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Refine quick start tutorial of pruning. (PaddlePaddle#49)
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from __future__ import absolute_import | ||
from .mobilenet import MobileNet | ||
from .resnet import ResNet34, ResNet50 | ||
from .resnet_vd import ResNet50_vd | ||
from .mobilenet_v2 import MobileNetV2 | ||
from .pvanet import PVANet | ||
|
||
__all__ = [ | ||
"model_list", "MobileNet", "ResNet34", "ResNet50", "MobileNetV2", "PVANet", | ||
"ResNet50_vd" | ||
] | ||
model_list = [ | ||
'MobileNet', 'ResNet34', 'ResNet50', 'MobileNetV2', 'PVANet', 'ResNet50_vd' | ||
] |
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demo/prune/image_classification_pruning_quick_start.ipynb
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# 图像分类模型通道剪裁-快速开始\n", | ||
"\n", | ||
"该教程以图像分类模型MobileNetV1为例,说明如何快速使用[PaddleSlim的卷积通道剪裁接口]()。\n", | ||
"该示例包含以下步骤:\n", | ||
"\n", | ||
"1. 导入依赖\n", | ||
"2. 构建模型\n", | ||
"3. 剪裁\n", | ||
"4. 训练剪裁后的模型\n", | ||
"\n", | ||
"以下章节依次次介绍每个步骤的内容。\n", | ||
"\n", | ||
"## 1. 导入依赖\n", | ||
"\n", | ||
"PaddleSlim依赖Paddle1.7版本,请确认已正确安装Paddle,然后按以下方式导入Paddle和PaddleSlim:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 22, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import paddle\n", | ||
"import paddle.fluid as fluid\n", | ||
"import paddleslim as slim" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## 2. 构建网络\n", | ||
"\n", | ||
"该章节构造一个用于对MNIST数据进行分类的分类模型,选用`MobileNetV1`,并将输入大小设置为`[1, 28, 28]`,输出类别数为10。\n", | ||
"为了方便展示示例,我们在`paddleslim.models`下预定义了用于构建分类模型的方法,执行以下代码构建分类模型:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 23, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"exe, train_program, val_program, inputs, outputs = slim.models.image_classification(\"MobileNet\", [1, 28, 28], 10, use_gpu=False)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
">注意:paddleslim.models下的API并非PaddleSlim常规API,是为了简化示例而封装预定义的一系列方法,比如:模型结构的定义、Program的构建等。" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## 3. 剪裁卷积层通道\n", | ||
"\n", | ||
"### 3.1 计算剪裁之前的FLOPs" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 24, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"FLOPs: 10907072.0\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"FLOPs = slim.analysis.flops(train_program)\n", | ||
"print(\"FLOPs: {}\".format(FLOPs))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### 3.2 剪裁\n", | ||
"\n", | ||
"我们这里对参数名为`conv2_1_sep_weights`和`conv2_2_sep_weights`的卷积层进行剪裁,分别剪掉20%和30%的通道数。\n", | ||
"代码如下所示:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 25, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"pruner = slim.prune.Pruner()\n", | ||
"pruned_program, _, _ = pruner.prune(\n", | ||
" train_program,\n", | ||
" fluid.global_scope(),\n", | ||
" params=[\"conv2_1_sep_weights\", \"conv2_2_sep_weights\"],\n", | ||
" ratios=[0.33] * 2,\n", | ||
" place=fluid.CPUPlace())" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"以上操作会修改`train_program`中对应卷积层参数的定义,同时对`fluid.global_scope()`中存储的参数数组进行裁剪。" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### 3.3 计算剪裁之后的FLOPs" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 26, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"FLOPs: 10907072.0\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"FLOPs = paddleslim.analysis.flops(train_program)\n", | ||
"print(\"FLOPs: {}\".format(FLOPs))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## 4. 训练剪裁后的模型\n", | ||
"\n", | ||
"### 4.1 定义输入数据\n", | ||
"\n", | ||
"为了快速执行该示例,我们选取简单的MNIST数据,Paddle框架的`paddle.dataset.mnist`包定义了MNIST数据的下载和读取。\n", | ||
"代码如下:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 27, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import paddle.dataset.mnist as reader\n", | ||
"train_reader = paddle.batch(\n", | ||
" reader.train(), batch_size=128, drop_last=True)\n", | ||
"train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### 4.2 执行训练\n", | ||
"以下代码执行了一个`epoch`的训练:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 21, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"[0.1484375] [0.4921875] [2.6727316]\n", | ||
"[0.125] [0.546875] [2.6547904]\n", | ||
"[0.125] [0.5546875] [2.795205]\n", | ||
"[0.1171875] [0.578125] [2.8561475]\n", | ||
"[0.1875] [0.59375] [2.470603]\n", | ||
"[0.1796875] [0.578125] [2.8031898]\n", | ||
"[0.1484375] [0.6015625] [2.7530417]\n", | ||
"[0.1953125] [0.640625] [2.711596]\n", | ||
"[0.125] [0.59375] [2.8637898]\n", | ||
"[0.1796875] [0.53125] [2.9473038]\n", | ||
"[0.25] [0.671875] [2.3943179]\n", | ||
"[0.25] [0.6953125] [2.632146]\n", | ||
"[0.2578125] [0.7265625] [2.723265]\n", | ||
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"[0.421875] [0.8203125] [1.8042578]\n", | ||
"[0.4765625] [0.890625] [1.6841211]\n", | ||
"[0.53125] [0.8671875] [2.1971617]\n", | ||
"[0.5546875] [0.8984375] [1.5361531]\n", | ||
"[0.53125] [0.890625] [1.7211896]\n", | ||
"[0.5078125] [0.8984375] [1.6586945]\n", | ||
"[0.53125] [0.9140625] [1.8980236]\n", | ||
"[0.546875] [0.9453125] [1.5279069]\n", | ||
"[0.5234375] [0.8828125] [1.7356458]\n", | ||
"[0.6015625] [0.9765625] [1.0375824]\n", | ||
"[0.5546875] [0.921875] [1.639497]\n", | ||
"[0.6015625] [0.9375] [1.5469061]\n", | ||
"[0.578125] [0.96875] [1.3573356]\n", | ||
"[0.65625] [0.9453125] [1.3787829]\n", | ||
"[0.640625] [0.9765625] [0.9946856]\n", | ||
"[0.65625] [0.96875] [1.1651027]\n", | ||
"[0.625] [0.984375] [1.0487883]\n", | ||
"[0.7265625] [0.9609375] [1.2526855]\n", | ||
"[0.7265625] [0.9765625] [1.2954011]\n", | ||
"[0.65625] [0.96875] [1.1181556]\n", | ||
"[0.71875] [0.9765625] [0.97891223]\n", | ||
"[0.640625] [0.9609375] [1.2135172]\n", | ||
"[0.7265625] [0.9921875] [0.8950747]\n", | ||
"[0.7578125] [0.96875] [1.0864108]\n", | ||
"[0.734375] [0.9921875] [0.8392239]\n", | ||
"[0.796875] [0.9609375] [0.7012155]\n", | ||
"[0.7734375] [0.9765625] [0.7409136]\n", | ||
"[0.8046875] [0.984375] [0.6108341]\n", | ||
"[0.796875] [0.9765625] [0.63867176]\n", | ||
"[0.7734375] [0.984375] [0.64099216]\n", | ||
"[0.7578125] [0.9453125] [0.83827704]\n", | ||
"[0.8046875] [0.9921875] [0.5311729]\n", | ||
"[0.8984375] [0.9921875] [0.36445504]\n", | ||
"[0.859375] [0.9921875] [0.40577835]\n", | ||
"[0.8125] [0.9765625] [0.64629185]\n", | ||
"[0.84375] [1.] [0.38400555]\n", | ||
"[0.890625] [0.9765625] [0.45866236]\n", | ||
"[0.8828125] [0.9921875] [0.3711415]\n", | ||
"[0.7578125] [0.9921875] [0.6650479]\n", | ||
"[0.7578125] [0.984375] [0.9030752]\n", | ||
"[0.8671875] [0.9921875] [0.3678714]\n", | ||
"[0.7421875] [0.9765625] [0.7424855]\n", | ||
"[0.7890625] [1.] [0.6212543]\n", | ||
"[0.8359375] [1.] [0.58529043]\n", | ||
"[0.8203125] [0.96875] [0.5860813]\n", | ||
"[0.8671875] [0.9921875] [0.415236]\n", | ||
"[0.8125] [1.] [0.60501564]\n", | ||
"[0.796875] [0.9765625] [0.60677457]\n", | ||
"[0.8515625] [1.] [0.5338207]\n", | ||
"[0.8046875] [0.9921875] [0.54180473]\n", | ||
"[0.875] [0.9921875] [0.7293667]\n", | ||
"[0.84375] [0.9765625] [0.5581689]\n", | ||
"[0.8359375] [1.] [0.50712734]\n", | ||
"[0.8671875] [0.9921875] [0.55217856]\n", | ||
"[0.765625] [0.96875] [0.8076792]\n", | ||
"[0.953125] [1.] [0.17031987]\n", | ||
"[0.890625] [0.9921875] [0.42383268]\n", | ||
"[0.828125] [0.9765625] [0.49300486]\n", | ||
"[0.8671875] [0.96875] [0.57985115]\n", | ||
"[0.8515625] [1.] [0.4901033]\n", | ||
"[0.921875] [1.] [0.34583277]\n", | ||
"[0.8984375] [0.984375] [0.41139168]\n", | ||
"[0.9296875] [1.] [0.20420414]\n", | ||
"[0.921875] [0.984375] [0.24322833]\n", | ||
"[0.921875] [0.9921875] [0.30570173]\n", | ||
"[0.875] [0.9921875] [0.3866225]\n", | ||
"[0.9140625] [0.9921875] [0.20813875]\n", | ||
"[0.9140625] [1.] [0.17933217]\n", | ||
"[0.8984375] [0.9921875] [0.32508463]\n", | ||
"[0.9375] [1.] [0.24799153]\n", | ||
"[0.9140625] [1.] [0.26146784]\n", | ||
"[0.90625] [1.] [0.24672262]\n", | ||
"[0.8828125] [1.] [0.34094217]\n", | ||
"[0.90625] [1.] [0.2964819]\n", | ||
"[0.9296875] [1.] [0.18237087]\n", | ||
"[0.84375] [1.] [0.7182543]\n", | ||
"[0.8671875] [0.984375] [0.508474]\n", | ||
"[0.8828125] [0.9921875] [0.367172]\n", | ||
"[0.9453125] [1.] [0.2366665]\n", | ||
"[0.9375] [1.] [0.12494276]\n", | ||
"[0.8984375] [1.] [0.3395289]\n", | ||
"[0.890625] [0.984375] [0.30877113]\n", | ||
"[0.90625] [1.] [0.29763448]\n", | ||
"[0.8828125] [0.984375] [0.4845504]\n", | ||
"[0.8515625] [1.] [0.45548072]\n", | ||
"[0.8828125] [1.] [0.33331633]\n", | ||
"[0.90625] [1.] [0.4024018]\n", | ||
"[0.890625] [0.984375] [0.73405886]\n", | ||
"[0.9609375] [0.9921875] [0.15409982]\n", | ||
"[0.9140625] [0.984375] [0.37103674]\n", | ||
"[0.953125] [1.] [0.17628372]\n", | ||
"[0.890625] [1.] [0.36522508]\n", | ||
"[0.8828125] [1.] [0.407708]\n", | ||
"[0.9375] [0.984375] [0.25090045]\n", | ||
"[0.890625] [0.984375] [0.35742313]\n", | ||
"[0.921875] [0.9921875] [0.2751101]\n", | ||
"[0.890625] [0.984375] [0.43053097]\n", | ||
"[0.875] [0.9921875] [0.34412643]\n", | ||
"[0.90625] [1.] [0.35595697]\n" | ||
] | ||
}, | ||
{ | ||
"ename": "KeyboardInterrupt", | ||
"evalue": "", | ||
"output_type": "error", | ||
"traceback": [ | ||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | ||
"\u001b[0;32m<ipython-input-21-92f72657bddc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_reader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0macc1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpruned_program\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_feeder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache)\u001b[0m\n\u001b[1;32m 776\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0mreturn_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_numpy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 778\u001b[0;31m use_program_cache=use_program_cache)\n\u001b[0m\u001b[1;32m 779\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEOFException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36m_run_impl\u001b[0;34m(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache)\u001b[0m\n\u001b[1;32m 829\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mreturn_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_numpy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 831\u001b[0;31m use_program_cache=use_program_cache)\n\u001b[0m\u001b[1;32m 832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 833\u001b[0m \u001b[0mprogram\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||
"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/paddle/fluid/executor.py\u001b[0m in \u001b[0;36m_run_program\u001b[0;34m(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache)\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0muse_program_cache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 904\u001b[0m self._default_executor.run(program.desc, scope, 0, True, True,\n\u001b[0;32m--> 905\u001b[0;31m fetch_var_name)\n\u001b[0m\u001b[1;32m 906\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 907\u001b[0m self._default_executor.run_prepared_ctx(ctx, scope, False, False,\n", | ||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: " | ||
] | ||
} | ||
], | ||
"source": [ | ||
"for data in train_reader():\n", | ||
" acc1, acc5, loss = exe.run(pruned_program, feed=train_feeder.feed(data), fetch_list=outputs)\n", | ||
" print(acc1, acc5, loss)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.5.2" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 4 | ||
} |
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