Refine quick start tutorial of pruning. (#49)

demo/models/__init__.py

+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'
 ]

demo/prune/image_classification_pruning_quick_start.ipynb

+{
+ "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",
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+      "[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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+      "[0.3828125] [0.8203125] [2.226284]\n",
+      "[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",
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+      "[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
+}

docs/docs/tutorials/pruning_demo.md

-# 卷积通道剪裁示例
-
-本示例将演示如何按指定的剪裁率对每个卷积层的通道数进行剪裁。该示例默认会自动下载并使用mnist数据。
-
-当前示例支持以下分类模型：
-
-- MobileNetV1
-- MobileNetV2
-- ResNet50
-- PVANet
-
-## 接口介绍
-
-该示例使用了`paddleslim.Pruner`工具类，用户接口使用介绍请参考：[API文档](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)
-
-## 确定待裁参数
-
-不同模型的参数命名不同，在剪裁前需要确定待裁卷积层的参数名称。可通过以下方法列出所有参数名：
-
-```python
-for param in program.global_block().all_parameters():
-    print("param name: {}; shape: {}".format(param.name, param.shape))
-```
-
-在`train.py`脚本中，提供了`get_pruned_params`方法，根据用户设置的选项`--model`确定要裁剪的参数。
-
-## 启动裁剪任务
-
-通过以下命令启动裁剪任务：
-
-```python
-export CUDA_VISIBLE_DEVICES=0
-python train.py
-```
-
-执行`python train.py --help`查看更多选项。
-
-## 注意
-
-1. 在接口`paddle.Pruner.prune`的参数中，`params`和`ratios`的长度需要一样。
-
-

docs/docs/tutorials/pruning_tutorial.md

+#  图像分类模型通道剪裁-快速开始
+
+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]()。
+该示例包含以下步骤：
+
+1. 导入依赖
+2. 构建模型
+3. 剪裁
+4. 训练剪裁后的模型
+
+以下章节依次次介绍每个步骤的内容。
+
+## 1. 导入依赖
+
+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:
+
+```
+import paddle
+import paddle.fluid as fluid
+import paddleslim as slim
+```
+
+## 2. 构建网络
+
+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。
+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：
+
+```
+exe, train_program, val_program, inputs, outputs =
+    slim.models.image_classification("MobileNet", [1, 28, 28], 10, use_gpu=False)
+```
+
+>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。
+
+## 3. 剪裁卷积层通道
+
+### 3.1 计算剪裁之前的FLOPs
+
+```
+FLOPs = slim.analysis.flops(train_program)
+print("FLOPs: {}".format(FLOPs))
+```
+
+### 3.2 剪裁
+
+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。
+代码如下所示：
+
+```
+pruner = slim.prune.Pruner()
+pruned_program, _, _ = pruner.prune(
+        train_program,
+        fluid.global_scope(),
+        params=["conv2_1_sep_weights", "conv2_2_sep_weights"],
+        ratios=[0.33] * 2,
+        place=fluid.CPUPlace())
+```
+
+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。
+
+### 3.3 计算剪裁之后的FLOPs
+
+```
+FLOPs = paddleslim.analysis.flops(train_program)
+print("FLOPs: {}".format(FLOPs))
+```
+
+## 4. 训练剪裁后的模型
+
+### 4.1 定义输入数据
+
+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。
+代码如下：
+
+```
+import paddle.dataset.mnist as reader
+train_reader = paddle.batch(
+        reader.train(), batch_size=128, drop_last=True)
+train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
+```
+
+### 4.2 执行训练
+以下代码执行了一个`epoch`的训练：
+
+```
+for data in train_reader():
+    acc1, acc5, loss = exe.run(pruned_program, feed=train_feeder.feed(data), fetch_list=outputs)
+    print(acc1, acc5, loss)
+```

docs/mkdocs.yml

@@ -4,6 +4,7 @@ nav:
 - Home: index.md
 - 模型库: model_zoo.md
 - 教程:
+  - 图像分类模型通道剪裁-快速开始: tutorials/pruning_tutorial.md
   - 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

paddleslim/__init__.py

@@ -11,3 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from __future__ import absolute_import
+from paddleslim import models
+from paddleslim import prune
+from paddleslim import nas
+from paddleslim import analysis
+from paddleslim import dist
+from paddleslim import quant
+__all__ = ['models', 'prune', 'nas', 'analysis', 'dist', 'quant']

paddleslim/models/util.py

+from __future__ import absolute_import
+import paddle.fluid as fluid
+from ..models import classification_models
+
+__all__ = ["image_classification"]
+
+model_list = classification_models.model_list
+
+
+def image_classification(model, image_shape, class_num, use_gpu=False):
+    assert model in model_list
+    train_program = fluid.Program()
+    startup_program = fluid.Program()
+    with fluid.program_guard(train_program, startup_program):
+        image = fluid.layers.data(
+            name='image', shape=image_shape, dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        model = classification_models.__dict__[model]()
+        out = model.net(input=image, class_dim=class_num)
+        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)
+        val_program = fluid.default_main_program().clone(for_test=True)
+
+        opt = fluid.optimizer.Momentum(0.1, 0.9)
+        opt.minimize(avg_cost)
+        place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+    return exe, train_program, val_program, (image, label), (
+        acc_top1.name, acc_top5.name, avg_cost.name)

paddleslim/prune/__init__.py

@@ -11,18 +11,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from __future__ import absolute_import
 from .pruner import *
-import pruner
+from ..prune import pruner
 from .auto_pruner import *
 import auto_pruner
 from .sensitive_pruner import *
-import sensitive_pruner
+from ..prune import sensitive_pruner
 from .sensitive import *
-import sensitive
-from prune_walker import *
-import prune_walker
+from ..prune import sensitive
+from .prune_walker import *
+from ..prune import prune_walker
 from io import *
-import io
+from ..prune import io
 
 __all__ = []
 

paddleslim/prune/sensitive.py

@@ -236,7 +236,7 @@ def get_ratios_by_loss(sensitivities, loss):
                     ratio = r0 + (loss - l0) * (r1 - r0) / (l1 - l0)
                     ratios[param] = ratio
                     if ratio > 1:
-                        print losses, ratio, (r1 - r0) / (l1 - l0), i
+                        _logger.info(losses, ratio, (r1 - r0) / (l1 - l0), i)
 
                 break
     return ratios

paddleslim/prune/sensitive_pruner.py

@@ -64,7 +64,7 @@ class SensitivePruner(object):
 
         exe = fluid.Executor(self._place)
         checkpoints = self._checkpoints if checkpoints is None else checkpoints
-        print("check points: {}".format(checkpoints))
+        _logger.info("check points: {}".format(checkpoints))
         main_program = None
         eval_program = None
         if checkpoints is not None:
@@ -87,8 +87,9 @@ class SensitivePruner(object):
                 with fluid.scope_guard(self._scope):
                     fluid.io.load_persistables(exe, latest_ck_path,
                                                main_program, "__params__")
-                print("load checkpoint from: {}".format(latest_ck_path))
-                print("flops of eval program: {}".format(flops(eval_program)))
+                _logger.info("load checkpoint from: {}".format(latest_ck_path))
+                _logger.info("flops of eval program: {}".format(
+                    flops(eval_program)))
         return main_program, eval_program, self._iter
 
     def greedy_prune(self,
@@ -108,7 +109,7 @@ class SensitivePruner(object):
                 self._eval_func,
                 sensitivities_file=sensitivities_file,
                 pruned_flops_rate=pruned_flops_rate)
-        print sensitivities
+        _logger.info(sensitivities)
         params, ratios = self._greedy_ratio_by_sensitive(sensitivities, topk)
 
         _logger.info("Pruning: {} by {}".format(params, ratios))
@@ -152,7 +153,7 @@ class SensitivePruner(object):
                 self._eval_func,
                 sensitivities_file=sensitivities_file,
                 step_size=0.1)
-        print sensitivities
+        _logger.info(sensitivities)
         _, ratios = self.get_ratios_by_sensitive(sensitivities, pruned_flops,
                                                  eval_program)
 

paddleslim/version.py

@@ -14,4 +14,4 @@
 # limitations under the License.
 """ PaddleSlim version string """
 __all__ = ["slim_version"]
-slim_version = "0.1"
+slim_version = "1.0.0"