Skip to content

X
X2Paddle

PaddlePaddle / X2Paddle
大约 1 年 前同步成功

通知 328
Star 698
Fork 167
X
X2Paddle
未验证 提交 108533b2 编写于 作者: S SunAhong1993 提交者： GitHub
浏览文件
操作

Merge pull request #1 from PaddlePaddle/develop 

add
上级 f75e11dc a72829d0
显示空白变更内容
内联 并排
Showing with 360 addition and 174 deletion
README.md
浏览文件 @ 108533b2
......@@ -5,7 +5,7 @@ X2Paddle支持将其余深度学习框架训练得到的模型，转换至Paddle
X2Paddle is a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks.
## 转换模型库
X2Paddle在多个主流的CV模型上，测试过TensorFlow/Caffe/ONNX模型的转换，可以在[X2Paddle-Model-Zoo](x2paddle_model_zoo.md)查看我们的模型测试列表。如果你在新的模型上进行了测试转换，也欢迎继续补充该列表；如若无法转换，可通过ISSUE反馈给我们，我们会尽快跟进。
X2Paddle在多个主流的CV模型上，测试过TensorFlow/Caffe/ONNX模型的转换，可以在[X2Paddle-Model-Zoo](x2paddle_model_zoo.md)查看我们的模型测试列表，可以在[OP-LIST](op_list.md)中查看目前X2Paddle支持的OP列表。如果你在新的模型上进行了测试转换，也欢迎继续补充该列表；如若无法转换，可通过ISSUE反馈给我们，我们会尽快跟进。
## 环境依赖
......@@ -29,7 +29,7 @@ python setup.py install
### 安装方式二
我们会定期更新pip源上的x2paddle版本
```
pip install x2paddle
pip install x2paddle --index https://pypi.Python.org/simple/
```
## 使用方法
### TensorFlow
......@@ -38,7 +38,7 @@ x2paddle --framework=tensorflow --model=tf_model.pb --save_dir=pd_model
```
### Caffe
```
x2paddle --framework=caffe --prototxt=deploy.proto --weight=deploy.caffemodel --save_dir=pd_model
x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel --save_dir=pd_model
```
### ONNX
```
......
op_list.md 0 → 100644
浏览文件 @ 108533b2
# X2Paddle支持OP列表
> 目前X2Paddle支持40+的TensorFlow OP，30+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。
**注：** 目前，部分OP暂未支持，如您在转换过程中出现OP不支持的情况，可自行添加或反馈给我们。欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：）
## TensorFlow
| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |
|------|------|------|------|------|------|------|------|
| 1 | Relu | 2 | Relu6 | 3 | Shape | 4 | Abs |
| 5 | Sigmoid | 6 | Exp | 7 | Rsqrt | 8 | swish_f32 |
| 9 | Tanh | 10 | LeakyRelu | 11 | Add | 12 | RealDiv |
| 13 | Sub | 14 | Maximum | 15 | Mul | 16 | FloorDiv |
| 17 | Placeholder | 18 | Const | 19 | Transpose | 20 | FusedBatchNorm |
| 21 | Conv2D | 22 | BiasAdd | 23 | MaxPool | 24 | DepthwiseConv2dNative |
| 25 | Reshape | 26 | AvgPool | 27 | SplitV | 28 | SquaredDifference |
| 29 | Tile | 30 | Pack | 31 | Pad | 32 | ResizeBilinear |
| 33 | Mean | 34 | MatMul | 35 | ArgMax | 36 | StridedSlice |
| 37 | Slice | 38 | Sum | 39 | Max | 40 | Conv2DBackpropInput |
| 41 | Cast | 42 | Split | 43 | Squeeze | 44 | ResizeNearestNeighbor |
| 45 | Softmax | 46 | Range | 47 | ConcatV2 |
## Caffe
| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |
|------|------|------|------|------|------|------|------|
| 1 | Input | 2 | Convolution | 3 | Deconvolution | 4 | Pooling |
| 5 | LRN | 6 | InnerProduct | 7 | Softmax | 8 | Slice |
| 9 | Concat | 10 | PReLU | 11 | Accuracy | 12 | Eltwise |
| 13 | BatchNorm | 14 | Scale | 15 | Reshape | 16 | ArgMax |
| 17 | Crop | 18 | Flatten | 19 | Power | 20 | Reduction |
| 21 | Axpy | 22 | ROIPolling | 23 | Permute | 24 | DetectionOutput |
| 25 | Normalize | 26 | Select | 27 | ShuffleChannel | 28 | ConvolutionDepthwise |
| 29 | ReLU | 30 | AbsVal | 31 | Sigmoid | 32 | TanH |
## ONNX
| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |
|------|------|------|------|------|------|------|------|
| 1 | Relu | 2 | LeakyRelu | 3 | Elu | 4 | ThresholdedRelu |
| 5 | Prelu | 6 | Tanh | 7 | Shrink | 8 | Sigmoid |
| 9 | Pow | 10 | Softplus | 11 | Softsign | 12 | HardSigmoid |
| 13 | Exp | 14 | Add | 15 | Div | 16 | Sub |
| 17 | Mul | 18 | Shape | 19 | Clip | 20 | AveragePool |
| 21 | Sqrt | 22 | ReduceSum | 23 | ReduceMin | 24 | ReduceMean |
| 25 | Constant | 26 | Pad | 27 | Unsqueeze | 28 | Resize |
| 29 | Upsample | 30 | Expand | 31 | Gather | 32 | Slice |
| 33 | Cast | 34 | Split | 35 | Reshape | 36 | ConstantOfShape |
| 37 | Ceil | 38 | Concat | 39 | Flatten | 40 | ConvTranspose |
| 41 | MatMul | 42 | Sum | 43 | Transpose | 44 | BatchNormalization |
| 45 | Squeeze | 46 | Equal | 47 | Identity | 48 | GlobalAveragePool |
| 49 | MaxPool | 50 | Conv | 51 | Gemm |
x2paddle/decoder/tf_decoder.py
浏览文件 @ 108533b2
......@@ -312,6 +312,10 @@ class TFDecoder(object):
right_shape_been_input = False
while not right_shape_been_input:
try:
shape = raw_input(
"Shape of Input(e.g. None,224,224,3): ")
except:
shape = input("Shape of Input(e.g. None,224,224,3): ")
if shape.count("None") > 1:
print("Only 1 dimension can be None, type again:)")
......
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @ 108533b2
......@@ -168,7 +168,11 @@ class TFOpMapper(OpMapper):
x_input = y
y_input = x
x_shape = y.out_shapes[0]
if len(x_shape) == 0:
x_shape = [1]
y_shape = x.out_shapes[0]
if len(y_shape) == 0:
y_shape = [1]
else:
if len(x_shape) == 1 and len(y_shape) == 4 and x_shape[
0] == y_shape[-1] and y_shape.count(-1) < 1:
......@@ -1006,7 +1010,7 @@ class TFOpMapper(OpMapper):
attr = {
"bias_attr": False,
"param_attr": string(kernel.layer_name),
"num_filters": k_size[3],
"num_filters": k_size[2],
"filter_size": k_size[0:2],
"stride": strides[2:4],
"dilation": dilations[2:4],
......@@ -1112,40 +1116,6 @@ class TFOpMapper(OpMapper):
output=node,
param_attr=attr)
def ResizeNearestNeighbor(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
self.add_omit_nodes(resize_shape.layer_name, node.layer_name)
if resize_shape.layer_type == "Const":
resize_shape = resize_shape.value.tolist()
else:
resize_shape = self.decoder.infer_shape_tensor(resize_shape)
align_corners = node.get_attr("align_corners")
attr = {"align_corners": align_corners, "out_shape": resize_shape}
node.fluid_code.add_layer("resize_nearest",
inputs=input,
output=node,
param_attr=attr)
def ResizeBilinear(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
self.add_omit_nodes(resize_shape.layer_name, node.layer_name)
if resize_shape.layer_type == "Const":
resize_shape = resize_shape.value.tolist()
else:
resize_shape = self.decoder.infer_shape_tensor(resize_shape)
align_corners = node.get_attr("align_corners")
attr = {
"align_corners": align_corners,
"out_shape": resize_shape,
"align_mode": 1
}
node.fluid_code.add_layer("resize_bilinear",
inputs=input,
output=node,
param_attr=attr)
def ResizeNearestNeighbor(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
resize_shape = self.graph.get_node(node.layer.input[1], copy=True)
......@@ -1191,37 +1161,6 @@ class TFOpMapper(OpMapper):
output=node,
param_attr=None)
def RandomUniform(self, node):
shape = self.graph.get_node(node.layer.input[0], copy=True)
self.add_omit_nodes(shape.layer_name, node.layer_name)
if shape.layer_type == "Const":
shape = shape.value.tolist()
else:
shape = self.decoder.infer_shape_tensor(shape)
if node.tf_data_format == "NHWC" and len(shape) == 4:
shape = [shape[i] for i in [0, 3, 1, 2]]
attr = {"shape": shape, "min": 0.0, "max": 0.9999}
if shape[0] < 0:
input = self.batch_node
node.fluid_code.add_layer("uniform_random_batch_size_like",
inputs=input,
output=node,
param_attr=attr)
else:
node.fluid_code.add_layer("uniform_random",
inputs=None,
output=node,
param_attr=attr)
def GreaterEqual(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
y = self.graph.get_node(node.layer.input[1], copy=True)
inputs = {"x": x, "y": y}
node.fluid_code.add_layer("greater_equal",
inputs=inputs,
output=node,
param_attr=None)
def RandomUniform(self, node):
shape = self.graph.get_node(node.layer.input[0], copy=True)
self.add_omit_nodes(shape.layer_name, node.layer_name)
......
x2paddle/op_mapper/tf_op_mapper_nhwc.py
浏览文件 @ 108533b2
......@@ -121,6 +121,25 @@ class TFOpMapperNHWC(OpMapper):
pd_param_name = list(param.values())[0]
tf_param = node.get_attr(tf_param_name)
attr[pd_param_name] = tf_param
if len(input.out_shapes[0]) == 4 and op_info[0] != 'shape':
attr1 = {"perm": [0, 3, 1, 2]}
node.fluid_code.add_layer('transpose',
inputs=input,
output=node,
param_attr=attr1)
input = node
node.fluid_code.add_layer(op_info[0],
inputs=input,
output=node,
param_attr=attr)
input = node
attr2 = {"perm": [0, 2, 3, 1]}
node.fluid_code.add_layer('transpose',
inputs=input,
output=node,
param_attr=attr2)
else:
node.fluid_code.add_layer(op_info[0],
inputs=input,
output=node,
......@@ -149,7 +168,11 @@ class TFOpMapperNHWC(OpMapper):
x_input = y
y_input = x
x_shape = y.out_shapes[0]
if len(x_shape) == 0:
x_shape = [1]
y_shape = x.out_shapes[0]
if len(y_shape) == 0:
y_shape = [1]
else:
raise Exception("Unexpected situation happend")
......@@ -193,6 +216,25 @@ class TFOpMapperNHWC(OpMapper):
output="y_tmp",
param_attr=attr)
y_input = "y_tmp"
if len(x_shape) == 4 and len(y_shape) == 4:
node.fluid_code.add_layer("transpose",
inputs=x_input,
output=x_input,
param_attr={'perm': [0, 3, 1, 2]})
node.fluid_code.add_layer("transpose",
inputs=y_input,
output=y_input,
param_attr={'perm': [0, 3, 1, 2]})
inputs = {"x": x_input, "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer("transpose",
inputs=node,
output=node,
param_attr={'perm': [0, 2, 3, 1]})
else:
inputs = {"x": x_input, "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
......@@ -965,7 +1007,7 @@ class TFOpMapperNHWC(OpMapper):
attr = {
"bias_attr": False,
"param_attr": string(kernel.layer_name),
"num_filters": k_size[3],
"num_filters": k_size[2],
"filter_size": k_size[0:2],
"stride": strides[2:4],
"dilation": dilations[2:4],
......@@ -978,9 +1020,7 @@ class TFOpMapperNHWC(OpMapper):
if pad_mode == "SAME":
if node.tf_data_format == "NHWC":
print(out_shape)
out_shape = [out_shape[i] for i in [0, 3, 1, 2]]
print(out_shape)
for i in range(4):
if out_shape[i] < 0:
out_shape[i] = 999999
......
x2paddle/optimizer/tf_optimizer.py
浏览文件 @ 108533b2
......@@ -232,84 +232,165 @@ class TFOptimizer(object):
'act']
node.fluid_code.clear()
self.graph.remove_node(node.layer_name)
self.graph.identity_map[node.layer_name] = input.layer_name
def remove_transpose(self):
graph_copy = cp.deepcopy(self.graph)
nhwc_insensitive_ops = [
'Relu', 'Relu6', 'Abs', 'Sigmoid', 'Exp', 'Rsqrt', 'swish_f32',
'LeakyRelu', 'Cast'
'LeakyRelu', 'Cast', 'Tanh'
]
elementwise_ops = [
'Sub', 'Add', 'RealDiv', 'Maximum', 'Mul', 'FloorDiv',
'GreaterEqual'
]
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in nhwc_insensitive_ops:
graph_copy.remove_node(node_name)
optimize_ops = [
'Conv2D', 'MaxPool', 'FusedBatchNorm', 'DepthwiseConv2dNative',
'AvgPool', 'Pad', 'Conv2DBackpropInput', 'ResizeNearestNeighbor',
'ResizeBilinear', "Placeholder"
]
can_be_optimized_ops = [
'Conv2D', 'MaxPool', 'FusedBatchNorm', 'DepthwiseConv2dNative',
'AvgPool', 'Pad', 'Conv2DBackpropInput', 'ResizeNearestNeighbor',
'ResizeBilinear', "Placeholder", 'Relu', 'Relu6', 'Abs', 'Sigmoid',
'Exp', 'Rsqrt', 'swish_f32', 'LeakyRelu', 'Cast', 'Tanh'
]
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
is_nhwc = True
for in_name in node.inputs:
in_node = graph_copy.get_node(in_name)
if hasattr(in_node, "is_nhwc"):
if not in_node.is_nhwc:
is_nhwc = False
else:
if len(in_node.fluid_code.layers) < 2:
is_nhwc = False
continue
if in_node.fluid_code.layers[
-1].op != "transpose" or in_node.fluid_code.layers[
if node.layer_type in can_be_optimized_ops:
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
is_nhwc = False
continue
node.is_nhwc = is_nhwc
can_be_removed = True
output_names = node.outputs
for out_name in output_names:
out_node = graph_copy.get_node(out_name)
if hasattr(out_node, "can_be_removed"):
if not out_node.can_be_removed:
can_be_removed = False
break
elif out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type in elementwise_ops:
can_be_removed = False
break
if can_be_removed and len(node.fluid_code.layers) > 1:
true_node = self.graph.get_node(node_name)
if true_node.layer_type == "Placeholder":
index = self.graph.input_nodes.index(
true_node.fluid_code.layers[-2].output)
if isinstance(true_node.fluid_code.layers[-1].output,
str):
self.graph.input_nodes[
index] = true_node.fluid_code.layers[-1].output
else:
self.graph.input_nodes[
index] = true_node.fluid_code.layers[
-1].output.layer_name
true_node.fluid_code.layers[
-2].output = true_node.fluid_code.layers[-1].output
node.removed = True
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
for i in range(len(self.graph.topo_sort)):
node_name = self.graph.topo_sort[-1 * i - 1]
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
can_be_removed = True
if len(node.fluid_code.layers) > 1:
can_be_removed = False
if not node.is_nhwc:
can_be_removed = False
for out_name in node.outputs:
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
continue
can_be_removed = True
output_names = node.outputs
for out_name in output_names:
out_node = graph_copy.get_node(out_name)
if hasattr(out_node, "is_nhwc"):
if not out_node.is_nhwc:
if len(out_node.fluid_code.layers) < 3:
can_be_removed = False
else:
if len(out_node.fluid_code.layers) < 2:
break
if hasattr(out_node, "can_be_removed"):
if not out_node.can_be_removed:
can_be_removed = False
break
if out_node.layer_type in can_be_optimized_ops:
if out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
node.can_be_removed = can_be_removed
elif out_node.layer_type in elementwise_ops:
if out_node.fluid_code.layers[
0].op != "transpose" and out_node.fluid_code.layers[
1].op != "transpose":
can_be_removed = False
break
if out_node.fluid_code.layers[0].op == "transpose":
if out_node.fluid_code.layers[0].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if out_node.fluid_code.layers[1].op == "transpose":
if out_node.fluid_code.layers[1].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if can_be_removed and len(node.fluid_code.layers) > 1:
true_node = self.graph.get_node(node_name)
true_node.fluid_code.layers[
-2].output = true_node.fluid_code.layers[-1].output
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
if out_node.layer_type in can_be_optimized_ops:
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
elif out_node.layer_type in elementwise_ops:
if out_node.inputs[0] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
else:
out_node.fluid_code.layers[1].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
elif out_node.inputs[1] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'y'] = out_node.fluid_code.layers[
1].inputs
del out_node.fluid_code.layers[1]
else:
out_node.fluid_code.layers[1].inputs[
'y'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
graph_copy = cp.deepcopy(self.graph)
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
if node is None or len(node.fluid_code.layers) < 2:
continue
if node.layer_type in optimize_ops:
if node.layer_type in can_be_optimized_ops and node.layer_type != "Placeholder":
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
......@@ -322,85 +403,154 @@ class TFOptimizer(object):
if not out_node.can_be_removed:
can_be_removed = False
break
elif out_node.fluid_code.layers[
if len(out_node.fluid_code.layers) < 2:
can_be_removed = False
break
if out_node.layer_type in can_be_optimized_ops:
if out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if can_be_removed and len(node.fluid_code.layers) > 1:
elif out_node.layer_type in elementwise_ops:
if out_node.fluid_code.layers[
0].op != "transpose" and out_node.fluid_code.layers[
1].op != "transpose":
can_be_removed = False
break
if out_node.fluid_code.layers[
0].op == "expand" or out_node.fluid_code.layers[
1].op == "expand":
can_be_removed = False
break
if out_node.fluid_code.layers[0].op == "transpose":
if out_node.fluid_code.layers[0].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if out_node.fluid_code.layers[1].op == "transpose":
if out_node.fluid_code.layers[1].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type not in elementwise_ops and out_node.layer_type not in can_be_optimized_ops:
can_be_removed = False
break
if can_be_removed:
true_node = self.graph.get_node(node_name)
if true_node.layer_type == "Placeholder":
index = self.graph.input_nodes.index(
true_node.fluid_code.layers[-2].output)
if isinstance(true_node.fluid_code.layers[-1].output,
str):
self.graph.input_nodes[
index] = true_node.fluid_code.layers[-1].output
else:
self.graph.input_nodes[
index] = true_node.fluid_code.layers[
-1].output.layer_name
if len(true_node.fluid_code.layers) < 2:
continue
true_node.fluid_code.layers[
-2].output = true_node.fluid_code.layers[-1].output
node.removed = True
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
if out_node.layer_type in elementwise_ops:
continue
if out_node.layer_type in can_be_optimized_ops:
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
elif out_node.layer_type in elementwise_ops:
if out_node.inputs[0] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
if out_node.fluid_code.layers[
2].op == 'transpose':
out_node.fluid_code.layers[3].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
else:
out_node.fluid_code.layers[2].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
else:
out_node.fluid_code.layers[1].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
elif out_node.inputs[1] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'y'] = out_node.fluid_code.layers[
1].inputs
del out_node.fluid_code.layers[1]
else:
out_node.fluid_code.layers[1].inputs[
'y'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
graph_copy = cp.deepcopy(self.graph)
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
if not node.can_be_removed:
can_be_removed = True
if len(node.fluid_code.layers) < 3:
continue
numTranspose = 0
numNotTranspose = 0
for i in range(len(node.fluid_code.layers)):
if node.fluid_code.layers[i].op == 'transpose':
numTranspose += 1
elif node.fluid_code.layers[i].op != 'expand':
numNotTranspose += 1
if numTranspose > numNotTranspose:
if node.fluid_code.layers[0].op == 'expand':
if node.fluid_code.layers[
1].op != 'transpose' or node.fluid_code.layers[
2].op != 'transpose':
continue
else:
true_node = self.graph.get_node(node_name)
for i, in_name in enumerate(node.inputs):
in_node = graph_copy.get_node(in_name)
if hasattr(in_node, "is_nhwc") and in_node.is_nhwc:
if i == 0:
l = Layer()
l.op = "transpose"
l.inputs = true_node.fluid_code.layers[
0].inputs["x"]
l.param_attr = {"perm": [0, 2, 3, 1]}
l.output = "nhwc_" + l.inputs.layer_name
true_node.fluid_code.layers[0].inputs[
"x"] = l.output
true_node.fluid_code.layers.insert(0, l)
elif i == 1:
true_node.fluid_code.layers[3].inputs[
'x'] = true_node.fluid_code.layers[1].inputs
true_node.fluid_code.layers[3].inputs[
'y'] = true_node.fluid_code.layers[2].inputs
l = Layer()
l.op = "transpose"
l.inputs = true_node.fluid_code.layers[
0].inputs["y"]
l.param_attr = {"perm": [0, 2, 3, 1]}
l.output = "nhwc_" + l.inputs.layer_name
true_node.fluid_code.layers[0].inputs[
"y"] = l.output
true_node.fluid_code.layers.insert(0, l)
l.op = 'transpose'
l.inputs = true_node.fluid_code.layers[3].output
l.param_attr = {'perm': [0, 3, 1, 2]}
if isinstance(l.inputs, six.string_types):
l.output = l.inputs
else:
raise Exception("Unexpected situation happend")
l.output = l.inputs.layer_name
true_node.fluid_code.layers.append(l)
del true_node.fluid_code.layers[1]
del true_node.fluid_code.layers[1]
else:
if node.fluid_code.layers[
0].op != 'transpose' or node.fluid_code.layers[
1].op != 'transpose':
continue
else:
for out_name in node.outputs:
out_node = self.graph.get_node(out_name)
if out_node.layer_type not in elementwise_ops:
assert out_node.fluid_code.layers[
0].op == "transpose", "unexpected situation happend"
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
true_node = self.graph.get_node(node_name)
true_node.fluid_code.layers[2].inputs[
'x'] = true_node.fluid_code.layers[0].inputs
true_node.fluid_code.layers[2].inputs[
'y'] = true_node.fluid_code.layers[1].inputs
l = Layer()
l.op = 'transpose'
l.inputs = true_node.fluid_code.layers[2].output
l.param_attr = {'perm': [0, 3, 1, 2]}
l.output = l.inputs.layer_name
true_node.fluid_code.layers.append(l)
del true_node.fluid_code.layers[0]
del true_node.fluid_code.layers[0]
def make_nchw_input_output(self):
for i, name in enumerate(self.graph.input_nodes):
node = self.graph.get_node(name)
if len(node.out_shapes[0]) == 4 and node.tf_data_format == "NHWC":
shape = node.fluid_code.layers[0].param_attr["shape"]
shape = [shape[i] for i in [0, 3, 1, 2]]
shape = [shape[j] for j in [0, 3, 1, 2]]
node.fluid_code.layers[0].param_attr["shape"] = shape
node.fluid_code.layers[0].output = "nhwc_" + name
attr = {"perm": [0, 2, 3, 1]}
......
x2paddle_model_zoo.md
浏览文件 @ 108533b2
......@@ -65,3 +65,4 @@
| 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|
|Ultra-Light-Fast-Generic-Face-Detector-1MB| [onnx_model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/models/onnx)| |
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或