From df7c9f2e636f8b7fef201c317cdb06a96ee08e9d Mon Sep 17 00:00:00 2001 From: Chang Xu Date: Tue, 29 Jun 2021 15:17:53 +0800 Subject: [PATCH] fix_docs_nas (#822) Co-authored-by: ceci3 --- docs/en/quick_start/nas_tutorial_en.md | 2 +- .../dygraph/ofa/convert_supernet_api.rst | 4 ++-- docs/zh_cn/api_cn/static/nas/nas_api.rst | 18 +++++++++--------- docs/zh_cn/quick_start/static/nas_tutorial.md | 4 ++-- docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md | 12 ++++++------ .../tutorials/nas/static/sanas_darts_space.md | 4 ++-- 6 files changed, 22 insertions(+), 22 deletions(-) diff --git a/docs/en/quick_start/nas_tutorial_en.md b/docs/en/quick_start/nas_tutorial_en.md index 3d15b92b..e5f5b228 100644 --- a/docs/en/quick_start/nas_tutorial_en.md +++ b/docs/en/quick_start/nas_tutorial_en.md @@ -33,7 +33,7 @@ import numpy as np Please set a unused port when build instance of SANAS. ```python -sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8337), save_checkpoint=None) +sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8911), save_checkpoint=None) ``` ## 3. define function about building program diff --git a/docs/zh_cn/api_cn/dygraph/ofa/convert_supernet_api.rst b/docs/zh_cn/api_cn/dygraph/ofa/convert_supernet_api.rst index ced8626b..56bb460e 100644 --- a/docs/zh_cn/api_cn/dygraph/ofa/convert_supernet_api.rst +++ b/docs/zh_cn/api_cn/dygraph/ofa/convert_supernet_api.rst @@ -85,8 +85,8 @@ PaddleSlim提供了三种方式构造超网络，下面分别介绍这三种方 models += [nn.Conv2D(4, 4, 3, groups=4)] self.models = paddle.nn.Sequential(*models) - def forward(self, inputs): - return self.models(inputs) + def forward(self, inputs): + return self.models(inputs) 方式三 ------------------ diff --git a/docs/zh_cn/api_cn/static/nas/nas_api.rst b/docs/zh_cn/api_cn/static/nas/nas_api.rst index e3f95ee6..211e3e6d 100644 --- a/docs/zh_cn/api_cn/static/nas/nas_api.rst +++ b/docs/zh_cn/api_cn/static/nas/nas_api.rst @@ -88,7 +88,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火 from paddleslim.nas import SANAS config = [('MobileNetV2Space')] paddle.enable_static() - sanas = SANAS(configs=config, , server_addr=("",8822)) + sanas = SANAS(configs=config, server_addr=("",8822)) input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32') archs = sanas.next_archs() for arch in archs: @@ -115,7 +115,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火 from paddleslim.nas import SANAS config = [('MobileNetV2Space')] paddle.enable_static() - sanas = SANAS(configs=config, server_addr=（"", 8883)) + sanas = SANAS(configs=config, server_addr=("", 8823)) archs = sanas.next_archs() ### 假设网络计算出来的score是1，实际代码中使用时需要返回真实score。 @@ -142,7 +142,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火 from paddleslim.nas import SANAS config = [('MobileNetV2Space')] paddle.enable_static() - sanas = SANAS(configs=config, server_addr=("",8823)) + sanas = SANAS(configs=config, server_addr=("", 8824)) input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32') tokens = ([0] * 25) archs = sanas.tokens2arch(tokens)[0] @@ -163,7 +163,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火 from paddleslim.nas import SANAS config = [('MobileNetV2Space')] paddle.enable_static() - sanas = SANAS(configs=config, server_addr=（"", 8885)) + sanas = SANAS(configs=config, server_addr=("", 8825)) print(sanas.current_info()) @@ -233,7 +233,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习 config = [('MobileNetV2Space')] paddle.enable_static() - rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8824)) + rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8826)) .. py:method:: next_archs(obs=None) @@ -255,7 +255,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习 from paddleslim.nas import RLNAS config = [('MobileNetV2Space')] paddle.enable_static() - rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8825)) + rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8827)) input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32') archs = rlnas.next_archs(1)[0] for arch in archs: @@ -280,7 +280,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习 from paddleslim.nas import RLNAS config = [('MobileNetV2Space')] paddle.enable_static() - rlnas = RLNAS(key='lstm', configs=config, server_addr=（"", 8888)) + rlnas = RLNAS(key='lstm', configs=config, server_addr=("", 8828)) rlnas.next_archs(1) rlnas.reward(1.0) @@ -307,7 +307,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习 from paddleslim.nas import RLNAS config = [('MobileNetV2Space')] paddle.enable_static() - rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8826)) + rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8829)) archs = rlnas.final_archs(1) print(archs) @@ -330,7 +330,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习 from paddleslim.nas import RLNAS config = [('MobileNetV2Space')] paddle.enable_static() - rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8827)) + rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8830)) input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32') tokens = ([0] * 25) archs = rlnas.tokens2arch(tokens)[0] diff --git a/docs/zh_cn/quick_start/static/nas_tutorial.md b/docs/zh_cn/quick_start/static/nas_tutorial.md index 2d636cde..12ee9ec3 100644 --- a/docs/zh_cn/quick_start/static/nas_tutorial.md +++ b/docs/zh_cn/quick_start/static/nas_tutorial.md @@ -160,8 +160,8 @@ sanas.reward(float(finally_reward[1])) ```python for step in range(3): archs = sanas.next_archs()[0] - exe, train_program, eval_program, inputs, avg_cost, acc_top1, acc_top5 = build_program(archs) - train_loader, eval_loader = input_data(inputs) + exe, train_program, eval_program, (images,label), avg_cost, acc_top1, acc_top5 = build_program(archs) + train_loader, eval_loader = input_data(images, label) current_flops = slim.analysis.flops(train_program) if current_flops > 321208544: diff --git a/docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md b/docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md index 05e54a95..0c5f1b12 100644 --- a/docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md +++ b/docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md @@ -16,13 +16,13 @@ OFA的基本流程分为以下步骤： PaddleSlim提供了三种获得超网络的方式，具体可以参考[超网络转换](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/dygraph/ofa/convert_supernet_api.html)。 ```python - import paddle - from paddle.vision.models import mobilenet_v1 - from paddleslim.nas.ofa.convert_super import Convert, supernet +import paddle +from paddle.vision.models import mobilenet_v1 +from paddleslim.nas.ofa.convert_super import Convert, supernet - model = mobilenet_v1() - sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4]) - sp_model = Convert(sp_net_config).convert(model) +model = mobilenet_v1() +sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4]) +sp_model = Convert(sp_net_config).convert(model) ``` ### 2. 训练配置 diff --git a/docs/zh_cn/tutorials/nas/static/sanas_darts_space.md b/docs/zh_cn/tutorials/nas/static/sanas_darts_space.md index 90ee4750..caad9f21 100644 --- a/docs/zh_cn/tutorials/nas/static/sanas_darts_space.md +++ b/docs/zh_cn/tutorials/nas/static/sanas_darts_space.md @@ -262,14 +262,14 @@ sa_nas.reward(float(valid_top1_list[-1] + valid_top1_list[-2]) / 2) ### 10. 利用demo下的脚本启动搜索 搜索文件位于: [darts_sanas_demo](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/nas/sanas_darts_space.py)，搜索过程中限制模型参数量为不大于3.77M。 -```python +```shell cd demo/nas/ python darts_nas.py ``` ### 11. 利用demo下的脚本启动最终实验 最终实验文件位于: [darts_sanas_demo](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/nas/sanas_darts_space.py)，最终实验需要训练600epoch。以下示例输入token为`[5, 5, 0, 5, 5, 10, 7, 7, 5, 7, 7, 11, 10, 12, 10, 0, 5, 3, 10, 8]`。 -```python +```shell cd demo/nas/ python darts_nas.py --token 5 5 0 5 5 10 7 7 5 7 7 11 10 12 10 0 5 3 10 8 --retain_epoch 600 ``` -- GitLab