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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# X2Paddle快速上手——PyTorch迁移至PaddlePaddle\n",
"***X2Paddle简介***：X2Paddle支持将Caffe/TensorFlow/ONNX/PyTorch深度学习框架训练得到的模型，迁移至PaddlePaddle模型。 \n",
"***X2Paddle代码GitHub链接***：[https://github.com/PaddlePaddle/X2Paddle](https://github.com/PaddlePaddle/X2Paddle) \n",
"***【注意】***前往GitHub给[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)点击Star，关注项目，即可随时了解X2Paddle的最新进展。 \n",
"本教程用于帮助用户学习将PyTorch训练后的预测模型迁移至PaddlePaddle框架，以PyTorch版本的[AlexNet](https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py)为例进行详细介绍。 \n",
"\n",
"## 安装及准备\n",
"### 1. 安装X2Paddle\n",
"***方式一：（推荐）***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! git clone https://github.com/PaddlePaddle/X2Paddle.git\n",
"! cd X2Paddle\n",
"! git checkout develop\n",
"! python setup.py install"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***方式二：***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install x2paddle==1.0.1 --index https://pypi.Python.org/simple/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 安装PyTorch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install torch==1.6.0 torchvision==0.7.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. 安装PaddlePaddle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install paddlepaddle==2.0.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 模型迁移\n",
"### 1. 获取AlexNet模型"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from torchvision.models import AlexNet\n",
"from torchvision.models.utils import load_state_dict_from_url\n",
"\n",
"torch_model = AlexNet()\n",
"torch_state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')\n",
"torch_model.load_state_dict(torch_state_dict)\n",
"torch_model.eval()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 转换\n",
"PyTorch到PaddlePaddle的转换需要传入输入的示例，才可以进行转换，以下为构建输入的过程（输入也可为值随机初始化的Tensor）："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from PIL import Image\n",
"img = Image.open(\"dog_pt.png\")\n",
"img = np.array(img).astype(\"float32\") / 255.0\n",
"img -= [0.485, 0.456, 0.406]\n",
"img /= [0.229, 0.224, 0.225]\n",
"img = np.transpose(img, (2, 0, 1))\n",
"img = np.expand_dims(img, 0)\n",
"\n",
"import torch\n",
"input_tensor = torch.tensor(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***方式一：*** Trace方式 \n",
"模型输入的shape固定，PyTorch模型基本均支持此方式转换。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from x2paddle.convert import pytorch2paddle\n",
"save_dir = \"pd_model_trace\"\n",
"jit_type = \"trace\"\n",
"pytorch2paddle(module=torch_model, \n",
" save_dir=save_dir, \n",
" jit_type=jit_type, \n",
" input_examples=[input_tensor])\n",
"# module (torch.nn.Module): PyTorch的Module。\n",
"# save_dir (str): 转换后模型的保存路径。\n",
"# jit_type (str): 转换方式，此时为\"trace\"。\n",
"# input_examples (list[torch.tensor]): torch.nn.Module的输入示例，list的长度必须与输入的长度一致。默认为None。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***方式二：*** Script方式 \n",
"模型输入的shape可不固定，由于PyTorch的Script方式可识别的代码格式有限，所以PyTorch模型在此方式下转换的支持度较低。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from x2paddle.convert import pytorch2paddle\n",
"save_dir = \"pd_model_script\"\n",
"jit_type = \"script\"\n",
"pytorch2paddle(module=torch_model, \n",
" save_dir=save_dir, \n",
" jit_type=jit_type, \n",
" input_examples=[input_tensor])\n",
"# module (torch.nn.Module): PyTorch的Module。\n",
"# save_dir (str): 转换后模型的保存路径。\n",
"# jit_type (str): 转换方式，此时为\"script\"。\n",
"# input_examples (list[torch.tensor]): torch.nn.Module的输入示例，list的长度必须与输入的长度一致。默认为None。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用方式一转换的PaddlePaddle预测模型进行预测： \n",
"（1）下载ImageNet类别文件"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! wget https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"（2）预测"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import paddle\n",
"paddle.enable_static()\n",
"exe = paddle.static.Executor(paddle.CPUPlace())\n",
"[prog, inputs, outputs] = paddle.static.load_inference_model(path_prefix=\"pd_model_trace/inference_model\", \n",
" executor=exe, \n",
" model_filename=\"model.pdmodel\",\n",
" params_filename=\"model.pdiparams\")\n",
"print(img.shape)\n",
"result = exe.run(prog, feed={inputs[0]: img}, fetch_list=outputs)\n",
"max_index = np.argmax(result)\n",
"with open('imagenet_classes.txt') as f:\n",
" classes = [line.strip() for line in f.readlines()]\n",
"print(\"The category of dog.jpg is: {}\".format(classes[max_index]))"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
docs/demo/tf2pd.ipynb 0 → 100644
浏览文件 @ 8ab1fcaf
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# X2Paddle快速上手——TensorFlow迁移至PaddlePaddle\n",
"***X2Paddle简介***：X2Paddle支持将Caffe/TensorFlow/ONNX/PyTorch深度学习框架训练得到的模型，迁移至PaddlePaddle模型。 \n",
"***X2Paddle代码GitHub链接***：[https://github.com/PaddlePaddle/X2Paddle](https://github.com/PaddlePaddle/X2Paddle) \n",
"***【注意】***前往GitHub给[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)点击Star，关注项目，即可随时了解X2Paddle的最新进展。 \n",
"本教程用于帮助用户学习将TensorFlow训练后的预测模型迁移至PaddlePaddle框架，以TensorFlow版本的[MobileNetV1](https://github.com/tensorflow/models/tree/master/research/slim)为例进行详细介绍。 \n",
"\n",
"## 安装及准备\n",
"### 1. 安装X2Paddle\n",
"***方式一：（推荐）***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! git clone https://github.com/PaddlePaddle/X2Paddle.git\n",
"! cd X2Paddle\n",
"! git checkout develop\n",
"! python setup.py install"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***方式二：***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install x2paddle==1.0.1 --index https://pypi.Python.org/simple/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 安装TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install tensorflow==1.14.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. 安装PaddlePaddle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! pip install paddlepaddle==2.0.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 模型迁移\n",
"### 1. 获取MobileNetV1的FrozenModel\n",
"由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! wget http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz\n",
"! tar zxvf mobilenet_v1_0.25_128.tgz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. 转换\n",
"需要传入的参数如下：\n",
"> --framework (-f)：源模型类型，此处设置为tensorflow。 \n",
"> --save_dir (-s)：指定转换后的模型保存目录路径。 \n",
"> --model (-m)：指定tensorflow的pb模型。 \n",
"> --paddle_type (-pt)：指定转换为动态图代码(dygraph)或者静态图代码(static)，默认为dygraph。 \n",
"\n",
"***方式一：***生成静态图代码，并保存成静态图预测模型"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! x2paddle -f tensorflow -m ./mobilenet_v1_0.25_128_frozen.pb -s pd_model_static -pt static"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***方式二：***生成动态图代码，并保存成静态图预测模型\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! x2paddle -f tensorflow -m ./mobilenet_v1_0.25_128_frozen.pb -s pd_model_dygraph -pt dygraph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用方式一转换得到的PaddlePaddle预测模型进行预测： \n",
"（1）下载ImageNet类别文件"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! wget https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"（2）预测"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 构造输入\n",
"import cv2\n",
"import numpy as np\n",
"img = cv2.imread(\"dog_tf.png\").astype(\"float32\") / 255.0\n",
"img = np.expand_dims(img, 0)\n",
"img -= 0.5\n",
"img *= 2.0\n",
" \n",
"# 进行预测\n",
"import paddle\n",
"import numpy as np\n",
"paddle.enable_static()\n",
"exe = paddle.static.Executor(paddle.CPUPlace())\n",
"[prog, inputs, outputs] = paddle.static.load_inference_model(path_prefix=\"pd_model_static/inference_model\", \n",
" executor=exe, \n",
" model_filename=\"model.pdmodel\",\n",
" params_filename=\"model.pdiparams\")\n",
"result = exe.run(prog, feed={inputs[0]: img}, fetch_list=outputs)\n",
"max_index = np.argmax(result[0])\n",
"with open('imagenet_classes.txt') as f:\n",
" classes = [line.strip() for line in f.readlines()]\n",
"print(\"The category of dog.jpg is: {}\".format(classes[max_index]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "all",
"language": "python",
"name": "all"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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