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PaddleSlim
未验证 提交 09af2257 编写于 作者: L Liufang Sang 提交者： GitHub
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add quant_aware and quant_post tutorial (#83)

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demo/quant/quant_aware/image_classification_training_aware_quantization_quick_start.ipynb 0 → 100755
浏览文件 @ 09af2257
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 图像分类模型量化训练-快速开始"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"该教程以图像分类模型MobileNetV1为例，说明如何快速使用PaddleSlim的[量化训练接口](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/api/quantization_api.md)。 该示例包含以下步骤：\n",
"\n",
"1. 导入依赖\n",
"2. 构建模型\n",
"3. 训练模型\n",
"4. 量化\n",
"5. 训练和测试量化后的模型\n",
"6. 保存量化后的模型"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 导入依赖\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": 1,
"metadata": {},
"outputs": [],
"source": [
"import paddle\n",
"import paddle.fluid as fluid\n",
"import paddleslim as slim\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 构建网络\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。 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：\n",
"\n",
">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": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"exe, train_program, val_program, inputs, outputs = \\\n",
" slim.models.image_classification(\"MobileNet\", [1, 28, 28], 10, use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 训练模型\n",
"该章节介绍了如何定义输入数据和如何训练和测试分类模型。先训练分类模型的原因是量化训练过程是在训练好的模型上进行的，也就是说是在训练好的模型的基础上加入量化反量化op之后，用小学习率进行参数微调。\n",
"\n",
"### 3.1 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": 3,
"metadata": {},
"outputs": [],
"source": [
"import paddle.dataset.mnist as reader\n",
"train_reader = paddle.batch(\n",
" reader.train(), batch_size=128, drop_last=True)\n",
"test_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": [
"### 3.2 训练和测试\n",
"先定义训练和测试函数，正常训练和量化训练时只需要调用函数即可。在训练函数中执行了一个epoch的训练，因为MNIST数据集数据较少，一个epoch就可将top1精度训练到95%以上。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def train(prog):\n",
" iter = 0\n",
" for data in train_reader():\n",
" acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)\n",
" if iter % 100 == 0:\n",
" print('train iter={}, top1={}, top5={}, loss={}'.format(iter, acc1.mean(), acc5.mean(), loss.mean()))\n",
" iter += 1\n",
" \n",
"def test(prog):\n",
" iter = 0\n",
" res = [[], []]\n",
" for data in train_reader():\n",
" acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)\n",
" if iter % 100 == 0:\n",
" print('test iter={}, top1={}, top5={}, loss={}'.format(iter, acc1.mean(), acc5.mean(), loss.mean()))\n",
" res[0].append(acc1.mean())\n",
" res[1].append(acc5.mean())\n",
" iter += 1\n",
" print('final test result top1={}, top5={}'.format(np.array(res[0]).mean(), np.array(res[1]).mean()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"调用``train``函数训练分类网络，``train_program``是在第2步：构建网络中定义的。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train iter=0, top1=0.1171875, top5=0.546875, loss=2.79680204391\n",
"train iter=100, top1=0.9296875, top5=1.0, loss=0.305284500122\n",
"train iter=200, top1=0.9609375, top5=0.9921875, loss=0.158525630832\n",
"train iter=300, top1=0.9609375, top5=0.9921875, loss=0.146427512169\n",
"train iter=400, top1=0.9609375, top5=1.0, loss=0.179066047072\n"
]
}
],
"source": [
"train(train_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"调用``test``函数测试分类网络，``val_program``是在第2步：构建网络中定义的。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test iter=0, top1=0.96875, top5=1.0, loss=0.0801232308149\n",
"test iter=100, top1=0.9609375, top5=1.0, loss=0.104892581701\n",
"test iter=200, top1=0.96875, top5=1.0, loss=0.156774014235\n",
"test iter=300, top1=0.984375, top5=1.0, loss=0.0931615754962\n",
"test iter=400, top1=0.9453125, top5=1.0, loss=0.184863254428\n",
"final test result top1=0.970469415188, top5=0.999282181263\n"
]
}
],
"source": [
"test(val_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. 量化\n",
"\n",
"按照[默认配置](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#_1)在``train_program``和``val_program``中加入量化和反量化op."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-02-06 09:08:49,489-INFO: quant_aware config {'moving_rate': 0.9, 'weight_quantize_type': 'channel_wise_abs_max', 'is_full_quantize': False, 'dtype': 'int8', 'weight_bits': 8, 'window_size': 10000, 'activation_bits': 8, 'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'], 'not_quant_pattern': ['skip_quant'], 'activation_quantize_type': 'moving_average_abs_max', 'for_tensorrt': False}\n",
"2020-02-06 09:08:50,943-INFO: quant_aware config {'moving_rate': 0.9, 'weight_quantize_type': 'channel_wise_abs_max', 'is_full_quantize': False, 'dtype': 'int8', 'weight_bits': 8, 'window_size': 10000, 'activation_bits': 8, 'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'], 'not_quant_pattern': ['skip_quant'], 'activation_quantize_type': 'moving_average_abs_max', 'for_tensorrt': False}\n"
]
}
],
"source": [
"quant_program = slim.quant.quant_aware(train_program, exe.place, for_test=False)\n",
"val_quant_program = slim.quant.quant_aware(val_program, exe.place, for_test=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. 训练和测试量化后的模型\n",
"微调量化后的模型，训练一个epoch后测试。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train iter=0, top1=0.953125, top5=1.0, loss=0.184170544147\n",
"train iter=100, top1=0.96875, top5=1.0, loss=0.0945074558258\n",
"train iter=200, top1=0.9765625, top5=1.0, loss=0.0915599390864\n",
"train iter=300, top1=0.9765625, top5=1.0, loss=0.0562560297549\n",
"train iter=400, top1=0.9609375, top5=1.0, loss=0.094195574522\n"
]
}
],
"source": [
"train(quant_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"测试量化后的模型，和``3.2 训练和测试``中得到的测试结果相比，精度相近，达到了无损量化。"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test iter=0, top1=0.984375, top5=1.0, loss=0.0542894415557\n",
"test iter=100, top1=0.9609375, top5=1.0, loss=0.0662319809198\n",
"test iter=200, top1=0.9609375, top5=1.0, loss=0.0832970961928\n",
"test iter=300, top1=0.9921875, top5=1.0, loss=0.0262515246868\n",
"test iter=400, top1=0.96875, top5=1.0, loss=0.123742781579\n",
"final test result top1=0.984057843685, top5=0.999799668789\n"
]
}
],
"source": [
"test(val_quant_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. 保存量化后的模型\n",
"\n",
"在``4. 量化``中使用接口``slim.quant.quant_aware``接口得到的模型只适合训练时使用，为了得到最终使用时的模型，需要使用[slim.quant.convert](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#convert)接口，然后使用[fluid.io.save_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/io_cn/save_inference_model_cn.html#save-inference-model)保存模型。``float_prog``的参数数据类型是float32，但是数据范围是int8, 保存之后可使用fluid或者paddle-lite加载使用，paddle-lite在使用时，会先将类型转换为int8。``int8_prog``的参数数据类型是int8, 保存后可看到量化后模型大小，不可加载使用。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-02-06 09:09:27,529-INFO: convert config {'moving_rate': 0.9, 'weight_quantize_type': 'channel_wise_abs_max', 'is_full_quantize': False, 'dtype': 'int8', 'weight_bits': 8, 'window_size': 10000, 'activation_bits': 8, 'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'], 'not_quant_pattern': ['skip_quant'], 'activation_quantize_type': 'moving_average_abs_max', 'for_tensorrt': False}\n"
]
},
{
"data": {
"text/plain": [
"[u'save_infer_model/scale_0',\n",
" u'save_infer_model/scale_1',\n",
" u'save_infer_model/scale_2']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float_prog, int8_prog = slim.quant.convert(val_quant_program, exe.place, save_int8=True)\n",
"target_vars = [float_prog.global_block().var(name) for name in outputs]\n",
"fluid.io.save_inference_model(dirname='./inference_model/float',\n",
" feeded_var_names=[var.name for var in inputs],\n",
" target_vars=target_vars,\n",
" executor=exe,\n",
" main_program=float_prog)\n",
"fluid.io.save_inference_model(dirname='./inference_model/int8',\n",
" feeded_var_names=[var.name for var in inputs],\n",
" target_vars=target_vars,\n",
" executor=exe,\n",
" main_program=int8_prog)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
demo/quant/quant_post/image_classification_post_training_quantization_quick_start.ipynb 0 → 100755
浏览文件 @ 09af2257
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 图像分类模型离线量化-快速开始"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"该教程以图像分类模型MobileNetV1为例，说明如何快速使用PaddleSlim的[离线量化接口](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/api/quantization_api.md)。 该示例包含以下步骤：\n",
"\n",
"1. 导入依赖\n",
"2. 构建模型\n",
"3. 训练模型\n",
"4. 离线量化"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 导入依赖\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": 1,
"metadata": {},
"outputs": [],
"source": [
"import paddle\n",
"import paddle.fluid as fluid\n",
"import paddleslim as slim\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 构建网络\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。 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：\n",
"\n",
">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": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"exe, train_program, val_program, inputs, outputs = \\\n",
" slim.models.image_classification(\"MobileNet\", [1, 28, 28], 10, use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 训练模型\n",
"该章节介绍了如何定义输入数据和如何训练和测试分类模型。先训练分类模型的原因是离线量化需要一个训练好的模型。\n",
"\n",
"### 3.1 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": 3,
"metadata": {},
"outputs": [],
"source": [
"import paddle.dataset.mnist as reader\n",
"train_reader = paddle.batch(\n",
" reader.train(), batch_size=128, drop_last=True)\n",
"test_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": [
"### 3.2 训练和测试\n",
"先定义训练和测试函数。在训练函数中执行了一个epoch的训练，因为MNIST数据集数据较少，一个epoch就可将top1精度训练到95%以上。\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def train(prog):\n",
" iter = 0\n",
" for data in train_reader():\n",
" acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)\n",
" if iter % 100 == 0:\n",
" print('train', acc1.mean(), acc5.mean(), loss.mean())\n",
" iter += 1\n",
" \n",
"def test(prog, outputs=outputs):\n",
" iter = 0\n",
" res = [[], []]\n",
" for data in train_reader():\n",
" acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)\n",
" if iter % 100 == 0:\n",
" print('test', acc1.mean(), acc5.mean(), loss.mean())\n",
" res[0].append(acc1.mean())\n",
" res[1].append(acc5.mean())\n",
" iter += 1\n",
" print('final test result', np.array(res[0]).mean(), np.array(res[1]).mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"调用``train``函数训练分类网络，``train_program``是在第2步：构建网络中定义的。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('train', 0.0625, 0.5234375, 2.6373053)\n",
"('train', 0.9375, 0.9921875, 0.20106347)\n",
"('train', 0.953125, 1.0, 0.13234669)\n",
"('train', 0.96875, 0.9921875, 0.18056682)\n",
"('train', 0.9453125, 1.0, 0.15847622)\n"
]
}
],
"source": [
"train(train_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"调用``test``函数测试分类网络，``val_program``是在第2步：构建网络中定义的。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('test', 0.9609375, 0.9921875, 0.12996897)\n",
"('test', 0.9609375, 1.0, 0.094265014)\n",
"('test', 0.9453125, 1.0, 0.10511534)\n",
"('test', 0.9765625, 1.0, 0.11341806)\n",
"('test', 0.953125, 1.0, 0.17046008)\n",
"('final test result', 0.9647603, 0.99943244)\n"
]
}
],
"source": [
"test(val_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"保存inference model，将训练好的分类模型保存在``'./inference_model'``下，后续进行离线量化时将加载保存在此处的模型。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[u'save_infer_model/scale_0',\n",
" u'save_infer_model/scale_1',\n",
" u'save_infer_model/scale_2']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_vars = [val_program.global_block().var(name) for name in outputs]\n",
"fluid.io.save_inference_model(dirname='./inference_model',\n",
" feeded_var_names=[var.name for var in inputs],\n",
" target_vars=target_vars,\n",
" executor=exe,\n",
" main_program=val_program)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. 离线量化\n",
"\n",
"调用离线量化接口，加载文件夹``'./inference_model'``训练好的分类模型，并使用10个batch的数据进行参数校正。此过程无需训练，只需跑前向过程来计算量化所需参数。离线量化后的模型保存在文件夹``'./quant_post_model'``下。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-02-06 09:32:42,944-INFO: run batch: 0\n",
"2020-02-06 09:32:42,944-INFO: run batch: 0\n",
"2020-02-06 09:32:43,233-INFO: run batch: 5\n",
"2020-02-06 09:32:43,233-INFO: run batch: 5\n",
"2020-02-06 09:32:43,362-INFO: all run batch: 10\n",
"2020-02-06 09:32:43,362-INFO: all run batch: 10\n",
"2020-02-06 09:32:43,365-INFO: calculate scale factor ...\n",
"2020-02-06 09:32:43,365-INFO: calculate scale factor ...\n",
"2020-02-06 09:32:54,841-INFO: update the program ...\n",
"2020-02-06 09:32:54,841-INFO: update the program ...\n"
]
}
],
"source": [
"slim.quant.quant_post(\n",
" executor=exe,\n",
" model_dir='./inference_model',\n",
" quantize_model_path='./quant_post_model',\n",
" sample_generator=reader.test(),\n",
" batch_nums=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"加载保存在文件夹``'./quant_post_model'``下的量化后的模型进行测试，可看到精度和``3.2 训练和测试``中得到的测试精度相近，因此离线量化过程对于此分类模型几乎无损。"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('test', 0.9765625, 0.9921875, 0.11411239)\n",
"('test', 0.953125, 1.0, 0.111179784)\n",
"('test', 0.953125, 1.0, 0.101078615)\n",
"('test', 0.96875, 1.0, 0.0993958)\n",
"('test', 0.9609375, 1.0, 0.16066414)\n",
"('final test result', 0.9643096, 0.99931556)\n"
]
}
],
"source": [
"quant_post_prog, feed_target_names, fetch_targets = fluid.io.load_inference_model(\n",
" dirname='./quant_post_model',\n",
" executor=exe)\n",
"test(quant_post_prog, fetch_targets)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
docs/zh_cn/quick_start/quant_aware_tutorial.md 0 → 100644
浏览文件 @ 09af2257
# 图像分类模型量化训练-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[量化训练接口](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/api/quantization_api.md)。 该示例包含以下步骤：
1. 导入依赖
2. 构建模型
3. 训练模型
4. 量化
5. 训练和测试量化后的模型
6. 保存量化后的模型
## 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:
```python
import paddle
import paddle.fluid as fluid
import paddleslim as slim
import numpy as np
```
## 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：
>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。
```python
exe, train_program, val_program, inputs, outputs = \
slim.models.image_classification("MobileNet", [1, 28, 28], 10, use_gpu=True)
```
## 3. 训练模型
该章节介绍了如何定义输入数据和如何训练和测试分类模型。先训练分类模型的原因是量化训练过程是在训练好的模型上进行的，也就是说是在训练好的模型的基础上加入量化反量化op之后，用小学习率进行参数微调。
### 3.1 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：
```python
import paddle.dataset.mnist as reader
train_reader = paddle.batch(
reader.train(), batch_size=128, drop_last=True)
test_reader = paddle.batch(
reader.train(), batch_size=128, drop_last=True)
train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
```
### 3.2 训练和测试
先定义训练和测试函数，正常训练和量化训练时只需要调用函数即可。在训练函数中执行了一个epoch的训练，因为MNIST数据集数据较少，一个epoch就可将top1精度训练到95%以上。
```python
def train(prog):
iter = 0
for data in train_reader():
acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)
if iter % 100 == 0:
print('train iter={}, top1={}, top5={}, loss={}'.format(iter, acc1.mean(), acc5.mean(), loss.mean()))
iter += 1
def test(prog):
iter = 0
res = [[], []]
for data in train_reader():
acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)
if iter % 100 == 0:
print('test iter={}, top1={}, top5={}, loss={}'.format(iter, acc1.mean(), acc5.mean(), loss.mean()))
res[0].append(acc1.mean())
res[1].append(acc5.mean())
iter += 1
print('final test result top1={}, top5={}'.format(np.array(res[0]).mean(), np.array(res[1]).mean()))
```
调用``train``函数训练分类网络，``train_program``是在第2步：构建网络中定义的。
```python
train(train_program)
```
调用``test``函数测试分类网络，``val_program``是在第2步：构建网络中定义的。
```python
test(val_program)
```
## 4. 量化
按照[默认配置](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#_1)在``train_program``和``val_program``中加入量化和反量化op.
```python
quant_program = slim.quant.quant_aware(train_program, exe.place, for_test=False)
val_quant_program = slim.quant.quant_aware(val_program, exe.place, for_test=True)
```
## 5. 训练和测试量化后的模型
微调量化后的模型，训练一个epoch后测试。
```python
train(quant_program)
```
测试量化后的模型，和``3.2 训练和测试``中得到的测试结果相比，精度相近，达到了无损量化。
```python
test(val_quant_program)
```
## 6. 保存量化后的模型
在``4. 量化``中使用接口``slim.quant.quant_aware``接口得到的模型只适合训练时使用，为了得到最终使用时的模型，需要使用[slim.quant.convert](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#convert)接口，然后使用[fluid.io.save_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/io_cn/save_inference_model_cn.html#save-inference-model)保存模型。``float_prog``的参数数据类型是float32，但是数据范围是int8, 保存之后可使用fluid或者paddle-lite加载使用，paddle-lite在使用时，会先将类型转换为int8。``int8_prog``的参数数据类型是int8, 保存后可看到量化后模型大小，不可加载使用。
```python
float_prog, int8_prog = slim.quant.convert(val_quant_program, exe.place, save_int8=True)
target_vars = [float_prog.global_block().var(name) for name in outputs]
fluid.io.save_inference_model(dirname='./inference_model/float',
feeded_var_names=[var.name for var in inputs],
target_vars=target_vars,
executor=exe,
main_program=float_prog)
fluid.io.save_inference_model(dirname='./inference_model/int8',
feeded_var_names=[var.name for var in inputs],
target_vars=target_vars,
executor=exe,
main_program=int8_prog)
```
docs/zh_cn/quick_start/quant_post_tutorial.md 0 → 100755
浏览文件 @ 09af2257
# 图像分类模型离线量化-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[离线量化接口](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/api/quantization_api.md)。 该示例包含以下步骤：
1. 导入依赖
2. 构建模型
3. 训练模型
4. 离线量化
## 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:
```python
import paddle
import paddle.fluid as fluid
import paddleslim as slim
import numpy as np
```
## 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：
>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。
```python
exe, train_program, val_program, inputs, outputs = \
slim.models.image_classification("MobileNet", [1, 28, 28], 10, use_gpu=True)
```
## 3. 训练模型
该章节介绍了如何定义输入数据和如何训练和测试分类模型。先训练分类模型的原因是离线量化需要一个训练好的模型。
### 3.1 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：
```python
import paddle.dataset.mnist as reader
train_reader = paddle.batch(
reader.train(), batch_size=128, drop_last=True)
test_reader = paddle.batch(
reader.train(), batch_size=128, drop_last=True)
train_feeder = fluid.DataFeeder(inputs, fluid.CPUPlace())
```
### 3.2 训练和测试
先定义训练和测试函数。在训练函数中执行了一个epoch的训练，因为MNIST数据集数据较少，一个epoch就可将top1精度训练到95%以上。
```python
def train(prog):
iter = 0
for data in train_reader():
acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)
if iter % 100 == 0:
print('train', acc1.mean(), acc5.mean(), loss.mean())
iter += 1
def test(prog, outputs=outputs):
iter = 0
res = [[], []]
for data in train_reader():
acc1, acc5, loss = exe.run(prog, feed=train_feeder.feed(data), fetch_list=outputs)
if iter % 100 == 0:
print('test', acc1.mean(), acc5.mean(), loss.mean())
res[0].append(acc1.mean())
res[1].append(acc5.mean())
iter += 1
print('final test result', np.array(res[0]).mean(), np.array(res[1]).mean())
```
调用``train``函数训练分类网络，``train_program``是在第2步：构建网络中定义的。
```python
train(train_program)
```
调用``test``函数测试分类网络，``val_program``是在第2步：构建网络中定义的。
```python
test(val_program)
```
保存inference model，将训练好的分类模型保存在``'./inference_model'``下，后续进行离线量化时将加载保存在此处的模型。
```python
target_vars = [val_program.global_block().var(name) for name in outputs]
fluid.io.save_inference_model(dirname='./inference_model',
feeded_var_names=[var.name for var in inputs],
target_vars=target_vars,
executor=exe,
main_program=val_program)
```
## 4. 离线量化
调用离线量化接口，加载文件夹``'./inference_model'``训练好的分类模型，并使用10个batch的数据进行参数校正。此过程无需训练，只需跑前向过程来计算量化所需参数。离线量化后的模型保存在文件夹``'./quant_post_model'``下。
```python
slim.quant.quant_post(
executor=exe,
model_dir='./inference_model',
quantize_model_path='./quant_post_model',
sample_generator=reader.test(),
batch_nums=10)
```
加载保存在文件夹``'./quant_post_model'``下的量化后的模型进行测试，可看到精度和``3.2 训练和测试``中得到的测试精度相近，因此离线量化过程对于此分类模型几乎无损。
```python
quant_post_prog, feed_target_names, fetch_targets = fluid.io.load_inference_model(
dirname='./quant_post_model',
executor=exe)
test(quant_post_prog, fetch_targets)
```
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