未验证 提交 5a20cb89 编写于 作者: J Jason 提交者: GitHub

Merge pull request #405 from SunAhong1993/syf_dev

add pytorch code and docs
...@@ -44,6 +44,10 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel ...@@ -44,6 +44,10 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
``` ```
x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model 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 ### Paddle2ONNX
``` ```
# 注意:paddle_infer_model_dir下需包含__model__和__params__两个文件 # 注意:paddle_infer_model_dir下需包含__model__和__params__两个文件
...@@ -52,7 +56,7 @@ x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_ ...@@ -52,7 +56,7 @@ x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_
### 参数选项 ### 参数选项
| 参数 | | | 参数 | |
|----------|--------------| |----------|--------------|
|--framework | 源模型类型 (tensorflow、caffe、onnx、paddle2onnx) | |--framework | 源模型类型 (tensorflow、caffe、onnx、pytorch、paddle2onnx) |
|--prototxt | 当framework为caffe时,该参数指定caffe模型的proto文件路径 | |--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 | |--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 | |--save_dir | 指定转换后的模型保存目录路径 |
...@@ -62,6 +66,7 @@ x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_ ...@@ -62,6 +66,7 @@ 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) | |--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__ | |--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 | |--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")
```
__version__ = "0.8.4" __version__ = "0.8.4"
from .core.program import PaddleProgram from .core.program import PaddleGraph
program = PaddleProgram() program = PaddleGraph()
name_counter = dict() name_counter = dict()
......
...@@ -87,10 +87,14 @@ def arg_parser(): ...@@ -87,10 +87,14 @@ def arg_parser():
action="store_true", action="store_true",
default=False, default=False,
help="define whether merge the params") help="define whether merge the params")
parser.add_argument(
"--input_shapes",
"-is",
action='append',
default=None,
help="define the inputs' shape")
return parser return parser
def tf2paddle(model_path, def tf2paddle(model_path,
save_dir, save_dir,
without_data_format_optimization, without_data_format_optimization,
...@@ -190,6 +194,45 @@ def onnx2paddle(model_path, save_dir, params_merge=False): ...@@ -190,6 +194,45 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
print("Paddle model and code generating ...") print("Paddle model and code generating ...")
mapper.save_inference_model(save_dir, params_merge) mapper.save_inference_model(save_dir, params_merge)
print("Paddle model and code generated.") print("Paddle model and code generated.")
def pytorch2paddle(model_path, save_dir, input_shapes):
# check pytorch installation and version
try:
import torch
version = torch.__version__
ver_part = version.split('.')
print(ver_part)
if int(ver_part[1]) < 5:
print("[ERROR] pytorch>=1.5.0 is required")
return
except:
print(
"[ERROR] Pytorch is not installed, use \"pip install torch==1.5.0 torchvision\"."
)
return
print("Now translating model from pytorch to paddle.")
from x2paddle.decoder.pytorch_decoder import PyTorchDecoder
from x2paddle.op_mapper.pytorch2paddle import pytorch_op_mapper
model = PyTorchDecoder(model_path)
mapper = pytorch_op_mapper.PyTorchOpMapper(model)
mapper.graph.build()
print("Model optimizing ...")
from x2paddle.optimizer.pytorch_optimizer.optimizer import GraphOptimizer
graph_opt = GraphOptimizer()
graph_opt.optimize(mapper.graph)
print("Model optimized.")
if input_shapes is not None:
real_input_shapes = list()
for shape in input_shapes:
sp = shape[1:-1].split(",")
for i, s in enumerate(sp):
sp[i] = int(s)
real_input_shapes.append(sp)
else:
real_input_shapes = None
mapper.graph.gen_model(save_dir, real_input_shapes)
def paddle2onnx(model_path, save_dir, opset_version=10): def paddle2onnx(model_path, save_dir, opset_version=10):
...@@ -267,6 +310,10 @@ def main(): ...@@ -267,6 +310,10 @@ def main():
if args.params_merge: if args.params_merge:
params_merge = True params_merge = True
onnx2paddle(args.model, args.save_dir, params_merge) 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": elif args.framework == "paddle2onnx":
assert args.model is not None, "--model should be defined while translating paddle model to onnx" assert args.model is not None, "--model should be defined while translating paddle model to onnx"
...@@ -278,4 +325,4 @@ def main(): ...@@ -278,4 +325,4 @@ def main():
if __name__ == "__main__": if __name__ == "__main__":
main() main()
\ No newline at end of file
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
import torch
class PyTorchDecoder(object):
def __init__(self, script_path):
self.script = torch.jit.load(script_path)
self.graph = self._optimize_graph(self.script.inlined_graph)
def _optimize_graph(self, graph):
torch._C._jit_pass_constant_propagation(graph)
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_peephole(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_canonicalize(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_constant_propagation(graph)
return graph
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 .adaptive_pool2d_fuser import AdaptivePool2dFuser
from .adaptive_pool2d_fuse_pass import AdaptivePool2dFusePass
from .batchnorm2d_fuser import BatchNorm2dFuser
from .batchnorm2d_fuse_pass import BatchNorm2dFusePass
from .constant_fuser import ConstantFuser
from .constant_fuse_pass import ConstantFusePass
from .dropout_fuser import DropoutFuser
from .dropout_fuse_pass import DropoutFusePass
from .fc_fuser import FcFuser
from .fc_fuse_pass import FcFusePass
from .interpolate_bilinear_fuser import InterpolateBilinearFuser
from .interpolate_bilinear_fuse_pass import InterpolateBilinearFusePass
from .reshape_fuser import ReshapeFuser
from .reshape_fuse_pass import ReshapeFusePass
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 x2paddle.optimizer.pytorch_optimizer.pass_ import Pass
from x2paddle.optimizer.pytorch_optimizer.fusion import AdaptivePool2dFuser
from x2paddle.optimizer.pytorch_optimizer.pass_manager import pass_register
@pass_register
class AdaptivePool2dFusePass(Pass):
name = "adaptive_pool2d_fuse_pass"
def __init__(self):
Pass.__init__(self)
def apply(self, graph):
fuser = AdaptivePool2dFuser()
fuser.operate(graph, match_kind="topo")
# 用于注册
adaptive_pool2d_fuse_pass = AdaptivePool2dFusePass()
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 x2paddle.optimizer.pytorch_optimizer.pass_ import Pass
from x2paddle.optimizer.pytorch_optimizer.fusion import BatchNorm2dFuser
from x2paddle.optimizer.pytorch_optimizer.pass_manager import pass_register
@pass_register
class BatchNorm2dFusePass(Pass):
name = "batchnorm2d_fuse_pass"
def __init__(self):
Pass.__init__(self)
def apply(self, graph):
fuser = BatchNorm2dFuser()
fuser.operate(graph, match_kind="topo")
# 用于注册
batchnorm2d_fuse_pass = BatchNorm2dFusePass()
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 x2paddle.optimizer.pytorch_optimizer.pass_ import Pass
from x2paddle.optimizer.pytorch_optimizer.fusion import ConstantFuser
from x2paddle.optimizer.pytorch_optimizer.pass_manager import pass_register
@pass_register
class ConstantFusePass(Pass):
name = "constant_fuse_pass"
def __init__(self):
Pass.__init__(self)
def apply(self, graph):
fuser = ConstantFuser()
fuser.operate(graph, match_kind="topo")
# 用于注册
constant_fuse_pass = ConstantFuser()
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