Merge branch 'develop' of https://github.com/PaddlePaddle/X2Paddle into develop

README.md

@@ -44,10 +44,7 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
 ```
 x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
 ```
-### PyTorch
-```
-x2paddle --framework=pytorch --model=resnet50.pt --save_dir=pd_model --input_shapes [-1,3,224,224]
-```
+
 ### Paddle2ONNX
 ```
 # 注意：paddle_infer_model_dir下需包含__model__和__params__两个文件
@@ -56,7 +53,7 @@ x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_
 ### 参数选项
 | 参数 | |
 |----------|--------------|
-|--framework | 源模型类型 (tensorflow、caffe、onnx、pytorch、paddle2onnx) |
+|--framework | 源模型类型 (tensorflow、caffe、onnx、paddle2onnx) |
 |--prototxt | 当framework为caffe时，该参数指定caffe模型的proto文件路径 |
 |--weight | 当framework为caffe时，该参数指定caffe模型的参数文件路径 |
 |--save_dir | 指定转换后的模型保存目录路径 |
@@ -66,7 +63,6 @@ x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_
 |--define_input_shape | **[可选]** For TensorFlow, 当指定该参数时，强制用户输入每个Placeholder的shape，见[文档Q2](FAQ.md) |
 |--params_merge | **[可选]** 当指定该参数时，转换完成后，inference_model中的所有模型参数将合并保存为一个文件__params__ |
 |--onnx_opset | **[可选]** 当framework为paddle2onnx时，该参数可设置转换为ONNX的OpSet版本，目前支持9、10、11，默认为10 |
-|--input_shapes |**[可选]** 当framework为pytorch时，该参数若设置，则根据输入的shape导出inference model（用于预测的静态模型）|
 
 
 

pytorch_to_script.md

-## PyTorch模型导出为ONNX模型
-
-目前pytorch2paddle主要支持pytorch ScriptModule。 用户可通过如下示例代码，将torchvison或者自己开发写的模型转换成ScriptModule model:
-```
-#coding: utf-8
-import torch
-import torch.nn as nn
-from torchvision.models.utils import load_state_dict_from_url
-# 定义模型
-class AlexNet(nn.Module):
-    def __init__(self, num_classes=1000):
-        super(AlexNet, self).__init__()
-        self.features = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-            nn.Conv2d(64, 192, kernel_size=5, padding=2),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-            nn.Conv2d(192, 384, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(384, 256, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, 256, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-        )
-        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
-        self.classifier = nn.Sequential(
-            nn.Dropout(0.0),
-            nn.Linear(256 * 6 * 6, 4096),
-            nn.ReLU(inplace=True),
-            nn.Dropout(0.0),
-            nn.Linear(4096, 4096),
-            nn.ReLU(inplace=True),
-            nn.Linear(4096, num_classes),
-        )
-
-    def forward(self, x):
-        x = self.features(x)
-        for i in range(1):
-            x = self.avgpool(x)
-        x = torch.flatten(x, 1)
-        x = self.classifier(x)
-        return x
-# 初始化模型
-model = AlexNet()
-# 加载参数
-state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
-                                      progress=True)
-model.load_state_dict(state_dict)
-# 设置模式
-model.eval()
-# 生成ScriptModule并保存
-script = torch.jit.script(model)
-torch.jit.save(script, "alexnet.pt")
-```

x2paddle/convert.py

@@ -304,10 +304,6 @@ def main():
             params_merge = True
         onnx2paddle(args.model, args.save_dir, params_merge)
         
-    elif args.framework == "pytorch":
-        assert args.model is not None, "--model should be defined while translating pytorch model"
-        pytorch2paddle(args.model, args.save_dir, args.input_shapes)
-
     elif args.framework == "paddle2onnx":
         assert args.model is not None, "--model should be defined while translating paddle model to onnx"
         paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset)