Merge pull request #121 from Channingss/develop

fix torch bug

FAQ.md

 ## 常见问题
 
 **Q1. TensorFlow模型转换过程中，提示『Unknown shape for input tensor[tensor name: "input"]， Please define shape of input here』？**  
-A：该提示信息表示无法从TensorFlow的pb模型中获取到输入tensor(tensor名为"input:)的shape信息，所以需要用户手动在提示后输入详细的shape信息，如None,224,224,3 其中None表示Batch 
+A：该提示信息表示无法从TensorFlow的pb模型中获取到输入tensor(tensor名为"input:)的shape信息，所以需要用户手动在提示后输入详细的shape信息，如None,224,224,3 其中None表示Batch
 
 
 **Q2. TensorFlow模型转换失败怎么解决?**  

add_caffe_custom_layer.md

 ## 如何转换Caffe自定义Layer
 
-本文档介绍如何将Caffe自定义Layer转换为PaddlePaddle模型中的对应实现, 用户可根据自己需要，添加代码实现自定义层，从而支持模型的完整转换。            
-***步骤一 下载代码***        
+本文档介绍如何将Caffe自定义Layer转换为PaddlePaddle模型中的对应实现, 用户可根据自己需要，添加代码实现自定义层，从而支持模型的完整转换。  
+***步骤一 下载代码***  
 此处涉及修改源码，应先卸载x2paddle，并且下载源码，主要有以下两步完成：
 ```
 pip uninstall x2paddle
 pip install git+https://github.com/PaddlePaddle/X2Paddle.git@develop
 ```
 
-***步骤二 编译caffe.proto***   
+***步骤二 编译caffe.proto***  
 该步骤依赖protobuf编译器，其安装过程有以下两种方式：
-> 选择一：pip install protobuf             
+> 选择一：pip install protobuf  
 > 选择二：使用[官方源码](https://github.com/protocolbuffers/protobuf)进行编译
 
-使用脚本./tools/compile.sh将caffe.proto（包含所需的自定义Layer信息）编译成我们所需的目标语言（Python）         
+使用脚本./tools/compile.sh将caffe.proto（包含所需的自定义Layer信息）编译成我们所需的目标语言（Python）  
 使用方式：
 ```
 bash ./toos/compile.sh /home/root/caffe/src/caffe/proto
@@ -25,14 +25,14 @@ bash ./toos/compile.sh /home/root/caffe/src/caffe/proto
 - 仿照./x2paddle/op_mapper/caffe_custom_layer中的其他文件，在mylayer.py中主要需要实现3个函数，下面以roipooling.py为例分析代码：
   1. `def roipooling_shape(input_shape, pooled_w=None, pooled_h=None)`  
      参数：
-     1. input_shape（list）：其中每个元素代表该层每个输入数据的shape，为必须传入的参数   
+     1. input_shape（list）：其中每个元素代表该层每个输入数据的shape，为必须传入的参数  
      2. pooled_w（int）：代表ROI Pooling的kernel的宽，其命名与.prototxt中roi_pooling_param中的key一致
-     3. pooled_h（int）：代表ROI Pooling的kernel的高，其命名与.prototxt中roi_pooling_param中的key一致          
+     3. pooled_h（int）：代表ROI Pooling的kernel的高，其命名与.prototxt中roi_pooling_param中的key一致  
 
-     功能：计算出进行ROI Pooling后的shape         
-     返回：一个list，其中每个元素代表每个输出数据的shape，由于ROI Pooling的输出数据只有一个，所以其list长度为1     
+     功能：计算出进行ROI Pooling后的shape  
+     返回：一个list，其中每个元素代表每个输出数据的shape，由于ROI Pooling的输出数据只有一个，所以其list长度为1  
 
-  2. `def roipooling_layer(inputs, input_shape=None, name=None, pooled_w=None, pooled_h=None, spatial_scale=None)` 
+  2. `def roipooling_layer(inputs, input_shape=None, name=None, pooled_w=None, pooled_h=None, spatial_scale=None)`
 
      参数：
      1. inputs（list）：其中每个元素代表该层每个输入数据，为必须传入的参数
@@ -40,9 +40,9 @@ bash ./toos/compile.sh /home/root/caffe/src/caffe/proto
      3. name（str）：ROI Pooling层的名字，为必须传入的参数  
      4. pooled_w（int）：代表ROI Pooling的kernel的宽，其命名与.prototxt中roi_pooling_param中的key一致
      5. pooled_h（int）：代表ROI Pooling的kernel的高，其命名与.prototxt中roi_pooling_param中的key一致
-     6. spatial_scale（float）：用于将ROI坐标从输入比例转换为池化时使用的比例，其命名与.prototxt中roi_pooling_param中的key一致            
+     6. spatial_scale（float）：用于将ROI坐标从输入比例转换为池化时使用的比例，其命名与.prototxt中roi_pooling_param中的key一致  
 
-     功能：运用PaddlePaddle完成组网来实现`roipooling_layer`的功能         
+     功能：运用PaddlePaddle完成组网来实现`roipooling_layer`的功能  
      返回：一个Variable，为组网后的结果
 
   3. `def roipooling_weights(name, data=None)`  
@@ -51,7 +51,7 @@ bash ./toos/compile.sh /home/root/caffe/src/caffe/proto
      1. name（str）：ROI Pooling层的名字，为必须传入的参数
      2. data（list）：由Caffe模型.caffemodel获得的关于roipooling的参数，roipooling的参数为None
 
-     功能：为每个参数（例如kernel、bias等）命名；同时，若Caffe中该层参数与PaddlePaddle中参数的格式不一致，则变换操作也在该函数中实现。     
+     功能：为每个参数（例如kernel、bias等）命名；同时，若Caffe中该层参数与PaddlePaddle中参数的格式不一致，则变换操作也在该函数中实现。  
      返回：一个list，包含每个参数的名字。
 
 - 在roipooling.py中注册`roipooling`，主要运用下述代码实现：
@@ -70,9 +70,9 @@ bash ./toos/compile.sh /home/root/caffe/src/caffe/proto
 # 在X2Paddle目录下安装x2paddle
 python setup.py install
 # 运行转换代码
-x2paddle --framework=caffe 
-         --prototxt=deploy.proto 
-         --weight=deploy.caffemodel 
-         --save_dir=pd_model 
+x2paddle --framework=caffe
+         --prototxt=deploy.proto
+         --weight=deploy.caffemodel
+         --save_dir=pd_model
          --caffe_proto=/home/root/caffe/src/caffe/proto/caffe_pb2.py
 ```

caffe_custom_layer.md

@@ -10,5 +10,3 @@
 | Normalize | [code](https://github.com/weiliu89/caffe/blob/ssd/src/caffe/layers/normalize_layer.cpp) |
 | ROIPooling | [code](https://github.com/rbgirshick/caffe-fast-rcnn/blob/0dcd397b29507b8314e252e850518c5695efbb83/src/caffe/layers/roi_pooling_layer.cpp) |
 | Axpy | [code](https://github.com/hujie-frank/SENet/blob/master/src/caffe/layers/axpy_layer.cpp) |
-
-

export_tf_model.md

@@ -28,7 +28,7 @@ def freeze_model(sess, output_tensor_names, freeze_model_path):
         f.write(out_graph.SerializeToString())
 
     print("freeze model saved in {}".format(freeze_model_path))
-    
+
 # 加载模型参数
 sess = tf.Session()
 inputs = tf.placeholder(dtype=tf.float32,

scripts/check_code_style.sh

@@ -7,7 +7,6 @@ function abort(){
 
 trap 'abort' 0
 set -e
-
 cd $TRAVIS_BUILD_DIR
 export PATH=/usr/bin:$PATH
 pre-commit install

x2paddle/convert.py

@@ -179,6 +179,9 @@ def main():
             x2paddle.__version__))
         return
 
+    assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
+    assert args.save_dir is not None, "--save_dir is not defined"
+
     try:
         import paddle
         v0, v1, v2 = paddle.__version__.split('.')
@@ -187,8 +190,6 @@ def main():
             return
     except:
         print("paddlepaddle not installed, use \"pip install paddlepaddle\"")
-    assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
-    assert args.save_dir is not None, "--save_dir is not defined"
 
     if args.framework == "tensorflow":
         assert args.model is not None, "--model should be defined while translating tensorflow model"

x2paddle/core/op_mapper.py

@@ -11,6 +11,7 @@
 # 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 paddle.fluid as fluid
 from paddle.fluid.proto import framework_pb2
 from x2paddle.core.util import *
 import inspect
@@ -46,6 +47,28 @@ def export_paddle_param(param, param_name, dir):
     fp.close()
 
 
+# This func will copy to generate code file
+def run_net(param_dir="./"):
+    import os
+    inputs, outputs = x2paddle_net()
+    for i, out in enumerate(outputs):
+        if isinstance(out, list):
+            for out_part in out:
+                outputs.append(out_part)
+            del outputs[i]
+    exe = fluid.Executor(fluid.CPUPlace())
+    exe.run(fluid.default_startup_program())
+
+    def if_exist(var):
+        b = os.path.exists(os.path.join(param_dir, var.name))
+        return b
+
+    fluid.io.load_vars(exe,
+                       param_dir,
+                       fluid.default_main_program(),
+                       predicate=if_exist)
+
+
 class OpMapper(object):
     def __init__(self):
         self.paddle_codes = ""

x2paddle/core/util.py

@@ -11,8 +11,6 @@
 # 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 paddle.fluid as fluid
 import numpy
 import math
 import os
@@ -20,25 +18,3 @@ import os
 
 def string(param):
     return "\'{}\'".format(param)
-
-
-# This func will copy to generate code file
-def run_net(param_dir="./"):
-    import os
-    inputs, outputs = x2paddle_net()
-    for i, out in enumerate(outputs):
-        if isinstance(out, list):
-            for out_part in out:
-                outputs.append(out_part)
-            del outputs[i]
-    exe = fluid.Executor(fluid.CPUPlace())
-    exe.run(fluid.default_startup_program())
-
-    def if_exist(var):
-        b = os.path.exists(os.path.join(param_dir, var.name))
-        return b
-
-    fluid.io.load_vars(exe,
-                       param_dir,
-                       fluid.default_main_program(),
-                       predicate=if_exist)

x2paddle/op_mapper/caffe_op_mapper.py

@@ -135,7 +135,8 @@ class CaffeOpMapper(OpMapper):
         if isinstance(params.kernel_size, numbers.Number):
             [k_h, k_w] = [params.kernel_size] * 2
         elif len(params.kernel_size) > 0:
-            k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[0]
+            k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[
+                0]
             k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[
                 len(params.kernel_size) - 1]
         elif params.kernel_h > 0 or params.kernel_w > 0:
@@ -156,8 +157,8 @@ class CaffeOpMapper(OpMapper):
             [p_h, p_w] = [params.pad] * 2
         elif len(params.pad) > 0:
             p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
-            p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) -
-                                                                  1]
+            p_w = params.pad_w if params.pad_w > 0 else params.pad[
+                len(params.pad) - 1]
         elif params.pad_h > 0 or params.pad_w > 0:
             p_h = params.pad_h
             p_w = params.pad_w
@@ -225,12 +226,17 @@ class CaffeOpMapper(OpMapper):
             node.layer_type, params)
         if data is None:
             data = []
-            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
-            node.layer_name, node.layer_type))
+            print(
+                'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
+                .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
             output_c = channel
-            data.append(np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype('float32'))
-            data.append(np.zeros([output_c,])).astype('float32')
+            data.append(
+                np.zeros([output_c, input_c, kernel[0],
+                          kernel[1]]).astype('float32'))
+            data.append(np.zeros([
+                output_c,
+            ])).astype('float32')
         else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
@@ -272,12 +278,17 @@ class CaffeOpMapper(OpMapper):
             node.layer_type, params)
         if data is None:
             data = []
-            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
-            node.layer_name, node.layer_type))
+            print(
+                'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
+                .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
             output_c = channel
-            data.append(np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype('float32'))
-            data.append(np.zeros([output_c,]).astype('float32'))
+            data.append(
+                np.zeros([output_c, input_c, kernel[0],
+                          kernel[1]]).astype('float32'))
+            data.append(np.zeros([
+                output_c,
+            ]).astype('float32'))
         else:
             data = self.adjust_parameters(node)
         self.weights[node.layer_name + '_weights'] = data[0]
@@ -369,13 +380,17 @@ class CaffeOpMapper(OpMapper):
         data = node.data
         params = node.layer.inner_product_param
         if data is None:
-            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0.'.format(
-            node.layer_name, node.layer_type))
+            print(
+                'The parameter of {} (type is {}) is not set. So we set the parameters as 0.'
+                .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
             output_c = params.num_output
             data = []
-            data.append(np.zeros([input_c, output_c]).astype('float32').astype('float32'))
-            data.append(np.zeros([output_c]).astype('float32').astype('float32'))
+            data.append(
+                np.zeros([input_c,
+                          output_c]).astype('float32').astype('float32'))
+            data.append(
+                np.zeros([output_c]).astype('float32').astype('float32'))
         else:
             data = self.adjust_parameters(node)
             # Reshape the parameters to Paddle's ordering
@@ -467,7 +482,7 @@ class CaffeOpMapper(OpMapper):
                 node.layer_name, node.layer_name + '_' + str(i)))
             if i == len(points) - 2:
                 break
-                
+
     def Concat(self, node):
         assert len(
             node.inputs
@@ -616,7 +631,8 @@ class CaffeOpMapper(OpMapper):
                                       param_attr=attr)
 
     def BatchNorm(self, node):
-        assert len(node.inputs) == 1, 'The count of BatchNorm node\'s input is not 1.'
+        assert len(
+            node.inputs) == 1, 'The count of BatchNorm node\'s input is not 1.'
         input = self.graph.get_bottom_node(node, idx=0, copy=True)
         params = node.layer.batch_norm_param
         if hasattr(params, 'eps'):
@@ -624,11 +640,16 @@ class CaffeOpMapper(OpMapper):
         else:
             eps = 1e-5
         if node.data is None or len(node.data) != 3:
-            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
-            node.layer_name, node.layer_type))
+            print(
+                'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
+                .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
-            mean = np.zeros([input_c,]).astype('float32')
-            variance = np.zeros([input_c,]).astype('float32')
+            mean = np.zeros([
+                input_c,
+            ]).astype('float32')
+            variance = np.zeros([
+                input_c,
+            ]).astype('float32')
             scale = 0
         else:
             node.data = [np.squeeze(i) for i in node.data]
@@ -655,11 +676,16 @@ class CaffeOpMapper(OpMapper):
 
     def Scale(self, node):
         if node.data is None:
-            print('The parameter of {} (type is {}) is not set. So we set the parameters as 0'.format(
-            node.layer_name, node.layer_type))
+            print(
+                'The parameter of {} (type is {}) is not set. So we set the parameters as 0'
+                .format(node.layer_name, node.layer_type))
             input_c = node.input_shape[0][1]
-            self.weights[node.layer_name + '_scale'] = np.zeros([input_c,]).astype('float32')
-            self.weights[node.layer_name + '_offset'] = np.zeros([input_c,]).astype('float32')
+            self.weights[node.layer_name + '_scale'] = np.zeros([
+                input_c,
+            ]).astype('float32')
+            self.weights[node.layer_name + '_offset'] = np.zeros([
+                input_c,
+            ]).astype('float32')
         else:
             self.weights[node.layer_name + '_scale'] = np.squeeze(node.data[0])
             self.weights[node.layer_name + '_offset'] = np.squeeze(node.data[1])

x2paddle/op_mapper/caffe_shape.py

@@ -43,7 +43,8 @@ def get_kernel_parameters(params):
         [p_h, p_w] = [params.pad] * 2
     elif len(params.pad) > 0:
         p_h = params.pad_h if params.pad_h > 0 else params.pad[0]
-        p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) - 1]
+        p_w = params.pad_w if params.pad_w > 0 else params.pad[len(params.pad) -
+                                                               1]
     elif params.pad_h > 0 or params.pad_w > 0:
         p_h = params.pad_h
         p_w = params.pad_w

x2paddle_model_zoo.md

@@ -65,4 +65,3 @@
 | mNASNet | [pytorch(personal practice)](https://github.com/rwightman/gen-efficientnet-pytorch) |9|
 | EfficientNet | [pytorch(personal practice)](https://github.com/rwightman/gen-efficientnet-pytorch) |9|
 | SqueezeNet | [onnx official](https://s3.amazonaws.com/download.onnx/models/opset_9/squeezenet.tar.gz) |9|
-