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css/base.css 0 → 100644
浏览文件 @ 01c4cf93
body {
padding-top: 70px;
}
/* Replacement for `body { background-attachment: fixed; }`, which has
performance issues when scrolling on large displays. See #1394. */
body::before {
content: ' ';
position: fixed;
width: 100%;
height: 100%;
top: 0;
left: 0;
background-color: #f8f8f8;
background: url(../img/grid.png) repeat-x;
will-change: transform;
z-index: -1;
}
body > .container {
min-height: 400px;
}
ul.nav .main {
font-weight: bold;
}
.col-md-3 {
padding-left: 0;
z-index: 1;
}
.col-md-9 {
padding-bottom: 100px;
}
.source-links {
float: right;
}
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max-width: 100%;
display: inline-block;
padding: 4px;
line-height: 1.428571429;
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border: 1px solid #ddd;
border-radius: 4px;
margin: 20px auto 30px auto;
}
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font-family: monospace,serif;
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width: 263px;
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.headerlink {
font-family: FontAwesome;
font-size: 14px;
display: none;
padding-left: .5em;
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.dropdown-submenu>.dropdown-menu {
top: 0;
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-moz-border-radius: 6px 0 6px 6px;
border-radius: 6px 0 6px 6px;
}
css/bootstrap-custom.min.css 0 → 100644
浏览文件 @ 01c4cf93
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<div class="col-md-9" role="main">
<h1 id="api">模型分析API文档</h1>
<h2 id="flops">flops</h2>
<blockquote>
<p>paddleslim.analysis.flops(program, detail=False) <a href="">源代码</a></p>
</blockquote>
<p>获得指定网络的每秒浮点运算次数(FLOPS)。</p>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>program(paddle.fluid.Program):</strong> 待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</p>
</li>
<li>
<p><strong>detail(bool):</strong> 是否返回每个卷积层的FLOPS。默认为False。</p>
</li>
</ul>
<p><strong>返回值：</strong></p>
<ul>
<li>
<p><strong>flops(float):</strong> 整个网络的FLOPS。</p>
</li>
<li>
<p><strong>params2flops(dict):</strong> 每层卷积对应的FLOPS，其中key为卷积层参数名称，value为FLOPS值。</p>
</li>
</ul>
<p><strong>示例：</strong></p>
<pre><code>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 + &quot;_weights&quot;),
bias_attr=False,
name=name + &quot;_out&quot;)
bn_name = name + &quot;_bn&quot;
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--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6
# | ^ | ^
# |____________| |____________________|
#
# X: prune output channels
# O: prune input channels
with fluid.program_guard(main_program, startup_program):
input = fluid.data(name=&quot;image&quot;, shape=[None, 3, 16, 16])
conv1 = conv_bn_layer(input, 8, 3, &quot;conv1&quot;)
conv2 = conv_bn_layer(conv1, 8, 3, &quot;conv2&quot;)
sum1 = conv1 + conv2
conv3 = conv_bn_layer(sum1, 8, 3, &quot;conv3&quot;)
conv4 = conv_bn_layer(conv3, 8, 3, &quot;conv4&quot;)
sum2 = conv4 + sum1
conv5 = conv_bn_layer(sum2, 8, 3, &quot;conv5&quot;)
conv6 = conv_bn_layer(conv5, 8, 3, &quot;conv6&quot;)
print(&quot;FLOPS: {}&quot;.format(flops(main_program)))
</code></pre>
<h2 id="model_size">model_size</h2>
<blockquote>
<p>paddleslim.analysis.model_size(program) <a href="">源代码</a></p>
</blockquote>
<p>获得指定网络的参数数量。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>program(paddle.fluid.Program):</strong> 待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</li>
</ul>
<p><strong>返回值：</strong></p>
<ul>
<li><strong>model_size(int):</strong> 整个网络的参数数量。</li>
</ul>
<p><strong>示例：</strong></p>
<pre><code>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 + &quot;_weights&quot;),
bias_attr=False,
name=name + &quot;_out&quot;)
return conv
main_program = fluid.Program()
startup_program = fluid.Program()
# X X O X O
# conv1--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6
# | ^ | ^
# |____________| |____________________|
#
# X: prune output channels
# O: prune input channels
with fluid.program_guard(main_program, startup_program):
input = fluid.data(name=&quot;image&quot;, shape=[None, 3, 16, 16])
conv1 = conv_layer(input, 8, 3, &quot;conv1&quot;)
conv2 = conv_layer(conv1, 8, 3, &quot;conv2&quot;)
sum1 = conv1 + conv2
conv3 = conv_layer(sum1, 8, 3, &quot;conv3&quot;)
conv4 = conv_layer(conv3, 8, 3, &quot;conv4&quot;)
sum2 = conv4 + sum1
conv5 = conv_layer(sum2, 8, 3, &quot;conv5&quot;)
conv6 = conv_layer(conv5, 8, 3, &quot;conv6&quot;)
print(&quot;FLOPS: {}&quot;.format(model_size(main_program)))
</code></pre>
<h2 id="tablelatencyevaluator">TableLatencyEvaluator</h2>
<blockquote>
<p>paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") <a href="">源代码</a></p>
</blockquote>
<p>基于硬件延时表的模型延时评估器。</p>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>table_file(str):</strong> 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：<a href="../paddleslim/analysis/table_latency.md">PaddleSlim硬件延时评估表格式</a></p>
</li>
<li>
<p><strong>delimiter(str):</strong> 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。</p>
</li>
</ul>
<p><strong>返回值：</strong></p>
<ul>
<li><strong>Evaluator:</strong> 硬件延时评估器的实例。</li>
</ul>
<blockquote>
<p>paddleslim.analysis.TableLatencyEvaluator.latency(graph) <a href="">源代码</a></p>
</blockquote>
<p>获得指定网络的预估延时。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>graph(Program):</strong> 待预估的目标网络。</li>
</ul>
<p><strong>返回值：</strong></p>
<ul>
<li><strong>latency:</strong> 目标网络的预估延时。</li>
</ul></div>
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<li><a href="#sanas-api">SANAS API文档</a></li>
<li><a href="#class-sanas">class SANAS</a></li>
</ul>
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<div class="col-md-9" role="main">
<h1 id="paddleslimnas-api">paddleslim.nas API文档</h1>
<h2 id="sanas-api">SANAS API文档</h2>
<h2 id="class-sanas">class SANAS</h2>
<p>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。</p>
<hr />
<blockquote>
<p>paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server)</p>
</blockquote>
<p><strong>参数：</strong>
- <strong>configs(list<tuple>):</strong> 搜索空间配置列表，格式是<code>[(key, {input_size, output_size, block_num, block_mask})]</code>或者<code>[(key)]</code>（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<code>key</code>即可）, <code>input_size</code> 和<code>output_size</code>表示输入和输出的特征图的大小，<code>block_num</code>是指搜索网络中的block数量，<code>block_mask</code>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。
- <strong>server_addr(tuple):</strong> 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）。
- <strong>init_temperature(float):</strong> 基于模拟退火进行搜索的初始温度。默认：100。
- <strong>reduce_rate(float):</strong> 基于模拟退火进行搜索的衰减率。默认：0.85。
- <strong>search_steps(int):</strong> 搜索过程迭代的次数。默认：300。
- <strong>save_checkpoint(str|None):</strong> 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：<code>./nas_checkpoint</code>。
- <strong>load_checkpoint(str|None):</strong> 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。
- <strong>is_server(bool):</strong> 当前实例是否要启动一个server。默认：True。</p>
<p><strong>返回：</strong>
一个SANAS类的实例</p>
<p><strong>示例代码：</strong></p>
<pre><code>from paddleslim.nas import SANAS
config = [('MobileNetV2Space')]
sanas = SANAS(config=config)
</code></pre>
<hr />
<blockquote>
<p>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。</p>
</blockquote>
<p><strong>参数：</strong>
- <strong>tokens(list):</strong> 一组token。</p>
<p><strong>返回</strong>
返回一个模型结构实例。</p>
<p><strong>示例代码：</strong></p>
<pre><code>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
</code></pre>
<hr />
<blockquote>
<p>next_archs():
获取下一组模型结构。</p>
</blockquote>
<p><strong>返回</strong>
返回模型结构实例的列表，形式为list。</p>
<p><strong>示例代码：</strong></p>
<pre><code>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
</code></pre>
<hr />
<blockquote>
<p>reward(score):
把当前模型结构的得分情况回传。</p>
</blockquote>
<p><strong>参数：</strong>
<strong>score<float>:</strong> 当前模型的得分，分数越大越好。</p>
<p><strong>返回</strong>
模型结构更新成功或者失败，成功则返回<code>True</code>，失败则返回<code>False</code>。</p>
<p><strong>代码示例</strong></p>
<pre><code class="python">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=(&quot;&quot;, 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=&quot;softmax&quot;)
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) &gt; 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]))
</code></pre></div>
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<li><a href="#class-pruner">class Pruner</a></li>
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<h1 id="api">卷积通道剪裁API文档</h1>
<h2 id="class-pruner">class Pruner</h2>
<hr />
<blockquote>
<p>paddleslim.prune.Pruner(criterion="l1_norm")<a href="">源代码</a></p>
</blockquote>
<p>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<code>[output_channel, input_channel, kernel_size, kernel_size]</code>，通过剪裁该权重的第一纬度达到剪裁输出通道数的目的。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>criterion:</strong> 评估一个卷积层内通道重要性所参考的指标。目前仅支持<code>l1_norm</code>。默认为<code>l1_norm</code>。</li>
</ul>
<p><strong>返回：</strong> 一个Pruner类的实例</p>
<p><strong>示例代码：</strong></p>
<pre><code>from paddleslim.prune import Pruner
pruner = Pruner()
</code></pre>
<hr />
<blockquote>
<p>prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False)</p>
</blockquote>
<p>对目标网络的一组卷积层的权重进行裁剪。</p>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>program(paddle.fluid.Program):</strong> 要裁剪的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</p>
</li>
<li>
<p><strong>scope(paddle.fluid.Scope):</strong> 要裁剪的权重所在的<code>scope</code>，Paddle中用<code>scope</code>实例存放模型参数和运行时变量的值。Scope中的参数值会被<code>inplace</code>的裁剪。更多介绍请参考<a href="">Scope概念介绍</a></p>
</li>
<li>
<p><strong>params(list<str>):</strong> 需要被裁剪的卷积层的参数的名称列表。可以通过以下方式查看模型中所有参数的名称:</p>
</li>
</ul>
<pre><code>for block in program.blocks:
for param in block.all_parameters():
print(&quot;param: {}; shape: {}&quot;.format(param.name, param.shape))
</code></pre>
<ul>
<li>
<p><strong>ratios(list<float>):</strong> 用于裁剪<code>params</code>的剪切率，类型为列表。该列表长度必须与<code>params</code>的长度一致。</p>
</li>
<li>
<p><strong>place(paddle.fluid.Place):</strong> 待裁剪参数所在的设备位置，可以是<code>CUDAPlace</code>或<code>CPUPlace</code>。<a href="">Place概念介绍</a></p>
</li>
<li>
<p><strong>lazy(bool):</strong> <code>lazy</code>为True时，通过将指定通道的参数置零达到裁剪的目的，参数的<code>shape保持不变</code>；<code>lazy</code>为False时，直接将要裁的通道的参数删除，参数的<code>shape</code>会发生变化。</p>
</li>
<li>
<p><strong>only_graph(bool):</strong> 是否只裁剪网络结构。在Paddle中，Program定义了网络结构，Scope存储参数的数值。一个Scope实例可以被多个Program使用，比如定义了训练网络的Program和定义了测试网络的Program是使用同一个Scope实例的。<code>only_graph</code>为True时，只对Program中定义的卷积的通道进行剪裁；<code>only_graph</code>为false时，Scope中卷积参数的数值也会被剪裁。默认为False。</p>
</li>
<li>
<p><strong>param_backup(bool):</strong> 是否返回对参数值的备份。默认为False。</p>
</li>
<li>
<p><strong>param_shape_backup(bool):</strong> 是否返回对参数<code>shape</code>的备份。默认为False。</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li>
<p><strong>pruned_program(paddle.fluid.Program):</strong> 被裁剪后的Program。</p>
</li>
<li>
<p><strong>param_backup(dict):</strong> 对参数数值的备份，用于恢复Scope中的参数数值。</p>
</li>
<li>
<p><strong>param_shape_backup(dict):</strong> 对参数形状的备份。</p>
</li>
</ul>
<p><strong>示例：</strong></p>
<p>点击<a href="https://aistudio.baidu.com/aistudio/projectDetail/200786">AIStudio</a>执行以下示例代码。</p>
<pre><code>
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 + &quot;_weights&quot;),
bias_attr=False,
name=name + &quot;_out&quot;)
bn_name = name + &quot;_bn&quot;
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--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6
# | ^ | ^
# |____________| |____________________|
#
# X: prune output channels
# O: prune input channels
with fluid.program_guard(main_program, startup_program):
input = fluid.data(name=&quot;image&quot;, shape=[None, 3, 16, 16])
conv1 = conv_bn_layer(input, 8, 3, &quot;conv1&quot;)
conv2 = conv_bn_layer(conv1, 8, 3, &quot;conv2&quot;)
sum1 = conv1 + conv2
conv3 = conv_bn_layer(sum1, 8, 3, &quot;conv3&quot;)
conv4 = conv_bn_layer(conv3, 8, 3, &quot;conv4&quot;)
sum2 = conv4 + sum1
conv5 = conv_bn_layer(sum2, 8, 3, &quot;conv5&quot;)
conv6 = conv_bn_layer(conv5, 8, 3, &quot;conv6&quot;)
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=[&quot;conv4_weights&quot;],
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 &quot;weights&quot; in param.name:
print(&quot;param name: {}; param shape: {}&quot;.format(param.name, param.shape))
</code></pre>
<hr />
<h2 id="sensitivity">sensitivity</h2>
<blockquote>
<p>paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) <a href="">源代码</a></p>
</blockquote>
<p>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。</p>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>program(paddle.fluid.Program):</strong> 待评估的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</p>
</li>
<li>
<p><strong>place(paddle.fluid.Place):</strong> 待分析的参数所在的设备位置，可以是<code>CUDAPlace</code>或<code>CPUPlace</code>。<a href="">Place概念介绍</a></p>
</li>
<li>
<p><strong>param_names(list<str>):</strong> 待分析的卷积层的参数的名称列表。可以通过以下方式查看模型中所有参数的名称:</p>
</li>
</ul>
<pre><code>for block in program.blocks:
for param in block.all_parameters():
print(&quot;param: {}; shape: {}&quot;.format(param.name, param.shape))
</code></pre>
<ul>
<li>
<p><strong>eval_func(function):</strong> 用于评估裁剪后模型效果的回调函数。该回调函数接受被裁剪后的<code>program</code>为参数，返回一个表示当前program的精度，用以计算当前裁剪带来的精度损失。</p>
</li>
<li>
<p><strong>sensitivities_file(str):</strong> 保存敏感度信息的本地文件系统的文件。在敏感度计算过程中，会持续将新计算出的敏感度信息追加到该文件中。重启任务后，文件中已有敏感度信息不会被重复计算。该文件可以用<code>pickle</code>加载。</p>
</li>
<li>
<p><strong>pruned_ratios(list<float>):</strong> 计算卷积层敏感度信息时，依次剪掉的通道数比例。默认为[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]。</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>sensitivities(dict):</strong> 存放敏感度信息的dict，其格式为：</li>
</ul>
<pre><code>{&quot;weight_0&quot;:
{0.1: 0.22,
0.2: 0.33
},
&quot;weight_1&quot;:
{0.1: 0.21,
0.2: 0.4
}
}
</code></pre>
<p>其中，<code>weight_0</code>是卷积层参数的名称，sensitivities['weight_0']的<code>value</code>为剪裁比例，<code>value</code>为精度损失的比例。</p>
<p><strong>示例：</strong></p>
<p>点击<a href="https://aistudio.baidu.com/aistudio/projectdetail/201401">AIStudio</a>运行以下示例代码。</p>
<pre><code>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 + &quot;_weights&quot;),
bias_attr=False,
name=name + &quot;_out&quot;)
bn_name = name + &quot;_bn&quot;
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--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;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, &quot;conv1&quot;)
conv2 = conv_bn_layer(conv1, 8, 3, &quot;conv2&quot;)
sum1 = conv1 + conv2
conv3 = conv_bn_layer(sum1, 8, 3, &quot;conv3&quot;)
conv4 = conv_bn_layer(conv3, 8, 3, &quot;conv4&quot;)
sum2 = conv4 + sum1
conv5 = conv_bn_layer(sum2, 8, 3, &quot;conv5&quot;)
conv6 = conv_bn_layer(conv5, 8, 3, &quot;conv6&quot;)
out = fluid.layers.fc(conv6, size=10, act=&quot;softmax&quot;)
# 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 &quot;weights&quot; in param.name:
param_names.append(param.name)
sensitivities = sensitivity(main_program,
place,
param_names,
eval_func,
sensitivities_file=&quot;./sensitive.data&quot;,
pruned_ratios=[0.1, 0.2, 0.3])
print(sensitivities)
</code></pre>
<h2 id="merge_sensitive">merge_sensitive</h2>
<blockquote>
<p>merge_sensitive(sensitivities)</p>
</blockquote>
<p>合并多个敏感度信息。</p>
<p>参数：</p>
<ul>
<li><strong>sensitivities(list<dict> | list<str>):</strong> 待合并的敏感度信息，可以是字典的列表，或者是存放敏感度信息的文件的路径列表。</li>
</ul>
<p>返回：</p>
<ul>
<li><strong>sensitivities(dict)：</strong> 合并后的敏感度信息。其格式为：</li>
</ul>
<pre><code>{&quot;weight_0&quot;:
{0.1: 0.22,
0.2: 0.33
},
&quot;weight_1&quot;:
{0.1: 0.21,
0.2: 0.4
}
}
</code></pre>
<p>其中，<code>weight_0</code>是卷积层参数的名称，sensitivities['weight_0']的<code>value</code>为剪裁比例，<code>value</code>为精度损失的比例。</p>
<p>示例：</p>
<h2 id="load_sensitivities">load_sensitivities</h2>
<blockquote>
<p>load_sensitivities(sensitivities_file)</p>
</blockquote>
<p>从文件中加载敏感度信息。</p>
<p>参数：</p>
<ul>
<li><strong>sensitivities_file(str):</strong> 存放敏感度信息的本地文件.</li>
</ul>
<p>返回：</p>
<ul>
<li><strong>sensitivities(dict)</strong>敏感度信息。</li>
</ul>
<p>示例：</p>
<h2 id="get_ratios_by_losssensitivities-loss">get_ratios_by_loss(sensitivities, loss)</h2>
<p>根据敏感度和精度损失阈值计算出一组剪切率。对于参数<code>w</code>, 其剪裁率为使精度损失低于<code>loss</code>的最大剪裁率。</p>
<p>参数：</p>
<ul>
<li>
<p><strong>sensitivities(dict):</strong> 敏感度信息。</p>
</li>
<li>
<p><strong>loss:</strong> 精度损失阈值。</p>
</li>
</ul>
<p>返回：</p>
<ul>
<li>ratios(dict): 一组剪切率。<code>key</code>是待剪裁参数的名称。<code>value</code>是对应参数的剪裁率。</li>
</ul>
<p>示例：</p>
<pre><code>
</code></pre></div>
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<li class="main active"><a href="#paddleslimquant-api">paddleslim.quant API文档</a></li>
<li><a href="#api">量化训练API</a></li>
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<h1 id="paddleslimquant-api">paddleslim.quant API文档</h1>
<h2 id="api">量化训练API</h2>
<h3 id="_1">量化配置</h3>
<pre><code>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,
# if set quant_weight_only True, then only quantize parameters of layers which need to be quantized,
# and activations will not be quantized.
'quant_weight_only': False
}
</code></pre>
<p>设置量化训练需要的配置。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>weight_quantize_type(str)</strong> - 参数量化方式。可选<code>'abs_max'</code>, <code>'channel_wise_abs_max'</code>, <code>'range_abs_max'</code>, <code>'moving_average_abs_max'</code>。 默认<code>'abs_max'</code>。</li>
<li><strong>activation_quantize_type(str)</strong> - 激活量化方式，可选<code>'abs_max'</code>, <code>'range_abs_max'</code>, <code>'moving_average_abs_max'</code>，默认<code>'abs_max'</code>。</li>
<li><strong>weight_bits(int)</strong> - 参数量化bit数，默认8, 推荐设为8。</li>
<li><strong>activation_bits(int)</strong> - 激活量化bit数，默认8， 推荐设为8。</li>
<li><strong>not_quant_pattern(str or list[str])</strong> - 所有<code>name_scope</code>包含<code>'not_quant_pattern'</code>字符串的<code>op</code>，都不量化, 设置方式请参考<code>fluid.name_scope()</code>。</li>
<li><strong>quantize_op_types(list[str])</strong> - 需要进行量化的<code>op</code>类型，目前支持<code>'conv2d', 'depthwise_conv2d', 'mul'</code>。</li>
<li><strong>dtype(int8)</strong> - 量化后的参数类型，默认 <code>int8</code>, 目前仅支持<code>int8</code>。</li>
<li><strong>window_size(int)</strong> - <code>'range_abs_max'</code>量化方式的<code>window size</code>，默认10000。</li>
<li><strong>moving_rate(int)</strong> - <code>'moving_average_abs_max'</code>量化方式的衰减系数，默认 0.9。</li>
<li><strong>quant_weight_only(bool)</strong> - 是否只量化参数，如果设为<code>True</code>，则激活不进行量化，默认<code>False</code>。目前暂不支持设置为<code>True</code>。 设置为<code>True</code>时，只量化参数，这种方式不能减少显存占用和加速，只能用来减少带宽。</li>
</ul>
<h3 id="paddleslimquantquant_awareprogram-place-config-scopenone-for_testfalse">paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False)</h3>
<p>在<code>program</code>中加入量化和反量化<code>op</code>, 用于量化训练。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>program (fluid.Program)</strong> - 传入训练或测试<code>program</code>。</li>
<li><strong>place(fluid.CPUPlace or fluid.CUDAPlace)</strong> - 该参数表示<code>Executor</code>执行所在的设备。</li>
<li><strong>config(dict)</strong> - 量化配置表。</li>
<li><strong>scope(fluid.Scope, optional)</strong> - 传入用于存储<code>Variable</code>的<code>scope</code>，需要传入<code>program</code>所使用的<code>scope</code>，一般情况下，是<code>fluid.global_scope()</code>。设置为<code>None</code>时将使用<code>fluid.global_scope()</code>，默认值为<code>None</code>。</li>
<li><strong>for_test(bool)</strong> - 如果<code>program</code>参数是一个测试<code>program</code>，<code>for_test</code>应设为<code>True</code>，否则设为<code>False</code>。</li>
</ul>
<p><strong>返回</strong></p>
<p>含有量化和反量化<code>operator</code>的<code>program</code></p>
<p><strong>返回类型</strong></p>
<ul>
<li>当<code>for_test=False</code>，返回类型为<code>fluid.CompiledProgram</code>， <strong>注意，此返回值不能用于保存参数</strong>。</li>
<li>当<code>for_test=True</code>，返回类型为<code>fluid.Program</code>。</li>
</ul>
<p><strong>注意事项</strong></p>
<ul>
<li>此接口会改变<code>program</code>结构，并且可能增加一些<code>persistable</code>的变量，所以加载模型参数时请注意和相应的<code>program</code>对应。</li>
<li>此接口底层经历了<code>fluid.Program</code>-&gt; <code>fluid.framework.IrGraph</code>-&gt;<code>fluid.Program</code>的转变，在<code>fluid.framework.IrGraph</code>中没有<code>Parameter</code>的概念，<code>Variable</code>只有<code>persistable</code>和<code>not persistable</code>的区别，所以在保存和加载参数时，请使用<code>fluid.io.save_persistables</code>和<code>fluid.io.load_persistables</code>接口。</li>
<li>由于此接口会根据<code>program</code>的结构和量化配置来对<code>program</code>添加op，所以<code>Paddle</code>中一些通过<code>fuse op</code>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<code>False</code>： <code>fuse_all_reduce_ops, sync_batch_norm</code>。</li>
<li>如果传入的<code>program</code>中存在和任何op都没有连接的<code>Variable</code>，则会在量化的过程中被优化掉。</li>
</ul>
<h3 id="paddleslimquantconvertprogram-place-config-scopenone-save_int8false">paddleslim.quant.convert(program, place, config, scope=None, save_int8=False)</h3>
<p>把训练好的量化<code>program</code>，转换为可用于保存<code>inference model</code>的<code>program</code>。</p>
<p><strong>参数：</strong>
- <strong>program (fluid.Program)</strong> - 传入测试<code>program</code>。
- <strong>place(fluid.CPUPlace or fluid.CUDAPlace)</strong> - 该参数表示<code>Executor</code>执行所在的设备。
- <strong>config(dict)</strong> - 量化配置表。
- <strong>scope(fluid.Scope)</strong> - 传入用于存储<code>Variable</code>的<code>scope</code>，需要传入<code>program</code>所使用的<code>scope</code>，一般情况下，是<code>fluid.global_scope()</code>。设置为<code>None</code>时将使用<code>fluid.global_scope()</code>，默认值为<code>None</code>。
- <strong>save_int8（bool)</strong> - 是否需要返回参数为<code>int8</code>的<code>program</code>。该功能目前只能用于确认模型大小。默认值为<code>False</code>。</p>
<p><strong>返回</strong></p>
<ul>
<li><strong>program (fluid.Program)</strong> - freezed program，可用于保存inference model，参数为<code>float32</code>类型，但其数值范围可用int8表示。</li>
<li><strong>int8_program (fluid.Program)</strong> - freezed program，可用于保存inference model，参数为<code>int8</code>类型。当<code>save_int8</code>为<code>False</code>时，不返回该值。</li>
</ul>
<p><strong>注意事项</strong></p>
<p>因为该接口会对<code>op</code>和<code>Variable</code>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。</p>
<p><strong>代码示例</strong></p>
<pre><code class="python">#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)
#配置
config = {'weight_quantize_type': 'abs_max',
'activation_quantize_type': 'moving_average_abs_max'}
build_strategy = fluid.BuildStrategy()
exec_strategy = fluid.ExecutionStrategy()
#调用api
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)
#关闭策略
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)
</code></pre>
<p>更详细的用法请参考 <a href='../../demo/quant/quant_aware/README.md'>量化训练demo</a>。</p>
<h2 id="api_1">离线量化API</h2>
<pre><code>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=[&quot;conv2d&quot;, &quot;depthwise_conv2d&quot;, &quot;mul&quot;])
</code></pre>
<p>对保存在<code>${model_dir}</code>下的模型进行量化，使用<code>sample_generator</code>的数据进行参数校正。</p>
<p><strong>参数:</strong>
- <strong>executor (fluid.Executor)</strong> - 执行模型的executor，可以在cpu或者gpu上执行。
- <strong>model_dir（str)</strong> - 需要量化的模型所在的文件夹。
- <strong>quantize_model_path(str)</strong> - 保存量化后的模型的路径
- <strong>sample_generator(python generator)</strong> - 读取数据样本，每次返回一个样本。
- <strong>model_filename(str, optional)</strong> - 模型文件名，如果需要量化的模型的参数存在一个文件中，则需要设置<code>model_filename</code>为模型文件的名称，否则设置为<code>None</code>即可。默认值是<code>None</code>。
- <strong>params_filename(str)</strong> - 参数文件名，如果需要量化的模型的参数存在一个文件中，则需要设置<code>params_filename</code>为参数文件的名称，否则设置为<code>None</code>即可。默认值是<code>None</code>。
- <strong>batch_size(int)</strong> - 每个batch的图片数量。默认值为16 。
- <strong>batch_nums(int, optional)</strong> - 迭代次数。如果设置为<code>None</code>，则会一直运行到<code>sample_generator</code> 迭代结束， 否则，迭代次数为<code>batch_nums</code>, 也就是说参与对<code>Scale</code>进行校正的样本个数为 <code>'batch_nums' * 'batch_size'</code>.
- <strong>scope(fluid.Scope, optional)</strong> - 用来获取和写入<code>Variable</code>, 如果设置为<code>None</code>,则使用<code>fluid.global_scope()</code>. 默认值是<code>None</code>.
- <strong>algo(str)</strong> - 量化时使用的算法名称，可为<code>'KL'</code>或者<code>'direct'</code>。该参数仅针对激活值的量化，因为参数值的量化使用的方式为<code>'channel_wise_abs_max'</code>. 当<code>algo</code> 设置为<code>'direct'</code>时，使用校正数据的激活值的绝对值的最大值当作<code>Scale</code>值，当设置为<code>'KL'</code>时，则使用<code>KL</code>散度的方法来计算<code>Scale</code>值。默认值为<code>'KL'</code>。
- <strong>quantizable_op_type(list[str])</strong> - 需要量化的<code>op</code>类型列表。默认值为<code>["conv2d", "depthwise_conv2d", "mul"]</code>。</p>
<p><strong>返回</strong></p>
<p>无。</p>
<p><strong>注意事项</strong></p>
<p>因为该接口会收集校正数据的所有的激活值，所以使用的校正图片不能太多。<code>'KL'</code>散度的计算也比较耗时。</p>
<p><strong>代码示例</strong></p>
<blockquote>
<p>注： 此示例不能直接运行，因为需要加载<code>${model_dir}</code>下的模型，所以不能直接运行。</p>
</blockquote>
<pre><code class="python">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)
</code></pre>
<p>更详细的用法请参考 <a href='../../demo/quant/quant_post/README.md'>离线量化demo</a>。</p>
<h2 id="embeddingapi">Embedding量化API</h2>
<pre><code>paddleslim.quant.quant_embedding(program, place, config, scope=None)
</code></pre>
<p>对<code>Embedding</code>参数进行量化。</p>
<p><strong>参数:</strong>
- <strong>program(fluid.Program)</strong> - 需要量化的program
- <strong>scope(fluid.Scope, optional)</strong> - 用来获取和写入<code>Variable</code>, 如果设置为<code>None</code>,则使用<code>fluid.global_scope()</code>.
- <strong>place(fluid.CPUPlace or fluid.CUDAPlace)</strong> - 运行program的设备
- <strong>config(dict)</strong> - 定义量化的配置。可以配置的参数有：
- <code>'params_name'</code> (str, required): 需要进行量化的参数名称，此参数必须设置。
- <code>'quantize_type'</code> (str, optional): 量化的类型，目前支持的类型是<code>'abs_max'</code>, 待支持的类型有 <code>'log', 'product_quantization'</code>。 默认值是<code>'abs_max'</code>.
- <code>'quantize_bits'</code>（int, optional): 量化的<code>bit</code>数，目前支持的<code>bit</code>数为8。默认值是8.
- <code>'dtype'</code>(str, optional): 量化之后的数据类型， 目前支持的是<code>'int8'</code>. 默认值是<code>int8</code>。
- <code>'threshold'</code>(float, optional): 量化之前将根据此阈值对需要量化的参数值进行<code>clip</code>. 如果不设置，则跳过<code>clip</code>过程直接量化。</p>
<p><strong>返回</strong></p>
<p>量化之后的program</p>
<p><strong>返回类型</strong></p>
<p><code>fluid.Program</code></p>
<p><strong>代码示例</strong></p>
<pre><code class="python">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=&quot;input_word&quot;, 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)
</code></pre>
<p>更详细的用法请参考 <a href='../../demo/quant/quant_embedding/README.md'>Embedding量化demo</a>。</p></div>
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<li class="main active"><a href="#paddleslimnas">paddleslim.nas 提供的搜索空间：</a></li>
<li><a href="#_1">搜索空间的配置介绍：</a></li>
<li><a href="#_2">搜索空间示例：</a></li>
<li class="main "><a href="#search-space">自定义搜索空间(search space)</a></li>
</ul>
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<div class="col-md-9" role="main">
<h1 id="paddleslimnas">paddleslim.nas 提供的搜索空间：</h1>
<ol>
<li>根据原本模型结构构造搜索空间：</li>
</ol>
<p>1.1 MobileNetV2Space</p>
<p>1.2 MobileNetV1Space</p>
<p>1.3 ResNetSpace</p>
<ol>
<li>根据相应模型的block构造搜索空间</li>
</ol>
<p>2.1 MobileNetV1BlockSpace</p>
<p>2.2 MobileNetV2BlockSpace</p>
<p>2.3 ResNetBlockSpace</p>
<p>2.4 InceptionABlockSpace</p>
<p>2.5 InceptionCBlockSpace</p>
<h2 id="_1">搜索空间的配置介绍：</h2>
<p><strong>input_size(int|None)</strong>：<code>input_size</code>表示输入feature map的大小。
<strong>output_size(int|None)</strong>：<code>output_size</code>表示输出feature map的大小。
<strong>block_num(int|None)</strong>：<code>block_num</code>表示搜索空间中block的数量。
<strong>block_mask(list|None)</strong>：<code>block_mask</code>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<code>block_mask</code>，则主要以<code>block_mask</code>为主要配置，<code>input_size</code>，<code>output_size</code>和<code>block_num</code>三种配置是无效的。</p>
<p><strong>Note:</strong>
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. <code>input_size</code>和<code>output_size</code>用来计算整个模型结构中reduction block数量。</p>
<h2 id="_2">搜索空间示例：</h2>
<ol>
<li>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')]。</li>
<li>使用paddleslim中提供的block搜索空间构造搜索空间：
2.1 使用<code>input_size</code>, <code>output_size</code>和<code>block_num</code>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<code>block_mask</code>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]})]。</li>
</ol>
<h1 id="search-space">自定义搜索空间(search space)</h1>
<p>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(<code>init_tokens</code>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(<code>range_table</code>函数)，tokens中每个token的索引范围。
3. 根据token产生模型结构(<code>token2arch</code>函数)，根据搜索到的tokens列表产生模型结构。</p>
<p>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。</p>
<pre><code class="python">### 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
### 需要调用注册函数把自定义搜索空间注册到space space中
@SEARCHSPACE.register
### 定义一个继承SearchSpaceBase基类的搜索空间的类函数
class ResNetBlockSpace2(SearchSpaceBase):
def __init__(self, input_size, output_size, block_num, block_mask):
### 定义一些实际想要搜索的内容，例如：通道数、每个卷积的重复次数、卷积核大小等等
### self.filter_num 代表通道数的搜索列表
self.filter_num = np.array([8, 16, 32, 40, 64, 128, 256, 512])
### 定义初始化token，初始化token的长度根据传入的block_num或者block_mask的长度来得到的
def init_tokens(self):
return [0] * 3 * len(self.block_mask)
### 定义
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
### 构造具体block的操作
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
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<li class="main active"><a href="#paddleslimdist-api">paddleslim.dist API文档</a></li>
<li><a href="#mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_">merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')</a></li>
<li><a href="#fsp_lossteacher_var1_name-teacher_var2_name-student_var1_name-student_var2_name-programfluiddefault_main_program">fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program())</a></li>
<li><a href="#l2_lossteacher_var_name-student_var_name-programfluiddefault_main_program">l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program())</a></li>
<li><a href="#soft_label_lossteacher_var_name-student_var_name-programfluiddefault_main_program-teacher_temperature1-student_temperature1">soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.)</a></li>
<li><a href="#lossloss_func-programfluiddefault_main_program-kwargs">loss(loss_func, program=fluid.default_main_program(), **kwargs)</a></li>
<li><a href="#_1">注意事项</a></li>
</ul>
</div></div>
<div class="col-md-9" role="main">
<h1 id="paddleslimdist-api">paddleslim.dist API文档</h1>
<h2 id="mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_">merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')</h2>
<p>该方法将两个fluid program（teacher_program, student_program）融合为一个program，并将融合得到的program返回。在融合的program中，可以为其中合适的teacher特征图和student特征图添加蒸馏损失函数，从而达到用teacher模型的暗知识（Dark Knowledge）指导student模型学习的目的。</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>teacher_program</strong>(Program)-定义了teacher模型的paddle program</li>
<li><strong>student_program</strong>(Program)-定义了student模型的paddle program</li>
<li><strong>data_name_map</strong>(dict)-teacher输入接口名与student输入接口名的映射，key为teacher的输入名，value为student的输入名。merge函数将会把这两个模型的输入按对应关系合并在一起，保证teacher与student输入数据相同</li>
<li><strong>place</strong>(fluid.CPUPlace()|fluid.CUDAPlace(N))-该参数表示程序运行在何种设备上，这里的N为GPU对应的ID</li>
<li><strong>scope</strong>(Scope)-该参数表示teacher variables和student variables所使用的作用域，如果不指定将使用默认的全局作用域。默认值：fluid.global_scope()</li>
<li><strong>name_prefix</strong>(str)-为了避免teacher variables和student variables存在同名变量而引起命名冲突，merge函数将统一为teacher variables添加一个名称前缀name_prefix，merge后的program中所有teacher variables都将带有这一名称前缀。默认值：'teacher_'</li>
</ul>
<p><strong>返回：</strong>由student_program和teacher_program merge得到的program</p>
<p><strong>使用示例：</strong></p>
<pre><code class="python">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)
</code></pre>
<h2 id="fsp_lossteacher_var1_name-teacher_var2_name-student_var1_name-student_var2_name-programfluiddefault_main_program">fsp_loss(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program())</h2>
<p>fsp_loss为program内的teacher var和student var添加fsp loss，出自论文<a href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Yim_A_Gift_From_CVPR_2017_paper.pdf">A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning</a></p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>teacher_var1_name</strong>(str): teacher_var1的名称. 对应的variable是一个形为<code>[batch_size, x_channel, height, width]</code>的4-D特征图Tensor，数据类型为float32或float64</li>
<li><strong>teacher_var2_name</strong>(str): teacher_var2的名称. 对应的variable是一个形为<code>[batch_size, y_channel, height, width]</code>的4-D特征图Tensor，数据类型为float32或float64。只有y_channel可以与teacher_var1的x_channel不同，其他维度必须与teacher_var1相同</li>
<li><strong>student_var1_name</strong>(str): student_var1的名称. 对应的variable需与teacher_var1尺寸保持一致，是一个形为<code>[batch_size, x_channel, height, width]</code>的4-D特征图Tensor，数据类型为float32或float64</li>
<li><strong>student_var2_name</strong>(str): student_var2的名称. 对应的variable需与teacher_var2尺寸保持一致，是一个形为<code>[batch_size, y_channel, height, width]</code>的4-D特征图Tensor，数据类型为float32或float64。只有y_channel可以与student_var1的x_channel不同，其他维度必须与student_var1相同</li>
<li><strong>program</strong>(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()</li>
</ul>
<p><strong>返回：</strong>由teacher_var1, teacher_var2, student_var1, student_var2组合得到的fsp_loss</p>
<p><strong>使用示例：</strong></p>
<pre><code class="python">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)
</code></pre>
<h2 id="l2_lossteacher_var_name-student_var_name-programfluiddefault_main_program">l2_loss(teacher_var_name, student_var_name, program=fluid.default_main_program())</h2>
<p>l2_loss为program内的teacher var和student var添加l2 loss</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>teacher_var_name</strong>(str): teacher_var的名称. </li>
<li><strong>student_var_name</strong>(str): student_var的名称.</li>
<li><strong>program</strong>(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()</li>
</ul>
<p><strong>返回：</strong>由teacher_var, student_var组合得到的l2_loss</p>
<p><strong>使用示例：</strong></p>
<pre><code class="python">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)
</code></pre>
<h2 id="soft_label_lossteacher_var_name-student_var_name-programfluiddefault_main_program-teacher_temperature1-student_temperature1">soft_label_loss(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.)</h2>
<p>soft_label_loss为program内的teacher var和student var添加soft label loss，出自论文<a href="https://arxiv.org/pdf/1503.02531.pdf">Distilling the Knowledge in a Neural Network</a></p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>teacher_var_name</strong>(str): teacher_var的名称. </li>
<li><strong>student_var_name</strong>(str): student_var的名称. </li>
<li><strong>program</strong>(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()</li>
<li><strong>teacher_temperature</strong>(float): 对teacher_var进行soft操作的温度值，温度值越大得到的特征图越平滑 </li>
<li><strong>student_temperature</strong>(float): 对student_var进行soft操作的温度值，温度值越大得到的特征图越平滑 </li>
</ul>
<p><strong>返回：</strong>由teacher_var, student_var组合得到的soft_label_loss</p>
<p><strong>使用示例：</strong></p>
<pre><code class="python">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.)
</code></pre>
<h2 id="lossloss_func-programfluiddefault_main_program-kwargs">loss(loss_func, program=fluid.default_main_program(), **kwargs)</h2>
<p>loss函数支持对任意多对teacher_var和student_var使用自定义损失函数</p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>loss_func</strong>(python function): 自定义的损失函数，输入为teacher var和student var，输出为自定义的loss </li>
<li><strong>program</strong>(Program): 用于蒸馏训练的fluid program。默认值：fluid.default_main_program()</li>
<li><strong>**kwargs</strong>: loss_func输入名与对应variable名称</li>
</ul>
<p><strong>返回</strong>：自定义的损失函数loss</p>
<p><strong>使用示例：</strong></p>
<pre><code class="python">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')
</code></pre>
<h2 id="_1">注意事项</h2>
<p>在添加蒸馏loss时会引入新的variable，需要注意新引入的variable不要与student variables命名冲突。这里建议两种用法：</p>
<ol>
<li>建议与student_program使用同一个命名空间，以避免一些未指定名称的variables(例如tmp_0, tmp_1...)多次定义为同一名称出现命名冲突</li>
<li>建议在添加蒸馏loss时指定一个命名空间前缀，具体用法请参考Paddle官方文档<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/name_scope_cn.html#name-scope">fluid.name_scope</a></li>
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<h1 id="paddleslim">PaddleSlim</h1>
<p>PaddleSlim是PaddlePaddle框架的一个子模块，主要用于压缩图像领域模型。在PaddleSlim中，不仅实现了目前主流的网络剪枝、量化、蒸馏三种压缩策略，还实现了超参数搜索和小模型网络结构搜索功能。在后续版本中，会添加更多的压缩策略，以及完善对NLP领域模型的支持。</p>
<h2 id="_1">功能</h2>
<ul>
<li>模型剪裁</li>
<li>支持通道均匀模型剪裁（uniform pruning)</li>
<li>基于敏感度的模型剪裁</li>
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<li>支持 FLOPS / 硬件延时约束</li>
<li>支持多平台模型延时评估</li>
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<h2 id="_2">安装</h2>
<p>安装PaddleSlim前，请确认已正确安装Paddle1.6版本或更新版本。Paddle安装请参考：<a href="https://www.paddlepaddle.org.cn/install/quick">Paddle安装教程</a>。</p>
<ul>
<li>安装develop版本</li>
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<pre><code>git clone https://github.com/PaddlePaddle/PaddleSlim.git
cd PaddleSlim
python setup.py install
</code></pre>
<ul>
<li>安装官方发布的最新版本</li>
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<pre><code>pip install paddleslim -i https://pypi.org/simple
</code></pre>
<ul>
<li>安装历史版本</li>
</ul>
<p>请点击<a href="https://pypi.org/project/paddleslim/#history">pypi.org</a>查看可安装历史版本。</p>
<h2 id="_3">使用</h2>
<ul>
<li><a href="doc/api_guide.md">API文档</a>：API使用介绍，包括<a href="">蒸馏</a>、<a href="">剪裁</a>、<a href="">量化</a>和<a href="">模型结构搜索</a>。</li>
<li><a href="doc/demo_guide.md">示例</a>：基于mnist和cifar10等简单分类任务的模型压缩示例，您可以通过该部分快速体验和了解PaddleSlim的功能。</li>
<li><a href="">实践教程</a>：经典模型的分析和压缩实验教程。</li>
<li><a href="">模型库</a>：经过压缩的分类、检测、语义分割模型，包括权重文件、网络结构文件和性能数据。</li>
<li><a href="">Paddle检测库</a>：介绍如何在检测库中使用PaddleSlim。</li>
<li><a href="">Paddle分割库</a>：介绍如何在分割库中使用PaddleSlim。</li>
<li><a href="">PaddleLite</a>：介绍如何使用预测库PaddleLite部署PaddleSlim产出的模型。</li>
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<h1 id="_1">硬件延时评估表</h1>
<p>硬件延时评估表用于快速评估一个模型在特定硬件环境和推理引擎上的推理速度。
该文档主要用于定义PaddleSlim支持的硬件延时评估表的格式。</p>
<h2 id="_2">概述</h2>
<p>硬件延时评估表中存放着所有可能的操作对应的延时信息，该表中的一个操作包括操作类型和操作参数，比如：操作类型可以是<code>conv2d</code>，对应的操作参数有输入特征图的大小、卷积核个数、卷积核大小等。
给定操作的延时依赖于硬件环境和推理引擎。</p>
<h2 id="_3">整体格式</h2>
<p>硬件延时评估表以文件或多行字符串的形式保存。</p>
<p>硬件延时评估表第一行保存版本信息，后续每行为一个操作和对应的延时信息。</p>
<h2 id="_4">版本信息</h2>
<p>版本信息以英文字符逗号分割，内容依次为硬件环境名称、推理引擎名称和时间戳。</p>
<ul>
<li>
<p><strong>硬件环境名称：</strong> 用于标识硬件环境，可以包含计算架构类型、版本号等信息。</p>
</li>
<li>
<p><strong>推理引擎名称：</strong> 用于标识推理引擎，可以包含推理引擎名称、版本号、优化选项等信息。</p>
</li>
<li>
<p><strong>时间戳：</strong> 该评估表的创建时间。</p>
</li>
</ul>
<h2 id="_5">操作信息</h2>
<p>操作信息字段之间以逗号分割。操作信息与延迟信息之间以制表符分割。</p>
<h3 id="conv2d">conv2d</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,flag_bias,flag_relu,n_in,c_in,h_in,w_in,c_out,groups,kernel,padding,stride,dilation\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>flag_bias (int)</strong> - 是否有 bias（0：无，1：有）。</li>
<li><strong>flag_relu (int)</strong> - 是否有 relu（0：无，1：有）。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>c_out (int)</strong> - 输出 Tensor 的通道 (channel) 数。</li>
<li><strong>groups (int)</strong> - 卷积二维层（Conv2D Layer）的组数。</li>
<li><strong>kernel (int)</strong> - 卷积核大小。</li>
<li><strong>padding (int)</strong> - 填充 (padding) 大小。</li>
<li><strong>stride (int)</strong> - 步长 (stride) 大小。</li>
<li><strong>dilation (int)</strong> - 膨胀 (dilation) 大小。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul>
<h3 id="activation">activation</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,n_in,c_in,h_in,w_in\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul>
<h3 id="batch_norm">batch_norm</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,active_type,n_in,c_in,h_in,w_in\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>active_type (string)</strong> - 激活函数类型，包含：relu, prelu, sigmoid, relu6, tanh。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul>
<h3 id="eltwise">eltwise</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,n_in,c_in,h_in,w_in\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul>
<h3 id="pooling">pooling</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,flag_global_pooling,n_in,c_in,h_in,w_in,kernel,padding,stride,ceil_mode,pool_type\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>flag_global_pooling (int)</strong> - 是否为全局池化（0：不是，1：是）。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>kernel (int)</strong> - 卷积核大小。</li>
<li><strong>padding (int)</strong> - 填充 (padding) 大小。</li>
<li><strong>stride (int)</strong> - 步长 (stride) 大小。</li>
<li><strong>ceil_mode (int)</strong> - 是否用 ceil 函数计算输出高度和宽度。0 表示使用 floor 函数，1 表示使用 ceil 函数。</li>
<li><strong>pool_type (int)</strong> - 池化类型，其中 1 表示 pooling_max，2 表示 pooling_average_include_padding，3 表示 pooling_average_exclude_padding。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul>
<h3 id="softmax">softmax</h3>
<p><strong>格式</strong></p>
<pre><code>op_type,axis,n_in,c_in,h_in,w_in\tlatency
</code></pre>
<p><strong>字段解释</strong></p>
<ul>
<li><strong>op_type(str)</strong> - 当前op类型。</li>
<li><strong>axis (int)</strong> - 执行 softmax 计算的维度索引，应该在 [−1，rank − 1] 范围内，其中 rank 是输入变量的秩。</li>
<li><strong>n_in (int)</strong> - 输入 Tensor 的批尺寸 (batch size)。</li>
<li><strong>c_in (int)</strong> - 输入 Tensor 的通道 (channel) 数。</li>
<li><strong>h_in (int)</strong> - 输入 Tensor 的特征高度。</li>
<li><strong>w_in (int)</strong> - 输入 Tensor 的特征宽度。</li>
<li><strong>latency (float)</strong> - 当前op的延时时间</li>
</ul></div>
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<h1 id="_1">网络结构搜索示例</h1>
<p>本示例介绍如何使用网络结构搜索接口，搜索到一个更小或者精度更高的模型，该文档仅介绍paddleslim中SANAS的使用及如何利用SANAS得到模型结构，完整示例代码请参考sa_nas_mobilenetv2.py或者block_sa_nas_mobilenetv2.py。</p>
<h2 id="_2">接口介绍</h2>
<p>请参考。</p>
<h3 id="1">1. 配置搜索空间</h3>
<p>详细的搜索空间配置可以参考<a href='../../../paddleslim/nas/nas_api.md'>神经网络搜索API文档</a>。</p>
<pre><code>config = [('MobileNetV2Space')]
</code></pre>
<h3 id="2-sanas">2. 利用搜索空间初始化SANAS实例</h3>
<pre><code>from paddleslim.nas import SANAS
sa_nas = SANAS(
config,
server_addr=(&quot;&quot;, 8881),
init_temperature=10.24,
reduce_rate=0.85,
search_steps=300,
is_server=True)
</code></pre>
<h3 id="3-nas">3. 根据实例化的NAS得到当前的网络结构</h3>
<pre><code>archs = sa_nas.next_archs()
</code></pre>
<h3 id="4-program">4. 根据得到的网络结构和输入构造训练和测试program</h3>
<pre><code>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)
</code></pre>
<h3 id="5-program">5. 根据构造的训练program添加限制条件</h3>
<pre><code>from paddleslim.analysis import flops
if flops(train_program) &gt; 321208544:
continue
</code></pre>
<h3 id="6-score">6. 回传score</h3>
<pre><code>sa_nas.reward(score)
</code></pre></div>
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<li><a href="#_3">分类模型的离线量化流程</a></li>
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<h1 id="_1">在线量化示例</h1>
<p>本示例介绍如何使用在线量化接口，来对训练好的分类模型进行量化, 可以减少模型的存储空间和显存占用。</p>
<h2 id="_2">接口介绍</h2>
<p>请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>量化API文档</a>。</p>
<h2 id="_3">分类模型的离线量化流程</h2>
<h3 id="1">1. 配置量化参数</h3>
<pre><code>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
}
</code></pre>
<h3 id="2-programop">2. 对训练和测试program插入可训练量化op</h3>
<pre><code>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)
</code></pre>
<h3 id="3build">3.关掉指定build策略</h3>
<pre><code>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)
</code></pre>
<h3 id="4-freeze-program">4. freeze program</h3>
<pre><code>float_program, int8_program = convert(val_program,
place,
quant_config,
scope=None,
save_int8=True)
</code></pre>
<h3 id="5">5.保存预测模型</h3>
<pre><code>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')
</code></pre></div>
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<li><a href="#skip-gramword2vector">基于skip-gram的word2vector模型</a></li>
<li><a href="#skip-gramword2vector_1">量化基于skip-gram的word2vector模型</a></li>
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<h1 id="embedding">Embedding量化示例</h1>
<p>本示例介绍如何使用Embedding量化的接口 <a href="">paddleslim.quant.quant_embedding</a> 。<code>quant_embedding</code>接口将网络中的Embedding参数从<code>float32</code>类型量化到 <code>8-bit</code>整数类型，在几乎不损失模型精度的情况下减少模型的存储空间和显存占用。</p>
<p>接口介绍请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>量化API文档</a>。</p>
<p>该接口对program的修改：</p>
<p>量化前:</p>
<p align="center">
<img src="./image/before.png" height=200 width=100 hspace='10'/> <br />
<strong>图1：量化前的模型结构</strong>
</p>
<p>量化后：</p>
<p align="center">
<img src="./image/after.png" height=300 width=300 hspace='10'/> <br />
<strong>图2: 量化后的模型结构</strong>
</p>
<p>以下将以 <code>基于skip-gram的word2vector模型</code> 为例来说明如何使用<code>quant_embedding</code>接口。首先介绍 <code>基于skip-gram的word2vector模型</code> 的正常训练和测试流程。</p>
<h2 id="skip-gramword2vector">基于skip-gram的word2vector模型</h2>
<p>以下是本例的简要目录结构及说明：</p>
<pre><code class="text">.
├── cluster_train.py # 分布式训练函数
├── cluster_train.sh # 本地模拟多机脚本
├── train.py # 训练函数
├── infer.py # 预测脚本
├── net.py # 网络结构
├── preprocess.py # 预处理脚本，包括构建词典和预处理文本
├── reader.py # 训练阶段的文本读写
├── train.py # 训练函数
└── utils.py # 通用函数
</code></pre>
<h3 id="_1">介绍</h3>
<p>本例实现了skip-gram模式的word2vector模型。</p>
<p>同时推荐用户参考<a href="https://aistudio.baidu.com/aistudio/projectDetail/124377"> IPython Notebook demo</a></p>
<h3 id="_2">数据下载</h3>
<p>全量数据集使用的是来自1 Billion Word Language Model Benchmark的(http://www.statmt.org/lm-benchmark) 的数据集.</p>
<pre><code class="bash">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/
</code></pre>
<p>备用数据地址下载命令如下</p>
<pre><code class="bash">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/
</code></pre>
<p>为了方便快速验证，我们也提供了经典的text8样例数据集，包含1700w个词。 下载命令如下</p>
<pre><code class="bash">mkdir data
wget https://paddlerec.bj.bcebos.com/word2vec/text.tar
tar xvf text.tar
mv text data/
</code></pre>
<h3 id="_3">数据预处理</h3>
<p>以样例数据集为例进行预处理。全量数据集注意解压后以training-monolingual.tokenized.shuffled 目录为预处理目录，和样例数据集的text目录并列。</p>
<p>词典格式: 词&lt;空格&gt;词频。注意低频词用'UNK'表示</p>
<p>可以按格式自建词典，如果自建词典跳过第一步。</p>
<pre><code>the 1061396
of 593677
and 416629
one 411764
in 372201
a 325873
&lt;UNK&gt; 324608
to 316376
zero 264975
nine 250430
</code></pre>
<p>第一步根据英文语料生成词典，中文语料可以通过修改text_strip方法自定义处理方法。</p>
<pre><code class="bash">python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict
</code></pre>
<p>第二步根据词典将文本转成id, 同时进行downsample，按照概率过滤常见词, 同时生成word和id映射的文件，文件名为词典+"<em>word_to_id</em>"。</p>
<pre><code class="bash">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
</code></pre>
<h3 id="_4">训练</h3>
<p>具体的参数配置可运行</p>
<pre><code class="bash">python train.py -h
</code></pre>
<p>单机多线程训练</p>
<pre><code class="bash">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
</code></pre>
<p>本地单机模拟多机训练</p>
<pre><code class="bash">sh cluster_train.sh
</code></pre>
<p>本示例中按照单机多线程训练的命令进行训练，训练完毕后，可看到在当前文件夹下保存模型的路径为: <code>v1_cpu5_b100_lr1dir</code>, 运行 <code>ls v1_cpu5_b100_lr1dir</code>可看到该文件夹下保存了训练的10个epoch的模型文件。</p>
<pre><code>pass-0 pass-1 pass-2 pass-3 pass-4 pass-5 pass-6 pass-7 pass-8 pass-9
</code></pre>
<h3 id="_5">预测</h3>
<p>测试集下载命令如下</p>
<pre><code class="bash">#全量数据集测试集
wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
#样本数据集测试集
wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
</code></pre>
<p>预测命令，注意词典名称需要加后缀"<em>word_to_id</em>", 此文件是预处理阶段生成的。</p>
<pre><code class="bash">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
</code></pre>
<p>运行该预测命令, 可看到如下输出</p>
<pre><code>('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
</code></pre>
<h2 id="skip-gramword2vector_1">量化<code>基于skip-gram的word2vector模型</code></h2>
<p>量化配置为:</p>
<pre><code>config = {
'params_name': 'emb',
'quantize_type': 'abs_max'
}
</code></pre>
<p>运行命令为：</p>
<pre><code class="bash">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
</code></pre>
<p>运行输出为:</p>
<pre><code>('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
</code></pre>
<p>量化后的模型保存在<code>./output_quant</code>中，可看到量化后的参数<code>'emb.int8'</code>的大小为3.9M, 在<code>./v1_cpu5_b100_lr1dir</code>中可看到量化前的参数<code>'emb'</code>的大小为16M。</p></div>
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<li class="main active"><a href="#_1">离线量化示例</a></li>
<li><a href="#_2">接口介绍</a></li>
<li><a href="#_3">分类模型的离线量化流程</a></li>
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<h1 id="_1">离线量化示例</h1>
<p>本示例介绍如何使用离线量化接口<code>paddleslim.quant.quant_post</code>来对训练好的分类模型进行离线量化, 该接口无需对模型进行训练就可得到量化模型，减少模型的存储空间和显存占用。</p>
<h2 id="_2">接口介绍</h2>
<p>请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>量化API文档</a>。</p>
<h2 id="_3">分类模型的离线量化流程</h2>
<h3 id="_4">准备数据</h3>
<p>在当前文件夹下创建<code>data</code>文件夹，将<code>imagenet</code>数据集解压在<code>data</code>文件夹下，解压后<code>data</code>文件夹下应包含以下文件：
- <code>'train'</code>文件夹，训练图片
- <code>'train_list.txt'</code>文件
- <code>'val'</code>文件夹，验证图片
- <code>'val_list.txt'</code>文件</p>
<h3 id="_5">准备需要量化的模型</h3>
<p>因为离线量化接口只支持加载通过<code>fluid.io.save_inference_model</code>接口保存的模型，因此如果您的模型是通过其他接口保存的，那需要先将模型进行转化。本示例将以分类模型为例进行说明。</p>
<p>首先在<a href="https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD">imagenet分类模型</a>中下载训练好的<code>mobilenetv1</code>模型。</p>
<p>在当前文件夹下创建<code>'pretrain'</code>文件夹，将<code>mobilenetv1</code>模型在该文件夹下解压，解压后的目录为<code>pretrain/MobileNetV1_pretrained</code></p>
<h3 id="_6">导出模型</h3>
<p>通过运行以下命令可将模型转化为离线量化接口可用的模型：</p>
<pre><code>python export_model.py --model &quot;MobileNet&quot; --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet
</code></pre>
<p>转化之后的模型存储在<code>inference_model/MobileNet/</code>文件夹下，可看到该文件夹下有<code>'model'</code>, <code>'weights'</code>两个文件。</p>
<h3 id="_7">离线量化</h3>
<p>接下来对导出的模型文件进行离线量化，离线量化的脚本为<a href="./quant_post.py">quant_post.py</a>，脚本中使用接口<code>paddleslim.quant.quant_post</code>对模型进行离线量化。运行命令为：</p>
<pre><code>python quant_post.py --model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights
</code></pre>
<ul>
<li><code>model_path</code>: 需要量化的模型坐在的文件夹</li>
<li><code>save_path</code>: 量化后的模型保存的路径</li>
<li><code>model_filename</code>: 如果需要量化的模型的参数文件保存在一个文件中，则设置为该模型的模型文件名称，如果参数文件保存在多个文件中，则不需要设置。</li>
<li><code>params_filename</code>: 如果需要量化的模型的参数文件保存在一个文件中，则设置为该模型的参数文件名称，如果参数文件保存在多个文件中，则不需要设置。</li>
</ul>
<p>运行以上命令后，可在<code>${save_path}</code>下看到量化后的模型文件和参数文件。</p>
<blockquote>
<p>使用的量化算法为<code>'KL'</code>, 使用训练集中的160张图片进行量化参数的校正。</p>
</blockquote>
<h3 id="_8">测试精度</h3>
<p>使用<a href="./eval.py">eval.py</a>脚本对量化前后的模型进行测试，得到模型的分类精度进行对比。</p>
<p>首先测试量化前的模型的精度，运行以下命令：</p>
<pre><code>python eval.py --model_path ./inference_model/MobileNet --model_name model --params_name weights
</code></pre>
<p>精度输出为:</p>
<pre><code>top1_acc/top5_acc= [0.70913923 0.89548034]
</code></pre>
<p>使用以下命令测试离线量化后的模型的精度：</p>
<pre><code>python eval.py --model_path ./quant_model_train/MobileNet
</code></pre>
<p>精度输出为</p>
<pre><code>top1_acc/top5_acc= [0.70141864 0.89086477]
</code></pre>
<p>从以上精度对比可以看出，对<code>mobilenet</code>在<code>imagenet</code>上的分类模型进行离线量化后 <code>top1</code>精度损失为<code>0.77%</code>， <code>top5</code>精度损失为<code>0.46%</code>. </p></div>
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index.html 0 → 100644
浏览文件 @ 01c4cf93
<!DOCTYPE html>
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<head>
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<script src="js/jquery-1.10.2.min.js" defer></script>
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<ul>
<li><code>mkdocs new [dir-name]</code> - Create a new project.</li>
<li><code>mkdocs serve</code> - Start the live-reloading docs server.</li>
<li><code>mkdocs build</code> - Build the documentation site.</li>
<li><code>mkdocs help</code> - Print this help message.</li>
</ul>
<h2 id="project-layout">Project layout</h2>
<pre><code>mkdocs.yml # The configuration file.
docs/
index.md # The documentation homepage.
... # Other markdown pages, images and other files.
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var base_url = ".",
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js/base.js 0 → 100644
浏览文件 @ 01c4cf93
function getSearchTerm()
{
var sPageURL = window.location.search.substring(1);
var sURLVariables = sPageURL.split('&');
for (var i = 0; i < sURLVariables.length; i++)
{
var sParameterName = sURLVariables[i].split('=');
if (sParameterName[0] == 'q')
{
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83: 's',
84: 't',
85: 'u',
86: 'v',
87: 'w',
88: 'x',
89: 'y',
90: 'z',
91: 'Left Windows Key / Left ⌘',
92: 'Right Windows Key',
93: 'Windows Menu / Right ⌘',
96: 'numpad 0',
97: 'numpad 1',
98: 'numpad 2',
99: 'numpad 3',
100: 'numpad 4',
101: 'numpad 5',
102: 'numpad 6',
103: 'numpad 7',
104: 'numpad 8',
105: 'numpad 9',
106: 'multiply',
107: 'add',
109: 'subtract',
110: 'decimal point',
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113: 'f2',
114: 'f3',
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118: 'f7',
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120: 'f9',
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122: 'f11',
123: 'f12',
124: 'f13',
125: 'f14',
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127: 'f16',
128: 'f17',
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130: 'f19',
131: 'f20',
132: 'f21',
133: 'f22',
134: 'f23',
135: 'f24',
144: 'num lock',
145: 'scroll lock',
186: '&semi;',
187: '&equals;',
188: '&comma;',
189: '&hyphen;',
190: '&period;',
191: '&quest;',
192: '&grave;',
219: '&lsqb;',
220: '&bsol;',
221: '&rsqb;',
222: '&apos;',
};
js/bootstrap-3.0.3.min.js 0 → 100644
浏览文件 @ 01c4cf93
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* Bootstrap v3.0.3 (http://getbootstrap.com)
* Copyright 2013 Twitter, Inc.
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mkdocs.yml 0 → 100644
浏览文件 @ 01c4cf93
site_name: PaddleSlim Docs
nav:
- Home: index.md
- 教程:
- 离线量化: tutorials/quant_post_demo.md
- 量化训练: tutorials/quant_aware_demo.md
- Embedding量化: tutorials/quant_embedding_demo.md
- SA搜索: tutorials/nas_demo.md
- API:
- 量化: api/quantization_api.md
- 剪枝: api/prune_api.md
- 敏感度分析: api/analysis_api.md
- 蒸馏: api/single_distiller_api.md
- SA搜索: api/nas_api.md
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/**
* lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.1.6
* Copyright (C) 2018 Oliver Nightingale
* @license MIT
*/
;(function(){
/**
* A convenience function for configuring and constructing
* a new lunr Index.
*
* A lunr.Builder instance is created and the pipeline setup
* with a trimmer, stop word filter and stemmer.
*
* This builder object is yielded to the configuration function
* that is passed as a parameter, allowing the list of fields
* and other builder parameters to be customised.
*
* All documents _must_ be added within the passed config function.
*
* @example
* var idx = lunr(function () {
* this.field('title')
* this.field('body')
* this.ref('id')
*
* documents.forEach(function (doc) {
* this.add(doc)
* }, this)
* })
*
* @see {@link lunr.Builder}
* @see {@link lunr.Pipeline}
* @see {@link lunr.trimmer}
* @see {@link lunr.stopWordFilter}
* @see {@link lunr.stemmer}
* @namespace {function} lunr
*/
var lunr = function (config) {
var builder = new lunr.Builder
builder.pipeline.add(
lunr.trimmer,
lunr.stopWordFilter,
lunr.stemmer
)
builder.searchPipeline.add(
lunr.stemmer
)
config.call(builder, builder)
return builder.build()
}
lunr.version = "2.1.6"
/*!
* lunr.utils
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* A namespace containing utils for the rest of the lunr library
*/
lunr.utils = {}
/**
* Print a warning message to the console.
*
* @param {String} message The message to be printed.
* @memberOf Utils
*/
lunr.utils.warn = (function (global) {
/* eslint-disable no-console */
return function (message) {
if (global.console && console.warn) {
console.warn(message)
}
}
/* eslint-enable no-console */
})(this)
/**
* Convert an object to a string.
*
* In the case of `null` and `undefined` the function returns
* the empty string, in all other cases the result of calling
* `toString` on the passed object is returned.
*
* @param {Any} obj The object to convert to a string.
* @return {String} string representation of the passed object.
* @memberOf Utils
*/
lunr.utils.asString = function (obj) {
if (obj === void 0 || obj === null) {
return ""
} else {
return obj.toString()
}
}
lunr.FieldRef = function (docRef, fieldName, stringValue) {
this.docRef = docRef
this.fieldName = fieldName
this._stringValue = stringValue
}
lunr.FieldRef.joiner = "/"
lunr.FieldRef.fromString = function (s) {
var n = s.indexOf(lunr.FieldRef.joiner)
if (n === -1) {
throw "malformed field ref string"
}
var fieldRef = s.slice(0, n),
docRef = s.slice(n + 1)
return new lunr.FieldRef (docRef, fieldRef, s)
}
lunr.FieldRef.prototype.toString = function () {
if (this._stringValue == undefined) {
this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef
}
return this._stringValue
}
/**
* A function to calculate the inverse document frequency for
* a posting. This is shared between the builder and the index
*
* @private
* @param {object} posting - The posting for a given term
* @param {number} documentCount - The total number of documents.
*/
lunr.idf = function (posting, documentCount) {
var documentsWithTerm = 0
for (var fieldName in posting) {
if (fieldName == '_index') continue // Ignore the term index, its not a field
documentsWithTerm += Object.keys(posting[fieldName]).length
}
var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)
return Math.log(1 + Math.abs(x))
}
/**
* A token wraps a string representation of a token
* as it is passed through the text processing pipeline.
*
* @constructor
* @param {string} [str=''] - The string token being wrapped.
* @param {object} [metadata={}] - Metadata associated with this token.
*/
lunr.Token = function (str, metadata) {
this.str = str || ""
this.metadata = metadata || {}
}
/**
* Returns the token string that is being wrapped by this object.
*
* @returns {string}
*/
lunr.Token.prototype.toString = function () {
return this.str
}
/**
* A token update function is used when updating or optionally
* when cloning a token.
*
* @callback lunr.Token~updateFunction
* @param {string} str - The string representation of the token.
* @param {Object} metadata - All metadata associated with this token.
*/
/**
* Applies the given function to the wrapped string token.
*
* @example
* token.update(function (str, metadata) {
* return str.toUpperCase()
* })
*
* @param {lunr.Token~updateFunction} fn - A function to apply to the token string.
* @returns {lunr.Token}
*/
lunr.Token.prototype.update = function (fn) {
this.str = fn(this.str, this.metadata)
return this
}
/**
* Creates a clone of this token. Optionally a function can be
* applied to the cloned token.
*
* @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.
* @returns {lunr.Token}
*/
lunr.Token.prototype.clone = function (fn) {
fn = fn || function (s) { return s }
return new lunr.Token (fn(this.str, this.metadata), this.metadata)
}
/*!
* lunr.tokenizer
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* A function for splitting a string into tokens ready to be inserted into
* the search index. Uses `lunr.tokenizer.separator` to split strings, change
* the value of this property to change how strings are split into tokens.
*
* This tokenizer will convert its parameter to a string by calling `toString` and
* then will split this string on the character in `lunr.tokenizer.separator`.
* Arrays will have their elements converted to strings and wrapped in a lunr.Token.
*
* @static
* @param {?(string|object|object[])} obj - The object to convert into tokens
* @returns {lunr.Token[]}
*/
lunr.tokenizer = function (obj) {
if (obj == null || obj == undefined) {
return []
}
if (Array.isArray(obj)) {
return obj.map(function (t) {
return new lunr.Token(lunr.utils.asString(t).toLowerCase())
})
}
var str = obj.toString().trim().toLowerCase(),
len = str.length,
tokens = []
for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {
var char = str.charAt(sliceEnd),
sliceLength = sliceEnd - sliceStart
if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {
if (sliceLength > 0) {
tokens.push(
new lunr.Token (str.slice(sliceStart, sliceEnd), {
position: [sliceStart, sliceLength],
index: tokens.length
})
)
}
sliceStart = sliceEnd + 1
}
}
return tokens
}
/**
* The separator used to split a string into tokens. Override this property to change the behaviour of
* `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.
*
* @static
* @see lunr.tokenizer
*/
lunr.tokenizer.separator = /[\s\-]+/
/*!
* lunr.Pipeline
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* lunr.Pipelines maintain an ordered list of functions to be applied to all
* tokens in documents entering the search index and queries being ran against
* the index.
*
* An instance of lunr.Index created with the lunr shortcut will contain a
* pipeline with a stop word filter and an English language stemmer. Extra
* functions can be added before or after either of these functions or these
* default functions can be removed.
*
* When run the pipeline will call each function in turn, passing a token, the
* index of that token in the original list of all tokens and finally a list of
* all the original tokens.
*
* The output of functions in the pipeline will be passed to the next function
* in the pipeline. To exclude a token from entering the index the function
* should return undefined, the rest of the pipeline will not be called with
* this token.
*
* For serialisation of pipelines to work, all functions used in an instance of
* a pipeline should be registered with lunr.Pipeline. Registered functions can
* then be loaded. If trying to load a serialised pipeline that uses functions
* that are not registered an error will be thrown.
*
* If not planning on serialising the pipeline then registering pipeline functions
* is not necessary.
*
* @constructor
*/
lunr.Pipeline = function () {
this._stack = []
}
lunr.Pipeline.registeredFunctions = Object.create(null)
/**
* A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token
* string as well as all known metadata. A pipeline function can mutate the token string
* or mutate (or add) metadata for a given token.
*
* A pipeline function can indicate that the passed token should be discarded by returning
* null. This token will not be passed to any downstream pipeline functions and will not be
* added to the index.
*
* Multiple tokens can be returned by returning an array of tokens. Each token will be passed
* to any downstream pipeline functions and all will returned tokens will be added to the index.
*
* Any number of pipeline functions may be chained together using a lunr.Pipeline.
*
* @interface lunr.PipelineFunction
* @param {lunr.Token} token - A token from the document being processed.
* @param {number} i - The index of this token in the complete list of tokens for this document/field.
* @param {lunr.Token[]} tokens - All tokens for this document/field.
* @returns {(?lunr.Token|lunr.Token[])}
*/
/**
* Register a function with the pipeline.
*
* Functions that are used in the pipeline should be registered if the pipeline
* needs to be serialised, or a serialised pipeline needs to be loaded.
*
* Registering a function does not add it to a pipeline, functions must still be
* added to instances of the pipeline for them to be used when running a pipeline.
*
* @param {lunr.PipelineFunction} fn - The function to check for.
* @param {String} label - The label to register this function with
*/
lunr.Pipeline.registerFunction = function (fn, label) {
if (label in this.registeredFunctions) {
lunr.utils.warn('Overwriting existing registered function: ' + label)
}
fn.label = label
lunr.Pipeline.registeredFunctions[fn.label] = fn
}
/**
* Warns if the function is not registered as a Pipeline function.
*
* @param {lunr.PipelineFunction} fn - The function to check for.
* @private
*/
lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {
var isRegistered = fn.label && (fn.label in this.registeredFunctions)
if (!isRegistered) {
lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn)
}
}
/**
* Loads a previously serialised pipeline.
*
* All functions to be loaded must already be registered with lunr.Pipeline.
* If any function from the serialised data has not been registered then an
* error will be thrown.
*
* @param {Object} serialised - The serialised pipeline to load.
* @returns {lunr.Pipeline}
*/
lunr.Pipeline.load = function (serialised) {
var pipeline = new lunr.Pipeline
serialised.forEach(function (fnName) {
var fn = lunr.Pipeline.registeredFunctions[fnName]
if (fn) {
pipeline.add(fn)
} else {
throw new Error('Cannot load unregistered function: ' + fnName)
}
})
return pipeline
}
/**
* Adds new functions to the end of the pipeline.
*
* Logs a warning if the function has not been registered.
*
* @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.
*/
lunr.Pipeline.prototype.add = function () {
var fns = Array.prototype.slice.call(arguments)
fns.forEach(function (fn) {
lunr.Pipeline.warnIfFunctionNotRegistered(fn)
this._stack.push(fn)
}, this)
}
/**
* Adds a single function after a function that already exists in the
* pipeline.
*
* Logs a warning if the function has not been registered.
*
* @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
* @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
*/
lunr.Pipeline.prototype.after = function (existingFn, newFn) {
lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
var pos = this._stack.indexOf(existingFn)
if (pos == -1) {
throw new Error('Cannot find existingFn')
}
pos = pos + 1
this._stack.splice(pos, 0, newFn)
}
/**
* Adds a single function before a function that already exists in the
* pipeline.
*
* Logs a warning if the function has not been registered.
*
* @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
* @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
*/
lunr.Pipeline.prototype.before = function (existingFn, newFn) {
lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
var pos = this._stack.indexOf(existingFn)
if (pos == -1) {
throw new Error('Cannot find existingFn')
}
this._stack.splice(pos, 0, newFn)
}
/**
* Removes a function from the pipeline.
*
* @param {lunr.PipelineFunction} fn The function to remove from the pipeline.
*/
lunr.Pipeline.prototype.remove = function (fn) {
var pos = this._stack.indexOf(fn)
if (pos == -1) {
return
}
this._stack.splice(pos, 1)
}
/**
* Runs the current list of functions that make up the pipeline against the
* passed tokens.
*
* @param {Array} tokens The tokens to run through the pipeline.
* @returns {Array}
*/
lunr.Pipeline.prototype.run = function (tokens) {
var stackLength = this._stack.length
for (var i = 0; i < stackLength; i++) {
var fn = this._stack[i]
var memo = []
for (var j = 0; j < tokens.length; j++) {
var result = fn(tokens[j], j, tokens)
if (result === void 0 || result === '') continue
if (result instanceof Array) {
for (var k = 0; k < result.length; k++) {
memo.push(result[k])
}
} else {
memo.push(result)
}
}
tokens = memo
}
return tokens
}
/**
* Convenience method for passing a string through a pipeline and getting
* strings out. This method takes care of wrapping the passed string in a
* token and mapping the resulting tokens back to strings.
*
* @param {string} str - The string to pass through the pipeline.
* @returns {string[]}
*/
lunr.Pipeline.prototype.runString = function (str) {
var token = new lunr.Token (str)
return this.run([token]).map(function (t) {
return t.toString()
})
}
/**
* Resets the pipeline by removing any existing processors.
*
*/
lunr.Pipeline.prototype.reset = function () {
this._stack = []
}
/**
* Returns a representation of the pipeline ready for serialisation.
*
* Logs a warning if the function has not been registered.
*
* @returns {Array}
*/
lunr.Pipeline.prototype.toJSON = function () {
return this._stack.map(function (fn) {
lunr.Pipeline.warnIfFunctionNotRegistered(fn)
return fn.label
})
}
/*!
* lunr.Vector
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* A vector is used to construct the vector space of documents and queries. These
* vectors support operations to determine the similarity between two documents or
* a document and a query.
*
* Normally no parameters are required for initializing a vector, but in the case of
* loading a previously dumped vector the raw elements can be provided to the constructor.
*
* For performance reasons vectors are implemented with a flat array, where an elements
* index is immediately followed by its value. E.g. [index, value, index, value]. This
* allows the underlying array to be as sparse as possible and still offer decent
* performance when being used for vector calculations.
*
* @constructor
* @param {Number[]} [elements] - The flat list of element index and element value pairs.
*/
lunr.Vector = function (elements) {
this._magnitude = 0
this.elements = elements || []
}
/**
* Calculates the position within the vector to insert a given index.
*
* This is used internally by insert and upsert. If there are duplicate indexes then
* the position is returned as if the value for that index were to be updated, but it
* is the callers responsibility to check whether there is a duplicate at that index
*
* @param {Number} insertIdx - The index at which the element should be inserted.
* @returns {Number}
*/
lunr.Vector.prototype.positionForIndex = function (index) {
// For an empty vector the tuple can be inserted at the beginning
if (this.elements.length == 0) {
return 0
}
var start = 0,
end = this.elements.length / 2,
sliceLength = end - start,
pivotPoint = Math.floor(sliceLength / 2),
pivotIndex = this.elements[pivotPoint * 2]
while (sliceLength > 1) {
if (pivotIndex < index) {
start = pivotPoint
}
if (pivotIndex > index) {
end = pivotPoint
}
if (pivotIndex == index) {
break
}
sliceLength = end - start
pivotPoint = start + Math.floor(sliceLength / 2)
pivotIndex = this.elements[pivotPoint * 2]
}
if (pivotIndex == index) {
return pivotPoint * 2
}
if (pivotIndex > index) {
return pivotPoint * 2
}
if (pivotIndex < index) {
return (pivotPoint + 1) * 2
}
}
/**
* Inserts an element at an index within the vector.
*
* Does not allow duplicates, will throw an error if there is already an entry
* for this index.
*
* @param {Number} insertIdx - The index at which the element should be inserted.
* @param {Number} val - The value to be inserted into the vector.
*/
lunr.Vector.prototype.insert = function (insertIdx, val) {
this.upsert(insertIdx, val, function () {
throw "duplicate index"
})
}
/**
* Inserts or updates an existing index within the vector.
*
* @param {Number} insertIdx - The index at which the element should be inserted.
* @param {Number} val - The value to be inserted into the vector.
* @param {function} fn - A function that is called for updates, the existing value and the
* requested value are passed as arguments
*/
lunr.Vector.prototype.upsert = function (insertIdx, val, fn) {
this._magnitude = 0
var position = this.positionForIndex(insertIdx)
if (this.elements[position] == insertIdx) {
this.elements[position + 1] = fn(this.elements[position + 1], val)
} else {
this.elements.splice(position, 0, insertIdx, val)
}
}
/**
* Calculates the magnitude of this vector.
*
* @returns {Number}
*/
lunr.Vector.prototype.magnitude = function () {
if (this._magnitude) return this._magnitude
var sumOfSquares = 0,
elementsLength = this.elements.length
for (var i = 1; i < elementsLength; i += 2) {
var val = this.elements[i]
sumOfSquares += val * val
}
return this._magnitude = Math.sqrt(sumOfSquares)
}
/**
* Calculates the dot product of this vector and another vector.
*
* @param {lunr.Vector} otherVector - The vector to compute the dot product with.
* @returns {Number}
*/
lunr.Vector.prototype.dot = function (otherVector) {
var dotProduct = 0,
a = this.elements, b = otherVector.elements,
aLen = a.length, bLen = b.length,
aVal = 0, bVal = 0,
i = 0, j = 0
while (i < aLen && j < bLen) {
aVal = a[i], bVal = b[j]
if (aVal < bVal) {
i += 2
} else if (aVal > bVal) {
j += 2
} else if (aVal == bVal) {
dotProduct += a[i + 1] * b[j + 1]
i += 2
j += 2
}
}
return dotProduct
}
/**
* Calculates the cosine similarity between this vector and another
* vector.
*
* @param {lunr.Vector} otherVector - The other vector to calculate the
* similarity with.
* @returns {Number}
*/
lunr.Vector.prototype.similarity = function (otherVector) {
return this.dot(otherVector) / (this.magnitude() * otherVector.magnitude())
}
/**
* Converts the vector to an array of the elements within the vector.
*
* @returns {Number[]}
*/
lunr.Vector.prototype.toArray = function () {
var output = new Array (this.elements.length / 2)
for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {
output[j] = this.elements[i]
}
return output
}
/**
* A JSON serializable representation of the vector.
*
* @returns {Number[]}
*/
lunr.Vector.prototype.toJSON = function () {
return this.elements
}
/* eslint-disable */
/*!
* lunr.stemmer
* Copyright (C) 2018 Oliver Nightingale
* Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
*/
/**
* lunr.stemmer is an english language stemmer, this is a JavaScript
* implementation of the PorterStemmer taken from http://tartarus.org/~martin
*
* @static
* @implements {lunr.PipelineFunction}
* @param {lunr.Token} token - The string to stem
* @returns {lunr.Token}
* @see {@link lunr.Pipeline}
*/
lunr.stemmer = (function(){
var step2list = {
"ational" : "ate",
"tional" : "tion",
"enci" : "ence",
"anci" : "ance",
"izer" : "ize",
"bli" : "ble",
"alli" : "al",
"entli" : "ent",
"eli" : "e",
"ousli" : "ous",
"ization" : "ize",
"ation" : "ate",
"ator" : "ate",
"alism" : "al",
"iveness" : "ive",
"fulness" : "ful",
"ousness" : "ous",
"aliti" : "al",
"iviti" : "ive",
"biliti" : "ble",
"logi" : "log"
},
step3list = {
"icate" : "ic",
"ative" : "",
"alize" : "al",
"iciti" : "ic",
"ical" : "ic",
"ful" : "",
"ness" : ""
},
c = "[^aeiou]", // consonant
v = "[aeiouy]", // vowel
C = c + "[^aeiouy]*", // consonant sequence
V = v + "[aeiou]*", // vowel sequence
mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0
meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1
mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1
s_v = "^(" + C + ")?" + v; // vowel in stem
var re_mgr0 = new RegExp(mgr0);
var re_mgr1 = new RegExp(mgr1);
var re_meq1 = new RegExp(meq1);
var re_s_v = new RegExp(s_v);
var re_1a = /^(.+?)(ss|i)es$/;
var re2_1a = /^(.+?)([^s])s$/;
var re_1b = /^(.+?)eed$/;
var re2_1b = /^(.+?)(ed|ing)$/;
var re_1b_2 = /.$/;
var re2_1b_2 = /(at|bl|iz)$/;
var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$");
var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$");
var re_1c = /^(.+?[^aeiou])y$/;
var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
var re2_4 = /^(.+?)(s|t)(ion)$/;
var re_5 = /^(.+?)e$/;
var re_5_1 = /ll$/;
var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$");
var porterStemmer = function porterStemmer(w) {
var stem,
suffix,
firstch,
re,
re2,
re3,
re4;
if (w.length < 3) { return w; }
firstch = w.substr(0,1);
if (firstch == "y") {
w = firstch.toUpperCase() + w.substr(1);
}
// Step 1a
re = re_1a
re2 = re2_1a;
if (re.test(w)) { w = w.replace(re,"$1$2"); }
else if (re2.test(w)) { w = w.replace(re2,"$1$2"); }
// Step 1b
re = re_1b;
re2 = re2_1b;
if (re.test(w)) {
var fp = re.exec(w);
re = re_mgr0;
if (re.test(fp[1])) {
re = re_1b_2;
w = w.replace(re,"");
}
} else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1];
re2 = re_s_v;
if (re2.test(stem)) {
w = stem;
re2 = re2_1b_2;
re3 = re3_1b_2;
re4 = re4_1b_2;
if (re2.test(w)) { w = w + "e"; }
else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); }
else if (re4.test(w)) { w = w + "e"; }
}
}
// Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)
re = re_1c;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
w = stem + "i";
}
// Step 2
re = re_2;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = re_mgr0;
if (re.test(stem)) {
w = stem + step2list[suffix];
}
}
// Step 3
re = re_3;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = re_mgr0;
if (re.test(stem)) {
w = stem + step3list[suffix];
}
}
// Step 4
re = re_4;
re2 = re2_4;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = re_mgr1;
if (re.test(stem)) {
w = stem;
}
} else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1] + fp[2];
re2 = re_mgr1;
if (re2.test(stem)) {
w = stem;
}
}
// Step 5
re = re_5;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = re_mgr1;
re2 = re_meq1;
re3 = re3_5;
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
w = stem;
}
}
re = re_5_1;
re2 = re_mgr1;
if (re.test(w) && re2.test(w)) {
re = re_1b_2;
w = w.replace(re,"");
}
// and turn initial Y back to y
if (firstch == "y") {
w = firstch.toLowerCase() + w.substr(1);
}
return w;
};
return function (token) {
return token.update(porterStemmer);
}
})();
lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')
/*!
* lunr.stopWordFilter
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* lunr.generateStopWordFilter builds a stopWordFilter function from the provided
* list of stop words.
*
* The built in lunr.stopWordFilter is built using this generator and can be used
* to generate custom stopWordFilters for applications or non English languages.
*
* @param {Array} token The token to pass through the filter
* @returns {lunr.PipelineFunction}
* @see lunr.Pipeline
* @see lunr.stopWordFilter
*/
lunr.generateStopWordFilter = function (stopWords) {
var words = stopWords.reduce(function (memo, stopWord) {
memo[stopWord] = stopWord
return memo
}, {})
return function (token) {
if (token && words[token.toString()] !== token.toString()) return token
}
}
/**
* lunr.stopWordFilter is an English language stop word list filter, any words
* contained in the list will not be passed through the filter.
*
* This is intended to be used in the Pipeline. If the token does not pass the
* filter then undefined will be returned.
*
* @implements {lunr.PipelineFunction}
* @params {lunr.Token} token - A token to check for being a stop word.
* @returns {lunr.Token}
* @see {@link lunr.Pipeline}
*/
lunr.stopWordFilter = lunr.generateStopWordFilter([
'a',
'able',
'about',
'across',
'after',
'all',
'almost',
'also',
'am',
'among',
'an',
'and',
'any',
'are',
'as',
'at',
'be',
'because',
'been',
'but',
'by',
'can',
'cannot',
'could',
'dear',
'did',
'do',
'does',
'either',
'else',
'ever',
'every',
'for',
'from',
'get',
'got',
'had',
'has',
'have',
'he',
'her',
'hers',
'him',
'his',
'how',
'however',
'i',
'if',
'in',
'into',
'is',
'it',
'its',
'just',
'least',
'let',
'like',
'likely',
'may',
'me',
'might',
'most',
'must',
'my',
'neither',
'no',
'nor',
'not',
'of',
'off',
'often',
'on',
'only',
'or',
'other',
'our',
'own',
'rather',
'said',
'say',
'says',
'she',
'should',
'since',
'so',
'some',
'than',
'that',
'the',
'their',
'them',
'then',
'there',
'these',
'they',
'this',
'tis',
'to',
'too',
'twas',
'us',
'wants',
'was',
'we',
'were',
'what',
'when',
'where',
'which',
'while',
'who',
'whom',
'why',
'will',
'with',
'would',
'yet',
'you',
'your'
])
lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')
/*!
* lunr.trimmer
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* lunr.trimmer is a pipeline function for trimming non word
* characters from the beginning and end of tokens before they
* enter the index.
*
* This implementation may not work correctly for non latin
* characters and should either be removed or adapted for use
* with languages with non-latin characters.
*
* @static
* @implements {lunr.PipelineFunction}
* @param {lunr.Token} token The token to pass through the filter
* @returns {lunr.Token}
* @see lunr.Pipeline
*/
lunr.trimmer = function (token) {
return token.update(function (s) {
return s.replace(/^\W+/, '').replace(/\W+$/, '')
})
}
lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')
/*!
* lunr.TokenSet
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* A token set is used to store the unique list of all tokens
* within an index. Token sets are also used to represent an
* incoming query to the index, this query token set and index
* token set are then intersected to find which tokens to look
* up in the inverted index.
*
* A token set can hold multiple tokens, as in the case of the
* index token set, or it can hold a single token as in the
* case of a simple query token set.
*
* Additionally token sets are used to perform wildcard matching.
* Leading, contained and trailing wildcards are supported, and
* from this edit distance matching can also be provided.
*
* Token sets are implemented as a minimal finite state automata,
* where both common prefixes and suffixes are shared between tokens.
* This helps to reduce the space used for storing the token set.
*
* @constructor
*/
lunr.TokenSet = function () {
this.final = false
this.edges = {}
this.id = lunr.TokenSet._nextId
lunr.TokenSet._nextId += 1
}
/**
* Keeps track of the next, auto increment, identifier to assign
* to a new tokenSet.
*
* TokenSets require a unique identifier to be correctly minimised.
*
* @private
*/
lunr.TokenSet._nextId = 1
/**
* Creates a TokenSet instance from the given sorted array of words.
*
* @param {String[]} arr - A sorted array of strings to create the set from.
* @returns {lunr.TokenSet}
* @throws Will throw an error if the input array is not sorted.
*/
lunr.TokenSet.fromArray = function (arr) {
var builder = new lunr.TokenSet.Builder
for (var i = 0, len = arr.length; i < len; i++) {
builder.insert(arr[i])
}
builder.finish()
return builder.root
}
/**
* Creates a token set from a query clause.
*
* @private
* @param {Object} clause - A single clause from lunr.Query.
* @param {string} clause.term - The query clause term.
* @param {number} [clause.editDistance] - The optional edit distance for the term.
* @returns {lunr.TokenSet}
*/
lunr.TokenSet.fromClause = function (clause) {
if ('editDistance' in clause) {
return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)
} else {
return lunr.TokenSet.fromString(clause.term)
}
}
/**
* Creates a token set representing a single string with a specified
* edit distance.
*
* Insertions, deletions, substitutions and transpositions are each
* treated as an edit distance of 1.
*
* Increasing the allowed edit distance will have a dramatic impact
* on the performance of both creating and intersecting these TokenSets.
* It is advised to keep the edit distance less than 3.
*
* @param {string} str - The string to create the token set from.
* @param {number} editDistance - The allowed edit distance to match.
* @returns {lunr.Vector}
*/
lunr.TokenSet.fromFuzzyString = function (str, editDistance) {
var root = new lunr.TokenSet
var stack = [{
node: root,
editsRemaining: editDistance,
str: str
}]
while (stack.length) {
var frame = stack.pop()
// no edit
if (frame.str.length > 0) {
var char = frame.str.charAt(0),
noEditNode
if (char in frame.node.edges) {
noEditNode = frame.node.edges[char]
} else {
noEditNode = new lunr.TokenSet
frame.node.edges[char] = noEditNode
}
if (frame.str.length == 1) {
noEditNode.final = true
} else {
stack.push({
node: noEditNode,
editsRemaining: frame.editsRemaining,
str: frame.str.slice(1)
})
}
}
// deletion
// can only do a deletion if we have enough edits remaining
// and if there are characters left to delete in the string
if (frame.editsRemaining > 0 && frame.str.length > 1) {
var char = frame.str.charAt(1),
deletionNode
if (char in frame.node.edges) {
deletionNode = frame.node.edges[char]
} else {
deletionNode = new lunr.TokenSet
frame.node.edges[char] = deletionNode
}
if (frame.str.length <= 2) {
deletionNode.final = true
} else {
stack.push({
node: deletionNode,
editsRemaining: frame.editsRemaining - 1,
str: frame.str.slice(2)
})
}
}
// deletion
// just removing the last character from the str
if (frame.editsRemaining > 0 && frame.str.length == 1) {
frame.node.final = true
}
// substitution
// can only do a substitution if we have enough edits remaining
// and if there are characters left to substitute
if (frame.editsRemaining > 0 && frame.str.length >= 1) {
if ("*" in frame.node.edges) {
var substitutionNode = frame.node.edges["*"]
} else {
var substitutionNode = new lunr.TokenSet
frame.node.edges["*"] = substitutionNode
}
if (frame.str.length == 1) {
substitutionNode.final = true
} else {
stack.push({
node: substitutionNode,
editsRemaining: frame.editsRemaining - 1,
str: frame.str.slice(1)
})
}
}
// insertion
// can only do insertion if there are edits remaining
if (frame.editsRemaining > 0) {
if ("*" in frame.node.edges) {
var insertionNode = frame.node.edges["*"]
} else {
var insertionNode = new lunr.TokenSet
frame.node.edges["*"] = insertionNode
}
if (frame.str.length == 0) {
insertionNode.final = true
} else {
stack.push({
node: insertionNode,
editsRemaining: frame.editsRemaining - 1,
str: frame.str
})
}
}
// transposition
// can only do a transposition if there are edits remaining
// and there are enough characters to transpose
if (frame.editsRemaining > 0 && frame.str.length > 1) {
var charA = frame.str.charAt(0),
charB = frame.str.charAt(1),
transposeNode
if (charB in frame.node.edges) {
transposeNode = frame.node.edges[charB]
} else {
transposeNode = new lunr.TokenSet
frame.node.edges[charB] = transposeNode
}
if (frame.str.length == 1) {
transposeNode.final = true
} else {
stack.push({
node: transposeNode,
editsRemaining: frame.editsRemaining - 1,
str: charA + frame.str.slice(2)
})
}
}
}
return root
}
/**
* Creates a TokenSet from a string.
*
* The string may contain one or more wildcard characters (*)
* that will allow wildcard matching when intersecting with
* another TokenSet.
*
* @param {string} str - The string to create a TokenSet from.
* @returns {lunr.TokenSet}
*/
lunr.TokenSet.fromString = function (str) {
var node = new lunr.TokenSet,
root = node,
wildcardFound = false
/*
* Iterates through all characters within the passed string
* appending a node for each character.
*
* As soon as a wildcard character is found then a self
* referencing edge is introduced to continually match
* any number of any characters.
*/
for (var i = 0, len = str.length; i < len; i++) {
var char = str[i],
final = (i == len - 1)
if (char == "*") {
wildcardFound = true
node.edges[char] = node
node.final = final
} else {
var next = new lunr.TokenSet
next.final = final
node.edges[char] = next
node = next
// TODO: is this needed anymore?
if (wildcardFound) {
node.edges["*"] = root
}
}
}
return root
}
/**
* Converts this TokenSet into an array of strings
* contained within the TokenSet.
*
* @returns {string[]}
*/
lunr.TokenSet.prototype.toArray = function () {
var words = []
var stack = [{
prefix: "",
node: this
}]
while (stack.length) {
var frame = stack.pop(),
edges = Object.keys(frame.node.edges),
len = edges.length
if (frame.node.final) {
words.push(frame.prefix)
}
for (var i = 0; i < len; i++) {
var edge = edges[i]
stack.push({
prefix: frame.prefix.concat(edge),
node: frame.node.edges[edge]
})
}
}
return words
}
/**
* Generates a string representation of a TokenSet.
*
* This is intended to allow TokenSets to be used as keys
* in objects, largely to aid the construction and minimisation
* of a TokenSet. As such it is not designed to be a human
* friendly representation of the TokenSet.
*
* @returns {string}
*/
lunr.TokenSet.prototype.toString = function () {
// NOTE: Using Object.keys here as this.edges is very likely
// to enter 'hash-mode' with many keys being added
//
// avoiding a for-in loop here as it leads to the function
// being de-optimised (at least in V8). From some simple
// benchmarks the performance is comparable, but allowing
// V8 to optimize may mean easy performance wins in the future.
if (this._str) {
return this._str
}
var str = this.final ? '1' : '0',
labels = Object.keys(this.edges).sort(),
len = labels.length
for (var i = 0; i < len; i++) {
var label = labels[i],
node = this.edges[label]
str = str + label + node.id
}
return str
}
/**
* Returns a new TokenSet that is the intersection of
* this TokenSet and the passed TokenSet.
*
* This intersection will take into account any wildcards
* contained within the TokenSet.
*
* @param {lunr.TokenSet} b - An other TokenSet to intersect with.
* @returns {lunr.TokenSet}
*/
lunr.TokenSet.prototype.intersect = function (b) {
var output = new lunr.TokenSet,
frame = undefined
var stack = [{
qNode: b,
output: output,
node: this
}]
while (stack.length) {
frame = stack.pop()
// NOTE: As with the #toString method, we are using
// Object.keys and a for loop instead of a for-in loop
// as both of these objects enter 'hash' mode, causing
// the function to be de-optimised in V8
var qEdges = Object.keys(frame.qNode.edges),
qLen = qEdges.length,
nEdges = Object.keys(frame.node.edges),
nLen = nEdges.length
for (var q = 0; q < qLen; q++) {
var qEdge = qEdges[q]
for (var n = 0; n < nLen; n++) {
var nEdge = nEdges[n]
if (nEdge == qEdge || qEdge == '*') {
var node = frame.node.edges[nEdge],
qNode = frame.qNode.edges[qEdge],
final = node.final && qNode.final,
next = undefined
if (nEdge in frame.output.edges) {
// an edge already exists for this character
// no need to create a new node, just set the finality
// bit unless this node is already final
next = frame.output.edges[nEdge]
next.final = next.final || final
} else {
// no edge exists yet, must create one
// set the finality bit and insert it
// into the output
next = new lunr.TokenSet
next.final = final
frame.output.edges[nEdge] = next
}
stack.push({
qNode: qNode,
output: next,
node: node
})
}
}
}
}
return output
}
lunr.TokenSet.Builder = function () {
this.previousWord = ""
this.root = new lunr.TokenSet
this.uncheckedNodes = []
this.minimizedNodes = {}
}
lunr.TokenSet.Builder.prototype.insert = function (word) {
var node,
commonPrefix = 0
if (word < this.previousWord) {
throw new Error ("Out of order word insertion")
}
for (var i = 0; i < word.length && i < this.previousWord.length; i++) {
if (word[i] != this.previousWord[i]) break
commonPrefix++
}
this.minimize(commonPrefix)
if (this.uncheckedNodes.length == 0) {
node = this.root
} else {
node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child
}
for (var i = commonPrefix; i < word.length; i++) {
var nextNode = new lunr.TokenSet,
char = word[i]
node.edges[char] = nextNode
this.uncheckedNodes.push({
parent: node,
char: char,
child: nextNode
})
node = nextNode
}
node.final = true
this.previousWord = word
}
lunr.TokenSet.Builder.prototype.finish = function () {
this.minimize(0)
}
lunr.TokenSet.Builder.prototype.minimize = function (downTo) {
for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {
var node = this.uncheckedNodes[i],
childKey = node.child.toString()
if (childKey in this.minimizedNodes) {
node.parent.edges[node.char] = this.minimizedNodes[childKey]
} else {
// Cache the key for this node since
// we know it can't change anymore
node.child._str = childKey
this.minimizedNodes[childKey] = node.child
}
this.uncheckedNodes.pop()
}
}
/*!
* lunr.Index
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* An index contains the built index of all documents and provides a query interface
* to the index.
*
* Usually instances of lunr.Index will not be created using this constructor, instead
* lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
* used to load previously built and serialized indexes.
*
* @constructor
* @param {Object} attrs - The attributes of the built search index.
* @param {Object} attrs.invertedIndex - An index of term/field to document reference.
* @param {Object<string, lunr.Vector>} attrs.documentVectors - Document vectors keyed by document reference.
* @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.
* @param {string[]} attrs.fields - The names of indexed document fields.
* @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.
*/
lunr.Index = function (attrs) {
this.invertedIndex = attrs.invertedIndex
this.fieldVectors = attrs.fieldVectors
this.tokenSet = attrs.tokenSet
this.fields = attrs.fields
this.pipeline = attrs.pipeline
}
/**
* A result contains details of a document matching a search query.
* @typedef {Object} lunr.Index~Result
* @property {string} ref - The reference of the document this result represents.
* @property {number} score - A number between 0 and 1 representing how similar this document is to the query.
* @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.
*/
/**
* Although lunr provides the ability to create queries using lunr.Query, it also provides a simple
* query language which itself is parsed into an instance of lunr.Query.
*
* For programmatically building queries it is advised to directly use lunr.Query, the query language
* is best used for human entered text rather than program generated text.
*
* At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported
* and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'
* or 'world', though those that contain both will rank higher in the results.
*
* Wildcards can be included in terms to match one or more unspecified characters, these wildcards can
* be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding
* wildcards will increase the number of documents that will be found but can also have a negative
* impact on query performance, especially with wildcards at the beginning of a term.
*
* Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term
* hello in the title field will match this query. Using a field not present in the index will lead
* to an error being thrown.
*
* Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term
* boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported
* to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.
* Avoid large values for edit distance to improve query performance.
*
* To escape special characters the backslash character '\' can be used, this allows searches to include
* characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead
* of attempting to apply a boost of 2 to the search term "foo".
*
* @typedef {string} lunr.Index~QueryString
* @example <caption>Simple single term query</caption>
* hello
* @example <caption>Multiple term query</caption>
* hello world
* @example <caption>term scoped to a field</caption>
* title:hello
* @example <caption>term with a boost of 10</caption>
* hello^10
* @example <caption>term with an edit distance of 2</caption>
* hello~2
*/
/**
* Performs a search against the index using lunr query syntax.
*
* Results will be returned sorted by their score, the most relevant results
* will be returned first.
*
* For more programmatic querying use lunr.Index#query.
*
* @param {lunr.Index~QueryString} queryString - A string containing a lunr query.
* @throws {lunr.QueryParseError} If the passed query string cannot be parsed.
* @returns {lunr.Index~Result[]}
*/
lunr.Index.prototype.search = function (queryString) {
return this.query(function (query) {
var parser = new lunr.QueryParser(queryString, query)
parser.parse()
})
}
/**
* A query builder callback provides a query object to be used to express
* the query to perform on the index.
*
* @callback lunr.Index~queryBuilder
* @param {lunr.Query} query - The query object to build up.
* @this lunr.Query
*/
/**
* Performs a query against the index using the yielded lunr.Query object.
*
* If performing programmatic queries against the index, this method is preferred
* over lunr.Index#search so as to avoid the additional query parsing overhead.
*
* A query object is yielded to the supplied function which should be used to
* express the query to be run against the index.
*
* Note that although this function takes a callback parameter it is _not_ an
* asynchronous operation, the callback is just yielded a query object to be
* customized.
*
* @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.
* @returns {lunr.Index~Result[]}
*/
lunr.Index.prototype.query = function (fn) {
// for each query clause
// * process terms
// * expand terms from token set
// * find matching documents and metadata
// * get document vectors
// * score documents
var query = new lunr.Query(this.fields),
matchingFields = Object.create(null),
queryVectors = Object.create(null),
termFieldCache = Object.create(null)
fn.call(query, query)
for (var i = 0; i < query.clauses.length; i++) {
/*
* Unless the pipeline has been disabled for this term, which is
* the case for terms with wildcards, we need to pass the clause
* term through the search pipeline. A pipeline returns an array
* of processed terms. Pipeline functions may expand the passed
* term, which means we may end up performing multiple index lookups
* for a single query term.
*/
var clause = query.clauses[i],
terms = null
if (clause.usePipeline) {
terms = this.pipeline.runString(clause.term)
} else {
terms = [clause.term]
}
for (var m = 0; m < terms.length; m++) {
var term = terms[m]
/*
* Each term returned from the pipeline needs to use the same query
* clause object, e.g. the same boost and or edit distance. The
* simplest way to do this is to re-use the clause object but mutate
* its term property.
*/
clause.term = term
/*
* From the term in the clause we create a token set which will then
* be used to intersect the indexes token set to get a list of terms
* to lookup in the inverted index
*/
var termTokenSet = lunr.TokenSet.fromClause(clause),
expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()
for (var j = 0; j < expandedTerms.length; j++) {
/*
* For each term get the posting and termIndex, this is required for
* building the query vector.
*/
var expandedTerm = expandedTerms[j],
posting = this.invertedIndex[expandedTerm],
termIndex = posting._index
for (var k = 0; k < clause.fields.length; k++) {
/*
* For each field that this query term is scoped by (by default
* all fields are in scope) we need to get all the document refs
* that have this term in that field.
*
* The posting is the entry in the invertedIndex for the matching
* term from above.
*/
var field = clause.fields[k],
fieldPosting = posting[field],
matchingDocumentRefs = Object.keys(fieldPosting),
termField = expandedTerm + "/" + field
/*
* To support field level boosts a query vector is created per
* field. This vector is populated using the termIndex found for
* the term and a unit value with the appropriate boost applied.
*
* If the query vector for this field does not exist yet it needs
* to be created.
*/
if (queryVectors[field] === undefined) {
queryVectors[field] = new lunr.Vector
}
/*
* Using upsert because there could already be an entry in the vector
* for the term we are working with. In that case we just add the scores
* together.
*/
queryVectors[field].upsert(termIndex, 1 * clause.boost, function (a, b) { return a + b })
/**
* If we've already seen this term, field combo then we've already collected
* the matching documents and metadata, no need to go through all that again
*/
if (termFieldCache[termField]) {
continue
}
for (var l = 0; l < matchingDocumentRefs.length; l++) {
/*
* All metadata for this term/field/document triple
* are then extracted and collected into an instance
* of lunr.MatchData ready to be returned in the query
* results
*/
var matchingDocumentRef = matchingDocumentRefs[l],
matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),
metadata = fieldPosting[matchingDocumentRef],
fieldMatch
if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {
matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)
} else {
fieldMatch.add(expandedTerm, field, metadata)
}
}
termFieldCache[termField] = true
}
}
}
}
var matchingFieldRefs = Object.keys(matchingFields),
results = [],
matches = Object.create(null)
for (var i = 0; i < matchingFieldRefs.length; i++) {
/*
* Currently we have document fields that match the query, but we
* need to return documents. The matchData and scores are combined
* from multiple fields belonging to the same document.
*
* Scores are calculated by field, using the query vectors created
* above, and combined into a final document score using addition.
*/
var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),
docRef = fieldRef.docRef,
fieldVector = this.fieldVectors[fieldRef],
score = queryVectors[fieldRef.fieldName].similarity(fieldVector),
docMatch
if ((docMatch = matches[docRef]) !== undefined) {
docMatch.score += score
docMatch.matchData.combine(matchingFields[fieldRef])
} else {
var match = {
ref: docRef,
score: score,
matchData: matchingFields[fieldRef]
}
matches[docRef] = match
results.push(match)
}
}
/*
* Sort the results objects by score, highest first.
*/
return results.sort(function (a, b) {
return b.score - a.score
})
}
/**
* Prepares the index for JSON serialization.
*
* The schema for this JSON blob will be described in a
* separate JSON schema file.
*
* @returns {Object}
*/
lunr.Index.prototype.toJSON = function () {
var invertedIndex = Object.keys(this.invertedIndex)
.sort()
.map(function (term) {
return [term, this.invertedIndex[term]]
}, this)
var fieldVectors = Object.keys(this.fieldVectors)
.map(function (ref) {
return [ref, this.fieldVectors[ref].toJSON()]
}, this)
return {
version: lunr.version,
fields: this.fields,
fieldVectors: fieldVectors,
invertedIndex: invertedIndex,
pipeline: this.pipeline.toJSON()
}
}
/**
* Loads a previously serialized lunr.Index
*
* @param {Object} serializedIndex - A previously serialized lunr.Index
* @returns {lunr.Index}
*/
lunr.Index.load = function (serializedIndex) {
var attrs = {},
fieldVectors = {},
serializedVectors = serializedIndex.fieldVectors,
invertedIndex = {},
serializedInvertedIndex = serializedIndex.invertedIndex,
tokenSetBuilder = new lunr.TokenSet.Builder,
pipeline = lunr.Pipeline.load(serializedIndex.pipeline)
if (serializedIndex.version != lunr.version) {
lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'")
}
for (var i = 0; i < serializedVectors.length; i++) {
var tuple = serializedVectors[i],
ref = tuple[0],
elements = tuple[1]
fieldVectors[ref] = new lunr.Vector(elements)
}
for (var i = 0; i < serializedInvertedIndex.length; i++) {
var tuple = serializedInvertedIndex[i],
term = tuple[0],
posting = tuple[1]
tokenSetBuilder.insert(term)
invertedIndex[term] = posting
}
tokenSetBuilder.finish()
attrs.fields = serializedIndex.fields
attrs.fieldVectors = fieldVectors
attrs.invertedIndex = invertedIndex
attrs.tokenSet = tokenSetBuilder.root
attrs.pipeline = pipeline
return new lunr.Index(attrs)
}
/*!
* lunr.Builder
* Copyright (C) 2018 Oliver Nightingale
*/
/**
* lunr.Builder performs indexing on a set of documents and
* returns instances of lunr.Index ready for querying.
*
* All configuration of the index is done via the builder, the
* fields to index, the document reference, the text processing
* pipeline and document scoring parameters are all set on the
* builder before indexing.
*
* @constructor
* @property {string} _ref - Internal reference to the document reference field.
* @property {string[]} _fields - Internal reference to the document fields to index.
* @property {object} invertedIndex - The inverted index maps terms to document fields.
* @property {object} documentTermFrequencies - Keeps track of document term frequencies.
* @property {object} documentLengths - Keeps track of the length of documents added to the index.
* @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.
* @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.
* @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.
* @property {number} documentCount - Keeps track of the total number of documents indexed.
* @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.
* @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.
* @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.
* @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.
*/
lunr.Builder = function () {
this._ref = "id"
this._fields = []
this.invertedIndex = Object.create(null)
this.fieldTermFrequencies = {}
this.fieldLengths = {}
this.tokenizer = lunr.tokenizer
this.pipeline = new lunr.Pipeline
this.searchPipeline = new lunr.Pipeline
this.documentCount = 0
this._b = 0.75
this._k1 = 1.2
this.termIndex = 0
this.metadataWhitelist = []
}
/**
* Sets the document field used as the document reference. Every document must have this field.
* The type of this field in the document should be a string, if it is not a string it will be
* coerced into a string by calling toString.
*
* The default ref is 'id'.
*
* The ref should _not_ be changed during indexing, it should be set before any documents are
* added to the index. Changing it during indexing can lead to inconsistent results.
*
* @param {string} ref - The name of the reference field in the document.
*/
lunr.Builder.prototype.ref = function (ref) {
this._ref = ref
}
/**
* Adds a field to the list of document fields that will be indexed. Every document being
* indexed should have this field. Null values for this field in indexed documents will
* not cause errors but will limit the chance of that document being retrieved by searches.
*
* All fields should be added before adding documents to the index. Adding fields after
* a document has been indexed will have no effect on already indexed documents.
*
* @param {string} field - The name of a field to index in all documents.
*/
lunr.Builder.prototype.field = function (field) {
this._fields.push(field)
}
/**
* A parameter to tune the amount of field length normalisation that is applied when
* calculating relevance scores. A value of 0 will completely disable any normalisation
* and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b
* will be clamped to the range 0 - 1.
*
* @param {number} number - The value to set for this tuning parameter.
*/
lunr.Builder.prototype.b = function (number) {
if (number < 0) {
this._b = 0
} else if (number > 1) {
this._b = 1
} else {
this._b = number
}
}
/**
* A parameter that controls the speed at which a rise in term frequency results in term
* frequency saturation. The default value is 1.2. Setting this to a higher value will give
* slower saturation levels, a lower value will result in quicker saturation.
*
* @param {number} number - The value to set for this tuning parameter.
*/
lunr.Builder.prototype.k1 = function (number) {
this._k1 = number
}
/**
* Adds a document to the index.
*
* Before adding fields to the index the index should have been fully setup, with the document
* ref and all fields to index already having been specified.
*
* The document must have a field name as specified by the ref (by default this is 'id') and
* it should have all fields defined for indexing, though null or undefined values will not
* cause errors.
*
* @param {object} doc - The document to add to the index.
*/
lunr.Builder.prototype.add = function (doc) {
var docRef = doc[this._ref]
this.documentCount += 1
for (var i = 0; i < this._fields.length; i++) {
var fieldName = this._fields[i],
field = doc[fieldName],
tokens = this.tokenizer(field),
terms = this.pipeline.run(tokens),
fieldRef = new lunr.FieldRef (docRef, fieldName),
fieldTerms = Object.create(null)
this.fieldTermFrequencies[fieldRef] = fieldTerms
this.fieldLengths[fieldRef] = 0
// store the length of this field for this document
this.fieldLengths[fieldRef] += terms.length
// calculate term frequencies for this field
for (var j = 0; j < terms.length; j++) {
var term = terms[j]
if (fieldTerms[term] == undefined) {
fieldTerms[term] = 0
}
fieldTerms[term] += 1
// add to inverted index
// create an initial posting if one doesn't exist
if (this.invertedIndex[term] == undefined) {
var posting = Object.create(null)
posting["_index"] = this.termIndex
this.termIndex += 1
for (var k = 0; k < this._fields.length; k++) {
posting[this._fields[k]] = Object.create(null)
}
this.invertedIndex[term] = posting
}
// add an entry for this term/fieldName/docRef to the invertedIndex
if (this.invertedIndex[term][fieldName][docRef] == undefined) {
this.invertedIndex[term][fieldName][docRef] = Object.create(null)
}
// store all whitelisted metadata about this token in the
// inverted index
for (var l = 0; l < this.metadataWhitelist.length; l++) {
var metadataKey = this.metadataWhitelist[l],
metadata = term.metadata[metadataKey]
if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {
this.invertedIndex[term][fieldName][docRef][metadataKey] = []
}
this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)
}
}
}
}
/**
* Calculates the average document length for this index
*
* @private
*/
lunr.Builder.prototype.calculateAverageFieldLengths = function () {
var fieldRefs = Object.keys(this.fieldLengths),
numberOfFields = fieldRefs.length,
accumulator = {},
documentsWithField = {}
for (var i = 0; i < numberOfFields; i++) {
var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
field = fieldRef.fieldName
documentsWithField[field] || (documentsWithField[field] = 0)
documentsWithField[field] += 1
accumulator[field] || (accumulator[field] = 0)
accumulator[field] += this.fieldLengths[fieldRef]
}
for (var i = 0; i < this._fields.length; i++) {
var field = this._fields[i]
accumulator[field] = accumulator[field] / documentsWithField[field]
}
this.averageFieldLength = accumulator
}
/**
* Builds a vector space model of every document using lunr.Vector
*
* @private
*/
lunr.Builder.prototype.createFieldVectors = function () {
var fieldVectors = {},
fieldRefs = Object.keys(this.fieldTermFrequencies),
fieldRefsLength = fieldRefs.length,
termIdfCache = Object.create(null)
for (var i = 0; i < fieldRefsLength; i++) {
var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
field = fieldRef.fieldName,
fieldLength = this.fieldLengths[fieldRef],
fieldVector = new lunr.Vector,
termFrequencies = this.fieldTermFrequencies[fieldRef],
terms = Object.keys(termFrequencies),
termsLength = terms.length
for (var j = 0; j < termsLength; j++) {
var term = terms[j],
tf = termFrequencies[term],
termIndex = this.invertedIndex[term]._index,
idf, score, scoreWithPrecision
if (termIdfCache[term] === undefined) {
idf = lunr.idf(this.invertedIndex[term], this.documentCount)
termIdfCache[term] = idf
} else {
idf = termIdfCache[term]
}
score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[field])) + tf)
scoreWithPrecision = Math.round(score * 1000) / 1000
// Converts 1.23456789 to 1.234.
// Reducing the precision so that the vectors take up less
// space when serialised. Doing it now so that they behave
// the same before and after serialisation. Also, this is
// the fastest approach to reducing a number's precision in
// JavaScript.
fieldVector.insert(termIndex, scoreWithPrecision)
}
fieldVectors[fieldRef] = fieldVector
}
this.fieldVectors = fieldVectors
}
/**
* Creates a token set of all tokens in the index using lunr.TokenSet
*
* @private
*/
lunr.Builder.prototype.createTokenSet = function () {
this.tokenSet = lunr.TokenSet.fromArray(
Object.keys(this.invertedIndex).sort()
)
}
/**
* Builds the index, creating an instance of lunr.Index.
*
* This completes the indexing process and should only be called
* once all documents have been added to the index.
*
* @returns {lunr.Index}
*/
lunr.Builder.prototype.build = function () {
this.calculateAverageFieldLengths()
this.createFieldVectors()
this.createTokenSet()
return new lunr.Index({
invertedIndex: this.invertedIndex,
fieldVectors: this.fieldVectors,
tokenSet: this.tokenSet,
fields: this._fields,
pipeline: this.searchPipeline
})
}
/**
* Applies a plugin to the index builder.
*
* A plugin is a function that is called with the index builder as its context.
* Plugins can be used to customise or extend the behaviour of the index
* in some way. A plugin is just a function, that encapsulated the custom
* behaviour that should be applied when building the index.
*
* The plugin function will be called with the index builder as its argument, additional
* arguments can also be passed when calling use. The function will be called
* with the index builder as its context.
*
* @param {Function} plugin The plugin to apply.
*/
lunr.Builder.prototype.use = function (fn) {
var args = Array.prototype.slice.call(arguments, 1)
args.unshift(this)
fn.apply(this, args)
}
/**
* Contains and collects metadata about a matching document.
* A single instance of lunr.MatchData is returned as part of every
* lunr.Index~Result.
*
* @constructor
* @param {string} term - The term this match data is associated with
* @param {string} field - The field in which the term was found
* @param {object} metadata - The metadata recorded about this term in this field
* @property {object} metadata - A cloned collection of metadata associated with this document.
* @see {@link lunr.Index~Result}
*/
lunr.MatchData = function (term, field, metadata) {
var clonedMetadata = Object.create(null),
metadataKeys = Object.keys(metadata)
// Cloning the metadata to prevent the original
// being mutated during match data combination.
// Metadata is kept in an array within the inverted
// index so cloning the data can be done with
// Array#slice
for (var i = 0; i < metadataKeys.length; i++) {
var key = metadataKeys[i]
clonedMetadata[key] = metadata[key].slice()
}
this.metadata = Object.create(null)
this.metadata[term] = Object.create(null)
this.metadata[term][field] = clonedMetadata
}
/**
* An instance of lunr.MatchData will be created for every term that matches a
* document. However only one instance is required in a lunr.Index~Result. This
* method combines metadata from another instance of lunr.MatchData with this
* objects metadata.
*
* @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.
* @see {@link lunr.Index~Result}
*/
lunr.MatchData.prototype.combine = function (otherMatchData) {
var terms = Object.keys(otherMatchData.metadata)
for (var i = 0; i < terms.length; i++) {
var term = terms[i],
fields = Object.keys(otherMatchData.metadata[term])
if (this.metadata[term] == undefined) {
this.metadata[term] = Object.create(null)
}
for (var j = 0; j < fields.length; j++) {
var field = fields[j],
keys = Object.keys(otherMatchData.metadata[term][field])
if (this.metadata[term][field] == undefined) {
this.metadata[term][field] = Object.create(null)
}
for (var k = 0; k < keys.length; k++) {
var key = keys[k]
if (this.metadata[term][field][key] == undefined) {
this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]
} else {
this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])
}
}
}
}
}
/**
* Add metadata for a term/field pair to this instance of match data.
*
* @param {string} term - The term this match data is associated with
* @param {string} field - The field in which the term was found
* @param {object} metadata - The metadata recorded about this term in this field
*/
lunr.MatchData.prototype.add = function (term, field, metadata) {
if (!(term in this.metadata)) {
this.metadata[term] = Object.create(null)
this.metadata[term][field] = metadata
return
}
if (!(field in this.metadata[term])) {
this.metadata[term][field] = metadata
return
}
var metadataKeys = Object.keys(metadata)
for (var i = 0; i < metadataKeys.length; i++) {
var key = metadataKeys[i]
if (key in this.metadata[term][field]) {
this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])
} else {
this.metadata[term][field][key] = metadata[key]
}
}
}
/**
* A lunr.Query provides a programmatic way of defining queries to be performed
* against a {@link lunr.Index}.
*
* Prefer constructing a lunr.Query using the {@link lunr.Index#query} method
* so the query object is pre-initialized with the right index fields.
*
* @constructor
* @property {lunr.Query~Clause[]} clauses - An array of query clauses.
* @property {string[]} allFields - An array of all available fields in a lunr.Index.
*/
lunr.Query = function (allFields) {
this.clauses = []
this.allFields = allFields
}
/**
* Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.
*
* This allows wildcards to be added to the beginning and end of a term without having to manually do any string
* concatenation.
*
* The wildcard constants can be bitwise combined to select both leading and trailing wildcards.
*
* @constant
* @default
* @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour
* @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists
* @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists
* @see lunr.Query~Clause
* @see lunr.Query#clause
* @see lunr.Query#term
* @example <caption>query term with trailing wildcard</caption>
* query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })
* @example <caption>query term with leading and trailing wildcard</caption>
* query.term('foo', {
* wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING
* })
*/
lunr.Query.wildcard = new String ("*")
lunr.Query.wildcard.NONE = 0
lunr.Query.wildcard.LEADING = 1
lunr.Query.wildcard.TRAILING = 2
/**
* A single clause in a {@link lunr.Query} contains a term and details on how to
* match that term against a {@link lunr.Index}.
*
* @typedef {Object} lunr.Query~Clause
* @property {string[]} fields - The fields in an index this clause should be matched against.
* @property {number} [boost=1] - Any boost that should be applied when matching this clause.
* @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.
* @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.
* @property {number} [wildcard=0] - Whether the term should have wildcards appended or prepended.
*/
/**
* Adds a {@link lunr.Query~Clause} to this query.
*
* Unless the clause contains the fields to be matched all fields will be matched. In addition
* a default boost of 1 is applied to the clause.
*
* @param {lunr.Query~Clause} clause - The clause to add to this query.
* @see lunr.Query~Clause
* @returns {lunr.Query}
*/
lunr.Query.prototype.clause = function (clause) {
if (!('fields' in clause)) {
clause.fields = this.allFields
}
if (!('boost' in clause)) {
clause.boost = 1
}
if (!('usePipeline' in clause)) {
clause.usePipeline = true
}
if (!('wildcard' in clause)) {
clause.wildcard = lunr.Query.wildcard.NONE
}
if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {
clause.term = "*" + clause.term
}
if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {
clause.term = "" + clause.term + "*"
}
this.clauses.push(clause)
return this
}
/**
* Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}
* to the list of clauses that make up this query.
*
* @param {string} term - The term to add to the query.
* @param {Object} [options] - Any additional properties to add to the query clause.
* @returns {lunr.Query}
* @see lunr.Query#clause
* @see lunr.Query~Clause
* @example <caption>adding a single term to a query</caption>
* query.term("foo")
* @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>
* query.term("foo", {
* fields: ["title"],
* boost: 10,
* wildcard: lunr.Query.wildcard.TRAILING
* })
*/
lunr.Query.prototype.term = function (term, options) {
var clause = options || {}
clause.term = term
this.clause(clause)
return this
}
lunr.QueryParseError = function (message, start, end) {
this.name = "QueryParseError"
this.message = message
this.start = start
this.end = end
}
lunr.QueryParseError.prototype = new Error
lunr.QueryLexer = function (str) {
this.lexemes = []
this.str = str
this.length = str.length
this.pos = 0
this.start = 0
this.escapeCharPositions = []
}
lunr.QueryLexer.prototype.run = function () {
var state = lunr.QueryLexer.lexText
while (state) {
state = state(this)
}
}
lunr.QueryLexer.prototype.sliceString = function () {
var subSlices = [],
sliceStart = this.start,
sliceEnd = this.pos
for (var i = 0; i < this.escapeCharPositions.length; i++) {
sliceEnd = this.escapeCharPositions[i]
subSlices.push(this.str.slice(sliceStart, sliceEnd))
sliceStart = sliceEnd + 1
}
subSlices.push(this.str.slice(sliceStart, this.pos))
this.escapeCharPositions.length = 0
return subSlices.join('')
}
lunr.QueryLexer.prototype.emit = function (type) {
this.lexemes.push({
type: type,
str: this.sliceString(),
start: this.start,
end: this.pos
})
this.start = this.pos
}
lunr.QueryLexer.prototype.escapeCharacter = function () {
this.escapeCharPositions.push(this.pos - 1)
this.pos += 1
}
lunr.QueryLexer.prototype.next = function () {
if (this.pos >= this.length) {
return lunr.QueryLexer.EOS
}
var char = this.str.charAt(this.pos)
this.pos += 1
return char
}
lunr.QueryLexer.prototype.width = function () {
return this.pos - this.start
}
lunr.QueryLexer.prototype.ignore = function () {
if (this.start == this.pos) {
this.pos += 1
}
this.start = this.pos
}
lunr.QueryLexer.prototype.backup = function () {
this.pos -= 1
}
lunr.QueryLexer.prototype.acceptDigitRun = function () {
var char, charCode
do {
char = this.next()
charCode = char.charCodeAt(0)
} while (charCode > 47 && charCode < 58)
if (char != lunr.QueryLexer.EOS) {
this.backup()
}
}
lunr.QueryLexer.prototype.more = function () {
return this.pos < this.length
}
lunr.QueryLexer.EOS = 'EOS'
lunr.QueryLexer.FIELD = 'FIELD'
lunr.QueryLexer.TERM = 'TERM'
lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'
lunr.QueryLexer.BOOST = 'BOOST'
lunr.QueryLexer.lexField = function (lexer) {
lexer.backup()
lexer.emit(lunr.QueryLexer.FIELD)
lexer.ignore()
return lunr.QueryLexer.lexText
}
lunr.QueryLexer.lexTerm = function (lexer) {
if (lexer.width() > 1) {
lexer.backup()
lexer.emit(lunr.QueryLexer.TERM)
}
lexer.ignore()
if (lexer.more()) {
return lunr.QueryLexer.lexText
}
}
lunr.QueryLexer.lexEditDistance = function (lexer) {
lexer.ignore()
lexer.acceptDigitRun()
lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)
return lunr.QueryLexer.lexText
}
lunr.QueryLexer.lexBoost = function (lexer) {
lexer.ignore()
lexer.acceptDigitRun()
lexer.emit(lunr.QueryLexer.BOOST)
return lunr.QueryLexer.lexText
}
lunr.QueryLexer.lexEOS = function (lexer) {
if (lexer.width() > 0) {
lexer.emit(lunr.QueryLexer.TERM)
}
}
// This matches the separator used when tokenising fields
// within a document. These should match otherwise it is
// not possible to search for some tokens within a document.
//
// It is possible for the user to change the separator on the
// tokenizer so it _might_ clash with any other of the special
// characters already used within the search string, e.g. :.
//
// This means that it is possible to change the separator in
// such a way that makes some words unsearchable using a search
// string.
lunr.QueryLexer.termSeparator = lunr.tokenizer.separator
lunr.QueryLexer.lexText = function (lexer) {
while (true) {
var char = lexer.next()
if (char == lunr.QueryLexer.EOS) {
return lunr.QueryLexer.lexEOS
}
// Escape character is '\'
if (char.charCodeAt(0) == 92) {
lexer.escapeCharacter()
continue
}
if (char == ":") {
return lunr.QueryLexer.lexField
}
if (char == "~") {
lexer.backup()
if (lexer.width() > 0) {
lexer.emit(lunr.QueryLexer.TERM)
}
return lunr.QueryLexer.lexEditDistance
}
if (char == "^") {
lexer.backup()
if (lexer.width() > 0) {
lexer.emit(lunr.QueryLexer.TERM)
}
return lunr.QueryLexer.lexBoost
}
if (char.match(lunr.QueryLexer.termSeparator)) {
return lunr.QueryLexer.lexTerm
}
}
}
lunr.QueryParser = function (str, query) {
this.lexer = new lunr.QueryLexer (str)
this.query = query
this.currentClause = {}
this.lexemeIdx = 0
}
lunr.QueryParser.prototype.parse = function () {
this.lexer.run()
this.lexemes = this.lexer.lexemes
var state = lunr.QueryParser.parseFieldOrTerm
while (state) {
state = state(this)
}
return this.query
}
lunr.QueryParser.prototype.peekLexeme = function () {
return this.lexemes[this.lexemeIdx]
}
lunr.QueryParser.prototype.consumeLexeme = function () {
var lexeme = this.peekLexeme()
this.lexemeIdx += 1
return lexeme
}
lunr.QueryParser.prototype.nextClause = function () {
var completedClause = this.currentClause
this.query.clause(completedClause)
this.currentClause = {}
}
lunr.QueryParser.parseFieldOrTerm = function (parser) {
var lexeme = parser.peekLexeme()
if (lexeme == undefined) {
return
}
switch (lexeme.type) {
case lunr.QueryLexer.FIELD:
return lunr.QueryParser.parseField
case lunr.QueryLexer.TERM:
return lunr.QueryParser.parseTerm
default:
var errorMessage = "expected either a field or a term, found " + lexeme.type
if (lexeme.str.length >= 1) {
errorMessage += " with value '" + lexeme.str + "'"
}
throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)
}
}
lunr.QueryParser.parseField = function (parser) {
var lexeme = parser.consumeLexeme()
if (lexeme == undefined) {
return
}
if (parser.query.allFields.indexOf(lexeme.str) == -1) {
var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '),
errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields
throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)
}
parser.currentClause.fields = [lexeme.str]
var nextLexeme = parser.peekLexeme()
if (nextLexeme == undefined) {
var errorMessage = "expecting term, found nothing"
throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)
}
switch (nextLexeme.type) {
case lunr.QueryLexer.TERM:
return lunr.QueryParser.parseTerm
default:
var errorMessage = "expecting term, found '" + nextLexeme.type + "'"
throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)
}
}
lunr.QueryParser.parseTerm = function (parser) {
var lexeme = parser.consumeLexeme()
if (lexeme == undefined) {
return
}
parser.currentClause.term = lexeme.str.toLowerCase()
if (lexeme.str.indexOf("*") != -1) {
parser.currentClause.usePipeline = false
}
var nextLexeme = parser.peekLexeme()
if (nextLexeme == undefined) {
parser.nextClause()
return
}
switch (nextLexeme.type) {
case lunr.QueryLexer.TERM:
parser.nextClause()
return lunr.QueryParser.parseTerm
case lunr.QueryLexer.FIELD:
parser.nextClause()
return lunr.QueryParser.parseField
case lunr.QueryLexer.EDIT_DISTANCE:
return lunr.QueryParser.parseEditDistance
case lunr.QueryLexer.BOOST:
return lunr.QueryParser.parseBoost
default:
var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'"
throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)
}
}
lunr.QueryParser.parseEditDistance = function (parser) {
var lexeme = parser.consumeLexeme()
if (lexeme == undefined) {
return
}
var editDistance = parseInt(lexeme.str, 10)
if (isNaN(editDistance)) {
var errorMessage = "edit distance must be numeric"
throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)
}
parser.currentClause.editDistance = editDistance
var nextLexeme = parser.peekLexeme()
if (nextLexeme == undefined) {
parser.nextClause()
return
}
switch (nextLexeme.type) {
case lunr.QueryLexer.TERM:
parser.nextClause()
return lunr.QueryParser.parseTerm
case lunr.QueryLexer.FIELD:
parser.nextClause()
return lunr.QueryParser.parseField
case lunr.QueryLexer.EDIT_DISTANCE:
return lunr.QueryParser.parseEditDistance
case lunr.QueryLexer.BOOST:
return lunr.QueryParser.parseBoost
default:
var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'"
throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)
}
}
lunr.QueryParser.parseBoost = function (parser) {
var lexeme = parser.consumeLexeme()
if (lexeme == undefined) {
return
}
var boost = parseInt(lexeme.str, 10)
if (isNaN(boost)) {
var errorMessage = "boost must be numeric"
throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)
}
parser.currentClause.boost = boost
var nextLexeme = parser.peekLexeme()
if (nextLexeme == undefined) {
parser.nextClause()
return
}
switch (nextLexeme.type) {
case lunr.QueryLexer.TERM:
parser.nextClause()
return lunr.QueryParser.parseTerm
case lunr.QueryLexer.FIELD:
parser.nextClause()
return lunr.QueryParser.parseField
case lunr.QueryLexer.EDIT_DISTANCE:
return lunr.QueryParser.parseEditDistance
case lunr.QueryLexer.BOOST:
return lunr.QueryParser.parseBoost
default:
var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'"
throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)
}
}
/**
* export the module via AMD, CommonJS or as a browser global
* Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
*/
;(function (root, factory) {
if (typeof define === 'function' && define.amd) {
// AMD. Register as an anonymous module.
define(factory)
} else if (typeof exports === 'object') {
/**
* Node. Does not work with strict CommonJS, but
* only CommonJS-like enviroments that support module.exports,
* like Node.
*/
module.exports = factory()
} else {
// Browser globals (root is window)
root.lunr = factory()
}
}(this, function () {
/**
* Just return a value to define the module export.
* This example returns an object, but the module
* can return a function as the exported value.
*/
return lunr
}))
})();
search/main.js 0 → 100644
浏览文件 @ 01c4cf93
function getSearchTermFromLocation() {
var sPageURL = window.location.search.substring(1);
var sURLVariables = sPageURL.split('&');
for (var i = 0; i < sURLVariables.length; i++) {
var sParameterName = sURLVariables[i].split('=');
if (sParameterName[0] == 'q') {
return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20'));
}
}
}
function joinUrl (base, path) {
if (path.substring(0, 1) === "/") {
// path starts with `/`. Thus it is absolute.
return path;
}
if (base.substring(base.length-1) === "/") {
// base ends with `/`
return base + path;
}
return base + "/" + path;
}
function formatResult (location, title, summary) {
return '<article><h3><a href="' + joinUrl(base_url, location) + '">'+ title + '</a></h3><p>' + summary +'</p></article>';
}
function displayResults (results) {
var search_results = document.getElementById("mkdocs-search-results");
while (search_results.firstChild) {
search_results.removeChild(search_results.firstChild);
}
if (results.length > 0){
for (var i=0; i < results.length; i++){
var result = results[i];
var html = formatResult(result.location, result.title, result.summary);
search_results.insertAdjacentHTML('beforeend', html);
}
} else {
search_results.insertAdjacentHTML('beforeend', "<p>No results found</p>");
}
}
function doSearch () {
var query = document.getElementById('mkdocs-search-query').value;
if (query.length > 2) {
if (!window.Worker) {
displayResults(search(query));
} else {
searchWorker.postMessage({query: query});
}
} else {
// Clear results for short queries
displayResults([]);
}
}
function initSearch () {
var search_input = document.getElementById('mkdocs-search-query');
if (search_input) {
search_input.addEventListener("keyup", doSearch);
}
var term = getSearchTermFromLocation();
if (term) {
search_input.value = term;
doSearch();
}
}
function onWorkerMessage (e) {
if (e.data.allowSearch) {
initSearch();
} else if (e.data.results) {
var results = e.data.results;
displayResults(results);
}
}
if (!window.Worker) {
console.log('Web Worker API not supported');
// load index in main thread
$.getScript(joinUrl(base_url, "search/worker.js")).done(function () {
console.log('Loaded worker');
init();
window.postMessage = function (msg) {
onWorkerMessage({data: msg});
};
}).fail(function (jqxhr, settings, exception) {
console.error('Could not load worker.js');
});
} else {
// Wrap search in a web worker
var searchWorker = new Worker(joinUrl(base_url, "search/worker.js"));
searchWorker.postMessage({init: true});
searchWorker.onmessage = onWorkerMessage;
}
search/search_index.json 0 → 100644
浏览文件 @ 01c4cf93
{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to MkDocs For full documentation visit mkdocs.org . Commands mkdocs new [dir-name] - Create a new project. mkdocs serve - Start the live-reloading docs server. mkdocs build - Build the documentation site. mkdocs help - Print this help message. Project layout mkdocs.yml # The configuration file. docs/ index.md # The documentation homepage. ... # Other markdown pages, images and other files.","title":"Welcome to MkDocs"},{"location":"#welcome-to-mkdocs","text":"For full documentation visit mkdocs.org .","title":"Welcome to MkDocs"},{"location":"#commands","text":"mkdocs new [dir-name] - Create a new project. mkdocs serve - Start the live-reloading docs server. mkdocs build - Build the documentation site. mkdocs help - Print this help message.","title":"Commands"},{"location":"#project-layout","text":"mkdocs.yml # The configuration file. docs/ index.md # The documentation homepage. ... # Other markdown pages, images and other files.","title":"Project layout"},{"location":"docs/","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 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 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":"PaddleSlim"},{"location":"docs/#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":"docs/#_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":"docs/#_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 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 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":"docs/#_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":"docs/#_4","text":"","title":"\u8d21\u732e\u4e0e\u53cd\u9988"},{"location":"docs/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 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 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 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) - \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 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 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 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":"docs/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":"docs/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":"docs/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":"docs/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":"docs/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":"docs/table_latency/#conv2d","text":"\u683c\u5f0f 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":"docs/table_latency/#activation","text":"\u683c\u5f0f 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":"docs/table_latency/#batch_norm","text":"\u683c\u5f0f 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) - \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":"docs/table_latency/#eltwise","text":"\u683c\u5f0f 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":"docs/table_latency/#pooling","text":"\u683c\u5f0f 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":"docs/table_latency/#softmax","text":"\u683c\u5f0f 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":"docs/api/analysis_api/","text":"\u6a21\u578b\u5206\u6790API\u6587\u6863 flops paddleslim.analysis.flops(program, detail=False) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6bcf\u79d2\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 \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 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 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\u6790API\u6587\u6863"},{"location":"docs/api/analysis_api/#api","text":"","title":"\u6a21\u578b\u5206\u6790API\u6587\u6863"},{"location":"docs/api/analysis_api/#flops","text":"paddleslim.analysis.flops(program, detail=False) \u6e90\u4ee3\u7801 \u83b7\u5f97\u6307\u5b9a\u7f51\u7edc\u7684\u6bcf\u79d2\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 \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 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":"docs/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 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":"docs/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":"docs/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":"docs/api/api_guide/#paddleslim-api","text":"","title":"PaddleSlim API\u6587\u6863\u5bfc\u822a"},{"location":"docs/api/api_guide/#_1","text":"","title":"\u6a21\u578b\u5206\u6790"},{"location":"docs/api/api_guide/#_2","text":"","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1"},{"location":"docs/api/api_guide/#_3","text":"\u5355\u8fdb\u7a0b\u84b8\u998f \u901a\u9053\u526a\u88c1","title":"\u84b8\u998f"},{"location":"docs/api/api_guide/#_4","text":"\u91cf\u5316\u8bad\u7ec3 \u79bb\u7ebf\u91cf\u5316 embedding\u91cf\u5316","title":"\u91cf\u5316"},{"location":"docs/api/api_guide/#_5","text":"nas API SearchSpace","title":"\u5c0f\u6a21\u578b\u7ed3\u6784\u641c\u7d22"},{"location":"docs/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 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 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 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 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":"paddleslim.nas API\u6587\u6863"},{"location":"docs/api/nas_api/#paddleslimnas-api","text":"","title":"paddleslim.nas API\u6587\u6863"},{"location":"docs/api/nas_api/#sanas-api","text":"","title":"SANAS API\u6587\u6863"},{"location":"docs/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 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 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 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 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":"docs/api/prune_api/","text":"\u5377\u79ef\u901a\u9053\u526a\u88c1API\u6587\u6863 class 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 from paddleslim.prune import Pruner pruner = Pruner() prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) \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: 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 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: 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 {\"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 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 merge_sensitive(sensitivities) \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)\uff1a \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a {\"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 load_sensitivities(sensitivities_file) \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(sensitivities, loss) \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 \u793a\u4f8b\uff1a","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1API\u6587\u6863"},{"location":"docs/api/prune_api/#api","text":"","title":"\u5377\u79ef\u901a\u9053\u526a\u88c1API\u6587\u6863"},{"location":"docs/api/prune_api/#class-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 from paddleslim.prune import Pruner pruner = Pruner() prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False) \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: 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 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":"class Pruner"},{"location":"docs/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: 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 {\"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 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":"docs/api/prune_api/#merge_sensitive","text":"merge_sensitive(sensitivities) \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)\uff1a \u5408\u5e76\u540e\u7684\u654f\u611f\u5ea6\u4fe1\u606f\u3002\u5176\u683c\u5f0f\u4e3a\uff1a {\"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":"docs/api/prune_api/#load_sensitivities","text":"load_sensitivities(sensitivities_file) \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":"docs/api/prune_api/#get_ratios_by_losssensitivities-loss","text":"\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 \u793a\u4f8b\uff1a","title":"get_ratios_by_loss(sensitivities, loss)"},{"location":"docs/api/quantization_api/","text":"paddleslim.quant API\u6587\u6863 \u91cf\u5316\u8bad\u7ec3API \u91cf\u5316\u914d\u7f6e 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, # if set quant_weight_only True, then only quantize parameters of layers which need to be quantized, # and activations will not be quantized. 'quant_weight_only': False } \u8bbe\u7f6e\u91cf\u5316\u8bad\u7ec3\u9700\u8981\u7684\u914d\u7f6e\u3002 \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 or 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_weight_only(bool) - \u662f\u5426\u53ea\u91cf\u5316\u53c2\u6570\uff0c\u5982\u679c\u8bbe\u4e3a True \uff0c\u5219\u6fc0\u6d3b\u4e0d\u8fdb\u884c\u91cf\u5316\uff0c\u9ed8\u8ba4 False \u3002\u76ee\u524d\u6682\u4e0d\u652f\u6301\u8bbe\u7f6e\u4e3a True \u3002 \u8bbe\u7f6e\u4e3a True \u65f6\uff0c\u53ea\u91cf\u5316\u53c2\u6570\uff0c\u8fd9\u79cd\u65b9\u5f0f\u4e0d\u80fd\u51cf\u5c11\u663e\u5b58\u5360\u7528\u548c\u52a0\u901f\uff0c\u53ea\u80fd\u7528\u6765\u51cf\u5c11\u5e26\u5bbd\u3002 paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False) \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 or 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 paddleslim.quant.convert(program, place, config, scope=None, save_int8=False) \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 or 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 #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 \u79bb\u7ebf\u91cf\u5316API 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\"]) \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 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 Embedding\u91cf\u5316API paddleslim.quant.quant_embedding(program, place, config, scope=None) \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 or 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 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":"paddleslim.quant API\u6587\u6863"},{"location":"docs/api/quantization_api/#paddleslimquant-api","text":"","title":"paddleslim.quant API\u6587\u6863"},{"location":"docs/api/quantization_api/#api","text":"","title":"\u91cf\u5316\u8bad\u7ec3API"},{"location":"docs/api/quantization_api/#_1","text":"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, # if set quant_weight_only True, then only quantize parameters of layers which need to be quantized, # and activations will not be quantized. 'quant_weight_only': False } \u8bbe\u7f6e\u91cf\u5316\u8bad\u7ec3\u9700\u8981\u7684\u914d\u7f6e\u3002 \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 or 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_weight_only(bool) - \u662f\u5426\u53ea\u91cf\u5316\u53c2\u6570\uff0c\u5982\u679c\u8bbe\u4e3a True \uff0c\u5219\u6fc0\u6d3b\u4e0d\u8fdb\u884c\u91cf\u5316\uff0c\u9ed8\u8ba4 False \u3002\u76ee\u524d\u6682\u4e0d\u652f\u6301\u8bbe\u7f6e\u4e3a True \u3002 \u8bbe\u7f6e\u4e3a True \u65f6\uff0c\u53ea\u91cf\u5316\u53c2\u6570\uff0c\u8fd9\u79cd\u65b9\u5f0f\u4e0d\u80fd\u51cf\u5c11\u663e\u5b58\u5360\u7528\u548c\u52a0\u901f\uff0c\u53ea\u80fd\u7528\u6765\u51cf\u5c11\u5e26\u5bbd\u3002","title":"\u91cf\u5316\u914d\u7f6e"},{"location":"docs/api/quantization_api/#paddleslimquantquant_awareprogram-place-config-scopenone-for_testfalse","text":"\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 or 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":"paddleslim.quant.quant_aware(program, place, config, scope=None, for_test=False)"},{"location":"docs/api/quantization_api/#paddleslimquantconvertprogram-place-config-scopenone-save_int8false","text":"\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 or 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 #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":"paddleslim.quant.convert(program, place, config, scope=None, save_int8=False)"},{"location":"docs/api/quantization_api/#api_1","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\"]) \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 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":"\u79bb\u7ebf\u91cf\u5316API"},{"location":"docs/api/quantization_api/#embeddingapi","text":"paddleslim.quant.quant_embedding(program, place, config, scope=None) \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 or 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 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":"Embedding\u91cf\u5316API"},{"location":"docs/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 ### \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":"paddleslim.nas \u63d0\u4f9b\u7684\u641c\u7d22\u7a7a\u95f4\uff1a"},{"location":"docs/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":"docs/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":"docs/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":"docs/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 ### \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":"docs/api/single_distiller_api/","text":"paddleslim.dist API\u6587\u6863 merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_') \u8be5\u65b9\u6cd5\u5c06\u4e24\u4e2afluid 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\u7684paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0ckey\u4e3ateacher\u7684\u8f93\u5165\u540d\uff0cvalue\u4e3astudent\u7684\u8f93\u5165\u540d\u3002merge\u51fd\u6570\u5c06\u4f1a\u628a\u8fd9\u4e24\u4e2a\u6a21\u578b\u7684\u8f93\u5165\u6309\u5bf9\u5e94\u5173\u7cfb\u5408\u5e76\u5728\u4e00\u8d77\uff0c\u4fdd\u8bc1teacher\u4e0estudent\u8f93\u5165\u6570\u636e\u76f8\u540c 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\u793ateacher variables\u548cstudent variables\u6240\u4f7f\u7528\u7684\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1afluid.global_scope() name_prefix (str)-\u4e3a\u4e86\u907f\u514dteacher variables\u548cstudent variables\u5b58\u5728\u540c\u540d\u53d8\u91cf\u800c\u5f15\u8d77\u547d\u540d\u51b2\u7a81\uff0cmerge\u51fd\u6570\u5c06\u7edf\u4e00\u4e3ateacher variables\u6dfb\u52a0\u4e00\u4e2a\u540d\u79f0\u524d\u7f00name_prefix\uff0cmerge\u540e\u7684program\u4e2d\u6240\u6709teacher variables\u90fd\u5c06\u5e26\u6709\u8fd9\u4e00\u540d\u79f0\u524d\u7f00\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program \u4f7f\u7528\u793a\u4f8b\uff1a 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(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program()) fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning \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\uff1afluid.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 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(teacher_var_name, student_var_name, program=fluid.default_main_program()) 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\uff1afluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 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(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.) soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 Distilling the Knowledge in a Neural Network \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\uff1afluid.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 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(loss_func, program=fluid.default_main_program(), **kwargs) 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\uff1afluid.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 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\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":"paddleslim.dist API\u6587\u6863"},{"location":"docs/api/single_distiller_api/#paddleslimdist-api","text":"","title":"paddleslim.dist API\u6587\u6863"},{"location":"docs/api/single_distiller_api/#mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_","text":"\u8be5\u65b9\u6cd5\u5c06\u4e24\u4e2afluid 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\u7684paddle program student_program (Program)-\u5b9a\u4e49\u4e86student\u6a21\u578b\u7684paddle program data_name_map (dict)-teacher\u8f93\u5165\u63a5\u53e3\u540d\u4e0estudent\u8f93\u5165\u63a5\u53e3\u540d\u7684\u6620\u5c04\uff0ckey\u4e3ateacher\u7684\u8f93\u5165\u540d\uff0cvalue\u4e3astudent\u7684\u8f93\u5165\u540d\u3002merge\u51fd\u6570\u5c06\u4f1a\u628a\u8fd9\u4e24\u4e2a\u6a21\u578b\u7684\u8f93\u5165\u6309\u5bf9\u5e94\u5173\u7cfb\u5408\u5e76\u5728\u4e00\u8d77\uff0c\u4fdd\u8bc1teacher\u4e0estudent\u8f93\u5165\u6570\u636e\u76f8\u540c 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\u793ateacher variables\u548cstudent variables\u6240\u4f7f\u7528\u7684\u4f5c\u7528\u57df\uff0c\u5982\u679c\u4e0d\u6307\u5b9a\u5c06\u4f7f\u7528\u9ed8\u8ba4\u7684\u5168\u5c40\u4f5c\u7528\u57df\u3002\u9ed8\u8ba4\u503c\uff1afluid.global_scope() name_prefix (str)-\u4e3a\u4e86\u907f\u514dteacher variables\u548cstudent variables\u5b58\u5728\u540c\u540d\u53d8\u91cf\u800c\u5f15\u8d77\u547d\u540d\u51b2\u7a81\uff0cmerge\u51fd\u6570\u5c06\u7edf\u4e00\u4e3ateacher variables\u6dfb\u52a0\u4e00\u4e2a\u540d\u79f0\u524d\u7f00name_prefix\uff0cmerge\u540e\u7684program\u4e2d\u6240\u6709teacher variables\u90fd\u5c06\u5e26\u6709\u8fd9\u4e00\u540d\u79f0\u524d\u7f00\u3002\u9ed8\u8ba4\u503c\uff1a'teacher_' \u8fd4\u56de\uff1a \u7531student_program\u548cteacher_program merge\u5f97\u5230\u7684program \u4f7f\u7528\u793a\u4f8b\uff1a 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(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')"},{"location":"docs/api/single_distiller_api/#fsp_lossteacher_var1_name-teacher_var2_name-student_var1_name-student_var2_name-programfluiddefault_main_program","text":"fsp_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0fsp loss\uff0c\u51fa\u81ea\u8bba\u6587 A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning \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\uff1afluid.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 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(teacher_var1_name, teacher_var2_name, student_var1_name, student_var2_name, program=fluid.default_main_program())"},{"location":"docs/api/single_distiller_api/#l2_lossteacher_var_name-student_var_name-programfluiddefault_main_program","text":"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\uff1afluid.default_main_program() \u8fd4\u56de\uff1a \u7531teacher_var, student_var\u7ec4\u5408\u5f97\u5230\u7684l2_loss \u4f7f\u7528\u793a\u4f8b\uff1a 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(teacher_var_name, student_var_name, program=fluid.default_main_program())"},{"location":"docs/api/single_distiller_api/#soft_label_lossteacher_var_name-student_var_name-programfluiddefault_main_program-teacher_temperature1-student_temperature1","text":"soft_label_loss\u4e3aprogram\u5185\u7684teacher var\u548cstudent var\u6dfb\u52a0soft label loss\uff0c\u51fa\u81ea\u8bba\u6587 Distilling the Knowledge in a Neural Network \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\uff1afluid.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 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(teacher_var_name, student_var_name, program=fluid.default_main_program(), teacher_temperature=1., student_temperature=1.)"},{"location":"docs/api/single_distiller_api/#lossloss_func-programfluiddefault_main_program-kwargs","text":"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\uff1afluid.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 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')","title":"loss(loss_func, program=fluid.default_main_program(), **kwargs)"},{"location":"docs/api/single_distiller_api/#_1","text":"\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\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":"\u6ce8\u610f\u4e8b\u9879"},{"location":"docs/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":"docs/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":"docs/tutorials/demo_guide/#_2","text":"","title":"\u91cf\u5316"},{"location":"docs/tutorials/demo_guide/#demo","text":"","title":"\u91cf\u5316\u8bad\u7ec3demo\u6587\u6863"},{"location":"docs/tutorials/demo_guide/#demo_1","text":"","title":"\u79bb\u7ebf\u91cf\u5316demo\u6587\u6863"},{"location":"docs/tutorials/demo_guide/#embeddingdemo","text":"","title":"Embedding\u91cf\u5316demo\u6587\u6863"},{"location":"docs/tutorials/demo_guide/#nas","text":"","title":"NAS"},{"location":"docs/tutorials/demo_guide/#nas_1","text":"","title":"NAS\u793a\u4f8b"},{"location":"docs/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 config = [('MobileNetV2Space')] 2. \u5229\u7528\u641c\u7d22\u7a7a\u95f4\u521d\u59cb\u5316SANAS\u5b9e\u4f8b 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 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 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 from paddleslim.analysis import flops if flops(train_program) > 321208544: continue 6. \u56de\u4f20score sa_nas.reward(score)","title":"\u7f51\u7edc\u7ed3\u6784\u641c\u7d22\u793a\u4f8b"},{"location":"docs/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":"docs/tutorials/nas_demo/#_2","text":"\u8bf7\u53c2\u8003\u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"docs/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 config = [('MobileNetV2Space')]","title":"1. \u914d\u7f6e\u641c\u7d22\u7a7a\u95f4"},{"location":"docs/tutorials/nas_demo/#2-sanas","text":"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":"docs/tutorials/nas_demo/#3-nas","text":"archs = sa_nas.next_archs()","title":"3. \u6839\u636e\u5b9e\u4f8b\u5316\u7684NAS\u5f97\u5230\u5f53\u524d\u7684\u7f51\u7edc\u7ed3\u6784"},{"location":"docs/tutorials/nas_demo/#4-program","text":"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":"docs/tutorials/nas_demo/#5-program","text":"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":"docs/tutorials/nas_demo/#6-score","text":"sa_nas.reward(score)","title":"6. \u56de\u4f20score"},{"location":"docs/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 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 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 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 float_program, int8_program = convert(val_program, place, quant_config, scope=None, save_int8=True) 5.\u4fdd\u5b58\u9884\u6d4b\u6a21\u578b 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":"\u5728\u7ebf\u91cf\u5316\u793a\u4f8b"},{"location":"docs/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":"docs/tutorials/quant_aware_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"docs/tutorials/quant_aware_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"docs/tutorials/quant_aware_demo/#1","text":"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":"docs/tutorials/quant_aware_demo/#2-programop","text":"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":"docs/tutorials/quant_aware_demo/#3build","text":"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":"docs/tutorials/quant_aware_demo/#4-freeze-program","text":"float_program, int8_program = convert(val_program, place, quant_config, scope=None, save_int8=True)","title":"4. freeze program"},{"location":"docs/tutorials/quant_aware_demo/#5","text":"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":"docs/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 . \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. 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 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 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 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 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 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 python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 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 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 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 #\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 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 ('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: config = { 'params_name': 'emb', 'quantize_type': 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 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: ('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\u793a\u4f8b"},{"location":"docs/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":"docs/tutorials/quant_embedding_demo/#skip-gramword2vector","text":"\u4ee5\u4e0b\u662f\u672c\u4f8b\u7684\u7b80\u8981\u76ee\u5f55\u7ed3\u6784\u53ca\u8bf4\u660e\uff1a . \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":"docs/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":"docs/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. 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 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 mkdir data wget https://paddlerec.bj.bcebos.com/word2vec/text.tar tar xvf text.tar mv text data/","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"docs/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 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 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 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":"docs/tutorials/quant_embedding_demo/#_4","text":"\u5177\u4f53\u7684\u53c2\u6570\u914d\u7f6e\u53ef\u8fd0\u884c python train.py -h \u5355\u673a\u591a\u7ebf\u7a0b\u8bad\u7ec3 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 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 pass-0 pass-1 pass-2 pass-3 pass-4 pass-5 pass-6 pass-7 pass-8 pass-9","title":"\u8bad\u7ec3"},{"location":"docs/tutorials/quant_embedding_demo/#_5","text":"\u6d4b\u8bd5\u96c6\u4e0b\u8f7d\u547d\u4ee4\u5982\u4e0b #\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 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 ('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":"docs/tutorials/quant_embedding_demo/#skip-gramword2vector_1","text":"\u91cf\u5316\u914d\u7f6e\u4e3a: config = { 'params_name': 'emb', 'quantize_type': 'abs_max' } \u8fd0\u884c\u547d\u4ee4\u4e3a\uff1a 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: ('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":"docs/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 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 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 python eval.py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 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 python eval.py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 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\u793a\u4f8b"},{"location":"docs/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":"docs/tutorials/quant_post_demo/#_2","text":"\u8bf7\u53c2\u8003 \u91cf\u5316API\u6587\u6863 \u3002","title":"\u63a5\u53e3\u4ecb\u7ecd"},{"location":"docs/tutorials/quant_post_demo/#_3","text":"","title":"\u5206\u7c7b\u6a21\u578b\u7684\u79bb\u7ebf\u91cf\u5316\u6d41\u7a0b"},{"location":"docs/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":"docs/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":"docs/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 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":"docs/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 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":"docs/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 python eval.py --model_path ./inference_model/MobileNet --model_name model --params_name weights \u7cbe\u5ea6\u8f93\u51fa\u4e3a: 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 python eval.py --model_path ./quant_model_train/MobileNet \u7cbe\u5ea6\u8f93\u51fa\u4e3a 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"}]}
\ No newline at end of file
search/worker.js 0 → 100644
浏览文件 @ 01c4cf93
var base_path = 'function' === typeof importScripts ? '.' : '/search/';
var allowSearch = false;
var index;
var documents = {};
var lang = ['en'];
var data;
function getScript(script, callback) {
console.log('Loading script: ' + script);
$.getScript(base_path + script).done(function () {
callback();
}).fail(function (jqxhr, settings, exception) {
console.log('Error: ' + exception);
});
}
function getScriptsInOrder(scripts, callback) {
if (scripts.length === 0) {
callback();
return;
}
getScript(scripts[0], function() {
getScriptsInOrder(scripts.slice(1), callback);
});
}
function loadScripts(urls, callback) {
if( 'function' === typeof importScripts ) {
importScripts.apply(null, urls);
callback();
} else {
getScriptsInOrder(urls, callback);
}
}
function onJSONLoaded () {
data = JSON.parse(this.responseText);
var scriptsToLoad = ['lunr.js'];
if (data.config && data.config.lang && data.config.lang.length) {
lang = data.config.lang;
}
if (lang.length > 1 || lang[0] !== "en") {
scriptsToLoad.push('lunr.stemmer.support.js');
if (lang.length > 1) {
scriptsToLoad.push('lunr.multi.js');
}
for (var i=0; i < lang.length; i++) {
if (lang[i] != 'en') {
scriptsToLoad.push(['lunr', lang[i], 'js'].join('.'));
}
}
}
loadScripts(scriptsToLoad, onScriptsLoaded);
}
function onScriptsLoaded () {
console.log('All search scripts loaded, building Lunr index...');
if (data.config && data.config.separator && data.config.separator.length) {
lunr.tokenizer.separator = new RegExp(data.config.separator);
}
if (data.index) {
index = lunr.Index.load(data.index);
data.docs.forEach(function (doc) {
documents[doc.location] = doc;
});
console.log('Lunr pre-built index loaded, search ready');
} else {
index = lunr(function () {
if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) {
this.use(lunr[lang[0]]);
} else if (lang.length > 1) {
this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility
}
this.field('title');
this.field('text');
this.ref('location');
for (var i=0; i < data.docs.length; i++) {
var doc = data.docs[i];
this.add(doc);
documents[doc.location] = doc;
}
});
console.log('Lunr index built, search ready');
}
allowSearch = true;
postMessage({allowSearch: allowSearch});
}
function init () {
var oReq = new XMLHttpRequest();
oReq.addEventListener("load", onJSONLoaded);
var index_path = base_path + '/search_index.json';
if( 'function' === typeof importScripts ){
index_path = 'search_index.json';
}
oReq.open("GET", index_path);
oReq.send();
}
function search (query) {
if (!allowSearch) {
console.error('Assets for search still loading');
return;
}
var resultDocuments = [];
var results = index.search(query);
for (var i=0; i < results.length; i++){
var result = results[i];
doc = documents[result.ref];
doc.summary = doc.text.substring(0, 200);
resultDocuments.push(doc);
}
return resultDocuments;
}
if( 'function' === typeof importScripts ) {
onmessage = function (e) {
if (e.data.init) {
init();
} else if (e.data.query) {
postMessage({ results: search(e.data.query) });
} else {
console.error("Worker - Unrecognized message: " + e);
}
};
}
sitemap.xml 0 → 100644
浏览文件 @ 01c4cf93
<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
<url>
<loc>None</loc>
<lastmod>2019-12-19</lastmod>
<changefreq>daily</changefreq>
</url>
</urlset>
\ No newline at end of file
sitemap.xml.gz 0 → 100644
浏览文件 @ 01c4cf93
文件已添加
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