95 41.35 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -51.77% COCO 8 26.0 (-3.3) 25.1 (-4.2) 22.6 (-4.4) 32 19.94 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 baseline COCO 8 39.1 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -9.37% COCO 8 39.3 (+0.2) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -24.68% COCO 8 37.3 (-1.8) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain baseline COCO 8 41.4 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -9.37% COCO 8 40.5 (-0.9) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -24.68% COCO 8 37.8 (-3.3) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5","title":"2.2 \u526a\u88c1"},{"location":"model_zoo/#23","text":"\u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 student Pascal VOC 8 76.2 76.7 75.3 94 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher Pascal VOC 8 82.6 81.9 80.1 162 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill Pascal VOC 8 79.0 (+2.8) 78.2 (+1.5) 75.5 (+0.2) 94 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 student COCO 8 29.3 29.3 27.0 95 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher COCO 8 36.2 34.3 31.4 163 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill COCO 8 31.4 (+2.1) 30.0 (+0.7) 27.1 (+0.1) 95 \u4e0b\u8f7d\u94fe\u63a5","title":"2.3 \u84b8\u998f"},{"location":"model_zoo/#3","text":"\u6570\u636e\u96c6\uff1aCityscapes","title":"3. \u56fe\u50cf\u5206\u5272"},{"location":"model_zoo/#31","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d DeepLabv3+/MobileNetv1 FP32 baseline 63.26 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 FP32 baseline 69.81 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5","title":"3.1 \u91cf\u5316"},{"location":"model_zoo/#32","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d fast-scnn baseline 69.64 11 14.41 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn uniform -17.07% 69.58 (-0.06) 8.5 11.95 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn sensitive -47.60% 66.68 (-2.96) 5.7 7.55 \u4e0b\u8f7d\u94fe\u63a5","title":"3.2 \u526a\u88c1"},{"location":"table_latency/","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868 # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7528\u4e8e\u5feb\u901f\u8bc4\u4f30\u4e00\u4e2a\u6a21\u578b\u5728\u7279\u5b9a\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u4e0a\u7684\u63a8\u7406\u901f\u5ea6\u3002 \u8be5\u6587\u6863\u4e3b\u8981\u7528\u4e8e\u5b9a\u4e49PaddleSlim\u652f\u6301\u7684\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u683c\u5f0f\u3002 \u6982\u8ff0 # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\u5b58\u653e\u7740\u6240\u6709\u53ef\u80fd\u7684\u64cd\u4f5c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\uff0c\u8be5\u8868\u4e2d\u7684\u4e00\u4e2a\u64cd\u4f5c\u5305\u62ec\u64cd\u4f5c\u7c7b\u578b\u548c\u64cd\u4f5c\u53c2\u6570\uff0c\u6bd4\u5982\uff1a\u64cd\u4f5c\u7c7b\u578b\u53ef\u4ee5\u662f conv2d \uff0c\u5bf9\u5e94\u7684\u64cd\u4f5c\u53c2\u6570\u6709\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5927\u5c0f\u3001\u5377\u79ef\u6838\u4e2a\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u3002 \u7ed9\u5b9a\u64cd\u4f5c\u7684\u5ef6\u65f6\u4f9d\u8d56\u4e8e\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u3002 \u6574\u4f53\u683c\u5f0f # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4ee5\u6587\u4ef6\u6216\u591a\u884c\u5b57\u7b26\u4e32\u7684\u5f62\u5f0f\u4fdd\u5b58\u3002 \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7b2c\u4e00\u884c\u4fdd\u5b58\u7248\u672c\u4fe1\u606f\uff0c\u540e\u7eed\u6bcf\u884c\u4e3a\u4e00\u4e2a\u64cd\u4f5c\u548c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\u3002 \u7248\u672c\u4fe1\u606f # \u7248\u672c\u4fe1\u606f\u4ee5\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u5206\u5272\uff0c\u5185\u5bb9\u4f9d\u6b21\u4e3a\u786c\u4ef6\u73af\u5883\u540d\u79f0\u3001\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u548c\u65f6\u95f4\u6233\u3002 \u786c\u4ef6\u73af\u5883\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u786c\u4ef6\u73af\u5883\uff0c\u53ef\u4ee5\u5305\u542b\u8ba1\u7b97\u67b6\u6784\u7c7b\u578b\u3001\u7248\u672c\u53f7\u7b49\u4fe1\u606f\u3002 \u63a8\u7406\u5f15\u64ce\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u63a8\u7406\u5f15\u64ce\uff0c\u53ef\u4ee5\u5305\u542b\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u3001\u7248\u672c\u53f7\u3001\u4f18\u5316\u9009\u9879\u7b49\u4fe1\u606f\u3002 \u65f6\u95f4\u6233\uff1a \u8be5\u8bc4\u4f30\u8868\u7684\u521b\u5efa\u65f6\u95f4\u3002 \u64cd\u4f5c\u4fe1\u606f # \u64cd\u4f5c\u4fe1\u606f\u5b57\u6bb5\u4e4b\u95f4\u4ee5\u9017\u53f7\u5206\u5272\u3002\u64cd\u4f5c\u4fe1\u606f\u4e0e\u5ef6\u8fdf\u4fe1\u606f\u4e4b\u95f4\u4ee5\u5236\u8868\u7b26\u5206\u5272\u3002 conv2d # \u683c\u5f0f 1 op_type , flag_bias , flag_relu , n_in , c_in , h_in , w_in , c_out , groups , kernel , padding , stride , dilation \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_bias (int) - \u662f\u5426\u6709 bias\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 flag_relu (int) - \u662f\u5426\u6709 relu\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 c_out (int) - \u8f93\u51fa Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 groups (int) - \u5377\u79ef\u4e8c\u7ef4\u5c42\uff08Conv2D Layer\uff09\u7684\u7ec4\u6570\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 dilation (int) - \u81a8\u80c0 (dilation) \u5927\u5c0f\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 activation # \u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 batch_norm # \u683c\u5f0f 1 op_type , active_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 active_type (string|None) - \u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b\uff0c\u5305\u542b\uff1arelu, prelu, sigmoid, relu6, tanh\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 eltwise # \u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 pooling # \u683c\u5f0f 1 op_type , flag_global_pooling , n_in , c_in , h_in , w_in , kernel , padding , stride , ceil_mode , pool_type \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_global_pooling (int) - \u662f\u5426\u4e3a\u5168\u5c40\u6c60\u5316\uff080\uff1a\u4e0d\u662f\uff0c1\uff1a\u662f\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 ceil_mode (int) - \u662f\u5426\u7528 ceil \u51fd\u6570\u8ba1\u7b97\u8f93\u51fa\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u30020 \u8868\u793a\u4f7f\u7528 floor \u51fd\u6570\uff0c1 \u8868\u793a\u4f7f\u7528 ceil \u51fd\u6570\u3002 pool_type (int) - \u6c60\u5316\u7c7b\u578b\uff0c\u5176\u4e2d 1 \u8868\u793a pooling_max\uff0c2 \u8868\u793a pooling_average_include_padding\uff0c3 \u8868\u793a pooling_average_exclude_padding\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 softmax # \u683c\u5f0f 1 op_type , axis , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 axis (int) - \u6267\u884c softmax \u8ba1\u7b97\u7684\u7ef4\u5ea6\u7d22\u5f15\uff0c\u5e94\u8be5\u5728 [\u22121\uff0crank \u2212 1] \u8303\u56f4\u5185\uff0c\u5176\u4e2d rank \u662f\u8f93\u5165\u53d8\u91cf\u7684\u79e9\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868"},{"location":"table_latency/#_1","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7528\u4e8e\u5feb\u901f\u8bc4\u4f30\u4e00\u4e2a\u6a21\u578b\u5728\u7279\u5b9a\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u4e0a\u7684\u63a8\u7406\u901f\u5ea6\u3002 \u8be5\u6587\u6863\u4e3b\u8981\u7528\u4e8e\u5b9a\u4e49PaddleSlim\u652f\u6301\u7684\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u683c\u5f0f\u3002","title":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868"},{"location":"table_latency/#_2","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\u5b58\u653e\u7740\u6240\u6709\u53ef\u80fd\u7684\u64cd\u4f5c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\uff0c\u8be5\u8868\u4e2d\u7684\u4e00\u4e2a\u64cd\u4f5c\u5305\u62ec\u64cd\u4f5c\u7c7b\u578b\u548c\u64cd\u4f5c\u53c2\u6570\uff0c\u6bd4\u5982\uff1a\u64cd\u4f5c\u7c7b\u578b\u53ef\u4ee5\u662f conv2d \uff0c\u5bf9\u5e94\u7684\u64cd\u4f5c\u53c2\u6570\u6709\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5927\u5c0f\u3001\u5377\u79ef\u6838\u4e2a\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u3002 \u7ed9\u5b9a\u64cd\u4f5c\u7684\u5ef6\u65f6\u4f9d\u8d56\u4e8e\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u3002","title":"\u6982\u8ff0"},{"location":"table_latency/#_3","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4ee5\u6587\u4ef6\u6216\u591a\u884c\u5b57\u7b26\u4e32\u7684\u5f62\u5f0f\u4fdd\u5b58\u3002 \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7b2c\u4e00\u884c\u4fdd\u5b58\u7248\u672c\u4fe1\u606f\uff0c\u540e\u7eed\u6bcf\u884c\u4e3a\u4e00\u4e2a\u64cd\u4f5c\u548c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\u3002","title":"\u6574\u4f53\u683c\u5f0f"},{"location":"table_latency/#_4","text":"\u7248\u672c\u4fe1\u606f\u4ee5\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u5206\u5272\uff0c\u5185\u5bb9\u4f9d\u6b21\u4e3a\u786c\u4ef6\u73af\u5883\u540d\u79f0\u3001\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u548c\u65f6\u95f4\u6233\u3002 \u786c\u4ef6\u73af\u5883\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u786c\u4ef6\u73af\u5883\uff0c\u53ef\u4ee5\u5305\u542b\u8ba1\u7b97\u67b6\u6784\u7c7b\u578b\u3001\u7248\u672c\u53f7\u7b49\u4fe1\u606f\u3002 \u63a8\u7406\u5f15\u64ce\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u63a8\u7406\u5f15\u64ce\uff0c\u53ef\u4ee5\u5305\u542b\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u3001\u7248\u672c\u53f7\u3001\u4f18\u5316\u9009\u9879\u7b49\u4fe1\u606f\u3002 \u65f6\u95f4\u6233\uff1a \u8be5\u8bc4\u4f30\u8868\u7684\u521b\u5efa\u65f6\u95f4\u3002","title":"\u7248\u672c\u4fe1\u606f"},{"location":"table_latency/#_5","text":"\u64cd\u4f5c\u4fe1\u606f\u5b57\u6bb5\u4e4b\u95f4\u4ee5\u9017\u53f7\u5206\u5272\u3002\u64cd\u4f5c\u4fe1\u606f\u4e0e\u5ef6\u8fdf\u4fe1\u606f\u4e4b\u95f4\u4ee5\u5236\u8868\u7b26\u5206\u5272\u3002","title":"\u64cd\u4f5c\u4fe1\u606f"},{"location":"table_latency/#conv2d","text":"\u683c\u5f0f 1 op_type , flag_bias , flag_relu , n_in , c_in , h_in , w_in , c_out , groups , kernel , padding , stride , dilation \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_bias (int) - \u662f\u5426\u6709 bias\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 flag_relu (int) - \u662f\u5426\u6709 relu\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 c_out (int) - \u8f93\u51fa Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 groups (int) - \u5377\u79ef\u4e8c\u7ef4\u5c42\uff08Conv2D Layer\uff09\u7684\u7ec4\u6570\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 dilation (int) - \u81a8\u80c0 (dilation) \u5927\u5c0f\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"conv2d"},{"location":"table_latency/#activation","text":"\u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"activation"},{"location":"table_latency/#batch_norm","text":"\u683c\u5f0f 1 op_type , active_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 active_type (string|None) - \u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b\uff0c\u5305\u542b\uff1arelu, prelu, sigmoid, relu6, tanh\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"batch_norm"},{"location":"table_latency/#eltwise","text":"\u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"eltwise"},{"location":"table_latency/#pooling","text":"\u683c\u5f0f 1 op_type , flag_global_pooling , n_in , c_in , h_in , w_in , kernel , padding , stride , ceil_mode , pool_type \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_global_pooling (int) - \u662f\u5426\u4e3a\u5168\u5c40\u6c60\u5316\uff080\uff1a\u4e0d\u662f\uff0c1\uff1a\u662f\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 ceil_mode (int) - \u662f\u5426\u7528 ceil \u51fd\u6570\u8ba1\u7b97\u8f93\u51fa\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u30020 \u8868\u793a\u4f7f\u7528 floor \u51fd\u6570\uff0c1 \u8868\u793a\u4f7f\u7528 ceil \u51fd\u6570\u3002 pool_type (int) - \u6c60\u5316\u7c7b\u578b\uff0c\u5176\u4e2d 1 \u8868\u793a pooling_max\uff0c2 \u8868\u793a pooling_average_include_padding\uff0c3 \u8868\u793a pooling_average_exclude_padding\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"pooling"},{"location":"table_latency/#softmax","text":"\u683c\u5f0f 1 op_type , axis , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 axis (int) - \u6267\u884c softmax \u8ba1\u7b97\u7684\u7ef4\u5ea6\u7d22\u5f15\uff0c\u5e94\u8be5\u5728 [\u22121\uff0crank \u2212 1] \u8303\u56f4\u5185\uff0c\u5176\u4e2d rank \u662f\u8f93\u5165\u53d8\u91cf\u7684\u79e9\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"softmax"},{"location":"algo/algo/","text":"\u76ee\u5f55 # \u91cf\u5316\u539f\u7406\u4ecb\u7ecd \u526a\u88c1\u539f\u7406\u4ecb\u7ecd \u84b8\u998f\u539f\u7406\u4ecb\u7ecd \u8f7b\u91cf\u7ea7\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u539f\u7406\u4ecb\u7ecd 1. Quantization Aware Training\u91cf\u5316\u4ecb\u7ecd # 1.1 \u80cc\u666f # \u8fd1\u5e74\u6765\uff0c\u5b9a\u70b9\u91cf\u5316\u4f7f\u7528\u66f4\u5c11\u7684\u6bd4\u7279\u6570\uff08\u59828-bit\u30013-bit\u30012-bit\u7b49\uff09\u8868\u793a\u795e\u7ecf\u7f51\u7edc\u7684\u6743\u91cd\u548c\u6fc0\u6d3b\u5df2\u88ab\u9a8c\u8bc1\u662f\u6709\u6548\u7684\u3002\u5b9a\u70b9\u91cf\u5316\u7684\u4f18\u70b9\u5305\u62ec\u4f4e\u5185\u5b58\u5e26\u5bbd\u3001\u4f4e\u529f\u8017\u3001\u4f4e\u8ba1\u7b97\u8d44\u6e90\u5360\u7528\u4ee5\u53ca\u4f4e\u6a21\u578b\u5b58\u50a8\u9700\u6c42\u7b49\u3002 \u88681: \u4e0d\u540c\u7c7b\u578b\u64cd\u4f5c\u7684\u5f00\u9500\u5bf9\u6bd4 \u7531\u88681\u53ef\u77e5\uff0c\u4f4e\u7cbe\u5ea6\u5b9a\u70b9\u6570\u64cd\u4f5c\u7684\u786c\u4ef6\u9762\u79ef\u5927\u5c0f\u53ca\u80fd\u8017\u6bd4\u9ad8\u7cbe\u5ea6\u6d6e\u70b9\u6570\u8981\u5c11\u51e0\u4e2a\u6570\u91cf\u7ea7\u3002 \u4f7f\u7528\u5b9a\u70b9\u91cf\u5316\u53ef\u5e26\u67654\u500d\u7684\u6a21\u578b\u538b\u7f29\u30014\u500d\u7684\u5185\u5b58\u5e26\u5bbd\u63d0\u5347\uff0c\u4ee5\u53ca\u66f4\u9ad8\u6548\u7684cache\u5229\u7528(\u5f88\u591a\u786c\u4ef6\u8bbe\u5907\uff0c\u5185\u5b58\u8bbf\u95ee\u662f\u4e3b\u8981\u80fd\u8017)\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8ba1\u7b97\u901f\u5ea6\u4e5f\u4f1a\u66f4\u5feb(\u901a\u5e38\u5177\u67092x-3x\u7684\u6027\u80fd\u63d0\u5347)\u3002\u7531\u88682\u53ef\u77e5\uff0c\u5728\u5f88\u591a\u573a\u666f\u4e0b\uff0c\u5b9a\u70b9\u91cf\u5316\u64cd\u4f5c\u5bf9\u7cbe\u5ea6\u5e76\u4e0d\u4f1a\u9020\u6210\u635f\u5931\u3002\u53e6\u5916\uff0c\u5b9a\u70b9\u91cf\u5316\u5bf9\u795e\u7ecf\u7f51\u7edc\u4e8e\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u7684\u63a8\u65ad\u6765\u8bf4\u662f\u6781\u5176\u91cd\u8981\u7684\u3002 \u88682\uff1a\u6a21\u578b\u91cf\u5316\u524d\u540e\u7cbe\u5ea6\u5bf9\u6bd4 \u76ee\u524d\uff0c\u5b66\u672f\u754c\u4e3b\u8981\u5c06\u91cf\u5316\u5206\u4e3a\u4e24\u5927\u7c7b\uff1a Post Training Quantization \u548c Quantization Aware Training \u3002 Post Training Quantization \u662f\u6307\u4f7f\u7528KL\u6563\u5ea6\u3001\u6ed1\u52a8\u5e73\u5747\u7b49\u65b9\u6cd5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\u4e14\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\u7684\u5b9a\u70b9\u91cf\u5316\u65b9\u6cd5\u3002 Quantization Aware Training \u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u91cf\u5316\u8fdb\u884c\u5efa\u6a21\u4ee5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\uff0c\u5b83\u4e0e Post Training Quantization \u6a21\u5f0f\u76f8\u6bd4\u53ef\u4ee5\u63d0\u4f9b\u66f4\u9ad8\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002 1.2 \u91cf\u5316\u539f\u7406 # 1.2.1 \u91cf\u5316\u65b9\u5f0f # \u76ee\u524d\uff0c\u5b58\u5728\u7740\u8bb8\u591a\u65b9\u6cd5\u53ef\u4ee5\u5c06\u6d6e\u70b9\u6570\u91cf\u5316\u6210\u5b9a\u70b9\u6570\u3002\u4f8b\u5982\uff1a r = min(max(x, a), b) s = \\frac{b - a}{n - 1} q = \\left \\lfloor \\frac{r - a}{s} \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c [a, b] [a, b] \u662f\u91cf\u5316\u8303\u56f4\uff0c a a \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5c0f\u503c\uff0c b b \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5982\u679c\u91cf\u5316\u7ea7\u522b\u4e3a k k \uff0c\u5219 n n \u4e3a 2^k 2^k \u3002\u4f8b\u5982\uff0c\u82e5 k k \u4e3a8\uff0c\u5219 n n \u4e3a256\u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 PaddleSlim\u6846\u67b6\u4e2d\u9009\u62e9\u7684\u91cf\u5316\u65b9\u6cd5\u4e3a\u6700\u5927\u7edd\u5bf9\u503c\u91cf\u5316( max-abs )\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a M = max(abs(x)) q = \\left \\lfloor \\frac{x}{M} * (n - 1) \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u88ab\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c M M \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u7edd\u5bf9\u503c\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5bf9\u4e8e8bit\u91cf\u5316\uff0cPaddleSlim\u91c7\u7528 int8_t \uff0c\u5373 n=2^7=128 n=2^7=128 \u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 \u65e0\u8bba\u662f min-max\u91cf\u5316 \u8fd8\u662f max-abs\u91cf\u5316 \uff0c\u4ed6\u4eec\u90fd\u53ef\u4ee5\u8868\u793a\u4e3a\u5982\u4e0b\u5f62\u5f0f\uff1a q = scale * r + b q = scale * r + b \u5176\u4e2d min-max \u548c max-abs \u88ab\u79f0\u4e3a\u91cf\u5316\u53c2\u6570\u6216\u8005\u91cf\u5316\u6bd4\u4f8b\u6216\u8005\u91cf\u5316\u8303\u56f4\u3002 1.2.2 \u91cf\u5316\u8bad\u7ec3 # 1.2.2.1 \u524d\u5411\u4f20\u64ad # \u524d\u5411\u4f20\u64ad\u8fc7\u7a0b\u91c7\u7528\u6a21\u62df\u91cf\u5316\u7684\u65b9\u5f0f\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a \u56fe1\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b \u7531\u56fe1\u53ef\u77e5\uff0c\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b\u53ef\u88ab\u63cf\u8ff0\u4e3a\u4ee5\u4e0b\u56db\u4e2a\u90e8\u5206\uff1a 1) \u8f93\u5165\u548c\u6743\u91cd\u5747\u88ab\u91cf\u5316\u62108-bit\u6574\u6570\u3002 2) \u57288-bit\u6574\u6570\u4e0a\u6267\u884c\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u3002 3) \u53cd\u91cf\u5316\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u7684\u8f93\u51fa\u7ed3\u679c\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002 4) \u572832-bit\u6d6e\u70b9\u578b\u6570\u636e\u4e0a\u6267\u884c\u504f\u7f6e\u52a0\u6cd5\u64cd\u4f5c\u3002\u6b64\u5904\uff0c\u504f\u7f6e\u5e76\u672a\u88ab\u91cf\u5316\u3002 \u5bf9\u4e8e\u901a\u7528\u77e9\u9635\u4e58\u6cd5( GEMM )\uff0c\u8f93\u5165 X X \u548c\u6743\u91cd W W \u7684\u91cf\u5316\u64cd\u4f5c\u53ef\u88ab\u8868\u8ff0\u4e3a\u5982\u4e0b\u8fc7\u7a0b\uff1a X_q = \\left \\lfloor \\frac{X}{X_m} * (n - 1) \\right \\rceil W_q = \\left \\lfloor \\frac{W}{W_m} * (n - 1) \\right \\rceil \u6267\u884c\u901a\u7528\u77e9\u9635\u4e58\u6cd5\uff1a Y_q = X_q * W_q \u5bf9\u91cf\u5316\u4e58\u79ef\u7ed3\u679c Yq Yq \u8fdb\u884c\u53cd\u91cf\u5316: \\begin{align} Y_{dq} = \\frac{Y_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =\\frac{X_q * W_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =(\\frac{X_q}{n - 1} * X_m) * (\\frac{W_q}{n - 1} * W_m) \\ \\end{align} \u4e0a\u8ff0\u516c\u5f0f\u8868\u660e\u53cd\u91cf\u5316\u64cd\u4f5c\u53ef\u4ee5\u88ab\u79fb\u52a8\u5230 GEMM \u4e4b\u524d\uff0c\u5373\u5148\u5bf9 Xq Xq \u548c Wq Wq \u6267\u884c\u53cd\u91cf\u5316\u64cd\u4f5c\u518d\u505a GEMM \u64cd\u4f5c\u3002\u56e0\u6b64\uff0c\u524d\u5411\u4f20\u64ad\u7684\u5de5\u4f5c\u6d41\u4ea6\u53ef\u8868\u793a\u4e3a\u5982\u4e0b\u65b9\u5f0f\uff1a \u56fe2\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u524d\u5411\u8fc7\u7a0b\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41 \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cPaddleSlim\u4f7f\u7528\u56fe2\u4e2d\u6240\u793a\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41\u3002\u5728\u8bbe\u8ba1\u4e2d\uff0c\u91cf\u5316Pass\u5728IrGraph\u4e2d\u63d2\u5165\u91cf\u5316op\u548c\u53cd\u91cf\u5316op\u3002\u56e0\u4e3a\u5728\u8fde\u7eed\u7684\u91cf\u5316\u3001\u53cd\u91cf\u5316\u64cd\u4f5c\u4e4b\u540e\u8f93\u5165\u4ecd\u7136\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u56e0\u6b64\uff0cPaddleSlim\u91cf\u5316\u8bad\u7ec3\u6846\u67b6\u6240\u91c7\u7528\u7684\u91cf\u5316\u65b9\u5f0f\u88ab\u79f0\u4e3a\u6a21\u62df\u91cf\u5316\u3002 1.2.2.2 \u53cd\u5411\u4f20\u64ad # \u7531\u56fe3\u53ef\u77e5\uff0c\u6743\u91cd\u66f4\u65b0\u6240\u9700\u7684\u68af\u5ea6\u503c\u53ef\u4ee5\u7531\u91cf\u5316\u540e\u7684\u6743\u91cd\u548c\u91cf\u5316\u540e\u7684\u6fc0\u6d3b\u6c42\u5f97\u3002\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u7684\u6240\u6709\u8f93\u5165\u548c\u8f93\u51fa\u5747\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u6ce8\u610f\uff0c\u68af\u5ea6\u66f4\u65b0\u64cd\u4f5c\u9700\u8981\u5728\u539f\u59cb\u6743\u91cd\u4e0a\u8fdb\u884c\uff0c\u5373\u8ba1\u7b97\u51fa\u7684\u68af\u5ea6\u5c06\u88ab\u52a0\u5230\u539f\u59cb\u6743\u91cd\u4e0a\u800c\u975e\u91cf\u5316\u540e\u6216\u53cd\u91cf\u5316\u540e\u7684\u6743\u91cd\u4e0a\u3002 \u56fe3\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u53cd\u5411\u4f20\u64ad\u548c\u6743\u91cd\u66f4\u65b0\u8fc7\u7a0b \u56e0\u6b64\uff0c\u91cf\u5316Pass\u4e5f\u4f1a\u6539\u53d8\u76f8\u5e94\u53cd\u5411\u7b97\u5b50\u7684\u67d0\u4e9b\u8f93\u5165\u3002 1.2.2.3 \u786e\u5b9a\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570 # \u5b58\u5728\u7740\u4e24\u79cd\u7b56\u7565\u53ef\u4ee5\u8ba1\u7b97\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\uff0c\u5373\u52a8\u6001\u7b56\u7565\u548c\u9759\u6001\u7b56\u7565\u3002\u52a8\u6001\u7b56\u7565\u4f1a\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u7684\u503c\u3002\u9759\u6001\u7b56\u7565\u5219\u5bf9\u4e0d\u540c\u7684\u8f93\u5165\u91c7\u7528\u76f8\u540c\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u3002 \u5bf9\u4e8e\u6743\u91cd\u800c\u8a00\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u91c7\u7528\u52a8\u6001\u7b56\u7565\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u5747\u4f1a\u88ab\u91cd\u65b0\u8ba1\u7b97\u5f97\u5230\u76f4\u81f3\u8bad\u7ec3\u8fc7\u7a0b\u7ed3\u675f\u3002 \u5bf9\u4e8e\u6fc0\u6d3b\u800c\u8a00\uff0c\u53ef\u4ee5\u9009\u62e9\u52a8\u6001\u7b56\u7565\u4e5f\u53ef\u4ee5\u9009\u62e9\u9759\u6001\u7b56\u7565\u3002\u82e5\u9009\u62e9\u4f7f\u7528\u9759\u6001\u7b56\u7565\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u4f1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u88ab\u8bc4\u4f30\u6c42\u5f97\uff0c\u4e14\u5728\u63a8\u65ad\u8fc7\u7a0b\u4e2d\u88ab\u4f7f\u7528(\u4e0d\u540c\u7684\u8f93\u5165\u5747\u4fdd\u6301\u4e0d\u53d8)\u3002\u9759\u6001\u7b56\u7565\u4e2d\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u53ef\u4e8e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u5982\u4e0b\u4e09\u79cd\u65b9\u5f0f\u8fdb\u884c\u8bc4\u4f30\uff1a \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u5e73\u5747\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6ed1\u52a8\u5e73\u5747\u503c\uff0c\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a Vt = (1 - k) * V + k * V_{t-1} Vt = (1 - k) * V + k * V_{t-1} \u5f0f\u4e2d\uff0c V V \u662f\u5f53\u524dbatch\u7684\u6700\u5927\u7edd\u5bf9\u503c\uff0c Vt Vt \u662f\u6ed1\u52a8\u5e73\u5747\u503c\u3002 k k \u662f\u4e00\u4e2a\u56e0\u5b50\uff0c\u4f8b\u5982\u5176\u503c\u53ef\u53d6\u4e3a0.9\u3002 1.2.4 \u8bad\u7ec3\u540e\u91cf\u5316 # \u8bad\u7ec3\u540e\u91cf\u5316\u662f\u57fa\u4e8e\u91c7\u6837\u6570\u636e\uff0c\u91c7\u7528KL\u6563\u5ea6\u7b49\u65b9\u6cd5\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7684\u65b9\u6cd5\u3002\u76f8\u6bd4\u91cf\u5316\u8bad\u7ec3\uff0c\u8bad\u7ec3\u540e\u91cf\u5316\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\uff0c\u53ef\u4ee5\u5feb\u901f\u5f97\u5230\u91cf\u5316\u6a21\u578b\u3002 \u8bad\u7ec3\u540e\u91cf\u5316\u7684\u76ee\u6807\u662f\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\uff0c\u4e3b\u8981\u6709\u4e24\u79cd\u65b9\u6cd5\uff1a\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5 ( No Saturation) \u548c\u9971\u548c\u91cf\u5316\u65b9\u6cd5 (Saturation)\u3002\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u8ba1\u7b97FP32\u7c7b\u578bTensor\u4e2d\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c abs_max \uff0c\u5c06\u5176\u6620\u5c04\u4e3a127\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7b49\u4e8e abs_max/127 \u3002\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u4f7f\u7528KL\u6563\u5ea6\u8ba1\u7b97\u4e00\u4e2a\u5408\u9002\u7684\u9608\u503c T ( 0=r \\end{cases} \\end{equation} \\begin{equation} P(r_k) = \\begin{cases} e^{\\frac{(r_k-r)}{T_k}} & r_k < r\\\\ 1 & r_k>=r \\end{cases} \\end{equation} \u5728\u7b2ck\u6b21\u8fed\u4ee3\uff0c\u641c\u5230\u7684\u7f51\u7edc\u4e3a N_k N_k , \u5bf9 N_k N_k \u8bad\u7ec3\u82e5\u5e72epoch\u540e\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u5f97\u5230reward\u4e3a r_k r_k , \u4ee5\u6982\u7387 P(r_k) P(r_k) \u63a5\u53d7 r_k r_k \uff0c\u5373\u6267\u884c r=r_k r=r_k \u3002 r r \u5728\u641c\u7d22\u8fc7\u7a0b\u8d77\u59cb\u65f6\u88ab\u521d\u59cb\u5316\u4e3a0. T_0 T_0 \u4e3a\u521d\u59cb\u5316\u6e29\u5ea6\uff0c \\theta \\theta \u4e3a\u6e29\u5ea6\u8870\u51cf\u7cfb\u6570\uff0c T_k T_k \u4e3a\u7b2ck\u6b21\u8fed\u4ee3\u7684\u6e29\u5ea6\u3002 \u5728\u6211\u4eec\u7684NAS\u4efb\u52a1\u4e2d\uff0c\u533a\u522b\u4e8eRL\u6bcf\u6b21\u91cd\u65b0\u751f\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u7f51\u7edc\uff0c\u6211\u4eec\u5c06\u7f51\u7edc\u7ed3\u6784\u6620\u5c04\u6210\u4e00\u6bb5\u7f16\u7801\uff0c\u7b2c\u4e00\u6b21\u968f\u673a\u521d\u59cb\u5316\uff0c\u7136\u540e\u6bcf\u6b21\u968f\u673a\u4fee\u6539\u7f16\u7801\u4e2d\u7684\u4e00\u90e8\u5206\uff08\u5bf9\u5e94\u4e8e\u7f51\u7edc\u7ed3\u6784\u7684\u4e00\u90e8\u5206\uff09\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u7f16\u7801\uff0c\u7136\u540e\u5c06\u8fd9\u4e2a\u7f16\u7801\u518d\u6620\u5c04\u56de\u7f51\u7edc\u7ed3\u6784\uff0c\u901a\u8fc7\u5728\u8bad\u7ec3\u96c6\u4e0a\u8bad\u7ec3\u4e00\u5b9a\u7684epochs\u540e\u7684\u7cbe\u5ea6\u4ee5\u53ca\u7f51\u7edc\u5ef6\u65f6\u878d\u5408\u83b7\u5f97reward\uff0c\u6765\u6307\u5bfc\u9000\u706b\u7b97\u6cd5\u7684\u6536\u655b\u3002 4.2 \u641c\u7d22\u7a7a\u95f4 # \u641c\u7d22\u7a7a\u95f4\u5b9a\u4e49\u4e86\u4f18\u5316\u95ee\u9898\u7684\u53d8\u91cf\uff0c\u53d8\u91cf\u89c4\u6a21\u51b3\u5b9a\u4e86\u641c\u7d22\u7b97\u6cd5\u7684\u96be\u5ea6\u548c\u641c\u7d22\u65f6\u95f4\u3002\u56e0\u6b64\u4e3a\u4e86\u52a0\u5feb\u641c\u7d22\u901f\u5ea6\uff0c\u5b9a\u4e49\u4e00\u4e2a\u5408\u7406\u7684\u641c\u7d22\u7a7a\u95f4\u81f3\u5173\u91cd\u8981\u3002\u5728Light-NAS\u4e2d\uff0c\u4e3a\u4e86\u52a0\u901f\u641c\u7d22\u901f\u5ea6\uff0c\u6211\u4eec\u5c06\u4e00\u4e2a\u7f51\u7edc\u5212\u5206\u4e3a\u591a\u4e2ablock\uff0c\u5148\u624b\u52a8\u6309\u94fe\u72b6\u5c42\u7ea7\u7ed3\u6784\u5806\u53e0c\uff0c\u518d \u4f7f\u7528\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u641c\u7d22\u6bcf\u4e2ablock\u5185\u90e8\u7684\u7ed3\u6784\u3002 \u56e0\u4e3a\u8981\u641c\u7d22\u51fa\u5728\u79fb\u52a8\u7aef\u8fd0\u884c\u901f\u5ea6\u5feb\u7684\u6a21\u578b\uff0c\u6211\u4eec\u53c2\u8003\u4e86MobileNetV2\u4e2d\u7684Linear Bottlenecks\u548cInverted residuals\u7ed3\u6784\uff0c\u641c\u7d22\u6bcf\u4e00\u4e2aInverted residuals\u4e2d\u7684\u5177\u4f53\u53c2\u6570\uff0c\u5305\u62eckernelsize\u3001channel\u6269\u5f20\u500d\u6570\u3001\u91cd\u590d\u6b21\u6570\u3001channels number\u3002\u5982\u56fe10\u6240\u793a\uff1a \u56fe10 4.3 \u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30 # \u641c\u7d22\u8fc7\u7a0b\u652f\u6301 FLOPS \u7ea6\u675f\u548c\u6a21\u578b\u5ef6\u65f6\u7ea6\u675f\u3002\u800c\u57fa\u4e8e Android/iOS \u79fb\u52a8\u7aef\u3001\u5f00\u53d1\u677f\u7b49\u786c\u4ef6\u5e73\u53f0\uff0c\u8fed\u4ee3\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u6d4b\u8bd5\u6a21\u578b\u7684\u5ef6\u65f6\u4e0d\u4ec5\u6d88\u8017\u65f6\u95f4\u800c\u4e14\u975e\u5e38\u4e0d\u65b9\u4fbf\uff0c\u56e0\u6b64\u6211\u4eec\u5f00\u53d1\u4e86\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u6765\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u6a21\u578b\u7684\u5ef6\u65f6\u3002\u901a\u8fc7\u5ef6\u65f6\u8bc4\u4f30\u5668\u8bc4\u4f30\u5f97\u5230\u7684\u5ef6\u65f6\u4e0e\u6a21\u578b\u5b9e\u9645\u6d4b\u8bd5\u7684\u5ef6\u65f6\u6ce2\u52a8\u504f\u5dee\u5c0f\u4e8e 10%\u3002 \u5ef6\u65f6\u8bc4\u4f30\u5668\u5206\u4e3a\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u548c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u4e24\u4e2a\u9636\u6bb5\uff0c\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u53ea\u9700\u8981\u6267\u884c\u4e00\u6b21\uff0c\u800c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u5219\u5728\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u7684\u6a21\u578b\u5ef6\u65f6\u3002 \u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668 \u83b7\u53d6\u641c\u7d22\u7a7a\u95f4\u4e2d\u6240\u6709\u4e0d\u91cd\u590d\u7684 op \u53ca\u5176\u53c2\u6570 \u83b7\u53d6\u6bcf\u7ec4 op \u53ca\u5176\u53c2\u6570\u7684\u5ef6\u65f6 \u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6 \u83b7\u53d6\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u5176\u53c2\u6570 \u6839\u636e\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u53c2\u6570\uff0c\u5229\u7528\u5ef6\u65f6\u8bc4\u4f30\u5668\u53bb\u4f30\u8ba1\u6a21\u578b\u7684\u5ef6\u65f6 5. \u53c2\u8003\u6587\u732e # High-Performance Hardware for Machine Learning Quantizing deep convolutional networks for efficient inference: A whitepaper Pruning Filters for Efficient ConvNets Distilling the Knowledge in a Neural Network A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning","title":"\u7b97\u6cd5\u539f\u7406"},{"location":"algo/algo/#_1","text":"\u91cf\u5316\u539f\u7406\u4ecb\u7ecd \u526a\u88c1\u539f\u7406\u4ecb\u7ecd \u84b8\u998f\u539f\u7406\u4ecb\u7ecd \u8f7b\u91cf\u7ea7\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u539f\u7406\u4ecb\u7ecd","title":"\u76ee\u5f55"},{"location":"algo/algo/#1-quantization-aware-training","text":"","title":"1. Quantization Aware Training\u91cf\u5316\u4ecb\u7ecd"},{"location":"algo/algo/#11","text":"\u8fd1\u5e74\u6765\uff0c\u5b9a\u70b9\u91cf\u5316\u4f7f\u7528\u66f4\u5c11\u7684\u6bd4\u7279\u6570\uff08\u59828-bit\u30013-bit\u30012-bit\u7b49\uff09\u8868\u793a\u795e\u7ecf\u7f51\u7edc\u7684\u6743\u91cd\u548c\u6fc0\u6d3b\u5df2\u88ab\u9a8c\u8bc1\u662f\u6709\u6548\u7684\u3002\u5b9a\u70b9\u91cf\u5316\u7684\u4f18\u70b9\u5305\u62ec\u4f4e\u5185\u5b58\u5e26\u5bbd\u3001\u4f4e\u529f\u8017\u3001\u4f4e\u8ba1\u7b97\u8d44\u6e90\u5360\u7528\u4ee5\u53ca\u4f4e\u6a21\u578b\u5b58\u50a8\u9700\u6c42\u7b49\u3002 \u88681: \u4e0d\u540c\u7c7b\u578b\u64cd\u4f5c\u7684\u5f00\u9500\u5bf9\u6bd4 \u7531\u88681\u53ef\u77e5\uff0c\u4f4e\u7cbe\u5ea6\u5b9a\u70b9\u6570\u64cd\u4f5c\u7684\u786c\u4ef6\u9762\u79ef\u5927\u5c0f\u53ca\u80fd\u8017\u6bd4\u9ad8\u7cbe\u5ea6\u6d6e\u70b9\u6570\u8981\u5c11\u51e0\u4e2a\u6570\u91cf\u7ea7\u3002 \u4f7f\u7528\u5b9a\u70b9\u91cf\u5316\u53ef\u5e26\u67654\u500d\u7684\u6a21\u578b\u538b\u7f29\u30014\u500d\u7684\u5185\u5b58\u5e26\u5bbd\u63d0\u5347\uff0c\u4ee5\u53ca\u66f4\u9ad8\u6548\u7684cache\u5229\u7528(\u5f88\u591a\u786c\u4ef6\u8bbe\u5907\uff0c\u5185\u5b58\u8bbf\u95ee\u662f\u4e3b\u8981\u80fd\u8017)\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8ba1\u7b97\u901f\u5ea6\u4e5f\u4f1a\u66f4\u5feb(\u901a\u5e38\u5177\u67092x-3x\u7684\u6027\u80fd\u63d0\u5347)\u3002\u7531\u88682\u53ef\u77e5\uff0c\u5728\u5f88\u591a\u573a\u666f\u4e0b\uff0c\u5b9a\u70b9\u91cf\u5316\u64cd\u4f5c\u5bf9\u7cbe\u5ea6\u5e76\u4e0d\u4f1a\u9020\u6210\u635f\u5931\u3002\u53e6\u5916\uff0c\u5b9a\u70b9\u91cf\u5316\u5bf9\u795e\u7ecf\u7f51\u7edc\u4e8e\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u7684\u63a8\u65ad\u6765\u8bf4\u662f\u6781\u5176\u91cd\u8981\u7684\u3002 \u88682\uff1a\u6a21\u578b\u91cf\u5316\u524d\u540e\u7cbe\u5ea6\u5bf9\u6bd4 \u76ee\u524d\uff0c\u5b66\u672f\u754c\u4e3b\u8981\u5c06\u91cf\u5316\u5206\u4e3a\u4e24\u5927\u7c7b\uff1a Post Training Quantization \u548c Quantization Aware Training \u3002 Post Training Quantization \u662f\u6307\u4f7f\u7528KL\u6563\u5ea6\u3001\u6ed1\u52a8\u5e73\u5747\u7b49\u65b9\u6cd5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\u4e14\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\u7684\u5b9a\u70b9\u91cf\u5316\u65b9\u6cd5\u3002 Quantization Aware Training \u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u91cf\u5316\u8fdb\u884c\u5efa\u6a21\u4ee5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\uff0c\u5b83\u4e0e Post Training Quantization \u6a21\u5f0f\u76f8\u6bd4\u53ef\u4ee5\u63d0\u4f9b\u66f4\u9ad8\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002","title":"1.1 \u80cc\u666f"},{"location":"algo/algo/#12","text":"","title":"1.2 \u91cf\u5316\u539f\u7406"},{"location":"algo/algo/#121","text":"\u76ee\u524d\uff0c\u5b58\u5728\u7740\u8bb8\u591a\u65b9\u6cd5\u53ef\u4ee5\u5c06\u6d6e\u70b9\u6570\u91cf\u5316\u6210\u5b9a\u70b9\u6570\u3002\u4f8b\u5982\uff1a r = min(max(x, a), b) s = \\frac{b - a}{n - 1} q = \\left \\lfloor \\frac{r - a}{s} \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c [a, b] [a, b] \u662f\u91cf\u5316\u8303\u56f4\uff0c a a \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5c0f\u503c\uff0c b b \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5982\u679c\u91cf\u5316\u7ea7\u522b\u4e3a k k \uff0c\u5219 n n \u4e3a 2^k 2^k \u3002\u4f8b\u5982\uff0c\u82e5 k k \u4e3a8\uff0c\u5219 n n \u4e3a256\u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 PaddleSlim\u6846\u67b6\u4e2d\u9009\u62e9\u7684\u91cf\u5316\u65b9\u6cd5\u4e3a\u6700\u5927\u7edd\u5bf9\u503c\u91cf\u5316( max-abs )\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a M = max(abs(x)) q = \\left \\lfloor \\frac{x}{M} * (n - 1) \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u88ab\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c M M \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u7edd\u5bf9\u503c\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5bf9\u4e8e8bit\u91cf\u5316\uff0cPaddleSlim\u91c7\u7528 int8_t \uff0c\u5373 n=2^7=128 n=2^7=128 \u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 \u65e0\u8bba\u662f min-max\u91cf\u5316 \u8fd8\u662f max-abs\u91cf\u5316 \uff0c\u4ed6\u4eec\u90fd\u53ef\u4ee5\u8868\u793a\u4e3a\u5982\u4e0b\u5f62\u5f0f\uff1a q = scale * r + b q = scale * r + b \u5176\u4e2d min-max \u548c max-abs \u88ab\u79f0\u4e3a\u91cf\u5316\u53c2\u6570\u6216\u8005\u91cf\u5316\u6bd4\u4f8b\u6216\u8005\u91cf\u5316\u8303\u56f4\u3002","title":"1.2.1 \u91cf\u5316\u65b9\u5f0f"},{"location":"algo/algo/#122","text":"","title":"1.2.2 \u91cf\u5316\u8bad\u7ec3"},{"location":"algo/algo/#1221","text":"\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b\u91c7\u7528\u6a21\u62df\u91cf\u5316\u7684\u65b9\u5f0f\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a \u56fe1\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b \u7531\u56fe1\u53ef\u77e5\uff0c\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b\u53ef\u88ab\u63cf\u8ff0\u4e3a\u4ee5\u4e0b\u56db\u4e2a\u90e8\u5206\uff1a 1) \u8f93\u5165\u548c\u6743\u91cd\u5747\u88ab\u91cf\u5316\u62108-bit\u6574\u6570\u3002 2) \u57288-bit\u6574\u6570\u4e0a\u6267\u884c\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u3002 3) \u53cd\u91cf\u5316\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u7684\u8f93\u51fa\u7ed3\u679c\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002 4) \u572832-bit\u6d6e\u70b9\u578b\u6570\u636e\u4e0a\u6267\u884c\u504f\u7f6e\u52a0\u6cd5\u64cd\u4f5c\u3002\u6b64\u5904\uff0c\u504f\u7f6e\u5e76\u672a\u88ab\u91cf\u5316\u3002 \u5bf9\u4e8e\u901a\u7528\u77e9\u9635\u4e58\u6cd5( GEMM )\uff0c\u8f93\u5165 X X \u548c\u6743\u91cd W W \u7684\u91cf\u5316\u64cd\u4f5c\u53ef\u88ab\u8868\u8ff0\u4e3a\u5982\u4e0b\u8fc7\u7a0b\uff1a X_q = \\left \\lfloor \\frac{X}{X_m} * (n - 1) \\right \\rceil W_q = \\left \\lfloor \\frac{W}{W_m} * (n - 1) \\right \\rceil \u6267\u884c\u901a\u7528\u77e9\u9635\u4e58\u6cd5\uff1a Y_q = X_q * W_q \u5bf9\u91cf\u5316\u4e58\u79ef\u7ed3\u679c Yq Yq \u8fdb\u884c\u53cd\u91cf\u5316: \\begin{align} Y_{dq} = \\frac{Y_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =\\frac{X_q * W_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =(\\frac{X_q}{n - 1} * X_m) * (\\frac{W_q}{n - 1} * W_m) \\ \\end{align} \u4e0a\u8ff0\u516c\u5f0f\u8868\u660e\u53cd\u91cf\u5316\u64cd\u4f5c\u53ef\u4ee5\u88ab\u79fb\u52a8\u5230 GEMM \u4e4b\u524d\uff0c\u5373\u5148\u5bf9 Xq Xq \u548c Wq Wq \u6267\u884c\u53cd\u91cf\u5316\u64cd\u4f5c\u518d\u505a GEMM \u64cd\u4f5c\u3002\u56e0\u6b64\uff0c\u524d\u5411\u4f20\u64ad\u7684\u5de5\u4f5c\u6d41\u4ea6\u53ef\u8868\u793a\u4e3a\u5982\u4e0b\u65b9\u5f0f\uff1a \u56fe2\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u524d\u5411\u8fc7\u7a0b\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41 \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cPaddleSlim\u4f7f\u7528\u56fe2\u4e2d\u6240\u793a\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41\u3002\u5728\u8bbe\u8ba1\u4e2d\uff0c\u91cf\u5316Pass\u5728IrGraph\u4e2d\u63d2\u5165\u91cf\u5316op\u548c\u53cd\u91cf\u5316op\u3002\u56e0\u4e3a\u5728\u8fde\u7eed\u7684\u91cf\u5316\u3001\u53cd\u91cf\u5316\u64cd\u4f5c\u4e4b\u540e\u8f93\u5165\u4ecd\u7136\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u56e0\u6b64\uff0cPaddleSlim\u91cf\u5316\u8bad\u7ec3\u6846\u67b6\u6240\u91c7\u7528\u7684\u91cf\u5316\u65b9\u5f0f\u88ab\u79f0\u4e3a\u6a21\u62df\u91cf\u5316\u3002","title":"1.2.2.1 \u524d\u5411\u4f20\u64ad"},{"location":"algo/algo/#1222","text":"\u7531\u56fe3\u53ef\u77e5\uff0c\u6743\u91cd\u66f4\u65b0\u6240\u9700\u7684\u68af\u5ea6\u503c\u53ef\u4ee5\u7531\u91cf\u5316\u540e\u7684\u6743\u91cd\u548c\u91cf\u5316\u540e\u7684\u6fc0\u6d3b\u6c42\u5f97\u3002\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u7684\u6240\u6709\u8f93\u5165\u548c\u8f93\u51fa\u5747\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u6ce8\u610f\uff0c\u68af\u5ea6\u66f4\u65b0\u64cd\u4f5c\u9700\u8981\u5728\u539f\u59cb\u6743\u91cd\u4e0a\u8fdb\u884c\uff0c\u5373\u8ba1\u7b97\u51fa\u7684\u68af\u5ea6\u5c06\u88ab\u52a0\u5230\u539f\u59cb\u6743\u91cd\u4e0a\u800c\u975e\u91cf\u5316\u540e\u6216\u53cd\u91cf\u5316\u540e\u7684\u6743\u91cd\u4e0a\u3002 \u56fe3\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u53cd\u5411\u4f20\u64ad\u548c\u6743\u91cd\u66f4\u65b0\u8fc7\u7a0b \u56e0\u6b64\uff0c\u91cf\u5316Pass\u4e5f\u4f1a\u6539\u53d8\u76f8\u5e94\u53cd\u5411\u7b97\u5b50\u7684\u67d0\u4e9b\u8f93\u5165\u3002","title":"1.2.2.2 \u53cd\u5411\u4f20\u64ad"},{"location":"algo/algo/#1223","text":"\u5b58\u5728\u7740\u4e24\u79cd\u7b56\u7565\u53ef\u4ee5\u8ba1\u7b97\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\uff0c\u5373\u52a8\u6001\u7b56\u7565\u548c\u9759\u6001\u7b56\u7565\u3002\u52a8\u6001\u7b56\u7565\u4f1a\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u7684\u503c\u3002\u9759\u6001\u7b56\u7565\u5219\u5bf9\u4e0d\u540c\u7684\u8f93\u5165\u91c7\u7528\u76f8\u540c\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u3002 \u5bf9\u4e8e\u6743\u91cd\u800c\u8a00\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u91c7\u7528\u52a8\u6001\u7b56\u7565\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u5747\u4f1a\u88ab\u91cd\u65b0\u8ba1\u7b97\u5f97\u5230\u76f4\u81f3\u8bad\u7ec3\u8fc7\u7a0b\u7ed3\u675f\u3002 \u5bf9\u4e8e\u6fc0\u6d3b\u800c\u8a00\uff0c\u53ef\u4ee5\u9009\u62e9\u52a8\u6001\u7b56\u7565\u4e5f\u53ef\u4ee5\u9009\u62e9\u9759\u6001\u7b56\u7565\u3002\u82e5\u9009\u62e9\u4f7f\u7528\u9759\u6001\u7b56\u7565\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u4f1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u88ab\u8bc4\u4f30\u6c42\u5f97\uff0c\u4e14\u5728\u63a8\u65ad\u8fc7\u7a0b\u4e2d\u88ab\u4f7f\u7528(\u4e0d\u540c\u7684\u8f93\u5165\u5747\u4fdd\u6301\u4e0d\u53d8)\u3002\u9759\u6001\u7b56\u7565\u4e2d\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u53ef\u4e8e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u5982\u4e0b\u4e09\u79cd\u65b9\u5f0f\u8fdb\u884c\u8bc4\u4f30\uff1a \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u5e73\u5747\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6ed1\u52a8\u5e73\u5747\u503c\uff0c\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a Vt = (1 - k) * V + k * V_{t-1} Vt = (1 - k) * V + k * V_{t-1} \u5f0f\u4e2d\uff0c V V \u662f\u5f53\u524dbatch\u7684\u6700\u5927\u7edd\u5bf9\u503c\uff0c Vt Vt \u662f\u6ed1\u52a8\u5e73\u5747\u503c\u3002 k k \u662f\u4e00\u4e2a\u56e0\u5b50\uff0c\u4f8b\u5982\u5176\u503c\u53ef\u53d6\u4e3a0.9\u3002","title":"1.2.2.3 \u786e\u5b9a\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570"},{"location":"algo/algo/#124","text":"\u8bad\u7ec3\u540e\u91cf\u5316\u662f\u57fa\u4e8e\u91c7\u6837\u6570\u636e\uff0c\u91c7\u7528KL\u6563\u5ea6\u7b49\u65b9\u6cd5\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7684\u65b9\u6cd5\u3002\u76f8\u6bd4\u91cf\u5316\u8bad\u7ec3\uff0c\u8bad\u7ec3\u540e\u91cf\u5316\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\uff0c\u53ef\u4ee5\u5feb\u901f\u5f97\u5230\u91cf\u5316\u6a21\u578b\u3002 \u8bad\u7ec3\u540e\u91cf\u5316\u7684\u76ee\u6807\u662f\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\uff0c\u4e3b\u8981\u6709\u4e24\u79cd\u65b9\u6cd5\uff1a\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5 ( No Saturation) \u548c\u9971\u548c\u91cf\u5316\u65b9\u6cd5 (Saturation)\u3002\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u8ba1\u7b97FP32\u7c7b\u578bTensor\u4e2d\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c abs_max \uff0c\u5c06\u5176\u6620\u5c04\u4e3a127\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7b49\u4e8e abs_max/127 \u3002\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u4f7f\u7528KL\u6563\u5ea6\u8ba1\u7b97\u4e00\u4e2a\u5408\u9002\u7684\u9608\u503c T ( 0=r \\end{cases} \\end{equation} \\begin{equation} P(r_k) = \\begin{cases} e^{\\frac{(r_k-r)}{T_k}} & r_k < r\\\\ 1 & r_k>=r \\end{cases} \\end{equation} \u5728\u7b2ck\u6b21\u8fed\u4ee3\uff0c\u641c\u5230\u7684\u7f51\u7edc\u4e3a N_k N_k , \u5bf9 N_k N_k \u8bad\u7ec3\u82e5\u5e72epoch\u540e\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u5f97\u5230reward\u4e3a r_k r_k , \u4ee5\u6982\u7387 P(r_k) P(r_k) \u63a5\u53d7 r_k r_k \uff0c\u5373\u6267\u884c r=r_k r=r_k \u3002 r r \u5728\u641c\u7d22\u8fc7\u7a0b\u8d77\u59cb\u65f6\u88ab\u521d\u59cb\u5316\u4e3a0. T_0 T_0 \u4e3a\u521d\u59cb\u5316\u6e29\u5ea6\uff0c \\theta \\theta \u4e3a\u6e29\u5ea6\u8870\u51cf\u7cfb\u6570\uff0c T_k T_k \u4e3a\u7b2ck\u6b21\u8fed\u4ee3\u7684\u6e29\u5ea6\u3002 \u5728\u6211\u4eec\u7684NAS\u4efb\u52a1\u4e2d\uff0c\u533a\u522b\u4e8eRL\u6bcf\u6b21\u91cd\u65b0\u751f\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u7f51\u7edc\uff0c\u6211\u4eec\u5c06\u7f51\u7edc\u7ed3\u6784\u6620\u5c04\u6210\u4e00\u6bb5\u7f16\u7801\uff0c\u7b2c\u4e00\u6b21\u968f\u673a\u521d\u59cb\u5316\uff0c\u7136\u540e\u6bcf\u6b21\u968f\u673a\u4fee\u6539\u7f16\u7801\u4e2d\u7684\u4e00\u90e8\u5206\uff08\u5bf9\u5e94\u4e8e\u7f51\u7edc\u7ed3\u6784\u7684\u4e00\u90e8\u5206\uff09\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u7f16\u7801\uff0c\u7136\u540e\u5c06\u8fd9\u4e2a\u7f16\u7801\u518d\u6620\u5c04\u56de\u7f51\u7edc\u7ed3\u6784\uff0c\u901a\u8fc7\u5728\u8bad\u7ec3\u96c6\u4e0a\u8bad\u7ec3\u4e00\u5b9a\u7684epochs\u540e\u7684\u7cbe\u5ea6\u4ee5\u53ca\u7f51\u7edc\u5ef6\u65f6\u878d\u5408\u83b7\u5f97reward\uff0c\u6765\u6307\u5bfc\u9000\u706b\u7b97\u6cd5\u7684\u6536\u655b\u3002","title":"4.1.1 \u6a21\u62df\u9000\u706b"},{"location":"algo/algo/#42","text":"\u641c\u7d22\u7a7a\u95f4\u5b9a\u4e49\u4e86\u4f18\u5316\u95ee\u9898\u7684\u53d8\u91cf\uff0c\u53d8\u91cf\u89c4\u6a21\u51b3\u5b9a\u4e86\u641c\u7d22\u7b97\u6cd5\u7684\u96be\u5ea6\u548c\u641c\u7d22\u65f6\u95f4\u3002\u56e0\u6b64\u4e3a\u4e86\u52a0\u5feb\u641c\u7d22\u901f\u5ea6\uff0c\u5b9a\u4e49\u4e00\u4e2a\u5408\u7406\u7684\u641c\u7d22\u7a7a\u95f4\u81f3\u5173\u91cd\u8981\u3002\u5728Light-NAS\u4e2d\uff0c\u4e3a\u4e86\u52a0\u901f\u641c\u7d22\u901f\u5ea6\uff0c\u6211\u4eec\u5c06\u4e00\u4e2a\u7f51\u7edc\u5212\u5206\u4e3a\u591a\u4e2ablock\uff0c\u5148\u624b\u52a8\u6309\u94fe\u72b6\u5c42\u7ea7\u7ed3\u6784\u5806\u53e0c\uff0c\u518d \u4f7f\u7528\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u641c\u7d22\u6bcf\u4e2ablock\u5185\u90e8\u7684\u7ed3\u6784\u3002 \u56e0\u4e3a\u8981\u641c\u7d22\u51fa\u5728\u79fb\u52a8\u7aef\u8fd0\u884c\u901f\u5ea6\u5feb\u7684\u6a21\u578b\uff0c\u6211\u4eec\u53c2\u8003\u4e86MobileNetV2\u4e2d\u7684Linear Bottlenecks\u548cInverted residuals\u7ed3\u6784\uff0c\u641c\u7d22\u6bcf\u4e00\u4e2aInverted residuals\u4e2d\u7684\u5177\u4f53\u53c2\u6570\uff0c\u5305\u62eckernelsize\u3001channel\u6269\u5f20\u500d\u6570\u3001\u91cd\u590d\u6b21\u6570\u3001channels number\u3002\u5982\u56fe10\u6240\u793a\uff1a \u56fe10","title":"4.2 \u641c\u7d22\u7a7a\u95f4"},{"location":"algo/algo/#43","text":"\u641c\u7d22\u8fc7\u7a0b\u652f\u6301 FLOPS \u7ea6\u675f\u548c\u6a21\u578b\u5ef6\u65f6\u7ea6\u675f\u3002\u800c\u57fa\u4e8e Android/iOS \u79fb\u52a8\u7aef\u3001\u5f00\u53d1\u677f\u7b49\u786c\u4ef6\u5e73\u53f0\uff0c\u8fed\u4ee3\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u6d4b\u8bd5\u6a21\u578b\u7684\u5ef6\u65f6\u4e0d\u4ec5\u6d88\u8017\u65f6\u95f4\u800c\u4e14\u975e\u5e38\u4e0d\u65b9\u4fbf\uff0c\u56e0\u6b64\u6211\u4eec\u5f00\u53d1\u4e86\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u6765\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u6a21\u578b\u7684\u5ef6\u65f6\u3002\u901a\u8fc7\u5ef6\u65f6\u8bc4\u4f30\u5668\u8bc4\u4f30\u5f97\u5230\u7684\u5ef6\u65f6\u4e0e\u6a21\u578b\u5b9e\u9645\u6d4b\u8bd5\u7684\u5ef6\u65f6\u6ce2\u52a8\u504f\u5dee\u5c0f\u4e8e 10%\u3002 \u5ef6\u65f6\u8bc4\u4f30\u5668\u5206\u4e3a\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u548c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u4e24\u4e2a\u9636\u6bb5\uff0c\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u53ea\u9700\u8981\u6267\u884c\u4e00\u6b21\uff0c\u800c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u5219\u5728\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u7684\u6a21\u578b\u5ef6\u65f6\u3002 \u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668 \u83b7\u53d6\u641c\u7d22\u7a7a\u95f4\u4e2d\u6240\u6709\u4e0d\u91cd\u590d\u7684 op \u53ca\u5176\u53c2\u6570 \u83b7\u53d6\u6bcf\u7ec4 op \u53ca\u5176\u53c2\u6570\u7684\u5ef6\u65f6 \u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6 \u83b7\u53d6\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u5176\u53c2\u6570 \u6839\u636e\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u53c2\u6570\uff0c\u5229\u7528\u5ef6\u65f6\u8bc4\u4f30\u5668\u53bb\u4f30\u8ba1\u6a21\u578b\u7684\u5ef6\u65f6","title":"4.3 \u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30"},{"location":"algo/algo/#5","text":"High-Performance Hardware for Machine Learning Quantizing deep convolutional networks for efficient inference: A whitepaper Pruning Filters for Efficient ConvNets Distilling the Knowledge in a Neural Network A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning","title":"5. \u53c2\u8003\u6587\u732e"},{"location":"api/analysis_api/","text":"FLOPs # paddleslim.analysis.flops(program, detail=False) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570(FLOPs)\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 detail(bool) - \u662f\u5426\u8fd4\u56de\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684FLOPs\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 only_conv(bool) - \u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4ec5\u8ba1\u7b97\u5377\u79ef\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u7684FLOPs\uff0c\u5373\u6d6e\u70b9\u6570\u7684\u4e58\u52a0\uff08multiplication-adds\uff09\u64cd\u4f5c\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aFalse\uff0c\u5219\u4e5f\u4f1a\u8ba1\u7b97\u5377\u79ef\u548c\u5168\u8fde\u63a5\u5c42\u4e4b\u5916\u7684\u64cd\u4f5c\u7684FLOPs\u3002 \u8fd4\u56de\u503c\uff1a flops(float) - \u6574\u4e2a\u7f51\u7edc\u7684FLOPs\u3002 params2flops(dict) - \u6bcf\u5c42\u5377\u79ef\u5bf9\u5e94\u7684FLOPs\uff0c\u5176\u4e2dkey\u4e3a\u5377\u79ef\u5c42\u53c2\u6570\u540d\u79f0\uff0cvalue\u4e3aFLOPs\u503c\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import flops def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( flops ( main_program ))) model_size # paddleslim.analysis.model_size(program) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 \u8fd4\u56de\u503c\uff1a model_size(int) - \u6574\u4e2a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import model_size def conv_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) return conv main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( model_size ( main_program ))) TableLatencyEvaluator # paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=\",\") \u6e90\u4ee3\u7801 \u57fa\u4e8e\u786c\u4ef6\u5ef6\u65f6\u8868\u7684\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u3002 \u53c2\u6570\uff1a table_file(str) - \u6240\u4f7f\u7528\u7684\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u7edd\u5bf9\u8def\u5f84\u3002\u5173\u4e8e\u6f14\u793a\u8bc4\u4f30\u8868\u683c\u5f0f\u8bf7\u53c2\u8003\uff1a PaddleSlim\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u683c\u5f0f delimiter(str) - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\uff0c\u64cd\u4f5c\u4fe1\u606f\u4e4b\u524d\u6240\u4f7f\u7528\u7684\u5206\u5272\u7b26\uff0c\u9ed8\u8ba4\u4e3a\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u3002 \u8fd4\u56de\u503c\uff1a Evaluator - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u7684\u5b9e\u4f8b\u3002 paddleslim.analysis.TableLatencyEvaluator.latency(graph) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002 \u53c2\u6570\uff1a graph(Program) - \u5f85\u9884\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002 \u8fd4\u56de\u503c\uff1a latency - \u76ee\u6807\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002","title":"\u6a21\u578b\u5206\u6790"},{"location":"api/analysis_api/#flops","text":"paddleslim.analysis.flops(program, detail=False) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570(FLOPs)\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 detail(bool) - \u662f\u5426\u8fd4\u56de\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684FLOPs\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 only_conv(bool) - \u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4ec5\u8ba1\u7b97\u5377\u79ef\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u7684FLOPs\uff0c\u5373\u6d6e\u70b9\u6570\u7684\u4e58\u52a0\uff08multiplication-adds\uff09\u64cd\u4f5c\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aFalse\uff0c\u5219\u4e5f\u4f1a\u8ba1\u7b97\u5377\u79ef\u548c\u5168\u8fde\u63a5\u5c42\u4e4b\u5916\u7684\u64cd\u4f5c\u7684FLOPs\u3002 \u8fd4\u56de\u503c\uff1a flops(float) - \u6574\u4e2a\u7f51\u7edc\u7684FLOPs\u3002 params2flops(dict) - \u6bcf\u5c42\u5377\u79ef\u5bf9\u5e94\u7684FLOPs\uff0c\u5176\u4e2dkey\u4e3a\u5377\u79ef\u5c42\u53c2\u6570\u540d\u79f0\uff0cvalue\u4e3aFLOPs\u503c\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import flops def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( flops ( main_program )))","title":"FLOPs"},{"location":"api/analysis_api/#model_size","text":"paddleslim.analysis.model_size(program) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 \u8fd4\u56de\u503c\uff1a model_size(int) - \u6574\u4e2a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import model_size def conv_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) return conv main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( model_size ( main_program )))","title":"model_size"},{"location":"api/analysis_api/#tablelatencyevaluator","text":"paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=\",\") \u6e90\u4ee3\u7801 \u57fa\u4e8e\u786c\u4ef6\u5ef6\u65f6\u8868\u7684\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u3002 \u53c2\u6570\uff1a table_file(str) - \u6240\u4f7f\u7528\u7684\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u7edd\u5bf9\u8def\u5f84\u3002\u5173\u4e8e\u6f14\u793a\u8bc4\u4f30\u8868\u683c\u5f0f\u8bf7\u53c2\u8003\uff1a PaddleSlim\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u683c\u5f0f delimiter(str) - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\uff0c\u64cd\u4f5c\u4fe1\u606f\u4e4b\u524d\u6240\u4f7f\u7528\u7684\u5206\u5272\u7b26\uff0c\u9ed8\u8ba4\u4e3a\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u3002 \u8fd4\u56de\u503c\uff1a Evaluator - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u7684\u5b9e\u4f8b\u3002 paddleslim.analysis.TableLatencyEvaluator.latency(graph) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002 \u53c2\u6570\uff1a graph(Program) - \u5f85\u9884\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002 \u8fd4\u56de\u503c\uff1a latency - \u76ee\u6807\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002","title":"TableLatencyEvaluator"},{"location":"api/api_guide/","text":"PaddleSlim API\u6587\u6863\u5bfc\u822a # \u6a21\u578b\u5206\u6790 # \u5377\u79ef\u901a\u9053\u526a\u88c1 # \u84b8\u998f # \u5355\u8fdb\u7a0b\u84b8\u998f \u901a\u9053\u526a\u88c1 \u91cf\u5316 # \u91cf\u5316\u8bad\u7ec3 \u79bb\u7ebf\u91cf\u5316 embedding\u91cf\u5316 \u5c0f\u6a21\u578b\u7ed3\u6784\u641c\u7d22 # nas API SearchSpace","title":"PaddleSlim API\u6587\u6863\u5bfc\u822a"},{"location":"api/api_guide/#paddleslim-api","text":"","title":"PaddleSlim API\u6587\u6863\u5bfc\u822a"},{"location":"api/api_guide/#_1","text":"","title":"\u6a21\u578b\u5206\u6790"},{"location":"api/api_guide/#_2","text":"","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1"},{"location":"api/api_guide/#_3","text":"\u5355\u8fdb\u7a0b\u84b8\u998f \u901a\u9053\u526a\u88c1","title":"\u84b8\u998f"},{"location":"api/api_guide/#_4","text":"\u91cf\u5316\u8bad\u7ec3 \u79bb\u7ebf\u91cf\u5316 embedding\u91cf\u5316","title":"\u91cf\u5316"},{"location":"api/api_guide/#_5","text":"nas API SearchSpace","title":"\u5c0f\u6a21\u578b\u7ed3\u6784\u641c\u7d22"},{"location":"api/nas_api/","text":"paddleslim.nas API\u6587\u6863 # SANAS API\u6587\u6863 # class SANAS # SANAS\uff08Simulated Annealing Neural Architecture Search\uff09\u662f\u57fa\u4e8e\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u8fdb\u884c\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u7684\u7b97\u6cd5\uff0c\u4e00\u822c\u7528\u4e8e\u79bb\u6563\u641c\u7d22\u4efb\u52a1\u3002 paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server) \u53c2\u6570\uff1a - configs(list ): \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u5217\u8868\uff0c\u683c\u5f0f\u662f [(key, {input_size, output_size, block_num, block_mask})] \u6216\u8005 [(key)] \uff08MobileNetV2\u3001MobilenetV1\u548cResNet\u7684\u641c\u7d22\u7a7a\u95f4\u4f7f\u7528\u548c\u539f\u672c\u7f51\u7edc\u7ed3\u6784\u76f8\u540c\u7684\u641c\u7d22\u7a7a\u95f4\uff0c\u6240\u4ee5\u4ec5\u9700\u6307\u5b9a key \u5373\u53ef\uff09, input_size \u548c output_size \u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u7684\u5927\u5c0f\uff0c block_num \u662f\u6307\u641c\u7d22\u7f51\u7edc\u4e2d\u7684block\u6570\u91cf\uff0c block_mask \u662f\u4e00\u7ec4\u75310\u548c1\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u4ee3\u8868\u4e0d\u8fdb\u884c\u4e0b\u91c7\u6837\u7684block\uff0c1\u4ee3\u8868\u4e0b\u91c7\u6837\u7684block\u3002 \u66f4\u591apaddleslim\u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003\u3002 - server_addr(tuple): SANAS\u7684\u5730\u5740\uff0c\u5305\u62ecserver\u7684ip\u5730\u5740\u548c\u7aef\u53e3\u53f7\uff0c\u5982\u679cip\u5730\u5740\u4e3aNone\u6216\u8005\u4e3a\"\"\u7684\u8bdd\u5219\u9ed8\u8ba4\u4f7f\u7528\u672c\u673aip\u3002\u9ed8\u8ba4\uff1a\uff08\"\", 8881\uff09\u3002 - init_temperature(float): \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u521d\u59cb\u6e29\u5ea6\u3002\u9ed8\u8ba4\uff1a100\u3002 - reduce_rate(float): \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u8870\u51cf\u7387\u3002\u9ed8\u8ba4\uff1a0.85\u3002 - search_steps(int): \u641c\u7d22\u8fc7\u7a0b\u8fed\u4ee3\u7684\u6b21\u6570\u3002\u9ed8\u8ba4\uff1a300\u3002 - save_checkpoint(str|None): \u4fdd\u5b58checkpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u4fdd\u5b58checkpoint\u3002\u9ed8\u8ba4\uff1a ./nas_checkpoint \u3002 - load_checkpoint(str|None): \u52a0\u8f7dcheckpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u52a0\u8f7dcheckpoint\u3002\u9ed8\u8ba4\uff1aNone\u3002 - is_server(bool): \u5f53\u524d\u5b9e\u4f8b\u662f\u5426\u8981\u542f\u52a8\u4e00\u4e2aserver\u3002\u9ed8\u8ba4\uff1aTrue\u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aSANAS\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 from paddleslim.nas import SANAS config = [( 'MobileNetV2Space' )] sanas = SANAS ( config = config ) tokens2arch(tokens) \u901a\u8fc7\u4e00\u7ec4token\u5f97\u5230\u5b9e\u9645\u7684\u6a21\u578b\u7ed3\u6784\uff0c\u4e00\u822c\u7528\u6765\u628a\u641c\u7d22\u5230\u6700\u4f18\u7684token\u8f6c\u6362\u4e3a\u6a21\u578b\u7ed3\u6784\u7528\u6765\u505a\u6700\u540e\u7684\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a - tokens(list): \u4e00\u7ec4token\u3002 \u8fd4\u56de \u8fd4\u56de\u4e00\u4e2a\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . token2arch ( tokens ) for arch in archs : output = arch ( input ) input = output next_archs(): \u83b7\u53d6\u4e0b\u4e00\u7ec4\u6a21\u578b\u7ed3\u6784\u3002 \u8fd4\u56de \u8fd4\u56de\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u7684\u5217\u8868\uff0c\u5f62\u5f0f\u4e3alist\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . next_archs () for arch in archs : output = arch ( input ) input = output reward(score): \u628a\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u7684\u5f97\u5206\u60c5\u51b5\u56de\u4f20\u3002 \u53c2\u6570\uff1a score : \u5f53\u524d\u6a21\u578b\u7684\u5f97\u5206\uff0c\u5206\u6570\u8d8a\u5927\u8d8a\u597d\u3002 \u8fd4\u56de \u6a21\u578b\u7ed3\u6784\u66f4\u65b0\u6210\u529f\u6216\u8005\u5931\u8d25\uff0c\u6210\u529f\u5219\u8fd4\u56de True \uff0c\u5931\u8d25\u5219\u8fd4\u56de False \u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 import numpy as np import paddle import paddle.fluid as fluid from paddleslim.nas import SANAS from paddleslim.analysis import flops max_flops = 321208544 batch_size = 256 # \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e config = [( 'MobileNetV2Space' )] # \u5b9e\u4f8b\u5316SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8887 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 100 , is_server = True ) for step in range ( 100 ): archs = sa_nas . next_archs () train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () ### \u6784\u9020\u8bad\u7ec3program with fluid . program_guard ( train_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : output = arch ( image ) out = fluid . layers . fc ( output , size = 10 , act = \"softmax\" ) softmax_out = fluid . layers . softmax ( input = out , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) ### \u6784\u9020\u6d4b\u8bd5program test_program = train_program . clone ( for_test = True ) ### \u5b9a\u4e49\u4f18\u5316\u5668 sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost ) ### \u589e\u52a0\u9650\u5236\u6761\u4ef6\uff0c\u5982\u679c\u6ca1\u6709\u5219\u8fdb\u884c\u65e0\u9650\u5236\u641c\u7d22 if flops ( train_program ) > max_flops : continue ### \u5b9a\u4e49\u4ee3\u7801\u662f\u5728cpu\u4e0a\u8fd0\u884c place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) ### \u5b9a\u4e49\u8bad\u7ec3\u8f93\u5165\u6570\u636e train_reader = paddle . batch ( paddle . reader . shuffle ( paddle . dataset . cifar . train10 ( cycle = False ), buf_size = 1024 ), batch_size = batch_size , drop_last = True ) ### \u5b9a\u4e49\u9884\u6d4b\u8f93\u5165\u6570\u636e test_reader = paddle . batch ( paddle . dataset . cifar . test10 ( cycle = False ), batch_size = batch_size , drop_last = False ) train_feeder = fluid . DataFeeder ([ image , label ], place , program = train_program ) test_feeder = fluid . DataFeeder ([ image , label ], place , program = test_program ) ### \u5f00\u59cb\u8bad\u7ec3\uff0c\u6bcf\u4e2a\u641c\u7d22\u7ed3\u679c\u8bad\u7ec35\u4e2aepoch for epoch_id in range ( 5 ): for batch_id , data in enumerate ( train_reader ()): fetches = [ avg_cost . name ] outs = exe . run ( train_program , feed = train_feeder . feed ( data ), fetch_list = fetches )[ 0 ] if batch_id % 10 == 0 : print ( 'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}' . format ( step , epoch_id , batch_id , outs [ 0 ])) ### \u5f00\u59cb\u9884\u6d4b\uff0c\u5f97\u5230\u6700\u7ec8\u7684\u6d4b\u8bd5\u7ed3\u679c\u4f5c\u4e3ascore\u56de\u4f20\u7ed9sa_nas reward = [] for batch_id , data in enumerate ( test_reader ()): test_fetches = [ avg_cost . name , acc_top1 . name ] batch_reward = exe . run ( test_program , feed = test_feeder . feed ( data ), fetch_list = test_fetches ) reward_avg = np . mean ( np . array ( batch_reward ), axis = 1 ) reward . append ( reward_avg ) print ( 'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}' . format ( step , batch_id , batch_reward [ 0 ], batch_reward [ 1 ])) finally_reward = np . mean ( np . array ( reward ), axis = 0 ) print ( 'FINAL TEST: avg_cost: {}, acc_top1: {}' . format ( finally_reward [ 0 ], finally_reward [ 1 ])) ### \u56de\u4f20score sa_nas . reward ( float ( finally_reward [ 1 ]))","title":"SA\u641c\u7d22"},{"location":"api/nas_api/#paddleslimnas-api","text":"","title":"paddleslim.nas API\u6587\u6863"},{"location":"api/nas_api/#sanas-api","text":"","title":"SANAS API\u6587\u6863"},{"location":"api/nas_api/#class-sanas","text":"SANAS\uff08Simulated Annealing Neural Architecture Search\uff09\u662f\u57fa\u4e8e\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u8fdb\u884c\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u7684\u7b97\u6cd5\uff0c\u4e00\u822c\u7528\u4e8e\u79bb\u6563\u641c\u7d22\u4efb\u52a1\u3002 paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server) \u53c2\u6570\uff1a - configs(list ): \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u5217\u8868\uff0c\u683c\u5f0f\u662f [(key, {input_size, output_size, block_num, block_mask})] \u6216\u8005 [(key)] \uff08MobileNetV2\u3001MobilenetV1\u548cResNet\u7684\u641c\u7d22\u7a7a\u95f4\u4f7f\u7528\u548c\u539f\u672c\u7f51\u7edc\u7ed3\u6784\u76f8\u540c\u7684\u641c\u7d22\u7a7a\u95f4\uff0c\u6240\u4ee5\u4ec5\u9700\u6307\u5b9a key \u5373\u53ef\uff09, input_size \u548c output_size \u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u7684\u5927\u5c0f\uff0c block_num \u662f\u6307\u641c\u7d22\u7f51\u7edc\u4e2d\u7684block\u6570\u91cf\uff0c block_mask \u662f\u4e00\u7ec4\u75310\u548c1\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u4ee3\u8868\u4e0d\u8fdb\u884c\u4e0b\u91c7\u6837\u7684block\uff0c1\u4ee3\u8868\u4e0b\u91c7\u6837\u7684block\u3002 \u66f4\u591apaddleslim\u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003\u3002 - server_addr(tuple): SANAS\u7684\u5730\u5740\uff0c\u5305\u62ecserver\u7684ip\u5730\u5740\u548c\u7aef\u53e3\u53f7\uff0c\u5982\u679cip\u5730\u5740\u4e3aNone\u6216\u8005\u4e3a\"\"\u7684\u8bdd\u5219\u9ed8\u8ba4\u4f7f\u7528\u672c\u673aip\u3002\u9ed8\u8ba4\uff1a\uff08\"\", 8881\uff09\u3002 - init_temperature(float): \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u521d\u59cb\u6e29\u5ea6\u3002\u9ed8\u8ba4\uff1a100\u3002 - reduce_rate(float): \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u8870\u51cf\u7387\u3002\u9ed8\u8ba4\uff1a0.85\u3002 - search_steps(int): \u641c\u7d22\u8fc7\u7a0b\u8fed\u4ee3\u7684\u6b21\u6570\u3002\u9ed8\u8ba4\uff1a300\u3002 - save_checkpoint(str|None): \u4fdd\u5b58checkpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u4fdd\u5b58checkpoint\u3002\u9ed8\u8ba4\uff1a ./nas_checkpoint \u3002 - load_checkpoint(str|None): \u52a0\u8f7dcheckpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u52a0\u8f7dcheckpoint\u3002\u9ed8\u8ba4\uff1aNone\u3002 - is_server(bool): \u5f53\u524d\u5b9e\u4f8b\u662f\u5426\u8981\u542f\u52a8\u4e00\u4e2aserver\u3002\u9ed8\u8ba4\uff1aTrue\u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aSANAS\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 from paddleslim.nas import SANAS config = [( 'MobileNetV2Space' )] sanas = SANAS ( config = config ) tokens2arch(tokens) \u901a\u8fc7\u4e00\u7ec4token\u5f97\u5230\u5b9e\u9645\u7684\u6a21\u578b\u7ed3\u6784\uff0c\u4e00\u822c\u7528\u6765\u628a\u641c\u7d22\u5230\u6700\u4f18\u7684token\u8f6c\u6362\u4e3a\u6a21\u578b\u7ed3\u6784\u7528\u6765\u505a\u6700\u540e\u7684\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a - tokens(list): \u4e00\u7ec4token\u3002 \u8fd4\u56de \u8fd4\u56de\u4e00\u4e2a\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . token2arch ( tokens ) for arch in archs : output = arch ( input ) input = output next_archs(): \u83b7\u53d6\u4e0b\u4e00\u7ec4\u6a21\u578b\u7ed3\u6784\u3002 \u8fd4\u56de \u8fd4\u56de\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u7684\u5217\u8868\uff0c\u5f62\u5f0f\u4e3alist\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . next_archs () for arch in archs : output = arch ( input ) input = output reward(score): \u628a\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u7684\u5f97\u5206\u60c5\u51b5\u56de\u4f20\u3002 \u53c2\u6570\uff1a score : \u5f53\u524d\u6a21\u578b\u7684\u5f97\u5206\uff0c\u5206\u6570\u8d8a\u5927\u8d8a\u597d\u3002 \u8fd4\u56de \u6a21\u578b\u7ed3\u6784\u66f4\u65b0\u6210\u529f\u6216\u8005\u5931\u8d25\uff0c\u6210\u529f\u5219\u8fd4\u56de True \uff0c\u5931\u8d25\u5219\u8fd4\u56de False \u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 import numpy as np import paddle import paddle.fluid as fluid from paddleslim.nas import SANAS from paddleslim.analysis import flops max_flops = 321208544 batch_size = 256 # \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e config = [( 'MobileNetV2Space' )] # \u5b9e\u4f8b\u5316SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8887 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 100 , is_server = True ) for step in range ( 100 ): archs = sa_nas . next_archs () train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () ### \u6784\u9020\u8bad\u7ec3program with fluid . program_guard ( train_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : output = arch ( image ) out = fluid . layers . fc ( output , size = 10 , act = \"softmax\" ) softmax_out = fluid . layers . softmax ( input = out , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) ### \u6784\u9020\u6d4b\u8bd5program test_program = train_program . clone ( for_test = True ) ### \u5b9a\u4e49\u4f18\u5316\u5668 sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost ) ### \u589e\u52a0\u9650\u5236\u6761\u4ef6\uff0c\u5982\u679c\u6ca1\u6709\u5219\u8fdb\u884c\u65e0\u9650\u5236\u641c\u7d22 if flops ( train_program ) > max_flops : continue ### \u5b9a\u4e49\u4ee3\u7801\u662f\u5728cpu\u4e0a\u8fd0\u884c place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) ### \u5b9a\u4e49\u8bad\u7ec3\u8f93\u5165\u6570\u636e train_reader = paddle . batch ( paddle . reader . shuffle ( paddle . dataset . cifar . train10 ( cycle = False ), buf_size = 1024 ), batch_size = batch_size , drop_last = True ) ### \u5b9a\u4e49\u9884\u6d4b\u8f93\u5165\u6570\u636e test_reader = paddle . batch ( paddle . dataset . cifar . test10 ( cycle = False ), batch_size = batch_size , drop_last = False ) train_feeder = fluid . DataFeeder ([ image , label ], place , program = train_program ) test_feeder = fluid . DataFeeder ([ image , label ], place , program = test_program ) ### \u5f00\u59cb\u8bad\u7ec3\uff0c\u6bcf\u4e2a\u641c\u7d22\u7ed3\u679c\u8bad\u7ec35\u4e2aepoch for epoch_id in range ( 5 ): for batch_id , data in enumerate ( train_reader ()): fetches = [ avg_cost . name ] outs = exe . run ( train_program , feed = train_feeder . feed ( data ), fetch_list = fetches )[ 0 ] if batch_id % 10 == 0 : print ( 'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}' . format ( step , epoch_id , batch_id , outs [ 0 ])) ### \u5f00\u59cb\u9884\u6d4b\uff0c\u5f97\u5230\u6700\u7ec8\u7684\u6d4b\u8bd5\u7ed3\u679c\u4f5c\u4e3ascore\u56de\u4f20\u7ed9sa_nas reward = [] for batch_id , data in enumerate ( test_reader ()): test_fetches = [ avg_cost . name , acc_top1 . name ] batch_reward = exe . run ( test_program , feed = test_feeder . feed ( data ), fetch_list = test_fetches ) reward_avg = np . mean ( np . array ( batch_reward ), axis = 1 ) reward . append ( reward_avg ) print ( 'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}' . format ( step , batch_id , batch_reward [ 0 ], batch_reward [ 1 ])) finally_reward = np . mean ( np . array ( reward ), axis = 0 ) print ( 'FINAL TEST: avg_cost: {}, acc_top1: {}' . format ( finally_reward [ 0 ], finally_reward [ 1 ])) ### \u56de\u4f20score sa_nas . reward ( float ( finally_reward [ 1 ]))","title":"class SANAS"},{"location":"api/prune_api/","text":"Pruner # paddleslim.prune.Pruner(criterion=\"l1_norm\") \u6e90\u4ee3\u7801 \u5bf9\u5377\u79ef\u7f51\u7edc\u7684\u901a\u9053\u8fdb\u884c\u4e00\u6b21\u526a\u88c1\u3002\u526a\u88c1\u4e00\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\uff0c\u662f\u6307\u526a\u88c1\u8be5\u5377\u79ef\u5c42\u8f93\u51fa\u7684\u901a\u9053\u3002\u5377\u79ef\u5c42\u7684\u6743\u91cd\u5f62\u72b6\u4e3a [output_channel, input_channel, kernel_size, kernel_size] \uff0c\u901a\u8fc7\u526a\u88c1\u8be5\u6743\u91cd\u7684\u7b2c\u4e00\u7eac\u5ea6\u8fbe\u5230\u526a\u88c1\u8f93\u51fa\u901a\u9053\u6570\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a criterion - \u8bc4\u4f30\u4e00\u4e2a\u5377\u79ef\u5c42\u5185\u901a\u9053\u91cd\u8981\u6027\u6240\u53c2\u8003\u7684\u6307\u6807\u3002\u76ee\u524d\u4ec5\u652f\u6301 l1_norm \u3002\u9ed8\u8ba4\u4e3a l1_norm \u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aPruner\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 from paddleslim.prune import Pruner pruner = Pruner () paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) \u6e90\u4ee3\u7801 \u5bf9\u76ee\u6807\u7f51\u7edc\u7684\u4e00\u7ec4\u5377\u79ef\u5c42\u7684\u6743\u91cd\u8fdb\u884c\u88c1\u526a\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u8981\u88c1\u526a\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 scope(paddle.fluid.Scope) - \u8981\u88c1\u526a\u7684\u6743\u91cd\u6240\u5728\u7684 scope \uff0cPaddle\u4e2d\u7528 scope \u5b9e\u4f8b\u5b58\u653e\u6a21\u578b\u53c2\u6570\u548c\u8fd0\u884c\u65f6\u53d8\u91cf\u7684\u503c\u3002Scope\u4e2d\u7684\u53c2\u6570\u503c\u4f1a\u88ab inplace \u7684\u88c1\u526a\u3002\u66f4\u591a\u4ecb\u7ecd\u8bf7\u53c2\u8003 Scope\u6982\u5ff5\u4ecb\u7ecd params(list ) - \u9700\u8981\u88ab\u88c1\u526a\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) ratios(list ) - \u7528\u4e8e\u88c1\u526a params \u7684\u526a\u5207\u7387\uff0c\u7c7b\u578b\u4e3a\u5217\u8868\u3002\u8be5\u5217\u8868\u957f\u5ea6\u5fc5\u987b\u4e0e params \u7684\u957f\u5ea6\u4e00\u81f4\u3002 place(paddle.fluid.Place) - \u5f85\u88c1\u526a\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd lazy(bool) - lazy \u4e3aTrue\u65f6\uff0c\u901a\u8fc7\u5c06\u6307\u5b9a\u901a\u9053\u7684\u53c2\u6570\u7f6e\u96f6\u8fbe\u5230\u88c1\u526a\u7684\u76ee\u7684\uff0c\u53c2\u6570\u7684 shape\u4fdd\u6301\u4e0d\u53d8 \uff1b lazy \u4e3aFalse\u65f6\uff0c\u76f4\u63a5\u5c06\u8981\u88c1\u7684\u901a\u9053\u7684\u53c2\u6570\u5220\u9664\uff0c\u53c2\u6570\u7684 shape \u4f1a\u53d1\u751f\u53d8\u5316\u3002 only_graph(bool) - \u662f\u5426\u53ea\u88c1\u526a\u7f51\u7edc\u7ed3\u6784\u3002\u5728Paddle\u4e2d\uff0cProgram\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\uff0cScope\u5b58\u50a8\u53c2\u6570\u7684\u6570\u503c\u3002\u4e00\u4e2aScope\u5b9e\u4f8b\u53ef\u4ee5\u88ab\u591a\u4e2aProgram\u4f7f\u7528\uff0c\u6bd4\u5982\u5b9a\u4e49\u4e86\u8bad\u7ec3\u7f51\u7edc\u7684Program\u548c\u5b9a\u4e49\u4e86\u6d4b\u8bd5\u7f51\u7edc\u7684Program\u662f\u4f7f\u7528\u540c\u4e00\u4e2aScope\u5b9e\u4f8b\u7684\u3002 only_graph \u4e3aTrue\u65f6\uff0c\u53ea\u5bf9Program\u4e2d\u5b9a\u4e49\u7684\u5377\u79ef\u7684\u901a\u9053\u8fdb\u884c\u526a\u88c1\uff1b only_graph \u4e3afalse\u65f6\uff0cScope\u4e2d\u5377\u79ef\u53c2\u6570\u7684\u6570\u503c\u4e5f\u4f1a\u88ab\u526a\u88c1\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570\u503c\u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_shape_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570 shape \u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 \u8fd4\u56de\uff1a pruned_program(paddle.fluid.Program) - \u88ab\u88c1\u526a\u540e\u7684Program\u3002 param_backup(dict) - \u5bf9\u53c2\u6570\u6570\u503c\u7684\u5907\u4efd\uff0c\u7528\u4e8e\u6062\u590dScope\u4e2d\u7684\u53c2\u6570\u6570\u503c\u3002 param_shape_backup(dict) - \u5bf9\u53c2\u6570\u5f62\u72b6\u7684\u5907\u4efd\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u6267\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import Pruner def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) place = fluid . CPUPlace () exe = fluid . Executor ( place ) scope = fluid . Scope () exe . run ( startup_program , scope = scope ) pruner = Pruner () main_program , _ , _ = pruner . prune ( main_program , scope , params = [ \"conv4_weights\" ], ratios = [ 0.5 ], place = place , lazy = False , only_graph = False , param_backup = False , param_shape_backup = False ) for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : print ( \"param name: {}; param shape: {}\" . format ( param . name , param . shape )) sensitivity # paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) \u6e90\u4ee3\u7801 \u8ba1\u7b97\u7f51\u7edc\u4e2d\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u3002\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u7edf\u8ba1\u65b9\u6cd5\u4e3a\uff1a\u4f9d\u6b21\u526a\u6389\u5f53\u524d\u5377\u79ef\u5c42\u4e0d\u540c\u6bd4\u4f8b\u7684\u8f93\u51fa\u901a\u9053\u6570\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8ba1\u7b97\u526a\u88c1\u540e\u7684\u7cbe\u5ea6\u635f\u5931\u3002\u5f97\u5230\u654f\u611f\u5ea6\u4fe1\u606f\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u6216\u5176\u5b83\u65b9\u5f0f\u786e\u5b9a\u6bcf\u5c42\u5377\u79ef\u7684\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u8bc4\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 place(paddle.fluid.Place) - \u5f85\u5206\u6790\u7684\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd param_names(list ) - \u5f85\u5206\u6790\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) eval_func(function) - \u7528\u4e8e\u8bc4\u4f30\u88c1\u526a\u540e\u6a21\u578b\u6548\u679c\u7684\u56de\u8c03\u51fd\u6570\u3002\u8be5\u56de\u8c03\u51fd\u6570\u63a5\u53d7\u88ab\u88c1\u526a\u540e\u7684 program \u4e3a\u53c2\u6570\uff0c\u8fd4\u56de\u4e00\u4e2a\u8868\u793a\u5f53\u524dprogram\u7684\u7cbe\u5ea6\uff0c\u7528\u4ee5\u8ba1\u7b97\u5f53\u524d\u88c1\u526a\u5e26\u6765\u7684\u7cbe\u5ea6\u635f\u5931\u3002 sensitivities_file(str) - \u4fdd\u5b58\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\u7684\u6587\u4ef6\u3002\u5728\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\uff0c\u4f1a\u6301\u7eed\u5c06\u65b0\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u8ffd\u52a0\u5230\u8be5\u6587\u4ef6\u4e2d\u3002\u91cd\u542f\u4efb\u52a1\u540e\uff0c\u6587\u4ef6\u4e2d\u5df2\u6709\u654f\u611f\u5ea6\u4fe1\u606f\u4e0d\u4f1a\u88ab\u91cd\u590d\u8ba1\u7b97\u3002\u8be5\u6587\u4ef6\u53ef\u4ee5\u7528 pickle \u52a0\u8f7d\u3002 pruned_ratios(list ) - \u8ba1\u7b97\u5377\u79ef\u5c42\u654f\u611f\u5ea6\u4fe1\u606f\u65f6\uff0c\u4f9d\u6b21\u526a\u6389\u7684\u901a\u9053\u6570\u6bd4\u4f8b\u3002\u9ed8\u8ba4\u4e3a[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684dict\uff0c\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u8fd0\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 import paddle import numpy as np import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import sensitivity import paddle.dataset.mnist as reader def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels image_shape = [ 1 , 28 , 28 ] with fluid . program_guard ( main_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None ] + image_shape , dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv1 = conv_bn_layer ( image , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) out = fluid . layers . fc ( conv6 , size = 10 , act = \"softmax\" ) # cost = fluid.layers.cross_entropy(input=out, label=label) # avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid . layers . accuracy ( input = out , label = label , k = 1 ) # acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) val_reader = paddle . batch ( reader . test (), batch_size = 128 ) val_feeder = feeder = fluid . DataFeeder ( [ image , label ], place , program = main_program ) def eval_func ( program ): acc_top1_ns = [] for data in val_reader (): acc_top1_n = exe . run ( program , feed = val_feeder . feed ( data ), fetch_list = [ acc_top1 . name ]) acc_top1_ns . append ( np . mean ( acc_top1_n )) return np . mean ( acc_top1_ns ) param_names = [] for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : param_names . append ( param . name ) sensitivities = sensitivity ( main_program , place , param_names , eval_func , sensitivities_file = \"./sensitive.data\" , pruned_ratios = [ 0.1 , 0.2 , 0.3 ]) print ( sensitivities ) merge_sensitive # paddleslim.prune.merge_sensitive(sensitivities) \u6e90\u4ee3\u7801 \u5408\u5e76\u591a\u4e2a\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities(list | list ) - \u5f85\u5408\u5e76\u7684\u654f\u611f\u5ea6\u4fe1\u606f\uff0c\u53ef\u4ee5\u662f\u5b57\u5178\u7684\u5217\u8868\uff0c\u6216\u8005\u662f\u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\u7684\u8def\u5f84\u5217\u8868\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a load_sensitivities # paddleslim.prune.load_sensitivities(sensitivities_file) \u6e90\u4ee3\u7801 \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities_file(str) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6. \u8fd4\u56de\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u793a\u4f8b\uff1a get_ratios_by_loss # paddleslim.prune.get_ratios_by_loss(sensitivities, loss) \u6e90\u4ee3\u7801 \u6839\u636e\u654f\u611f\u5ea6\u548c\u7cbe\u5ea6\u635f\u5931\u9608\u503c\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u5207\u7387\u3002\u5bf9\u4e8e\u53c2\u6570 w , \u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e loss \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 loss - \u7cbe\u5ea6\u635f\u5931\u9608\u503c\u3002 \u8fd4\u56de\uff1a ratios(dict) - \u4e00\u7ec4\u526a\u5207\u7387\u3002 key \u662f\u5f85\u526a\u88c1\u53c2\u6570\u7684\u540d\u79f0\u3002 value \u662f\u5bf9\u5e94\u53c2\u6570\u7684\u526a\u88c1\u7387\u3002","title":"\u526a\u679d\u4e0e\u654f\u611f\u5ea6"},{"location":"api/prune_api/#pruner","text":"paddleslim.prune.Pruner(criterion=\"l1_norm\") \u6e90\u4ee3\u7801 \u5bf9\u5377\u79ef\u7f51\u7edc\u7684\u901a\u9053\u8fdb\u884c\u4e00\u6b21\u526a\u88c1\u3002\u526a\u88c1\u4e00\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\uff0c\u662f\u6307\u526a\u88c1\u8be5\u5377\u79ef\u5c42\u8f93\u51fa\u7684\u901a\u9053\u3002\u5377\u79ef\u5c42\u7684\u6743\u91cd\u5f62\u72b6\u4e3a [output_channel, input_channel, kernel_size, kernel_size] \uff0c\u901a\u8fc7\u526a\u88c1\u8be5\u6743\u91cd\u7684\u7b2c\u4e00\u7eac\u5ea6\u8fbe\u5230\u526a\u88c1\u8f93\u51fa\u901a\u9053\u6570\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a criterion - \u8bc4\u4f30\u4e00\u4e2a\u5377\u79ef\u5c42\u5185\u901a\u9053\u91cd\u8981\u6027\u6240\u53c2\u8003\u7684\u6307\u6807\u3002\u76ee\u524d\u4ec5\u652f\u6301 l1_norm \u3002\u9ed8\u8ba4\u4e3a l1_norm \u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aPruner\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 from paddleslim.prune import Pruner pruner = Pruner () paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) \u6e90\u4ee3\u7801 \u5bf9\u76ee\u6807\u7f51\u7edc\u7684\u4e00\u7ec4\u5377\u79ef\u5c42\u7684\u6743\u91cd\u8fdb\u884c\u88c1\u526a\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u8981\u88c1\u526a\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 scope(paddle.fluid.Scope) - \u8981\u88c1\u526a\u7684\u6743\u91cd\u6240\u5728\u7684 scope \uff0cPaddle\u4e2d\u7528 scope \u5b9e\u4f8b\u5b58\u653e\u6a21\u578b\u53c2\u6570\u548c\u8fd0\u884c\u65f6\u53d8\u91cf\u7684\u503c\u3002Scope\u4e2d\u7684\u53c2\u6570\u503c\u4f1a\u88ab inplace \u7684\u88c1\u526a\u3002\u66f4\u591a\u4ecb\u7ecd\u8bf7\u53c2\u8003 Scope\u6982\u5ff5\u4ecb\u7ecd params(list ) - \u9700\u8981\u88ab\u88c1\u526a\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) ratios(list ) - \u7528\u4e8e\u88c1\u526a params \u7684\u526a\u5207\u7387\uff0c\u7c7b\u578b\u4e3a\u5217\u8868\u3002\u8be5\u5217\u8868\u957f\u5ea6\u5fc5\u987b\u4e0e params \u7684\u957f\u5ea6\u4e00\u81f4\u3002 place(paddle.fluid.Place) - \u5f85\u88c1\u526a\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd lazy(bool) - lazy \u4e3aTrue\u65f6\uff0c\u901a\u8fc7\u5c06\u6307\u5b9a\u901a\u9053\u7684\u53c2\u6570\u7f6e\u96f6\u8fbe\u5230\u88c1\u526a\u7684\u76ee\u7684\uff0c\u53c2\u6570\u7684 shape\u4fdd\u6301\u4e0d\u53d8 \uff1b lazy \u4e3aFalse\u65f6\uff0c\u76f4\u63a5\u5c06\u8981\u88c1\u7684\u901a\u9053\u7684\u53c2\u6570\u5220\u9664\uff0c\u53c2\u6570\u7684 shape \u4f1a\u53d1\u751f\u53d8\u5316\u3002 only_graph(bool) - \u662f\u5426\u53ea\u88c1\u526a\u7f51\u7edc\u7ed3\u6784\u3002\u5728Paddle\u4e2d\uff0cProgram\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\uff0cScope\u5b58\u50a8\u53c2\u6570\u7684\u6570\u503c\u3002\u4e00\u4e2aScope\u5b9e\u4f8b\u53ef\u4ee5\u88ab\u591a\u4e2aProgram\u4f7f\u7528\uff0c\u6bd4\u5982\u5b9a\u4e49\u4e86\u8bad\u7ec3\u7f51\u7edc\u7684Program\u548c\u5b9a\u4e49\u4e86\u6d4b\u8bd5\u7f51\u7edc\u7684Program\u662f\u4f7f\u7528\u540c\u4e00\u4e2aScope\u5b9e\u4f8b\u7684\u3002 only_graph \u4e3aTrue\u65f6\uff0c\u53ea\u5bf9Program\u4e2d\u5b9a\u4e49\u7684\u5377\u79ef\u7684\u901a\u9053\u8fdb\u884c\u526a\u88c1\uff1b only_graph \u4e3afalse\u65f6\uff0cScope\u4e2d\u5377\u79ef\u53c2\u6570\u7684\u6570\u503c\u4e5f\u4f1a\u88ab\u526a\u88c1\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570\u503c\u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_shape_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570 shape \u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 \u8fd4\u56de\uff1a pruned_program(paddle.fluid.Program) - \u88ab\u88c1\u526a\u540e\u7684Program\u3002 param_backup(dict) - \u5bf9\u53c2\u6570\u6570\u503c\u7684\u5907\u4efd\uff0c\u7528\u4e8e\u6062\u590dScope\u4e2d\u7684\u53c2\u6570\u6570\u503c\u3002 param_shape_backup(dict) - \u5bf9\u53c2\u6570\u5f62\u72b6\u7684\u5907\u4efd\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u6267\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import Pruner def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) place = fluid . CPUPlace () exe = fluid . Executor ( place ) scope = fluid . Scope () exe . run ( startup_program , scope = scope ) pruner = Pruner () main_program , _ , _ = pruner . prune ( main_program , scope , params = [ \"conv4_weights\" ], ratios = [ 0.5 ], place = place , lazy = False , only_graph = False , param_backup = False , param_shape_backup = False ) for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : print ( \"param name: {}; param shape: {}\" . format ( param . name , param . shape ))","title":"Pruner"},{"location":"api/prune_api/#sensitivity","text":"paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) \u6e90\u4ee3\u7801 \u8ba1\u7b97\u7f51\u7edc\u4e2d\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u3002\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u7edf\u8ba1\u65b9\u6cd5\u4e3a\uff1a\u4f9d\u6b21\u526a\u6389\u5f53\u524d\u5377\u79ef\u5c42\u4e0d\u540c\u6bd4\u4f8b\u7684\u8f93\u51fa\u901a\u9053\u6570\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8ba1\u7b97\u526a\u88c1\u540e\u7684\u7cbe\u5ea6\u635f\u5931\u3002\u5f97\u5230\u654f\u611f\u5ea6\u4fe1\u606f\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u6216\u5176\u5b83\u65b9\u5f0f\u786e\u5b9a\u6bcf\u5c42\u5377\u79ef\u7684\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u8bc4\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 place(paddle.fluid.Place) - \u5f85\u5206\u6790\u7684\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd param_names(list ) - \u5f85\u5206\u6790\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) eval_func(function) - \u7528\u4e8e\u8bc4\u4f30\u88c1\u526a\u540e\u6a21\u578b\u6548\u679c\u7684\u56de\u8c03\u51fd\u6570\u3002\u8be5\u56de\u8c03\u51fd\u6570\u63a5\u53d7\u88ab\u88c1\u526a\u540e\u7684 program \u4e3a\u53c2\u6570\uff0c\u8fd4\u56de\u4e00\u4e2a\u8868\u793a\u5f53\u524dprogram\u7684\u7cbe\u5ea6\uff0c\u7528\u4ee5\u8ba1\u7b97\u5f53\u524d\u88c1\u526a\u5e26\u6765\u7684\u7cbe\u5ea6\u635f\u5931\u3002 sensitivities_file(str) - \u4fdd\u5b58\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\u7684\u6587\u4ef6\u3002\u5728\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\uff0c\u4f1a\u6301\u7eed\u5c06\u65b0\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u8ffd\u52a0\u5230\u8be5\u6587\u4ef6\u4e2d\u3002\u91cd\u542f\u4efb\u52a1\u540e\uff0c\u6587\u4ef6\u4e2d\u5df2\u6709\u654f\u611f\u5ea6\u4fe1\u606f\u4e0d\u4f1a\u88ab\u91cd\u590d\u8ba1\u7b97\u3002\u8be5\u6587\u4ef6\u53ef\u4ee5\u7528 pickle \u52a0\u8f7d\u3002 pruned_ratios(list ) - \u8ba1\u7b97\u5377\u79ef\u5c42\u654f\u611f\u5ea6\u4fe1\u606f\u65f6\uff0c\u4f9d\u6b21\u526a\u6389\u7684\u901a\u9053\u6570\u6bd4\u4f8b\u3002\u9ed8\u8ba4\u4e3a[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684dict\uff0c\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u8fd0\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 import paddle import numpy as np import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import sensitivity import paddle.dataset.mnist as reader def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels image_shape = [ 1 , 28 , 28 ] with fluid . program_guard ( main_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None ] + image_shape , dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv1 = conv_bn_layer ( image , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) out = fluid . layers . fc ( conv6 , size = 10 , act = \"softmax\" ) # cost = fluid.layers.cross_entropy(input=out, label=label) # avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid . layers . accuracy ( input = out , label = label , k = 1 ) # acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) val_reader = paddle . batch ( reader . test (), batch_size = 128 ) val_feeder = feeder = fluid . DataFeeder ( [ image , label ], place , program = main_program ) def eval_func ( program ): acc_top1_ns = [] for data in val_reader (): acc_top1_n = exe . run ( program , feed = val_feeder . feed ( data ), fetch_list = [ acc_top1 . name ]) acc_top1_ns . append ( np . mean ( acc_top1_n )) return np . mean ( acc_top1_ns ) param_names = [] for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : param_names . append ( param . name ) sensitivities = sensitivity ( main_program , place , param_names , eval_func , sensitivities_file = \"./sensitive.data\" , pruned_ratios = [ 0.1 , 0.2 , 0.3 ]) print ( sensitivities )","title":"sensitivity"},{"location":"api/prune_api/#merge_sensitive","text":"paddleslim.prune.merge_sensitive(sensitivities) \u6e90\u4ee3\u7801 \u5408\u5e76\u591a\u4e2a\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities(list | list ) - \u5f85\u5408\u5e76\u7684\u654f\u611f\u5ea6\u4fe1\u606f\uff0c\u53ef\u4ee5\u662f\u5b57\u5178\u7684\u5217\u8868\uff0c\u6216\u8005\u662f\u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\u7684\u8def\u5f84\u5217\u8868\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a","title":"merge_sensitive"},{"location":"api/prune_api/#load_sensitivities","text":"paddleslim.prune.load_sensitivities(sensitivities_file) \u6e90\u4ee3\u7801 \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities_file(str) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6. \u8fd4\u56de\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u793a\u4f8b\uff1a","title":"load_sensitivities"},{"location":"api/prune_api/#get_ratios_by_loss","text":"paddleslim.prune.get_ratios_by_loss(sensitivities, loss) \u6e90\u4ee3\u7801 \u6839\u636e\u654f\u611f\u5ea6\u548c\u7cbe\u5ea6\u635f\u5931\u9608\u503c\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u5207\u7387\u3002\u5bf9\u4e8e\u53c2\u6570 w , \u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e loss \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 loss - \u7cbe\u5ea6\u635f\u5931\u9608\u503c\u3002 \u8fd4\u56de\uff1a ratios(dict) - \u4e00\u7ec4\u526a\u5207\u7387\u3002 key \u662f\u5f85\u526a\u88c1\u53c2\u6570\u7684\u540d\u79f0\u3002 value \u662f\u5bf9\u5e94\u53c2\u6570\u7684\u526a\u88c1\u7387\u3002","title":"get_ratios_by_loss"},{"location":"api/quantization_api/","text":"\u91cf\u5316\u914d\u7f6e # \u901a\u8fc7\u5b57\u5178\u914d\u7f6e\u91cf\u5316\u53c2\u6570 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 quant_config_default = { ' weight_quantize_type ' : ' abs_max ' , ' activation_quantize_type ' : ' abs_max ' , ' weight_bits ' : 8 , ' activation_bits ' : 8 , # ops of name_scope in not_quant_pattern list , will not be quantized ' not_quant_pattern ' : [ ' skip_quant ' ], # ops of type in quantize_op_types , will be quantized ' quantize_op_types ' : [ ' conv2d ' , ' depthwise_conv2d ' , ' mul ' , ' elementwise_add ' , ' pool2d ' ], # data type after quantization , such as ' uint8 ' , ' int8 ' , etc . default is ' int8 ' ' dtype ' : ' int8 ' , # window size for ' range_abs_max ' quantization . defaulf is 10000 ' window_size ' : 10000 , # The decay coefficient of moving average , default is 0 . 9 ' moving_rate ' : 0 . 9 , } \u53c2\u6570\uff1a weight_quantize_type(str) - \u53c2\u6570\u91cf\u5316\u65b9\u5f0f\u3002\u53ef\u9009 'abs_max' , 'channel_wise_abs_max' , 'range_abs_max' , 'moving_average_abs_max' \u3002 \u9ed8\u8ba4 'abs_max' \u3002 activation_quantize_type(str) - \u6fc0\u6d3b\u91cf\u5316\u65b9\u5f0f\uff0c\u53ef\u9009 'abs_max' , 'range_abs_max' , 'moving_average_abs_max' \uff0c\u9ed8\u8ba4 'abs_max' \u3002 weight_bits(int) - \u53c2\u6570\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48, \u63a8\u8350\u8bbe\u4e3a8\u3002 activation_bits(int) - \u6fc0\u6d3b\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48\uff0c \u63a8\u8350\u8bbe\u4e3a8\u3002 not_quant_pattern(str | list[str]) - \u6240\u6709 name_scope \u5305\u542b 'not_quant_pattern' \u5b57\u7b26\u4e32\u7684 op \uff0c\u90fd\u4e0d\u91cf\u5316, \u8bbe\u7f6e\u65b9\u5f0f\u8bf7\u53c2\u8003 fluid.name_scope \u3002 quantize_op_types(list[str]) - \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684 op \u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301 'conv2d', 'depthwise_conv2d', 'mul' \u3002 dtype(int8) - \u91cf\u5316\u540e\u7684\u53c2\u6570\u7c7b\u578b\uff0c\u9ed8\u8ba4 int8 , \u76ee\u524d\u4ec5\u652f\u6301 int8 \u3002 window_size(int) - 'range_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684 window size \uff0c\u9ed8\u8ba410000\u3002 moving_rate(int) - 'moving_average_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684\u8870\u51cf\u7cfb\u6570\uff0c\u9ed8\u8ba4 0.9\u3002 quant_aware # paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False) [\u6e90\u4ee3\u7801] \u5728 program \u4e2d\u52a0\u5165\u91cf\u5316\u548c\u53cd\u91cf\u5316 op , \u7528\u4e8e\u91cf\u5316\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u8bad\u7ec3\u6216\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope, optional) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 for_test(bool) - \u5982\u679c program \u53c2\u6570\u662f\u4e00\u4e2a\u6d4b\u8bd5 program \uff0c for_test \u5e94\u8bbe\u4e3a True \uff0c\u5426\u5219\u8bbe\u4e3a False \u3002 \u8fd4\u56de \u542b\u6709\u91cf\u5316\u548c\u53cd\u91cf\u5316 operator \u7684 program \u8fd4\u56de\u7c7b\u578b \u5f53 for_test=False \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.CompiledProgram \uff0c \u6ce8\u610f\uff0c\u6b64\u8fd4\u56de\u503c\u4e0d\u80fd\u7528\u4e8e\u4fdd\u5b58\u53c2\u6570 \u3002 \u5f53 for_test=True \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.Program \u3002 \u6ce8\u610f\u4e8b\u9879 \u6b64\u63a5\u53e3\u4f1a\u6539\u53d8 program \u7ed3\u6784\uff0c\u5e76\u4e14\u53ef\u80fd\u589e\u52a0\u4e00\u4e9b persistable \u7684\u53d8\u91cf\uff0c\u6240\u4ee5\u52a0\u8f7d\u6a21\u578b\u53c2\u6570\u65f6\u8bf7\u6ce8\u610f\u548c\u76f8\u5e94\u7684 program \u5bf9\u5e94\u3002 \u6b64\u63a5\u53e3\u5e95\u5c42\u7ecf\u5386\u4e86 fluid.Program -> fluid.framework.IrGraph -> fluid.Program \u7684\u8f6c\u53d8\uff0c\u5728 fluid.framework.IrGraph \u4e2d\u6ca1\u6709 Parameter \u7684\u6982\u5ff5\uff0c Variable \u53ea\u6709 persistable \u548c not persistable \u7684\u533a\u522b\uff0c\u6240\u4ee5\u5728\u4fdd\u5b58\u548c\u52a0\u8f7d\u53c2\u6570\u65f6\uff0c\u8bf7\u4f7f\u7528 fluid.io.save_persistables \u548c fluid.io.load_persistables \u63a5\u53e3\u3002 \u7531\u4e8e\u6b64\u63a5\u53e3\u4f1a\u6839\u636e program \u7684\u7ed3\u6784\u548c\u91cf\u5316\u914d\u7f6e\u6765\u5bf9 program \u6dfb\u52a0op\uff0c\u6240\u4ee5 Paddle \u4e2d\u4e00\u4e9b\u901a\u8fc7 fuse op \u6765\u52a0\u901f\u8bad\u7ec3\u7684\u7b56\u7565\u4e0d\u80fd\u4f7f\u7528\u3002\u5df2\u77e5\u4ee5\u4e0b\u7b56\u7565\u5728\u4f7f\u7528\u91cf\u5316\u65f6\u5fc5\u987b\u8bbe\u4e3a False \uff1a fuse_all_reduce_ops, sync_batch_norm \u3002 \u5982\u679c\u4f20\u5165\u7684 program \u4e2d\u5b58\u5728\u548c\u4efb\u4f55op\u90fd\u6ca1\u6709\u8fde\u63a5\u7684 Variable \uff0c\u5219\u4f1a\u5728\u91cf\u5316\u7684\u8fc7\u7a0b\u4e2d\u88ab\u4f18\u5316\u6389\u3002 convert # paddleslim.quant.convert(program, place, config, scope=None, save_int8=False) [\u6e90\u4ee3\u7801] \u628a\u8bad\u7ec3\u597d\u7684\u91cf\u5316 program \uff0c\u8f6c\u6362\u4e3a\u53ef\u7528\u4e8e\u4fdd\u5b58 inference model \u7684 program \u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 save_int8\uff08bool) - \u662f\u5426\u9700\u8981\u8fd4\u56de\u53c2\u6570\u4e3a int8 \u7684 program \u3002\u8be5\u529f\u80fd\u76ee\u524d\u53ea\u80fd\u7528\u4e8e\u786e\u8ba4\u6a21\u578b\u5927\u5c0f\u3002\u9ed8\u8ba4\u503c\u4e3a False \u3002 \u8fd4\u56de program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a float32 \u7c7b\u578b\uff0c\u4f46\u5176\u6570\u503c\u8303\u56f4\u53ef\u7528int8\u8868\u793a\u3002 int8_program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a int8 \u7c7b\u578b\u3002\u5f53 save_int8 \u4e3a False \u65f6\uff0c\u4e0d\u8fd4\u56de\u8be5\u503c\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u5bf9 op \u548c Variable \u505a\u76f8\u5e94\u7684\u5220\u9664\u548c\u4fee\u6539\uff0c\u6240\u4ee5\u6b64\u63a5\u53e3\u53ea\u80fd\u5728\u8bad\u7ec3\u5b8c\u6210\u4e4b\u540e\u8c03\u7528\u3002\u5982\u679c\u60f3\u8f6c\u5316\u8bad\u7ec3\u7684\u4e2d\u95f4\u6a21\u578b\uff0c\u53ef\u52a0\u8f7d\u76f8\u5e94\u7684\u53c2\u6570\u4e4b\u540e\u518d\u4f7f\u7528\u6b64\u63a5\u53e3\u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 #encoding=utf8 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): image = fluid . data ( name = 'x' , shape = [ None , 1 , 28 , 28 ]) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv = fluid . layers . conv2d ( image , 32 , 1 ) feat = fluid . layers . fc ( conv , 10 , act = 'softmax' ) cost = fluid . layers . cross_entropy ( input = feat , label = label ) avg_cost = fluid . layers . mean ( x = cost ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) eval_program = train_program . clone ( for_test = True ) #\u914d\u7f6e config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' } build_strategy = fluid . BuildStrategy () exec_strategy = fluid . ExecutionStrategy () #\u8c03\u7528api quant_train_program = quant . quant_aware ( train_program , place , config , for_test = False ) quant_eval_program = quant . quant_aware ( eval_program , place , config , for_test = True ) #\u5173\u95ed\u7b56\u7565 build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False quant_train_program = quant_train_program . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) inference_prog = quant . convert ( quant_eval_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u91cf\u5316\u8bad\u7ec3demo \u3002 quant_post # paddleslim.quant.quant_post(executor, model_dir, quantize_model_path,sample_generator, model_filename=None, params_filename=None, batch_size=16,batch_nums=None, scope=None, algo='KL', quantizable_op_type=[\"conv2d\", \"depthwise_conv2d\", \"mul\"]) [\u6e90\u4ee3\u7801] \u5bf9\u4fdd\u5b58\u5728 ${model_dir} \u4e0b\u7684\u6a21\u578b\u8fdb\u884c\u91cf\u5316\uff0c\u4f7f\u7528 sample_generator \u7684\u6570\u636e\u8fdb\u884c\u53c2\u6570\u6821\u6b63\u3002 \u53c2\u6570: executor (fluid.Executor) - \u6267\u884c\u6a21\u578b\u7684executor\uff0c\u53ef\u4ee5\u5728cpu\u6216\u8005gpu\u4e0a\u6267\u884c\u3002 model_dir\uff08str) - \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u6240\u5728\u7684\u6587\u4ef6\u5939\u3002 quantize_model_path(str) - \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u8def\u5f84 sample_generator(python generator) - \u8bfb\u53d6\u6570\u636e\u6837\u672c\uff0c\u6bcf\u6b21\u8fd4\u56de\u4e00\u4e2a\u6837\u672c\u3002 model_filename(str, optional) - \u6a21\u578b\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e model_filename \u4e3a\u6a21\u578b\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 params_filename(str) - \u53c2\u6570\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e params_filename \u4e3a\u53c2\u6570\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 batch_size(int) - \u6bcf\u4e2abatch\u7684\u56fe\u7247\u6570\u91cf\u3002\u9ed8\u8ba4\u503c\u4e3a16 \u3002 batch_nums(int, optional) - \u8fed\u4ee3\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a None \uff0c\u5219\u4f1a\u4e00\u76f4\u8fd0\u884c\u5230 sample_generator \u8fed\u4ee3\u7ed3\u675f\uff0c \u5426\u5219\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a batch_nums , \u4e5f\u5c31\u662f\u8bf4\u53c2\u4e0e\u5bf9 Scale \u8fdb\u884c\u6821\u6b63\u7684\u6837\u672c\u4e2a\u6570\u4e3a 'batch_nums' * 'batch_size' . scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . \u9ed8\u8ba4\u503c\u662f None . algo(str) - \u91cf\u5316\u65f6\u4f7f\u7528\u7684\u7b97\u6cd5\u540d\u79f0\uff0c\u53ef\u4e3a 'KL' \u6216\u8005 'direct' \u3002\u8be5\u53c2\u6570\u4ec5\u9488\u5bf9\u6fc0\u6d3b\u503c\u7684\u91cf\u5316\uff0c\u56e0\u4e3a\u53c2\u6570\u503c\u7684\u91cf\u5316\u4f7f\u7528\u7684\u65b9\u5f0f\u4e3a 'channel_wise_abs_max' . \u5f53 algo \u8bbe\u7f6e\u4e3a 'direct' \u65f6\uff0c\u4f7f\u7528\u6821\u6b63\u6570\u636e\u7684\u6fc0\u6d3b\u503c\u7684\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u5f53\u4f5c Scale \u503c\uff0c\u5f53\u8bbe\u7f6e\u4e3a 'KL' \u65f6\uff0c\u5219\u4f7f\u7528 KL \u6563\u5ea6\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97 Scale \u503c\u3002\u9ed8\u8ba4\u503c\u4e3a 'KL' \u3002 quantizable_op_type(list[str]) - \u9700\u8981\u91cf\u5316\u7684 op \u7c7b\u578b\u5217\u8868\u3002\u9ed8\u8ba4\u503c\u4e3a [\"conv2d\", \"depthwise_conv2d\", \"mul\"] \u3002 \u8fd4\u56de \u65e0\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u6536\u96c6\u6821\u6b63\u6570\u636e\u7684\u6240\u6709\u7684\u6fc0\u6d3b\u503c\uff0c\u6240\u4ee5\u4f7f\u7528\u7684\u6821\u6b63\u56fe\u7247\u4e0d\u80fd\u592a\u591a\u3002 'KL' \u6563\u5ea6\u7684\u8ba1\u7b97\u4e5f\u6bd4\u8f83\u8017\u65f6\u3002 \u4ee3\u7801\u793a\u4f8b \u6ce8\uff1a \u6b64\u793a\u4f8b\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\uff0c\u56e0\u4e3a\u9700\u8981\u52a0\u8f7d ${model_dir} \u4e0b\u7684\u6a21\u578b\uff0c\u6240\u4ee5\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import paddle.fluid as fluid import paddle.dataset.mnist as reader from paddleslim.quant import quant_post val_reader = reader . train () use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) quant_post ( executor = exe , model_dir = './model_path' , quantize_model_path = './save_path' , sample_generator = val_reader , model_filename = '__model__' , params_filename = '__params__' , batch_size = 16 , batch_nums = 10 ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u79bb\u7ebf\u91cf\u5316demo \u3002 quant_embedding # paddleslim.quant.quant_embedding(program, place, config, scope=None) [\u6e90\u4ee3\u7801] \u5bf9 Embedding \u53c2\u6570\u8fdb\u884c\u91cf\u5316\u3002 \u53c2\u6570: program(fluid.Program) - \u9700\u8981\u91cf\u5316\u7684program scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . place(fluid.CPUPlace | fluid.CUDAPlace) - \u8fd0\u884cprogram\u7684\u8bbe\u5907 config(dict) - \u5b9a\u4e49\u91cf\u5316\u7684\u914d\u7f6e\u3002\u53ef\u4ee5\u914d\u7f6e\u7684\u53c2\u6570\u6709\uff1a 'params_name' (str, required): \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684\u53c2\u6570\u540d\u79f0\uff0c\u6b64\u53c2\u6570\u5fc5\u987b\u8bbe\u7f6e\u3002 'quantize_type' (str, optional): \u91cf\u5316\u7684\u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301\u7684\u7c7b\u578b\u662f 'abs_max' , \u5f85\u652f\u6301\u7684\u7c7b\u578b\u6709 'log', 'product_quantization' \u3002 \u9ed8\u8ba4\u503c\u662f 'abs_max' . 'quantize_bits' \uff08int, optional): \u91cf\u5316\u7684 bit \u6570\uff0c\u76ee\u524d\u652f\u6301\u7684 bit \u6570\u4e3a8\u3002\u9ed8\u8ba4\u503c\u662f8. 'dtype' (str, optional): \u91cf\u5316\u4e4b\u540e\u7684\u6570\u636e\u7c7b\u578b\uff0c \u76ee\u524d\u652f\u6301\u7684\u662f 'int8' . \u9ed8\u8ba4\u503c\u662f int8 \u3002 'threshold' (float, optional): \u91cf\u5316\u4e4b\u524d\u5c06\u6839\u636e\u6b64\u9608\u503c\u5bf9\u9700\u8981\u91cf\u5316\u7684\u53c2\u6570\u503c\u8fdb\u884c clip . \u5982\u679c\u4e0d\u8bbe\u7f6e\uff0c\u5219\u8df3\u8fc7 clip \u8fc7\u7a0b\u76f4\u63a5\u91cf\u5316\u3002 \u8fd4\u56de \u91cf\u5316\u4e4b\u540e\u7684program \u8fd4\u56de\u7c7b\u578b fluid.Program \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): input_word = fluid . data ( name = \"input_word\" , shape = [ None , 1 ], dtype = 'int64' ) input_emb = fluid . embedding ( input = input_word , is_sparse = False , size = [ 100 , 128 ], param_attr = fluid . ParamAttr ( name = 'emb' , initializer = fluid . initializer . Uniform ( - 0.005 , 0.005 ))) infer_program = train_program . clone ( for_test = True ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } quant_program = quant . quant_embedding ( infer_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 Embedding\u91cf\u5316demo \u3002","title":"\u91cf\u5316"},{"location":"api/quantization_api/#_1","text":"\u901a\u8fc7\u5b57\u5178\u914d\u7f6e\u91cf\u5316\u53c2\u6570 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 quant_config_default = { ' weight_quantize_type ' : ' abs_max ' , ' activation_quantize_type ' : ' abs_max ' , ' weight_bits ' : 8 , ' activation_bits ' : 8 , # ops of name_scope in not_quant_pattern list , will not be quantized ' not_quant_pattern ' : [ ' skip_quant ' ], # ops of type in quantize_op_types , will be quantized ' quantize_op_types ' : [ ' conv2d ' , ' depthwise_conv2d ' , ' mul ' , ' elementwise_add ' , ' pool2d ' ], # data type after quantization , such as ' uint8 ' , ' int8 ' , etc . default is ' int8 ' ' dtype ' : ' int8 ' , # window size for ' range_abs_max ' quantization . defaulf is 10000 ' window_size ' : 10000 , # The decay coefficient of moving average , default is 0 . 9 ' moving_rate ' : 0 . 9 , } \u53c2\u6570\uff1a weight_quantize_type(str) - \u53c2\u6570\u91cf\u5316\u65b9\u5f0f\u3002\u53ef\u9009 'abs_max' , 'channel_wise_abs_max' , 'range_abs_max' , 'moving_average_abs_max' \u3002 \u9ed8\u8ba4 'abs_max' \u3002 activation_quantize_type(str) - \u6fc0\u6d3b\u91cf\u5316\u65b9\u5f0f\uff0c\u53ef\u9009 'abs_max' , 'range_abs_max' , 'moving_average_abs_max' \uff0c\u9ed8\u8ba4 'abs_max' \u3002 weight_bits(int) - \u53c2\u6570\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48, \u63a8\u8350\u8bbe\u4e3a8\u3002 activation_bits(int) - \u6fc0\u6d3b\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48\uff0c \u63a8\u8350\u8bbe\u4e3a8\u3002 not_quant_pattern(str | list[str]) - \u6240\u6709 name_scope \u5305\u542b 'not_quant_pattern' \u5b57\u7b26\u4e32\u7684 op \uff0c\u90fd\u4e0d\u91cf\u5316, \u8bbe\u7f6e\u65b9\u5f0f\u8bf7\u53c2\u8003 fluid.name_scope \u3002 quantize_op_types(list[str]) - \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684 op \u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301 'conv2d', 'depthwise_conv2d', 'mul' \u3002 dtype(int8) - \u91cf\u5316\u540e\u7684\u53c2\u6570\u7c7b\u578b\uff0c\u9ed8\u8ba4 int8 , \u76ee\u524d\u4ec5\u652f\u6301 int8 \u3002 window_size(int) - 'range_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684 window size \uff0c\u9ed8\u8ba410000\u3002 moving_rate(int) - 'moving_average_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684\u8870\u51cf\u7cfb\u6570\uff0c\u9ed8\u8ba4 0.9\u3002","title":"\u91cf\u5316\u914d\u7f6e"},{"location":"api/quantization_api/#quant_aware","text":"paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False) [\u6e90\u4ee3\u7801] \u5728 program \u4e2d\u52a0\u5165\u91cf\u5316\u548c\u53cd\u91cf\u5316 op , \u7528\u4e8e\u91cf\u5316\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u8bad\u7ec3\u6216\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope, optional) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 for_test(bool) - \u5982\u679c program \u53c2\u6570\u662f\u4e00\u4e2a\u6d4b\u8bd5 program \uff0c for_test \u5e94\u8bbe\u4e3a True \uff0c\u5426\u5219\u8bbe\u4e3a False \u3002 \u8fd4\u56de \u542b\u6709\u91cf\u5316\u548c\u53cd\u91cf\u5316 operator \u7684 program \u8fd4\u56de\u7c7b\u578b \u5f53 for_test=False \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.CompiledProgram \uff0c \u6ce8\u610f\uff0c\u6b64\u8fd4\u56de\u503c\u4e0d\u80fd\u7528\u4e8e\u4fdd\u5b58\u53c2\u6570 \u3002 \u5f53 for_test=True \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.Program \u3002 \u6ce8\u610f\u4e8b\u9879 \u6b64\u63a5\u53e3\u4f1a\u6539\u53d8 program \u7ed3\u6784\uff0c\u5e76\u4e14\u53ef\u80fd\u589e\u52a0\u4e00\u4e9b persistable \u7684\u53d8\u91cf\uff0c\u6240\u4ee5\u52a0\u8f7d\u6a21\u578b\u53c2\u6570\u65f6\u8bf7\u6ce8\u610f\u548c\u76f8\u5e94\u7684 program \u5bf9\u5e94\u3002 \u6b64\u63a5\u53e3\u5e95\u5c42\u7ecf\u5386\u4e86 fluid.Program -> fluid.framework.IrGraph -> fluid.Program \u7684\u8f6c\u53d8\uff0c\u5728 fluid.framework.IrGraph \u4e2d\u6ca1\u6709 Parameter \u7684\u6982\u5ff5\uff0c Variable \u53ea\u6709 persistable \u548c not persistable \u7684\u533a\u522b\uff0c\u6240\u4ee5\u5728\u4fdd\u5b58\u548c\u52a0\u8f7d\u53c2\u6570\u65f6\uff0c\u8bf7\u4f7f\u7528 fluid.io.save_persistables \u548c fluid.io.load_persistables \u63a5\u53e3\u3002 \u7531\u4e8e\u6b64\u63a5\u53e3\u4f1a\u6839\u636e program \u7684\u7ed3\u6784\u548c\u91cf\u5316\u914d\u7f6e\u6765\u5bf9 program \u6dfb\u52a0op\uff0c\u6240\u4ee5 Paddle \u4e2d\u4e00\u4e9b\u901a\u8fc7 fuse op \u6765\u52a0\u901f\u8bad\u7ec3\u7684\u7b56\u7565\u4e0d\u80fd\u4f7f\u7528\u3002\u5df2\u77e5\u4ee5\u4e0b\u7b56\u7565\u5728\u4f7f\u7528\u91cf\u5316\u65f6\u5fc5\u987b\u8bbe\u4e3a False \uff1a fuse_all_reduce_ops, sync_batch_norm \u3002 \u5982\u679c\u4f20\u5165\u7684 program \u4e2d\u5b58\u5728\u548c\u4efb\u4f55op\u90fd\u6ca1\u6709\u8fde\u63a5\u7684 Variable \uff0c\u5219\u4f1a\u5728\u91cf\u5316\u7684\u8fc7\u7a0b\u4e2d\u88ab\u4f18\u5316\u6389\u3002","title":"quant_aware"},{"location":"api/quantization_api/#convert","text":"paddleslim.quant.convert(program, place, config, scope=None, save_int8=False) [\u6e90\u4ee3\u7801] \u628a\u8bad\u7ec3\u597d\u7684\u91cf\u5316 program \uff0c\u8f6c\u6362\u4e3a\u53ef\u7528\u4e8e\u4fdd\u5b58 inference model \u7684 program \u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 save_int8\uff08bool) - \u662f\u5426\u9700\u8981\u8fd4\u56de\u53c2\u6570\u4e3a int8 \u7684 program \u3002\u8be5\u529f\u80fd\u76ee\u524d\u53ea\u80fd\u7528\u4e8e\u786e\u8ba4\u6a21\u578b\u5927\u5c0f\u3002\u9ed8\u8ba4\u503c\u4e3a False \u3002 \u8fd4\u56de program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a float32 \u7c7b\u578b\uff0c\u4f46\u5176\u6570\u503c\u8303\u56f4\u53ef\u7528int8\u8868\u793a\u3002 int8_program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a int8 \u7c7b\u578b\u3002\u5f53 save_int8 \u4e3a False \u65f6\uff0c\u4e0d\u8fd4\u56de\u8be5\u503c\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u5bf9 op \u548c Variable \u505a\u76f8\u5e94\u7684\u5220\u9664\u548c\u4fee\u6539\uff0c\u6240\u4ee5\u6b64\u63a5\u53e3\u53ea\u80fd\u5728\u8bad\u7ec3\u5b8c\u6210\u4e4b\u540e\u8c03\u7528\u3002\u5982\u679c\u60f3\u8f6c\u5316\u8bad\u7ec3\u7684\u4e2d\u95f4\u6a21\u578b\uff0c\u53ef\u52a0\u8f7d\u76f8\u5e94\u7684\u53c2\u6570\u4e4b\u540e\u518d\u4f7f\u7528\u6b64\u63a5\u53e3\u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 #encoding=utf8 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): image = fluid . data ( name = 'x' , shape = [ None , 1 , 28 , 28 ]) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv = fluid . layers . conv2d ( image , 32 , 1 ) feat = fluid . layers . fc ( conv , 10 , act = 'softmax' ) cost = fluid . layers . cross_entropy ( input = feat , label = label ) avg_cost = fluid . layers . mean ( x = cost ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) eval_program = train_program . clone ( for_test = True ) #\u914d\u7f6e config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' } build_strategy = fluid . BuildStrategy () exec_strategy = fluid . ExecutionStrategy () #\u8c03\u7528api quant_train_program = quant . quant_aware ( train_program , place , config , for_test = False ) quant_eval_program = quant . quant_aware ( eval_program , place , config , for_test = True ) #\u5173\u95ed\u7b56\u7565 build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False quant_train_program = quant_train_program . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) inference_prog = quant . convert ( quant_eval_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u91cf\u5316\u8bad\u7ec3demo \u3002","title":"convert"},{"location":"api/quantization_api/#quant_post","text":"paddleslim.quant.quant_post(executor, model_dir, quantize_model_path,sample_generator, model_filename=None, params_filename=None, batch_size=16,batch_nums=None, scope=None, algo='KL', quantizable_op_type=[\"conv2d\", \"depthwise_conv2d\", \"mul\"]) [\u6e90\u4ee3\u7801] \u5bf9\u4fdd\u5b58\u5728 ${model_dir} \u4e0b\u7684\u6a21\u578b\u8fdb\u884c\u91cf\u5316\uff0c\u4f7f\u7528 sample_generator \u7684\u6570\u636e\u8fdb\u884c\u53c2\u6570\u6821\u6b63\u3002 \u53c2\u6570: executor (fluid.Executor) - \u6267\u884c\u6a21\u578b\u7684executor\uff0c\u53ef\u4ee5\u5728cpu\u6216\u8005gpu\u4e0a\u6267\u884c\u3002 model_dir\uff08str) - \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u6240\u5728\u7684\u6587\u4ef6\u5939\u3002 quantize_model_path(str) - \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u8def\u5f84 sample_generator(python generator) - \u8bfb\u53d6\u6570\u636e\u6837\u672c\uff0c\u6bcf\u6b21\u8fd4\u56de\u4e00\u4e2a\u6837\u672c\u3002 model_filename(str, optional) - \u6a21\u578b\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e model_filename \u4e3a\u6a21\u578b\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 params_filename(str) - \u53c2\u6570\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e params_filename \u4e3a\u53c2\u6570\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 batch_size(int) - \u6bcf\u4e2abatch\u7684\u56fe\u7247\u6570\u91cf\u3002\u9ed8\u8ba4\u503c\u4e3a16 \u3002 batch_nums(int, optional) - \u8fed\u4ee3\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a None \uff0c\u5219\u4f1a\u4e00\u76f4\u8fd0\u884c\u5230 sample_generator \u8fed\u4ee3\u7ed3\u675f\uff0c \u5426\u5219\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a batch_nums , \u4e5f\u5c31\u662f\u8bf4\u53c2\u4e0e\u5bf9 Scale \u8fdb\u884c\u6821\u6b63\u7684\u6837\u672c\u4e2a\u6570\u4e3a 'batch_nums' * 'batch_size' . scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . \u9ed8\u8ba4\u503c\u662f None . algo(str) - \u91cf\u5316\u65f6\u4f7f\u7528\u7684\u7b97\u6cd5\u540d\u79f0\uff0c\u53ef\u4e3a 'KL' \u6216\u8005 'direct' \u3002\u8be5\u53c2\u6570\u4ec5\u9488\u5bf9\u6fc0\u6d3b\u503c\u7684\u91cf\u5316\uff0c\u56e0\u4e3a\u53c2\u6570\u503c\u7684\u91cf\u5316\u4f7f\u7528\u7684\u65b9\u5f0f\u4e3a 'channel_wise_abs_max' . \u5f53 algo \u8bbe\u7f6e\u4e3a 'direct' \u65f6\uff0c\u4f7f\u7528\u6821\u6b63\u6570\u636e\u7684\u6fc0\u6d3b\u503c\u7684\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u5f53\u4f5c Scale \u503c\uff0c\u5f53\u8bbe\u7f6e\u4e3a 'KL' \u65f6\uff0c\u5219\u4f7f\u7528 KL \u6563\u5ea6\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97 Scale \u503c\u3002\u9ed8\u8ba4\u503c\u4e3a 'KL' \u3002 quantizable_op_type(list[str]) - \u9700\u8981\u91cf\u5316\u7684 op \u7c7b\u578b\u5217\u8868\u3002\u9ed8\u8ba4\u503c\u4e3a [\"conv2d\", \"depthwise_conv2d\", \"mul\"] \u3002 \u8fd4\u56de \u65e0\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u6536\u96c6\u6821\u6b63\u6570\u636e\u7684\u6240\u6709\u7684\u6fc0\u6d3b\u503c\uff0c\u6240\u4ee5\u4f7f\u7528\u7684\u6821\u6b63\u56fe\u7247\u4e0d\u80fd\u592a\u591a\u3002 'KL' \u6563\u5ea6\u7684\u8ba1\u7b97\u4e5f\u6bd4\u8f83\u8017\u65f6\u3002 \u4ee3\u7801\u793a\u4f8b \u6ce8\uff1a \u6b64\u793a\u4f8b\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\uff0c\u56e0\u4e3a\u9700\u8981\u52a0\u8f7d ${model_dir} \u4e0b\u7684\u6a21\u578b\uff0c\u6240\u4ee5\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import paddle.fluid as fluid import paddle.dataset.mnist as reader from paddleslim.quant import quant_post val_reader = reader . train () use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) quant_post ( executor = exe , model_dir = './model_path' , quantize_model_path = './save_path' , sample_generator = val_reader , model_filename = '__model__' , params_filename = '__params__' , batch_size = 16 , batch_nums = 10 ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u79bb\u7ebf\u91cf\u5316demo \u3002","title":"quant_post"},{"location":"api/quantization_api/#quant_embedding","text":"paddleslim.quant.quant_embedding(program, place, config, scope=None) [\u6e90\u4ee3\u7801] \u5bf9 Embedding \u53c2\u6570\u8fdb\u884c\u91cf\u5316\u3002 \u53c2\u6570: program(fluid.Program) - \u9700\u8981\u91cf\u5316\u7684program scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . place(fluid.CPUPlace | fluid.CUDAPlace) - \u8fd0\u884cprogram\u7684\u8bbe\u5907 config(dict) - \u5b9a\u4e49\u91cf\u5316\u7684\u914d\u7f6e\u3002\u53ef\u4ee5\u914d\u7f6e\u7684\u53c2\u6570\u6709\uff1a 'params_name' (str, required): \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684\u53c2\u6570\u540d\u79f0\uff0c\u6b64\u53c2\u6570\u5fc5\u987b\u8bbe\u7f6e\u3002 'quantize_type' (str, optional): \u91cf\u5316\u7684\u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301\u7684\u7c7b\u578b\u662f 'abs_max' , \u5f85\u652f\u6301\u7684\u7c7b\u578b\u6709 'log', 'product_quantization' \u3002 \u9ed8\u8ba4\u503c\u662f 'abs_max' . 'quantize_bits' \uff08int, optional): \u91cf\u5316\u7684 bit \u6570\uff0c\u76ee\u524d\u652f\u6301\u7684 bit \u6570\u4e3a8\u3002\u9ed8\u8ba4\u503c\u662f8. 'dtype' (str, optional): \u91cf\u5316\u4e4b\u540e\u7684\u6570\u636e\u7c7b\u578b\uff0c \u76ee\u524d\u652f\u6301\u7684\u662f 'int8' . \u9ed8\u8ba4\u503c\u662f int8 \u3002 'threshold' (float, optional): \u91cf\u5316\u4e4b\u524d\u5c06\u6839\u636e\u6b64\u9608\u503c\u5bf9\u9700\u8981\u91cf\u5316\u7684\u53c2\u6570\u503c\u8fdb\u884c clip . \u5982\u679c\u4e0d\u8bbe\u7f6e\uff0c\u5219\u8df3\u8fc7 clip \u8fc7\u7a0b\u76f4\u63a5\u91cf\u5316\u3002 \u8fd4\u56de \u91cf\u5316\u4e4b\u540e\u7684program \u8fd4\u56de\u7c7b\u578b fluid.Program \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): input_word = fluid . data ( name = \"input_word\" , shape = [ None , 1 ], dtype = 'int64' ) input_emb = fluid . embedding ( input = input_word , is_sparse = False , size = [ 100 , 128 ], param_attr = fluid . ParamAttr ( name = 'emb' , initializer = fluid . initializer . Uniform ( - 0.005 , 0.005 ))) infer_program = train_program . clone ( for_test = True ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } quant_program = quant . quant_embedding ( infer_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 Embedding\u91cf\u5316demo \u3002","title":"quant_embedding"},{"location":"api/search_space/","text":"paddleslim.nas \u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\uff1a # \u6839\u636e\u539f\u672c\u6a21\u578b\u7ed3\u6784\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 1.1 MobileNetV2Space 1.2 MobileNetV1Space 1.3 ResNetSpace \u6839\u636e\u76f8\u5e94\u6a21\u578b\u7684block\u6784\u9020\u641c\u7d22\u7a7a\u95f4 2.1 MobileNetV1BlockSpace 2.2 MobileNetV2BlockSpace 2.3 ResNetBlockSpace 2.4 InceptionABlockSpace 2.5 InceptionCBlockSpace \u641c\u7d22\u7a7a\u95f4\u7684\u914d\u7f6e\u4ecb\u7ecd\uff1a # input_size(int|None) \uff1a input_size \u8868\u793a\u8f93\u5165feature map\u7684\u5927\u5c0f\u3002 output_size(int|None) \uff1a output_size \u8868\u793a\u8f93\u51fafeature map\u7684\u5927\u5c0f\u3002 block_num(int|None) \uff1a block_num \u8868\u793a\u641c\u7d22\u7a7a\u95f4\u4e2dblock\u7684\u6570\u91cf\u3002 block_mask(list|None) \uff1a block_mask \u8868\u793a\u5f53\u524d\u7684block\u662f\u4e00\u4e2areduction block\u8fd8\u662f\u4e00\u4e2anormal block\uff0c\u662f\u4e00\u7ec4\u75310\u30011\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u8868\u793a\u5f53\u524dblock\u662fnormal block\uff0c1\u8868\u793a\u5f53\u524dblock\u662freduction block\u3002\u5982\u679c\u8bbe\u7f6e\u4e86 block_mask \uff0c\u5219\u4e3b\u8981\u4ee5 block_mask \u4e3a\u4e3b\u8981\u914d\u7f6e\uff0c input_size \uff0c output_size \u548c block_num \u4e09\u79cd\u914d\u7f6e\u662f\u65e0\u6548\u7684\u3002 Note: 1. reduction block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540e\u7684feature map\u5927\u5c0f\u4e0b\u964d\u4e3a\u4e4b\u524d\u7684\u4e00\u534a\uff0cnormal block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540efeature map\u5927\u5c0f\u4e0d\u53d8\u3002 2. input_size \u548c output_size \u7528\u6765\u8ba1\u7b97\u6574\u4e2a\u6a21\u578b\u7ed3\u6784\u4e2dreduction block\u6570\u91cf\u3002 \u641c\u7d22\u7a7a\u95f4\u793a\u4f8b\uff1a # \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7528\u539f\u672c\u7684\u6a21\u578b\u7ed3\u6784\u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u7684\u8bdd\uff0c\u4ec5\u9700\u8981\u6307\u5b9a\u641c\u7d22\u7a7a\u95f4\u540d\u5b57\u5373\u53ef\u3002\u4f8b\u5982\uff1a\u5982\u679c\u4f7f\u7528\u539f\u672c\u7684MobileNetV2\u7684\u641c\u7d22\u7a7a\u95f4\u8fdb\u884c\u641c\u7d22\u7684\u8bdd\uff0c\u4f20\u5165SANAS\u4e2d\u7684config\u76f4\u63a5\u6307\u5b9a\u4e3a[('MobileNetV2Space')]\u3002 \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7684block\u641c\u7d22\u7a7a\u95f4\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 2.1 \u4f7f\u7528 input_size , output_size \u548c block_num \u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]\u3002 2.2 \u4f7f\u7528 block_mask \u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]\u3002 \u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4(search space) # \u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u7c7b\u9700\u8981\u7ee7\u627f\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u5e76\u91cd\u5199\u4ee5\u4e0b\u51e0\u90e8\u5206\uff1a 1. \u521d\u59cb\u5316\u7684tokens( init_tokens \u51fd\u6570)\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u81ea\u5df1\u60f3\u8981\u7684tokens\u5217\u8868, tokens\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u6570\u5b57\u6307\u7684\u662f\u5f53\u524d\u6570\u5b57\u5728\u76f8\u5e94\u7684\u641c\u7d22\u5217\u8868\u4e2d\u7684\u7d22\u5f15\u3002\u4f8b\u5982\u672c\u793a\u4f8b\u4e2d\u82e5tokens=[0, 3, 5]\uff0c\u5219\u4ee3\u8868\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u5230\u7684\u901a\u9053\u6570\u4e3a[8, 40, 128]\u3002 2. token\u4e2d\u6bcf\u4e2a\u6570\u5b57\u7684\u641c\u7d22\u5217\u8868\u957f\u5ea6( range_table \u51fd\u6570)\uff0ctokens\u4e2d\u6bcf\u4e2atoken\u7684\u7d22\u5f15\u8303\u56f4\u3002 3. \u6839\u636etoken\u4ea7\u751f\u6a21\u578b\u7ed3\u6784( token2arch \u51fd\u6570)\uff0c\u6839\u636e\u641c\u7d22\u5230\u7684tokens\u5217\u8868\u4ea7\u751f\u6a21\u578b\u7ed3\u6784\u3002 \u4ee5\u65b0\u589ereset block\u4e3a\u4f8b\u8bf4\u660e\u5982\u4f55\u6784\u9020\u81ea\u5df1\u7684search space\u3002\u81ea\u5b9a\u4e49\u7684search space\u4e0d\u80fd\u548c\u5df2\u6709\u7684search space\u540c\u540d\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 ### \u5f15\u5165\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u51fd\u6570\u548csearch space\u7684\u6ce8\u518c\u7c7b\u51fd\u6570 from .search_space_base import SearchSpaceBase from .search_space_registry import SEARCHSPACE import numpy as np ### \u9700\u8981\u8c03\u7528\u6ce8\u518c\u51fd\u6570\u628a\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u6ce8\u518c\u5230space space\u4e2d @SEARCHSPACE.register ### \u5b9a\u4e49\u4e00\u4e2a\u7ee7\u627fSearchSpaceBase\u57fa\u7c7b\u7684\u641c\u7d22\u7a7a\u95f4\u7684\u7c7b\u51fd\u6570 class ResNetBlockSpace2 ( SearchSpaceBase ): def __init__ ( self , input_size , output_size , block_num , block_mask ): ### \u5b9a\u4e49\u4e00\u4e9b\u5b9e\u9645\u60f3\u8981\u641c\u7d22\u7684\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a\u901a\u9053\u6570\u3001\u6bcf\u4e2a\u5377\u79ef\u7684\u91cd\u590d\u6b21\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u7b49 ### self.filter_num \u4ee3\u8868\u901a\u9053\u6570\u7684\u641c\u7d22\u5217\u8868 self . filter_num = np . array ([ 8 , 16 , 32 , 40 , 64 , 128 , 256 , 512 ]) ### \u5b9a\u4e49\u521d\u59cb\u5316token\uff0c\u521d\u59cb\u5316token\u7684\u957f\u5ea6\u6839\u636e\u4f20\u5165\u7684block_num\u6216\u8005block_mask\u7684\u957f\u5ea6\u6765\u5f97\u5230\u7684 def init_tokens ( self ): return [ 0 ] * 3 * len ( self . block_mask ) ### \u5b9a\u4e49 def range_table ( self ): return [ len ( self . filter_num )] * 3 * len ( self . block_mask ) def token2arch ( self , tokens = None ): if tokens == None : tokens = self . init_tokens () self . bottleneck_params_list = [] for i in range ( len ( self . block_mask )): self . bottleneck_params_list . append ( self . filter_num [ tokens [ i * 3 + 0 ]], self . filter_num [ tokens [ i * 3 + 1 ]], self . filter_num [ tokens [ i * 3 + 2 ]], 2 if self . block_mask [ i ] == 1 else 1 ) def net_arch ( input ): for i , layer_setting in enumerate ( self . bottleneck_params_list ): channel_num , stride = layer_setting [: - 1 ], layer_setting [ - 1 ] input = self . _resnet_block ( input , channel_num , stride , name = 'resnet_layer{}' . format ( i + 1 )) return input return net_arch ### \u6784\u9020\u5177\u4f53block\u7684\u64cd\u4f5c def _resnet_block ( self , input , channel_num , stride , name = None ): shortcut_conv = self . _shortcut ( input , channel_num [ 2 ], stride , name = name ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 0 ], filter_size = 1 , act = 'relu' , name = name + '_conv0' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 1 ], filter_size = 3 , stride = stride , act = 'relu' , name = name + '_conv1' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 2 ], filter_size = 1 , name = name + '_conv2' ) return fluid . layers . elementwise_add ( x = shortcut_conv , y = input , axis = 0 , name = name + '_elementwise_add' ) def _shortcut ( self , input , channel_num , stride , name = None ): channel_in = input . shape [ 1 ] if channel_in != channel_num or stride != 1 : return self . conv_bn_layer ( input , num_filters = channel_num , filter_size = 1 , stride = stride , name = name + '_shortcut' ) else : return input def _conv_bn_layer ( self , input , num_filters , filter_size , stride = 1 , padding = 'SAME' , act = None , name = None ): conv = fluid . layers . conv2d ( input , num_filters , filter_size , stride , name = name + '_conv' ) bn = fluid . layers . batch_norm ( conv , act = act , name = name + '_bn' ) return bn","title":"\u641c\u7d22\u7a7a\u95f4"},{"location":"api/search_space/#paddleslimnas","text":"\u6839\u636e\u539f\u672c\u6a21\u578b\u7ed3\u6784\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 1.1 MobileNetV2Space 1.2 MobileNetV1Space 1.3 ResNetSpace \u6839\u636e\u76f8\u5e94\u6a21\u578b\u7684block\u6784\u9020\u641c\u7d22\u7a7a\u95f4 2.1 MobileNetV1BlockSpace 2.2 MobileNetV2BlockSpace 2.3 ResNetBlockSpace 2.4 InceptionABlockSpace 2.5 InceptionCBlockSpace","title":"paddleslim.nas \u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\uff1a"},{"location":"api/search_space/#_1","text":"input_size(int|None) \uff1a input_size \u8868\u793a\u8f93\u5165feature map\u7684\u5927\u5c0f\u3002 output_size(int|None) \uff1a output_size \u8868\u793a\u8f93\u51fafeature map\u7684\u5927\u5c0f\u3002 block_num(int|None) \uff1a block_num \u8868\u793a\u641c\u7d22\u7a7a\u95f4\u4e2dblock\u7684\u6570\u91cf\u3002 block_mask(list|None) \uff1a block_mask \u8868\u793a\u5f53\u524d\u7684block\u662f\u4e00\u4e2areduction block\u8fd8\u662f\u4e00\u4e2anormal block\uff0c\u662f\u4e00\u7ec4\u75310\u30011\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u8868\u793a\u5f53\u524dblock\u662fnormal block\uff0c1\u8868\u793a\u5f53\u524dblock\u662freduction block\u3002\u5982\u679c\u8bbe\u7f6e\u4e86 block_mask \uff0c\u5219\u4e3b\u8981\u4ee5 block_mask \u4e3a\u4e3b\u8981\u914d\u7f6e\uff0c input_size \uff0c output_size \u548c block_num \u4e09\u79cd\u914d\u7f6e\u662f\u65e0\u6548\u7684\u3002 Note: 1. reduction block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540e\u7684feature map\u5927\u5c0f\u4e0b\u964d\u4e3a\u4e4b\u524d\u7684\u4e00\u534a\uff0cnormal block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540efeature map\u5927\u5c0f\u4e0d\u53d8\u3002 2. input_size \u548c output_size \u7528\u6765\u8ba1\u7b97\u6574\u4e2a\u6a21\u578b\u7ed3\u6784\u4e2dreduction block\u6570\u91cf\u3002","title":"\u641c\u7d22\u7a7a\u95f4\u7684\u914d\u7f6e\u4ecb\u7ecd\uff1a"},{"location":"api/search_space/#_2","text":"\u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7528\u539f\u672c\u7684\u6a21\u578b\u7ed3\u6784\u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u7684\u8bdd\uff0c\u4ec5\u9700\u8981\u6307\u5b9a\u641c\u7d22\u7a7a\u95f4\u540d\u5b57\u5373\u53ef\u3002\u4f8b\u5982\uff1a\u5982\u679c\u4f7f\u7528\u539f\u672c\u7684MobileNetV2\u7684\u641c\u7d22\u7a7a\u95f4\u8fdb\u884c\u641c\u7d22\u7684\u8bdd\uff0c\u4f20\u5165SANAS\u4e2d\u7684config\u76f4\u63a5\u6307\u5b9a\u4e3a[('MobileNetV2Space')]\u3002 \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7684block\u641c\u7d22\u7a7a\u95f4\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 2.1 \u4f7f\u7528 input_size , output_size \u548c block_num \u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]\u3002 2.2 \u4f7f\u7528 block_mask \u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]\u3002","title":"\u641c\u7d22\u7a7a\u95f4\u793a\u4f8b\uff1a"},{"location":"api/search_space/#search-space","text":"\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u7c7b\u9700\u8981\u7ee7\u627f\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u5e76\u91cd\u5199\u4ee5\u4e0b\u51e0\u90e8\u5206\uff1a 1. \u521d\u59cb\u5316\u7684tokens( init_tokens \u51fd\u6570)\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u81ea\u5df1\u60f3\u8981\u7684tokens\u5217\u8868, tokens\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u6570\u5b57\u6307\u7684\u662f\u5f53\u524d\u6570\u5b57\u5728\u76f8\u5e94\u7684\u641c\u7d22\u5217\u8868\u4e2d\u7684\u7d22\u5f15\u3002\u4f8b\u5982\u672c\u793a\u4f8b\u4e2d\u82e5tokens=[0, 3, 5]\uff0c\u5219\u4ee3\u8868\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u5230\u7684\u901a\u9053\u6570\u4e3a[8, 40, 128]\u3002 2. token\u4e2d\u6bcf\u4e2a\u6570\u5b57\u7684\u641c\u7d22\u5217\u8868\u957f\u5ea6( range_table \u51fd\u6570)\uff0ctokens\u4e2d\u6bcf\u4e2atoken\u7684\u7d22\u5f15\u8303\u56f4\u3002 3. \u6839\u636etoken\u4ea7\u751f\u6a21\u578b\u7ed3\u6784( token2arch \u51fd\u6570)\uff0c\u6839\u636e\u641c\u7d22\u5230\u7684tokens\u5217\u8868\u4ea7\u751f\u6a21\u578b\u7ed3\u6784\u3002 \u4ee5\u65b0\u589ereset block\u4e3a\u4f8b\u8bf4\u660e\u5982\u4f55\u6784\u9020\u81ea\u5df1\u7684search space\u3002\u81ea\u5b9a\u4e49\u7684search space\u4e0d\u80fd\u548c\u5df2\u6709\u7684search space\u540c\u540d\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 ### \u5f15\u5165\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u51fd\u6570\u548csearch space\u7684\u6ce8\u518c\u7c7b\u51fd\u6570 from .search_space_base import SearchSpaceBase from .search_space_registry import SEARCHSPACE import numpy as np ### \u9700\u8981\u8c03\u7528\u6ce8\u518c\u51fd\u6570\u628a\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u6ce8\u518c\u5230space space\u4e2d @SEARCHSPACE.register ### \u5b9a\u4e49\u4e00\u4e2a\u7ee7\u627fSearchSpaceBase\u57fa\u7c7b\u7684\u641c\u7d22\u7a7a\u95f4\u7684\u7c7b\u51fd\u6570 class ResNetBlockSpace2 ( SearchSpaceBase ): def __init__ ( self , input_size , output_size , block_num , block_mask ): ### \u5b9a\u4e49\u4e00\u4e9b\u5b9e\u9645\u60f3\u8981\u641c\u7d22\u7684\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a\u901a\u9053\u6570\u3001\u6bcf\u4e2a\u5377\u79ef\u7684\u91cd\u590d\u6b21\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u7b49 ### self.filter_num \u4ee3\u8868\u901a\u9053\u6570\u7684\u641c\u7d22\u5217\u8868 self . filter_num = np . array ([ 8 , 16 , 32 , 40 , 64 , 128 , 256 , 512 ]) ### \u5b9a\u4e49\u521d\u59cb\u5316token\uff0c\u521d\u59cb\u5316token\u7684\u957f\u5ea6\u6839\u636e\u4f20\u5165\u7684block_num\u6216\u8005block_mask\u7684\u957f\u5ea6\u6765\u5f97\u5230\u7684 def init_tokens ( self ): return [ 0 ] * 3 * len ( self . block_mask ) ### \u5b9a\u4e49 def range_table ( self ): return [ len ( self . filter_num )] * 3 * len ( self . block_mask ) def token2arch ( self , tokens = None ): if tokens == None : tokens = self . init_tokens () self . bottleneck_params_list = [] for i in range ( len ( self . block_mask )): self . bottleneck_params_list . append ( self . filter_num [ tokens [ i * 3 + 0 ]], self . filter_num [ tokens [ i * 3 + 1 ]], self . filter_num [ tokens [ i * 3 + 2 ]], 2 if self . block_mask [ i ] == 1 else 1 ) def net_arch ( input ): for i , layer_setting in enumerate ( self . bottleneck_params_list ): channel_num , stride = layer_setting [: - 1 ], layer_setting [ - 1 ] input = self . _resnet_block ( input , channel_num , stride , name = 'resnet_layer{}' . format ( i + 1 )) return input return net_arch ### \u6784\u9020\u5177\u4f53block\u7684\u64cd\u4f5c def _resnet_block ( self , input , channel_num , stride , name = None ): shortcut_conv = self . _shortcut ( input , channel_num [ 2 ], stride , name = name ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 0 ], filter_size = 1 , act = 'relu' , name = name + '_conv0' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 1 ], filter_size = 3 , stride = stride , act = 'relu' , name = name + '_conv1' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 2 ], filter_size = 1 , name = name + '_conv2' ) return fluid . layers . elementwise_add ( x = shortcut_conv , y = input , axis = 0 , name = name + '_elementwise_add' ) def _shortcut ( self , input , channel_num , stride , name = None ): channel_in = input . shape [ 1 ] if channel_in != channel_num or stride != 1 : return self . conv_bn_layer ( input , num_filters = channel_num , filter_size = 1 , stride = stride , name = name + '_shortcut' ) else : return input def _conv_bn_layer ( self , input , num_filters , filter_size , stride = 1 , padding = 'SAME' , act = None , name = None ): conv = fluid . layers . conv2d ( input , num_filters , filter_size , stride , name = name + '_conv' ) bn = fluid . layers . batch_norm ( conv , act = act , name = name + '_bn' ) return bn","title":"\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4(search space)"},{"location":"api/single_distiller_api/","text":"merge # paddleslim.dist.merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_') [\u6e90\u4ee3\u7801] merge\u5c06\u4e24\u4e2apaddle program\uff08teacher_program, student_program\uff09\u878d\u5408\u4e3a\u4e00\u4e2aprogram\uff0c\u5e76\u5c06\u878d\u5408\u5f97\u5230\u7684program\u8fd4\u56de\u3002\u5728\u878d\u5408\u7684program\u4e2d\uff0c\u53ef\u4ee5\u4e3a\u5176\u4e2d\u5408\u9002\u7684teacher\u7279\u5f81\u56fe\u548cstudent\u7279\u5f81\u56fe\u6dfb\u52a0\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4ece\u800c\u8fbe\u5230\u7528teacher\u6a21\u578b\u7684\u6697\u77e5\u8bc6\uff08Dark Knowledge\uff09\u6307\u5bfcstudent\u6a21\u578b\u5b66\u4e60\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a teacher_program (Program)-\u5b9a\u4e49\u4e86teacher\u6a21\u578b\u7684 paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684 paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0c\u5176\u4e2ddict\u7684 key \u4e3ateacher\u7684\u8f93\u5165\u540d\uff0c value \u4e3astudent\u7684\u8f93\u5165\u540d place (fluid.CPUPlace()|fluid.CUDAPlace(N))-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u8fd0\u884c\u5728\u4f55\u79cd\u8bbe\u5907\u4e0a\uff0c\u8fd9\u91cc\u7684N\u4e3aGPU\u5bf9\u5e94\u7684ID scope (Scope)-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u4f7f\u7528\u7684\u53d8\u91cf\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1a fluid.global_scope() name_prefix (str)-merge\u64cd\u4f5c\u5c06\u7edf\u4e00\u4e3ateacher\u7684 Variables \u6dfb\u52a0\u7684\u540d\u79f0\u524d\u7f00name_prefix\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program Note data_name_map \u662f teacher_var name\u5230student_var name\u7684\u6620\u5c04 \uff0c\u5982\u679c\u5199\u53cd\u53ef\u80fd\u65e0\u6cd5\u6b63\u786e\u8fdb\u884cmerge \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = dist . merge ( teacher_program , student_program , data_name_map , place ) fsp_loss # paddleslim.dist.fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var1_name (str): teacher_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 teacher_var2_name (str): teacher_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0eteacher_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0eteacher_var1\u76f8\u540c student_var1_name (str): student_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var1\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 student_var2_name (str): student_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var2\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0estudent_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0estudent_var1\u76f8\u540c program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var1, teacher_var2, student_var1, student_var2\u7ec4\u5408\u5f97\u5230\u7684fsp_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . fsp_loss ( 'teacher_t1.tmp_1' , 'teacher_t2.tmp_1' , 's1.tmp_1' , 's2.tmp_1' , main_program ) l2_loss # paddleslim.dist.l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] l2_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0l2 loss \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . l2_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program ) soft_label_loss # paddleslim.dist.soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.) [\u6e90\u4ee3\u7801] soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() teacher_temperature (float): \u5bf9teacher_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 student_temperature (float): \u5bf9student_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684soft_label_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . soft_label_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program , 1. , 1. ) loss # paddleslim.dist.loss(loss_func, program=fluid.default_main_program(), **kwargs) [\u6e90\u4ee3\u7801] loss\u51fd\u6570\u652f\u6301\u5bf9\u4efb\u610f\u591a\u5bf9teacher_var\u548cstudent_var\u4f7f\u7528\u81ea\u5b9a\u4e49\u635f\u5931\u51fd\u6570 \u53c2\u6570\uff1a loss_func (python function): \u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570\uff0c\u8f93\u5165\u4e3ateacher var\u548cstudent var\uff0c\u8f93\u51fa\u4e3a\u81ea\u5b9a\u4e49\u7684loss program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() **kwargs : loss_func\u8f93\u5165\u540d\u4e0e\u5bf9\u5e94variable\u540d\u79f0 \u8fd4\u56de \uff1a\u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) def adaptation_loss ( t_var , s_var ): teacher_channel = t_var . shape [ 1 ] s_hint = fluid . layers . conv2d ( s_var , teacher_channel , 1 ) hint_loss = fluid . layers . reduce_mean ( fluid . layers . square ( s_hint - t_var )) return hint_loss with fluid . program_guard ( main_program ): distillation_loss = dist . loss ( main_program , adaptation_loss , t_var = 'teacher_t2.tmp_1' , s_var = 's2.tmp_1' ) \u6ce8\u610f\u4e8b\u9879 \u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u4f1a\u5f15\u5165\u65b0\u7684variable\uff0c\u9700\u8981\u6ce8\u610f\u65b0\u5f15\u5165\u7684variable\u4e0d\u8981\u4e0estudent variables\u547d\u540d\u51b2\u7a81\u3002\u8fd9\u91cc\u5efa\u8bae\u4e24\u79cd\u7528\u6cd5\uff08\u4e24\u79cd\u65b9\u6cd5\u4efb\u9009\u5176\u4e00\u5373\u53ef\uff09\uff1a \u5efa\u8bae\u4e0estudent_program\u4f7f\u7528\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\uff0c\u4ee5\u907f\u514d\u4e00\u4e9b\u672a\u6307\u5b9a\u540d\u79f0\u7684variables(\u4f8b\u5982tmp_0, tmp_1...)\u591a\u6b21\u5b9a\u4e49\u4e3a\u540c\u4e00\u540d\u79f0\u51fa\u73b0\u547d\u540d\u51b2\u7a81 \u5efa\u8bae\u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u6307\u5b9a\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u524d\u7f00\uff0c\u5177\u4f53\u7528\u6cd5\u8bf7\u53c2\u8003Paddle\u5b98\u65b9\u6587\u6863 fluid.name_scope","title":"\u77e5\u8bc6\u84b8\u998f"},{"location":"api/single_distiller_api/#merge","text":"paddleslim.dist.merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_') [\u6e90\u4ee3\u7801] merge\u5c06\u4e24\u4e2apaddle program\uff08teacher_program, student_program\uff09\u878d\u5408\u4e3a\u4e00\u4e2aprogram\uff0c\u5e76\u5c06\u878d\u5408\u5f97\u5230\u7684program\u8fd4\u56de\u3002\u5728\u878d\u5408\u7684program\u4e2d\uff0c\u53ef\u4ee5\u4e3a\u5176\u4e2d\u5408\u9002\u7684teacher\u7279\u5f81\u56fe\u548cstudent\u7279\u5f81\u56fe\u6dfb\u52a0\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4ece\u800c\u8fbe\u5230\u7528teacher\u6a21\u578b\u7684\u6697\u77e5\u8bc6\uff08Dark Knowledge\uff09\u6307\u5bfcstudent\u6a21\u578b\u5b66\u4e60\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a teacher_program (Program)-\u5b9a\u4e49\u4e86teacher\u6a21\u578b\u7684 paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684 paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0c\u5176\u4e2ddict\u7684 key \u4e3ateacher\u7684\u8f93\u5165\u540d\uff0c value \u4e3astudent\u7684\u8f93\u5165\u540d place (fluid.CPUPlace()|fluid.CUDAPlace(N))-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u8fd0\u884c\u5728\u4f55\u79cd\u8bbe\u5907\u4e0a\uff0c\u8fd9\u91cc\u7684N\u4e3aGPU\u5bf9\u5e94\u7684ID scope (Scope)-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u4f7f\u7528\u7684\u53d8\u91cf\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1a fluid.global_scope() name_prefix (str)-merge\u64cd\u4f5c\u5c06\u7edf\u4e00\u4e3ateacher\u7684 Variables \u6dfb\u52a0\u7684\u540d\u79f0\u524d\u7f00name_prefix\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program Note data_name_map \u662f teacher_var name\u5230student_var name\u7684\u6620\u5c04 \uff0c\u5982\u679c\u5199\u53cd\u53ef\u80fd\u65e0\u6cd5\u6b63\u786e\u8fdb\u884cmerge \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = dist . merge ( teacher_program , student_program , data_name_map , place )","title":"merge"},{"location":"api/single_distiller_api/#fsp_loss","text":"paddleslim.dist.fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var1_name (str): teacher_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 teacher_var2_name (str): teacher_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0eteacher_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0eteacher_var1\u76f8\u540c student_var1_name (str): student_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var1\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 student_var2_name (str): student_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var2\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0estudent_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0estudent_var1\u76f8\u540c program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var1, teacher_var2, student_var1, student_var2\u7ec4\u5408\u5f97\u5230\u7684fsp_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . fsp_loss ( 'teacher_t1.tmp_1' , 'teacher_t2.tmp_1' , 's1.tmp_1' , 's2.tmp_1' , main_program )","title":"fsp_loss"},{"location":"api/single_distiller_api/#l2_loss","text":"paddleslim.dist.l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] l2_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0l2 loss \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . l2_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program )","title":"l2_loss"},{"location":"api/single_distiller_api/#soft_label_loss","text":"paddleslim.dist.soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.) [\u6e90\u4ee3\u7801] soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() teacher_temperature (float): \u5bf9teacher_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 student_temperature (float): \u5bf9student_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684soft_label_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . soft_label_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program , 1. , 1. )","title":"soft_label_loss"},{"location":"api/single_distiller_api/#loss","text":"paddleslim.dist.loss(loss_func, program=fluid.default_main_program(), **kwargs) [\u6e90\u4ee3\u7801] loss\u51fd\u6570\u652f\u6301\u5bf9\u4efb\u610f\u591a\u5bf9teacher_var\u548cstudent_var\u4f7f\u7528\u81ea\u5b9a\u4e49\u635f\u5931\u51fd\u6570 \u53c2\u6570\uff1a loss_func (python function): \u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570\uff0c\u8f93\u5165\u4e3ateacher var\u548cstudent var\uff0c\u8f93\u51fa\u4e3a\u81ea\u5b9a\u4e49\u7684loss program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() **kwargs : loss_func\u8f93\u5165\u540d\u4e0e\u5bf9\u5e94variable\u540d\u79f0 \u8fd4\u56de \uff1a\u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) def adaptation_loss ( t_var , s_var ): teacher_channel = t_var . shape [ 1 ] s_hint = fluid . layers . conv2d ( s_var , teacher_channel , 1 ) hint_loss = fluid . layers . reduce_mean ( fluid . layers . square ( s_hint - t_var )) return hint_loss with fluid . program_guard ( main_program ): distillation_loss = dist . loss ( main_program , adaptation_loss , t_var = 'teacher_t2.tmp_1' , s_var = 's2.tmp_1' ) \u6ce8\u610f\u4e8b\u9879 \u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u4f1a\u5f15\u5165\u65b0\u7684variable\uff0c\u9700\u8981\u6ce8\u610f\u65b0\u5f15\u5165\u7684variable\u4e0d\u8981\u4e0estudent variables\u547d\u540d\u51b2\u7a81\u3002\u8fd9\u91cc\u5efa\u8bae\u4e24\u79cd\u7528\u6cd5\uff08\u4e24\u79cd\u65b9\u6cd5\u4efb\u9009\u5176\u4e00\u5373\u53ef\uff09\uff1a \u5efa\u8bae\u4e0estudent_program\u4f7f\u7528\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\uff0c\u4ee5\u907f\u514d\u4e00\u4e9b\u672a\u6307\u5b9a\u540d\u79f0\u7684variables(\u4f8b\u5982tmp_0, tmp_1...)\u591a\u6b21\u5b9a\u4e49\u4e3a\u540c\u4e00\u540d\u79f0\u51fa\u73b0\u547d\u540d\u51b2\u7a81 \u5efa\u8bae\u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u6307\u5b9a\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u524d\u7f00\uff0c\u5177\u4f53\u7528\u6cd5\u8bf7\u53c2\u8003Paddle\u5b98\u65b9\u6587\u6863 fluid.name_scope","title":"loss"},{"location":"tutorials/demo_guide/","text":"\u84b8\u998f # \u84b8\u998fdemo\u9ed8\u8ba4\u4f7f\u7528ResNet50\u4f5c\u4e3ateacher\u7f51\u7edc\uff0cMobileNet\u4f5c\u4e3astudent\u7f51\u7edc\uff0c\u6b64\u5916\u8fd8\u652f\u6301\u5c06teacher\u548cstudent\u6362\u6210 models\u76ee\u5f55 \u652f\u6301\u7684\u4efb\u610f\u6a21\u578b\u3002 demo\u4e2d\u5bf9teahcer\u6a21\u578b\u548cstudent\u6a21\u578b\u7684\u4e00\u5c42\u7279\u5f81\u56fe\u6dfb\u52a0\u4e86l2_loss\u7684\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4f7f\u7528\u65f6\u4e5f\u53ef\u6839\u636e\u9700\u8981\u9009\u62e9fsp_loss, soft_label_loss\u4ee5\u53ca\u81ea\u5b9a\u4e49\u7684loss\u51fd\u6570\u3002 \u8bad\u7ec3\u9ed8\u8ba4\u4f7f\u7528\u7684\u662fcifar10\u6570\u636e\u96c6\uff0cpiecewise_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\uff0cmomentum\u4f18\u5316\u5668\u8fdb\u884c120\u8f6e\u84b8\u998f\u8bad\u7ec3\u3002\u4f7f\u7528\u8005\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u7528args\u53c2\u6570\u5207\u6362\u4e3a\u4f7f\u7528ImageNet\u6570\u636e\u96c6\uff0ccosine_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\u7b49\u5176\u4ed6\u8bad\u7ec3\u914d\u7f6e\u3002 \u91cf\u5316 # \u91cf\u5316\u8bad\u7ec3demo\u6587\u6863 # \u79bb\u7ebf\u91cf\u5316demo\u6587\u6863 # Embedding\u91cf\u5316demo\u6587\u6863 # NAS # NAS\u793a\u4f8b #","title":"Demo guide"},{"location":"tutorials/demo_guide/#_1","text":"\u84b8\u998fdemo\u9ed8\u8ba4\u4f7f\u7528ResNet50\u4f5c\u4e3ateacher\u7f51\u7edc\uff0cMobileNet\u4f5c\u4e3astudent\u7f51\u7edc\uff0c\u6b64\u5916\u8fd8\u652f\u6301\u5c06teacher\u548cstudent\u6362\u6210 models\u76ee\u5f55 \u652f\u6301\u7684\u4efb\u610f\u6a21\u578b\u3002 demo\u4e2d\u5bf9teahcer\u6a21\u578b\u548cstudent\u6a21\u578b\u7684\u4e00\u5c42\u7279\u5f81\u56fe\u6dfb\u52a0\u4e86l2_loss\u7684\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4f7f\u7528\u65f6\u4e5f\u53ef\u6839\u636e\u9700\u8981\u9009\u62e9fsp_loss, soft_label_loss\u4ee5\u53ca\u81ea\u5b9a\u4e49\u7684loss\u51fd\u6570\u3002 \u8bad\u7ec3\u9ed8\u8ba4\u4f7f\u7528\u7684\u662fcifar10\u6570\u636e\u96c6\uff0cpiecewise_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\uff0cmomentum\u4f18\u5316\u5668\u8fdb\u884c120\u8f6e\u84b8\u998f\u8bad\u7ec3\u3002\u4f7f\u7528\u8005\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u7528args\u53c2\u6570\u5207\u6362\u4e3a\u4f7f\u7528ImageNet\u6570\u636e\u96c6\uff0ccosine_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\u7b49\u5176\u4ed6\u8bad\u7ec3\u914d\u7f6e\u3002","title":"\u84b8\u998f"},{"location":"tutorials/demo_guide/#_2","text":"","title":"\u91cf\u5316"},{"location":"tutorials/demo_guide/#demo","text":"","title":"\u91cf\u5316\u8bad\u7ec3demo\u6587\u6863"},{"location":"tutorials/demo_guide/#demo_1","text":"","title":"\u79bb\u7ebf\u91cf\u5316demo\u6587\u6863"},{"location":"tutorials/demo_guide/#embeddingdemo","text":"","title":"Embedding\u91cf\u5316demo\u6587\u6863"},{"location":"tutorials/demo_guide/#nas","text":"","title":"NAS"},{"location":"tutorials/demo_guide/#nas_1","text":"","title":"NAS\u793a\u4f8b"},{"location":"tutorials/distillation_demo/","text":"\u672c\u793a\u4f8b\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528PaddleSlim\u84b8\u998f\u63a5\u53e3\u6765\u5bf9\u6a21\u578b\u8fdb\u884c\u84b8\u998f\u8bad\u7ec3\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u84b8\u998fAPI\u6587\u6863 \u3002 PaddleSlim\u84b8\u998f\u8bad\u7ec3\u6d41\u7a0b # \u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u53c2\u6570\u91cf\u8d8a\u591a\uff0c\u7ed3\u6784\u8d8a\u590d\u6742\uff0c\u5176\u6027\u80fd\u8d8a\u597d\uff0c\u4f46\u8fd0\u7b97\u91cf\u548c\u8d44\u6e90\u6d88\u8017\u4e5f\u8d8a\u5927\u3002 \u77e5\u8bc6\u84b8\u998f \u5c31\u662f\u4e00\u79cd\u5c06\u5927\u6a21\u578b\u5b66\u4e60\u5230\u7684\u6709\u7528\u4fe1\u606f\uff08Dark Knowledge\uff09\u538b\u7f29\u8fdb\u66f4\u5c0f\u66f4\u5feb\u7684\u6a21\u578b\uff0c\u800c\u83b7\u5f97\u53ef\u4ee5\u5339\u654c\u5927\u6a21\u578b\u7ed3\u679c\u7684\u65b9\u6cd5\u3002 \u5728\u672c\u793a\u4f8b\u4e2d\u7cbe\u5ea6\u8f83\u9ad8\u7684\u5927\u6a21\u578b\u88ab\u79f0\u4e3ateacher\uff0c\u7cbe\u5ea6\u7a0d\u900a\u4f46\u901f\u5ea6\u66f4\u5feb\u7684\u5c0f\u6a21\u578b\u88ab\u79f0\u4e3astudent\u3002 1. \u5b9a\u4e49student_program # 1 2 3 4 5 6 7 8 9 10 11 student_program = fluid . Program () student_startup = fluid . Program () with fluid . program_guard ( student_program , student_startup ): image = fluid . data ( name = 'image' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) # student model definition model = MobileNet () out = model . net ( input = image , class_dim = 1000 ) cost = fluid . layers . cross_entropy ( input = out , label = label ) avg_cost = fluid . layers . mean ( x = cost ) 2. \u5b9a\u4e49teacher_program # \u5728\u5b9a\u4e49\u597d teacher_program \u540e\uff0c\u53ef\u4ee5\u4e00\u5e76\u52a0\u8f7d\u8bad\u7ec3\u597d\u7684pretrained_model\u3002 \u5728 teacher_program \u5185\u9700\u8981\u52a0\u4e0a with fluid.unique_name.guard(): \uff0c\u4fdd\u8bc1teacher\u7684\u53d8\u91cf\u547d\u540d\u4e0d\u88ab student_program \u5f71\u54cd\uff0c\u4ece\u800c\u80fd\u591f\u6b63\u786e\u5730\u52a0\u8f7d\u9884\u8bad\u7ec3\u53c2\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 teacher_program = fluid . Program () teacher_startup = fluid . Program () with fluid . program_guard ( teacher_program , teacher_startup ): with fluid . unique_name . guard (): image = fluid . data ( name = 'data' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) # teacher model definition teacher_model = ResNet () predict = teacher_model . net ( image , class_dim = 1000 ) exe . run ( teacher_startup ) def if_exist ( var ): return os . path . exists ( os . path . join ( \"./pretrained\" , var . name ) fluid . io . load_vars ( exe , \"./pretrained\" , main_program = teacher_program , predicate = if_exist ) 3.\u9009\u62e9\u7279\u5f81\u56fe # \u5b9a\u4e49\u597d student_program \u548c teacher_program \u540e\uff0c\u6211\u4eec\u9700\u8981\u4ece\u4e2d\u4e24\u4e24\u5bf9\u5e94\u5730\u6311\u9009\u51fa\u82e5\u5e72\u4e2a\u7279\u5f81\u56fe\uff0c\u7559\u5f85\u540e\u7eed\u4e3a\u5176\u6dfb\u52a0\u77e5\u8bc6\u84b8\u998f\u635f\u5931\u51fd\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # get all student variables student_vars = [] for v in student_program . list_vars (): try : student_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"student_model_vars\" + \"=\" * 50 ) print ( student_vars ) # get all teacher variables teacher_vars = [] for v in teacher_program . list_vars (): try : teacher_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"teacher_model_vars\" + \"=\" * 50 ) print ( teacher_vars ) 4. \u5408\u5e76Program\uff08merge\uff09 # PaddlePaddle\u4f7f\u7528Program\u6765\u63cf\u8ff0\u8ba1\u7b97\u56fe\uff0c\u4e3a\u4e86\u540c\u65f6\u8ba1\u7b97student\u548cteacher\u4e24\u4e2aProgram\uff0c\u8fd9\u91cc\u9700\u8981\u5c06\u5176\u4e24\u8005\u5408\u5e76\uff08merge\uff09\u4e3a\u4e00\u4e2aProgram\u3002 merge\u8fc7\u7a0b\u64cd\u4f5c\u8f83\u591a\uff0c\u5177\u4f53\u7ec6\u8282\u8bf7\u53c2\u8003 merge API\u6587\u6863 \u3002 1 2 data_name_map = { 'data' : 'image' } student_program = merge ( teacher_program , student_program , data_name_map , place ) 5.\u6dfb\u52a0\u84b8\u998floss # \u5728\u6dfb\u52a0\u84b8\u998floss\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u8fd8\u4f1a\u5f15\u5165\u90e8\u5206\u53d8\u91cf\uff08Variable\uff09\uff0c\u4e3a\u4e86\u907f\u514d\u547d\u540d\u91cd\u590d\u8fd9\u91cc\u53ef\u4ee5\u4f7f\u7528 with fluid.name_scope(\"distill\"): \u4e3a\u65b0\u5f15\u5165\u7684\u53d8\u91cf\u52a0\u4e00\u4e2a\u547d\u540d\u4f5c\u7528\u57df\u3002 \u53e6\u5916\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cmerge\u8fc7\u7a0b\u4e3a teacher_program \u7684\u53d8\u91cf\u7edf\u4e00\u52a0\u4e86\u540d\u79f0\u524d\u7f00\uff0c\u9ed8\u8ba4\u662f \"teacher_\" , \u8fd9\u91cc\u5728\u6dfb\u52a0 l2_loss \u65f6\u4e5f\u8981\u4e3ateacher\u7684\u53d8\u91cf\u52a0\u4e0a\u8fd9\u4e2a\u524d\u7f00\u3002 1 2 3 4 5 6 7 8 9 with fluid . program_guard ( student_program , student_startup ): with fluid . name_scope ( \"distill\" ): distill_loss = l2_loss ( 'teacher_bn5c_branch2b.output.1.tmp_3' , 'depthwise_conv2d_11.tmp_0' , student_program ) distill_weight = 1 loss = avg_cost + distill_loss * distill_weight opt = create_optimizer () opt . minimize ( loss ) exe . run ( student_startup ) \u81f3\u6b64\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684 student_program \uff0c\u540e\u9762\u5c31\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u666e\u901aprogram\u4e00\u6837\u5bf9\u5176\u5f00\u59cb\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002","title":"\u77e5\u8bc6\u84b8\u998f"},{"location":"tutorials/distillation_demo/#_1","text":"\u8bf7\u53c2\u8003 \u84b8\u998fAPI\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/distillation_demo/#paddleslim","text":"\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u53c2\u6570\u91cf\u8d8a\u591a\uff0c\u7ed3\u6784\u8d8a\u590d\u6742\uff0c\u5176\u6027\u80fd\u8d8a\u597d\uff0c\u4f46\u8fd0\u7b97\u91cf\u548c\u8d44\u6e90\u6d88\u8017\u4e5f\u8d8a\u5927\u3002 \u77e5\u8bc6\u84b8\u998f \u5c31\u662f\u4e00\u79cd\u5c06\u5927\u6a21\u578b\u5b66\u4e60\u5230\u7684\u6709\u7528\u4fe1\u606f\uff08Dark Knowledge\uff09\u538b\u7f29\u8fdb\u66f4\u5c0f\u66f4\u5feb\u7684\u6a21\u578b\uff0c\u800c\u83b7\u5f97\u53ef\u4ee5\u5339\u654c\u5927\u6a21\u578b\u7ed3\u679c\u7684\u65b9\u6cd5\u3002 \u5728\u672c\u793a\u4f8b\u4e2d\u7cbe\u5ea6\u8f83\u9ad8\u7684\u5927\u6a21\u578b\u88ab\u79f0\u4e3ateacher\uff0c\u7cbe\u5ea6\u7a0d\u900a\u4f46\u901f\u5ea6\u66f4\u5feb\u7684\u5c0f\u6a21\u578b\u88ab\u79f0\u4e3astudent\u3002","title":"PaddleSlim\u84b8\u998f\u8bad\u7ec3\u6d41\u7a0b"},{"location":"tutorials/distillation_demo/#1-student_program","text":"1 2 3 4 5 6 7 8 9 10 11 student_program = fluid . Program () student_startup = fluid . Program () with fluid . program_guard ( student_program , student_startup ): image = fluid . data ( name = 'image' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) # student model definition model = MobileNet () out = model . net ( input = image , class_dim = 1000 ) cost = fluid . layers . cross_entropy ( input = out , label = label ) avg_cost = fluid . layers . mean ( x = cost )","title":"1. \u5b9a\u4e49student_program"},{"location":"tutorials/distillation_demo/#2-teacher_program","text":"\u5728\u5b9a\u4e49\u597d teacher_program \u540e\uff0c\u53ef\u4ee5\u4e00\u5e76\u52a0\u8f7d\u8bad\u7ec3\u597d\u7684pretrained_model\u3002 \u5728 teacher_program \u5185\u9700\u8981\u52a0\u4e0a with fluid.unique_name.guard(): \uff0c\u4fdd\u8bc1teacher\u7684\u53d8\u91cf\u547d\u540d\u4e0d\u88ab student_program \u5f71\u54cd\uff0c\u4ece\u800c\u80fd\u591f\u6b63\u786e\u5730\u52a0\u8f7d\u9884\u8bad\u7ec3\u53c2\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 teacher_program = fluid . Program () teacher_startup = fluid . Program () with fluid . program_guard ( teacher_program , teacher_startup ): with fluid . unique_name . guard (): image = fluid . data ( name = 'data' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) # teacher model definition teacher_model = ResNet () predict = teacher_model . net ( image , class_dim = 1000 ) exe . run ( teacher_startup ) def if_exist ( var ): return os . path . exists ( os . path . join ( \"./pretrained\" , var . name ) fluid . io . load_vars ( exe , \"./pretrained\" , main_program = teacher_program , predicate = if_exist )","title":"2. \u5b9a\u4e49teacher_program"},{"location":"tutorials/distillation_demo/#3","text":"\u5b9a\u4e49\u597d student_program \u548c teacher_program \u540e\uff0c\u6211\u4eec\u9700\u8981\u4ece\u4e2d\u4e24\u4e24\u5bf9\u5e94\u5730\u6311\u9009\u51fa\u82e5\u5e72\u4e2a\u7279\u5f81\u56fe\uff0c\u7559\u5f85\u540e\u7eed\u4e3a\u5176\u6dfb\u52a0\u77e5\u8bc6\u84b8\u998f\u635f\u5931\u51fd\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # get all student variables student_vars = [] for v in student_program . list_vars (): try : student_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"student_model_vars\" + \"=\" * 50 ) print ( student_vars ) # get all teacher variables teacher_vars = [] for v in teacher_program . list_vars (): try : teacher_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"teacher_model_vars\" + \"=\" * 50 ) print ( teacher_vars )","title":"3.\u9009\u62e9\u7279\u5f81\u56fe"},{"location":"tutorials/distillation_demo/#4-programmerge","text":"PaddlePaddle\u4f7f\u7528Program\u6765\u63cf\u8ff0\u8ba1\u7b97\u56fe\uff0c\u4e3a\u4e86\u540c\u65f6\u8ba1\u7b97student\u548cteacher\u4e24\u4e2aProgram\uff0c\u8fd9\u91cc\u9700\u8981\u5c06\u5176\u4e24\u8005\u5408\u5e76\uff08merge\uff09\u4e3a\u4e00\u4e2aProgram\u3002 merge\u8fc7\u7a0b\u64cd\u4f5c\u8f83\u591a\uff0c\u5177\u4f53\u7ec6\u8282\u8bf7\u53c2\u8003 merge API\u6587\u6863 \u3002 1 2 data_name_map = { 'data' : 'image' } student_program = merge ( teacher_program , student_program , data_name_map , place )","title":"4. \u5408\u5e76Program\uff08merge\uff09"},{"location":"tutorials/distillation_demo/#5loss","text":"\u5728\u6dfb\u52a0\u84b8\u998floss\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u8fd8\u4f1a\u5f15\u5165\u90e8\u5206\u53d8\u91cf\uff08Variable\uff09\uff0c\u4e3a\u4e86\u907f\u514d\u547d\u540d\u91cd\u590d\u8fd9\u91cc\u53ef\u4ee5\u4f7f\u7528 with fluid.name_scope(\"distill\"): \u4e3a\u65b0\u5f15\u5165\u7684\u53d8\u91cf\u52a0\u4e00\u4e2a\u547d\u540d\u4f5c\u7528\u57df\u3002 \u53e6\u5916\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cmerge\u8fc7\u7a0b\u4e3a teacher_program \u7684\u53d8\u91cf\u7edf\u4e00\u52a0\u4e86\u540d\u79f0\u524d\u7f00\uff0c\u9ed8\u8ba4\u662f \"teacher_\" , \u8fd9\u91cc\u5728\u6dfb\u52a0 l2_loss \u65f6\u4e5f\u8981\u4e3ateacher\u7684\u53d8\u91cf\u52a0\u4e0a\u8fd9\u4e2a\u524d\u7f00\u3002 1 2 3 4 5 6 7 8 9 with fluid . program_guard ( student_program , student_startup ): with fluid . name_scope ( \"distill\" ): distill_loss = l2_loss ( 'teacher_bn5c_branch2b.output.1.tmp_3' , 'depthwise_conv2d_11.tmp_0' , student_program ) distill_weight = 1 loss = avg_cost + distill_loss * distill_weight opt = create_optimizer () opt . minimize ( loss ) exe . run ( student_startup ) \u81f3\u6b64\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684 student_program \uff0c\u540e\u9762\u5c31\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u666e\u901aprogram\u4e00\u6837\u5bf9\u5176\u5f00\u59cb\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002","title":"5.\u6dfb\u52a0\u84b8\u998floss"},{"location":"tutorials/nas_demo/","text":"\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u63a5\u53e3\uff0c\u641c\u7d22\u5230\u4e00\u4e2a\u66f4\u5c0f\u6216\u8005\u7cbe\u5ea6\u66f4\u9ad8\u7684\u6a21\u578b\uff0c\u8be5\u6587\u6863\u4ec5\u4ecb\u7ecdpaddleslim\u4e2dSANAS\u7684\u4f7f\u7528\u53ca\u5982\u4f55\u5229\u7528SANAS\u5f97\u5230\u6a21\u578b\u7ed3\u6784\uff0c\u5b8c\u6574\u793a\u4f8b\u4ee3\u7801\u8bf7\u53c2\u8003sa_nas_mobilenetv2.py\u6216\u8005block_sa_nas_mobilenetv2.py\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003\u3002 1. \u914d\u7f6e\u641c\u7d22\u7a7a\u95f4 # \u8be6\u7ec6\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003 \u795e\u7ecf\u7f51\u7edc\u641c\u7d22API\u6587\u6863 \u3002 1 config = [( 'MobileNetV2Space' )] 2. \u5229\u7528\u641c\u7d22\u7a7a\u95f4\u521d\u59cb\u5316SANAS\u5b9e\u4f8b # 1 2 3 4 5 6 7 8 9 from paddleslim.nas import SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8881 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 300 , is_server = True ) 3. \u6839\u636e\u5b9e\u4f8b\u5316\u7684NAS\u5f97\u5230\u5f53\u524d\u7684\u7f51\u7edc\u7ed3\u6784 # 1 archs = sa_nas . next_archs () 4. \u6839\u636e\u5f97\u5230\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u8f93\u5165\u6784\u9020\u8bad\u7ec3\u548c\u6d4b\u8bd5program # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () with fluid . program_guard ( train_program , startup_program ): data = fluid . data ( name = 'data' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : data = arch ( data ) output = fluid . layers . fc ( data , 10 ) softmax_out = fluid . layers . softmax ( input = output , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) test_program = train_program . clone ( for_test = True ) sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost ) 5. \u6839\u636e\u6784\u9020\u7684\u8bad\u7ec3program\u6dfb\u52a0\u9650\u5236\u6761\u4ef6 # 1 2 3 4 from paddleslim.analysis import flops if flops ( train_program ) > 321208544 : continue 6. \u56de\u4f20score # 1 sa_nas . reward ( score )","title":"SA\u641c\u7d22"},{"location":"tutorials/nas_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u63a5\u53e3\uff0c\u641c\u7d22\u5230\u4e00\u4e2a\u66f4\u5c0f\u6216\u8005\u7cbe\u5ea6\u66f4\u9ad8\u7684\u6a21\u578b\uff0c\u8be5\u6587\u6863\u4ec5\u4ecb\u7ecdpaddleslim\u4e2dSANAS\u7684\u4f7f\u7528\u53ca\u5982\u4f55\u5229\u7528SANAS\u5f97\u5230\u6a21\u578b\u7ed3\u6784\uff0c\u5b8c\u6574\u793a\u4f8b\u4ee3\u7801\u8bf7\u53c2\u8003sa_nas_mobilenetv2.py\u6216\u8005block_sa_nas_mobilenetv2.py\u3002","title":"\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u793a\u4f8b"},{"location":"tutorials/nas_demo/#_2","text":"\u8bf7\u53c2\u8003\u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/nas_demo/#1","text":"\u8be6\u7ec6\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003 \u795e\u7ecf\u7f51\u7edc\u641c\u7d22API\u6587\u6863 \u3002 1 config = [( 'MobileNetV2Space' )]","title":"1. \u914d\u7f6e\u641c\u7d22\u7a7a\u95f4"},{"location":"tutorials/nas_demo/#2-sanas","text":"1 2 3 4 5 6 7 8 9 from paddleslim.nas import SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8881 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 300 , is_server = True )","title":"2. \u5229\u7528\u641c\u7d22\u7a7a\u95f4\u521d\u59cb\u5316SANAS\u5b9e\u4f8b"},{"location":"tutorials/nas_demo/#3-nas","text":"1 archs = sa_nas . next_archs ()","title":"3. \u6839\u636e\u5b9e\u4f8b\u5316\u7684NAS\u5f97\u5230\u5f53\u524d\u7684\u7f51\u7edc\u7ed3\u6784"},{"location":"tutorials/nas_demo/#4-program","text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () with fluid . program_guard ( train_program , startup_program ): data = fluid . data ( name = 'data' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : data = arch ( data ) output = fluid . layers . fc ( data , 10 ) softmax_out = fluid . layers . softmax ( input = output , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) test_program = train_program . clone ( for_test = True ) sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost )","title":"4. \u6839\u636e\u5f97\u5230\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u8f93\u5165\u6784\u9020\u8bad\u7ec3\u548c\u6d4b\u8bd5program"},{"location":"tutorials/nas_demo/#5-program","text":"1 2 3 4 from paddleslim.analysis import flops if flops ( train_program ) > 321208544 : continue","title":"5. \u6839\u636e\u6784\u9020\u7684\u8bad\u7ec3program\u6dfb\u52a0\u9650\u5236\u6761\u4ef6"},{"location":"tutorials/nas_demo/#6-score","text":"1 sa_nas . reward ( score )","title":"6. \u56de\u4f20score"},{"location":"tutorials/pruning_demo/","text":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b # \u672c\u793a\u4f8b\u5c06\u6f14\u793a\u5982\u4f55\u6309\u6307\u5b9a\u7684\u526a\u88c1\u7387\u5bf9\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\u6570\u8fdb\u884c\u526a\u88c1\u3002\u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528mnist\u6570\u636e\u3002 \u5f53\u524d\u793a\u4f8b\u652f\u6301\u4ee5\u4e0b\u5206\u7c7b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 PVANet \u63a5\u53e3\u4ecb\u7ecd # \u8be5\u793a\u4f8b\u4f7f\u7528\u4e86 paddleslim.Pruner \u5de5\u5177\u7c7b\uff0c\u7528\u6237\u63a5\u53e3\u4f7f\u7528\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a API\u6587\u6863 \u786e\u5b9a\u5f85\u88c1\u53c2\u6570 # \u4e0d\u540c\u6a21\u578b\u7684\u53c2\u6570\u547d\u540d\u4e0d\u540c\uff0c\u5728\u526a\u88c1\u524d\u9700\u8981\u786e\u5b9a\u5f85\u88c1\u5377\u79ef\u5c42\u7684\u53c2\u6570\u540d\u79f0\u3002\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5217\u51fa\u6240\u6709\u53c2\u6570\u540d\uff1a 1 2 for param in program . global_block () . all_parameters () : print ( \" param name: {}; shape: {} \" . format ( param . name , param . shape )) \u5728 train.py \u811a\u672c\u4e2d\uff0c\u63d0\u4f9b\u4e86 get_pruned_params \u65b9\u6cd5\uff0c\u6839\u636e\u7528\u6237\u8bbe\u7f6e\u7684\u9009\u9879 --model \u786e\u5b9a\u8981\u88c1\u526a\u7684\u53c2\u6570\u3002 \u542f\u52a8\u88c1\u526a\u4efb\u52a1 # \u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u542f\u52a8\u88c1\u526a\u4efb\u52a1\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py \u6267\u884c python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002 \u6ce8\u610f # \u5728\u63a5\u53e3 paddle.Pruner.prune \u7684\u53c2\u6570\u4e2d\uff0c params \u548c ratios \u7684\u957f\u5ea6\u9700\u8981\u4e00\u6837\u3002","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b"},{"location":"tutorials/pruning_demo/#_1","text":"\u672c\u793a\u4f8b\u5c06\u6f14\u793a\u5982\u4f55\u6309\u6307\u5b9a\u7684\u526a\u88c1\u7387\u5bf9\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\u6570\u8fdb\u884c\u526a\u88c1\u3002\u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528mnist\u6570\u636e\u3002 \u5f53\u524d\u793a\u4f8b\u652f\u6301\u4ee5\u4e0b\u5206\u7c7b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 PVANet","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b"},{"location":"tutorials/pruning_demo/#_2","text":"\u8be5\u793a\u4f8b\u4f7f\u7528\u4e86 paddleslim.Pruner \u5de5\u5177\u7c7b\uff0c\u7528\u6237\u63a5\u53e3\u4f7f\u7528\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a API\u6587\u6863","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/pruning_demo/#_3","text":"\u4e0d\u540c\u6a21\u578b\u7684\u53c2\u6570\u547d\u540d\u4e0d\u540c\uff0c\u5728\u526a\u88c1\u524d\u9700\u8981\u786e\u5b9a\u5f85\u88c1\u5377\u79ef\u5c42\u7684\u53c2\u6570\u540d\u79f0\u3002\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5217\u51fa\u6240\u6709\u53c2\u6570\u540d\uff1a 1 2 for param in program . global_block () . all_parameters () : print ( \" param name: {}; shape: {} \" . format ( param . name , param . shape )) \u5728 train.py \u811a\u672c\u4e2d\uff0c\u63d0\u4f9b\u4e86 get_pruned_params \u65b9\u6cd5\uff0c\u6839\u636e\u7528\u6237\u8bbe\u7f6e\u7684\u9009\u9879 --model \u786e\u5b9a\u8981\u88c1\u526a\u7684\u53c2\u6570\u3002","title":"\u786e\u5b9a\u5f85\u88c1\u53c2\u6570"},{"location":"tutorials/pruning_demo/#_4","text":"\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u542f\u52a8\u88c1\u526a\u4efb\u52a1\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py \u6267\u884c python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002","title":"\u542f\u52a8\u88c1\u526a\u4efb\u52a1"},{"location":"tutorials/pruning_demo/#_5","text":"\u5728\u63a5\u53e3 paddle.Pruner.prune \u7684\u53c2\u6570\u4e2d\uff0c params \u548c ratios \u7684\u957f\u5ea6\u9700\u8981\u4e00\u6837\u3002","title":"\u6ce8\u610f"},{"location":"tutorials/quant_aware_demo/","text":"\u5728\u7ebf\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5728\u7ebf\u91cf\u5316\u63a5\u53e3\uff0c\u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u91cf\u5316, \u53ef\u4ee5\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b # 1. \u914d\u7f6e\u91cf\u5316\u53c2\u6570 # 1 2 3 4 5 6 7 8 9 10 11 12 quant_config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' , 'weight_bits' : 8 , 'activation_bits' : 8 , 'not_quant_pattern' : [ 'skip_quant' ], 'quantize_op_types' : [ 'conv2d' , 'depthwise_conv2d' , 'mul' ], 'dtype' : 'int8' , 'window_size' : 10000 , 'moving_rate' : 0 . 9 , 'quant_weight_only' : False } 2. \u5bf9\u8bad\u7ec3\u548c\u6d4b\u8bd5program\u63d2\u5165\u53ef\u8bad\u7ec3\u91cf\u5316op # 1 2 3 val_program = quant_aware ( val_program , place , quant_config , scope = None , for_test = True ) compiled_train_prog = quant_aware ( train_prog , place , quant_config , scope = None , for_test = False ) 3.\u5173\u6389\u6307\u5b9abuild\u7b56\u7565 # 1 2 3 4 5 6 7 8 build_strategy = fluid . BuildStrategy () build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False exec_strategy = fluid . ExecutionStrategy () compiled_train_prog = compiled_train_prog . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) 4. freeze program # 1 2 3 4 5 float_program , int8_program = convert ( val_program , place , quant_config , scope = None , save_int8 = True ) 5.\u4fdd\u5b58\u9884\u6d4b\u6a21\u578b # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 fluid . io . save_inference_model ( dirname = float_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = float_program , model_filename = float_path + ' /model ' , params_filename = float_path + ' /params ' ) fluid . io . save_inference_model ( dirname = int8_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = int8_program , model_filename = int8_path + ' /model ' , params_filename = int8_path + ' /params ' )","title":"\u91cf\u5316\u8bad\u7ec3"},{"location":"tutorials/quant_aware_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5728\u7ebf\u91cf\u5316\u63a5\u53e3\uff0c\u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u91cf\u5316, \u53ef\u4ee5\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002","title":"\u5728\u7ebf\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_aware_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/quant_aware_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"tutorials/quant_aware_demo/#1","text":"1 2 3 4 5 6 7 8 9 10 11 12 quant_config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' , 'weight_bits' : 8 , 'activation_bits' : 8 , 'not_quant_pattern' : [ 'skip_quant' ], 'quantize_op_types' : [ 'conv2d' , 'depthwise_conv2d' , 'mul' ], 'dtype' : 'int8' , 'window_size' : 10000 , 'moving_rate' : 0 . 9 , 'quant_weight_only' : False }","title":"1. \u914d\u7f6e\u91cf\u5316\u53c2\u6570"},{"location":"tutorials/quant_aware_demo/#2-programop","text":"1 2 3 val_program = quant_aware ( val_program , place , quant_config , scope = None , for_test = True ) compiled_train_prog = quant_aware ( train_prog , place , quant_config , scope = None , for_test = False )","title":"2. \u5bf9\u8bad\u7ec3\u548c\u6d4b\u8bd5program\u63d2\u5165\u53ef\u8bad\u7ec3\u91cf\u5316op"},{"location":"tutorials/quant_aware_demo/#3build","text":"1 2 3 4 5 6 7 8 build_strategy = fluid . BuildStrategy () build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False exec_strategy = fluid . ExecutionStrategy () compiled_train_prog = compiled_train_prog . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy )","title":"3.\u5173\u6389\u6307\u5b9abuild\u7b56\u7565"},{"location":"tutorials/quant_aware_demo/#4-freeze-program","text":"1 2 3 4 5 float_program , int8_program = convert ( val_program , place , quant_config , scope = None , save_int8 = True )","title":"4. freeze program"},{"location":"tutorials/quant_aware_demo/#5","text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 fluid . io . save_inference_model ( dirname = float_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = float_program , model_filename = float_path + ' /model ' , params_filename = float_path + ' /params ' ) fluid . io . save_inference_model ( dirname = int8_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = int8_program , model_filename = int8_path + ' /model ' , params_filename = int8_path + ' /params ' )","title":"5.\u4fdd\u5b58\u9884\u6d4b\u6a21\u578b"},{"location":"tutorials/quant_embedding_demo/","text":"Embedding\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Embedding\u91cf\u5316\u7684\u63a5\u53e3 paddleslim.quant.quant_embedding \u3002 quant_embedding \u63a5\u53e3\u5c06\u7f51\u7edc\u4e2d\u7684Embedding\u53c2\u6570\u4ece float32 \u7c7b\u578b\u91cf\u5316\u5230 8-bit \u6574\u6570\u7c7b\u578b\uff0c\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u6a21\u578b\u7cbe\u5ea6\u7684\u60c5\u51b5\u4e0b\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u8be5\u63a5\u53e3\u5bf9program\u7684\u4fee\u6539\uff1a \u91cf\u5316\u524d: \u56fe1\uff1a\u91cf\u5316\u524d\u7684\u6a21\u578b\u7ed3\u6784 \u91cf\u5316\u540e\uff1a \u56fe2: \u91cf\u5316\u540e\u7684\u6a21\u578b\u7ed3\u6784 \u4ee5\u4e0b\u5c06\u4ee5 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u4e3a\u4f8b\u6765\u8bf4\u660e\u5982\u4f55\u4f7f\u7528 quant_embedding \u63a5\u53e3\u3002\u9996\u5148\u4ecb\u7ecd \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u7684\u6b63\u5e38\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6d41\u7a0b\u3002 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b # \u4ee5\u4e0b\u662f\u672c\u4f8b\u7684\u7b80\u8981\u76ee\u5f55\u7ed3\u6784\u53ca\u8bf4\u660e\uff1a 1 2 3 4 5 6 7 8 9 10 . \u251c\u2500\u2500 cluster_train.py # \u5206\u5e03\u5f0f\u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 cluster_train.sh # \u672c\u5730\u6a21\u62df\u591a\u673a\u811a\u672c \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 infer.py # \u9884\u6d4b\u811a\u672c \u251c\u2500\u2500 net.py # \u7f51\u7edc\u7ed3\u6784 \u251c\u2500\u2500 preprocess.py # \u9884\u5904\u7406\u811a\u672c\uff0c\u5305\u62ec\u6784\u5efa\u8bcd\u5178\u548c\u9884\u5904\u7406\u6587\u672c \u251c\u2500\u2500 reader.py # \u8bad\u7ec3\u9636\u6bb5\u7684\u6587\u672c\u8bfb\u5199 \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u2514\u2500\u2500 utils.py # \u901a\u7528\u51fd\u6570 \u4ecb\u7ecd # \u672c\u4f8b\u5b9e\u73b0\u4e86skip-gram\u6a21\u5f0f\u7684word2vector\u6a21\u578b\u3002 \u540c\u65f6\u63a8\u8350\u7528\u6237\u53c2\u8003 IPython Notebook demo \u6570\u636e\u4e0b\u8f7d # \u5168\u91cf\u6570\u636e\u96c6\u4f7f\u7528\u7684\u662f\u6765\u81ea1 Billion Word Language Model Benchmark\u7684( http://www.statmt.org/lm-benchmark ) \u7684\u6570\u636e\u96c6. 1 2 3 4 mkdir data wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz tar xzvf 1 -billion-word-language-modeling-benchmark-r13output.tar.gz mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u5907\u7528\u6570\u636e\u5730\u5740\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar tar xvf 1 -billion-word-language-modeling-benchmark-r13output.tar mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u4e3a\u4e86\u65b9\u4fbf\u5feb\u901f\u9a8c\u8bc1\uff0c\u6211\u4eec\u4e5f\u63d0\u4f9b\u4e86\u7ecf\u5178\u7684text8\u6837\u4f8b\u6570\u636e\u96c6\uff0c\u5305\u542b1700w\u4e2a\u8bcd\u3002 \u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/ \u6570\u636e\u9884\u5904\u7406 # \u4ee5\u6837\u4f8b\u6570\u636e\u96c6\u4e3a\u4f8b\u8fdb\u884c\u9884\u5904\u7406\u3002\u5168\u91cf\u6570\u636e\u96c6\u6ce8\u610f\u89e3\u538b\u540e\u4ee5training-monolingual.tokenized.shuffled \u76ee\u5f55\u4e3a\u9884\u5904\u7406\u76ee\u5f55\uff0c\u548c\u6837\u4f8b\u6570\u636e\u96c6\u7684text\u76ee\u5f55\u5e76\u5217\u3002 \u8bcd\u5178\u683c\u5f0f: \u8bcd<\u7a7a\u683c>\u8bcd\u9891\u3002\u6ce8\u610f\u4f4e\u9891\u8bcd\u7528'UNK'\u8868\u793a \u53ef\u4ee5\u6309\u683c\u5f0f\u81ea\u5efa\u8bcd\u5178\uff0c\u5982\u679c\u81ea\u5efa\u8bcd\u5178\u8df3\u8fc7\u7b2c\u4e00\u6b65\u3002 1 2 3 4 5 6 7 8 9 10 the 1061396 of 593677 and 416629 one 411764 in 372201 a 325873 < UNK > 324608 to 316376 zero 264975 nine 250430 \u7b2c\u4e00\u6b65\u6839\u636e\u82f1\u6587\u8bed\u6599\u751f\u6210\u8bcd\u5178\uff0c\u4e2d\u6587\u8bed\u6599\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539text_strip\u65b9\u6cd5\u81ea\u5b9a\u4e49\u5904\u7406\u65b9\u6cd5\u3002 1 python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict \u7b2c\u4e8c\u6b65\u6839\u636e\u8bcd\u5178\u5c06\u6587\u672c\u8f6c\u6210id, \u540c\u65f6\u8fdb\u884cdownsample\uff0c\u6309\u7167\u6982\u7387\u8fc7\u6ee4\u5e38\u89c1\u8bcd, \u540c\u65f6\u751f\u6210word\u548cid\u6620\u5c04\u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u540d\u4e3a\u8bcd\u5178+\" word_to_id \"\u3002 1 python preprocess.py --filter_corpus --dict_path data/test_build_dict --input_corpus_dir data/text --output_corpus_dir data/convert_text8 --min_count 5 --downsample 0 .001 \u8bad\u7ec3 # \u5177\u4f53\u7684\u53c2\u6570\u914d\u7f6e\u53ef\u8fd0\u884c 1 python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 1 OPENBLAS_NUM_THREADS = 1 CPU_NUM = 5 python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes 10 --batch_size 100 --model_output_dir v1_cpu5_b100_lr1dir --base_lr 1 .0 --print_batch 1000 --with_speed --is_sparse \u672c\u5730\u5355\u673a\u6a21\u62df\u591a\u673a\u8bad\u7ec3 1 sh cluster_train.sh \u672c\u793a\u4f8b\u4e2d\u6309\u7167\u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3\u7684\u547d\u4ee4\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u5b8c\u6bd5\u540e\uff0c\u53ef\u770b\u5230\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u6a21\u578b\u7684\u8def\u5f84\u4e3a: v1_cpu5_b100_lr1dir , \u8fd0\u884c ls v1_cpu5_b100_lr1dir \u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u4e86\u8bad\u7ec3\u768410\u4e2aepoch\u7684\u6a21\u578b\u6587\u4ef6\u3002 1 pass - 0 pass - 1 pass - 2 pass - 3 pass - 4 pass - 5 pass - 6 pass - 7 pass - 8 pass - 9 \u9884\u6d4b # \u6d4b\u8bd5\u96c6\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 #\u5168\u91cf\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar #\u6837\u672c\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar \u9884\u6d4b\u547d\u4ee4\uff0c\u6ce8\u610f\u8bcd\u5178\u540d\u79f0\u9700\u8981\u52a0\u540e\u7f00\" word_to_id \", \u6b64\u6587\u4ef6\u662f\u9884\u5904\u7406\u9636\u6bb5\u751f\u6210\u7684\u3002 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 \u8fd0\u884c\u8be5\u9884\u6d4b\u547d\u4ee4, \u53ef\u770b\u5230\u5982\u4e0b\u8f93\u51fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) step : 1 249 epoch : 0 acc : 0 . 014 step : 1 590 epoch : 1 acc : 0 . 033 step : 1 982 epoch : 2 acc : 0 . 055 step : 1 1338 epoch : 3 acc : 0 . 075 step : 1 1653 epoch : 4 acc : 0 . 093 step : 1 1914 epoch : 5 acc : 0 . 107 step : 1 2204 epoch : 6 acc : 0 . 124 step : 1 2416 epoch : 7 acc : 0 . 136 step : 1 2606 epoch : 8 acc : 0 . 146 step : 1 2722 epoch : 9 acc : 0 . 153 \u91cf\u5316 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b # \u91cf\u5316\u914d\u7f6e\u4e3a: 1 2 3 4 config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 --emb_quant True \u8fd0\u884c\u8f93\u51fa\u4e3a: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 253 epoch : 0 acc : 0 . 014 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 586 epoch : 1 acc : 0 . 033 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 970 epoch : 2 acc : 0 . 054 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1364 epoch : 3 acc : 0 . 077 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1642 epoch : 4 acc : 0 . 092 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1936 epoch : 5 acc : 0 . 109 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2216 epoch : 6 acc : 0 . 124 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2419 epoch : 7 acc : 0 . 136 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2603 epoch : 8 acc : 0 . 146 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2719 epoch : 9 acc : 0 . 153 \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u5728 ./output_quant \u4e2d\uff0c\u53ef\u770b\u5230\u91cf\u5316\u540e\u7684\u53c2\u6570 'emb.int8' \u7684\u5927\u5c0f\u4e3a3.9M, \u5728 ./v1_cpu5_b100_lr1dir \u4e2d\u53ef\u770b\u5230\u91cf\u5316\u524d\u7684\u53c2\u6570 'emb' \u7684\u5927\u5c0f\u4e3a16M\u3002","title":"Embedding\u91cf\u5316"},{"location":"tutorials/quant_embedding_demo/#embedding","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Embedding\u91cf\u5316\u7684\u63a5\u53e3 paddleslim.quant.quant_embedding \u3002 quant_embedding \u63a5\u53e3\u5c06\u7f51\u7edc\u4e2d\u7684Embedding\u53c2\u6570\u4ece float32 \u7c7b\u578b\u91cf\u5316\u5230 8-bit \u6574\u6570\u7c7b\u578b\uff0c\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u6a21\u578b\u7cbe\u5ea6\u7684\u60c5\u51b5\u4e0b\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u8be5\u63a5\u53e3\u5bf9program\u7684\u4fee\u6539\uff1a \u91cf\u5316\u524d: \u56fe1\uff1a\u91cf\u5316\u524d\u7684\u6a21\u578b\u7ed3\u6784 \u91cf\u5316\u540e\uff1a \u56fe2: \u91cf\u5316\u540e\u7684\u6a21\u578b\u7ed3\u6784 \u4ee5\u4e0b\u5c06\u4ee5 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u4e3a\u4f8b\u6765\u8bf4\u660e\u5982\u4f55\u4f7f\u7528 quant_embedding \u63a5\u53e3\u3002\u9996\u5148\u4ecb\u7ecd \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u7684\u6b63\u5e38\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6d41\u7a0b\u3002","title":"Embedding\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_embedding_demo/#skip-gramword2vector","text":"\u4ee5\u4e0b\u662f\u672c\u4f8b\u7684\u7b80\u8981\u76ee\u5f55\u7ed3\u6784\u53ca\u8bf4\u660e\uff1a 1 2 3 4 5 6 7 8 9 10 . \u251c\u2500\u2500 cluster_train.py # \u5206\u5e03\u5f0f\u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 cluster_train.sh # \u672c\u5730\u6a21\u62df\u591a\u673a\u811a\u672c \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 infer.py # \u9884\u6d4b\u811a\u672c \u251c\u2500\u2500 net.py # \u7f51\u7edc\u7ed3\u6784 \u251c\u2500\u2500 preprocess.py # \u9884\u5904\u7406\u811a\u672c\uff0c\u5305\u62ec\u6784\u5efa\u8bcd\u5178\u548c\u9884\u5904\u7406\u6587\u672c \u251c\u2500\u2500 reader.py # \u8bad\u7ec3\u9636\u6bb5\u7684\u6587\u672c\u8bfb\u5199 \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u2514\u2500\u2500 utils.py # \u901a\u7528\u51fd\u6570","title":"\u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b"},{"location":"tutorials/quant_embedding_demo/#_1","text":"\u672c\u4f8b\u5b9e\u73b0\u4e86skip-gram\u6a21\u5f0f\u7684word2vector\u6a21\u578b\u3002 \u540c\u65f6\u63a8\u8350\u7528\u6237\u53c2\u8003 IPython Notebook demo","title":"\u4ecb\u7ecd"},{"location":"tutorials/quant_embedding_demo/#_2","text":"\u5168\u91cf\u6570\u636e\u96c6\u4f7f\u7528\u7684\u662f\u6765\u81ea1 Billion Word Language Model Benchmark\u7684( http://www.statmt.org/lm-benchmark ) \u7684\u6570\u636e\u96c6. 1 2 3 4 mkdir data wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz tar xzvf 1 -billion-word-language-modeling-benchmark-r13output.tar.gz mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u5907\u7528\u6570\u636e\u5730\u5740\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar tar xvf 1 -billion-word-language-modeling-benchmark-r13output.tar mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u4e3a\u4e86\u65b9\u4fbf\u5feb\u901f\u9a8c\u8bc1\uff0c\u6211\u4eec\u4e5f\u63d0\u4f9b\u4e86\u7ecf\u5178\u7684text8\u6837\u4f8b\u6570\u636e\u96c6\uff0c\u5305\u542b1700w\u4e2a\u8bcd\u3002 \u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"tutorials/quant_embedding_demo/#_3","text":"\u4ee5\u6837\u4f8b\u6570\u636e\u96c6\u4e3a\u4f8b\u8fdb\u884c\u9884\u5904\u7406\u3002\u5168\u91cf\u6570\u636e\u96c6\u6ce8\u610f\u89e3\u538b\u540e\u4ee5training-monolingual.tokenized.shuffled \u76ee\u5f55\u4e3a\u9884\u5904\u7406\u76ee\u5f55\uff0c\u548c\u6837\u4f8b\u6570\u636e\u96c6\u7684text\u76ee\u5f55\u5e76\u5217\u3002 \u8bcd\u5178\u683c\u5f0f: \u8bcd<\u7a7a\u683c>\u8bcd\u9891\u3002\u6ce8\u610f\u4f4e\u9891\u8bcd\u7528'UNK'\u8868\u793a \u53ef\u4ee5\u6309\u683c\u5f0f\u81ea\u5efa\u8bcd\u5178\uff0c\u5982\u679c\u81ea\u5efa\u8bcd\u5178\u8df3\u8fc7\u7b2c\u4e00\u6b65\u3002 1 2 3 4 5 6 7 8 9 10 the 1061396 of 593677 and 416629 one 411764 in 372201 a 325873 < UNK > 324608 to 316376 zero 264975 nine 250430 \u7b2c\u4e00\u6b65\u6839\u636e\u82f1\u6587\u8bed\u6599\u751f\u6210\u8bcd\u5178\uff0c\u4e2d\u6587\u8bed\u6599\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539text_strip\u65b9\u6cd5\u81ea\u5b9a\u4e49\u5904\u7406\u65b9\u6cd5\u3002 1 python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict \u7b2c\u4e8c\u6b65\u6839\u636e\u8bcd\u5178\u5c06\u6587\u672c\u8f6c\u6210id, \u540c\u65f6\u8fdb\u884cdownsample\uff0c\u6309\u7167\u6982\u7387\u8fc7\u6ee4\u5e38\u89c1\u8bcd, \u540c\u65f6\u751f\u6210word\u548cid\u6620\u5c04\u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u540d\u4e3a\u8bcd\u5178+\" word_to_id \"\u3002 1 python preprocess.py --filter_corpus --dict_path data/test_build_dict --input_corpus_dir data/text --output_corpus_dir data/convert_text8 --min_count 5 --downsample 0 .001","title":"\u6570\u636e\u9884\u5904\u7406"},{"location":"tutorials/quant_embedding_demo/#_4","text":"\u5177\u4f53\u7684\u53c2\u6570\u914d\u7f6e\u53ef\u8fd0\u884c 1 python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 1 OPENBLAS_NUM_THREADS = 1 CPU_NUM = 5 python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes 10 --batch_size 100 --model_output_dir v1_cpu5_b100_lr1dir --base_lr 1 .0 --print_batch 1000 --with_speed --is_sparse \u672c\u5730\u5355\u673a\u6a21\u62df\u591a\u673a\u8bad\u7ec3 1 sh cluster_train.sh \u672c\u793a\u4f8b\u4e2d\u6309\u7167\u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3\u7684\u547d\u4ee4\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u5b8c\u6bd5\u540e\uff0c\u53ef\u770b\u5230\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u6a21\u578b\u7684\u8def\u5f84\u4e3a: v1_cpu5_b100_lr1dir , \u8fd0\u884c ls v1_cpu5_b100_lr1dir \u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u4e86\u8bad\u7ec3\u768410\u4e2aepoch\u7684\u6a21\u578b\u6587\u4ef6\u3002 1 pass - 0 pass - 1 pass - 2 pass - 3 pass - 4 pass - 5 pass - 6 pass - 7 pass - 8 pass - 9","title":"\u8bad\u7ec3"},{"location":"tutorials/quant_embedding_demo/#_5","text":"\u6d4b\u8bd5\u96c6\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 #\u5168\u91cf\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar #\u6837\u672c\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar \u9884\u6d4b\u547d\u4ee4\uff0c\u6ce8\u610f\u8bcd\u5178\u540d\u79f0\u9700\u8981\u52a0\u540e\u7f00\" word_to_id \", \u6b64\u6587\u4ef6\u662f\u9884\u5904\u7406\u9636\u6bb5\u751f\u6210\u7684\u3002 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 \u8fd0\u884c\u8be5\u9884\u6d4b\u547d\u4ee4, \u53ef\u770b\u5230\u5982\u4e0b\u8f93\u51fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) step : 1 249 epoch : 0 acc : 0 . 014 step : 1 590 epoch : 1 acc : 0 . 033 step : 1 982 epoch : 2 acc : 0 . 055 step : 1 1338 epoch : 3 acc : 0 . 075 step : 1 1653 epoch : 4 acc : 0 . 093 step : 1 1914 epoch : 5 acc : 0 . 107 step : 1 2204 epoch : 6 acc : 0 . 124 step : 1 2416 epoch : 7 acc : 0 . 136 step : 1 2606 epoch : 8 acc : 0 . 146 step : 1 2722 epoch : 9 acc : 0 . 153","title":"\u9884\u6d4b"},{"location":"tutorials/quant_embedding_demo/#skip-gramword2vector_1","text":"\u91cf\u5316\u914d\u7f6e\u4e3a: 1 2 3 4 config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 --emb_quant True \u8fd0\u884c\u8f93\u51fa\u4e3a: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 253 epoch : 0 acc : 0 . 014 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 586 epoch : 1 acc : 0 . 033 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 970 epoch : 2 acc : 0 . 054 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1364 epoch : 3 acc : 0 . 077 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1642 epoch : 4 acc : 0 . 092 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1936 epoch : 5 acc : 0 . 109 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2216 epoch : 6 acc : 0 . 124 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2419 epoch : 7 acc : 0 . 136 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2603 epoch : 8 acc : 0 . 146 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2719 epoch : 9 acc : 0 . 153 \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u5728 ./output_quant \u4e2d\uff0c\u53ef\u770b\u5230\u91cf\u5316\u540e\u7684\u53c2\u6570 'emb.int8' \u7684\u5927\u5c0f\u4e3a3.9M, \u5728 ./v1_cpu5_b100_lr1dir \u4e2d\u53ef\u770b\u5230\u91cf\u5316\u524d\u7684\u53c2\u6570 'emb' \u7684\u5927\u5c0f\u4e3a16M\u3002","title":"\u91cf\u5316\u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b"},{"location":"tutorials/quant_post_demo/","text":"\u79bb\u7ebf\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3 paddleslim.quant.quant_post \u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316, \u8be5\u63a5\u53e3\u65e0\u9700\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u5c31\u53ef\u5f97\u5230\u91cf\u5316\u6a21\u578b\uff0c\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b # \u51c6\u5907\u6570\u636e # \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa data \u6587\u4ef6\u5939\uff0c\u5c06 imagenet \u6570\u636e\u96c6\u89e3\u538b\u5728 data \u6587\u4ef6\u5939\u4e0b\uff0c\u89e3\u538b\u540e data \u6587\u4ef6\u5939\u4e0b\u5e94\u5305\u542b\u4ee5\u4e0b\u6587\u4ef6\uff1a - 'train' \u6587\u4ef6\u5939\uff0c\u8bad\u7ec3\u56fe\u7247 - 'train_list.txt' \u6587\u4ef6 - 'val' \u6587\u4ef6\u5939\uff0c\u9a8c\u8bc1\u56fe\u7247 - 'val_list.txt' \u6587\u4ef6 \u51c6\u5907\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b # \u56e0\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ea\u652f\u6301\u52a0\u8f7d\u901a\u8fc7 fluid.io.save_inference_model \u63a5\u53e3\u4fdd\u5b58\u7684\u6a21\u578b\uff0c\u56e0\u6b64\u5982\u679c\u60a8\u7684\u6a21\u578b\u662f\u901a\u8fc7\u5176\u4ed6\u63a5\u53e3\u4fdd\u5b58\u7684\uff0c\u90a3\u9700\u8981\u5148\u5c06\u6a21\u578b\u8fdb\u884c\u8f6c\u5316\u3002\u672c\u793a\u4f8b\u5c06\u4ee5\u5206\u7c7b\u6a21\u578b\u4e3a\u4f8b\u8fdb\u884c\u8bf4\u660e\u3002 \u9996\u5148\u5728 imagenet\u5206\u7c7b\u6a21\u578b \u4e2d\u4e0b\u8f7d\u8bad\u7ec3\u597d\u7684 mobilenetv1 \u6a21\u578b\u3002 \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa 'pretrain' \u6587\u4ef6\u5939\uff0c\u5c06 mobilenetv1 \u6a21\u578b\u5728\u8be5\u6587\u4ef6\u5939\u4e0b\u89e3\u538b\uff0c\u89e3\u538b\u540e\u7684\u76ee\u5f55\u4e3a pretrain/MobileNetV1_pretrained \u5bfc\u51fa\u6a21\u578b # \u901a\u8fc7\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u53ef\u5c06\u6a21\u578b\u8f6c\u5316\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ef\u7528\u7684\u6a21\u578b\uff1a 1 python export_model . py --model \"MobileNet\" --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet \u8f6c\u5316\u4e4b\u540e\u7684\u6a21\u578b\u5b58\u50a8\u5728 inference_model/MobileNet/ \u6587\u4ef6\u5939\u4e0b\uff0c\u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u6709 'model' , 'weights' \u4e24\u4e2a\u6587\u4ef6\u3002 \u79bb\u7ebf\u91cf\u5316 # \u63a5\u4e0b\u6765\u5bf9\u5bfc\u51fa\u7684\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\uff0c\u79bb\u7ebf\u91cf\u5316\u7684\u811a\u672c\u4e3a quant_post.py \uff0c\u811a\u672c\u4e2d\u4f7f\u7528\u63a5\u53e3 paddleslim.quant.quant_post \u5bf9\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u3002\u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python quant_post . py --model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights model_path : \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u5750\u5728\u7684\u6587\u4ef6\u5939 save_path : \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u7684\u8def\u5f84 model_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u6a21\u578b\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 params_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 \u8fd0\u884c\u4ee5\u4e0a\u547d\u4ee4\u540e\uff0c\u53ef\u5728 ${save_path} \u4e0b\u770b\u5230\u91cf\u5316\u540e\u7684\u6a21\u578b\u6587\u4ef6\u548c\u53c2\u6570\u6587\u4ef6\u3002 \u4f7f\u7528\u7684\u91cf\u5316\u7b97\u6cd5\u4e3a 'KL' , \u4f7f\u7528\u8bad\u7ec3\u96c6\u4e2d\u7684160\u5f20\u56fe\u7247\u8fdb\u884c\u91cf\u5316\u53c2\u6570\u7684\u6821\u6b63\u3002 \u6d4b\u8bd5\u7cbe\u5ea6 # \u4f7f\u7528 eval.py \u811a\u672c\u5bf9\u91cf\u5316\u524d\u540e\u7684\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5\uff0c\u5f97\u5230\u6a21\u578b\u7684\u5206\u7c7b\u7cbe\u5ea6\u8fdb\u884c\u5bf9\u6bd4\u3002 \u9996\u5148\u6d4b\u8bd5\u91cf\u5316\u524d\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff0c\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a 1 python eval . py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 1 top1_acc / top5_acc = [ 0 . 70913923 0 . 89548034 ] \u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6d4b\u8bd5\u79bb\u7ebf\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff1a 1 python eval . py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 1 top1_acc / top5_acc = [ 0 . 70141864 0 . 89086477 ] \u4ece\u4ee5\u4e0a\u7cbe\u5ea6\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51fa\uff0c\u5bf9 mobilenet \u5728 imagenet \u4e0a\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u540e top1 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.77% \uff0c top5 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.46% .","title":"\u79bb\u7ebf\u91cf\u5316"},{"location":"tutorials/quant_post_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3 paddleslim.quant.quant_post \u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316, \u8be5\u63a5\u53e3\u65e0\u9700\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u5c31\u53ef\u5f97\u5230\u91cf\u5316\u6a21\u578b\uff0c\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002","title":"\u79bb\u7ebf\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_post_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/quant_post_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"tutorials/quant_post_demo/#_4","text":"\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa data \u6587\u4ef6\u5939\uff0c\u5c06 imagenet \u6570\u636e\u96c6\u89e3\u538b\u5728 data \u6587\u4ef6\u5939\u4e0b\uff0c\u89e3\u538b\u540e data \u6587\u4ef6\u5939\u4e0b\u5e94\u5305\u542b\u4ee5\u4e0b\u6587\u4ef6\uff1a - 'train' \u6587\u4ef6\u5939\uff0c\u8bad\u7ec3\u56fe\u7247 - 'train_list.txt' \u6587\u4ef6 - 'val' \u6587\u4ef6\u5939\uff0c\u9a8c\u8bc1\u56fe\u7247 - 'val_list.txt' \u6587\u4ef6","title":"\u51c6\u5907\u6570\u636e"},{"location":"tutorials/quant_post_demo/#_5","text":"\u56e0\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ea\u652f\u6301\u52a0\u8f7d\u901a\u8fc7 fluid.io.save_inference_model \u63a5\u53e3\u4fdd\u5b58\u7684\u6a21\u578b\uff0c\u56e0\u6b64\u5982\u679c\u60a8\u7684\u6a21\u578b\u662f\u901a\u8fc7\u5176\u4ed6\u63a5\u53e3\u4fdd\u5b58\u7684\uff0c\u90a3\u9700\u8981\u5148\u5c06\u6a21\u578b\u8fdb\u884c\u8f6c\u5316\u3002\u672c\u793a\u4f8b\u5c06\u4ee5\u5206\u7c7b\u6a21\u578b\u4e3a\u4f8b\u8fdb\u884c\u8bf4\u660e\u3002 \u9996\u5148\u5728 imagenet\u5206\u7c7b\u6a21\u578b \u4e2d\u4e0b\u8f7d\u8bad\u7ec3\u597d\u7684 mobilenetv1 \u6a21\u578b\u3002 \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa 'pretrain' \u6587\u4ef6\u5939\uff0c\u5c06 mobilenetv1 \u6a21\u578b\u5728\u8be5\u6587\u4ef6\u5939\u4e0b\u89e3\u538b\uff0c\u89e3\u538b\u540e\u7684\u76ee\u5f55\u4e3a pretrain/MobileNetV1_pretrained","title":"\u51c6\u5907\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b"},{"location":"tutorials/quant_post_demo/#_6","text":"\u901a\u8fc7\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u53ef\u5c06\u6a21\u578b\u8f6c\u5316\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ef\u7528\u7684\u6a21\u578b\uff1a 1 python export_model . py --model \"MobileNet\" --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet \u8f6c\u5316\u4e4b\u540e\u7684\u6a21\u578b\u5b58\u50a8\u5728 inference_model/MobileNet/ \u6587\u4ef6\u5939\u4e0b\uff0c\u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u6709 'model' , 'weights' \u4e24\u4e2a\u6587\u4ef6\u3002","title":"\u5bfc\u51fa\u6a21\u578b"},{"location":"tutorials/quant_post_demo/#_7","text":"\u63a5\u4e0b\u6765\u5bf9\u5bfc\u51fa\u7684\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\uff0c\u79bb\u7ebf\u91cf\u5316\u7684\u811a\u672c\u4e3a quant_post.py \uff0c\u811a\u672c\u4e2d\u4f7f\u7528\u63a5\u53e3 paddleslim.quant.quant_post \u5bf9\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u3002\u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python quant_post . py --model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights model_path : \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u5750\u5728\u7684\u6587\u4ef6\u5939 save_path : \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u7684\u8def\u5f84 model_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u6a21\u578b\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 params_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 \u8fd0\u884c\u4ee5\u4e0a\u547d\u4ee4\u540e\uff0c\u53ef\u5728 ${save_path} \u4e0b\u770b\u5230\u91cf\u5316\u540e\u7684\u6a21\u578b\u6587\u4ef6\u548c\u53c2\u6570\u6587\u4ef6\u3002 \u4f7f\u7528\u7684\u91cf\u5316\u7b97\u6cd5\u4e3a 'KL' , \u4f7f\u7528\u8bad\u7ec3\u96c6\u4e2d\u7684160\u5f20\u56fe\u7247\u8fdb\u884c\u91cf\u5316\u53c2\u6570\u7684\u6821\u6b63\u3002","title":"\u79bb\u7ebf\u91cf\u5316"},{"location":"tutorials/quant_post_demo/#_8","text":"\u4f7f\u7528 eval.py \u811a\u672c\u5bf9\u91cf\u5316\u524d\u540e\u7684\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5\uff0c\u5f97\u5230\u6a21\u578b\u7684\u5206\u7c7b\u7cbe\u5ea6\u8fdb\u884c\u5bf9\u6bd4\u3002 \u9996\u5148\u6d4b\u8bd5\u91cf\u5316\u524d\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff0c\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a 1 python eval . py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 1 top1_acc / top5_acc = [ 0 . 70913923 0 . 89548034 ] \u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6d4b\u8bd5\u79bb\u7ebf\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff1a 1 python eval . py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 1 top1_acc / top5_acc = [ 0 . 70141864 0 . 89086477 ] \u4ece\u4ee5\u4e0a\u7cbe\u5ea6\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51fa\uff0c\u5bf9 mobilenet \u5728 imagenet \u4e0a\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u540e top1 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.77% \uff0c top5 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.46% .","title":"\u6d4b\u8bd5\u7cbe\u5ea6"},{"location":"tutorials/sensitivity_demo/","text":"\u8be5\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u5206\u6790\u5377\u79ef\u7f51\u7edc\u4e2d\u5404\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\uff0c\u4ee5\u53ca\u5982\u4f55\u6839\u636e\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u9009\u62e9\u4e00\u7ec4\u5408\u9002\u7684\u526a\u88c1\u7387\u3002 \u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528MNIST\u6570\u636e\u3002\u652f\u6301\u4ee5\u4e0b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 1. \u63a5\u53e3\u4ecb\u7ecd # \u8be5\u793a\u4f8b\u6d89\u53ca\u4ee5\u4e0b\u63a5\u53e3\uff1a paddleslim.prune.sensitivity paddleslim.prune.merge_sensitive paddleslim.prune.get_ratios_by_loss 2. \u8fd0\u884c\u793a\u4f8b # \u5728\u8def\u5f84 PaddleSlim/demo/sensitive \u4e0b\u6267\u884c\u4ee5\u4e0b\u4ee3\u7801\u8fd0\u884c\u793a\u4f8b\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py --model \"MobileNetV1\" \u901a\u8fc7 python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002 3. \u91cd\u8981\u6b65\u9aa4\u8bf4\u660e # 3.1 \u8ba1\u7b97\u654f\u611f\u5ea6 # \u8ba1\u7b97\u654f\u611f\u5ea6\u4e4b\u524d\uff0c\u7528\u6237\u9700\u8981\u642d\u5efa\u597d\u7528\u4e8e\u6d4b\u8bd5\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u5b9e\u73b0\u8bc4\u4f30\u6a21\u578b\u7cbe\u5ea6\u7684\u56de\u8c03\u51fd\u6570\u3002 \u8c03\u7528 paddleslim.prune.sensitivity \u63a5\u53e3\u8ba1\u7b97\u654f\u611f\u5ea6\u3002\u654f\u611f\u5ea6\u4fe1\u606f\u4f1a\u8ffd\u52a0\u5230 sensitivities_file \u9009\u9879\u6240\u6307\u5b9a\u7684\u6587\u4ef6\u4e2d\uff0c\u5982\u679c\u9700\u8981\u91cd\u65b0\u8ba1\u7b97\u654f\u611f\u5ea6\uff0c\u9700\u8981\u5148\u5220\u9664 sensitivities_file \u6587\u4ef6\u3002 \u5982\u679c\u6a21\u578b\u8bc4\u4f30\u901f\u5ea6\u8f83\u6162\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u52a0\u901f\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u3002\u6bd4\u5982\u5728\u8fdb\u7a0b1\u4e2d\u8bbe\u7f6e pruned_ratios=[0.1, 0.2, 0.3, 0.4] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u653e\u5728\u6587\u4ef6 sensitivities_0.data \u4e2d\uff0c\u7136\u540e\u5728\u8fdb\u7a0b2\u4e2d\u8bbe\u7f6e pruned_ratios=[0.5, 0.6, 0.7] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u6587\u4ef6 sensitivities_1.data \u4e2d\u3002\u8fd9\u6837\u6bcf\u4e2a\u8fdb\u7a0b\u53ea\u4f1a\u8ba1\u7b97\u6307\u5b9a\u526a\u5207\u7387\u4e0b\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u591a\u8fdb\u7a0b\u53ef\u4ee5\u8fd0\u884c\u5728\u5355\u673a\u591a\u5361\uff0c\u6216\u591a\u673a\u591a\u5361\u3002 \u4ee3\u7801\u5982\u4e0b\uff1a 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 1 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_0.data\" , pruned_ratios = [ 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 ]) 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 2 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_1.data\" , pruned_ratios = [ 0 . 5 , 0 . 6 , 0 . 7 ]) 3.2 \u5408\u5e76\u654f\u611f\u5ea6 # \u5982\u679c\u7528\u6237\u901a\u8fc7\u4e0a\u4e00\u8282\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u751f\u6210\u4e86\u591a\u4e2a\u5b58\u50a8\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u901a\u8fc7 paddleslim.prune.merge_sensitive \u5c06\u5176\u5408\u5e76\uff0c\u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u4e00\u4e2a dict \u4e2d\u3002\u4ee3\u7801\u5982\u4e0b\uff1a 1 sens = merge_sensitive ([ \"./sensitivities_0.data\" , \"./sensitivities_1.data\" ]) 3.3 \u8ba1\u7b97\u526a\u88c1\u7387 # \u8c03\u7528 paddleslim.prune.get_ratios_by_loss \u63a5\u53e3\u8ba1\u7b97\u4e00\u7ec4\u526a\u88c1\u7387\u3002 1 ratios = get_ratios_by_loss ( sens , 0 . 01 ) \u5176\u4e2d\uff0c 0.01 \u4e3a\u4e00\u4e2a\u9608\u503c\uff0c\u5bf9\u4e8e\u4efb\u610f\u5377\u79ef\u5c42\uff0c\u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e\u9608\u503c 0.01 \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u7528\u6237\u5728\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u4e4b\u540e\u53ef\u4ee5\u901a\u8fc7\u63a5\u53e3 paddleslim.prune.Pruner \u526a\u88c1\u7f51\u7edc\uff0c\u5e76\u7528\u63a5\u53e3 paddleslim.analysis.flops \u8ba1\u7b97 FLOPs \u3002\u5982\u679c FLOPs \u4e0d\u6ee1\u8db3\u8981\u6c42\uff0c\u8c03\u6574\u9608\u503c\u91cd\u65b0\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u3002","title":"Sensitivity demo"},{"location":"tutorials/sensitivity_demo/#1","text":"\u8be5\u793a\u4f8b\u6d89\u53ca\u4ee5\u4e0b\u63a5\u53e3\uff1a paddleslim.prune.sensitivity paddleslim.prune.merge_sensitive paddleslim.prune.get_ratios_by_loss","title":"1. \u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/sensitivity_demo/#2","text":"\u5728\u8def\u5f84 PaddleSlim/demo/sensitive \u4e0b\u6267\u884c\u4ee5\u4e0b\u4ee3\u7801\u8fd0\u884c\u793a\u4f8b\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py --model \"MobileNetV1\" \u901a\u8fc7 python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002","title":"2. \u8fd0\u884c\u793a\u4f8b"},{"location":"tutorials/sensitivity_demo/#3","text":"","title":"3. \u91cd\u8981\u6b65\u9aa4\u8bf4\u660e"},{"location":"tutorials/sensitivity_demo/#31","text":"\u8ba1\u7b97\u654f\u611f\u5ea6\u4e4b\u524d\uff0c\u7528\u6237\u9700\u8981\u642d\u5efa\u597d\u7528\u4e8e\u6d4b\u8bd5\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u5b9e\u73b0\u8bc4\u4f30\u6a21\u578b\u7cbe\u5ea6\u7684\u56de\u8c03\u51fd\u6570\u3002 \u8c03\u7528 paddleslim.prune.sensitivity \u63a5\u53e3\u8ba1\u7b97\u654f\u611f\u5ea6\u3002\u654f\u611f\u5ea6\u4fe1\u606f\u4f1a\u8ffd\u52a0\u5230 sensitivities_file \u9009\u9879\u6240\u6307\u5b9a\u7684\u6587\u4ef6\u4e2d\uff0c\u5982\u679c\u9700\u8981\u91cd\u65b0\u8ba1\u7b97\u654f\u611f\u5ea6\uff0c\u9700\u8981\u5148\u5220\u9664 sensitivities_file \u6587\u4ef6\u3002 \u5982\u679c\u6a21\u578b\u8bc4\u4f30\u901f\u5ea6\u8f83\u6162\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u52a0\u901f\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u3002\u6bd4\u5982\u5728\u8fdb\u7a0b1\u4e2d\u8bbe\u7f6e pruned_ratios=[0.1, 0.2, 0.3, 0.4] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u653e\u5728\u6587\u4ef6 sensitivities_0.data \u4e2d\uff0c\u7136\u540e\u5728\u8fdb\u7a0b2\u4e2d\u8bbe\u7f6e pruned_ratios=[0.5, 0.6, 0.7] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u6587\u4ef6 sensitivities_1.data \u4e2d\u3002\u8fd9\u6837\u6bcf\u4e2a\u8fdb\u7a0b\u53ea\u4f1a\u8ba1\u7b97\u6307\u5b9a\u526a\u5207\u7387\u4e0b\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u591a\u8fdb\u7a0b\u53ef\u4ee5\u8fd0\u884c\u5728\u5355\u673a\u591a\u5361\uff0c\u6216\u591a\u673a\u591a\u5361\u3002 \u4ee3\u7801\u5982\u4e0b\uff1a 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 1 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_0.data\" , pruned_ratios = [ 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 ]) 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 2 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_1.data\" , pruned_ratios = [ 0 . 5 , 0 . 6 , 0 . 7 ])","title":"3.1 \u8ba1\u7b97\u654f\u611f\u5ea6"},{"location":"tutorials/sensitivity_demo/#32","text":"\u5982\u679c\u7528\u6237\u901a\u8fc7\u4e0a\u4e00\u8282\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u751f\u6210\u4e86\u591a\u4e2a\u5b58\u50a8\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u901a\u8fc7 paddleslim.prune.merge_sensitive \u5c06\u5176\u5408\u5e76\uff0c\u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u4e00\u4e2a dict \u4e2d\u3002\u4ee3\u7801\u5982\u4e0b\uff1a 1 sens = merge_sensitive ([ \"./sensitivities_0.data\" , \"./sensitivities_1.data\" ])","title":"3.2 \u5408\u5e76\u654f\u611f\u5ea6"},{"location":"tutorials/sensitivity_demo/#33","text":"\u8c03\u7528 paddleslim.prune.get_ratios_by_loss \u63a5\u53e3\u8ba1\u7b97\u4e00\u7ec4\u526a\u88c1\u7387\u3002 1 ratios = get_ratios_by_loss ( sens , 0 . 01 ) \u5176\u4e2d\uff0c 0.01 \u4e3a\u4e00\u4e2a\u9608\u503c\uff0c\u5bf9\u4e8e\u4efb\u610f\u5377\u79ef\u5c42\uff0c\u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e\u9608\u503c 0.01 \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u7528\u6237\u5728\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u4e4b\u540e\u53ef\u4ee5\u901a\u8fc7\u63a5\u53e3 paddleslim.prune.Pruner \u526a\u88c1\u7f51\u7edc\uff0c\u5e76\u7528\u63a5\u53e3 paddleslim.analysis.flops \u8ba1\u7b97 FLOPs \u3002\u5982\u679c FLOPs \u4e0d\u6ee1\u8db3\u8981\u6c42\uff0c\u8c03\u6574\u9608\u503c\u91cd\u65b0\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u3002","title":"3.3 \u8ba1\u7b97\u526a\u88c1\u7387"}]} \ No newline at end of file +{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"PaddleSlim # PaddleSlim\u662fPaddlePaddle\u6846\u67b6\u7684\u4e00\u4e2a\u5b50\u6a21\u5757\uff0c\u4e3b\u8981\u7528\u4e8e\u538b\u7f29\u56fe\u50cf\u9886\u57df\u6a21\u578b\u3002\u5728PaddleSlim\u4e2d\uff0c\u4e0d\u4ec5\u5b9e\u73b0\u4e86\u76ee\u524d\u4e3b\u6d41\u7684\u7f51\u7edc\u526a\u679d\u3001\u91cf\u5316\u3001\u84b8\u998f\u4e09\u79cd\u538b\u7f29\u7b56\u7565\uff0c\u8fd8\u5b9e\u73b0\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u548c\u5c0f\u6a21\u578b\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u529f\u80fd\u3002\u5728\u540e\u7eed\u7248\u672c\u4e2d\uff0c\u4f1a\u6dfb\u52a0\u66f4\u591a\u7684\u538b\u7f29\u7b56\u7565\uff0c\u4ee5\u53ca\u5b8c\u5584\u5bf9NLP\u9886\u57df\u6a21\u578b\u7684\u652f\u6301\u3002 \u529f\u80fd # \u6a21\u578b\u526a\u88c1 \u652f\u6301\u901a\u9053\u5747\u5300\u6a21\u578b\u526a\u88c1\uff08uniform pruning) \u57fa\u4e8e\u654f\u611f\u5ea6\u7684\u6a21\u578b\u526a\u88c1 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u81ea\u52a8\u6a21\u578b\u526a\u88c1\u4e09\u79cd\u65b9\u5f0f \u91cf\u5316\u8bad\u7ec3 \u5728\u7ebf\u91cf\u5316\u8bad\u7ec3\uff08training aware\uff09 \u79bb\u7ebf\u91cf\u5316\uff08post training\uff09 \u652f\u6301\u5bf9\u6743\u91cd\u5168\u5c40\u91cf\u5316\u548cChannel-Wise\u91cf\u5316 \u84b8\u998f \u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301\u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301 FLOPS / \u786c\u4ef6\u5ef6\u65f6\u7ea6\u675f \u652f\u6301\u591a\u5e73\u53f0\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30 \u5b89\u88c5 # \u5b89\u88c5PaddleSlim\u524d\uff0c\u8bf7\u786e\u8ba4\u5df2\u6b63\u786e\u5b89\u88c5Paddle1.6\u7248\u672c\u6216\u66f4\u65b0\u7248\u672c\u3002Paddle\u5b89\u88c5\u8bf7\u53c2\u8003\uff1a Paddle\u5b89\u88c5\u6559\u7a0b \u3002 \u5b89\u88c5develop\u7248\u672c 1 2 3 git clone https : // github . com / PaddlePaddle / PaddleSlim . git cd PaddleSlim python setup . py install \u5b89\u88c5\u5b98\u65b9\u53d1\u5e03\u7684\u6700\u65b0\u7248\u672c 1 pip install paddleslim - i https : // pypi . org / simple \u5b89\u88c5\u5386\u53f2\u7248\u672c \u8bf7\u70b9\u51fb pypi.org \u67e5\u770b\u53ef\u5b89\u88c5\u5386\u53f2\u7248\u672c\u3002 \u4f7f\u7528 # API\u6587\u6863 \uff1aAPI\u4f7f\u7528\u4ecb\u7ecd\uff0c\u5305\u62ec \u84b8\u998f \u3001 \u526a\u88c1 \u3001 \u91cf\u5316 \u548c \u6a21\u578b\u7ed3\u6784\u641c\u7d22 \u3002 \u793a\u4f8b \uff1a\u57fa\u4e8emnist\u548ccifar10\u7b49\u7b80\u5355\u5206\u7c7b\u4efb\u52a1\u7684\u6a21\u578b\u538b\u7f29\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8be5\u90e8\u5206\u5feb\u901f\u4f53\u9a8c\u548c\u4e86\u89e3PaddleSlim\u7684\u529f\u80fd\u3002 \u5b9e\u8df5\u6559\u7a0b \uff1a\u7ecf\u5178\u6a21\u578b\u7684\u5206\u6790\u548c\u538b\u7f29\u5b9e\u9a8c\u6559\u7a0b\u3002 \u6a21\u578b\u5e93 \uff1a\u7ecf\u8fc7\u538b\u7f29\u7684\u5206\u7c7b\u3001\u68c0\u6d4b\u3001\u8bed\u4e49\u5206\u5272\u6a21\u578b\uff0c\u5305\u62ec\u6743\u91cd\u6587\u4ef6\u3001\u7f51\u7edc\u7ed3\u6784\u6587\u4ef6\u548c\u6027\u80fd\u6570\u636e\u3002 Paddle\u68c0\u6d4b\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u68c0\u6d4b\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 Paddle\u5206\u5272\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u5206\u5272\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 PaddleLite \uff1a\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u9884\u6d4b\u5e93PaddleLite\u90e8\u7f72PaddleSlim\u4ea7\u51fa\u7684\u6a21\u578b\u3002 \u8d21\u732e\u4e0e\u53cd\u9988 #","title":"Home"},{"location":"#paddleslim","text":"PaddleSlim\u662fPaddlePaddle\u6846\u67b6\u7684\u4e00\u4e2a\u5b50\u6a21\u5757\uff0c\u4e3b\u8981\u7528\u4e8e\u538b\u7f29\u56fe\u50cf\u9886\u57df\u6a21\u578b\u3002\u5728PaddleSlim\u4e2d\uff0c\u4e0d\u4ec5\u5b9e\u73b0\u4e86\u76ee\u524d\u4e3b\u6d41\u7684\u7f51\u7edc\u526a\u679d\u3001\u91cf\u5316\u3001\u84b8\u998f\u4e09\u79cd\u538b\u7f29\u7b56\u7565\uff0c\u8fd8\u5b9e\u73b0\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u548c\u5c0f\u6a21\u578b\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u529f\u80fd\u3002\u5728\u540e\u7eed\u7248\u672c\u4e2d\uff0c\u4f1a\u6dfb\u52a0\u66f4\u591a\u7684\u538b\u7f29\u7b56\u7565\uff0c\u4ee5\u53ca\u5b8c\u5584\u5bf9NLP\u9886\u57df\u6a21\u578b\u7684\u652f\u6301\u3002","title":"PaddleSlim"},{"location":"#_1","text":"\u6a21\u578b\u526a\u88c1 \u652f\u6301\u901a\u9053\u5747\u5300\u6a21\u578b\u526a\u88c1\uff08uniform pruning) \u57fa\u4e8e\u654f\u611f\u5ea6\u7684\u6a21\u578b\u526a\u88c1 \u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u81ea\u52a8\u6a21\u578b\u526a\u88c1\u4e09\u79cd\u65b9\u5f0f \u91cf\u5316\u8bad\u7ec3 \u5728\u7ebf\u91cf\u5316\u8bad\u7ec3\uff08training aware\uff09 \u79bb\u7ebf\u91cf\u5316\uff08post training\uff09 \u652f\u6301\u5bf9\u6743\u91cd\u5168\u5c40\u91cf\u5316\u548cChannel-Wise\u91cf\u5316 \u84b8\u998f \u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301\u57fa\u4e8e\u8fdb\u5316\u7b97\u6cd5\u7684\u8f7b\u91cf\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u81ea\u52a8\u641c\u7d22\uff08Light-NAS\uff09 \u652f\u6301 FLOPS / \u786c\u4ef6\u5ef6\u65f6\u7ea6\u675f \u652f\u6301\u591a\u5e73\u53f0\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30","title":"\u529f\u80fd"},{"location":"#_2","text":"\u5b89\u88c5PaddleSlim\u524d\uff0c\u8bf7\u786e\u8ba4\u5df2\u6b63\u786e\u5b89\u88c5Paddle1.6\u7248\u672c\u6216\u66f4\u65b0\u7248\u672c\u3002Paddle\u5b89\u88c5\u8bf7\u53c2\u8003\uff1a Paddle\u5b89\u88c5\u6559\u7a0b \u3002 \u5b89\u88c5develop\u7248\u672c 1 2 3 git clone https : // github . com / PaddlePaddle / PaddleSlim . git cd PaddleSlim python setup . py install \u5b89\u88c5\u5b98\u65b9\u53d1\u5e03\u7684\u6700\u65b0\u7248\u672c 1 pip install paddleslim - i https : // pypi . org / simple \u5b89\u88c5\u5386\u53f2\u7248\u672c \u8bf7\u70b9\u51fb pypi.org \u67e5\u770b\u53ef\u5b89\u88c5\u5386\u53f2\u7248\u672c\u3002","title":"\u5b89\u88c5"},{"location":"#_3","text":"API\u6587\u6863 \uff1aAPI\u4f7f\u7528\u4ecb\u7ecd\uff0c\u5305\u62ec \u84b8\u998f \u3001 \u526a\u88c1 \u3001 \u91cf\u5316 \u548c \u6a21\u578b\u7ed3\u6784\u641c\u7d22 \u3002 \u793a\u4f8b \uff1a\u57fa\u4e8emnist\u548ccifar10\u7b49\u7b80\u5355\u5206\u7c7b\u4efb\u52a1\u7684\u6a21\u578b\u538b\u7f29\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8be5\u90e8\u5206\u5feb\u901f\u4f53\u9a8c\u548c\u4e86\u89e3PaddleSlim\u7684\u529f\u80fd\u3002 \u5b9e\u8df5\u6559\u7a0b \uff1a\u7ecf\u5178\u6a21\u578b\u7684\u5206\u6790\u548c\u538b\u7f29\u5b9e\u9a8c\u6559\u7a0b\u3002 \u6a21\u578b\u5e93 \uff1a\u7ecf\u8fc7\u538b\u7f29\u7684\u5206\u7c7b\u3001\u68c0\u6d4b\u3001\u8bed\u4e49\u5206\u5272\u6a21\u578b\uff0c\u5305\u62ec\u6743\u91cd\u6587\u4ef6\u3001\u7f51\u7edc\u7ed3\u6784\u6587\u4ef6\u548c\u6027\u80fd\u6570\u636e\u3002 Paddle\u68c0\u6d4b\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u68c0\u6d4b\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 Paddle\u5206\u5272\u5e93 \uff1a\u4ecb\u7ecd\u5982\u4f55\u5728\u5206\u5272\u5e93\u4e2d\u4f7f\u7528PaddleSlim\u3002 PaddleLite \uff1a\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u9884\u6d4b\u5e93PaddleLite\u90e8\u7f72PaddleSlim\u4ea7\u51fa\u7684\u6a21\u578b\u3002","title":"\u4f7f\u7528"},{"location":"#_4","text":"","title":"\u8d21\u732e\u4e0e\u53cd\u9988"},{"location":"model_zoo/","text":"1. \u56fe\u50cf\u5206\u7c7b # \u6570\u636e\u96c6\uff1aImageNet1000\u7c7b 1.1 \u91cf\u5316 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 FP32 baseline 70.99%/89.68% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 FP32 baseline 72.15%/90.65% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 FP32 baseline 76.50%/93.00% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 1.2 \u526a\u88c1 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d MobileNetV1 baseline 70.99%/89.68% 17 1.11 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 uniform -50% 69.4%/88.66% (-1.59%/-1.02%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -30% 70.4%/89.3% (-0.59%/-0.38%) 12 0.74 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -50% 69.8% / 88.9% (-1.19%/-0.78%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 baseline 72.15%/90.65% 15 0.59 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 uniform -50% 65.79%/86.11% (-6.35%/-4.47%) 11 0.296 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 baseline 72.15%/90.65% 84 7.36 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 uniform -50% 70.99%/89.95% (-1.36%/-0.87%) 41 3.67 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 auto -55.05% 70.24%/89.63% (-2.04%/-1.06%) 33 3.31 \u4e0b\u8f7d\u94fe\u63a5 1.3 \u84b8\u998f # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 student 70.99%/89.68% 17 \u4e0b\u8f7d\u94fe\u63a5 ResNet50_vd teacher 79.12%/94.44% 99 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 ResNet50_vd 1 distill 72.77%/90.68% (+1.78%/+1.00%) 17 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 student 72.15%/90.65% 15 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 ResNet50_vd distill 74.28%/91.53% (+2.13%/+0.88%) 15 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 student 76.50%/93.00% 99 \u4e0b\u8f7d\u94fe\u63a5 ResNet101 teacher 77.56%/93.64% 173 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 ResNet101 distill 77.29%/93.65% (+0.79%/+0.65%) 99 \u4e0b\u8f7d\u94fe\u63a5 Note [1] \uff1a\u5e26_vd\u540e\u7f00\u4ee3\u8868\u8be5\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u4e86Mixup\uff0cMixup\u76f8\u5173\u4ecb\u7ecd\u53c2\u8003 mixup: Beyond Empirical Risk Minimization 2. \u76ee\u6807\u68c0\u6d4b # 2.1 \u91cf\u5316 # \u6570\u636e\u96c6\uff1a COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 FP32 baseline COCO 8 29.3 29.3 27.1 xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain FP32 baseline COCO 8 41.4 xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 \u6570\u636e\u96c6\uff1aWIDER-FACE \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Image/GPU \u8f93\u5165\u5c3a\u5bf8 Easy/Medium/Hard \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d BlazeFace FP32 baseline 8 640 0.915/0.892/0.797 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite FP32 baseline 8 640 0.909/0.885/0.781 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS FP32 baseline 8 640 0.837/0.807/0.658 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 2.2 \u526a\u88c1 # \u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef(MB) GFLOPs (608*608) \u4e0b\u8f7d MobileNet-V1-YOLOv3 baseline Pascal VOC 8 76.2 76.7 75.3 94 40.49 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -52.88% Pascal VOC 8 77.6 (+1.4) 77.7 (1.0) 75.5 (+0.2) 31 19.08 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 baseline COCO 8 29.3 29.3 27.0 95 41.35 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -51.77% COCO 8 26.0 (-3.3) 25.1 (-4.2) 22.6 (-4.4) 32 19.94 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 baseline COCO 8 39.1 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -9.37% COCO 8 39.3 (+0.2) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -24.68% COCO 8 37.3 (-1.8) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain baseline COCO 8 41.4 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -9.37% COCO 8 40.5 (-0.9) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -24.68% COCO 8 37.8 (-3.3) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 2.3 \u84b8\u998f # \u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 student Pascal VOC 8 76.2 76.7 75.3 94 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher Pascal VOC 8 82.6 81.9 80.1 162 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill Pascal VOC 8 79.0 (+2.8) 78.2 (+1.5) 75.5 (+0.2) 94 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 student COCO 8 29.3 29.3 27.0 95 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher COCO 8 36.2 34.3 31.4 163 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill COCO 8 31.4 (+2.1) 30.0 (+0.7) 27.1 (+0.1) 95 \u4e0b\u8f7d\u94fe\u63a5 3. \u56fe\u50cf\u5206\u5272 # \u6570\u636e\u96c6\uff1aCityscapes 3.1 \u91cf\u5316 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d DeepLabv3+/MobileNetv1 FP32 baseline 63.26 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 FP32 baseline 69.81 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 3.2 \u526a\u88c1 # \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d fast-scnn baseline 69.64 11 14.41 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn uniform -17.07% 69.58 (-0.06) 8.5 11.95 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn sensitive -47.60% 66.68 (-2.96) 5.7 7.55 \u4e0b\u8f7d\u94fe\u63a5","title":"\u6a21\u578b\u5e93"},{"location":"model_zoo/#1","text":"\u6570\u636e\u96c6\uff1aImageNet1000\u7c7b","title":"1. \u56fe\u50cf\u5206\u7c7b"},{"location":"model_zoo/#11","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 FP32 baseline 70.99%/89.68% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 FP32 baseline 72.15%/90.65% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 FP32 baseline 76.50%/93.00% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_post xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5 ResNet50 quant_aware xx%/xx% xx \u4e0b\u8f7d\u94fe\u63a5","title":"1.1 \u91cf\u5316"},{"location":"model_zoo/#12","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d MobileNetV1 baseline 70.99%/89.68% 17 1.11 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 uniform -50% 69.4%/88.66% (-1.59%/-1.02%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -30% 70.4%/89.3% (-0.59%/-0.38%) 12 0.74 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 sensitive -50% 69.8% / 88.9% (-1.19%/-0.78%) 9 0.56 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 baseline 72.15%/90.65% 15 0.59 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 uniform -50% 65.79%/86.11% (-6.35%/-4.47%) 11 0.296 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 baseline 72.15%/90.65% 84 7.36 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 uniform -50% 70.99%/89.95% (-1.36%/-0.87%) 41 3.67 \u4e0b\u8f7d\u94fe\u63a5 ResNet34 auto -55.05% 70.24%/89.63% (-2.04%/-1.06%) 33 3.31 \u4e0b\u8f7d\u94fe\u63a5","title":"1.2 \u526a\u88c1"},{"location":"model_zoo/#13","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Top-1/Top-5 Acc \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNetV1 student 70.99%/89.68% 17 \u4e0b\u8f7d\u94fe\u63a5 ResNet50_vd teacher 79.12%/94.44% 99 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV1 ResNet50_vd 1 distill 72.77%/90.68% (+1.78%/+1.00%) 17 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 student 72.15%/90.65% 15 \u4e0b\u8f7d\u94fe\u63a5 MobileNetV2 ResNet50_vd distill 74.28%/91.53% (+2.13%/+0.88%) 15 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 student 76.50%/93.00% 99 \u4e0b\u8f7d\u94fe\u63a5 ResNet101 teacher 77.56%/93.64% 173 \u4e0b\u8f7d\u94fe\u63a5 ResNet50 ResNet101 distill 77.29%/93.65% (+0.79%/+0.65%) 99 \u4e0b\u8f7d\u94fe\u63a5 Note [1] \uff1a\u5e26_vd\u540e\u7f00\u4ee3\u8868\u8be5\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u4e86Mixup\uff0cMixup\u76f8\u5173\u4ecb\u7ecd\u53c2\u8003 mixup: Beyond Empirical Risk Minimization","title":"1.3 \u84b8\u998f"},{"location":"model_zoo/#2","text":"","title":"2. \u76ee\u6807\u68c0\u6d4b"},{"location":"model_zoo/#21","text":"\u6570\u636e\u96c6\uff1a COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 FP32 baseline COCO 8 29.3 29.3 27.1 xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain FP32 baseline COCO 8 41.4 xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_post COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain quant_aware COCO 8 xx xx xx xx \u4e0b\u8f7d\u94fe\u63a5 \u6570\u636e\u96c6\uff1aWIDER-FACE \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 Image/GPU \u8f93\u5165\u5c3a\u5bf8 Easy/Medium/Hard \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d BlazeFace FP32 baseline 8 640 0.915/0.892/0.797 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite FP32 baseline 8 640 0.909/0.885/0.781 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-Lite quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS FP32 baseline 8 640 0.837/0.807/0.658 xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_post 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5 BlazeFace-NAS quant_aware 8 640 xx/xx/xx xx \u4e0b\u8f7d\u94fe\u63a5","title":"2.1 \u91cf\u5316"},{"location":"model_zoo/#22","text":"\u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef(MB) GFLOPs (608*608) \u4e0b\u8f7d MobileNet-V1-YOLOv3 baseline Pascal VOC 8 76.2 76.7 75.3 94 40.49 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -52.88% Pascal VOC 8 77.6 (+1.4) 77.7 (1.0) 75.5 (+0.2) 31 19.08 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 baseline COCO 8 29.3 29.3 27.0 95 41.35 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 sensitive -51.77% COCO 8 26.0 (-3.3) 25.1 (-4.2) 22.6 (-4.4) 32 19.94 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 baseline COCO 8 39.1 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -9.37% COCO 8 39.3 (+0.2) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 sensitive -24.68% COCO 8 37.3 (-1.8) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain baseline COCO 8 41.4 - - 177 89.60 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -9.37% COCO 8 40.5 (-0.9) - - 150 81.20 \u4e0b\u8f7d\u94fe\u63a5 R50-dcn-YOLOv3 obj365_pretrain sensitive -24.68% COCO 8 37.8 (-3.3) - - 113 67.48 \u4e0b\u8f7d\u94fe\u63a5","title":"2.2 \u526a\u88c1"},{"location":"model_zoo/#23","text":"\u6570\u636e\u96c6\uff1aPasacl VOC & COCO 2017 \u6a21\u578b \u538b\u7f29\u65b9\u6cd5 \u6570\u636e\u96c6 Image/GPU \u8f93\u5165608 Box AP \u8f93\u5165416 Box AP \u8f93\u5165320 Box AP \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d MobileNet-V1-YOLOv3 student Pascal VOC 8 76.2 76.7 75.3 94 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher Pascal VOC 8 82.6 81.9 80.1 162 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill Pascal VOC 8 79.0 (+2.8) 78.2 (+1.5) 75.5 (+0.2) 94 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 student COCO 8 29.3 29.3 27.0 95 \u4e0b\u8f7d\u94fe\u63a5 ResNet34-YOLOv3 teacher COCO 8 36.2 34.3 31.4 163 \u4e0b\u8f7d\u94fe\u63a5 MobileNet-V1-YOLOv3 ResNet34-YOLOv3 distill COCO 8 31.4 (+2.1) 30.0 (+0.7) 27.1 (+0.1) 95 \u4e0b\u8f7d\u94fe\u63a5","title":"2.3 \u84b8\u998f"},{"location":"model_zoo/#3","text":"\u6570\u636e\u96c6\uff1aCityscapes","title":"3. \u56fe\u50cf\u5206\u5272"},{"location":"model_zoo/#31","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 \u4e0b\u8f7d DeepLabv3+/MobileNetv1 FP32 baseline 63.26 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv1 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 FP32 baseline 69.81 xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_post xx xx \u4e0b\u8f7d\u94fe\u63a5 DeepLabv3+/MobileNetv2 quant_aware xx xx \u4e0b\u8f7d\u94fe\u63a5","title":"3.1 \u91cf\u5316"},{"location":"model_zoo/#32","text":"\u6a21\u578b \u538b\u7f29\u65b9\u6cd5 mIoU \u6a21\u578b\u4f53\u79ef\uff08MB\uff09 GFLOPs \u4e0b\u8f7d fast-scnn baseline 69.64 11 14.41 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn uniform -17.07% 69.58 (-0.06) 8.5 11.95 \u4e0b\u8f7d\u94fe\u63a5 fast-scnn sensitive -47.60% 66.68 (-2.96) 5.7 7.55 \u4e0b\u8f7d\u94fe\u63a5","title":"3.2 \u526a\u88c1"},{"location":"search_space/","text":"\u641c\u7d22\u7a7a\u95f4\u7b80\u4ecb # \u641c\u7d22\u7a7a\u95f4\u662f\u795e\u7ecf\u7f51\u7edc\u641c\u7d22\u4e2d\u7684\u4e00\u4e2a\u6982\u5ff5\u3002\u641c\u7d22\u7a7a\u95f4\u662f\u4e00\u7cfb\u5217\u6a21\u578b\u7ed3\u6784\u7684\u6c47\u96c6, SANAS\u4e3b\u8981\u662f\u5229\u7528\u6a21\u62df\u9000\u706b\u7684\u601d\u60f3\u5728\u641c\u7d22\u7a7a\u95f4\u4e2d\u641c\u7d22\u5230\u4e00\u4e2a\u6bd4\u8f83\u5c0f\u7684\u6a21\u578b\u7ed3\u6784\u6216\u8005\u4e00\u4e2a\u7cbe\u5ea6\u6bd4\u8f83\u9ad8\u7684\u6a21\u578b\u7ed3\u6784\u3002 paddleslim.nas \u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4 # \u6839\u636e\u521d\u59cb\u6a21\u578b\u7ed3\u6784\u6784\u9020\u641c\u7d22\u7a7a\u95f4 # MobileNetV2Space \u2003 MobileNetV2\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587 MobileNetV1Space \u2003 MobilNetV1\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587 ResNetSpace \u2003 ResNetSpace\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587 \u6839\u636e\u76f8\u5e94\u6a21\u578b\u7684block\u6784\u9020\u641c\u7d22\u7a7a\u95f4 # MobileNetV1BlockSpace \u2003 MobileNetV1Block\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 MobileNetV2BlockSpace \u2003 MobileNetV2Block\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 ResNetBlockSpace \u2003 ResNetBlock\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 InceptionABlockSpace \u2003 InceptionABlock\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 InceptionCBlockSpace \u2003 InceptionCBlock\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \u641c\u7d22\u7a7a\u95f4\u793a\u4f8b # \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7528\u521d\u59cb\u7684\u6a21\u578b\u7ed3\u6784\u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u7684\u8bdd\uff0c\u4ec5\u9700\u8981\u6307\u5b9a\u641c\u7d22\u7a7a\u95f4\u540d\u5b57\u5373\u53ef\u3002\u4f8b\u5982\uff1a\u5982\u679c\u4f7f\u7528\u539f\u672c\u7684MobileNetV2\u7684\u641c\u7d22\u7a7a\u95f4\u8fdb\u884c\u641c\u7d22\u7684\u8bdd\uff0c\u4f20\u5165SANAS\u4e2d\u7684config\u76f4\u63a5\u6307\u5b9a\u4e3a[('MobileNetV2Space')]\u3002 \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7684block\u641c\u7d22\u7a7a\u95f4\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 2.1 \u4f7f\u7528 input_size , output_size \u548c block_num \u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]\u3002 2.2 \u4f7f\u7528 block_mask \u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]\u3002 \u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4(search space) # \u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u7c7b\u9700\u8981\u7ee7\u627f\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u5e76\u91cd\u5199\u4ee5\u4e0b\u51e0\u90e8\u5206\uff1a \u2003 1. \u521d\u59cb\u5316\u7684tokens( init_tokens \u51fd\u6570)\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u81ea\u5df1\u60f3\u8981\u7684tokens\u5217\u8868, tokens\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u6570\u5b57\u6307\u7684\u662f\u5f53\u524d\u6570\u5b57\u5728\u76f8\u5e94\u7684\u641c\u7d22\u5217\u8868\u4e2d\u7684\u7d22\u5f15\u3002\u4f8b\u5982\u672c\u793a\u4f8b\u4e2d\u82e5tokens=[0, 3, 5]\uff0c\u5219\u4ee3\u8868\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u5230\u7684\u901a\u9053\u6570\u4e3a[8, 40, 128]\u3002 \u2003 2. token\u4e2d\u6bcf\u4e2a\u6570\u5b57\u7684\u641c\u7d22\u5217\u8868\u957f\u5ea6( range_table \u51fd\u6570)\uff0ctokens\u4e2d\u6bcf\u4e2atoken\u7684\u7d22\u5f15\u8303\u56f4\u3002 \u2003 3. \u6839\u636etoken\u4ea7\u751f\u6a21\u578b\u7ed3\u6784( token2arch \u51fd\u6570)\uff0c\u6839\u636e\u641c\u7d22\u5230\u7684tokens\u5217\u8868\u4ea7\u751f\u6a21\u578b\u7ed3\u6784\u3002 \u4ee5\u65b0\u589ereset block\u4e3a\u4f8b\u8bf4\u660e\u5982\u4f55\u6784\u9020\u81ea\u5df1\u7684search space\u3002\u81ea\u5b9a\u4e49\u7684search space\u4e0d\u80fd\u548c\u5df2\u6709\u7684search space\u540c\u540d\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 ### \u5f15\u5165\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u51fd\u6570\u548csearch space\u7684\u6ce8\u518c\u7c7b\u51fd\u6570 from .search_space_base import SearchSpaceBase from .search_space_registry import SEARCHSPACE import numpy as np ### \u9700\u8981\u8c03\u7528\u6ce8\u518c\u51fd\u6570\u628a\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u6ce8\u518c\u5230space space\u4e2d @SEARCHSPACE.register ### \u5b9a\u4e49\u4e00\u4e2a\u7ee7\u627fSearchSpaceBase\u57fa\u7c7b\u7684\u641c\u7d22\u7a7a\u95f4\u7684\u7c7b\u51fd\u6570 class ResNetBlockSpace2 ( SearchSpaceBase ): def __init__ ( self , input_size , output_size , block_num , block_mask ): ### \u5b9a\u4e49\u4e00\u4e9b\u5b9e\u9645\u60f3\u8981\u641c\u7d22\u7684\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a\u901a\u9053\u6570\u3001\u6bcf\u4e2a\u5377\u79ef\u7684\u91cd\u590d\u6b21\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u7b49 ### self.filter_num \u4ee3\u8868\u901a\u9053\u6570\u7684\u641c\u7d22\u5217\u8868 self . filter_num = np . array ([ 8 , 16 , 32 , 40 , 64 , 128 , 256 , 512 ]) ### \u5b9a\u4e49\u521d\u59cb\u5316token\uff0c\u521d\u59cb\u5316token\u7684\u957f\u5ea6\u6839\u636e\u4f20\u5165\u7684block_num\u6216\u8005block_mask\u7684\u957f\u5ea6\u6765\u5f97\u5230\u7684 def init_tokens ( self ): return [ 0 ] * 3 * len ( self . block_mask ) ### \u5b9a\u4e49token\u7684index\u7684\u53d6\u503c\u8303\u56f4 def range_table ( self ): return [ len ( self . filter_num )] * 3 * len ( self . block_mask ) ### \u628atoken\u8f6c\u6362\u6210\u6a21\u578b\u7ed3\u6784 def token2arch ( self , tokens = None ): if tokens == None : tokens = self . init_tokens () self . bottleneck_params_list = [] for i in range ( len ( self . block_mask )): self . bottleneck_params_list . append ( self . filter_num [ tokens [ i * 3 + 0 ]], self . filter_num [ tokens [ i * 3 + 1 ]], self . filter_num [ tokens [ i * 3 + 2 ]], 2 if self . block_mask [ i ] == 1 else 1 ) def net_arch ( input ): for i , layer_setting in enumerate ( self . bottleneck_params_list ): channel_num , stride = layer_setting [: - 1 ], layer_setting [ - 1 ] input = self . _resnet_block ( input , channel_num , stride , name = 'resnet_layer{}' . format ( i + 1 )) return input return net_arch ### \u6784\u9020\u5177\u4f53block\u7684\u64cd\u4f5c def _resnet_block ( self , input , channel_num , stride , name = None ): shortcut_conv = self . _shortcut ( input , channel_num [ 2 ], stride , name = name ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 0 ], filter_size = 1 , act = 'relu' , name = name + '_conv0' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 1 ], filter_size = 3 , stride = stride , act = 'relu' , name = name + '_conv1' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 2 ], filter_size = 1 , name = name + '_conv2' ) return fluid . layers . elementwise_add ( x = shortcut_conv , y = input , axis = 0 , name = name + '_elementwise_add' ) def _shortcut ( self , input , channel_num , stride , name = None ): channel_in = input . shape [ 1 ] if channel_in != channel_num or stride != 1 : return self . conv_bn_layer ( input , num_filters = channel_num , filter_size = 1 , stride = stride , name = name + '_shortcut' ) else : return input def _conv_bn_layer ( self , input , num_filters , filter_size , stride = 1 , padding = 'SAME' , act = None , name = None ): conv = fluid . layers . conv2d ( input , num_filters , filter_size , stride , name = name + '_conv' ) bn = fluid . layers . batch_norm ( conv , act = act , name = name + '_bn' ) return bn","title":"Search space"},{"location":"search_space/#_1","text":"\u641c\u7d22\u7a7a\u95f4\u662f\u795e\u7ecf\u7f51\u7edc\u641c\u7d22\u4e2d\u7684\u4e00\u4e2a\u6982\u5ff5\u3002\u641c\u7d22\u7a7a\u95f4\u662f\u4e00\u7cfb\u5217\u6a21\u578b\u7ed3\u6784\u7684\u6c47\u96c6, SANAS\u4e3b\u8981\u662f\u5229\u7528\u6a21\u62df\u9000\u706b\u7684\u601d\u60f3\u5728\u641c\u7d22\u7a7a\u95f4\u4e2d\u641c\u7d22\u5230\u4e00\u4e2a\u6bd4\u8f83\u5c0f\u7684\u6a21\u578b\u7ed3\u6784\u6216\u8005\u4e00\u4e2a\u7cbe\u5ea6\u6bd4\u8f83\u9ad8\u7684\u6a21\u578b\u7ed3\u6784\u3002","title":"\u641c\u7d22\u7a7a\u95f4\u7b80\u4ecb"},{"location":"search_space/#paddleslimnas","text":"","title":"paddleslim.nas \u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4"},{"location":"search_space/#_2","text":"MobileNetV2Space \u2003 MobileNetV2\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587 MobileNetV1Space \u2003 MobilNetV1\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587 ResNetSpace \u2003 ResNetSpace\u7684\u7f51\u7edc\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 \uff0c \u8bba\u6587","title":"\u6839\u636e\u521d\u59cb\u6a21\u578b\u7ed3\u6784\u6784\u9020\u641c\u7d22\u7a7a\u95f4"},{"location":"search_space/#block","text":"MobileNetV1BlockSpace \u2003 MobileNetV1Block\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 MobileNetV2BlockSpace \u2003 MobileNetV2Block\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 ResNetBlockSpace \u2003 ResNetBlock\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 InceptionABlockSpace \u2003 InceptionABlock\u7684\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801 InceptionCBlockSpace \u2003 InceptionCBlock\u7ed3\u6784\u53ef\u4ee5\u53c2\u8003\uff1a \u4ee3\u7801","title":"\u6839\u636e\u76f8\u5e94\u6a21\u578b\u7684block\u6784\u9020\u641c\u7d22\u7a7a\u95f4"},{"location":"search_space/#_3","text":"\u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7528\u521d\u59cb\u7684\u6a21\u578b\u7ed3\u6784\u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u7684\u8bdd\uff0c\u4ec5\u9700\u8981\u6307\u5b9a\u641c\u7d22\u7a7a\u95f4\u540d\u5b57\u5373\u53ef\u3002\u4f8b\u5982\uff1a\u5982\u679c\u4f7f\u7528\u539f\u672c\u7684MobileNetV2\u7684\u641c\u7d22\u7a7a\u95f4\u8fdb\u884c\u641c\u7d22\u7684\u8bdd\uff0c\u4f20\u5165SANAS\u4e2d\u7684config\u76f4\u63a5\u6307\u5b9a\u4e3a[('MobileNetV2Space')]\u3002 \u4f7f\u7528paddleslim\u4e2d\u63d0\u4f9b\u7684block\u641c\u7d22\u7a7a\u95f4\u6784\u9020\u641c\u7d22\u7a7a\u95f4\uff1a 2.1 \u4f7f\u7528 input_size , output_size \u548c block_num \u6765\u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]\u3002 2.2 \u4f7f\u7528 block_mask \u6784\u9020\u641c\u7d22\u7a7a\u95f4\u3002\u4f8b\u5982\uff1a\u4f20\u5165SANAS\u7684config\u53ef\u4ee5\u6307\u5b9a\u4e3a[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]\u3002","title":"\u641c\u7d22\u7a7a\u95f4\u793a\u4f8b"},{"location":"search_space/#search-space","text":"\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u7c7b\u9700\u8981\u7ee7\u627f\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u5e76\u91cd\u5199\u4ee5\u4e0b\u51e0\u90e8\u5206\uff1a \u2003 1. \u521d\u59cb\u5316\u7684tokens( init_tokens \u51fd\u6570)\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u81ea\u5df1\u60f3\u8981\u7684tokens\u5217\u8868, tokens\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u6570\u5b57\u6307\u7684\u662f\u5f53\u524d\u6570\u5b57\u5728\u76f8\u5e94\u7684\u641c\u7d22\u5217\u8868\u4e2d\u7684\u7d22\u5f15\u3002\u4f8b\u5982\u672c\u793a\u4f8b\u4e2d\u82e5tokens=[0, 3, 5]\uff0c\u5219\u4ee3\u8868\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u5230\u7684\u901a\u9053\u6570\u4e3a[8, 40, 128]\u3002 \u2003 2. token\u4e2d\u6bcf\u4e2a\u6570\u5b57\u7684\u641c\u7d22\u5217\u8868\u957f\u5ea6( range_table \u51fd\u6570)\uff0ctokens\u4e2d\u6bcf\u4e2atoken\u7684\u7d22\u5f15\u8303\u56f4\u3002 \u2003 3. \u6839\u636etoken\u4ea7\u751f\u6a21\u578b\u7ed3\u6784( token2arch \u51fd\u6570)\uff0c\u6839\u636e\u641c\u7d22\u5230\u7684tokens\u5217\u8868\u4ea7\u751f\u6a21\u578b\u7ed3\u6784\u3002 \u4ee5\u65b0\u589ereset block\u4e3a\u4f8b\u8bf4\u660e\u5982\u4f55\u6784\u9020\u81ea\u5df1\u7684search space\u3002\u81ea\u5b9a\u4e49\u7684search space\u4e0d\u80fd\u548c\u5df2\u6709\u7684search space\u540c\u540d\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 ### \u5f15\u5165\u641c\u7d22\u7a7a\u95f4\u57fa\u7c7b\u51fd\u6570\u548csearch space\u7684\u6ce8\u518c\u7c7b\u51fd\u6570 from .search_space_base import SearchSpaceBase from .search_space_registry import SEARCHSPACE import numpy as np ### \u9700\u8981\u8c03\u7528\u6ce8\u518c\u51fd\u6570\u628a\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4\u6ce8\u518c\u5230space space\u4e2d @SEARCHSPACE.register ### \u5b9a\u4e49\u4e00\u4e2a\u7ee7\u627fSearchSpaceBase\u57fa\u7c7b\u7684\u641c\u7d22\u7a7a\u95f4\u7684\u7c7b\u51fd\u6570 class ResNetBlockSpace2 ( SearchSpaceBase ): def __init__ ( self , input_size , output_size , block_num , block_mask ): ### \u5b9a\u4e49\u4e00\u4e9b\u5b9e\u9645\u60f3\u8981\u641c\u7d22\u7684\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a\u901a\u9053\u6570\u3001\u6bcf\u4e2a\u5377\u79ef\u7684\u91cd\u590d\u6b21\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u7b49 ### self.filter_num \u4ee3\u8868\u901a\u9053\u6570\u7684\u641c\u7d22\u5217\u8868 self . filter_num = np . array ([ 8 , 16 , 32 , 40 , 64 , 128 , 256 , 512 ]) ### \u5b9a\u4e49\u521d\u59cb\u5316token\uff0c\u521d\u59cb\u5316token\u7684\u957f\u5ea6\u6839\u636e\u4f20\u5165\u7684block_num\u6216\u8005block_mask\u7684\u957f\u5ea6\u6765\u5f97\u5230\u7684 def init_tokens ( self ): return [ 0 ] * 3 * len ( self . block_mask ) ### \u5b9a\u4e49token\u7684index\u7684\u53d6\u503c\u8303\u56f4 def range_table ( self ): return [ len ( self . filter_num )] * 3 * len ( self . block_mask ) ### \u628atoken\u8f6c\u6362\u6210\u6a21\u578b\u7ed3\u6784 def token2arch ( self , tokens = None ): if tokens == None : tokens = self . init_tokens () self . bottleneck_params_list = [] for i in range ( len ( self . block_mask )): self . bottleneck_params_list . append ( self . filter_num [ tokens [ i * 3 + 0 ]], self . filter_num [ tokens [ i * 3 + 1 ]], self . filter_num [ tokens [ i * 3 + 2 ]], 2 if self . block_mask [ i ] == 1 else 1 ) def net_arch ( input ): for i , layer_setting in enumerate ( self . bottleneck_params_list ): channel_num , stride = layer_setting [: - 1 ], layer_setting [ - 1 ] input = self . _resnet_block ( input , channel_num , stride , name = 'resnet_layer{}' . format ( i + 1 )) return input return net_arch ### \u6784\u9020\u5177\u4f53block\u7684\u64cd\u4f5c def _resnet_block ( self , input , channel_num , stride , name = None ): shortcut_conv = self . _shortcut ( input , channel_num [ 2 ], stride , name = name ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 0 ], filter_size = 1 , act = 'relu' , name = name + '_conv0' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 1 ], filter_size = 3 , stride = stride , act = 'relu' , name = name + '_conv1' ) input = self . _conv_bn_layer ( input = input , num_filters = channel_num [ 2 ], filter_size = 1 , name = name + '_conv2' ) return fluid . layers . elementwise_add ( x = shortcut_conv , y = input , axis = 0 , name = name + '_elementwise_add' ) def _shortcut ( self , input , channel_num , stride , name = None ): channel_in = input . shape [ 1 ] if channel_in != channel_num or stride != 1 : return self . conv_bn_layer ( input , num_filters = channel_num , filter_size = 1 , stride = stride , name = name + '_shortcut' ) else : return input def _conv_bn_layer ( self , input , num_filters , filter_size , stride = 1 , padding = 'SAME' , act = None , name = None ): conv = fluid . layers . conv2d ( input , num_filters , filter_size , stride , name = name + '_conv' ) bn = fluid . layers . batch_norm ( conv , act = act , name = name + '_bn' ) return bn","title":"\u81ea\u5b9a\u4e49\u641c\u7d22\u7a7a\u95f4(search space)"},{"location":"table_latency/","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868 # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7528\u4e8e\u5feb\u901f\u8bc4\u4f30\u4e00\u4e2a\u6a21\u578b\u5728\u7279\u5b9a\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u4e0a\u7684\u63a8\u7406\u901f\u5ea6\u3002 \u8be5\u6587\u6863\u4e3b\u8981\u7528\u4e8e\u5b9a\u4e49PaddleSlim\u652f\u6301\u7684\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u683c\u5f0f\u3002 \u6982\u8ff0 # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\u5b58\u653e\u7740\u6240\u6709\u53ef\u80fd\u7684\u64cd\u4f5c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\uff0c\u8be5\u8868\u4e2d\u7684\u4e00\u4e2a\u64cd\u4f5c\u5305\u62ec\u64cd\u4f5c\u7c7b\u578b\u548c\u64cd\u4f5c\u53c2\u6570\uff0c\u6bd4\u5982\uff1a\u64cd\u4f5c\u7c7b\u578b\u53ef\u4ee5\u662f conv2d \uff0c\u5bf9\u5e94\u7684\u64cd\u4f5c\u53c2\u6570\u6709\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5927\u5c0f\u3001\u5377\u79ef\u6838\u4e2a\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u3002 \u7ed9\u5b9a\u64cd\u4f5c\u7684\u5ef6\u65f6\u4f9d\u8d56\u4e8e\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u3002 \u6574\u4f53\u683c\u5f0f # \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4ee5\u6587\u4ef6\u6216\u591a\u884c\u5b57\u7b26\u4e32\u7684\u5f62\u5f0f\u4fdd\u5b58\u3002 \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7b2c\u4e00\u884c\u4fdd\u5b58\u7248\u672c\u4fe1\u606f\uff0c\u540e\u7eed\u6bcf\u884c\u4e3a\u4e00\u4e2a\u64cd\u4f5c\u548c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\u3002 \u7248\u672c\u4fe1\u606f # \u7248\u672c\u4fe1\u606f\u4ee5\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u5206\u5272\uff0c\u5185\u5bb9\u4f9d\u6b21\u4e3a\u786c\u4ef6\u73af\u5883\u540d\u79f0\u3001\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u548c\u65f6\u95f4\u6233\u3002 \u786c\u4ef6\u73af\u5883\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u786c\u4ef6\u73af\u5883\uff0c\u53ef\u4ee5\u5305\u542b\u8ba1\u7b97\u67b6\u6784\u7c7b\u578b\u3001\u7248\u672c\u53f7\u7b49\u4fe1\u606f\u3002 \u63a8\u7406\u5f15\u64ce\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u63a8\u7406\u5f15\u64ce\uff0c\u53ef\u4ee5\u5305\u542b\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u3001\u7248\u672c\u53f7\u3001\u4f18\u5316\u9009\u9879\u7b49\u4fe1\u606f\u3002 \u65f6\u95f4\u6233\uff1a \u8be5\u8bc4\u4f30\u8868\u7684\u521b\u5efa\u65f6\u95f4\u3002 \u64cd\u4f5c\u4fe1\u606f # \u64cd\u4f5c\u4fe1\u606f\u5b57\u6bb5\u4e4b\u95f4\u4ee5\u9017\u53f7\u5206\u5272\u3002\u64cd\u4f5c\u4fe1\u606f\u4e0e\u5ef6\u8fdf\u4fe1\u606f\u4e4b\u95f4\u4ee5\u5236\u8868\u7b26\u5206\u5272\u3002 conv2d # \u683c\u5f0f 1 op_type , flag_bias , flag_relu , n_in , c_in , h_in , w_in , c_out , groups , kernel , padding , stride , dilation \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_bias (int) - \u662f\u5426\u6709 bias\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 flag_relu (int) - \u662f\u5426\u6709 relu\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 c_out (int) - \u8f93\u51fa Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 groups (int) - \u5377\u79ef\u4e8c\u7ef4\u5c42\uff08Conv2D Layer\uff09\u7684\u7ec4\u6570\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 dilation (int) - \u81a8\u80c0 (dilation) \u5927\u5c0f\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 activation # \u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 batch_norm # \u683c\u5f0f 1 op_type , active_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 active_type (string|None) - \u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b\uff0c\u5305\u542b\uff1arelu, prelu, sigmoid, relu6, tanh\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 eltwise # \u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 pooling # \u683c\u5f0f 1 op_type , flag_global_pooling , n_in , c_in , h_in , w_in , kernel , padding , stride , ceil_mode , pool_type \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_global_pooling (int) - \u662f\u5426\u4e3a\u5168\u5c40\u6c60\u5316\uff080\uff1a\u4e0d\u662f\uff0c1\uff1a\u662f\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 ceil_mode (int) - \u662f\u5426\u7528 ceil \u51fd\u6570\u8ba1\u7b97\u8f93\u51fa\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u30020 \u8868\u793a\u4f7f\u7528 floor \u51fd\u6570\uff0c1 \u8868\u793a\u4f7f\u7528 ceil \u51fd\u6570\u3002 pool_type (int) - \u6c60\u5316\u7c7b\u578b\uff0c\u5176\u4e2d 1 \u8868\u793a pooling_max\uff0c2 \u8868\u793a pooling_average_include_padding\uff0c3 \u8868\u793a pooling_average_exclude_padding\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4 softmax # \u683c\u5f0f 1 op_type , axis , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 axis (int) - \u6267\u884c softmax \u8ba1\u7b97\u7684\u7ef4\u5ea6\u7d22\u5f15\uff0c\u5e94\u8be5\u5728 [\u22121\uff0crank \u2212 1] \u8303\u56f4\u5185\uff0c\u5176\u4e2d rank \u662f\u8f93\u5165\u53d8\u91cf\u7684\u79e9\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868"},{"location":"table_latency/#_1","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7528\u4e8e\u5feb\u901f\u8bc4\u4f30\u4e00\u4e2a\u6a21\u578b\u5728\u7279\u5b9a\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u4e0a\u7684\u63a8\u7406\u901f\u5ea6\u3002 \u8be5\u6587\u6863\u4e3b\u8981\u7528\u4e8e\u5b9a\u4e49PaddleSlim\u652f\u6301\u7684\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u683c\u5f0f\u3002","title":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868"},{"location":"table_latency/#_2","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\u5b58\u653e\u7740\u6240\u6709\u53ef\u80fd\u7684\u64cd\u4f5c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\uff0c\u8be5\u8868\u4e2d\u7684\u4e00\u4e2a\u64cd\u4f5c\u5305\u62ec\u64cd\u4f5c\u7c7b\u578b\u548c\u64cd\u4f5c\u53c2\u6570\uff0c\u6bd4\u5982\uff1a\u64cd\u4f5c\u7c7b\u578b\u53ef\u4ee5\u662f conv2d \uff0c\u5bf9\u5e94\u7684\u64cd\u4f5c\u53c2\u6570\u6709\u8f93\u5165\u7279\u5f81\u56fe\u7684\u5927\u5c0f\u3001\u5377\u79ef\u6838\u4e2a\u6570\u3001\u5377\u79ef\u6838\u5927\u5c0f\u7b49\u3002 \u7ed9\u5b9a\u64cd\u4f5c\u7684\u5ef6\u65f6\u4f9d\u8d56\u4e8e\u786c\u4ef6\u73af\u5883\u548c\u63a8\u7406\u5f15\u64ce\u3002","title":"\u6982\u8ff0"},{"location":"table_latency/#_3","text":"\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4ee5\u6587\u4ef6\u6216\u591a\u884c\u5b57\u7b26\u4e32\u7684\u5f62\u5f0f\u4fdd\u5b58\u3002 \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u7b2c\u4e00\u884c\u4fdd\u5b58\u7248\u672c\u4fe1\u606f\uff0c\u540e\u7eed\u6bcf\u884c\u4e3a\u4e00\u4e2a\u64cd\u4f5c\u548c\u5bf9\u5e94\u7684\u5ef6\u65f6\u4fe1\u606f\u3002","title":"\u6574\u4f53\u683c\u5f0f"},{"location":"table_latency/#_4","text":"\u7248\u672c\u4fe1\u606f\u4ee5\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u5206\u5272\uff0c\u5185\u5bb9\u4f9d\u6b21\u4e3a\u786c\u4ef6\u73af\u5883\u540d\u79f0\u3001\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u548c\u65f6\u95f4\u6233\u3002 \u786c\u4ef6\u73af\u5883\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u786c\u4ef6\u73af\u5883\uff0c\u53ef\u4ee5\u5305\u542b\u8ba1\u7b97\u67b6\u6784\u7c7b\u578b\u3001\u7248\u672c\u53f7\u7b49\u4fe1\u606f\u3002 \u63a8\u7406\u5f15\u64ce\u540d\u79f0\uff1a \u7528\u4e8e\u6807\u8bc6\u63a8\u7406\u5f15\u64ce\uff0c\u53ef\u4ee5\u5305\u542b\u63a8\u7406\u5f15\u64ce\u540d\u79f0\u3001\u7248\u672c\u53f7\u3001\u4f18\u5316\u9009\u9879\u7b49\u4fe1\u606f\u3002 \u65f6\u95f4\u6233\uff1a \u8be5\u8bc4\u4f30\u8868\u7684\u521b\u5efa\u65f6\u95f4\u3002","title":"\u7248\u672c\u4fe1\u606f"},{"location":"table_latency/#_5","text":"\u64cd\u4f5c\u4fe1\u606f\u5b57\u6bb5\u4e4b\u95f4\u4ee5\u9017\u53f7\u5206\u5272\u3002\u64cd\u4f5c\u4fe1\u606f\u4e0e\u5ef6\u8fdf\u4fe1\u606f\u4e4b\u95f4\u4ee5\u5236\u8868\u7b26\u5206\u5272\u3002","title":"\u64cd\u4f5c\u4fe1\u606f"},{"location":"table_latency/#conv2d","text":"\u683c\u5f0f 1 op_type , flag_bias , flag_relu , n_in , c_in , h_in , w_in , c_out , groups , kernel , padding , stride , dilation \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_bias (int) - \u662f\u5426\u6709 bias\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 flag_relu (int) - \u662f\u5426\u6709 relu\uff080\uff1a\u65e0\uff0c1\uff1a\u6709\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 c_out (int) - \u8f93\u51fa Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 groups (int) - \u5377\u79ef\u4e8c\u7ef4\u5c42\uff08Conv2D Layer\uff09\u7684\u7ec4\u6570\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 dilation (int) - \u81a8\u80c0 (dilation) \u5927\u5c0f\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"conv2d"},{"location":"table_latency/#activation","text":"\u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"activation"},{"location":"table_latency/#batch_norm","text":"\u683c\u5f0f 1 op_type , active_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 active_type (string|None) - \u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b\uff0c\u5305\u542b\uff1arelu, prelu, sigmoid, relu6, tanh\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"batch_norm"},{"location":"table_latency/#eltwise","text":"\u683c\u5f0f 1 op_type , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"eltwise"},{"location":"table_latency/#pooling","text":"\u683c\u5f0f 1 op_type , flag_global_pooling , n_in , c_in , h_in , w_in , kernel , padding , stride , ceil_mode , pool_type \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 flag_global_pooling (int) - \u662f\u5426\u4e3a\u5168\u5c40\u6c60\u5316\uff080\uff1a\u4e0d\u662f\uff0c1\uff1a\u662f\uff09\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 kernel (int) - \u5377\u79ef\u6838\u5927\u5c0f\u3002 padding (int) - \u586b\u5145 (padding) \u5927\u5c0f\u3002 stride (int) - \u6b65\u957f (stride) \u5927\u5c0f\u3002 ceil_mode (int) - \u662f\u5426\u7528 ceil \u51fd\u6570\u8ba1\u7b97\u8f93\u51fa\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u30020 \u8868\u793a\u4f7f\u7528 floor \u51fd\u6570\uff0c1 \u8868\u793a\u4f7f\u7528 ceil \u51fd\u6570\u3002 pool_type (int) - \u6c60\u5316\u7c7b\u578b\uff0c\u5176\u4e2d 1 \u8868\u793a pooling_max\uff0c2 \u8868\u793a pooling_average_include_padding\uff0c3 \u8868\u793a pooling_average_exclude_padding\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"pooling"},{"location":"table_latency/#softmax","text":"\u683c\u5f0f 1 op_type , axis , n_in , c_in , h_in , w_in \\ tlatency \u5b57\u6bb5\u89e3\u91ca op_type(str) - \u5f53\u524dop\u7c7b\u578b\u3002 axis (int) - \u6267\u884c softmax \u8ba1\u7b97\u7684\u7ef4\u5ea6\u7d22\u5f15\uff0c\u5e94\u8be5\u5728 [\u22121\uff0crank \u2212 1] \u8303\u56f4\u5185\uff0c\u5176\u4e2d rank \u662f\u8f93\u5165\u53d8\u91cf\u7684\u79e9\u3002 n_in (int) - \u8f93\u5165 Tensor \u7684\u6279\u5c3a\u5bf8 (batch size)\u3002 c_in (int) - \u8f93\u5165 Tensor \u7684\u901a\u9053 (channel) \u6570\u3002 h_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u9ad8\u5ea6\u3002 w_in (int) - \u8f93\u5165 Tensor \u7684\u7279\u5f81\u5bbd\u5ea6\u3002 latency (float) - \u5f53\u524dop\u7684\u5ef6\u65f6\u65f6\u95f4","title":"softmax"},{"location":"algo/algo/","text":"\u76ee\u5f55 # \u91cf\u5316\u539f\u7406\u4ecb\u7ecd \u526a\u88c1\u539f\u7406\u4ecb\u7ecd \u84b8\u998f\u539f\u7406\u4ecb\u7ecd \u8f7b\u91cf\u7ea7\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u539f\u7406\u4ecb\u7ecd 1. Quantization Aware Training\u91cf\u5316\u4ecb\u7ecd # 1.1 \u80cc\u666f # \u8fd1\u5e74\u6765\uff0c\u5b9a\u70b9\u91cf\u5316\u4f7f\u7528\u66f4\u5c11\u7684\u6bd4\u7279\u6570\uff08\u59828-bit\u30013-bit\u30012-bit\u7b49\uff09\u8868\u793a\u795e\u7ecf\u7f51\u7edc\u7684\u6743\u91cd\u548c\u6fc0\u6d3b\u5df2\u88ab\u9a8c\u8bc1\u662f\u6709\u6548\u7684\u3002\u5b9a\u70b9\u91cf\u5316\u7684\u4f18\u70b9\u5305\u62ec\u4f4e\u5185\u5b58\u5e26\u5bbd\u3001\u4f4e\u529f\u8017\u3001\u4f4e\u8ba1\u7b97\u8d44\u6e90\u5360\u7528\u4ee5\u53ca\u4f4e\u6a21\u578b\u5b58\u50a8\u9700\u6c42\u7b49\u3002 \u88681: \u4e0d\u540c\u7c7b\u578b\u64cd\u4f5c\u7684\u5f00\u9500\u5bf9\u6bd4 \u7531\u88681\u53ef\u77e5\uff0c\u4f4e\u7cbe\u5ea6\u5b9a\u70b9\u6570\u64cd\u4f5c\u7684\u786c\u4ef6\u9762\u79ef\u5927\u5c0f\u53ca\u80fd\u8017\u6bd4\u9ad8\u7cbe\u5ea6\u6d6e\u70b9\u6570\u8981\u5c11\u51e0\u4e2a\u6570\u91cf\u7ea7\u3002 \u4f7f\u7528\u5b9a\u70b9\u91cf\u5316\u53ef\u5e26\u67654\u500d\u7684\u6a21\u578b\u538b\u7f29\u30014\u500d\u7684\u5185\u5b58\u5e26\u5bbd\u63d0\u5347\uff0c\u4ee5\u53ca\u66f4\u9ad8\u6548\u7684cache\u5229\u7528(\u5f88\u591a\u786c\u4ef6\u8bbe\u5907\uff0c\u5185\u5b58\u8bbf\u95ee\u662f\u4e3b\u8981\u80fd\u8017)\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8ba1\u7b97\u901f\u5ea6\u4e5f\u4f1a\u66f4\u5feb(\u901a\u5e38\u5177\u67092x-3x\u7684\u6027\u80fd\u63d0\u5347)\u3002\u7531\u88682\u53ef\u77e5\uff0c\u5728\u5f88\u591a\u573a\u666f\u4e0b\uff0c\u5b9a\u70b9\u91cf\u5316\u64cd\u4f5c\u5bf9\u7cbe\u5ea6\u5e76\u4e0d\u4f1a\u9020\u6210\u635f\u5931\u3002\u53e6\u5916\uff0c\u5b9a\u70b9\u91cf\u5316\u5bf9\u795e\u7ecf\u7f51\u7edc\u4e8e\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u7684\u63a8\u65ad\u6765\u8bf4\u662f\u6781\u5176\u91cd\u8981\u7684\u3002 \u88682\uff1a\u6a21\u578b\u91cf\u5316\u524d\u540e\u7cbe\u5ea6\u5bf9\u6bd4 \u76ee\u524d\uff0c\u5b66\u672f\u754c\u4e3b\u8981\u5c06\u91cf\u5316\u5206\u4e3a\u4e24\u5927\u7c7b\uff1a Post Training Quantization \u548c Quantization Aware Training \u3002 Post Training Quantization \u662f\u6307\u4f7f\u7528KL\u6563\u5ea6\u3001\u6ed1\u52a8\u5e73\u5747\u7b49\u65b9\u6cd5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\u4e14\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\u7684\u5b9a\u70b9\u91cf\u5316\u65b9\u6cd5\u3002 Quantization Aware Training \u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u91cf\u5316\u8fdb\u884c\u5efa\u6a21\u4ee5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\uff0c\u5b83\u4e0e Post Training Quantization \u6a21\u5f0f\u76f8\u6bd4\u53ef\u4ee5\u63d0\u4f9b\u66f4\u9ad8\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002 1.2 \u91cf\u5316\u539f\u7406 # 1.2.1 \u91cf\u5316\u65b9\u5f0f # \u76ee\u524d\uff0c\u5b58\u5728\u7740\u8bb8\u591a\u65b9\u6cd5\u53ef\u4ee5\u5c06\u6d6e\u70b9\u6570\u91cf\u5316\u6210\u5b9a\u70b9\u6570\u3002\u4f8b\u5982\uff1a r = min(max(x, a), b) s = \\frac{b - a}{n - 1} q = \\left \\lfloor \\frac{r - a}{s} \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c [a, b] [a, b] \u662f\u91cf\u5316\u8303\u56f4\uff0c a a \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5c0f\u503c\uff0c b b \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5982\u679c\u91cf\u5316\u7ea7\u522b\u4e3a k k \uff0c\u5219 n n \u4e3a 2^k 2^k \u3002\u4f8b\u5982\uff0c\u82e5 k k \u4e3a8\uff0c\u5219 n n \u4e3a256\u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 PaddleSlim\u6846\u67b6\u4e2d\u9009\u62e9\u7684\u91cf\u5316\u65b9\u6cd5\u4e3a\u6700\u5927\u7edd\u5bf9\u503c\u91cf\u5316( max-abs )\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a M = max(abs(x)) q = \\left \\lfloor \\frac{x}{M} * (n - 1) \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u88ab\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c M M \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u7edd\u5bf9\u503c\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5bf9\u4e8e8bit\u91cf\u5316\uff0cPaddleSlim\u91c7\u7528 int8_t \uff0c\u5373 n=2^7=128 n=2^7=128 \u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 \u65e0\u8bba\u662f min-max\u91cf\u5316 \u8fd8\u662f max-abs\u91cf\u5316 \uff0c\u4ed6\u4eec\u90fd\u53ef\u4ee5\u8868\u793a\u4e3a\u5982\u4e0b\u5f62\u5f0f\uff1a q = scale * r + b q = scale * r + b \u5176\u4e2d min-max \u548c max-abs \u88ab\u79f0\u4e3a\u91cf\u5316\u53c2\u6570\u6216\u8005\u91cf\u5316\u6bd4\u4f8b\u6216\u8005\u91cf\u5316\u8303\u56f4\u3002 1.2.2 \u91cf\u5316\u8bad\u7ec3 # 1.2.2.1 \u524d\u5411\u4f20\u64ad # \u524d\u5411\u4f20\u64ad\u8fc7\u7a0b\u91c7\u7528\u6a21\u62df\u91cf\u5316\u7684\u65b9\u5f0f\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a \u56fe1\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b \u7531\u56fe1\u53ef\u77e5\uff0c\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b\u53ef\u88ab\u63cf\u8ff0\u4e3a\u4ee5\u4e0b\u56db\u4e2a\u90e8\u5206\uff1a 1) \u8f93\u5165\u548c\u6743\u91cd\u5747\u88ab\u91cf\u5316\u62108-bit\u6574\u6570\u3002 2) \u57288-bit\u6574\u6570\u4e0a\u6267\u884c\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u3002 3) \u53cd\u91cf\u5316\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u7684\u8f93\u51fa\u7ed3\u679c\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002 4) \u572832-bit\u6d6e\u70b9\u578b\u6570\u636e\u4e0a\u6267\u884c\u504f\u7f6e\u52a0\u6cd5\u64cd\u4f5c\u3002\u6b64\u5904\uff0c\u504f\u7f6e\u5e76\u672a\u88ab\u91cf\u5316\u3002 \u5bf9\u4e8e\u901a\u7528\u77e9\u9635\u4e58\u6cd5( GEMM )\uff0c\u8f93\u5165 X X \u548c\u6743\u91cd W W \u7684\u91cf\u5316\u64cd\u4f5c\u53ef\u88ab\u8868\u8ff0\u4e3a\u5982\u4e0b\u8fc7\u7a0b\uff1a X_q = \\left \\lfloor \\frac{X}{X_m} * (n - 1) \\right \\rceil W_q = \\left \\lfloor \\frac{W}{W_m} * (n - 1) \\right \\rceil \u6267\u884c\u901a\u7528\u77e9\u9635\u4e58\u6cd5\uff1a Y_q = X_q * W_q \u5bf9\u91cf\u5316\u4e58\u79ef\u7ed3\u679c Yq Yq \u8fdb\u884c\u53cd\u91cf\u5316: \\begin{align} Y_{dq} = \\frac{Y_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =\\frac{X_q * W_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =(\\frac{X_q}{n - 1} * X_m) * (\\frac{W_q}{n - 1} * W_m) \\ \\end{align} \u4e0a\u8ff0\u516c\u5f0f\u8868\u660e\u53cd\u91cf\u5316\u64cd\u4f5c\u53ef\u4ee5\u88ab\u79fb\u52a8\u5230 GEMM \u4e4b\u524d\uff0c\u5373\u5148\u5bf9 Xq Xq \u548c Wq Wq \u6267\u884c\u53cd\u91cf\u5316\u64cd\u4f5c\u518d\u505a GEMM \u64cd\u4f5c\u3002\u56e0\u6b64\uff0c\u524d\u5411\u4f20\u64ad\u7684\u5de5\u4f5c\u6d41\u4ea6\u53ef\u8868\u793a\u4e3a\u5982\u4e0b\u65b9\u5f0f\uff1a \u56fe2\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u524d\u5411\u8fc7\u7a0b\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41 \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cPaddleSlim\u4f7f\u7528\u56fe2\u4e2d\u6240\u793a\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41\u3002\u5728\u8bbe\u8ba1\u4e2d\uff0c\u91cf\u5316Pass\u5728IrGraph\u4e2d\u63d2\u5165\u91cf\u5316op\u548c\u53cd\u91cf\u5316op\u3002\u56e0\u4e3a\u5728\u8fde\u7eed\u7684\u91cf\u5316\u3001\u53cd\u91cf\u5316\u64cd\u4f5c\u4e4b\u540e\u8f93\u5165\u4ecd\u7136\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u56e0\u6b64\uff0cPaddleSlim\u91cf\u5316\u8bad\u7ec3\u6846\u67b6\u6240\u91c7\u7528\u7684\u91cf\u5316\u65b9\u5f0f\u88ab\u79f0\u4e3a\u6a21\u62df\u91cf\u5316\u3002 1.2.2.2 \u53cd\u5411\u4f20\u64ad # \u7531\u56fe3\u53ef\u77e5\uff0c\u6743\u91cd\u66f4\u65b0\u6240\u9700\u7684\u68af\u5ea6\u503c\u53ef\u4ee5\u7531\u91cf\u5316\u540e\u7684\u6743\u91cd\u548c\u91cf\u5316\u540e\u7684\u6fc0\u6d3b\u6c42\u5f97\u3002\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u7684\u6240\u6709\u8f93\u5165\u548c\u8f93\u51fa\u5747\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u6ce8\u610f\uff0c\u68af\u5ea6\u66f4\u65b0\u64cd\u4f5c\u9700\u8981\u5728\u539f\u59cb\u6743\u91cd\u4e0a\u8fdb\u884c\uff0c\u5373\u8ba1\u7b97\u51fa\u7684\u68af\u5ea6\u5c06\u88ab\u52a0\u5230\u539f\u59cb\u6743\u91cd\u4e0a\u800c\u975e\u91cf\u5316\u540e\u6216\u53cd\u91cf\u5316\u540e\u7684\u6743\u91cd\u4e0a\u3002 \u56fe3\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u53cd\u5411\u4f20\u64ad\u548c\u6743\u91cd\u66f4\u65b0\u8fc7\u7a0b \u56e0\u6b64\uff0c\u91cf\u5316Pass\u4e5f\u4f1a\u6539\u53d8\u76f8\u5e94\u53cd\u5411\u7b97\u5b50\u7684\u67d0\u4e9b\u8f93\u5165\u3002 1.2.2.3 \u786e\u5b9a\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570 # \u5b58\u5728\u7740\u4e24\u79cd\u7b56\u7565\u53ef\u4ee5\u8ba1\u7b97\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\uff0c\u5373\u52a8\u6001\u7b56\u7565\u548c\u9759\u6001\u7b56\u7565\u3002\u52a8\u6001\u7b56\u7565\u4f1a\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u7684\u503c\u3002\u9759\u6001\u7b56\u7565\u5219\u5bf9\u4e0d\u540c\u7684\u8f93\u5165\u91c7\u7528\u76f8\u540c\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u3002 \u5bf9\u4e8e\u6743\u91cd\u800c\u8a00\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u91c7\u7528\u52a8\u6001\u7b56\u7565\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u5747\u4f1a\u88ab\u91cd\u65b0\u8ba1\u7b97\u5f97\u5230\u76f4\u81f3\u8bad\u7ec3\u8fc7\u7a0b\u7ed3\u675f\u3002 \u5bf9\u4e8e\u6fc0\u6d3b\u800c\u8a00\uff0c\u53ef\u4ee5\u9009\u62e9\u52a8\u6001\u7b56\u7565\u4e5f\u53ef\u4ee5\u9009\u62e9\u9759\u6001\u7b56\u7565\u3002\u82e5\u9009\u62e9\u4f7f\u7528\u9759\u6001\u7b56\u7565\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u4f1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u88ab\u8bc4\u4f30\u6c42\u5f97\uff0c\u4e14\u5728\u63a8\u65ad\u8fc7\u7a0b\u4e2d\u88ab\u4f7f\u7528(\u4e0d\u540c\u7684\u8f93\u5165\u5747\u4fdd\u6301\u4e0d\u53d8)\u3002\u9759\u6001\u7b56\u7565\u4e2d\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u53ef\u4e8e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u5982\u4e0b\u4e09\u79cd\u65b9\u5f0f\u8fdb\u884c\u8bc4\u4f30\uff1a \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u5e73\u5747\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6ed1\u52a8\u5e73\u5747\u503c\uff0c\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a Vt = (1 - k) * V + k * V_{t-1} Vt = (1 - k) * V + k * V_{t-1} \u5f0f\u4e2d\uff0c V V \u662f\u5f53\u524dbatch\u7684\u6700\u5927\u7edd\u5bf9\u503c\uff0c Vt Vt \u662f\u6ed1\u52a8\u5e73\u5747\u503c\u3002 k k \u662f\u4e00\u4e2a\u56e0\u5b50\uff0c\u4f8b\u5982\u5176\u503c\u53ef\u53d6\u4e3a0.9\u3002 1.2.4 \u8bad\u7ec3\u540e\u91cf\u5316 # \u8bad\u7ec3\u540e\u91cf\u5316\u662f\u57fa\u4e8e\u91c7\u6837\u6570\u636e\uff0c\u91c7\u7528KL\u6563\u5ea6\u7b49\u65b9\u6cd5\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7684\u65b9\u6cd5\u3002\u76f8\u6bd4\u91cf\u5316\u8bad\u7ec3\uff0c\u8bad\u7ec3\u540e\u91cf\u5316\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\uff0c\u53ef\u4ee5\u5feb\u901f\u5f97\u5230\u91cf\u5316\u6a21\u578b\u3002 \u8bad\u7ec3\u540e\u91cf\u5316\u7684\u76ee\u6807\u662f\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\uff0c\u4e3b\u8981\u6709\u4e24\u79cd\u65b9\u6cd5\uff1a\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5 ( No Saturation) \u548c\u9971\u548c\u91cf\u5316\u65b9\u6cd5 (Saturation)\u3002\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u8ba1\u7b97FP32\u7c7b\u578bTensor\u4e2d\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c abs_max \uff0c\u5c06\u5176\u6620\u5c04\u4e3a127\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7b49\u4e8e abs_max/127 \u3002\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u4f7f\u7528KL\u6563\u5ea6\u8ba1\u7b97\u4e00\u4e2a\u5408\u9002\u7684\u9608\u503c T ( 0=r \\end{cases} \\end{equation} \\begin{equation} P(r_k) = \\begin{cases} e^{\\frac{(r_k-r)}{T_k}} & r_k < r\\\\ 1 & r_k>=r \\end{cases} \\end{equation} \u5728\u7b2ck\u6b21\u8fed\u4ee3\uff0c\u641c\u5230\u7684\u7f51\u7edc\u4e3a N_k N_k , \u5bf9 N_k N_k \u8bad\u7ec3\u82e5\u5e72epoch\u540e\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u5f97\u5230reward\u4e3a r_k r_k , \u4ee5\u6982\u7387 P(r_k) P(r_k) \u63a5\u53d7 r_k r_k \uff0c\u5373\u6267\u884c r=r_k r=r_k \u3002 r r \u5728\u641c\u7d22\u8fc7\u7a0b\u8d77\u59cb\u65f6\u88ab\u521d\u59cb\u5316\u4e3a0. T_0 T_0 \u4e3a\u521d\u59cb\u5316\u6e29\u5ea6\uff0c \\theta \\theta \u4e3a\u6e29\u5ea6\u8870\u51cf\u7cfb\u6570\uff0c T_k T_k \u4e3a\u7b2ck\u6b21\u8fed\u4ee3\u7684\u6e29\u5ea6\u3002 \u5728\u6211\u4eec\u7684NAS\u4efb\u52a1\u4e2d\uff0c\u533a\u522b\u4e8eRL\u6bcf\u6b21\u91cd\u65b0\u751f\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u7f51\u7edc\uff0c\u6211\u4eec\u5c06\u7f51\u7edc\u7ed3\u6784\u6620\u5c04\u6210\u4e00\u6bb5\u7f16\u7801\uff0c\u7b2c\u4e00\u6b21\u968f\u673a\u521d\u59cb\u5316\uff0c\u7136\u540e\u6bcf\u6b21\u968f\u673a\u4fee\u6539\u7f16\u7801\u4e2d\u7684\u4e00\u90e8\u5206\uff08\u5bf9\u5e94\u4e8e\u7f51\u7edc\u7ed3\u6784\u7684\u4e00\u90e8\u5206\uff09\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u7f16\u7801\uff0c\u7136\u540e\u5c06\u8fd9\u4e2a\u7f16\u7801\u518d\u6620\u5c04\u56de\u7f51\u7edc\u7ed3\u6784\uff0c\u901a\u8fc7\u5728\u8bad\u7ec3\u96c6\u4e0a\u8bad\u7ec3\u4e00\u5b9a\u7684epochs\u540e\u7684\u7cbe\u5ea6\u4ee5\u53ca\u7f51\u7edc\u5ef6\u65f6\u878d\u5408\u83b7\u5f97reward\uff0c\u6765\u6307\u5bfc\u9000\u706b\u7b97\u6cd5\u7684\u6536\u655b\u3002 4.2 \u641c\u7d22\u7a7a\u95f4 # \u641c\u7d22\u7a7a\u95f4\u5b9a\u4e49\u4e86\u4f18\u5316\u95ee\u9898\u7684\u53d8\u91cf\uff0c\u53d8\u91cf\u89c4\u6a21\u51b3\u5b9a\u4e86\u641c\u7d22\u7b97\u6cd5\u7684\u96be\u5ea6\u548c\u641c\u7d22\u65f6\u95f4\u3002\u56e0\u6b64\u4e3a\u4e86\u52a0\u5feb\u641c\u7d22\u901f\u5ea6\uff0c\u5b9a\u4e49\u4e00\u4e2a\u5408\u7406\u7684\u641c\u7d22\u7a7a\u95f4\u81f3\u5173\u91cd\u8981\u3002\u5728Light-NAS\u4e2d\uff0c\u4e3a\u4e86\u52a0\u901f\u641c\u7d22\u901f\u5ea6\uff0c\u6211\u4eec\u5c06\u4e00\u4e2a\u7f51\u7edc\u5212\u5206\u4e3a\u591a\u4e2ablock\uff0c\u5148\u624b\u52a8\u6309\u94fe\u72b6\u5c42\u7ea7\u7ed3\u6784\u5806\u53e0c\uff0c\u518d \u4f7f\u7528\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u641c\u7d22\u6bcf\u4e2ablock\u5185\u90e8\u7684\u7ed3\u6784\u3002 \u56e0\u4e3a\u8981\u641c\u7d22\u51fa\u5728\u79fb\u52a8\u7aef\u8fd0\u884c\u901f\u5ea6\u5feb\u7684\u6a21\u578b\uff0c\u6211\u4eec\u53c2\u8003\u4e86MobileNetV2\u4e2d\u7684Linear Bottlenecks\u548cInverted residuals\u7ed3\u6784\uff0c\u641c\u7d22\u6bcf\u4e00\u4e2aInverted residuals\u4e2d\u7684\u5177\u4f53\u53c2\u6570\uff0c\u5305\u62eckernelsize\u3001channel\u6269\u5f20\u500d\u6570\u3001\u91cd\u590d\u6b21\u6570\u3001channels number\u3002\u5982\u56fe10\u6240\u793a\uff1a \u56fe10 4.3 \u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30 # \u641c\u7d22\u8fc7\u7a0b\u652f\u6301 FLOPS \u7ea6\u675f\u548c\u6a21\u578b\u5ef6\u65f6\u7ea6\u675f\u3002\u800c\u57fa\u4e8e Android/iOS \u79fb\u52a8\u7aef\u3001\u5f00\u53d1\u677f\u7b49\u786c\u4ef6\u5e73\u53f0\uff0c\u8fed\u4ee3\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u6d4b\u8bd5\u6a21\u578b\u7684\u5ef6\u65f6\u4e0d\u4ec5\u6d88\u8017\u65f6\u95f4\u800c\u4e14\u975e\u5e38\u4e0d\u65b9\u4fbf\uff0c\u56e0\u6b64\u6211\u4eec\u5f00\u53d1\u4e86\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u6765\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u6a21\u578b\u7684\u5ef6\u65f6\u3002\u901a\u8fc7\u5ef6\u65f6\u8bc4\u4f30\u5668\u8bc4\u4f30\u5f97\u5230\u7684\u5ef6\u65f6\u4e0e\u6a21\u578b\u5b9e\u9645\u6d4b\u8bd5\u7684\u5ef6\u65f6\u6ce2\u52a8\u504f\u5dee\u5c0f\u4e8e 10%\u3002 \u5ef6\u65f6\u8bc4\u4f30\u5668\u5206\u4e3a\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u548c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u4e24\u4e2a\u9636\u6bb5\uff0c\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u53ea\u9700\u8981\u6267\u884c\u4e00\u6b21\uff0c\u800c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u5219\u5728\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u7684\u6a21\u578b\u5ef6\u65f6\u3002 \u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668 \u83b7\u53d6\u641c\u7d22\u7a7a\u95f4\u4e2d\u6240\u6709\u4e0d\u91cd\u590d\u7684 op \u53ca\u5176\u53c2\u6570 \u83b7\u53d6\u6bcf\u7ec4 op \u53ca\u5176\u53c2\u6570\u7684\u5ef6\u65f6 \u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6 \u83b7\u53d6\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u5176\u53c2\u6570 \u6839\u636e\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u53c2\u6570\uff0c\u5229\u7528\u5ef6\u65f6\u8bc4\u4f30\u5668\u53bb\u4f30\u8ba1\u6a21\u578b\u7684\u5ef6\u65f6 5. \u53c2\u8003\u6587\u732e # High-Performance Hardware for Machine Learning Quantizing deep convolutional networks for efficient inference: A whitepaper Pruning Filters for Efficient ConvNets Distilling the Knowledge in a Neural Network A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning","title":"\u7b97\u6cd5\u539f\u7406"},{"location":"algo/algo/#_1","text":"\u91cf\u5316\u539f\u7406\u4ecb\u7ecd \u526a\u88c1\u539f\u7406\u4ecb\u7ecd \u84b8\u998f\u539f\u7406\u4ecb\u7ecd \u8f7b\u91cf\u7ea7\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u539f\u7406\u4ecb\u7ecd","title":"\u76ee\u5f55"},{"location":"algo/algo/#1-quantization-aware-training","text":"","title":"1. Quantization Aware Training\u91cf\u5316\u4ecb\u7ecd"},{"location":"algo/algo/#11","text":"\u8fd1\u5e74\u6765\uff0c\u5b9a\u70b9\u91cf\u5316\u4f7f\u7528\u66f4\u5c11\u7684\u6bd4\u7279\u6570\uff08\u59828-bit\u30013-bit\u30012-bit\u7b49\uff09\u8868\u793a\u795e\u7ecf\u7f51\u7edc\u7684\u6743\u91cd\u548c\u6fc0\u6d3b\u5df2\u88ab\u9a8c\u8bc1\u662f\u6709\u6548\u7684\u3002\u5b9a\u70b9\u91cf\u5316\u7684\u4f18\u70b9\u5305\u62ec\u4f4e\u5185\u5b58\u5e26\u5bbd\u3001\u4f4e\u529f\u8017\u3001\u4f4e\u8ba1\u7b97\u8d44\u6e90\u5360\u7528\u4ee5\u53ca\u4f4e\u6a21\u578b\u5b58\u50a8\u9700\u6c42\u7b49\u3002 \u88681: \u4e0d\u540c\u7c7b\u578b\u64cd\u4f5c\u7684\u5f00\u9500\u5bf9\u6bd4 \u7531\u88681\u53ef\u77e5\uff0c\u4f4e\u7cbe\u5ea6\u5b9a\u70b9\u6570\u64cd\u4f5c\u7684\u786c\u4ef6\u9762\u79ef\u5927\u5c0f\u53ca\u80fd\u8017\u6bd4\u9ad8\u7cbe\u5ea6\u6d6e\u70b9\u6570\u8981\u5c11\u51e0\u4e2a\u6570\u91cf\u7ea7\u3002 \u4f7f\u7528\u5b9a\u70b9\u91cf\u5316\u53ef\u5e26\u67654\u500d\u7684\u6a21\u578b\u538b\u7f29\u30014\u500d\u7684\u5185\u5b58\u5e26\u5bbd\u63d0\u5347\uff0c\u4ee5\u53ca\u66f4\u9ad8\u6548\u7684cache\u5229\u7528(\u5f88\u591a\u786c\u4ef6\u8bbe\u5907\uff0c\u5185\u5b58\u8bbf\u95ee\u662f\u4e3b\u8981\u80fd\u8017)\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u8ba1\u7b97\u901f\u5ea6\u4e5f\u4f1a\u66f4\u5feb(\u901a\u5e38\u5177\u67092x-3x\u7684\u6027\u80fd\u63d0\u5347)\u3002\u7531\u88682\u53ef\u77e5\uff0c\u5728\u5f88\u591a\u573a\u666f\u4e0b\uff0c\u5b9a\u70b9\u91cf\u5316\u64cd\u4f5c\u5bf9\u7cbe\u5ea6\u5e76\u4e0d\u4f1a\u9020\u6210\u635f\u5931\u3002\u53e6\u5916\uff0c\u5b9a\u70b9\u91cf\u5316\u5bf9\u795e\u7ecf\u7f51\u7edc\u4e8e\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u7684\u63a8\u65ad\u6765\u8bf4\u662f\u6781\u5176\u91cd\u8981\u7684\u3002 \u88682\uff1a\u6a21\u578b\u91cf\u5316\u524d\u540e\u7cbe\u5ea6\u5bf9\u6bd4 \u76ee\u524d\uff0c\u5b66\u672f\u754c\u4e3b\u8981\u5c06\u91cf\u5316\u5206\u4e3a\u4e24\u5927\u7c7b\uff1a Post Training Quantization \u548c Quantization Aware Training \u3002 Post Training Quantization \u662f\u6307\u4f7f\u7528KL\u6563\u5ea6\u3001\u6ed1\u52a8\u5e73\u5747\u7b49\u65b9\u6cd5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\u4e14\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\u7684\u5b9a\u70b9\u91cf\u5316\u65b9\u6cd5\u3002 Quantization Aware Training \u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5bf9\u91cf\u5316\u8fdb\u884c\u5efa\u6a21\u4ee5\u786e\u5b9a\u91cf\u5316\u53c2\u6570\uff0c\u5b83\u4e0e Post Training Quantization \u6a21\u5f0f\u76f8\u6bd4\u53ef\u4ee5\u63d0\u4f9b\u66f4\u9ad8\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002","title":"1.1 \u80cc\u666f"},{"location":"algo/algo/#12","text":"","title":"1.2 \u91cf\u5316\u539f\u7406"},{"location":"algo/algo/#121","text":"\u76ee\u524d\uff0c\u5b58\u5728\u7740\u8bb8\u591a\u65b9\u6cd5\u53ef\u4ee5\u5c06\u6d6e\u70b9\u6570\u91cf\u5316\u6210\u5b9a\u70b9\u6570\u3002\u4f8b\u5982\uff1a r = min(max(x, a), b) s = \\frac{b - a}{n - 1} q = \\left \\lfloor \\frac{r - a}{s} \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c [a, b] [a, b] \u662f\u91cf\u5316\u8303\u56f4\uff0c a a \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5c0f\u503c\uff0c b b \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5982\u679c\u91cf\u5316\u7ea7\u522b\u4e3a k k \uff0c\u5219 n n \u4e3a 2^k 2^k \u3002\u4f8b\u5982\uff0c\u82e5 k k \u4e3a8\uff0c\u5219 n n \u4e3a256\u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 PaddleSlim\u6846\u67b6\u4e2d\u9009\u62e9\u7684\u91cf\u5316\u65b9\u6cd5\u4e3a\u6700\u5927\u7edd\u5bf9\u503c\u91cf\u5316( max-abs )\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a M = max(abs(x)) q = \\left \\lfloor \\frac{x}{M} * (n - 1) \\right \\rceil \u5f0f\u4e2d\uff0c x x \u662f\u5f85\u88ab\u91cf\u5316\u7684\u6d6e\u70b9\u503c\uff0c M M \u662f\u5f85\u91cf\u5316\u6d6e\u70b9\u6570\u4e2d\u7684\u7edd\u5bf9\u503c\u6700\u5927\u503c\u3002 \\left \\lfloor \\right \\rceil \\left \\lfloor \\right \\rceil \u8868\u793a\u5c06\u7ed3\u679c\u56db\u820d\u4e94\u5165\u5230\u6700\u8fd1\u7684\u6574\u6570\u3002\u5bf9\u4e8e8bit\u91cf\u5316\uff0cPaddleSlim\u91c7\u7528 int8_t \uff0c\u5373 n=2^7=128 n=2^7=128 \u3002 q q \u662f\u91cf\u5316\u5f97\u5230\u7684\u6574\u6570\u3002 \u65e0\u8bba\u662f min-max\u91cf\u5316 \u8fd8\u662f max-abs\u91cf\u5316 \uff0c\u4ed6\u4eec\u90fd\u53ef\u4ee5\u8868\u793a\u4e3a\u5982\u4e0b\u5f62\u5f0f\uff1a q = scale * r + b q = scale * r + b \u5176\u4e2d min-max \u548c max-abs \u88ab\u79f0\u4e3a\u91cf\u5316\u53c2\u6570\u6216\u8005\u91cf\u5316\u6bd4\u4f8b\u6216\u8005\u91cf\u5316\u8303\u56f4\u3002","title":"1.2.1 \u91cf\u5316\u65b9\u5f0f"},{"location":"algo/algo/#122","text":"","title":"1.2.2 \u91cf\u5316\u8bad\u7ec3"},{"location":"algo/algo/#1221","text":"\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b\u91c7\u7528\u6a21\u62df\u91cf\u5316\u7684\u65b9\u5f0f\uff0c\u5177\u4f53\u63cf\u8ff0\u5982\u4e0b\uff1a \u56fe1\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b \u7531\u56fe1\u53ef\u77e5\uff0c\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u524d\u5411\u8fc7\u7a0b\u53ef\u88ab\u63cf\u8ff0\u4e3a\u4ee5\u4e0b\u56db\u4e2a\u90e8\u5206\uff1a 1) \u8f93\u5165\u548c\u6743\u91cd\u5747\u88ab\u91cf\u5316\u62108-bit\u6574\u6570\u3002 2) \u57288-bit\u6574\u6570\u4e0a\u6267\u884c\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u3002 3) \u53cd\u91cf\u5316\u77e9\u9635\u4e58\u6cd5\u6216\u8005\u5377\u79ef\u64cd\u4f5c\u7684\u8f93\u51fa\u7ed3\u679c\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002 4) \u572832-bit\u6d6e\u70b9\u578b\u6570\u636e\u4e0a\u6267\u884c\u504f\u7f6e\u52a0\u6cd5\u64cd\u4f5c\u3002\u6b64\u5904\uff0c\u504f\u7f6e\u5e76\u672a\u88ab\u91cf\u5316\u3002 \u5bf9\u4e8e\u901a\u7528\u77e9\u9635\u4e58\u6cd5( GEMM )\uff0c\u8f93\u5165 X X \u548c\u6743\u91cd W W \u7684\u91cf\u5316\u64cd\u4f5c\u53ef\u88ab\u8868\u8ff0\u4e3a\u5982\u4e0b\u8fc7\u7a0b\uff1a X_q = \\left \\lfloor \\frac{X}{X_m} * (n - 1) \\right \\rceil W_q = \\left \\lfloor \\frac{W}{W_m} * (n - 1) \\right \\rceil \u6267\u884c\u901a\u7528\u77e9\u9635\u4e58\u6cd5\uff1a Y_q = X_q * W_q \u5bf9\u91cf\u5316\u4e58\u79ef\u7ed3\u679c Yq Yq \u8fdb\u884c\u53cd\u91cf\u5316: \\begin{align} Y_{dq} = \\frac{Y_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =\\frac{X_q * W_q}{(n - 1) * (n - 1)} * X_m * W_m \\ =(\\frac{X_q}{n - 1} * X_m) * (\\frac{W_q}{n - 1} * W_m) \\ \\end{align} \u4e0a\u8ff0\u516c\u5f0f\u8868\u660e\u53cd\u91cf\u5316\u64cd\u4f5c\u53ef\u4ee5\u88ab\u79fb\u52a8\u5230 GEMM \u4e4b\u524d\uff0c\u5373\u5148\u5bf9 Xq Xq \u548c Wq Wq \u6267\u884c\u53cd\u91cf\u5316\u64cd\u4f5c\u518d\u505a GEMM \u64cd\u4f5c\u3002\u56e0\u6b64\uff0c\u524d\u5411\u4f20\u64ad\u7684\u5de5\u4f5c\u6d41\u4ea6\u53ef\u8868\u793a\u4e3a\u5982\u4e0b\u65b9\u5f0f\uff1a \u56fe2\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u524d\u5411\u8fc7\u7a0b\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41 \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cPaddleSlim\u4f7f\u7528\u56fe2\u4e2d\u6240\u793a\u7684\u7b49\u4ef7\u5de5\u4f5c\u6d41\u3002\u5728\u8bbe\u8ba1\u4e2d\uff0c\u91cf\u5316Pass\u5728IrGraph\u4e2d\u63d2\u5165\u91cf\u5316op\u548c\u53cd\u91cf\u5316op\u3002\u56e0\u4e3a\u5728\u8fde\u7eed\u7684\u91cf\u5316\u3001\u53cd\u91cf\u5316\u64cd\u4f5c\u4e4b\u540e\u8f93\u5165\u4ecd\u7136\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u56e0\u6b64\uff0cPaddleSlim\u91cf\u5316\u8bad\u7ec3\u6846\u67b6\u6240\u91c7\u7528\u7684\u91cf\u5316\u65b9\u5f0f\u88ab\u79f0\u4e3a\u6a21\u62df\u91cf\u5316\u3002","title":"1.2.2.1 \u524d\u5411\u4f20\u64ad"},{"location":"algo/algo/#1222","text":"\u7531\u56fe3\u53ef\u77e5\uff0c\u6743\u91cd\u66f4\u65b0\u6240\u9700\u7684\u68af\u5ea6\u503c\u53ef\u4ee5\u7531\u91cf\u5316\u540e\u7684\u6743\u91cd\u548c\u91cf\u5316\u540e\u7684\u6fc0\u6d3b\u6c42\u5f97\u3002\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u7684\u6240\u6709\u8f93\u5165\u548c\u8f93\u51fa\u5747\u4e3a32-bit\u6d6e\u70b9\u578b\u6570\u636e\u3002\u6ce8\u610f\uff0c\u68af\u5ea6\u66f4\u65b0\u64cd\u4f5c\u9700\u8981\u5728\u539f\u59cb\u6743\u91cd\u4e0a\u8fdb\u884c\uff0c\u5373\u8ba1\u7b97\u51fa\u7684\u68af\u5ea6\u5c06\u88ab\u52a0\u5230\u539f\u59cb\u6743\u91cd\u4e0a\u800c\u975e\u91cf\u5316\u540e\u6216\u53cd\u91cf\u5316\u540e\u7684\u6743\u91cd\u4e0a\u3002 \u56fe3\uff1a\u57fa\u4e8e\u6a21\u62df\u91cf\u5316\u8bad\u7ec3\u7684\u53cd\u5411\u4f20\u64ad\u548c\u6743\u91cd\u66f4\u65b0\u8fc7\u7a0b \u56e0\u6b64\uff0c\u91cf\u5316Pass\u4e5f\u4f1a\u6539\u53d8\u76f8\u5e94\u53cd\u5411\u7b97\u5b50\u7684\u67d0\u4e9b\u8f93\u5165\u3002","title":"1.2.2.2 \u53cd\u5411\u4f20\u64ad"},{"location":"algo/algo/#1223","text":"\u5b58\u5728\u7740\u4e24\u79cd\u7b56\u7565\u53ef\u4ee5\u8ba1\u7b97\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\uff0c\u5373\u52a8\u6001\u7b56\u7565\u548c\u9759\u6001\u7b56\u7565\u3002\u52a8\u6001\u7b56\u7565\u4f1a\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u7684\u503c\u3002\u9759\u6001\u7b56\u7565\u5219\u5bf9\u4e0d\u540c\u7684\u8f93\u5165\u91c7\u7528\u76f8\u540c\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u3002 \u5bf9\u4e8e\u6743\u91cd\u800c\u8a00\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u91c7\u7528\u52a8\u6001\u7b56\u7565\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5728\u6bcf\u6b21\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u5747\u4f1a\u88ab\u91cd\u65b0\u8ba1\u7b97\u5f97\u5230\u76f4\u81f3\u8bad\u7ec3\u8fc7\u7a0b\u7ed3\u675f\u3002 \u5bf9\u4e8e\u6fc0\u6d3b\u800c\u8a00\uff0c\u53ef\u4ee5\u9009\u62e9\u52a8\u6001\u7b56\u7565\u4e5f\u53ef\u4ee5\u9009\u62e9\u9759\u6001\u7b56\u7565\u3002\u82e5\u9009\u62e9\u4f7f\u7528\u9759\u6001\u7b56\u7565\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u4f1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u88ab\u8bc4\u4f30\u6c42\u5f97\uff0c\u4e14\u5728\u63a8\u65ad\u8fc7\u7a0b\u4e2d\u88ab\u4f7f\u7528(\u4e0d\u540c\u7684\u8f93\u5165\u5747\u4fdd\u6301\u4e0d\u53d8)\u3002\u9759\u6001\u7b56\u7565\u4e2d\u7684\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570\u53ef\u4e8e\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u5982\u4e0b\u4e09\u79cd\u65b9\u5f0f\u8fdb\u884c\u8bc4\u4f30\uff1a \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u5e73\u5747\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u3002 \u5728\u4e00\u4e2a\u7a97\u53e3\u4e2d\u8ba1\u7b97\u6fc0\u6d3b\u6700\u5927\u7edd\u5bf9\u503c\u7684\u6ed1\u52a8\u5e73\u5747\u503c\uff0c\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a Vt = (1 - k) * V + k * V_{t-1} Vt = (1 - k) * V + k * V_{t-1} \u5f0f\u4e2d\uff0c V V \u662f\u5f53\u524dbatch\u7684\u6700\u5927\u7edd\u5bf9\u503c\uff0c Vt Vt \u662f\u6ed1\u52a8\u5e73\u5747\u503c\u3002 k k \u662f\u4e00\u4e2a\u56e0\u5b50\uff0c\u4f8b\u5982\u5176\u503c\u53ef\u53d6\u4e3a0.9\u3002","title":"1.2.2.3 \u786e\u5b9a\u91cf\u5316\u6bd4\u4f8b\u7cfb\u6570"},{"location":"algo/algo/#124","text":"\u8bad\u7ec3\u540e\u91cf\u5316\u662f\u57fa\u4e8e\u91c7\u6837\u6570\u636e\uff0c\u91c7\u7528KL\u6563\u5ea6\u7b49\u65b9\u6cd5\u8ba1\u7b97\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7684\u65b9\u6cd5\u3002\u76f8\u6bd4\u91cf\u5316\u8bad\u7ec3\uff0c\u8bad\u7ec3\u540e\u91cf\u5316\u4e0d\u9700\u8981\u91cd\u65b0\u8bad\u7ec3\uff0c\u53ef\u4ee5\u5feb\u901f\u5f97\u5230\u91cf\u5316\u6a21\u578b\u3002 \u8bad\u7ec3\u540e\u91cf\u5316\u7684\u76ee\u6807\u662f\u6c42\u53d6\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\uff0c\u4e3b\u8981\u6709\u4e24\u79cd\u65b9\u6cd5\uff1a\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5 ( No Saturation) \u548c\u9971\u548c\u91cf\u5316\u65b9\u6cd5 (Saturation)\u3002\u975e\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u8ba1\u7b97FP32\u7c7b\u578bTensor\u4e2d\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c abs_max \uff0c\u5c06\u5176\u6620\u5c04\u4e3a127\uff0c\u5219\u91cf\u5316\u6bd4\u4f8b\u56e0\u5b50\u7b49\u4e8e abs_max/127 \u3002\u9971\u548c\u91cf\u5316\u65b9\u6cd5\u4f7f\u7528KL\u6563\u5ea6\u8ba1\u7b97\u4e00\u4e2a\u5408\u9002\u7684\u9608\u503c T ( 0=r \\end{cases} \\end{equation} \\begin{equation} P(r_k) = \\begin{cases} e^{\\frac{(r_k-r)}{T_k}} & r_k < r\\\\ 1 & r_k>=r \\end{cases} \\end{equation} \u5728\u7b2ck\u6b21\u8fed\u4ee3\uff0c\u641c\u5230\u7684\u7f51\u7edc\u4e3a N_k N_k , \u5bf9 N_k N_k \u8bad\u7ec3\u82e5\u5e72epoch\u540e\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u5f97\u5230reward\u4e3a r_k r_k , \u4ee5\u6982\u7387 P(r_k) P(r_k) \u63a5\u53d7 r_k r_k \uff0c\u5373\u6267\u884c r=r_k r=r_k \u3002 r r \u5728\u641c\u7d22\u8fc7\u7a0b\u8d77\u59cb\u65f6\u88ab\u521d\u59cb\u5316\u4e3a0. T_0 T_0 \u4e3a\u521d\u59cb\u5316\u6e29\u5ea6\uff0c \\theta \\theta \u4e3a\u6e29\u5ea6\u8870\u51cf\u7cfb\u6570\uff0c T_k T_k \u4e3a\u7b2ck\u6b21\u8fed\u4ee3\u7684\u6e29\u5ea6\u3002 \u5728\u6211\u4eec\u7684NAS\u4efb\u52a1\u4e2d\uff0c\u533a\u522b\u4e8eRL\u6bcf\u6b21\u91cd\u65b0\u751f\u6210\u4e00\u4e2a\u5b8c\u6574\u7684\u7f51\u7edc\uff0c\u6211\u4eec\u5c06\u7f51\u7edc\u7ed3\u6784\u6620\u5c04\u6210\u4e00\u6bb5\u7f16\u7801\uff0c\u7b2c\u4e00\u6b21\u968f\u673a\u521d\u59cb\u5316\uff0c\u7136\u540e\u6bcf\u6b21\u968f\u673a\u4fee\u6539\u7f16\u7801\u4e2d\u7684\u4e00\u90e8\u5206\uff08\u5bf9\u5e94\u4e8e\u7f51\u7edc\u7ed3\u6784\u7684\u4e00\u90e8\u5206\uff09\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u7f16\u7801\uff0c\u7136\u540e\u5c06\u8fd9\u4e2a\u7f16\u7801\u518d\u6620\u5c04\u56de\u7f51\u7edc\u7ed3\u6784\uff0c\u901a\u8fc7\u5728\u8bad\u7ec3\u96c6\u4e0a\u8bad\u7ec3\u4e00\u5b9a\u7684epochs\u540e\u7684\u7cbe\u5ea6\u4ee5\u53ca\u7f51\u7edc\u5ef6\u65f6\u878d\u5408\u83b7\u5f97reward\uff0c\u6765\u6307\u5bfc\u9000\u706b\u7b97\u6cd5\u7684\u6536\u655b\u3002","title":"4.1.1 \u6a21\u62df\u9000\u706b"},{"location":"algo/algo/#42","text":"\u641c\u7d22\u7a7a\u95f4\u5b9a\u4e49\u4e86\u4f18\u5316\u95ee\u9898\u7684\u53d8\u91cf\uff0c\u53d8\u91cf\u89c4\u6a21\u51b3\u5b9a\u4e86\u641c\u7d22\u7b97\u6cd5\u7684\u96be\u5ea6\u548c\u641c\u7d22\u65f6\u95f4\u3002\u56e0\u6b64\u4e3a\u4e86\u52a0\u5feb\u641c\u7d22\u901f\u5ea6\uff0c\u5b9a\u4e49\u4e00\u4e2a\u5408\u7406\u7684\u641c\u7d22\u7a7a\u95f4\u81f3\u5173\u91cd\u8981\u3002\u5728Light-NAS\u4e2d\uff0c\u4e3a\u4e86\u52a0\u901f\u641c\u7d22\u901f\u5ea6\uff0c\u6211\u4eec\u5c06\u4e00\u4e2a\u7f51\u7edc\u5212\u5206\u4e3a\u591a\u4e2ablock\uff0c\u5148\u624b\u52a8\u6309\u94fe\u72b6\u5c42\u7ea7\u7ed3\u6784\u5806\u53e0c\uff0c\u518d \u4f7f\u7528\u641c\u7d22\u7b97\u6cd5\u81ea\u52a8\u641c\u7d22\u6bcf\u4e2ablock\u5185\u90e8\u7684\u7ed3\u6784\u3002 \u56e0\u4e3a\u8981\u641c\u7d22\u51fa\u5728\u79fb\u52a8\u7aef\u8fd0\u884c\u901f\u5ea6\u5feb\u7684\u6a21\u578b\uff0c\u6211\u4eec\u53c2\u8003\u4e86MobileNetV2\u4e2d\u7684Linear Bottlenecks\u548cInverted residuals\u7ed3\u6784\uff0c\u641c\u7d22\u6bcf\u4e00\u4e2aInverted residuals\u4e2d\u7684\u5177\u4f53\u53c2\u6570\uff0c\u5305\u62eckernelsize\u3001channel\u6269\u5f20\u500d\u6570\u3001\u91cd\u590d\u6b21\u6570\u3001channels number\u3002\u5982\u56fe10\u6240\u793a\uff1a \u56fe10","title":"4.2 \u641c\u7d22\u7a7a\u95f4"},{"location":"algo/algo/#43","text":"\u641c\u7d22\u8fc7\u7a0b\u652f\u6301 FLOPS \u7ea6\u675f\u548c\u6a21\u578b\u5ef6\u65f6\u7ea6\u675f\u3002\u800c\u57fa\u4e8e Android/iOS \u79fb\u52a8\u7aef\u3001\u5f00\u53d1\u677f\u7b49\u786c\u4ef6\u5e73\u53f0\uff0c\u8fed\u4ee3\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u6d4b\u8bd5\u6a21\u578b\u7684\u5ef6\u65f6\u4e0d\u4ec5\u6d88\u8017\u65f6\u95f4\u800c\u4e14\u975e\u5e38\u4e0d\u65b9\u4fbf\uff0c\u56e0\u6b64\u6211\u4eec\u5f00\u53d1\u4e86\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u6765\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u6a21\u578b\u7684\u5ef6\u65f6\u3002\u901a\u8fc7\u5ef6\u65f6\u8bc4\u4f30\u5668\u8bc4\u4f30\u5f97\u5230\u7684\u5ef6\u65f6\u4e0e\u6a21\u578b\u5b9e\u9645\u6d4b\u8bd5\u7684\u5ef6\u65f6\u6ce2\u52a8\u504f\u5dee\u5c0f\u4e8e 10%\u3002 \u5ef6\u65f6\u8bc4\u4f30\u5668\u5206\u4e3a\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u548c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u4e24\u4e2a\u9636\u6bb5\uff0c\u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u53ea\u9700\u8981\u6267\u884c\u4e00\u6b21\uff0c\u800c\u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6\u5219\u5728\u641c\u7d22\u8fc7\u7a0b\u4e2d\u4e0d\u65ad\u8bc4\u4f30\u641c\u7d22\u5f97\u5230\u7684\u6a21\u578b\u5ef6\u65f6\u3002 \u914d\u7f6e\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668 \u83b7\u53d6\u641c\u7d22\u7a7a\u95f4\u4e2d\u6240\u6709\u4e0d\u91cd\u590d\u7684 op \u53ca\u5176\u53c2\u6570 \u83b7\u53d6\u6bcf\u7ec4 op \u53ca\u5176\u53c2\u6570\u7684\u5ef6\u65f6 \u8bc4\u4f30\u6a21\u578b\u5ef6\u65f6 \u83b7\u53d6\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u5176\u53c2\u6570 \u6839\u636e\u7ed9\u5b9a\u6a21\u578b\u7684\u6240\u6709 op \u53ca\u53c2\u6570\uff0c\u5229\u7528\u5ef6\u65f6\u8bc4\u4f30\u5668\u53bb\u4f30\u8ba1\u6a21\u578b\u7684\u5ef6\u65f6","title":"4.3 \u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30"},{"location":"algo/algo/#5","text":"High-Performance Hardware for Machine Learning Quantizing deep convolutional networks for efficient inference: A whitepaper Pruning Filters for Efficient ConvNets Distilling the Knowledge in a Neural Network A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning","title":"5. \u53c2\u8003\u6587\u732e"},{"location":"api/analysis_api/","text":"FLOPs # paddleslim.analysis.flops(program, detail=False) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570(FLOPs)\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 detail(bool) - \u662f\u5426\u8fd4\u56de\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684FLOPs\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 only_conv(bool) - \u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4ec5\u8ba1\u7b97\u5377\u79ef\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u7684FLOPs\uff0c\u5373\u6d6e\u70b9\u6570\u7684\u4e58\u52a0\uff08multiplication-adds\uff09\u64cd\u4f5c\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aFalse\uff0c\u5219\u4e5f\u4f1a\u8ba1\u7b97\u5377\u79ef\u548c\u5168\u8fde\u63a5\u5c42\u4e4b\u5916\u7684\u64cd\u4f5c\u7684FLOPs\u3002 \u8fd4\u56de\u503c\uff1a flops(float) - \u6574\u4e2a\u7f51\u7edc\u7684FLOPs\u3002 params2flops(dict) - \u6bcf\u5c42\u5377\u79ef\u5bf9\u5e94\u7684FLOPs\uff0c\u5176\u4e2dkey\u4e3a\u5377\u79ef\u5c42\u53c2\u6570\u540d\u79f0\uff0cvalue\u4e3aFLOPs\u503c\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import flops def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( flops ( main_program ))) model_size # paddleslim.analysis.model_size(program) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 \u8fd4\u56de\u503c\uff1a model_size(int) - \u6574\u4e2a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import model_size def conv_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) return conv main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( model_size ( main_program ))) TableLatencyEvaluator # paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=\",\") [\u6e90\u4ee3\u7801] \u57fa\u4e8e\u786c\u4ef6\u5ef6\u65f6\u8868\u7684\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u3002 \u53c2\u6570\uff1a table_file(str) - \u6240\u4f7f\u7528\u7684\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u7edd\u5bf9\u8def\u5f84\u3002\u5173\u4e8e\u6f14\u793a\u8bc4\u4f30\u8868\u683c\u5f0f\u8bf7\u53c2\u8003\uff1a PaddleSlim\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u683c\u5f0f delimiter(str) - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\uff0c\u64cd\u4f5c\u4fe1\u606f\u4e4b\u524d\u6240\u4f7f\u7528\u7684\u5206\u5272\u7b26\uff0c\u9ed8\u8ba4\u4e3a\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u3002 \u8fd4\u56de\u503c\uff1a Evaluator - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u7684\u5b9e\u4f8b\u3002 paddleslim.analysis.TableLatencyEvaluator.latency(graph) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002 \u53c2\u6570\uff1a graph(Program) - \u5f85\u9884\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002 \u8fd4\u56de\u503c\uff1a latency - \u76ee\u6807\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002","title":"\u6a21\u578b\u5206\u6790"},{"location":"api/analysis_api/#flops","text":"paddleslim.analysis.flops(program, detail=False) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570(FLOPs)\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 detail(bool) - \u662f\u5426\u8fd4\u56de\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684FLOPs\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 only_conv(bool) - \u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4ec5\u8ba1\u7b97\u5377\u79ef\u5c42\u548c\u5168\u8fde\u63a5\u5c42\u7684FLOPs\uff0c\u5373\u6d6e\u70b9\u6570\u7684\u4e58\u52a0\uff08multiplication-adds\uff09\u64cd\u4f5c\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aFalse\uff0c\u5219\u4e5f\u4f1a\u8ba1\u7b97\u5377\u79ef\u548c\u5168\u8fde\u63a5\u5c42\u4e4b\u5916\u7684\u64cd\u4f5c\u7684FLOPs\u3002 \u8fd4\u56de\u503c\uff1a flops(float) - \u6574\u4e2a\u7f51\u7edc\u7684FLOPs\u3002 params2flops(dict) - \u6bcf\u5c42\u5377\u79ef\u5bf9\u5e94\u7684FLOPs\uff0c\u5176\u4e2dkey\u4e3a\u5377\u79ef\u5c42\u53c2\u6570\u540d\u79f0\uff0cvalue\u4e3aFLOPs\u503c\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import flops def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( flops ( main_program )))","title":"FLOPs"},{"location":"api/analysis_api/#model_size","text":"paddleslim.analysis.model_size(program) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u5206\u6790\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 \u8fd4\u56de\u503c\uff1a model_size(int) - \u6574\u4e2a\u7f51\u7edc\u7684\u53c2\u6570\u6570\u91cf\u3002 \u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import model_size def conv_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) return conv main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_layer ( conv5 , 8 , 3 , \"conv6\" ) print ( \"FLOPs: {}\" . format ( model_size ( main_program )))","title":"model_size"},{"location":"api/analysis_api/#tablelatencyevaluator","text":"paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=\",\") [\u6e90\u4ee3\u7801] \u57fa\u4e8e\u786c\u4ef6\u5ef6\u65f6\u8868\u7684\u6a21\u578b\u5ef6\u65f6\u8bc4\u4f30\u5668\u3002 \u53c2\u6570\uff1a table_file(str) - \u6240\u4f7f\u7528\u7684\u5ef6\u65f6\u8bc4\u4f30\u8868\u7684\u7edd\u5bf9\u8def\u5f84\u3002\u5173\u4e8e\u6f14\u793a\u8bc4\u4f30\u8868\u683c\u5f0f\u8bf7\u53c2\u8003\uff1a PaddleSlim\u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u683c\u5f0f delimiter(str) - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u8868\u4e2d\uff0c\u64cd\u4f5c\u4fe1\u606f\u4e4b\u524d\u6240\u4f7f\u7528\u7684\u5206\u5272\u7b26\uff0c\u9ed8\u8ba4\u4e3a\u82f1\u6587\u5b57\u7b26\u9017\u53f7\u3002 \u8fd4\u56de\u503c\uff1a Evaluator - \u786c\u4ef6\u5ef6\u65f6\u8bc4\u4f30\u5668\u7684\u5b9e\u4f8b\u3002 paddleslim.analysis.TableLatencyEvaluator.latency(graph) [\u6e90\u4ee3\u7801] \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002 \u53c2\u6570\uff1a graph(Program) - \u5f85\u9884\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002 \u8fd4\u56de\u503c\uff1a latency - \u76ee\u6807\u7f51\u7edc\u7684\u9884\u4f30\u5ef6\u65f6\u3002","title":"TableLatencyEvaluator"},{"location":"api/api_guide/","text":"PaddleSlim API\u6587\u6863\u5bfc\u822a # \u6a21\u578b\u5206\u6790 # \u5377\u79ef\u901a\u9053\u526a\u88c1 # \u84b8\u998f # \u5355\u8fdb\u7a0b\u84b8\u998f \u901a\u9053\u526a\u88c1 \u91cf\u5316 # \u91cf\u5316\u8bad\u7ec3 \u79bb\u7ebf\u91cf\u5316 embedding\u91cf\u5316 \u5c0f\u6a21\u578b\u7ed3\u6784\u641c\u7d22 # nas API SearchSpace","title":"PaddleSlim API\u6587\u6863\u5bfc\u822a"},{"location":"api/api_guide/#paddleslim-api","text":"","title":"PaddleSlim API\u6587\u6863\u5bfc\u822a"},{"location":"api/api_guide/#_1","text":"","title":"\u6a21\u578b\u5206\u6790"},{"location":"api/api_guide/#_2","text":"","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1"},{"location":"api/api_guide/#_3","text":"\u5355\u8fdb\u7a0b\u84b8\u998f \u901a\u9053\u526a\u88c1","title":"\u84b8\u998f"},{"location":"api/api_guide/#_4","text":"\u91cf\u5316\u8bad\u7ec3 \u79bb\u7ebf\u91cf\u5316 embedding\u91cf\u5316","title":"\u91cf\u5316"},{"location":"api/api_guide/#_5","text":"nas API SearchSpace","title":"\u5c0f\u6a21\u578b\u7ed3\u6784\u641c\u7d22"},{"location":"api/nas_api/","text":"\u641c\u7d22\u7a7a\u95f4\u53c2\u6570\u7684\u914d\u7f6e # \u901a\u8fc7\u53c2\u6570\u914d\u7f6e\u641c\u7d22\u7a7a\u95f4\u3002\u66f4\u591a\u641c\u7d22\u7a7a\u95f4\u7684\u4f7f\u7528\u53ef\u4ee5\u53c2\u8003 search_space \u53c2\u6570\uff1a input_size(int|None) \uff1a- input_size \u8868\u793a\u8f93\u5165feature map\u7684\u5927\u5c0f\u3002 output_size(int|None) \uff1a- output_size \u8868\u793a\u8f93\u51fafeature map\u7684\u5927\u5c0f\u3002 block_num(int|None) \uff1a- block_num \u8868\u793a\u641c\u7d22\u7a7a\u95f4\u4e2dblock\u7684\u6570\u91cf\u3002 block_mask(list|None) \uff1a- block_mask \u662f\u4e00\u7ec4\u75310\u30011\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u8868\u793a\u5f53\u524dblock\u662fnormal block\uff0c1\u8868\u793a\u5f53\u524dblock\u662freduction block\u3002\u5982\u679c\u8bbe\u7f6e\u4e86 block_mask \uff0c\u5219\u4e3b\u8981\u4ee5 block_mask \u4e3a\u4e3b\u8981\u914d\u7f6e\uff0c input_size \uff0c output_size \u548c block_num \u4e09\u79cd\u914d\u7f6e\u662f\u65e0\u6548\u7684\u3002 Note: 1. reduction block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540e\u7684feature map\u5927\u5c0f\u4e0b\u964d\u4e3a\u4e4b\u524d\u7684\u4e00\u534a\uff0cnormal block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540efeature map\u5927\u5c0f\u4e0d\u53d8\u3002 2. input_size \u548c output_size \u7528\u6765\u8ba1\u7b97\u6574\u4e2a\u6a21\u578b\u7ed3\u6784\u4e2dreduction block\u6570\u91cf\u3002 SANAS # paddleslim.nas.SANAS(configs, server_addr=(\"\", 8881), init_temperature=100, reduce_rate=0.85, search_steps=300, save_checkpoint='./nas_checkpoint', load_checkpoint=None, is_server=True) \u6e90\u4ee3\u7801 SANAS\uff08Simulated Annealing Neural Architecture Search\uff09\u662f\u57fa\u4e8e\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u8fdb\u884c\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u7684\u7b97\u6cd5\uff0c\u4e00\u822c\u7528\u4e8e\u79bb\u6563\u641c\u7d22\u4efb\u52a1\u3002 \u53c2\u6570\uff1a configs(list ) - \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u5217\u8868\uff0c\u683c\u5f0f\u662f [(key, {input_size, output_size, block_num, block_mask})] \u6216\u8005 [(key)] \uff08MobileNetV2\u3001MobilenetV1\u548cResNet\u7684\u641c\u7d22\u7a7a\u95f4\u4f7f\u7528\u548c\u539f\u672c\u7f51\u7edc\u7ed3\u6784\u76f8\u540c\u7684\u641c\u7d22\u7a7a\u95f4\uff0c\u6240\u4ee5\u4ec5\u9700\u6307\u5b9a key \u5373\u53ef\uff09, input_size \u548c output_size \u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u7684\u5927\u5c0f\uff0c block_num \u662f\u6307\u641c\u7d22\u7f51\u7edc\u4e2d\u7684block\u6570\u91cf\uff0c block_mask \u662f\u4e00\u7ec4\u75310\u548c1\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u4ee3\u8868\u4e0d\u8fdb\u884c\u4e0b\u91c7\u6837\u7684block\uff0c1\u4ee3\u8868\u4e0b\u91c7\u6837\u7684block\u3002 \u66f4\u591apaddleslim\u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003\u3002 server_addr(tuple) - SANAS\u7684\u5730\u5740\uff0c\u5305\u62ecserver\u7684ip\u5730\u5740\u548c\u7aef\u53e3\u53f7\uff0c\u5982\u679cip\u5730\u5740\u4e3aNone\u6216\u8005\u4e3a\"\"\u7684\u8bdd\u5219\u9ed8\u8ba4\u4f7f\u7528\u672c\u673aip\u3002\u9ed8\u8ba4\uff1a\uff08\"\", 8881\uff09\u3002 init_temperature(float) - \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u521d\u59cb\u6e29\u5ea6\u3002\u9ed8\u8ba4\uff1a100\u3002 reduce_rate(float) - \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u8870\u51cf\u7387\u3002\u9ed8\u8ba4\uff1a0.85\u3002 search_steps(int) - \u641c\u7d22\u8fc7\u7a0b\u8fed\u4ee3\u7684\u6b21\u6570\u3002\u9ed8\u8ba4\uff1a300\u3002 save_checkpoint(str|None) - \u4fdd\u5b58checkpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u4fdd\u5b58checkpoint\u3002\u9ed8\u8ba4\uff1a ./nas_checkpoint \u3002 load_checkpoint(str|None) - \u52a0\u8f7dcheckpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u52a0\u8f7dcheckpoint\u3002\u9ed8\u8ba4\uff1aNone\u3002 is_server(bool) - \u5f53\u524d\u5b9e\u4f8b\u662f\u5426\u8981\u542f\u52a8\u4e00\u4e2aserver\u3002\u9ed8\u8ba4\uff1aTrue\u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aSANAS\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 from paddleslim.nas import SANAS config = [( 'MobileNetV2Space' )] sanas = SANAS ( config = config ) paddlesim.nas.SANAS.tokens2arch(tokens) \u901a\u8fc7\u4e00\u7ec4token\u5f97\u5230\u5b9e\u9645\u7684\u6a21\u578b\u7ed3\u6784\uff0c\u4e00\u822c\u7528\u6765\u628a\u641c\u7d22\u5230\u6700\u4f18\u7684token\u8f6c\u6362\u4e3a\u6a21\u578b\u7ed3\u6784\u7528\u6765\u505a\u6700\u540e\u7684\u8bad\u7ec3\u3002 Note: tokens\u662f\u4e00\u4e2a\u5217\u8868\uff0ctoken\u6620\u5c04\u5230\u641c\u7d22\u7a7a\u95f4\u8f6c\u6362\u6210\u76f8\u5e94\u7684\u7f51\u7edc\u7ed3\u6784\uff0c\u4e00\u7ec4token\u5bf9\u5e94\u552f\u4e00\u7684\u4e00\u4e2a\u7f51\u7edc\u7ed3\u6784\u3002 \u53c2\u6570\uff1a tokens(list): - \u4e00\u7ec4token\u3002 \u8fd4\u56de\uff1a \u6839\u636e\u4f20\u5165\u7684token\u5f97\u5230\u4e00\u4e2a\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . token2arch ( tokens ) for arch in archs : output = arch ( input ) input = output paddleslim.nas.SANAS.next_archs() \u83b7\u53d6\u4e0b\u4e00\u7ec4\u6a21\u578b\u7ed3\u6784\u3002 \u8fd4\u56de\uff1a \u8fd4\u56de\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u7684\u5217\u8868\uff0c\u5f62\u5f0f\u4e3alist\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . next_archs () for arch in archs : output = arch ( input ) input = output paddleslim.nas.SANAS.reward(score) \u628a\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u7684\u5f97\u5206\u60c5\u51b5\u56de\u4f20\u3002 \u53c2\u6570\uff1a score : - \u5f53\u524d\u6a21\u578b\u7684\u5f97\u5206\uff0c\u5206\u6570\u8d8a\u5927\u8d8a\u597d\u3002 \u8fd4\u56de\uff1a \u6a21\u578b\u7ed3\u6784\u66f4\u65b0\u6210\u529f\u6216\u8005\u5931\u8d25\uff0c\u6210\u529f\u5219\u8fd4\u56de True \uff0c\u5931\u8d25\u5219\u8fd4\u56de False \u3002 paddleslim.nas.SANAS.current_info() \u8fd4\u56de\u5f53\u524dtoken\u548c\u641c\u7d22\u8fc7\u7a0b\u4e2d\u6700\u597d\u7684token\u548creward\u3002 \u8fd4\u56de\uff1a \u641c\u7d22\u8fc7\u7a0b\u4e2d\u6700\u597d\u7684token\uff0creward\u548c\u5f53\u524d\u8bad\u7ec3\u7684token\uff0c\u5f62\u5f0f\u4e3adict\u3002","title":"SA\u641c\u7d22"},{"location":"api/nas_api/#_1","text":"\u901a\u8fc7\u53c2\u6570\u914d\u7f6e\u641c\u7d22\u7a7a\u95f4\u3002\u66f4\u591a\u641c\u7d22\u7a7a\u95f4\u7684\u4f7f\u7528\u53ef\u4ee5\u53c2\u8003 search_space \u53c2\u6570\uff1a input_size(int|None) \uff1a- input_size \u8868\u793a\u8f93\u5165feature map\u7684\u5927\u5c0f\u3002 output_size(int|None) \uff1a- output_size \u8868\u793a\u8f93\u51fafeature map\u7684\u5927\u5c0f\u3002 block_num(int|None) \uff1a- block_num \u8868\u793a\u641c\u7d22\u7a7a\u95f4\u4e2dblock\u7684\u6570\u91cf\u3002 block_mask(list|None) \uff1a- block_mask \u662f\u4e00\u7ec4\u75310\u30011\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u8868\u793a\u5f53\u524dblock\u662fnormal block\uff0c1\u8868\u793a\u5f53\u524dblock\u662freduction block\u3002\u5982\u679c\u8bbe\u7f6e\u4e86 block_mask \uff0c\u5219\u4e3b\u8981\u4ee5 block_mask \u4e3a\u4e3b\u8981\u914d\u7f6e\uff0c input_size \uff0c output_size \u548c block_num \u4e09\u79cd\u914d\u7f6e\u662f\u65e0\u6548\u7684\u3002 Note: 1. reduction block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540e\u7684feature map\u5927\u5c0f\u4e0b\u964d\u4e3a\u4e4b\u524d\u7684\u4e00\u534a\uff0cnormal block\u8868\u793a\u7ecf\u8fc7\u8fd9\u4e2ablock\u4e4b\u540efeature map\u5927\u5c0f\u4e0d\u53d8\u3002 2. input_size \u548c output_size \u7528\u6765\u8ba1\u7b97\u6574\u4e2a\u6a21\u578b\u7ed3\u6784\u4e2dreduction block\u6570\u91cf\u3002","title":"\u641c\u7d22\u7a7a\u95f4\u53c2\u6570\u7684\u914d\u7f6e"},{"location":"api/nas_api/#sanas","text":"paddleslim.nas.SANAS(configs, server_addr=(\"\", 8881), init_temperature=100, reduce_rate=0.85, search_steps=300, save_checkpoint='./nas_checkpoint', load_checkpoint=None, is_server=True) \u6e90\u4ee3\u7801 SANAS\uff08Simulated Annealing Neural Architecture Search\uff09\u662f\u57fa\u4e8e\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u8fdb\u884c\u6a21\u578b\u7ed3\u6784\u641c\u7d22\u7684\u7b97\u6cd5\uff0c\u4e00\u822c\u7528\u4e8e\u79bb\u6563\u641c\u7d22\u4efb\u52a1\u3002 \u53c2\u6570\uff1a configs(list ) - \u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u5217\u8868\uff0c\u683c\u5f0f\u662f [(key, {input_size, output_size, block_num, block_mask})] \u6216\u8005 [(key)] \uff08MobileNetV2\u3001MobilenetV1\u548cResNet\u7684\u641c\u7d22\u7a7a\u95f4\u4f7f\u7528\u548c\u539f\u672c\u7f51\u7edc\u7ed3\u6784\u76f8\u540c\u7684\u641c\u7d22\u7a7a\u95f4\uff0c\u6240\u4ee5\u4ec5\u9700\u6307\u5b9a key \u5373\u53ef\uff09, input_size \u548c output_size \u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u7684\u5927\u5c0f\uff0c block_num \u662f\u6307\u641c\u7d22\u7f51\u7edc\u4e2d\u7684block\u6570\u91cf\uff0c block_mask \u662f\u4e00\u7ec4\u75310\u548c1\u7ec4\u6210\u7684\u5217\u8868\uff0c0\u4ee3\u8868\u4e0d\u8fdb\u884c\u4e0b\u91c7\u6837\u7684block\uff0c1\u4ee3\u8868\u4e0b\u91c7\u6837\u7684block\u3002 \u66f4\u591apaddleslim\u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003\u3002 server_addr(tuple) - SANAS\u7684\u5730\u5740\uff0c\u5305\u62ecserver\u7684ip\u5730\u5740\u548c\u7aef\u53e3\u53f7\uff0c\u5982\u679cip\u5730\u5740\u4e3aNone\u6216\u8005\u4e3a\"\"\u7684\u8bdd\u5219\u9ed8\u8ba4\u4f7f\u7528\u672c\u673aip\u3002\u9ed8\u8ba4\uff1a\uff08\"\", 8881\uff09\u3002 init_temperature(float) - \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u521d\u59cb\u6e29\u5ea6\u3002\u9ed8\u8ba4\uff1a100\u3002 reduce_rate(float) - \u57fa\u4e8e\u6a21\u62df\u9000\u706b\u8fdb\u884c\u641c\u7d22\u7684\u8870\u51cf\u7387\u3002\u9ed8\u8ba4\uff1a0.85\u3002 search_steps(int) - \u641c\u7d22\u8fc7\u7a0b\u8fed\u4ee3\u7684\u6b21\u6570\u3002\u9ed8\u8ba4\uff1a300\u3002 save_checkpoint(str|None) - \u4fdd\u5b58checkpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u4fdd\u5b58checkpoint\u3002\u9ed8\u8ba4\uff1a ./nas_checkpoint \u3002 load_checkpoint(str|None) - \u52a0\u8f7dcheckpoint\u7684\u6587\u4ef6\u76ee\u5f55\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aNone\u7684\u8bdd\u5219\u4e0d\u52a0\u8f7dcheckpoint\u3002\u9ed8\u8ba4\uff1aNone\u3002 is_server(bool) - \u5f53\u524d\u5b9e\u4f8b\u662f\u5426\u8981\u542f\u52a8\u4e00\u4e2aserver\u3002\u9ed8\u8ba4\uff1aTrue\u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aSANAS\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 from paddleslim.nas import SANAS config = [( 'MobileNetV2Space' )] sanas = SANAS ( config = config ) paddlesim.nas.SANAS.tokens2arch(tokens) \u901a\u8fc7\u4e00\u7ec4token\u5f97\u5230\u5b9e\u9645\u7684\u6a21\u578b\u7ed3\u6784\uff0c\u4e00\u822c\u7528\u6765\u628a\u641c\u7d22\u5230\u6700\u4f18\u7684token\u8f6c\u6362\u4e3a\u6a21\u578b\u7ed3\u6784\u7528\u6765\u505a\u6700\u540e\u7684\u8bad\u7ec3\u3002 Note: tokens\u662f\u4e00\u4e2a\u5217\u8868\uff0ctoken\u6620\u5c04\u5230\u641c\u7d22\u7a7a\u95f4\u8f6c\u6362\u6210\u76f8\u5e94\u7684\u7f51\u7edc\u7ed3\u6784\uff0c\u4e00\u7ec4token\u5bf9\u5e94\u552f\u4e00\u7684\u4e00\u4e2a\u7f51\u7edc\u7ed3\u6784\u3002 \u53c2\u6570\uff1a tokens(list): - \u4e00\u7ec4token\u3002 \u8fd4\u56de\uff1a \u6839\u636e\u4f20\u5165\u7684token\u5f97\u5230\u4e00\u4e2a\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . token2arch ( tokens ) for arch in archs : output = arch ( input ) input = output paddleslim.nas.SANAS.next_archs() \u83b7\u53d6\u4e0b\u4e00\u7ec4\u6a21\u578b\u7ed3\u6784\u3002 \u8fd4\u56de\uff1a \u8fd4\u56de\u6a21\u578b\u7ed3\u6784\u5b9e\u4f8b\u7684\u5217\u8868\uff0c\u5f62\u5f0f\u4e3alist\u3002 \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 3 4 5 6 import paddle.fluid as fluid input = fluid . data ( name = 'input' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) archs = sanas . next_archs () for arch in archs : output = arch ( input ) input = output paddleslim.nas.SANAS.reward(score) \u628a\u5f53\u524d\u6a21\u578b\u7ed3\u6784\u7684\u5f97\u5206\u60c5\u51b5\u56de\u4f20\u3002 \u53c2\u6570\uff1a score : - \u5f53\u524d\u6a21\u578b\u7684\u5f97\u5206\uff0c\u5206\u6570\u8d8a\u5927\u8d8a\u597d\u3002 \u8fd4\u56de\uff1a \u6a21\u578b\u7ed3\u6784\u66f4\u65b0\u6210\u529f\u6216\u8005\u5931\u8d25\uff0c\u6210\u529f\u5219\u8fd4\u56de True \uff0c\u5931\u8d25\u5219\u8fd4\u56de False \u3002 paddleslim.nas.SANAS.current_info() \u8fd4\u56de\u5f53\u524dtoken\u548c\u641c\u7d22\u8fc7\u7a0b\u4e2d\u6700\u597d\u7684token\u548creward\u3002 \u8fd4\u56de\uff1a \u641c\u7d22\u8fc7\u7a0b\u4e2d\u6700\u597d\u7684token\uff0creward\u548c\u5f53\u524d\u8bad\u7ec3\u7684token\uff0c\u5f62\u5f0f\u4e3adict\u3002","title":"SANAS"},{"location":"api/prune_api/","text":"Pruner # paddleslim.prune.Pruner(criterion=\"l1_norm\") [\u6e90\u4ee3\u7801] \u5bf9\u5377\u79ef\u7f51\u7edc\u7684\u901a\u9053\u8fdb\u884c\u4e00\u6b21\u526a\u88c1\u3002\u526a\u88c1\u4e00\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\uff0c\u662f\u6307\u526a\u88c1\u8be5\u5377\u79ef\u5c42\u8f93\u51fa\u7684\u901a\u9053\u3002\u5377\u79ef\u5c42\u7684\u6743\u91cd\u5f62\u72b6\u4e3a [output_channel, input_channel, kernel_size, kernel_size] \uff0c\u901a\u8fc7\u526a\u88c1\u8be5\u6743\u91cd\u7684\u7b2c\u4e00\u7eac\u5ea6\u8fbe\u5230\u526a\u88c1\u8f93\u51fa\u901a\u9053\u6570\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a criterion - \u8bc4\u4f30\u4e00\u4e2a\u5377\u79ef\u5c42\u5185\u901a\u9053\u91cd\u8981\u6027\u6240\u53c2\u8003\u7684\u6307\u6807\u3002\u76ee\u524d\u4ec5\u652f\u6301 l1_norm \u3002\u9ed8\u8ba4\u4e3a l1_norm \u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aPruner\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 from paddleslim.prune import Pruner pruner = Pruner () paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) [\u6e90\u4ee3\u7801] \u5bf9\u76ee\u6807\u7f51\u7edc\u7684\u4e00\u7ec4\u5377\u79ef\u5c42\u7684\u6743\u91cd\u8fdb\u884c\u88c1\u526a\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u8981\u88c1\u526a\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 scope(paddle.fluid.Scope) - \u8981\u88c1\u526a\u7684\u6743\u91cd\u6240\u5728\u7684 scope \uff0cPaddle\u4e2d\u7528 scope \u5b9e\u4f8b\u5b58\u653e\u6a21\u578b\u53c2\u6570\u548c\u8fd0\u884c\u65f6\u53d8\u91cf\u7684\u503c\u3002Scope\u4e2d\u7684\u53c2\u6570\u503c\u4f1a\u88ab inplace \u7684\u88c1\u526a\u3002\u66f4\u591a\u4ecb\u7ecd\u8bf7\u53c2\u8003 Scope\u6982\u5ff5\u4ecb\u7ecd params(list ) - \u9700\u8981\u88ab\u88c1\u526a\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) ratios(list ) - \u7528\u4e8e\u88c1\u526a params \u7684\u526a\u5207\u7387\uff0c\u7c7b\u578b\u4e3a\u5217\u8868\u3002\u8be5\u5217\u8868\u957f\u5ea6\u5fc5\u987b\u4e0e params \u7684\u957f\u5ea6\u4e00\u81f4\u3002 place(paddle.fluid.Place) - \u5f85\u88c1\u526a\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd lazy(bool) - lazy \u4e3aTrue\u65f6\uff0c\u901a\u8fc7\u5c06\u6307\u5b9a\u901a\u9053\u7684\u53c2\u6570\u7f6e\u96f6\u8fbe\u5230\u88c1\u526a\u7684\u76ee\u7684\uff0c\u53c2\u6570\u7684 shape\u4fdd\u6301\u4e0d\u53d8 \uff1b lazy \u4e3aFalse\u65f6\uff0c\u76f4\u63a5\u5c06\u8981\u88c1\u7684\u901a\u9053\u7684\u53c2\u6570\u5220\u9664\uff0c\u53c2\u6570\u7684 shape \u4f1a\u53d1\u751f\u53d8\u5316\u3002 only_graph(bool) - \u662f\u5426\u53ea\u88c1\u526a\u7f51\u7edc\u7ed3\u6784\u3002\u5728Paddle\u4e2d\uff0cProgram\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\uff0cScope\u5b58\u50a8\u53c2\u6570\u7684\u6570\u503c\u3002\u4e00\u4e2aScope\u5b9e\u4f8b\u53ef\u4ee5\u88ab\u591a\u4e2aProgram\u4f7f\u7528\uff0c\u6bd4\u5982\u5b9a\u4e49\u4e86\u8bad\u7ec3\u7f51\u7edc\u7684Program\u548c\u5b9a\u4e49\u4e86\u6d4b\u8bd5\u7f51\u7edc\u7684Program\u662f\u4f7f\u7528\u540c\u4e00\u4e2aScope\u5b9e\u4f8b\u7684\u3002 only_graph \u4e3aTrue\u65f6\uff0c\u53ea\u5bf9Program\u4e2d\u5b9a\u4e49\u7684\u5377\u79ef\u7684\u901a\u9053\u8fdb\u884c\u526a\u88c1\uff1b only_graph \u4e3afalse\u65f6\uff0cScope\u4e2d\u5377\u79ef\u53c2\u6570\u7684\u6570\u503c\u4e5f\u4f1a\u88ab\u526a\u88c1\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570\u503c\u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_shape_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570 shape \u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 \u8fd4\u56de\uff1a pruned_program(paddle.fluid.Program) - \u88ab\u88c1\u526a\u540e\u7684Program\u3002 param_backup(dict) - \u5bf9\u53c2\u6570\u6570\u503c\u7684\u5907\u4efd\uff0c\u7528\u4e8e\u6062\u590dScope\u4e2d\u7684\u53c2\u6570\u6570\u503c\u3002 param_shape_backup(dict) - \u5bf9\u53c2\u6570\u5f62\u72b6\u7684\u5907\u4efd\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u6267\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import Pruner def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) place = fluid . CPUPlace () exe = fluid . Executor ( place ) scope = fluid . Scope () exe . run ( startup_program , scope = scope ) pruner = Pruner () main_program , _ , _ = pruner . prune ( main_program , scope , params = [ \"conv4_weights\" ], ratios = [ 0.5 ], place = place , lazy = False , only_graph = False , param_backup = False , param_shape_backup = False ) for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : print ( \"param name: {}; param shape: {}\" . format ( param . name , param . shape )) sensitivity # paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) [\u6e90\u4ee3\u7801] \u8ba1\u7b97\u7f51\u7edc\u4e2d\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u3002\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u7edf\u8ba1\u65b9\u6cd5\u4e3a\uff1a\u4f9d\u6b21\u526a\u6389\u5f53\u524d\u5377\u79ef\u5c42\u4e0d\u540c\u6bd4\u4f8b\u7684\u8f93\u51fa\u901a\u9053\u6570\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8ba1\u7b97\u526a\u88c1\u540e\u7684\u7cbe\u5ea6\u635f\u5931\u3002\u5f97\u5230\u654f\u611f\u5ea6\u4fe1\u606f\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u6216\u5176\u5b83\u65b9\u5f0f\u786e\u5b9a\u6bcf\u5c42\u5377\u79ef\u7684\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u8bc4\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 place(paddle.fluid.Place) - \u5f85\u5206\u6790\u7684\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd param_names(list ) - \u5f85\u5206\u6790\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) eval_func(function) - \u7528\u4e8e\u8bc4\u4f30\u88c1\u526a\u540e\u6a21\u578b\u6548\u679c\u7684\u56de\u8c03\u51fd\u6570\u3002\u8be5\u56de\u8c03\u51fd\u6570\u63a5\u53d7\u88ab\u88c1\u526a\u540e\u7684 program \u4e3a\u53c2\u6570\uff0c\u8fd4\u56de\u4e00\u4e2a\u8868\u793a\u5f53\u524dprogram\u7684\u7cbe\u5ea6\uff0c\u7528\u4ee5\u8ba1\u7b97\u5f53\u524d\u88c1\u526a\u5e26\u6765\u7684\u7cbe\u5ea6\u635f\u5931\u3002 sensitivities_file(str) - \u4fdd\u5b58\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\u7684\u6587\u4ef6\u3002\u5728\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\uff0c\u4f1a\u6301\u7eed\u5c06\u65b0\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u8ffd\u52a0\u5230\u8be5\u6587\u4ef6\u4e2d\u3002\u91cd\u542f\u4efb\u52a1\u540e\uff0c\u6587\u4ef6\u4e2d\u5df2\u6709\u654f\u611f\u5ea6\u4fe1\u606f\u4e0d\u4f1a\u88ab\u91cd\u590d\u8ba1\u7b97\u3002\u8be5\u6587\u4ef6\u53ef\u4ee5\u7528 pickle \u52a0\u8f7d\u3002 pruned_ratios(list ) - \u8ba1\u7b97\u5377\u79ef\u5c42\u654f\u611f\u5ea6\u4fe1\u606f\u65f6\uff0c\u4f9d\u6b21\u526a\u6389\u7684\u901a\u9053\u6570\u6bd4\u4f8b\u3002\u9ed8\u8ba4\u4e3a[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684dict\uff0c\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u8fd0\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 import paddle import numpy as np import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import sensitivity import paddle.dataset.mnist as reader def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels image_shape = [ 1 , 28 , 28 ] with fluid . program_guard ( main_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None ] + image_shape , dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv1 = conv_bn_layer ( image , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) out = fluid . layers . fc ( conv6 , size = 10 , act = \"softmax\" ) # cost = fluid.layers.cross_entropy(input=out, label=label) # avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid . layers . accuracy ( input = out , label = label , k = 1 ) # acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) val_reader = paddle . batch ( reader . test (), batch_size = 128 ) val_feeder = feeder = fluid . DataFeeder ( [ image , label ], place , program = main_program ) def eval_func ( program ): acc_top1_ns = [] for data in val_reader (): acc_top1_n = exe . run ( program , feed = val_feeder . feed ( data ), fetch_list = [ acc_top1 . name ]) acc_top1_ns . append ( np . mean ( acc_top1_n )) return np . mean ( acc_top1_ns ) param_names = [] for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : param_names . append ( param . name ) sensitivities = sensitivity ( main_program , place , param_names , eval_func , sensitivities_file = \"./sensitive.data\" , pruned_ratios = [ 0.1 , 0.2 , 0.3 ]) print ( sensitivities ) merge_sensitive # paddleslim.prune.merge_sensitive(sensitivities) [\u6e90\u4ee3\u7801] \u5408\u5e76\u591a\u4e2a\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities(list | list ) - \u5f85\u5408\u5e76\u7684\u654f\u611f\u5ea6\u4fe1\u606f\uff0c\u53ef\u4ee5\u662f\u5b57\u5178\u7684\u5217\u8868\uff0c\u6216\u8005\u662f\u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\u7684\u8def\u5f84\u5217\u8868\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a load_sensitivities # paddleslim.prune.load_sensitivities(sensitivities_file) [\u6e90\u4ee3\u7801] \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities_file(str) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6. \u8fd4\u56de\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u793a\u4f8b\uff1a get_ratios_by_loss # paddleslim.prune.get_ratios_by_loss(sensitivities, loss) [\u6e90\u4ee3\u7801] \u6839\u636e\u654f\u611f\u5ea6\u548c\u7cbe\u5ea6\u635f\u5931\u9608\u503c\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u5207\u7387\u3002\u5bf9\u4e8e\u53c2\u6570 w , \u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e loss \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 loss - \u7cbe\u5ea6\u635f\u5931\u9608\u503c\u3002 \u8fd4\u56de\uff1a ratios(dict) - \u4e00\u7ec4\u526a\u5207\u7387\u3002 key \u662f\u5f85\u526a\u88c1\u53c2\u6570\u7684\u540d\u79f0\u3002 value \u662f\u5bf9\u5e94\u53c2\u6570\u7684\u526a\u88c1\u7387\u3002","title":"\u526a\u679d\u4e0e\u654f\u611f\u5ea6"},{"location":"api/prune_api/#pruner","text":"paddleslim.prune.Pruner(criterion=\"l1_norm\") [\u6e90\u4ee3\u7801] \u5bf9\u5377\u79ef\u7f51\u7edc\u7684\u901a\u9053\u8fdb\u884c\u4e00\u6b21\u526a\u88c1\u3002\u526a\u88c1\u4e00\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\uff0c\u662f\u6307\u526a\u88c1\u8be5\u5377\u79ef\u5c42\u8f93\u51fa\u7684\u901a\u9053\u3002\u5377\u79ef\u5c42\u7684\u6743\u91cd\u5f62\u72b6\u4e3a [output_channel, input_channel, kernel_size, kernel_size] \uff0c\u901a\u8fc7\u526a\u88c1\u8be5\u6743\u91cd\u7684\u7b2c\u4e00\u7eac\u5ea6\u8fbe\u5230\u526a\u88c1\u8f93\u51fa\u901a\u9053\u6570\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a criterion - \u8bc4\u4f30\u4e00\u4e2a\u5377\u79ef\u5c42\u5185\u901a\u9053\u91cd\u8981\u6027\u6240\u53c2\u8003\u7684\u6307\u6807\u3002\u76ee\u524d\u4ec5\u652f\u6301 l1_norm \u3002\u9ed8\u8ba4\u4e3a l1_norm \u3002 \u8fd4\u56de\uff1a \u4e00\u4e2aPruner\u7c7b\u7684\u5b9e\u4f8b \u793a\u4f8b\u4ee3\u7801\uff1a 1 2 from paddleslim.prune import Pruner pruner = Pruner () paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) [\u6e90\u4ee3\u7801] \u5bf9\u76ee\u6807\u7f51\u7edc\u7684\u4e00\u7ec4\u5377\u79ef\u5c42\u7684\u6743\u91cd\u8fdb\u884c\u88c1\u526a\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u8981\u88c1\u526a\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 scope(paddle.fluid.Scope) - \u8981\u88c1\u526a\u7684\u6743\u91cd\u6240\u5728\u7684 scope \uff0cPaddle\u4e2d\u7528 scope \u5b9e\u4f8b\u5b58\u653e\u6a21\u578b\u53c2\u6570\u548c\u8fd0\u884c\u65f6\u53d8\u91cf\u7684\u503c\u3002Scope\u4e2d\u7684\u53c2\u6570\u503c\u4f1a\u88ab inplace \u7684\u88c1\u526a\u3002\u66f4\u591a\u4ecb\u7ecd\u8bf7\u53c2\u8003 Scope\u6982\u5ff5\u4ecb\u7ecd params(list ) - \u9700\u8981\u88ab\u88c1\u526a\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) ratios(list ) - \u7528\u4e8e\u88c1\u526a params \u7684\u526a\u5207\u7387\uff0c\u7c7b\u578b\u4e3a\u5217\u8868\u3002\u8be5\u5217\u8868\u957f\u5ea6\u5fc5\u987b\u4e0e params \u7684\u957f\u5ea6\u4e00\u81f4\u3002 place(paddle.fluid.Place) - \u5f85\u88c1\u526a\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd lazy(bool) - lazy \u4e3aTrue\u65f6\uff0c\u901a\u8fc7\u5c06\u6307\u5b9a\u901a\u9053\u7684\u53c2\u6570\u7f6e\u96f6\u8fbe\u5230\u88c1\u526a\u7684\u76ee\u7684\uff0c\u53c2\u6570\u7684 shape\u4fdd\u6301\u4e0d\u53d8 \uff1b lazy \u4e3aFalse\u65f6\uff0c\u76f4\u63a5\u5c06\u8981\u88c1\u7684\u901a\u9053\u7684\u53c2\u6570\u5220\u9664\uff0c\u53c2\u6570\u7684 shape \u4f1a\u53d1\u751f\u53d8\u5316\u3002 only_graph(bool) - \u662f\u5426\u53ea\u88c1\u526a\u7f51\u7edc\u7ed3\u6784\u3002\u5728Paddle\u4e2d\uff0cProgram\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\uff0cScope\u5b58\u50a8\u53c2\u6570\u7684\u6570\u503c\u3002\u4e00\u4e2aScope\u5b9e\u4f8b\u53ef\u4ee5\u88ab\u591a\u4e2aProgram\u4f7f\u7528\uff0c\u6bd4\u5982\u5b9a\u4e49\u4e86\u8bad\u7ec3\u7f51\u7edc\u7684Program\u548c\u5b9a\u4e49\u4e86\u6d4b\u8bd5\u7f51\u7edc\u7684Program\u662f\u4f7f\u7528\u540c\u4e00\u4e2aScope\u5b9e\u4f8b\u7684\u3002 only_graph \u4e3aTrue\u65f6\uff0c\u53ea\u5bf9Program\u4e2d\u5b9a\u4e49\u7684\u5377\u79ef\u7684\u901a\u9053\u8fdb\u884c\u526a\u88c1\uff1b only_graph \u4e3afalse\u65f6\uff0cScope\u4e2d\u5377\u79ef\u53c2\u6570\u7684\u6570\u503c\u4e5f\u4f1a\u88ab\u526a\u88c1\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570\u503c\u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 param_shape_backup(bool) - \u662f\u5426\u8fd4\u56de\u5bf9\u53c2\u6570 shape \u7684\u5907\u4efd\u3002\u9ed8\u8ba4\u4e3aFalse\u3002 \u8fd4\u56de\uff1a pruned_program(paddle.fluid.Program) - \u88ab\u88c1\u526a\u540e\u7684Program\u3002 param_backup(dict) - \u5bf9\u53c2\u6570\u6570\u503c\u7684\u5907\u4efd\uff0c\u7528\u4e8e\u6062\u590dScope\u4e2d\u7684\u53c2\u6570\u6570\u503c\u3002 param_shape_backup(dict) - \u5bf9\u53c2\u6570\u5f62\u72b6\u7684\u5907\u4efd\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u6267\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import Pruner def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels with fluid . program_guard ( main_program , startup_program ): input = fluid . data ( name = \"image\" , shape = [ None , 3 , 16 , 16 ]) conv1 = conv_bn_layer ( input , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) place = fluid . CPUPlace () exe = fluid . Executor ( place ) scope = fluid . Scope () exe . run ( startup_program , scope = scope ) pruner = Pruner () main_program , _ , _ = pruner . prune ( main_program , scope , params = [ \"conv4_weights\" ], ratios = [ 0.5 ], place = place , lazy = False , only_graph = False , param_backup = False , param_shape_backup = False ) for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : print ( \"param name: {}; param shape: {}\" . format ( param . name , param . shape ))","title":"Pruner"},{"location":"api/prune_api/#sensitivity","text":"paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) [\u6e90\u4ee3\u7801] \u8ba1\u7b97\u7f51\u7edc\u4e2d\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u3002\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u7edf\u8ba1\u65b9\u6cd5\u4e3a\uff1a\u4f9d\u6b21\u526a\u6389\u5f53\u524d\u5377\u79ef\u5c42\u4e0d\u540c\u6bd4\u4f8b\u7684\u8f93\u51fa\u901a\u9053\u6570\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8ba1\u7b97\u526a\u88c1\u540e\u7684\u7cbe\u5ea6\u635f\u5931\u3002\u5f97\u5230\u654f\u611f\u5ea6\u4fe1\u606f\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u6216\u5176\u5b83\u65b9\u5f0f\u786e\u5b9a\u6bcf\u5c42\u5377\u79ef\u7684\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a program(paddle.fluid.Program) - \u5f85\u8bc4\u4f30\u7684\u76ee\u6807\u7f51\u7edc\u3002\u66f4\u591a\u5173\u4e8eProgram\u7684\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a Program\u6982\u5ff5\u4ecb\u7ecd \u3002 place(paddle.fluid.Place) - \u5f85\u5206\u6790\u7684\u53c2\u6570\u6240\u5728\u7684\u8bbe\u5907\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u662f CUDAPlace \u6216 CPUPlace \u3002 Place\u6982\u5ff5\u4ecb\u7ecd param_names(list ) - \u5f85\u5206\u6790\u7684\u5377\u79ef\u5c42\u7684\u53c2\u6570\u7684\u540d\u79f0\u5217\u8868\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u67e5\u770b\u6a21\u578b\u4e2d\u6240\u6709\u53c2\u6570\u7684\u540d\u79f0: 1 2 3 for block in program . blocks : for param in block . all_parameters () : print ( \" param: {}; shape: {} \" . format ( param . name , param . shape )) eval_func(function) - \u7528\u4e8e\u8bc4\u4f30\u88c1\u526a\u540e\u6a21\u578b\u6548\u679c\u7684\u56de\u8c03\u51fd\u6570\u3002\u8be5\u56de\u8c03\u51fd\u6570\u63a5\u53d7\u88ab\u88c1\u526a\u540e\u7684 program \u4e3a\u53c2\u6570\uff0c\u8fd4\u56de\u4e00\u4e2a\u8868\u793a\u5f53\u524dprogram\u7684\u7cbe\u5ea6\uff0c\u7528\u4ee5\u8ba1\u7b97\u5f53\u524d\u88c1\u526a\u5e26\u6765\u7684\u7cbe\u5ea6\u635f\u5931\u3002 sensitivities_file(str) - \u4fdd\u5b58\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6\u7cfb\u7edf\u7684\u6587\u4ef6\u3002\u5728\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\uff0c\u4f1a\u6301\u7eed\u5c06\u65b0\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u8ffd\u52a0\u5230\u8be5\u6587\u4ef6\u4e2d\u3002\u91cd\u542f\u4efb\u52a1\u540e\uff0c\u6587\u4ef6\u4e2d\u5df2\u6709\u654f\u611f\u5ea6\u4fe1\u606f\u4e0d\u4f1a\u88ab\u91cd\u590d\u8ba1\u7b97\u3002\u8be5\u6587\u4ef6\u53ef\u4ee5\u7528 pickle \u52a0\u8f7d\u3002 pruned_ratios(list ) - \u8ba1\u7b97\u5377\u79ef\u5c42\u654f\u611f\u5ea6\u4fe1\u606f\u65f6\uff0c\u4f9d\u6b21\u526a\u6389\u7684\u901a\u9053\u6570\u6bd4\u4f8b\u3002\u9ed8\u8ba4\u4e3a[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684dict\uff0c\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a \u70b9\u51fb AIStudio \u8fd0\u884c\u4ee5\u4e0b\u793a\u4f8b\u4ee3\u7801\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 import paddle import numpy as np import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.prune import sensitivity import paddle.dataset.mnist as reader def conv_bn_layer ( input , num_filters , filter_size , name , stride = 1 , groups = 1 , act = None ): conv = fluid . layers . conv2d ( input = input , num_filters = num_filters , filter_size = filter_size , stride = stride , padding = ( filter_size - 1 ) // 2 , groups = groups , act = None , param_attr = ParamAttr ( name = name + \"_weights\" ), bias_attr = False , name = name + \"_out\" ) bn_name = name + \"_bn\" return fluid . layers . batch_norm ( input = conv , act = act , name = bn_name + '_output' , param_attr = ParamAttr ( name = bn_name + '_scale' ), bias_attr = ParamAttr ( bn_name + '_offset' ), moving_mean_name = bn_name + '_mean' , moving_variance_name = bn_name + '_variance' , ) main_program = fluid . Program () startup_program = fluid . Program () # X X O X O # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6 # | ^ | ^ # |____________| |____________________| # # X: prune output channels # O: prune input channels image_shape = [ 1 , 28 , 28 ] with fluid . program_guard ( main_program , startup_program ): image = fluid . data ( name = 'image' , shape = [ None ] + image_shape , dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv1 = conv_bn_layer ( image , 8 , 3 , \"conv1\" ) conv2 = conv_bn_layer ( conv1 , 8 , 3 , \"conv2\" ) sum1 = conv1 + conv2 conv3 = conv_bn_layer ( sum1 , 8 , 3 , \"conv3\" ) conv4 = conv_bn_layer ( conv3 , 8 , 3 , \"conv4\" ) sum2 = conv4 + sum1 conv5 = conv_bn_layer ( sum2 , 8 , 3 , \"conv5\" ) conv6 = conv_bn_layer ( conv5 , 8 , 3 , \"conv6\" ) out = fluid . layers . fc ( conv6 , size = 10 , act = \"softmax\" ) # cost = fluid.layers.cross_entropy(input=out, label=label) # avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid . layers . accuracy ( input = out , label = label , k = 1 ) # acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) place = fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( startup_program ) val_reader = paddle . batch ( reader . test (), batch_size = 128 ) val_feeder = feeder = fluid . DataFeeder ( [ image , label ], place , program = main_program ) def eval_func ( program ): acc_top1_ns = [] for data in val_reader (): acc_top1_n = exe . run ( program , feed = val_feeder . feed ( data ), fetch_list = [ acc_top1 . name ]) acc_top1_ns . append ( np . mean ( acc_top1_n )) return np . mean ( acc_top1_ns ) param_names = [] for param in main_program . global_block () . all_parameters (): if \"weights\" in param . name : param_names . append ( param . name ) sensitivities = sensitivity ( main_program , place , param_names , eval_func , sensitivities_file = \"./sensitive.data\" , pruned_ratios = [ 0.1 , 0.2 , 0.3 ]) print ( sensitivities )","title":"sensitivity"},{"location":"api/prune_api/#merge_sensitive","text":"paddleslim.prune.merge_sensitive(sensitivities) [\u6e90\u4ee3\u7801] \u5408\u5e76\u591a\u4e2a\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities(list | list ) - \u5f85\u5408\u5e76\u7684\u654f\u611f\u5ea6\u4fe1\u606f\uff0c\u53ef\u4ee5\u662f\u5b57\u5178\u7684\u5217\u8868\uff0c\u6216\u8005\u662f\u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\u7684\u8def\u5f84\u5217\u8868\u3002 \u8fd4\u56de\uff1a sensitivities(dict) - \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a 1 2 3 4 5 6 7 8 9 { \"weight_0\" : { 0 . 1 : 0 . 22 , 0 . 2 : 0 . 33 } , \"weight_1\" : { 0 . 1 : 0 . 21 , 0 . 2 : 0 . 4 } } \u5176\u4e2d\uff0c weight_0 \u662f\u5377\u79ef\u5c42\u53c2\u6570\u7684\u540d\u79f0\uff0csensitivities['weight_0']\u7684 value \u4e3a\u526a\u88c1\u6bd4\u4f8b\uff0c value \u4e3a\u7cbe\u5ea6\u635f\u5931\u7684\u6bd4\u4f8b\u3002 \u793a\u4f8b\uff1a","title":"merge_sensitive"},{"location":"api/prune_api/#load_sensitivities","text":"paddleslim.prune.load_sensitivities(sensitivities_file) [\u6e90\u4ee3\u7801] \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u53c2\u6570\uff1a sensitivities_file(str) - \u5b58\u653e\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u672c\u5730\u6587\u4ef6. \u8fd4\u56de\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 \u793a\u4f8b\uff1a","title":"load_sensitivities"},{"location":"api/prune_api/#get_ratios_by_loss","text":"paddleslim.prune.get_ratios_by_loss(sensitivities, loss) [\u6e90\u4ee3\u7801] \u6839\u636e\u654f\u611f\u5ea6\u548c\u7cbe\u5ea6\u635f\u5931\u9608\u503c\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u5207\u7387\u3002\u5bf9\u4e8e\u53c2\u6570 w , \u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e loss \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u53c2\u6570\uff1a sensitivities(dict) - \u654f\u611f\u5ea6\u4fe1\u606f\u3002 loss - \u7cbe\u5ea6\u635f\u5931\u9608\u503c\u3002 \u8fd4\u56de\uff1a ratios(dict) - \u4e00\u7ec4\u526a\u5207\u7387\u3002 key \u662f\u5f85\u526a\u88c1\u53c2\u6570\u7684\u540d\u79f0\u3002 value \u662f\u5bf9\u5e94\u53c2\u6570\u7684\u526a\u88c1\u7387\u3002","title":"get_ratios_by_loss"},{"location":"api/quantization_api/","text":"\u91cf\u5316\u914d\u7f6e # \u901a\u8fc7\u5b57\u5178\u914d\u7f6e\u91cf\u5316\u53c2\u6570 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 quant_config_default = { ' weight_quantize_type ' : ' abs_max ' , ' activation_quantize_type ' : ' abs_max ' , ' weight_bits ' : 8 , ' activation_bits ' : 8 , # ops of name_scope in not_quant_pattern list , will not be quantized ' not_quant_pattern ' : [ ' skip_quant ' ], # ops of type in quantize_op_types , will be quantized ' quantize_op_types ' : [ ' conv2d ' , ' depthwise_conv2d ' , ' mul ' , ' elementwise_add ' , ' pool2d ' ], # data type after quantization , such as ' uint8 ' , ' int8 ' , etc . default is ' int8 ' ' dtype ' : ' int8 ' , # window size for ' range_abs_max ' quantization . defaulf is 10000 ' window_size ' : 10000 , # The decay coefficient of moving average , default is 0 . 9 ' moving_rate ' : 0 . 9 , } \u53c2\u6570\uff1a weight_quantize_type(str) - \u53c2\u6570\u91cf\u5316\u65b9\u5f0f\u3002\u53ef\u9009 'abs_max' , 'channel_wise_abs_max' , 'range_abs_max' , 'moving_average_abs_max' \u3002 \u9ed8\u8ba4 'abs_max' \u3002 activation_quantize_type(str) - \u6fc0\u6d3b\u91cf\u5316\u65b9\u5f0f\uff0c\u53ef\u9009 'abs_max' , 'range_abs_max' , 'moving_average_abs_max' \uff0c\u9ed8\u8ba4 'abs_max' \u3002 weight_bits(int) - \u53c2\u6570\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48, \u63a8\u8350\u8bbe\u4e3a8\u3002 activation_bits(int) - \u6fc0\u6d3b\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48\uff0c \u63a8\u8350\u8bbe\u4e3a8\u3002 not_quant_pattern(str | list[str]) - \u6240\u6709 name_scope \u5305\u542b 'not_quant_pattern' \u5b57\u7b26\u4e32\u7684 op \uff0c\u90fd\u4e0d\u91cf\u5316, \u8bbe\u7f6e\u65b9\u5f0f\u8bf7\u53c2\u8003 fluid.name_scope \u3002 quantize_op_types(list[str]) - \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684 op \u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301 'conv2d', 'depthwise_conv2d', 'mul' \u3002 dtype(int8) - \u91cf\u5316\u540e\u7684\u53c2\u6570\u7c7b\u578b\uff0c\u9ed8\u8ba4 int8 , \u76ee\u524d\u4ec5\u652f\u6301 int8 \u3002 window_size(int) - 'range_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684 window size \uff0c\u9ed8\u8ba410000\u3002 moving_rate(int) - 'moving_average_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684\u8870\u51cf\u7cfb\u6570\uff0c\u9ed8\u8ba4 0.9\u3002 quant_aware # paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False) [\u6e90\u4ee3\u7801] \u5728 program \u4e2d\u52a0\u5165\u91cf\u5316\u548c\u53cd\u91cf\u5316 op , \u7528\u4e8e\u91cf\u5316\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u8bad\u7ec3\u6216\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope, optional) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 for_test(bool) - \u5982\u679c program \u53c2\u6570\u662f\u4e00\u4e2a\u6d4b\u8bd5 program \uff0c for_test \u5e94\u8bbe\u4e3a True \uff0c\u5426\u5219\u8bbe\u4e3a False \u3002 \u8fd4\u56de \u542b\u6709\u91cf\u5316\u548c\u53cd\u91cf\u5316 operator \u7684 program \u8fd4\u56de\u7c7b\u578b \u5f53 for_test=False \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.CompiledProgram \uff0c \u6ce8\u610f\uff0c\u6b64\u8fd4\u56de\u503c\u4e0d\u80fd\u7528\u4e8e\u4fdd\u5b58\u53c2\u6570 \u3002 \u5f53 for_test=True \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.Program \u3002 \u6ce8\u610f\u4e8b\u9879 \u6b64\u63a5\u53e3\u4f1a\u6539\u53d8 program \u7ed3\u6784\uff0c\u5e76\u4e14\u53ef\u80fd\u589e\u52a0\u4e00\u4e9b persistable \u7684\u53d8\u91cf\uff0c\u6240\u4ee5\u52a0\u8f7d\u6a21\u578b\u53c2\u6570\u65f6\u8bf7\u6ce8\u610f\u548c\u76f8\u5e94\u7684 program \u5bf9\u5e94\u3002 \u6b64\u63a5\u53e3\u5e95\u5c42\u7ecf\u5386\u4e86 fluid.Program -> fluid.framework.IrGraph -> fluid.Program \u7684\u8f6c\u53d8\uff0c\u5728 fluid.framework.IrGraph \u4e2d\u6ca1\u6709 Parameter \u7684\u6982\u5ff5\uff0c Variable \u53ea\u6709 persistable \u548c not persistable \u7684\u533a\u522b\uff0c\u6240\u4ee5\u5728\u4fdd\u5b58\u548c\u52a0\u8f7d\u53c2\u6570\u65f6\uff0c\u8bf7\u4f7f\u7528 fluid.io.save_persistables \u548c fluid.io.load_persistables \u63a5\u53e3\u3002 \u7531\u4e8e\u6b64\u63a5\u53e3\u4f1a\u6839\u636e program \u7684\u7ed3\u6784\u548c\u91cf\u5316\u914d\u7f6e\u6765\u5bf9 program \u6dfb\u52a0op\uff0c\u6240\u4ee5 Paddle \u4e2d\u4e00\u4e9b\u901a\u8fc7 fuse op \u6765\u52a0\u901f\u8bad\u7ec3\u7684\u7b56\u7565\u4e0d\u80fd\u4f7f\u7528\u3002\u5df2\u77e5\u4ee5\u4e0b\u7b56\u7565\u5728\u4f7f\u7528\u91cf\u5316\u65f6\u5fc5\u987b\u8bbe\u4e3a False \uff1a fuse_all_reduce_ops, sync_batch_norm \u3002 \u5982\u679c\u4f20\u5165\u7684 program \u4e2d\u5b58\u5728\u548c\u4efb\u4f55op\u90fd\u6ca1\u6709\u8fde\u63a5\u7684 Variable \uff0c\u5219\u4f1a\u5728\u91cf\u5316\u7684\u8fc7\u7a0b\u4e2d\u88ab\u4f18\u5316\u6389\u3002 convert # paddleslim.quant.convert(program, place, config, scope=None, save_int8=False) [\u6e90\u4ee3\u7801] \u628a\u8bad\u7ec3\u597d\u7684\u91cf\u5316 program \uff0c\u8f6c\u6362\u4e3a\u53ef\u7528\u4e8e\u4fdd\u5b58 inference model \u7684 program \u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 save_int8\uff08bool) - \u662f\u5426\u9700\u8981\u8fd4\u56de\u53c2\u6570\u4e3a int8 \u7684 program \u3002\u8be5\u529f\u80fd\u76ee\u524d\u53ea\u80fd\u7528\u4e8e\u786e\u8ba4\u6a21\u578b\u5927\u5c0f\u3002\u9ed8\u8ba4\u503c\u4e3a False \u3002 \u8fd4\u56de program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a float32 \u7c7b\u578b\uff0c\u4f46\u5176\u6570\u503c\u8303\u56f4\u53ef\u7528int8\u8868\u793a\u3002 int8_program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a int8 \u7c7b\u578b\u3002\u5f53 save_int8 \u4e3a False \u65f6\uff0c\u4e0d\u8fd4\u56de\u8be5\u503c\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u5bf9 op \u548c Variable \u505a\u76f8\u5e94\u7684\u5220\u9664\u548c\u4fee\u6539\uff0c\u6240\u4ee5\u6b64\u63a5\u53e3\u53ea\u80fd\u5728\u8bad\u7ec3\u5b8c\u6210\u4e4b\u540e\u8c03\u7528\u3002\u5982\u679c\u60f3\u8f6c\u5316\u8bad\u7ec3\u7684\u4e2d\u95f4\u6a21\u578b\uff0c\u53ef\u52a0\u8f7d\u76f8\u5e94\u7684\u53c2\u6570\u4e4b\u540e\u518d\u4f7f\u7528\u6b64\u63a5\u53e3\u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 #encoding=utf8 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): image = fluid . data ( name = 'x' , shape = [ None , 1 , 28 , 28 ]) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv = fluid . layers . conv2d ( image , 32 , 1 ) feat = fluid . layers . fc ( conv , 10 , act = 'softmax' ) cost = fluid . layers . cross_entropy ( input = feat , label = label ) avg_cost = fluid . layers . mean ( x = cost ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) eval_program = train_program . clone ( for_test = True ) #\u914d\u7f6e config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' } build_strategy = fluid . BuildStrategy () exec_strategy = fluid . ExecutionStrategy () #\u8c03\u7528api quant_train_program = quant . quant_aware ( train_program , place , config , for_test = False ) quant_eval_program = quant . quant_aware ( eval_program , place , config , for_test = True ) #\u5173\u95ed\u7b56\u7565 build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False quant_train_program = quant_train_program . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) inference_prog = quant . convert ( quant_eval_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u91cf\u5316\u8bad\u7ec3demo \u3002 quant_post # paddleslim.quant.quant_post(executor, model_dir, quantize_model_path,sample_generator, model_filename=None, params_filename=None, batch_size=16,batch_nums=None, scope=None, algo='KL', quantizable_op_type=[\"conv2d\", \"depthwise_conv2d\", \"mul\"]) [\u6e90\u4ee3\u7801] \u5bf9\u4fdd\u5b58\u5728 ${model_dir} \u4e0b\u7684\u6a21\u578b\u8fdb\u884c\u91cf\u5316\uff0c\u4f7f\u7528 sample_generator \u7684\u6570\u636e\u8fdb\u884c\u53c2\u6570\u6821\u6b63\u3002 \u53c2\u6570: executor (fluid.Executor) - \u6267\u884c\u6a21\u578b\u7684executor\uff0c\u53ef\u4ee5\u5728cpu\u6216\u8005gpu\u4e0a\u6267\u884c\u3002 model_dir\uff08str) - \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u6240\u5728\u7684\u6587\u4ef6\u5939\u3002 quantize_model_path(str) - \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u8def\u5f84 sample_generator(python generator) - \u8bfb\u53d6\u6570\u636e\u6837\u672c\uff0c\u6bcf\u6b21\u8fd4\u56de\u4e00\u4e2a\u6837\u672c\u3002 model_filename(str, optional) - \u6a21\u578b\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e model_filename \u4e3a\u6a21\u578b\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 params_filename(str) - \u53c2\u6570\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e params_filename \u4e3a\u53c2\u6570\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 batch_size(int) - \u6bcf\u4e2abatch\u7684\u56fe\u7247\u6570\u91cf\u3002\u9ed8\u8ba4\u503c\u4e3a16 \u3002 batch_nums(int, optional) - \u8fed\u4ee3\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a None \uff0c\u5219\u4f1a\u4e00\u76f4\u8fd0\u884c\u5230 sample_generator \u8fed\u4ee3\u7ed3\u675f\uff0c \u5426\u5219\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a batch_nums , \u4e5f\u5c31\u662f\u8bf4\u53c2\u4e0e\u5bf9 Scale \u8fdb\u884c\u6821\u6b63\u7684\u6837\u672c\u4e2a\u6570\u4e3a 'batch_nums' * 'batch_size' . scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . \u9ed8\u8ba4\u503c\u662f None . algo(str) - \u91cf\u5316\u65f6\u4f7f\u7528\u7684\u7b97\u6cd5\u540d\u79f0\uff0c\u53ef\u4e3a 'KL' \u6216\u8005 'direct' \u3002\u8be5\u53c2\u6570\u4ec5\u9488\u5bf9\u6fc0\u6d3b\u503c\u7684\u91cf\u5316\uff0c\u56e0\u4e3a\u53c2\u6570\u503c\u7684\u91cf\u5316\u4f7f\u7528\u7684\u65b9\u5f0f\u4e3a 'channel_wise_abs_max' . \u5f53 algo \u8bbe\u7f6e\u4e3a 'direct' \u65f6\uff0c\u4f7f\u7528\u6821\u6b63\u6570\u636e\u7684\u6fc0\u6d3b\u503c\u7684\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u5f53\u4f5c Scale \u503c\uff0c\u5f53\u8bbe\u7f6e\u4e3a 'KL' \u65f6\uff0c\u5219\u4f7f\u7528 KL \u6563\u5ea6\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97 Scale \u503c\u3002\u9ed8\u8ba4\u503c\u4e3a 'KL' \u3002 quantizable_op_type(list[str]) - \u9700\u8981\u91cf\u5316\u7684 op \u7c7b\u578b\u5217\u8868\u3002\u9ed8\u8ba4\u503c\u4e3a [\"conv2d\", \"depthwise_conv2d\", \"mul\"] \u3002 \u8fd4\u56de \u65e0\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u6536\u96c6\u6821\u6b63\u6570\u636e\u7684\u6240\u6709\u7684\u6fc0\u6d3b\u503c\uff0c\u6240\u4ee5\u4f7f\u7528\u7684\u6821\u6b63\u56fe\u7247\u4e0d\u80fd\u592a\u591a\u3002 'KL' \u6563\u5ea6\u7684\u8ba1\u7b97\u4e5f\u6bd4\u8f83\u8017\u65f6\u3002 \u4ee3\u7801\u793a\u4f8b \u6ce8\uff1a \u6b64\u793a\u4f8b\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\uff0c\u56e0\u4e3a\u9700\u8981\u52a0\u8f7d ${model_dir} \u4e0b\u7684\u6a21\u578b\uff0c\u6240\u4ee5\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import paddle.fluid as fluid import paddle.dataset.mnist as reader from paddleslim.quant import quant_post val_reader = reader . train () use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) quant_post ( executor = exe , model_dir = './model_path' , quantize_model_path = './save_path' , sample_generator = val_reader , model_filename = '__model__' , params_filename = '__params__' , batch_size = 16 , batch_nums = 10 ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u79bb\u7ebf\u91cf\u5316demo \u3002 quant_embedding # paddleslim.quant.quant_embedding(program, place, config, scope=None) [\u6e90\u4ee3\u7801] \u5bf9 Embedding \u53c2\u6570\u8fdb\u884c\u91cf\u5316\u3002 \u53c2\u6570: program(fluid.Program) - \u9700\u8981\u91cf\u5316\u7684program scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . place(fluid.CPUPlace | fluid.CUDAPlace) - \u8fd0\u884cprogram\u7684\u8bbe\u5907 config(dict) - \u5b9a\u4e49\u91cf\u5316\u7684\u914d\u7f6e\u3002\u53ef\u4ee5\u914d\u7f6e\u7684\u53c2\u6570\u6709\uff1a 'params_name' (str, required): \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684\u53c2\u6570\u540d\u79f0\uff0c\u6b64\u53c2\u6570\u5fc5\u987b\u8bbe\u7f6e\u3002 'quantize_type' (str, optional): \u91cf\u5316\u7684\u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301\u7684\u7c7b\u578b\u662f 'abs_max' , \u5f85\u652f\u6301\u7684\u7c7b\u578b\u6709 'log', 'product_quantization' \u3002 \u9ed8\u8ba4\u503c\u662f 'abs_max' . 'quantize_bits' \uff08int, optional): \u91cf\u5316\u7684 bit \u6570\uff0c\u76ee\u524d\u652f\u6301\u7684 bit \u6570\u4e3a8\u3002\u9ed8\u8ba4\u503c\u662f8. 'dtype' (str, optional): \u91cf\u5316\u4e4b\u540e\u7684\u6570\u636e\u7c7b\u578b\uff0c \u76ee\u524d\u652f\u6301\u7684\u662f 'int8' . \u9ed8\u8ba4\u503c\u662f int8 \u3002 'threshold' (float, optional): \u91cf\u5316\u4e4b\u524d\u5c06\u6839\u636e\u6b64\u9608\u503c\u5bf9\u9700\u8981\u91cf\u5316\u7684\u53c2\u6570\u503c\u8fdb\u884c clip . \u5982\u679c\u4e0d\u8bbe\u7f6e\uff0c\u5219\u8df3\u8fc7 clip \u8fc7\u7a0b\u76f4\u63a5\u91cf\u5316\u3002 \u8fd4\u56de \u91cf\u5316\u4e4b\u540e\u7684program \u8fd4\u56de\u7c7b\u578b fluid.Program \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): input_word = fluid . data ( name = \"input_word\" , shape = [ None , 1 ], dtype = 'int64' ) input_emb = fluid . embedding ( input = input_word , is_sparse = False , size = [ 100 , 128 ], param_attr = fluid . ParamAttr ( name = 'emb' , initializer = fluid . initializer . Uniform ( - 0.005 , 0.005 ))) infer_program = train_program . clone ( for_test = True ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } quant_program = quant . quant_embedding ( infer_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 Embedding\u91cf\u5316demo \u3002","title":"\u91cf\u5316"},{"location":"api/quantization_api/#_1","text":"\u901a\u8fc7\u5b57\u5178\u914d\u7f6e\u91cf\u5316\u53c2\u6570 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 quant_config_default = { ' weight_quantize_type ' : ' abs_max ' , ' activation_quantize_type ' : ' abs_max ' , ' weight_bits ' : 8 , ' activation_bits ' : 8 , # ops of name_scope in not_quant_pattern list , will not be quantized ' not_quant_pattern ' : [ ' skip_quant ' ], # ops of type in quantize_op_types , will be quantized ' quantize_op_types ' : [ ' conv2d ' , ' depthwise_conv2d ' , ' mul ' , ' elementwise_add ' , ' pool2d ' ], # data type after quantization , such as ' uint8 ' , ' int8 ' , etc . default is ' int8 ' ' dtype ' : ' int8 ' , # window size for ' range_abs_max ' quantization . defaulf is 10000 ' window_size ' : 10000 , # The decay coefficient of moving average , default is 0 . 9 ' moving_rate ' : 0 . 9 , } \u53c2\u6570\uff1a weight_quantize_type(str) - \u53c2\u6570\u91cf\u5316\u65b9\u5f0f\u3002\u53ef\u9009 'abs_max' , 'channel_wise_abs_max' , 'range_abs_max' , 'moving_average_abs_max' \u3002 \u9ed8\u8ba4 'abs_max' \u3002 activation_quantize_type(str) - \u6fc0\u6d3b\u91cf\u5316\u65b9\u5f0f\uff0c\u53ef\u9009 'abs_max' , 'range_abs_max' , 'moving_average_abs_max' \uff0c\u9ed8\u8ba4 'abs_max' \u3002 weight_bits(int) - \u53c2\u6570\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48, \u63a8\u8350\u8bbe\u4e3a8\u3002 activation_bits(int) - \u6fc0\u6d3b\u91cf\u5316bit\u6570\uff0c\u9ed8\u8ba48\uff0c \u63a8\u8350\u8bbe\u4e3a8\u3002 not_quant_pattern(str | list[str]) - \u6240\u6709 name_scope \u5305\u542b 'not_quant_pattern' \u5b57\u7b26\u4e32\u7684 op \uff0c\u90fd\u4e0d\u91cf\u5316, \u8bbe\u7f6e\u65b9\u5f0f\u8bf7\u53c2\u8003 fluid.name_scope \u3002 quantize_op_types(list[str]) - \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684 op \u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301 'conv2d', 'depthwise_conv2d', 'mul' \u3002 dtype(int8) - \u91cf\u5316\u540e\u7684\u53c2\u6570\u7c7b\u578b\uff0c\u9ed8\u8ba4 int8 , \u76ee\u524d\u4ec5\u652f\u6301 int8 \u3002 window_size(int) - 'range_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684 window size \uff0c\u9ed8\u8ba410000\u3002 moving_rate(int) - 'moving_average_abs_max' \u91cf\u5316\u65b9\u5f0f\u7684\u8870\u51cf\u7cfb\u6570\uff0c\u9ed8\u8ba4 0.9\u3002","title":"\u91cf\u5316\u914d\u7f6e"},{"location":"api/quantization_api/#quant_aware","text":"paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False) [\u6e90\u4ee3\u7801] \u5728 program \u4e2d\u52a0\u5165\u91cf\u5316\u548c\u53cd\u91cf\u5316 op , \u7528\u4e8e\u91cf\u5316\u8bad\u7ec3\u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u8bad\u7ec3\u6216\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope, optional) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 for_test(bool) - \u5982\u679c program \u53c2\u6570\u662f\u4e00\u4e2a\u6d4b\u8bd5 program \uff0c for_test \u5e94\u8bbe\u4e3a True \uff0c\u5426\u5219\u8bbe\u4e3a False \u3002 \u8fd4\u56de \u542b\u6709\u91cf\u5316\u548c\u53cd\u91cf\u5316 operator \u7684 program \u8fd4\u56de\u7c7b\u578b \u5f53 for_test=False \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.CompiledProgram \uff0c \u6ce8\u610f\uff0c\u6b64\u8fd4\u56de\u503c\u4e0d\u80fd\u7528\u4e8e\u4fdd\u5b58\u53c2\u6570 \u3002 \u5f53 for_test=True \uff0c\u8fd4\u56de\u7c7b\u578b\u4e3a fluid.Program \u3002 \u6ce8\u610f\u4e8b\u9879 \u6b64\u63a5\u53e3\u4f1a\u6539\u53d8 program \u7ed3\u6784\uff0c\u5e76\u4e14\u53ef\u80fd\u589e\u52a0\u4e00\u4e9b persistable \u7684\u53d8\u91cf\uff0c\u6240\u4ee5\u52a0\u8f7d\u6a21\u578b\u53c2\u6570\u65f6\u8bf7\u6ce8\u610f\u548c\u76f8\u5e94\u7684 program \u5bf9\u5e94\u3002 \u6b64\u63a5\u53e3\u5e95\u5c42\u7ecf\u5386\u4e86 fluid.Program -> fluid.framework.IrGraph -> fluid.Program \u7684\u8f6c\u53d8\uff0c\u5728 fluid.framework.IrGraph \u4e2d\u6ca1\u6709 Parameter \u7684\u6982\u5ff5\uff0c Variable \u53ea\u6709 persistable \u548c not persistable \u7684\u533a\u522b\uff0c\u6240\u4ee5\u5728\u4fdd\u5b58\u548c\u52a0\u8f7d\u53c2\u6570\u65f6\uff0c\u8bf7\u4f7f\u7528 fluid.io.save_persistables \u548c fluid.io.load_persistables \u63a5\u53e3\u3002 \u7531\u4e8e\u6b64\u63a5\u53e3\u4f1a\u6839\u636e program \u7684\u7ed3\u6784\u548c\u91cf\u5316\u914d\u7f6e\u6765\u5bf9 program \u6dfb\u52a0op\uff0c\u6240\u4ee5 Paddle \u4e2d\u4e00\u4e9b\u901a\u8fc7 fuse op \u6765\u52a0\u901f\u8bad\u7ec3\u7684\u7b56\u7565\u4e0d\u80fd\u4f7f\u7528\u3002\u5df2\u77e5\u4ee5\u4e0b\u7b56\u7565\u5728\u4f7f\u7528\u91cf\u5316\u65f6\u5fc5\u987b\u8bbe\u4e3a False \uff1a fuse_all_reduce_ops, sync_batch_norm \u3002 \u5982\u679c\u4f20\u5165\u7684 program \u4e2d\u5b58\u5728\u548c\u4efb\u4f55op\u90fd\u6ca1\u6709\u8fde\u63a5\u7684 Variable \uff0c\u5219\u4f1a\u5728\u91cf\u5316\u7684\u8fc7\u7a0b\u4e2d\u88ab\u4f18\u5316\u6389\u3002","title":"quant_aware"},{"location":"api/quantization_api/#convert","text":"paddleslim.quant.convert(program, place, config, scope=None, save_int8=False) [\u6e90\u4ee3\u7801] \u628a\u8bad\u7ec3\u597d\u7684\u91cf\u5316 program \uff0c\u8f6c\u6362\u4e3a\u53ef\u7528\u4e8e\u4fdd\u5b58 inference model \u7684 program \u3002 \u53c2\u6570\uff1a program (fluid.Program) - \u4f20\u5165\u6d4b\u8bd5 program \u3002 place(fluid.CPUPlace | fluid.CUDAPlace) - \u8be5\u53c2\u6570\u8868\u793a Executor \u6267\u884c\u6240\u5728\u7684\u8bbe\u5907\u3002 config(dict) - \u91cf\u5316\u914d\u7f6e\u8868\u3002 scope(fluid.Scope) - \u4f20\u5165\u7528\u4e8e\u5b58\u50a8 Variable \u7684 scope \uff0c\u9700\u8981\u4f20\u5165 program \u6240\u4f7f\u7528\u7684 scope \uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u662f fluid.global_scope() \u3002\u8bbe\u7f6e\u4e3a None \u65f6\u5c06\u4f7f\u7528 fluid.global_scope() \uff0c\u9ed8\u8ba4\u503c\u4e3a None \u3002 save_int8\uff08bool) - \u662f\u5426\u9700\u8981\u8fd4\u56de\u53c2\u6570\u4e3a int8 \u7684 program \u3002\u8be5\u529f\u80fd\u76ee\u524d\u53ea\u80fd\u7528\u4e8e\u786e\u8ba4\u6a21\u578b\u5927\u5c0f\u3002\u9ed8\u8ba4\u503c\u4e3a False \u3002 \u8fd4\u56de program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a float32 \u7c7b\u578b\uff0c\u4f46\u5176\u6570\u503c\u8303\u56f4\u53ef\u7528int8\u8868\u793a\u3002 int8_program (fluid.Program) - freezed program\uff0c\u53ef\u7528\u4e8e\u4fdd\u5b58inference model\uff0c\u53c2\u6570\u4e3a int8 \u7c7b\u578b\u3002\u5f53 save_int8 \u4e3a False \u65f6\uff0c\u4e0d\u8fd4\u56de\u8be5\u503c\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u5bf9 op \u548c Variable \u505a\u76f8\u5e94\u7684\u5220\u9664\u548c\u4fee\u6539\uff0c\u6240\u4ee5\u6b64\u63a5\u53e3\u53ea\u80fd\u5728\u8bad\u7ec3\u5b8c\u6210\u4e4b\u540e\u8c03\u7528\u3002\u5982\u679c\u60f3\u8f6c\u5316\u8bad\u7ec3\u7684\u4e2d\u95f4\u6a21\u578b\uff0c\u53ef\u52a0\u8f7d\u76f8\u5e94\u7684\u53c2\u6570\u4e4b\u540e\u518d\u4f7f\u7528\u6b64\u63a5\u53e3\u3002 \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 #encoding=utf8 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): image = fluid . data ( name = 'x' , shape = [ None , 1 , 28 , 28 ]) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) conv = fluid . layers . conv2d ( image , 32 , 1 ) feat = fluid . layers . fc ( conv , 10 , act = 'softmax' ) cost = fluid . layers . cross_entropy ( input = feat , label = label ) avg_cost = fluid . layers . mean ( x = cost ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) eval_program = train_program . clone ( for_test = True ) #\u914d\u7f6e config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' } build_strategy = fluid . BuildStrategy () exec_strategy = fluid . ExecutionStrategy () #\u8c03\u7528api quant_train_program = quant . quant_aware ( train_program , place , config , for_test = False ) quant_eval_program = quant . quant_aware ( eval_program , place , config , for_test = True ) #\u5173\u95ed\u7b56\u7565 build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False quant_train_program = quant_train_program . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) inference_prog = quant . convert ( quant_eval_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u91cf\u5316\u8bad\u7ec3demo \u3002","title":"convert"},{"location":"api/quantization_api/#quant_post","text":"paddleslim.quant.quant_post(executor, model_dir, quantize_model_path,sample_generator, model_filename=None, params_filename=None, batch_size=16,batch_nums=None, scope=None, algo='KL', quantizable_op_type=[\"conv2d\", \"depthwise_conv2d\", \"mul\"]) [\u6e90\u4ee3\u7801] \u5bf9\u4fdd\u5b58\u5728 ${model_dir} \u4e0b\u7684\u6a21\u578b\u8fdb\u884c\u91cf\u5316\uff0c\u4f7f\u7528 sample_generator \u7684\u6570\u636e\u8fdb\u884c\u53c2\u6570\u6821\u6b63\u3002 \u53c2\u6570: executor (fluid.Executor) - \u6267\u884c\u6a21\u578b\u7684executor\uff0c\u53ef\u4ee5\u5728cpu\u6216\u8005gpu\u4e0a\u6267\u884c\u3002 model_dir\uff08str) - \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u6240\u5728\u7684\u6587\u4ef6\u5939\u3002 quantize_model_path(str) - \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u8def\u5f84 sample_generator(python generator) - \u8bfb\u53d6\u6570\u636e\u6837\u672c\uff0c\u6bcf\u6b21\u8fd4\u56de\u4e00\u4e2a\u6837\u672c\u3002 model_filename(str, optional) - \u6a21\u578b\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e model_filename \u4e3a\u6a21\u578b\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 params_filename(str) - \u53c2\u6570\u6587\u4ef6\u540d\uff0c\u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u9700\u8981\u8bbe\u7f6e params_filename \u4e3a\u53c2\u6570\u6587\u4ef6\u7684\u540d\u79f0\uff0c\u5426\u5219\u8bbe\u7f6e\u4e3a None \u5373\u53ef\u3002\u9ed8\u8ba4\u503c\u662f None \u3002 batch_size(int) - \u6bcf\u4e2abatch\u7684\u56fe\u7247\u6570\u91cf\u3002\u9ed8\u8ba4\u503c\u4e3a16 \u3002 batch_nums(int, optional) - \u8fed\u4ee3\u6b21\u6570\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a None \uff0c\u5219\u4f1a\u4e00\u76f4\u8fd0\u884c\u5230 sample_generator \u8fed\u4ee3\u7ed3\u675f\uff0c \u5426\u5219\uff0c\u8fed\u4ee3\u6b21\u6570\u4e3a batch_nums , \u4e5f\u5c31\u662f\u8bf4\u53c2\u4e0e\u5bf9 Scale \u8fdb\u884c\u6821\u6b63\u7684\u6837\u672c\u4e2a\u6570\u4e3a 'batch_nums' * 'batch_size' . scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . \u9ed8\u8ba4\u503c\u662f None . algo(str) - \u91cf\u5316\u65f6\u4f7f\u7528\u7684\u7b97\u6cd5\u540d\u79f0\uff0c\u53ef\u4e3a 'KL' \u6216\u8005 'direct' \u3002\u8be5\u53c2\u6570\u4ec5\u9488\u5bf9\u6fc0\u6d3b\u503c\u7684\u91cf\u5316\uff0c\u56e0\u4e3a\u53c2\u6570\u503c\u7684\u91cf\u5316\u4f7f\u7528\u7684\u65b9\u5f0f\u4e3a 'channel_wise_abs_max' . \u5f53 algo \u8bbe\u7f6e\u4e3a 'direct' \u65f6\uff0c\u4f7f\u7528\u6821\u6b63\u6570\u636e\u7684\u6fc0\u6d3b\u503c\u7684\u7edd\u5bf9\u503c\u7684\u6700\u5927\u503c\u5f53\u4f5c Scale \u503c\uff0c\u5f53\u8bbe\u7f6e\u4e3a 'KL' \u65f6\uff0c\u5219\u4f7f\u7528 KL \u6563\u5ea6\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97 Scale \u503c\u3002\u9ed8\u8ba4\u503c\u4e3a 'KL' \u3002 quantizable_op_type(list[str]) - \u9700\u8981\u91cf\u5316\u7684 op \u7c7b\u578b\u5217\u8868\u3002\u9ed8\u8ba4\u503c\u4e3a [\"conv2d\", \"depthwise_conv2d\", \"mul\"] \u3002 \u8fd4\u56de \u65e0\u3002 \u6ce8\u610f\u4e8b\u9879 \u56e0\u4e3a\u8be5\u63a5\u53e3\u4f1a\u6536\u96c6\u6821\u6b63\u6570\u636e\u7684\u6240\u6709\u7684\u6fc0\u6d3b\u503c\uff0c\u6240\u4ee5\u4f7f\u7528\u7684\u6821\u6b63\u56fe\u7247\u4e0d\u80fd\u592a\u591a\u3002 'KL' \u6563\u5ea6\u7684\u8ba1\u7b97\u4e5f\u6bd4\u8f83\u8017\u65f6\u3002 \u4ee3\u7801\u793a\u4f8b \u6ce8\uff1a \u6b64\u793a\u4f8b\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\uff0c\u56e0\u4e3a\u9700\u8981\u52a0\u8f7d ${model_dir} \u4e0b\u7684\u6a21\u578b\uff0c\u6240\u4ee5\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import paddle.fluid as fluid import paddle.dataset.mnist as reader from paddleslim.quant import quant_post val_reader = reader . train () use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) quant_post ( executor = exe , model_dir = './model_path' , quantize_model_path = './save_path' , sample_generator = val_reader , model_filename = '__model__' , params_filename = '__params__' , batch_size = 16 , batch_nums = 10 ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 \u79bb\u7ebf\u91cf\u5316demo \u3002","title":"quant_post"},{"location":"api/quantization_api/#quant_embedding","text":"paddleslim.quant.quant_embedding(program, place, config, scope=None) [\u6e90\u4ee3\u7801] \u5bf9 Embedding \u53c2\u6570\u8fdb\u884c\u91cf\u5316\u3002 \u53c2\u6570: program(fluid.Program) - \u9700\u8981\u91cf\u5316\u7684program scope(fluid.Scope, optional) - \u7528\u6765\u83b7\u53d6\u548c\u5199\u5165 Variable , \u5982\u679c\u8bbe\u7f6e\u4e3a None ,\u5219\u4f7f\u7528 fluid.global_scope() . place(fluid.CPUPlace | fluid.CUDAPlace) - \u8fd0\u884cprogram\u7684\u8bbe\u5907 config(dict) - \u5b9a\u4e49\u91cf\u5316\u7684\u914d\u7f6e\u3002\u53ef\u4ee5\u914d\u7f6e\u7684\u53c2\u6570\u6709\uff1a 'params_name' (str, required): \u9700\u8981\u8fdb\u884c\u91cf\u5316\u7684\u53c2\u6570\u540d\u79f0\uff0c\u6b64\u53c2\u6570\u5fc5\u987b\u8bbe\u7f6e\u3002 'quantize_type' (str, optional): \u91cf\u5316\u7684\u7c7b\u578b\uff0c\u76ee\u524d\u652f\u6301\u7684\u7c7b\u578b\u662f 'abs_max' , \u5f85\u652f\u6301\u7684\u7c7b\u578b\u6709 'log', 'product_quantization' \u3002 \u9ed8\u8ba4\u503c\u662f 'abs_max' . 'quantize_bits' \uff08int, optional): \u91cf\u5316\u7684 bit \u6570\uff0c\u76ee\u524d\u652f\u6301\u7684 bit \u6570\u4e3a8\u3002\u9ed8\u8ba4\u503c\u662f8. 'dtype' (str, optional): \u91cf\u5316\u4e4b\u540e\u7684\u6570\u636e\u7c7b\u578b\uff0c \u76ee\u524d\u652f\u6301\u7684\u662f 'int8' . \u9ed8\u8ba4\u503c\u662f int8 \u3002 'threshold' (float, optional): \u91cf\u5316\u4e4b\u524d\u5c06\u6839\u636e\u6b64\u9608\u503c\u5bf9\u9700\u8981\u91cf\u5316\u7684\u53c2\u6570\u503c\u8fdb\u884c clip . \u5982\u679c\u4e0d\u8bbe\u7f6e\uff0c\u5219\u8df3\u8fc7 clip \u8fc7\u7a0b\u76f4\u63a5\u91cf\u5316\u3002 \u8fd4\u56de \u91cf\u5316\u4e4b\u540e\u7684program \u8fd4\u56de\u7c7b\u578b fluid.Program \u4ee3\u7801\u793a\u4f8b 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import paddle.fluid as fluid import paddleslim.quant as quant train_program = fluid . Program () with fluid . program_guard ( train_program ): input_word = fluid . data ( name = \"input_word\" , shape = [ None , 1 ], dtype = 'int64' ) input_emb = fluid . embedding ( input = input_word , is_sparse = False , size = [ 100 , 128 ], param_attr = fluid . ParamAttr ( name = 'emb' , initializer = fluid . initializer . Uniform ( - 0.005 , 0.005 ))) infer_program = train_program . clone ( for_test = True ) use_gpu = True place = fluid . CUDAPlace ( 0 ) if use_gpu else fluid . CPUPlace () exe = fluid . Executor ( place ) exe . run ( fluid . default_startup_program ()) config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } quant_program = quant . quant_embedding ( infer_program , place , config ) \u66f4\u8be6\u7ec6\u7684\u7528\u6cd5\u8bf7\u53c2\u8003 Embedding\u91cf\u5316demo \u3002","title":"quant_embedding"},{"location":"api/single_distiller_api/","text":"merge # paddleslim.dist.merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_') [\u6e90\u4ee3\u7801] merge\u5c06\u4e24\u4e2apaddle program\uff08teacher_program, student_program\uff09\u878d\u5408\u4e3a\u4e00\u4e2aprogram\uff0c\u5e76\u5c06\u878d\u5408\u5f97\u5230\u7684program\u8fd4\u56de\u3002\u5728\u878d\u5408\u7684program\u4e2d\uff0c\u53ef\u4ee5\u4e3a\u5176\u4e2d\u5408\u9002\u7684teacher\u7279\u5f81\u56fe\u548cstudent\u7279\u5f81\u56fe\u6dfb\u52a0\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4ece\u800c\u8fbe\u5230\u7528teacher\u6a21\u578b\u7684\u6697\u77e5\u8bc6\uff08Dark Knowledge\uff09\u6307\u5bfcstudent\u6a21\u578b\u5b66\u4e60\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a teacher_program (Program)-\u5b9a\u4e49\u4e86teacher\u6a21\u578b\u7684 paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684 paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0c\u5176\u4e2ddict\u7684 key \u4e3ateacher\u7684\u8f93\u5165\u540d\uff0c value \u4e3astudent\u7684\u8f93\u5165\u540d place (fluid.CPUPlace()|fluid.CUDAPlace(N))-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u8fd0\u884c\u5728\u4f55\u79cd\u8bbe\u5907\u4e0a\uff0c\u8fd9\u91cc\u7684N\u4e3aGPU\u5bf9\u5e94\u7684ID scope (Scope)-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u4f7f\u7528\u7684\u53d8\u91cf\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1a fluid.global_scope() name_prefix (str)-merge\u64cd\u4f5c\u5c06\u7edf\u4e00\u4e3ateacher\u7684 Variables \u6dfb\u52a0\u7684\u540d\u79f0\u524d\u7f00name_prefix\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program Note data_name_map \u662f teacher_var name\u5230student_var name\u7684\u6620\u5c04 \uff0c\u5982\u679c\u5199\u53cd\u53ef\u80fd\u65e0\u6cd5\u6b63\u786e\u8fdb\u884cmerge \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = dist . merge ( teacher_program , student_program , data_name_map , place ) fsp_loss # paddleslim.dist.fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var1_name (str): teacher_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 teacher_var2_name (str): teacher_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0eteacher_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0eteacher_var1\u76f8\u540c student_var1_name (str): student_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var1\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 student_var2_name (str): student_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var2\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0estudent_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0estudent_var1\u76f8\u540c program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var1, teacher_var2, student_var1, student_var2\u7ec4\u5408\u5f97\u5230\u7684fsp_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . fsp_loss ( 'teacher_t1.tmp_1' , 'teacher_t2.tmp_1' , 's1.tmp_1' , 's2.tmp_1' , main_program ) l2_loss # paddleslim.dist.l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] l2_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0l2 loss \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . l2_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program ) soft_label_loss # paddleslim.dist.soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.) [\u6e90\u4ee3\u7801] soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() teacher_temperature (float): \u5bf9teacher_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 student_temperature (float): \u5bf9student_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684soft_label_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . soft_label_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program , 1. , 1. ) loss # paddleslim.dist.loss(loss_func, program=fluid.default_main_program(), **kwargs) [\u6e90\u4ee3\u7801] loss\u51fd\u6570\u652f\u6301\u5bf9\u4efb\u610f\u591a\u5bf9teacher_var\u548cstudent_var\u4f7f\u7528\u81ea\u5b9a\u4e49\u635f\u5931\u51fd\u6570 \u53c2\u6570\uff1a loss_func (python function): \u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570\uff0c\u8f93\u5165\u4e3ateacher var\u548cstudent var\uff0c\u8f93\u51fa\u4e3a\u81ea\u5b9a\u4e49\u7684loss program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() **kwargs : loss_func\u8f93\u5165\u540d\u4e0e\u5bf9\u5e94variable\u540d\u79f0 \u8fd4\u56de \uff1a\u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) def adaptation_loss ( t_var , s_var ): teacher_channel = t_var . shape [ 1 ] s_hint = fluid . layers . conv2d ( s_var , teacher_channel , 1 ) hint_loss = fluid . layers . reduce_mean ( fluid . layers . square ( s_hint - t_var )) return hint_loss with fluid . program_guard ( main_program ): distillation_loss = dist . loss ( main_program , adaptation_loss , t_var = 'teacher_t2.tmp_1' , s_var = 's2.tmp_1' ) \u6ce8\u610f\u4e8b\u9879 \u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u4f1a\u5f15\u5165\u65b0\u7684variable\uff0c\u9700\u8981\u6ce8\u610f\u65b0\u5f15\u5165\u7684variable\u4e0d\u8981\u4e0estudent variables\u547d\u540d\u51b2\u7a81\u3002\u8fd9\u91cc\u5efa\u8bae\u4e24\u79cd\u7528\u6cd5\uff08\u4e24\u79cd\u65b9\u6cd5\u4efb\u9009\u5176\u4e00\u5373\u53ef\uff09\uff1a \u5efa\u8bae\u4e0estudent_program\u4f7f\u7528\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\uff0c\u4ee5\u907f\u514d\u4e00\u4e9b\u672a\u6307\u5b9a\u540d\u79f0\u7684variables(\u4f8b\u5982tmp_0, tmp_1...)\u591a\u6b21\u5b9a\u4e49\u4e3a\u540c\u4e00\u540d\u79f0\u51fa\u73b0\u547d\u540d\u51b2\u7a81 \u5efa\u8bae\u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u6307\u5b9a\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u524d\u7f00\uff0c\u5177\u4f53\u7528\u6cd5\u8bf7\u53c2\u8003Paddle\u5b98\u65b9\u6587\u6863 fluid.name_scope","title":"\u77e5\u8bc6\u84b8\u998f"},{"location":"api/single_distiller_api/#merge","text":"paddleslim.dist.merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_') [\u6e90\u4ee3\u7801] merge\u5c06\u4e24\u4e2apaddle program\uff08teacher_program, student_program\uff09\u878d\u5408\u4e3a\u4e00\u4e2aprogram\uff0c\u5e76\u5c06\u878d\u5408\u5f97\u5230\u7684program\u8fd4\u56de\u3002\u5728\u878d\u5408\u7684program\u4e2d\uff0c\u53ef\u4ee5\u4e3a\u5176\u4e2d\u5408\u9002\u7684teacher\u7279\u5f81\u56fe\u548cstudent\u7279\u5f81\u56fe\u6dfb\u52a0\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4ece\u800c\u8fbe\u5230\u7528teacher\u6a21\u578b\u7684\u6697\u77e5\u8bc6\uff08Dark Knowledge\uff09\u6307\u5bfcstudent\u6a21\u578b\u5b66\u4e60\u7684\u76ee\u7684\u3002 \u53c2\u6570\uff1a teacher_program (Program)-\u5b9a\u4e49\u4e86teacher\u6a21\u578b\u7684 paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684 paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0c\u5176\u4e2ddict\u7684 key \u4e3ateacher\u7684\u8f93\u5165\u540d\uff0c value \u4e3astudent\u7684\u8f93\u5165\u540d place (fluid.CPUPlace()|fluid.CUDAPlace(N))-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u8fd0\u884c\u5728\u4f55\u79cd\u8bbe\u5907\u4e0a\uff0c\u8fd9\u91cc\u7684N\u4e3aGPU\u5bf9\u5e94\u7684ID scope (Scope)-\u8be5\u53c2\u6570\u8868\u793a\u7a0b\u5e8f\u4f7f\u7528\u7684\u53d8\u91cf\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1a fluid.global_scope() name_prefix (str)-merge\u64cd\u4f5c\u5c06\u7edf\u4e00\u4e3ateacher\u7684 Variables \u6dfb\u52a0\u7684\u540d\u79f0\u524d\u7f00name_prefix\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program Note data_name_map \u662f teacher_var name\u5230student_var name\u7684\u6620\u5c04 \uff0c\u5982\u679c\u5199\u53cd\u53ef\u80fd\u65e0\u6cd5\u6b63\u786e\u8fdb\u884cmerge \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = dist . merge ( teacher_program , student_program , data_name_map , place )","title":"merge"},{"location":"api/single_distiller_api/#fsp_loss","text":"paddleslim.dist.fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var1_name (str): teacher_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 teacher_var2_name (str): teacher_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0eteacher_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0eteacher_var1\u76f8\u540c student_var1_name (str): student_var1\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var1\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, x_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64 student_var2_name (str): student_var2\u7684\u540d\u79f0. \u5bf9\u5e94\u7684variable\u9700\u4e0eteacher_var2\u5c3a\u5bf8\u4fdd\u6301\u4e00\u81f4\uff0c\u662f\u4e00\u4e2a\u5f62\u4e3a [batch_size, y_channel, height, width] \u76844-D\u7279\u5f81\u56feTensor\uff0c\u6570\u636e\u7c7b\u578b\u4e3afloat32\u6216float64\u3002\u53ea\u6709y_channel\u53ef\u4ee5\u4e0estudent_var1\u7684x_channel\u4e0d\u540c\uff0c\u5176\u4ed6\u7ef4\u5ea6\u5fc5\u987b\u4e0estudent_var1\u76f8\u540c program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var1, teacher_var2, student_var1, student_var2\u7ec4\u5408\u5f97\u5230\u7684fsp_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . fsp_loss ( 'teacher_t1.tmp_1' , 'teacher_t2.tmp_1' , 's1.tmp_1' , 's2.tmp_1' , main_program )","title":"fsp_loss"},{"location":"api/single_distiller_api/#l2_loss","text":"paddleslim.dist.l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program()) [\u6e90\u4ee3\u7801] l2_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0l2 loss \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . l2_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program )","title":"l2_loss"},{"location":"api/single_distiller_api/#soft_label_loss","text":"paddleslim.dist.soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.) [\u6e90\u4ee3\u7801] soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 <> \u53c2\u6570\uff1a teacher_var_name (str): teacher_var\u7684\u540d\u79f0. student_var_name (str): student_var\u7684\u540d\u79f0. program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() teacher_temperature (float): \u5bf9teacher_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 student_temperature (float): \u5bf9student_var\u8fdb\u884csoft\u64cd\u4f5c\u7684\u6e29\u5ea6\u503c\uff0c\u6e29\u5ea6\u503c\u8d8a\u5927\u5f97\u5230\u7684\u7279\u5f81\u56fe\u8d8a\u5e73\u6ed1 \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684soft_label_loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) with fluid . program_guard ( main_program ): distillation_loss = dist . soft_label_loss ( 'teacher_t2.tmp_1' , 's2.tmp_1' , main_program , 1. , 1. )","title":"soft_label_loss"},{"location":"api/single_distiller_api/#loss","text":"paddleslim.dist.loss(loss_func, program=fluid.default_main_program(), **kwargs) [\u6e90\u4ee3\u7801] loss\u51fd\u6570\u652f\u6301\u5bf9\u4efb\u610f\u591a\u5bf9teacher_var\u548cstudent_var\u4f7f\u7528\u81ea\u5b9a\u4e49\u635f\u5931\u51fd\u6570 \u53c2\u6570\uff1a loss_func (python function): \u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570\uff0c\u8f93\u5165\u4e3ateacher var\u548cstudent var\uff0c\u8f93\u51fa\u4e3a\u81ea\u5b9a\u4e49\u7684loss program (Program): \u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684fluid program\u3002\u9ed8\u8ba4\u503c\uff1a fluid.default_main_program() **kwargs : loss_func\u8f93\u5165\u540d\u4e0e\u5bf9\u5e94variable\u540d\u79f0 \u8fd4\u56de \uff1a\u81ea\u5b9a\u4e49\u7684\u635f\u5931\u51fd\u6570loss \u4f7f\u7528\u793a\u4f8b\uff1a 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import paddle.fluid as fluid import paddleslim.dist as dist student_program = fluid . Program () with fluid . program_guard ( student_program ): x = fluid . layers . data ( name = 'x' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( x , 32 , 1 , name = 's1' ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 's2' ) teacher_program = fluid . Program () with fluid . program_guard ( teacher_program ): y = fluid . layers . data ( name = 'y' , shape = [ 1 , 28 , 28 ]) conv = fluid . layers . conv2d ( y , 32 , 1 , name = 't1' ) conv = fluid . layers . conv2d ( conv , 32 , 3 , padding = 1 ) out = fluid . layers . conv2d ( conv , 64 , 3 , padding = 1 , name = 't2' ) data_name_map = { 'y' : 'x' } USE_GPU = False place = fluid . CUDAPlace ( 0 ) if USE_GPU else fluid . CPUPlace () main_program = merge ( teacher_program , student_program , data_name_map , place ) def adaptation_loss ( t_var , s_var ): teacher_channel = t_var . shape [ 1 ] s_hint = fluid . layers . conv2d ( s_var , teacher_channel , 1 ) hint_loss = fluid . layers . reduce_mean ( fluid . layers . square ( s_hint - t_var )) return hint_loss with fluid . program_guard ( main_program ): distillation_loss = dist . loss ( main_program , adaptation_loss , t_var = 'teacher_t2.tmp_1' , s_var = 's2.tmp_1' ) \u6ce8\u610f\u4e8b\u9879 \u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u4f1a\u5f15\u5165\u65b0\u7684variable\uff0c\u9700\u8981\u6ce8\u610f\u65b0\u5f15\u5165\u7684variable\u4e0d\u8981\u4e0estudent variables\u547d\u540d\u51b2\u7a81\u3002\u8fd9\u91cc\u5efa\u8bae\u4e24\u79cd\u7528\u6cd5\uff08\u4e24\u79cd\u65b9\u6cd5\u4efb\u9009\u5176\u4e00\u5373\u53ef\uff09\uff1a \u5efa\u8bae\u4e0estudent_program\u4f7f\u7528\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\uff0c\u4ee5\u907f\u514d\u4e00\u4e9b\u672a\u6307\u5b9a\u540d\u79f0\u7684variables(\u4f8b\u5982tmp_0, tmp_1...)\u591a\u6b21\u5b9a\u4e49\u4e3a\u540c\u4e00\u540d\u79f0\u51fa\u73b0\u547d\u540d\u51b2\u7a81 \u5efa\u8bae\u5728\u6dfb\u52a0\u84b8\u998floss\u65f6\u6307\u5b9a\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u524d\u7f00\uff0c\u5177\u4f53\u7528\u6cd5\u8bf7\u53c2\u8003Paddle\u5b98\u65b9\u6587\u6863 fluid.name_scope","title":"loss"},{"location":"tutorials/demo_guide/","text":"\u84b8\u998f # \u84b8\u998fdemo\u9ed8\u8ba4\u4f7f\u7528ResNet50\u4f5c\u4e3ateacher\u7f51\u7edc\uff0cMobileNet\u4f5c\u4e3astudent\u7f51\u7edc\uff0c\u6b64\u5916\u8fd8\u652f\u6301\u5c06teacher\u548cstudent\u6362\u6210 models\u76ee\u5f55 \u652f\u6301\u7684\u4efb\u610f\u6a21\u578b\u3002 demo\u4e2d\u5bf9teahcer\u6a21\u578b\u548cstudent\u6a21\u578b\u7684\u4e00\u5c42\u7279\u5f81\u56fe\u6dfb\u52a0\u4e86l2_loss\u7684\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4f7f\u7528\u65f6\u4e5f\u53ef\u6839\u636e\u9700\u8981\u9009\u62e9fsp_loss, soft_label_loss\u4ee5\u53ca\u81ea\u5b9a\u4e49\u7684loss\u51fd\u6570\u3002 \u8bad\u7ec3\u9ed8\u8ba4\u4f7f\u7528\u7684\u662fcifar10\u6570\u636e\u96c6\uff0cpiecewise_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\uff0cmomentum\u4f18\u5316\u5668\u8fdb\u884c120\u8f6e\u84b8\u998f\u8bad\u7ec3\u3002\u4f7f\u7528\u8005\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u7528args\u53c2\u6570\u5207\u6362\u4e3a\u4f7f\u7528ImageNet\u6570\u636e\u96c6\uff0ccosine_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\u7b49\u5176\u4ed6\u8bad\u7ec3\u914d\u7f6e\u3002 \u91cf\u5316 # \u91cf\u5316\u8bad\u7ec3demo\u6587\u6863 # \u79bb\u7ebf\u91cf\u5316demo\u6587\u6863 # Embedding\u91cf\u5316demo\u6587\u6863 # NAS # NAS\u793a\u4f8b #","title":"Demo guide"},{"location":"tutorials/demo_guide/#_1","text":"\u84b8\u998fdemo\u9ed8\u8ba4\u4f7f\u7528ResNet50\u4f5c\u4e3ateacher\u7f51\u7edc\uff0cMobileNet\u4f5c\u4e3astudent\u7f51\u7edc\uff0c\u6b64\u5916\u8fd8\u652f\u6301\u5c06teacher\u548cstudent\u6362\u6210 models\u76ee\u5f55 \u652f\u6301\u7684\u4efb\u610f\u6a21\u578b\u3002 demo\u4e2d\u5bf9teahcer\u6a21\u578b\u548cstudent\u6a21\u578b\u7684\u4e00\u5c42\u7279\u5f81\u56fe\u6dfb\u52a0\u4e86l2_loss\u7684\u84b8\u998f\u635f\u5931\u51fd\u6570\uff0c\u4f7f\u7528\u65f6\u4e5f\u53ef\u6839\u636e\u9700\u8981\u9009\u62e9fsp_loss, soft_label_loss\u4ee5\u53ca\u81ea\u5b9a\u4e49\u7684loss\u51fd\u6570\u3002 \u8bad\u7ec3\u9ed8\u8ba4\u4f7f\u7528\u7684\u662fcifar10\u6570\u636e\u96c6\uff0cpiecewise_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\uff0cmomentum\u4f18\u5316\u5668\u8fdb\u884c120\u8f6e\u84b8\u998f\u8bad\u7ec3\u3002\u4f7f\u7528\u8005\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u7528args\u53c2\u6570\u5207\u6362\u4e3a\u4f7f\u7528ImageNet\u6570\u636e\u96c6\uff0ccosine_decay\u5b66\u4e60\u7387\u8870\u51cf\u7b56\u7565\u7b49\u5176\u4ed6\u8bad\u7ec3\u914d\u7f6e\u3002","title":"\u84b8\u998f"},{"location":"tutorials/demo_guide/#_2","text":"","title":"\u91cf\u5316"},{"location":"tutorials/demo_guide/#demo","text":"","title":"\u91cf\u5316\u8bad\u7ec3demo\u6587\u6863"},{"location":"tutorials/demo_guide/#demo_1","text":"","title":"\u79bb\u7ebf\u91cf\u5316demo\u6587\u6863"},{"location":"tutorials/demo_guide/#embeddingdemo","text":"","title":"Embedding\u91cf\u5316demo\u6587\u6863"},{"location":"tutorials/demo_guide/#nas","text":"","title":"NAS"},{"location":"tutorials/demo_guide/#nas_1","text":"","title":"NAS\u793a\u4f8b"},{"location":"tutorials/distillation_demo/","text":"\u672c\u793a\u4f8b\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528PaddleSlim\u84b8\u998f\u63a5\u53e3\u6765\u5bf9\u6a21\u578b\u8fdb\u884c\u84b8\u998f\u8bad\u7ec3\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u84b8\u998fAPI\u6587\u6863 \u3002 PaddleSlim\u84b8\u998f\u8bad\u7ec3\u6d41\u7a0b # \u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u53c2\u6570\u91cf\u8d8a\u591a\uff0c\u7ed3\u6784\u8d8a\u590d\u6742\uff0c\u5176\u6027\u80fd\u8d8a\u597d\uff0c\u4f46\u8fd0\u7b97\u91cf\u548c\u8d44\u6e90\u6d88\u8017\u4e5f\u8d8a\u5927\u3002 \u77e5\u8bc6\u84b8\u998f \u5c31\u662f\u4e00\u79cd\u5c06\u5927\u6a21\u578b\u5b66\u4e60\u5230\u7684\u6709\u7528\u4fe1\u606f\uff08Dark Knowledge\uff09\u538b\u7f29\u8fdb\u66f4\u5c0f\u66f4\u5feb\u7684\u6a21\u578b\uff0c\u800c\u83b7\u5f97\u53ef\u4ee5\u5339\u654c\u5927\u6a21\u578b\u7ed3\u679c\u7684\u65b9\u6cd5\u3002 \u5728\u672c\u793a\u4f8b\u4e2d\u7cbe\u5ea6\u8f83\u9ad8\u7684\u5927\u6a21\u578b\u88ab\u79f0\u4e3ateacher\uff0c\u7cbe\u5ea6\u7a0d\u900a\u4f46\u901f\u5ea6\u66f4\u5feb\u7684\u5c0f\u6a21\u578b\u88ab\u79f0\u4e3astudent\u3002 1. \u5b9a\u4e49student_program # 1 2 3 4 5 6 7 8 9 10 11 student_program = fluid . Program () student_startup = fluid . Program () with fluid . program_guard ( student_program , student_startup ): image = fluid . data ( name = 'image' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) # student model definition model = MobileNet () out = model . net ( input = image , class_dim = 1000 ) cost = fluid . layers . cross_entropy ( input = out , label = label ) avg_cost = fluid . layers . mean ( x = cost ) 2. \u5b9a\u4e49teacher_program # \u5728\u5b9a\u4e49\u597d teacher_program \u540e\uff0c\u53ef\u4ee5\u4e00\u5e76\u52a0\u8f7d\u8bad\u7ec3\u597d\u7684pretrained_model\u3002 \u5728 teacher_program \u5185\u9700\u8981\u52a0\u4e0a with fluid.unique_name.guard(): \uff0c\u4fdd\u8bc1teacher\u7684\u53d8\u91cf\u547d\u540d\u4e0d\u88ab student_program \u5f71\u54cd\uff0c\u4ece\u800c\u80fd\u591f\u6b63\u786e\u5730\u52a0\u8f7d\u9884\u8bad\u7ec3\u53c2\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 teacher_program = fluid . Program () teacher_startup = fluid . Program () with fluid . program_guard ( teacher_program , teacher_startup ): with fluid . unique_name . guard (): image = fluid . data ( name = 'data' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) # teacher model definition teacher_model = ResNet () predict = teacher_model . net ( image , class_dim = 1000 ) exe . run ( teacher_startup ) def if_exist ( var ): return os . path . exists ( os . path . join ( \"./pretrained\" , var . name ) fluid . io . load_vars ( exe , \"./pretrained\" , main_program = teacher_program , predicate = if_exist ) 3.\u9009\u62e9\u7279\u5f81\u56fe # \u5b9a\u4e49\u597d student_program \u548c teacher_program \u540e\uff0c\u6211\u4eec\u9700\u8981\u4ece\u4e2d\u4e24\u4e24\u5bf9\u5e94\u5730\u6311\u9009\u51fa\u82e5\u5e72\u4e2a\u7279\u5f81\u56fe\uff0c\u7559\u5f85\u540e\u7eed\u4e3a\u5176\u6dfb\u52a0\u77e5\u8bc6\u84b8\u998f\u635f\u5931\u51fd\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # get all student variables student_vars = [] for v in student_program . list_vars (): try : student_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"student_model_vars\" + \"=\" * 50 ) print ( student_vars ) # get all teacher variables teacher_vars = [] for v in teacher_program . list_vars (): try : teacher_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"teacher_model_vars\" + \"=\" * 50 ) print ( teacher_vars ) 4. \u5408\u5e76Program\uff08merge\uff09 # PaddlePaddle\u4f7f\u7528Program\u6765\u63cf\u8ff0\u8ba1\u7b97\u56fe\uff0c\u4e3a\u4e86\u540c\u65f6\u8ba1\u7b97student\u548cteacher\u4e24\u4e2aProgram\uff0c\u8fd9\u91cc\u9700\u8981\u5c06\u5176\u4e24\u8005\u5408\u5e76\uff08merge\uff09\u4e3a\u4e00\u4e2aProgram\u3002 merge\u8fc7\u7a0b\u64cd\u4f5c\u8f83\u591a\uff0c\u5177\u4f53\u7ec6\u8282\u8bf7\u53c2\u8003 merge API\u6587\u6863 \u3002 1 2 data_name_map = { 'data' : 'image' } student_program = merge ( teacher_program , student_program , data_name_map , place ) 5.\u6dfb\u52a0\u84b8\u998floss # \u5728\u6dfb\u52a0\u84b8\u998floss\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u8fd8\u4f1a\u5f15\u5165\u90e8\u5206\u53d8\u91cf\uff08Variable\uff09\uff0c\u4e3a\u4e86\u907f\u514d\u547d\u540d\u91cd\u590d\u8fd9\u91cc\u53ef\u4ee5\u4f7f\u7528 with fluid.name_scope(\"distill\"): \u4e3a\u65b0\u5f15\u5165\u7684\u53d8\u91cf\u52a0\u4e00\u4e2a\u547d\u540d\u4f5c\u7528\u57df\u3002 \u53e6\u5916\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cmerge\u8fc7\u7a0b\u4e3a teacher_program \u7684\u53d8\u91cf\u7edf\u4e00\u52a0\u4e86\u540d\u79f0\u524d\u7f00\uff0c\u9ed8\u8ba4\u662f \"teacher_\" , \u8fd9\u91cc\u5728\u6dfb\u52a0 l2_loss \u65f6\u4e5f\u8981\u4e3ateacher\u7684\u53d8\u91cf\u52a0\u4e0a\u8fd9\u4e2a\u524d\u7f00\u3002 1 2 3 4 5 6 7 8 9 with fluid . program_guard ( student_program , student_startup ): with fluid . name_scope ( \"distill\" ): distill_loss = l2_loss ( 'teacher_bn5c_branch2b.output.1.tmp_3' , 'depthwise_conv2d_11.tmp_0' , student_program ) distill_weight = 1 loss = avg_cost + distill_loss * distill_weight opt = create_optimizer () opt . minimize ( loss ) exe . run ( student_startup ) \u81f3\u6b64\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684 student_program \uff0c\u540e\u9762\u5c31\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u666e\u901aprogram\u4e00\u6837\u5bf9\u5176\u5f00\u59cb\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002","title":"\u77e5\u8bc6\u84b8\u998f"},{"location":"tutorials/distillation_demo/#_1","text":"\u8bf7\u53c2\u8003 \u84b8\u998fAPI\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/distillation_demo/#paddleslim","text":"\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u53c2\u6570\u91cf\u8d8a\u591a\uff0c\u7ed3\u6784\u8d8a\u590d\u6742\uff0c\u5176\u6027\u80fd\u8d8a\u597d\uff0c\u4f46\u8fd0\u7b97\u91cf\u548c\u8d44\u6e90\u6d88\u8017\u4e5f\u8d8a\u5927\u3002 \u77e5\u8bc6\u84b8\u998f \u5c31\u662f\u4e00\u79cd\u5c06\u5927\u6a21\u578b\u5b66\u4e60\u5230\u7684\u6709\u7528\u4fe1\u606f\uff08Dark Knowledge\uff09\u538b\u7f29\u8fdb\u66f4\u5c0f\u66f4\u5feb\u7684\u6a21\u578b\uff0c\u800c\u83b7\u5f97\u53ef\u4ee5\u5339\u654c\u5927\u6a21\u578b\u7ed3\u679c\u7684\u65b9\u6cd5\u3002 \u5728\u672c\u793a\u4f8b\u4e2d\u7cbe\u5ea6\u8f83\u9ad8\u7684\u5927\u6a21\u578b\u88ab\u79f0\u4e3ateacher\uff0c\u7cbe\u5ea6\u7a0d\u900a\u4f46\u901f\u5ea6\u66f4\u5feb\u7684\u5c0f\u6a21\u578b\u88ab\u79f0\u4e3astudent\u3002","title":"PaddleSlim\u84b8\u998f\u8bad\u7ec3\u6d41\u7a0b"},{"location":"tutorials/distillation_demo/#1-student_program","text":"1 2 3 4 5 6 7 8 9 10 11 student_program = fluid . Program () student_startup = fluid . Program () with fluid . program_guard ( student_program , student_startup ): image = fluid . data ( name = 'image' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) # student model definition model = MobileNet () out = model . net ( input = image , class_dim = 1000 ) cost = fluid . layers . cross_entropy ( input = out , label = label ) avg_cost = fluid . layers . mean ( x = cost )","title":"1. \u5b9a\u4e49student_program"},{"location":"tutorials/distillation_demo/#2-teacher_program","text":"\u5728\u5b9a\u4e49\u597d teacher_program \u540e\uff0c\u53ef\u4ee5\u4e00\u5e76\u52a0\u8f7d\u8bad\u7ec3\u597d\u7684pretrained_model\u3002 \u5728 teacher_program \u5185\u9700\u8981\u52a0\u4e0a with fluid.unique_name.guard(): \uff0c\u4fdd\u8bc1teacher\u7684\u53d8\u91cf\u547d\u540d\u4e0d\u88ab student_program \u5f71\u54cd\uff0c\u4ece\u800c\u80fd\u591f\u6b63\u786e\u5730\u52a0\u8f7d\u9884\u8bad\u7ec3\u53c2\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 teacher_program = fluid . Program () teacher_startup = fluid . Program () with fluid . program_guard ( teacher_program , teacher_startup ): with fluid . unique_name . guard (): image = fluid . data ( name = 'data' , shape = [ None ] + [ 3 , 224 , 224 ], dtype = 'float32' ) # teacher model definition teacher_model = ResNet () predict = teacher_model . net ( image , class_dim = 1000 ) exe . run ( teacher_startup ) def if_exist ( var ): return os . path . exists ( os . path . join ( \"./pretrained\" , var . name ) fluid . io . load_vars ( exe , \"./pretrained\" , main_program = teacher_program , predicate = if_exist )","title":"2. \u5b9a\u4e49teacher_program"},{"location":"tutorials/distillation_demo/#3","text":"\u5b9a\u4e49\u597d student_program \u548c teacher_program \u540e\uff0c\u6211\u4eec\u9700\u8981\u4ece\u4e2d\u4e24\u4e24\u5bf9\u5e94\u5730\u6311\u9009\u51fa\u82e5\u5e72\u4e2a\u7279\u5f81\u56fe\uff0c\u7559\u5f85\u540e\u7eed\u4e3a\u5176\u6dfb\u52a0\u77e5\u8bc6\u84b8\u998f\u635f\u5931\u51fd\u6570\u3002 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # get all student variables student_vars = [] for v in student_program . list_vars (): try : student_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"student_model_vars\" + \"=\" * 50 ) print ( student_vars ) # get all teacher variables teacher_vars = [] for v in teacher_program . list_vars (): try : teacher_vars . append (( v . name , v . shape )) except : pass print ( \"=\" * 50 + \"teacher_model_vars\" + \"=\" * 50 ) print ( teacher_vars )","title":"3.\u9009\u62e9\u7279\u5f81\u56fe"},{"location":"tutorials/distillation_demo/#4-programmerge","text":"PaddlePaddle\u4f7f\u7528Program\u6765\u63cf\u8ff0\u8ba1\u7b97\u56fe\uff0c\u4e3a\u4e86\u540c\u65f6\u8ba1\u7b97student\u548cteacher\u4e24\u4e2aProgram\uff0c\u8fd9\u91cc\u9700\u8981\u5c06\u5176\u4e24\u8005\u5408\u5e76\uff08merge\uff09\u4e3a\u4e00\u4e2aProgram\u3002 merge\u8fc7\u7a0b\u64cd\u4f5c\u8f83\u591a\uff0c\u5177\u4f53\u7ec6\u8282\u8bf7\u53c2\u8003 merge API\u6587\u6863 \u3002 1 2 data_name_map = { 'data' : 'image' } student_program = merge ( teacher_program , student_program , data_name_map , place )","title":"4. \u5408\u5e76Program\uff08merge\uff09"},{"location":"tutorials/distillation_demo/#5loss","text":"\u5728\u6dfb\u52a0\u84b8\u998floss\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u8fd8\u4f1a\u5f15\u5165\u90e8\u5206\u53d8\u91cf\uff08Variable\uff09\uff0c\u4e3a\u4e86\u907f\u514d\u547d\u540d\u91cd\u590d\u8fd9\u91cc\u53ef\u4ee5\u4f7f\u7528 with fluid.name_scope(\"distill\"): \u4e3a\u65b0\u5f15\u5165\u7684\u53d8\u91cf\u52a0\u4e00\u4e2a\u547d\u540d\u4f5c\u7528\u57df\u3002 \u53e6\u5916\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cmerge\u8fc7\u7a0b\u4e3a teacher_program \u7684\u53d8\u91cf\u7edf\u4e00\u52a0\u4e86\u540d\u79f0\u524d\u7f00\uff0c\u9ed8\u8ba4\u662f \"teacher_\" , \u8fd9\u91cc\u5728\u6dfb\u52a0 l2_loss \u65f6\u4e5f\u8981\u4e3ateacher\u7684\u53d8\u91cf\u52a0\u4e0a\u8fd9\u4e2a\u524d\u7f00\u3002 1 2 3 4 5 6 7 8 9 with fluid . program_guard ( student_program , student_startup ): with fluid . name_scope ( \"distill\" ): distill_loss = l2_loss ( 'teacher_bn5c_branch2b.output.1.tmp_3' , 'depthwise_conv2d_11.tmp_0' , student_program ) distill_weight = 1 loss = avg_cost + distill_loss * distill_weight opt = create_optimizer () opt . minimize ( loss ) exe . run ( student_startup ) \u81f3\u6b64\uff0c\u6211\u4eec\u5c31\u5f97\u5230\u4e86\u7528\u4e8e\u84b8\u998f\u8bad\u7ec3\u7684 student_program \uff0c\u540e\u9762\u5c31\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u666e\u901aprogram\u4e00\u6837\u5bf9\u5176\u5f00\u59cb\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002","title":"5.\u6dfb\u52a0\u84b8\u998floss"},{"location":"tutorials/nas_demo/","text":"\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u63a5\u53e3\uff0c\u641c\u7d22\u5230\u4e00\u4e2a\u66f4\u5c0f\u6216\u8005\u7cbe\u5ea6\u66f4\u9ad8\u7684\u6a21\u578b\uff0c\u8be5\u6587\u6863\u4ec5\u4ecb\u7ecdpaddleslim\u4e2dSANAS\u7684\u4f7f\u7528\u53ca\u5982\u4f55\u5229\u7528SANAS\u5f97\u5230\u6a21\u578b\u7ed3\u6784\uff0c\u5b8c\u6574\u793a\u4f8b\u4ee3\u7801\u8bf7\u53c2\u8003sa_nas_mobilenetv2.py\u6216\u8005block_sa_nas_mobilenetv2.py\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003\u3002 1. \u914d\u7f6e\u641c\u7d22\u7a7a\u95f4 # \u8be6\u7ec6\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003 \u795e\u7ecf\u7f51\u7edc\u641c\u7d22API\u6587\u6863 \u3002 1 config = [( 'MobileNetV2Space' )] 2. \u5229\u7528\u641c\u7d22\u7a7a\u95f4\u521d\u59cb\u5316SANAS\u5b9e\u4f8b # 1 2 3 4 5 6 7 8 9 from paddleslim.nas import SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8881 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 300 , is_server = True ) 3. \u6839\u636e\u5b9e\u4f8b\u5316\u7684NAS\u5f97\u5230\u5f53\u524d\u7684\u7f51\u7edc\u7ed3\u6784 # 1 archs = sa_nas . next_archs () 4. \u6839\u636e\u5f97\u5230\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u8f93\u5165\u6784\u9020\u8bad\u7ec3\u548c\u6d4b\u8bd5program # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () with fluid . program_guard ( train_program , startup_program ): data = fluid . data ( name = 'data' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : data = arch ( data ) output = fluid . layers . fc ( data , 10 ) softmax_out = fluid . layers . softmax ( input = output , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) test_program = train_program . clone ( for_test = True ) sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost ) 5. \u6839\u636e\u6784\u9020\u7684\u8bad\u7ec3program\u6dfb\u52a0\u9650\u5236\u6761\u4ef6 # 1 2 3 4 from paddleslim.analysis import flops if flops ( train_program ) > 321208544 : continue 6. \u56de\u4f20score # 1 sa_nas . reward ( score )","title":"SA\u641c\u7d22"},{"location":"tutorials/nas_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u63a5\u53e3\uff0c\u641c\u7d22\u5230\u4e00\u4e2a\u66f4\u5c0f\u6216\u8005\u7cbe\u5ea6\u66f4\u9ad8\u7684\u6a21\u578b\uff0c\u8be5\u6587\u6863\u4ec5\u4ecb\u7ecdpaddleslim\u4e2dSANAS\u7684\u4f7f\u7528\u53ca\u5982\u4f55\u5229\u7528SANAS\u5f97\u5230\u6a21\u578b\u7ed3\u6784\uff0c\u5b8c\u6574\u793a\u4f8b\u4ee3\u7801\u8bf7\u53c2\u8003sa_nas_mobilenetv2.py\u6216\u8005block_sa_nas_mobilenetv2.py\u3002","title":"\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u793a\u4f8b"},{"location":"tutorials/nas_demo/#_2","text":"\u8bf7\u53c2\u8003\u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/nas_demo/#1","text":"\u8be6\u7ec6\u7684\u641c\u7d22\u7a7a\u95f4\u914d\u7f6e\u53ef\u4ee5\u53c2\u8003 \u795e\u7ecf\u7f51\u7edc\u641c\u7d22API\u6587\u6863 \u3002 1 config = [( 'MobileNetV2Space' )]","title":"1. \u914d\u7f6e\u641c\u7d22\u7a7a\u95f4"},{"location":"tutorials/nas_demo/#2-sanas","text":"1 2 3 4 5 6 7 8 9 from paddleslim.nas import SANAS sa_nas = SANAS ( config , server_addr = ( \"\" , 8881 ), init_temperature = 10.24 , reduce_rate = 0.85 , search_steps = 300 , is_server = True )","title":"2. \u5229\u7528\u641c\u7d22\u7a7a\u95f4\u521d\u59cb\u5316SANAS\u5b9e\u4f8b"},{"location":"tutorials/nas_demo/#3-nas","text":"1 archs = sa_nas . next_archs ()","title":"3. \u6839\u636e\u5b9e\u4f8b\u5316\u7684NAS\u5f97\u5230\u5f53\u524d\u7684\u7f51\u7edc\u7ed3\u6784"},{"location":"tutorials/nas_demo/#4-program","text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 import paddle.fluid as fluid train_program = fluid . Program () test_program = fluid . Program () startup_program = fluid . Program () with fluid . program_guard ( train_program , startup_program ): data = fluid . data ( name = 'data' , shape = [ None , 3 , 32 , 32 ], dtype = 'float32' ) label = fluid . data ( name = 'label' , shape = [ None , 1 ], dtype = 'int64' ) for arch in archs : data = arch ( data ) output = fluid . layers . fc ( data , 10 ) softmax_out = fluid . layers . softmax ( input = output , use_cudnn = False ) cost = fluid . layers . cross_entropy ( input = softmax_out , label = label ) avg_cost = fluid . layers . mean ( cost ) acc_top1 = fluid . layers . accuracy ( input = softmax_out , label = label , k = 1 ) test_program = train_program . clone ( for_test = True ) sgd = fluid . optimizer . SGD ( learning_rate = 1e-3 ) sgd . minimize ( avg_cost )","title":"4. \u6839\u636e\u5f97\u5230\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u8f93\u5165\u6784\u9020\u8bad\u7ec3\u548c\u6d4b\u8bd5program"},{"location":"tutorials/nas_demo/#5-program","text":"1 2 3 4 from paddleslim.analysis import flops if flops ( train_program ) > 321208544 : continue","title":"5. \u6839\u636e\u6784\u9020\u7684\u8bad\u7ec3program\u6dfb\u52a0\u9650\u5236\u6761\u4ef6"},{"location":"tutorials/nas_demo/#6-score","text":"1 sa_nas . reward ( score )","title":"6. \u56de\u4f20score"},{"location":"tutorials/pruning_demo/","text":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b # \u672c\u793a\u4f8b\u5c06\u6f14\u793a\u5982\u4f55\u6309\u6307\u5b9a\u7684\u526a\u88c1\u7387\u5bf9\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\u6570\u8fdb\u884c\u526a\u88c1\u3002\u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528mnist\u6570\u636e\u3002 \u5f53\u524d\u793a\u4f8b\u652f\u6301\u4ee5\u4e0b\u5206\u7c7b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 PVANet \u63a5\u53e3\u4ecb\u7ecd # \u8be5\u793a\u4f8b\u4f7f\u7528\u4e86 paddleslim.Pruner \u5de5\u5177\u7c7b\uff0c\u7528\u6237\u63a5\u53e3\u4f7f\u7528\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a API\u6587\u6863 \u786e\u5b9a\u5f85\u88c1\u53c2\u6570 # \u4e0d\u540c\u6a21\u578b\u7684\u53c2\u6570\u547d\u540d\u4e0d\u540c\uff0c\u5728\u526a\u88c1\u524d\u9700\u8981\u786e\u5b9a\u5f85\u88c1\u5377\u79ef\u5c42\u7684\u53c2\u6570\u540d\u79f0\u3002\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5217\u51fa\u6240\u6709\u53c2\u6570\u540d\uff1a 1 2 for param in program . global_block () . all_parameters () : print ( \" param name: {}; shape: {} \" . format ( param . name , param . shape )) \u5728 train.py \u811a\u672c\u4e2d\uff0c\u63d0\u4f9b\u4e86 get_pruned_params \u65b9\u6cd5\uff0c\u6839\u636e\u7528\u6237\u8bbe\u7f6e\u7684\u9009\u9879 --model \u786e\u5b9a\u8981\u88c1\u526a\u7684\u53c2\u6570\u3002 \u542f\u52a8\u88c1\u526a\u4efb\u52a1 # \u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u542f\u52a8\u88c1\u526a\u4efb\u52a1\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py \u6267\u884c python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002 \u6ce8\u610f # \u5728\u63a5\u53e3 paddle.Pruner.prune \u7684\u53c2\u6570\u4e2d\uff0c params \u548c ratios \u7684\u957f\u5ea6\u9700\u8981\u4e00\u6837\u3002","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b"},{"location":"tutorials/pruning_demo/#_1","text":"\u672c\u793a\u4f8b\u5c06\u6f14\u793a\u5982\u4f55\u6309\u6307\u5b9a\u7684\u526a\u88c1\u7387\u5bf9\u6bcf\u4e2a\u5377\u79ef\u5c42\u7684\u901a\u9053\u6570\u8fdb\u884c\u526a\u88c1\u3002\u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528mnist\u6570\u636e\u3002 \u5f53\u524d\u793a\u4f8b\u652f\u6301\u4ee5\u4e0b\u5206\u7c7b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 PVANet","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b"},{"location":"tutorials/pruning_demo/#_2","text":"\u8be5\u793a\u4f8b\u4f7f\u7528\u4e86 paddleslim.Pruner \u5de5\u5177\u7c7b\uff0c\u7528\u6237\u63a5\u53e3\u4f7f\u7528\u4ecb\u7ecd\u8bf7\u53c2\u8003\uff1a API\u6587\u6863","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/pruning_demo/#_3","text":"\u4e0d\u540c\u6a21\u578b\u7684\u53c2\u6570\u547d\u540d\u4e0d\u540c\uff0c\u5728\u526a\u88c1\u524d\u9700\u8981\u786e\u5b9a\u5f85\u88c1\u5377\u79ef\u5c42\u7684\u53c2\u6570\u540d\u79f0\u3002\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u5217\u51fa\u6240\u6709\u53c2\u6570\u540d\uff1a 1 2 for param in program . global_block () . all_parameters () : print ( \" param name: {}; shape: {} \" . format ( param . name , param . shape )) \u5728 train.py \u811a\u672c\u4e2d\uff0c\u63d0\u4f9b\u4e86 get_pruned_params \u65b9\u6cd5\uff0c\u6839\u636e\u7528\u6237\u8bbe\u7f6e\u7684\u9009\u9879 --model \u786e\u5b9a\u8981\u88c1\u526a\u7684\u53c2\u6570\u3002","title":"\u786e\u5b9a\u5f85\u88c1\u53c2\u6570"},{"location":"tutorials/pruning_demo/#_4","text":"\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u542f\u52a8\u88c1\u526a\u4efb\u52a1\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py \u6267\u884c python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002","title":"\u542f\u52a8\u88c1\u526a\u4efb\u52a1"},{"location":"tutorials/pruning_demo/#_5","text":"\u5728\u63a5\u53e3 paddle.Pruner.prune \u7684\u53c2\u6570\u4e2d\uff0c params \u548c ratios \u7684\u957f\u5ea6\u9700\u8981\u4e00\u6837\u3002","title":"\u6ce8\u610f"},{"location":"tutorials/quant_aware_demo/","text":"\u5728\u7ebf\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5728\u7ebf\u91cf\u5316\u63a5\u53e3\uff0c\u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u91cf\u5316, \u53ef\u4ee5\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b # 1. \u914d\u7f6e\u91cf\u5316\u53c2\u6570 # 1 2 3 4 5 6 7 8 9 10 11 12 quant_config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' , 'weight_bits' : 8 , 'activation_bits' : 8 , 'not_quant_pattern' : [ 'skip_quant' ], 'quantize_op_types' : [ 'conv2d' , 'depthwise_conv2d' , 'mul' ], 'dtype' : 'int8' , 'window_size' : 10000 , 'moving_rate' : 0 . 9 , 'quant_weight_only' : False } 2. \u5bf9\u8bad\u7ec3\u548c\u6d4b\u8bd5program\u63d2\u5165\u53ef\u8bad\u7ec3\u91cf\u5316op # 1 2 3 val_program = quant_aware ( val_program , place , quant_config , scope = None , for_test = True ) compiled_train_prog = quant_aware ( train_prog , place , quant_config , scope = None , for_test = False ) 3.\u5173\u6389\u6307\u5b9abuild\u7b56\u7565 # 1 2 3 4 5 6 7 8 build_strategy = fluid . BuildStrategy () build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False exec_strategy = fluid . ExecutionStrategy () compiled_train_prog = compiled_train_prog . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy ) 4. freeze program # 1 2 3 4 5 float_program , int8_program = convert ( val_program , place , quant_config , scope = None , save_int8 = True ) 5.\u4fdd\u5b58\u9884\u6d4b\u6a21\u578b # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 fluid . io . save_inference_model ( dirname = float_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = float_program , model_filename = float_path + ' /model ' , params_filename = float_path + ' /params ' ) fluid . io . save_inference_model ( dirname = int8_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = int8_program , model_filename = int8_path + ' /model ' , params_filename = int8_path + ' /params ' )","title":"\u91cf\u5316\u8bad\u7ec3"},{"location":"tutorials/quant_aware_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5728\u7ebf\u91cf\u5316\u63a5\u53e3\uff0c\u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u91cf\u5316, \u53ef\u4ee5\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002","title":"\u5728\u7ebf\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_aware_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/quant_aware_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"tutorials/quant_aware_demo/#1","text":"1 2 3 4 5 6 7 8 9 10 11 12 quant_config = { 'weight_quantize_type' : 'abs_max' , 'activation_quantize_type' : 'moving_average_abs_max' , 'weight_bits' : 8 , 'activation_bits' : 8 , 'not_quant_pattern' : [ 'skip_quant' ], 'quantize_op_types' : [ 'conv2d' , 'depthwise_conv2d' , 'mul' ], 'dtype' : 'int8' , 'window_size' : 10000 , 'moving_rate' : 0 . 9 , 'quant_weight_only' : False }","title":"1. \u914d\u7f6e\u91cf\u5316\u53c2\u6570"},{"location":"tutorials/quant_aware_demo/#2-programop","text":"1 2 3 val_program = quant_aware ( val_program , place , quant_config , scope = None , for_test = True ) compiled_train_prog = quant_aware ( train_prog , place , quant_config , scope = None , for_test = False )","title":"2. \u5bf9\u8bad\u7ec3\u548c\u6d4b\u8bd5program\u63d2\u5165\u53ef\u8bad\u7ec3\u91cf\u5316op"},{"location":"tutorials/quant_aware_demo/#3build","text":"1 2 3 4 5 6 7 8 build_strategy = fluid . BuildStrategy () build_strategy . fuse_all_reduce_ops = False build_strategy . sync_batch_norm = False exec_strategy = fluid . ExecutionStrategy () compiled_train_prog = compiled_train_prog . with_data_parallel ( loss_name = avg_cost . name , build_strategy = build_strategy , exec_strategy = exec_strategy )","title":"3.\u5173\u6389\u6307\u5b9abuild\u7b56\u7565"},{"location":"tutorials/quant_aware_demo/#4-freeze-program","text":"1 2 3 4 5 float_program , int8_program = convert ( val_program , place , quant_config , scope = None , save_int8 = True )","title":"4. freeze program"},{"location":"tutorials/quant_aware_demo/#5","text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 fluid . io . save_inference_model ( dirname = float_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = float_program , model_filename = float_path + ' /model ' , params_filename = float_path + ' /params ' ) fluid . io . save_inference_model ( dirname = int8_path , feeded_var_names = [ image . name ], target_vars = [ out ], executor = exe , main_program = int8_program , model_filename = int8_path + ' /model ' , params_filename = int8_path + ' /params ' )","title":"5.\u4fdd\u5b58\u9884\u6d4b\u6a21\u578b"},{"location":"tutorials/quant_embedding_demo/","text":"Embedding\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Embedding\u91cf\u5316\u7684\u63a5\u53e3 paddleslim.quant.quant_embedding \u3002 quant_embedding \u63a5\u53e3\u5c06\u7f51\u7edc\u4e2d\u7684Embedding\u53c2\u6570\u4ece float32 \u7c7b\u578b\u91cf\u5316\u5230 8-bit \u6574\u6570\u7c7b\u578b\uff0c\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u6a21\u578b\u7cbe\u5ea6\u7684\u60c5\u51b5\u4e0b\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u8be5\u63a5\u53e3\u5bf9program\u7684\u4fee\u6539\uff1a \u91cf\u5316\u524d: \u56fe1\uff1a\u91cf\u5316\u524d\u7684\u6a21\u578b\u7ed3\u6784 \u91cf\u5316\u540e\uff1a \u56fe2: \u91cf\u5316\u540e\u7684\u6a21\u578b\u7ed3\u6784 \u4ee5\u4e0b\u5c06\u4ee5 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u4e3a\u4f8b\u6765\u8bf4\u660e\u5982\u4f55\u4f7f\u7528 quant_embedding \u63a5\u53e3\u3002\u9996\u5148\u4ecb\u7ecd \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u7684\u6b63\u5e38\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6d41\u7a0b\u3002 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b # \u4ee5\u4e0b\u662f\u672c\u4f8b\u7684\u7b80\u8981\u76ee\u5f55\u7ed3\u6784\u53ca\u8bf4\u660e\uff1a 1 2 3 4 5 6 7 8 9 10 . \u251c\u2500\u2500 cluster_train.py # \u5206\u5e03\u5f0f\u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 cluster_train.sh # \u672c\u5730\u6a21\u62df\u591a\u673a\u811a\u672c \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 infer.py # \u9884\u6d4b\u811a\u672c \u251c\u2500\u2500 net.py # \u7f51\u7edc\u7ed3\u6784 \u251c\u2500\u2500 preprocess.py # \u9884\u5904\u7406\u811a\u672c\uff0c\u5305\u62ec\u6784\u5efa\u8bcd\u5178\u548c\u9884\u5904\u7406\u6587\u672c \u251c\u2500\u2500 reader.py # \u8bad\u7ec3\u9636\u6bb5\u7684\u6587\u672c\u8bfb\u5199 \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u2514\u2500\u2500 utils.py # \u901a\u7528\u51fd\u6570 \u4ecb\u7ecd # \u672c\u4f8b\u5b9e\u73b0\u4e86skip-gram\u6a21\u5f0f\u7684word2vector\u6a21\u578b\u3002 \u540c\u65f6\u63a8\u8350\u7528\u6237\u53c2\u8003 IPython Notebook demo \u6570\u636e\u4e0b\u8f7d # \u5168\u91cf\u6570\u636e\u96c6\u4f7f\u7528\u7684\u662f\u6765\u81ea1 Billion Word Language Model Benchmark\u7684( http://www.statmt.org/lm-benchmark ) \u7684\u6570\u636e\u96c6. 1 2 3 4 mkdir data wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz tar xzvf 1 -billion-word-language-modeling-benchmark-r13output.tar.gz mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u5907\u7528\u6570\u636e\u5730\u5740\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar tar xvf 1 -billion-word-language-modeling-benchmark-r13output.tar mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u4e3a\u4e86\u65b9\u4fbf\u5feb\u901f\u9a8c\u8bc1\uff0c\u6211\u4eec\u4e5f\u63d0\u4f9b\u4e86\u7ecf\u5178\u7684text8\u6837\u4f8b\u6570\u636e\u96c6\uff0c\u5305\u542b1700w\u4e2a\u8bcd\u3002 \u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/ \u6570\u636e\u9884\u5904\u7406 # \u4ee5\u6837\u4f8b\u6570\u636e\u96c6\u4e3a\u4f8b\u8fdb\u884c\u9884\u5904\u7406\u3002\u5168\u91cf\u6570\u636e\u96c6\u6ce8\u610f\u89e3\u538b\u540e\u4ee5training-monolingual.tokenized.shuffled \u76ee\u5f55\u4e3a\u9884\u5904\u7406\u76ee\u5f55\uff0c\u548c\u6837\u4f8b\u6570\u636e\u96c6\u7684text\u76ee\u5f55\u5e76\u5217\u3002 \u8bcd\u5178\u683c\u5f0f: \u8bcd<\u7a7a\u683c>\u8bcd\u9891\u3002\u6ce8\u610f\u4f4e\u9891\u8bcd\u7528'UNK'\u8868\u793a \u53ef\u4ee5\u6309\u683c\u5f0f\u81ea\u5efa\u8bcd\u5178\uff0c\u5982\u679c\u81ea\u5efa\u8bcd\u5178\u8df3\u8fc7\u7b2c\u4e00\u6b65\u3002 1 2 3 4 5 6 7 8 9 10 the 1061396 of 593677 and 416629 one 411764 in 372201 a 325873 < UNK > 324608 to 316376 zero 264975 nine 250430 \u7b2c\u4e00\u6b65\u6839\u636e\u82f1\u6587\u8bed\u6599\u751f\u6210\u8bcd\u5178\uff0c\u4e2d\u6587\u8bed\u6599\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539text_strip\u65b9\u6cd5\u81ea\u5b9a\u4e49\u5904\u7406\u65b9\u6cd5\u3002 1 python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict \u7b2c\u4e8c\u6b65\u6839\u636e\u8bcd\u5178\u5c06\u6587\u672c\u8f6c\u6210id, \u540c\u65f6\u8fdb\u884cdownsample\uff0c\u6309\u7167\u6982\u7387\u8fc7\u6ee4\u5e38\u89c1\u8bcd, \u540c\u65f6\u751f\u6210word\u548cid\u6620\u5c04\u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u540d\u4e3a\u8bcd\u5178+\" word_to_id \"\u3002 1 python preprocess.py --filter_corpus --dict_path data/test_build_dict --input_corpus_dir data/text --output_corpus_dir data/convert_text8 --min_count 5 --downsample 0 .001 \u8bad\u7ec3 # \u5177\u4f53\u7684\u53c2\u6570\u914d\u7f6e\u53ef\u8fd0\u884c 1 python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 1 OPENBLAS_NUM_THREADS = 1 CPU_NUM = 5 python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes 10 --batch_size 100 --model_output_dir v1_cpu5_b100_lr1dir --base_lr 1 .0 --print_batch 1000 --with_speed --is_sparse \u672c\u5730\u5355\u673a\u6a21\u62df\u591a\u673a\u8bad\u7ec3 1 sh cluster_train.sh \u672c\u793a\u4f8b\u4e2d\u6309\u7167\u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3\u7684\u547d\u4ee4\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u5b8c\u6bd5\u540e\uff0c\u53ef\u770b\u5230\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u6a21\u578b\u7684\u8def\u5f84\u4e3a: v1_cpu5_b100_lr1dir , \u8fd0\u884c ls v1_cpu5_b100_lr1dir \u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u4e86\u8bad\u7ec3\u768410\u4e2aepoch\u7684\u6a21\u578b\u6587\u4ef6\u3002 1 pass - 0 pass - 1 pass - 2 pass - 3 pass - 4 pass - 5 pass - 6 pass - 7 pass - 8 pass - 9 \u9884\u6d4b # \u6d4b\u8bd5\u96c6\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 #\u5168\u91cf\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar #\u6837\u672c\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar \u9884\u6d4b\u547d\u4ee4\uff0c\u6ce8\u610f\u8bcd\u5178\u540d\u79f0\u9700\u8981\u52a0\u540e\u7f00\" word_to_id \", \u6b64\u6587\u4ef6\u662f\u9884\u5904\u7406\u9636\u6bb5\u751f\u6210\u7684\u3002 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 \u8fd0\u884c\u8be5\u9884\u6d4b\u547d\u4ee4, \u53ef\u770b\u5230\u5982\u4e0b\u8f93\u51fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) step : 1 249 epoch : 0 acc : 0 . 014 step : 1 590 epoch : 1 acc : 0 . 033 step : 1 982 epoch : 2 acc : 0 . 055 step : 1 1338 epoch : 3 acc : 0 . 075 step : 1 1653 epoch : 4 acc : 0 . 093 step : 1 1914 epoch : 5 acc : 0 . 107 step : 1 2204 epoch : 6 acc : 0 . 124 step : 1 2416 epoch : 7 acc : 0 . 136 step : 1 2606 epoch : 8 acc : 0 . 146 step : 1 2722 epoch : 9 acc : 0 . 153 \u91cf\u5316 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b # \u91cf\u5316\u914d\u7f6e\u4e3a: 1 2 3 4 config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 --emb_quant True \u8fd0\u884c\u8f93\u51fa\u4e3a: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 253 epoch : 0 acc : 0 . 014 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 586 epoch : 1 acc : 0 . 033 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 970 epoch : 2 acc : 0 . 054 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1364 epoch : 3 acc : 0 . 077 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1642 epoch : 4 acc : 0 . 092 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1936 epoch : 5 acc : 0 . 109 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2216 epoch : 6 acc : 0 . 124 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2419 epoch : 7 acc : 0 . 136 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2603 epoch : 8 acc : 0 . 146 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2719 epoch : 9 acc : 0 . 153 \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u5728 ./output_quant \u4e2d\uff0c\u53ef\u770b\u5230\u91cf\u5316\u540e\u7684\u53c2\u6570 'emb.int8' \u7684\u5927\u5c0f\u4e3a3.9M, \u5728 ./v1_cpu5_b100_lr1dir \u4e2d\u53ef\u770b\u5230\u91cf\u5316\u524d\u7684\u53c2\u6570 'emb' \u7684\u5927\u5c0f\u4e3a16M\u3002","title":"Embedding\u91cf\u5316"},{"location":"tutorials/quant_embedding_demo/#embedding","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Embedding\u91cf\u5316\u7684\u63a5\u53e3 paddleslim.quant.quant_embedding \u3002 quant_embedding \u63a5\u53e3\u5c06\u7f51\u7edc\u4e2d\u7684Embedding\u53c2\u6570\u4ece float32 \u7c7b\u578b\u91cf\u5316\u5230 8-bit \u6574\u6570\u7c7b\u578b\uff0c\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u6a21\u578b\u7cbe\u5ea6\u7684\u60c5\u51b5\u4e0b\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u8be5\u63a5\u53e3\u5bf9program\u7684\u4fee\u6539\uff1a \u91cf\u5316\u524d: \u56fe1\uff1a\u91cf\u5316\u524d\u7684\u6a21\u578b\u7ed3\u6784 \u91cf\u5316\u540e\uff1a \u56fe2: \u91cf\u5316\u540e\u7684\u6a21\u578b\u7ed3\u6784 \u4ee5\u4e0b\u5c06\u4ee5 \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u4e3a\u4f8b\u6765\u8bf4\u660e\u5982\u4f55\u4f7f\u7528 quant_embedding \u63a5\u53e3\u3002\u9996\u5148\u4ecb\u7ecd \u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b \u7684\u6b63\u5e38\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6d41\u7a0b\u3002","title":"Embedding\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_embedding_demo/#skip-gramword2vector","text":"\u4ee5\u4e0b\u662f\u672c\u4f8b\u7684\u7b80\u8981\u76ee\u5f55\u7ed3\u6784\u53ca\u8bf4\u660e\uff1a 1 2 3 4 5 6 7 8 9 10 . \u251c\u2500\u2500 cluster_train.py # \u5206\u5e03\u5f0f\u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 cluster_train.sh # \u672c\u5730\u6a21\u62df\u591a\u673a\u811a\u672c \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u251c\u2500\u2500 infer.py # \u9884\u6d4b\u811a\u672c \u251c\u2500\u2500 net.py # \u7f51\u7edc\u7ed3\u6784 \u251c\u2500\u2500 preprocess.py # \u9884\u5904\u7406\u811a\u672c\uff0c\u5305\u62ec\u6784\u5efa\u8bcd\u5178\u548c\u9884\u5904\u7406\u6587\u672c \u251c\u2500\u2500 reader.py # \u8bad\u7ec3\u9636\u6bb5\u7684\u6587\u672c\u8bfb\u5199 \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u51fd\u6570 \u2514\u2500\u2500 utils.py # \u901a\u7528\u51fd\u6570","title":"\u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b"},{"location":"tutorials/quant_embedding_demo/#_1","text":"\u672c\u4f8b\u5b9e\u73b0\u4e86skip-gram\u6a21\u5f0f\u7684word2vector\u6a21\u578b\u3002 \u540c\u65f6\u63a8\u8350\u7528\u6237\u53c2\u8003 IPython Notebook demo","title":"\u4ecb\u7ecd"},{"location":"tutorials/quant_embedding_demo/#_2","text":"\u5168\u91cf\u6570\u636e\u96c6\u4f7f\u7528\u7684\u662f\u6765\u81ea1 Billion Word Language Model Benchmark\u7684( http://www.statmt.org/lm-benchmark ) \u7684\u6570\u636e\u96c6. 1 2 3 4 mkdir data wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz tar xzvf 1 -billion-word-language-modeling-benchmark-r13output.tar.gz mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u5907\u7528\u6570\u636e\u5730\u5740\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar tar xvf 1 -billion-word-language-modeling-benchmark-r13output.tar mv 1 -billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/ \u4e3a\u4e86\u65b9\u4fbf\u5feb\u901f\u9a8c\u8bc1\uff0c\u6211\u4eec\u4e5f\u63d0\u4f9b\u4e86\u7ecf\u5178\u7684text8\u6837\u4f8b\u6570\u636e\u96c6\uff0c\u5305\u542b1700w\u4e2a\u8bcd\u3002 \u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"tutorials/quant_embedding_demo/#_3","text":"\u4ee5\u6837\u4f8b\u6570\u636e\u96c6\u4e3a\u4f8b\u8fdb\u884c\u9884\u5904\u7406\u3002\u5168\u91cf\u6570\u636e\u96c6\u6ce8\u610f\u89e3\u538b\u540e\u4ee5training-monolingual.tokenized.shuffled \u76ee\u5f55\u4e3a\u9884\u5904\u7406\u76ee\u5f55\uff0c\u548c\u6837\u4f8b\u6570\u636e\u96c6\u7684text\u76ee\u5f55\u5e76\u5217\u3002 \u8bcd\u5178\u683c\u5f0f: \u8bcd<\u7a7a\u683c>\u8bcd\u9891\u3002\u6ce8\u610f\u4f4e\u9891\u8bcd\u7528'UNK'\u8868\u793a \u53ef\u4ee5\u6309\u683c\u5f0f\u81ea\u5efa\u8bcd\u5178\uff0c\u5982\u679c\u81ea\u5efa\u8bcd\u5178\u8df3\u8fc7\u7b2c\u4e00\u6b65\u3002 1 2 3 4 5 6 7 8 9 10 the 1061396 of 593677 and 416629 one 411764 in 372201 a 325873 < UNK > 324608 to 316376 zero 264975 nine 250430 \u7b2c\u4e00\u6b65\u6839\u636e\u82f1\u6587\u8bed\u6599\u751f\u6210\u8bcd\u5178\uff0c\u4e2d\u6587\u8bed\u6599\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539text_strip\u65b9\u6cd5\u81ea\u5b9a\u4e49\u5904\u7406\u65b9\u6cd5\u3002 1 python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict \u7b2c\u4e8c\u6b65\u6839\u636e\u8bcd\u5178\u5c06\u6587\u672c\u8f6c\u6210id, \u540c\u65f6\u8fdb\u884cdownsample\uff0c\u6309\u7167\u6982\u7387\u8fc7\u6ee4\u5e38\u89c1\u8bcd, \u540c\u65f6\u751f\u6210word\u548cid\u6620\u5c04\u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u540d\u4e3a\u8bcd\u5178+\" word_to_id \"\u3002 1 python preprocess.py --filter_corpus --dict_path data/test_build_dict --input_corpus_dir data/text --output_corpus_dir data/convert_text8 --min_count 5 --downsample 0 .001","title":"\u6570\u636e\u9884\u5904\u7406"},{"location":"tutorials/quant_embedding_demo/#_4","text":"\u5177\u4f53\u7684\u53c2\u6570\u914d\u7f6e\u53ef\u8fd0\u884c 1 python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 1 OPENBLAS_NUM_THREADS = 1 CPU_NUM = 5 python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes 10 --batch_size 100 --model_output_dir v1_cpu5_b100_lr1dir --base_lr 1 .0 --print_batch 1000 --with_speed --is_sparse \u672c\u5730\u5355\u673a\u6a21\u62df\u591a\u673a\u8bad\u7ec3 1 sh cluster_train.sh \u672c\u793a\u4f8b\u4e2d\u6309\u7167\u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3\u7684\u547d\u4ee4\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u5b8c\u6bd5\u540e\uff0c\u53ef\u770b\u5230\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u6a21\u578b\u7684\u8def\u5f84\u4e3a: v1_cpu5_b100_lr1dir , \u8fd0\u884c ls v1_cpu5_b100_lr1dir \u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u4fdd\u5b58\u4e86\u8bad\u7ec3\u768410\u4e2aepoch\u7684\u6a21\u578b\u6587\u4ef6\u3002 1 pass - 0 pass - 1 pass - 2 pass - 3 pass - 4 pass - 5 pass - 6 pass - 7 pass - 8 pass - 9","title":"\u8bad\u7ec3"},{"location":"tutorials/quant_embedding_demo/#_5","text":"\u6d4b\u8bd5\u96c6\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b 1 2 3 4 #\u5168\u91cf\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar #\u6837\u672c\u6570\u636e\u96c6\u6d4b\u8bd5\u96c6 wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar \u9884\u6d4b\u547d\u4ee4\uff0c\u6ce8\u610f\u8bcd\u5178\u540d\u79f0\u9700\u8981\u52a0\u540e\u7f00\" word_to_id \", \u6b64\u6587\u4ef6\u662f\u9884\u5904\u7406\u9636\u6bb5\u751f\u6210\u7684\u3002 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 \u8fd0\u884c\u8be5\u9884\u6d4b\u547d\u4ee4, \u53ef\u770b\u5230\u5982\u4e0b\u8f93\u51fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) step : 1 249 epoch : 0 acc : 0 . 014 step : 1 590 epoch : 1 acc : 0 . 033 step : 1 982 epoch : 2 acc : 0 . 055 step : 1 1338 epoch : 3 acc : 0 . 075 step : 1 1653 epoch : 4 acc : 0 . 093 step : 1 1914 epoch : 5 acc : 0 . 107 step : 1 2204 epoch : 6 acc : 0 . 124 step : 1 2416 epoch : 7 acc : 0 . 136 step : 1 2606 epoch : 8 acc : 0 . 146 step : 1 2722 epoch : 9 acc : 0 . 153","title":"\u9884\u6d4b"},{"location":"tutorials/quant_embedding_demo/#skip-gramword2vector_1","text":"\u91cf\u5316\u914d\u7f6e\u4e3a: 1 2 3 4 config = { 'params_name' : 'emb' , 'quantize_type' : 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size 20000 --model_dir v1_cpu5_b100_lr1dir/ --start_index 0 --last_index 9 --emb_quant True \u8fd0\u884c\u8f93\u51fa\u4e3a: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ( 'start index: ' , 0 , ' last_index:' , 9 ) ( 'vocab_size:' , 63642 ) quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 253 epoch : 0 acc : 0 . 014 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 586 epoch : 1 acc : 0 . 033 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 970 epoch : 2 acc : 0 . 054 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1364 epoch : 3 acc : 0 . 077 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1642 epoch : 4 acc : 0 . 092 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 1936 epoch : 5 acc : 0 . 109 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2216 epoch : 6 acc : 0 . 124 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2419 epoch : 7 acc : 0 . 136 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2603 epoch : 8 acc : 0 . 146 quant_embedding config { 'quantize_type' : 'abs_max' , 'params_name' : 'emb' , 'quantize_bits' : 8 , 'dtype' : 'int8' } step : 1 2719 epoch : 9 acc : 0 . 153 \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u5728 ./output_quant \u4e2d\uff0c\u53ef\u770b\u5230\u91cf\u5316\u540e\u7684\u53c2\u6570 'emb.int8' \u7684\u5927\u5c0f\u4e3a3.9M, \u5728 ./v1_cpu5_b100_lr1dir \u4e2d\u53ef\u770b\u5230\u91cf\u5316\u524d\u7684\u53c2\u6570 'emb' \u7684\u5927\u5c0f\u4e3a16M\u3002","title":"\u91cf\u5316\u57fa\u4e8eskip-gram\u7684word2vector\u6a21\u578b"},{"location":"tutorials/quant_post_demo/","text":"\u79bb\u7ebf\u91cf\u5316\u793a\u4f8b # \u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3 paddleslim.quant.quant_post \u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316, \u8be5\u63a5\u53e3\u65e0\u9700\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u5c31\u53ef\u5f97\u5230\u91cf\u5316\u6a21\u578b\uff0c\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002 \u63a5\u53e3\u4ecb\u7ecd # \u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002 \u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b # \u51c6\u5907\u6570\u636e # \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa data \u6587\u4ef6\u5939\uff0c\u5c06 imagenet \u6570\u636e\u96c6\u89e3\u538b\u5728 data \u6587\u4ef6\u5939\u4e0b\uff0c\u89e3\u538b\u540e data \u6587\u4ef6\u5939\u4e0b\u5e94\u5305\u542b\u4ee5\u4e0b\u6587\u4ef6\uff1a - 'train' \u6587\u4ef6\u5939\uff0c\u8bad\u7ec3\u56fe\u7247 - 'train_list.txt' \u6587\u4ef6 - 'val' \u6587\u4ef6\u5939\uff0c\u9a8c\u8bc1\u56fe\u7247 - 'val_list.txt' \u6587\u4ef6 \u51c6\u5907\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b # \u56e0\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ea\u652f\u6301\u52a0\u8f7d\u901a\u8fc7 fluid.io.save_inference_model \u63a5\u53e3\u4fdd\u5b58\u7684\u6a21\u578b\uff0c\u56e0\u6b64\u5982\u679c\u60a8\u7684\u6a21\u578b\u662f\u901a\u8fc7\u5176\u4ed6\u63a5\u53e3\u4fdd\u5b58\u7684\uff0c\u90a3\u9700\u8981\u5148\u5c06\u6a21\u578b\u8fdb\u884c\u8f6c\u5316\u3002\u672c\u793a\u4f8b\u5c06\u4ee5\u5206\u7c7b\u6a21\u578b\u4e3a\u4f8b\u8fdb\u884c\u8bf4\u660e\u3002 \u9996\u5148\u5728 imagenet\u5206\u7c7b\u6a21\u578b \u4e2d\u4e0b\u8f7d\u8bad\u7ec3\u597d\u7684 mobilenetv1 \u6a21\u578b\u3002 \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa 'pretrain' \u6587\u4ef6\u5939\uff0c\u5c06 mobilenetv1 \u6a21\u578b\u5728\u8be5\u6587\u4ef6\u5939\u4e0b\u89e3\u538b\uff0c\u89e3\u538b\u540e\u7684\u76ee\u5f55\u4e3a pretrain/MobileNetV1_pretrained \u5bfc\u51fa\u6a21\u578b # \u901a\u8fc7\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u53ef\u5c06\u6a21\u578b\u8f6c\u5316\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ef\u7528\u7684\u6a21\u578b\uff1a 1 python export_model . py --model \"MobileNet\" --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet \u8f6c\u5316\u4e4b\u540e\u7684\u6a21\u578b\u5b58\u50a8\u5728 inference_model/MobileNet/ \u6587\u4ef6\u5939\u4e0b\uff0c\u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u6709 'model' , 'weights' \u4e24\u4e2a\u6587\u4ef6\u3002 \u79bb\u7ebf\u91cf\u5316 # \u63a5\u4e0b\u6765\u5bf9\u5bfc\u51fa\u7684\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\uff0c\u79bb\u7ebf\u91cf\u5316\u7684\u811a\u672c\u4e3a quant_post.py \uff0c\u811a\u672c\u4e2d\u4f7f\u7528\u63a5\u53e3 paddleslim.quant.quant_post \u5bf9\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u3002\u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python quant_post . py --model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights model_path : \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u5750\u5728\u7684\u6587\u4ef6\u5939 save_path : \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u7684\u8def\u5f84 model_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u6a21\u578b\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 params_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 \u8fd0\u884c\u4ee5\u4e0a\u547d\u4ee4\u540e\uff0c\u53ef\u5728 ${save_path} \u4e0b\u770b\u5230\u91cf\u5316\u540e\u7684\u6a21\u578b\u6587\u4ef6\u548c\u53c2\u6570\u6587\u4ef6\u3002 \u4f7f\u7528\u7684\u91cf\u5316\u7b97\u6cd5\u4e3a 'KL' , \u4f7f\u7528\u8bad\u7ec3\u96c6\u4e2d\u7684160\u5f20\u56fe\u7247\u8fdb\u884c\u91cf\u5316\u53c2\u6570\u7684\u6821\u6b63\u3002 \u6d4b\u8bd5\u7cbe\u5ea6 # \u4f7f\u7528 eval.py \u811a\u672c\u5bf9\u91cf\u5316\u524d\u540e\u7684\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5\uff0c\u5f97\u5230\u6a21\u578b\u7684\u5206\u7c7b\u7cbe\u5ea6\u8fdb\u884c\u5bf9\u6bd4\u3002 \u9996\u5148\u6d4b\u8bd5\u91cf\u5316\u524d\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff0c\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a 1 python eval . py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 1 top1_acc / top5_acc = [ 0 . 70913923 0 . 89548034 ] \u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6d4b\u8bd5\u79bb\u7ebf\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff1a 1 python eval . py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 1 top1_acc / top5_acc = [ 0 . 70141864 0 . 89086477 ] \u4ece\u4ee5\u4e0a\u7cbe\u5ea6\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51fa\uff0c\u5bf9 mobilenet \u5728 imagenet \u4e0a\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u540e top1 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.77% \uff0c top5 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.46% .","title":"\u79bb\u7ebf\u91cf\u5316"},{"location":"tutorials/quant_post_demo/#_1","text":"\u672c\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3 paddleslim.quant.quant_post \u6765\u5bf9\u8bad\u7ec3\u597d\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316, \u8be5\u63a5\u53e3\u65e0\u9700\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u5c31\u53ef\u5f97\u5230\u91cf\u5316\u6a21\u578b\uff0c\u51cf\u5c11\u6a21\u578b\u7684\u5b58\u50a8\u7a7a\u95f4\u548c\u663e\u5b58\u5360\u7528\u3002","title":"\u79bb\u7ebf\u91cf\u5316\u793a\u4f8b"},{"location":"tutorials/quant_post_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/quant_post_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"tutorials/quant_post_demo/#_4","text":"\u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa data \u6587\u4ef6\u5939\uff0c\u5c06 imagenet \u6570\u636e\u96c6\u89e3\u538b\u5728 data \u6587\u4ef6\u5939\u4e0b\uff0c\u89e3\u538b\u540e data \u6587\u4ef6\u5939\u4e0b\u5e94\u5305\u542b\u4ee5\u4e0b\u6587\u4ef6\uff1a - 'train' \u6587\u4ef6\u5939\uff0c\u8bad\u7ec3\u56fe\u7247 - 'train_list.txt' \u6587\u4ef6 - 'val' \u6587\u4ef6\u5939\uff0c\u9a8c\u8bc1\u56fe\u7247 - 'val_list.txt' \u6587\u4ef6","title":"\u51c6\u5907\u6570\u636e"},{"location":"tutorials/quant_post_demo/#_5","text":"\u56e0\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ea\u652f\u6301\u52a0\u8f7d\u901a\u8fc7 fluid.io.save_inference_model \u63a5\u53e3\u4fdd\u5b58\u7684\u6a21\u578b\uff0c\u56e0\u6b64\u5982\u679c\u60a8\u7684\u6a21\u578b\u662f\u901a\u8fc7\u5176\u4ed6\u63a5\u53e3\u4fdd\u5b58\u7684\uff0c\u90a3\u9700\u8981\u5148\u5c06\u6a21\u578b\u8fdb\u884c\u8f6c\u5316\u3002\u672c\u793a\u4f8b\u5c06\u4ee5\u5206\u7c7b\u6a21\u578b\u4e3a\u4f8b\u8fdb\u884c\u8bf4\u660e\u3002 \u9996\u5148\u5728 imagenet\u5206\u7c7b\u6a21\u578b \u4e2d\u4e0b\u8f7d\u8bad\u7ec3\u597d\u7684 mobilenetv1 \u6a21\u578b\u3002 \u5728\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u521b\u5efa 'pretrain' \u6587\u4ef6\u5939\uff0c\u5c06 mobilenetv1 \u6a21\u578b\u5728\u8be5\u6587\u4ef6\u5939\u4e0b\u89e3\u538b\uff0c\u89e3\u538b\u540e\u7684\u76ee\u5f55\u4e3a pretrain/MobileNetV1_pretrained","title":"\u51c6\u5907\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b"},{"location":"tutorials/quant_post_demo/#_6","text":"\u901a\u8fc7\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u53ef\u5c06\u6a21\u578b\u8f6c\u5316\u4e3a\u79bb\u7ebf\u91cf\u5316\u63a5\u53e3\u53ef\u7528\u7684\u6a21\u578b\uff1a 1 python export_model . py --model \"MobileNet\" --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet \u8f6c\u5316\u4e4b\u540e\u7684\u6a21\u578b\u5b58\u50a8\u5728 inference_model/MobileNet/ \u6587\u4ef6\u5939\u4e0b\uff0c\u53ef\u770b\u5230\u8be5\u6587\u4ef6\u5939\u4e0b\u6709 'model' , 'weights' \u4e24\u4e2a\u6587\u4ef6\u3002","title":"\u5bfc\u51fa\u6a21\u578b"},{"location":"tutorials/quant_post_demo/#_7","text":"\u63a5\u4e0b\u6765\u5bf9\u5bfc\u51fa\u7684\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\uff0c\u79bb\u7ebf\u91cf\u5316\u7684\u811a\u672c\u4e3a quant_post.py \uff0c\u811a\u672c\u4e2d\u4f7f\u7528\u63a5\u53e3 paddleslim.quant.quant_post \u5bf9\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u3002\u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 1 python quant_post . py --model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights model_path : \u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u5750\u5728\u7684\u6587\u4ef6\u5939 save_path : \u91cf\u5316\u540e\u7684\u6a21\u578b\u4fdd\u5b58\u7684\u8def\u5f84 model_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u6a21\u578b\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 params_filename : \u5982\u679c\u9700\u8981\u91cf\u5316\u7684\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u8bbe\u7f6e\u4e3a\u8be5\u6a21\u578b\u7684\u53c2\u6570\u6587\u4ef6\u540d\u79f0\uff0c\u5982\u679c\u53c2\u6570\u6587\u4ef6\u4fdd\u5b58\u5728\u591a\u4e2a\u6587\u4ef6\u4e2d\uff0c\u5219\u4e0d\u9700\u8981\u8bbe\u7f6e\u3002 \u8fd0\u884c\u4ee5\u4e0a\u547d\u4ee4\u540e\uff0c\u53ef\u5728 ${save_path} \u4e0b\u770b\u5230\u91cf\u5316\u540e\u7684\u6a21\u578b\u6587\u4ef6\u548c\u53c2\u6570\u6587\u4ef6\u3002 \u4f7f\u7528\u7684\u91cf\u5316\u7b97\u6cd5\u4e3a 'KL' , \u4f7f\u7528\u8bad\u7ec3\u96c6\u4e2d\u7684160\u5f20\u56fe\u7247\u8fdb\u884c\u91cf\u5316\u53c2\u6570\u7684\u6821\u6b63\u3002","title":"\u79bb\u7ebf\u91cf\u5316"},{"location":"tutorials/quant_post_demo/#_8","text":"\u4f7f\u7528 eval.py \u811a\u672c\u5bf9\u91cf\u5316\u524d\u540e\u7684\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5\uff0c\u5f97\u5230\u6a21\u578b\u7684\u5206\u7c7b\u7cbe\u5ea6\u8fdb\u884c\u5bf9\u6bd4\u3002 \u9996\u5148\u6d4b\u8bd5\u91cf\u5316\u524d\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff0c\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a 1 python eval . py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 1 top1_acc / top5_acc = [ 0 . 70913923 0 . 89548034 ] \u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6d4b\u8bd5\u79bb\u7ebf\u91cf\u5316\u540e\u7684\u6a21\u578b\u7684\u7cbe\u5ea6\uff1a 1 python eval . py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 1 top1_acc / top5_acc = [ 0 . 70141864 0 . 89086477 ] \u4ece\u4ee5\u4e0a\u7cbe\u5ea6\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51fa\uff0c\u5bf9 mobilenet \u5728 imagenet \u4e0a\u7684\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u79bb\u7ebf\u91cf\u5316\u540e top1 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.77% \uff0c top5 \u7cbe\u5ea6\u635f\u5931\u4e3a 0.46% .","title":"\u6d4b\u8bd5\u7cbe\u5ea6"},{"location":"tutorials/sensitivity_demo/","text":"\u8be5\u793a\u4f8b\u4ecb\u7ecd\u5982\u4f55\u5206\u6790\u5377\u79ef\u7f51\u7edc\u4e2d\u5404\u5377\u79ef\u5c42\u7684\u654f\u611f\u5ea6\uff0c\u4ee5\u53ca\u5982\u4f55\u6839\u636e\u8ba1\u7b97\u51fa\u7684\u654f\u611f\u5ea6\u9009\u62e9\u4e00\u7ec4\u5408\u9002\u7684\u526a\u88c1\u7387\u3002 \u8be5\u793a\u4f8b\u9ed8\u8ba4\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u4f7f\u7528MNIST\u6570\u636e\u3002\u652f\u6301\u4ee5\u4e0b\u6a21\u578b\uff1a MobileNetV1 MobileNetV2 ResNet50 1. \u63a5\u53e3\u4ecb\u7ecd # \u8be5\u793a\u4f8b\u6d89\u53ca\u4ee5\u4e0b\u63a5\u53e3\uff1a paddleslim.prune.sensitivity paddleslim.prune.merge_sensitive paddleslim.prune.get_ratios_by_loss 2. \u8fd0\u884c\u793a\u4f8b # \u5728\u8def\u5f84 PaddleSlim/demo/sensitive \u4e0b\u6267\u884c\u4ee5\u4e0b\u4ee3\u7801\u8fd0\u884c\u793a\u4f8b\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py --model \"MobileNetV1\" \u901a\u8fc7 python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002 3. \u91cd\u8981\u6b65\u9aa4\u8bf4\u660e # 3.1 \u8ba1\u7b97\u654f\u611f\u5ea6 # \u8ba1\u7b97\u654f\u611f\u5ea6\u4e4b\u524d\uff0c\u7528\u6237\u9700\u8981\u642d\u5efa\u597d\u7528\u4e8e\u6d4b\u8bd5\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u5b9e\u73b0\u8bc4\u4f30\u6a21\u578b\u7cbe\u5ea6\u7684\u56de\u8c03\u51fd\u6570\u3002 \u8c03\u7528 paddleslim.prune.sensitivity \u63a5\u53e3\u8ba1\u7b97\u654f\u611f\u5ea6\u3002\u654f\u611f\u5ea6\u4fe1\u606f\u4f1a\u8ffd\u52a0\u5230 sensitivities_file \u9009\u9879\u6240\u6307\u5b9a\u7684\u6587\u4ef6\u4e2d\uff0c\u5982\u679c\u9700\u8981\u91cd\u65b0\u8ba1\u7b97\u654f\u611f\u5ea6\uff0c\u9700\u8981\u5148\u5220\u9664 sensitivities_file \u6587\u4ef6\u3002 \u5982\u679c\u6a21\u578b\u8bc4\u4f30\u901f\u5ea6\u8f83\u6162\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u52a0\u901f\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u3002\u6bd4\u5982\u5728\u8fdb\u7a0b1\u4e2d\u8bbe\u7f6e pruned_ratios=[0.1, 0.2, 0.3, 0.4] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u653e\u5728\u6587\u4ef6 sensitivities_0.data \u4e2d\uff0c\u7136\u540e\u5728\u8fdb\u7a0b2\u4e2d\u8bbe\u7f6e pruned_ratios=[0.5, 0.6, 0.7] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u6587\u4ef6 sensitivities_1.data \u4e2d\u3002\u8fd9\u6837\u6bcf\u4e2a\u8fdb\u7a0b\u53ea\u4f1a\u8ba1\u7b97\u6307\u5b9a\u526a\u5207\u7387\u4e0b\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u591a\u8fdb\u7a0b\u53ef\u4ee5\u8fd0\u884c\u5728\u5355\u673a\u591a\u5361\uff0c\u6216\u591a\u673a\u591a\u5361\u3002 \u4ee3\u7801\u5982\u4e0b\uff1a 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 1 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_0.data\" , pruned_ratios = [ 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 ]) 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 2 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_1.data\" , pruned_ratios = [ 0 . 5 , 0 . 6 , 0 . 7 ]) 3.2 \u5408\u5e76\u654f\u611f\u5ea6 # \u5982\u679c\u7528\u6237\u901a\u8fc7\u4e0a\u4e00\u8282\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u751f\u6210\u4e86\u591a\u4e2a\u5b58\u50a8\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u901a\u8fc7 paddleslim.prune.merge_sensitive \u5c06\u5176\u5408\u5e76\uff0c\u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u4e00\u4e2a dict \u4e2d\u3002\u4ee3\u7801\u5982\u4e0b\uff1a 1 sens = merge_sensitive ([ \"./sensitivities_0.data\" , \"./sensitivities_1.data\" ]) 3.3 \u8ba1\u7b97\u526a\u88c1\u7387 # \u8c03\u7528 paddleslim.prune.get_ratios_by_loss \u63a5\u53e3\u8ba1\u7b97\u4e00\u7ec4\u526a\u88c1\u7387\u3002 1 ratios = get_ratios_by_loss ( sens , 0 . 01 ) \u5176\u4e2d\uff0c 0.01 \u4e3a\u4e00\u4e2a\u9608\u503c\uff0c\u5bf9\u4e8e\u4efb\u610f\u5377\u79ef\u5c42\uff0c\u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e\u9608\u503c 0.01 \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u7528\u6237\u5728\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u4e4b\u540e\u53ef\u4ee5\u901a\u8fc7\u63a5\u53e3 paddleslim.prune.Pruner \u526a\u88c1\u7f51\u7edc\uff0c\u5e76\u7528\u63a5\u53e3 paddleslim.analysis.flops \u8ba1\u7b97 FLOPs \u3002\u5982\u679c FLOPs \u4e0d\u6ee1\u8db3\u8981\u6c42\uff0c\u8c03\u6574\u9608\u503c\u91cd\u65b0\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u3002","title":"Sensitivity demo"},{"location":"tutorials/sensitivity_demo/#1","text":"\u8be5\u793a\u4f8b\u6d89\u53ca\u4ee5\u4e0b\u63a5\u53e3\uff1a paddleslim.prune.sensitivity paddleslim.prune.merge_sensitive paddleslim.prune.get_ratios_by_loss","title":"1. \u63a5\u53e3\u4ecb\u7ecd"},{"location":"tutorials/sensitivity_demo/#2","text":"\u5728\u8def\u5f84 PaddleSlim/demo/sensitive \u4e0b\u6267\u884c\u4ee5\u4e0b\u4ee3\u7801\u8fd0\u884c\u793a\u4f8b\uff1a 1 2 export CUDA_VISIBLE_DEVICES = 0 python train . py --model \"MobileNetV1\" \u901a\u8fc7 python train.py --help \u67e5\u770b\u66f4\u591a\u9009\u9879\u3002","title":"2. \u8fd0\u884c\u793a\u4f8b"},{"location":"tutorials/sensitivity_demo/#3","text":"","title":"3. \u91cd\u8981\u6b65\u9aa4\u8bf4\u660e"},{"location":"tutorials/sensitivity_demo/#31","text":"\u8ba1\u7b97\u654f\u611f\u5ea6\u4e4b\u524d\uff0c\u7528\u6237\u9700\u8981\u642d\u5efa\u597d\u7528\u4e8e\u6d4b\u8bd5\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u5b9e\u73b0\u8bc4\u4f30\u6a21\u578b\u7cbe\u5ea6\u7684\u56de\u8c03\u51fd\u6570\u3002 \u8c03\u7528 paddleslim.prune.sensitivity \u63a5\u53e3\u8ba1\u7b97\u654f\u611f\u5ea6\u3002\u654f\u611f\u5ea6\u4fe1\u606f\u4f1a\u8ffd\u52a0\u5230 sensitivities_file \u9009\u9879\u6240\u6307\u5b9a\u7684\u6587\u4ef6\u4e2d\uff0c\u5982\u679c\u9700\u8981\u91cd\u65b0\u8ba1\u7b97\u654f\u611f\u5ea6\uff0c\u9700\u8981\u5148\u5220\u9664 sensitivities_file \u6587\u4ef6\u3002 \u5982\u679c\u6a21\u578b\u8bc4\u4f30\u901f\u5ea6\u8f83\u6162\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u52a0\u901f\u654f\u611f\u5ea6\u8ba1\u7b97\u8fc7\u7a0b\u3002\u6bd4\u5982\u5728\u8fdb\u7a0b1\u4e2d\u8bbe\u7f6e pruned_ratios=[0.1, 0.2, 0.3, 0.4] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u653e\u5728\u6587\u4ef6 sensitivities_0.data \u4e2d\uff0c\u7136\u540e\u5728\u8fdb\u7a0b2\u4e2d\u8bbe\u7f6e pruned_ratios=[0.5, 0.6, 0.7] \uff0c\u5e76\u5c06\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u6587\u4ef6 sensitivities_1.data \u4e2d\u3002\u8fd9\u6837\u6bcf\u4e2a\u8fdb\u7a0b\u53ea\u4f1a\u8ba1\u7b97\u6307\u5b9a\u526a\u5207\u7387\u4e0b\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u591a\u8fdb\u7a0b\u53ef\u4ee5\u8fd0\u884c\u5728\u5355\u673a\u591a\u5361\uff0c\u6216\u591a\u673a\u591a\u5361\u3002 \u4ee3\u7801\u5982\u4e0b\uff1a 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 1 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_0.data\" , pruned_ratios = [ 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 ]) 1 2 3 4 5 6 7 8 # \u8fdb\u7a0b 2 sensitivity ( val_program , place , params , test , sensitivities_file = \"sensitivities_1.data\" , pruned_ratios = [ 0 . 5 , 0 . 6 , 0 . 7 ])","title":"3.1 \u8ba1\u7b97\u654f\u611f\u5ea6"},{"location":"tutorials/sensitivity_demo/#32","text":"\u5982\u679c\u7528\u6237\u901a\u8fc7\u4e0a\u4e00\u8282\u591a\u8fdb\u7a0b\u7684\u65b9\u5f0f\u751f\u6210\u4e86\u591a\u4e2a\u5b58\u50a8\u654f\u611f\u5ea6\u4fe1\u606f\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u901a\u8fc7 paddleslim.prune.merge_sensitive \u5c06\u5176\u5408\u5e76\uff0c\u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u5b58\u50a8\u5728\u4e00\u4e2a dict \u4e2d\u3002\u4ee3\u7801\u5982\u4e0b\uff1a 1 sens = merge_sensitive ([ \"./sensitivities_0.data\" , \"./sensitivities_1.data\" ])","title":"3.2 \u5408\u5e76\u654f\u611f\u5ea6"},{"location":"tutorials/sensitivity_demo/#33","text":"\u8c03\u7528 paddleslim.prune.get_ratios_by_loss \u63a5\u53e3\u8ba1\u7b97\u4e00\u7ec4\u526a\u88c1\u7387\u3002 1 ratios = get_ratios_by_loss ( sens , 0 . 01 ) \u5176\u4e2d\uff0c 0.01 \u4e3a\u4e00\u4e2a\u9608\u503c\uff0c\u5bf9\u4e8e\u4efb\u610f\u5377\u79ef\u5c42\uff0c\u5176\u526a\u88c1\u7387\u4e3a\u4f7f\u7cbe\u5ea6\u635f\u5931\u4f4e\u4e8e\u9608\u503c 0.01 \u7684\u6700\u5927\u526a\u88c1\u7387\u3002 \u7528\u6237\u5728\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u4e4b\u540e\u53ef\u4ee5\u901a\u8fc7\u63a5\u53e3 paddleslim.prune.Pruner \u526a\u88c1\u7f51\u7edc\uff0c\u5e76\u7528\u63a5\u53e3 paddleslim.analysis.flops \u8ba1\u7b97 FLOPs \u3002\u5982\u679c FLOPs \u4e0d\u6ee1\u8db3\u8981\u6c42\uff0c\u8c03\u6574\u9608\u503c\u91cd\u65b0\u8ba1\u7b97\u51fa\u4e00\u7ec4\u526a\u88c1\u7387\u3002","title":"3.3 \u8ba1\u7b97\u526a\u88c1\u7387"}]} \ No newline at end of file diff --git a/api/search_space/index.html b/search_space/index.html similarity index 78% rename from api/search_space/index.html rename to search_space/index.html index 89e48ef7..d264dec5 100644 --- a/api/search_space/index.html +++ b/search_space/index.html @@ -7,23 +7,23 @@ - - 搜索空间 - PaddleSlim Docs + + Search space - PaddleSlim Docs - - + + - - + + @@ -36,9 +36,9 @@
  • - 算法原理 + 算法原理
    • @@ -152,7 +135,7 @@ @@ -160,18 +143,14 @@
      -
    • Docs »
      • +
    • Docs »
      • - -
    • API »
      • - - -
    • 搜索空间
      • +
    • Search space
    • - Edit on GitHub
      • @@ -181,41 +160,61 @@
      -

      paddleslim.nas 提供的搜索空间：#

      +

      搜索空间简介#

      +
      +
      搜索空间是神经网络搜索中的一个概念。搜索空间是一系列模型结构的汇集, SANAS主要是利用模拟退火的思想在搜索空间中搜索到一个比较小的模型结构或者一个精度比较高的模型结构。
      +
      +

      paddleslim.nas 提供的搜索空间#

      +
      根据初始模型结构构造搜索空间#
        -
      1. 根据原本模型结构构造搜索空间：
        2. +
      2. +

        MobileNetV2Space
        +  MobileNetV2的网络结构可以参考：代码，论文

        +
        3. +
      3. +

        MobileNetV1Space
        +  MobilNetV1的网络结构可以参考：代码，论文

        +
        4. +
      4. +

        ResNetSpace
        +  ResNetSpace的网络结构可以参考：代码，论文

        +
      -

      1.1 MobileNetV2Space

      -

      1.2 MobileNetV1Space

      -

      1.3 ResNetSpace

      +
      根据相应模型的block构造搜索空间#
        -
      1. 根据相应模型的block构造搜索空间
        2. +
      2. +

        MobileNetV1BlockSpace
        +  MobileNetV1Block的结构可以参考：代码

        +
        3. +
      3. +

        MobileNetV2BlockSpace
        +  MobileNetV2Block的结构可以参考：代码

        +
        4. +
      4. +

        ResNetBlockSpace
        +  ResNetBlock的结构可以参考：代码

        +
        5. +
      5. +

        InceptionABlockSpace
        +  InceptionABlock的结构可以参考：代码

        +
        6. +
      6. +

        InceptionCBlockSpace
        +  InceptionCBlock结构可以参考：代码

        +
      -

      2.1 MobileNetV1BlockSpace

      -

      2.2 MobileNetV2BlockSpace

      -

      2.3 ResNetBlockSpace

      -

      2.4 InceptionABlockSpace

      -

      2.5 InceptionCBlockSpace

      -

      搜索空间的配置介绍：#

      -

      input_size(int|None)：input_size表示输入feature map的大小。 -output_size(int|None)：output_size表示输出feature map的大小。 -block_num(int|None)：block_num表示搜索空间中block的数量。 -block_mask(list|None)：block_mask表示当前的block是一个reduction block还是一个normal block，是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了block_mask，则主要以block_mask为主要配置，input_size，output_size和block_num三种配置是无效的。

      -

      Note: -1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。 -2. input_size和output_size用来计算整个模型结构中reduction block数量。

      -

      搜索空间示例：#

      +

      搜索空间示例#

        -
      1. 使用paddleslim中提供用原本的模型结构来构造搜索空间的话，仅需要指定搜索空间名字即可。例如：如果使用原本的MobileNetV2的搜索空间进行搜索的话，传入SANAS中的config直接指定为[('MobileNetV2Space')]。
        2. -
      2. 使用paddleslim中提供的block搜索空间构造搜索空间： - 2.1 使用input_size, output_size和block_num来构造搜索空间。例如：传入SANAS的config可以指定为[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]。 +
      3. 使用paddleslim中提供用初始的模型结构来构造搜索空间的话，仅需要指定搜索空间名字即可。例如：如果使用原本的MobileNetV2的搜索空间进行搜索的话，传入SANAS中的config直接指定为[('MobileNetV2Space')]。
        4. +
      4. 使用paddleslim中提供的block搜索空间构造搜索空间：
        + 2.1 使用input_size, output_size和block_num来构造搜索空间。例如：传入SANAS的config可以指定为[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]。
        2.2 使用block_mask构造搜索空间。例如：传入SANAS的config可以指定为[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]。
      -

      自定义搜索空间(search space)#

      -

      自定义搜索空间类需要继承搜索空间基类并重写以下几部分： - 1. 初始化的tokens(init_tokens函数)，可以设置为自己想要的tokens列表, tokens列表中的每个数字指的是当前数字在相应的搜索列表中的索引。例如本示例中若tokens=[0, 3, 5]，则代表当前模型结构搜索到的通道数为[8, 40, 128]。 - 2. token中每个数字的搜索列表长度(range_table函数)，tokens中每个token的索引范围。 - 3. 根据token产生模型结构(token2arch函数)，根据搜索到的tokens列表产生模型结构。

      +

      自定义搜索空间(search space)#

      +

      自定义搜索空间类需要继承搜索空间基类并重写以下几部分：
      +  1. 初始化的tokens(init_tokens函数)，可以设置为自己想要的tokens列表, tokens列表中的每个数字指的是当前数字在相应的搜索列表中的索引。例如本示例中若tokens=[0, 3, 5]，则代表当前模型结构搜索到的通道数为[8, 40, 128]。
      +  2. token中每个数字的搜索列表长度(range_table函数)，tokens中每个token的索引范围。
      +  3. 根据token产生模型结构(token2arch函数)，根据搜索到的tokens列表产生模型结构。

      以新增reset block为例说明如何构造自己的search space。自定义的search space不能和已有的search space同名。

       1
  2
@@ -277,7 +276,8 @@
 58
 59
 60
-61
      ### 引入搜索空间基类函数和search space的注册类函数
+61
+62
      ### 引入搜索空间基类函数和search space的注册类函数
 from .search_space_base import SearchSpaceBase
 from .search_space_registry import SEARCHSPACE
 import numpy as np
@@ -295,17 +295,18 @@
     def init_tokens(self):
         return [0] * 3 * len(self.block_mask)
 
-    ### 定义
+    ### 定义token的index的取值范围
     def range_table(self):
         return [len(self.filter_num)] * 3 * len(self.block_mask)
 
+    ### 把token转换成模型结构
     def token2arch(self, tokens=None):
         if tokens == None:
             tokens = self.init_tokens()
 
         self.bottleneck_params_list = []
         for i in range(len(self.block_mask)):
-            self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]], 
+            self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]],
                                                self.filter_num[tokens[i * 3 + 1]],
                                                self.filter_num[tokens[i * 3 + 2]],
                                                2 if self.block_mask[i] == 1 else 1)
@@ -345,15 +346,6 @@