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X2Paddle

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    https://gitcode.net/paddlepaddle/X2Paddle/-/commit/8b183f71608d02fe51a0f52074e02591178f79d4 Support GFPGAN 2022-11-02T15:14:11+08:00 wjj19950828 wjjisloser@163.com https://gitcode.net/paddlepaddle/X2Paddle/-/commit/0423b067da519b435d7ca6cb2c484aeeb085fbcd Support GFPGAN 2022-11-02T15:20:00+08:00 wjj19950828 wjjisloser@163.com https://gitcode.net/paddlepaddle/X2Paddle/-/commit/80439dab6f8f5c2eef09555bec8dc6e17edd8354 Merge pull request #906 from wjj19950828/support_GFPGAN 2022-11-07T19:31:49+08:00 Jason jiangjiajun@baidu.com Support GFPGAN
隐藏空白更改
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Showing with 284 addition and 103 deletion
docs/inference_model_convertor/op_list.md
浏览文件 @ 80439dab
......@@ -117,7 +117,8 @@ Aten:
| 125 | aten::complex | 126 | aten::real | 127 | aten::imag | 128 | aten::fft\_rfftn |
| 129 | aten::fft\_irfftn | 130 | aten::hardsigmoid | 131 | aten::hardswish | 132 | aten::linear |
| 133 | aten::rsqrt | 134 | aten::replication\_pad1d | 135 | aten::full | 136 | aten::group\_norm |
| 137 | aten::argmax | 138 | aten::copy | 139 | aten::upsample\_trilinear3d | | |
| 137 | aten::argmax | 138 | aten::copy | 139 | aten::upsample\_trilinear3d | 140 | aten::clone |
| 141 | aten::rand | 142 | aten::randn | | | | |
Prim:
| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |
......
x2paddle/op_mapper/pytorch2paddle/aten.py
浏览文件 @ 80439dab
......@@ -1087,6 +1087,36 @@ def aten_clamp_min(mapper, graph, node):
return current_inputs, current_outputs
def aten_clone(mapper, graph, node):
"""
TorchScript Code:
%55 : Tensor = aten::clone(%54)
Parameter meaning:
%55 (Tensor): output tensor
%54 (Tensor): input tensor
"""
scope_name = mapper.normalize_scope_name(node)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# outputs list
current_outputs = [output_name]
# inputs list
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.clone",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
def aten_complex(mapper, graph, node):
"""
TorchScript示例:
......@@ -1347,123 +1377,191 @@ def aten_conv2d(mapper, graph, node):
def aten__convolution(mapper, graph, node):
""" 构造conv2d的PaddleLayer。
TorchScript示例:
"""
TorchScript Code:
%input.10 : Tensor = aten::_convolution(%input.1, %18, %10, %19, %20, %21, %13, %22, %12, %13, %13, %15)
参数含义:
%input.10 (Tensor): 输出，卷积后的结果。
%input.8 (Tensor): 需要进行卷积的特征层。
%18 (Tensor): weights。
%10 (Tensor): bias。
%19 (list): 步长大小。
%20 (list): 填充大小。
%21 (list): 空洞大小。
%13 (bool): 是否进行转置卷积。
%22 (list): 输出形状上一侧额外添加的大小。
%12 (int): 卷积的组数。
Parameter meaning:
%input.10 (Tensor): Output Tensor
%input.8 (Tensor): Input Tensor
%18 (Tensor): weights
%10 (Tensor): bias
%19 (list): stride
%20 (list): padding
%21 (list): dilation
%13 (bool): whether transpose
%22 (list): output_padding
%12 (int): groups
"""
scope_name = mapper.normalize_scope_name(node)
inputs_name, inputs_node = mapper._get_inputs_name(node)
weights = mapper.pytorch_params[inputs_name[1]]
if len(weights.shape) == 3:
op_name = name_generator("conv1d", mapper.nn_name2id)
elif len(weights.shape) == 4:
op_name = name_generator("conv2d", mapper.nn_name2id)
else:
op_name = name_generator("conv3d", mapper.nn_name2id)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [op_name, output_name]
layer_inputs = {}
layer_attrs = {}
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.8
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
# 处理输入1，即%18
mapper.paddle_params[op_name +
".weight"] = weights #np.swapaxes(weights, 0, 1)
if mapper.attrs[inputs_name[6]]:
layer_attrs["out_channels"] = weights.shape[1]
else:
layer_attrs["out_channels"] = weights.shape[0]
layer_attrs["kernel_size"] = weights.shape[2:]
# 处理输入2，即%10
if inputs_name[2] in mapper.pytorch_params:
bias = mapper.pytorch_params[inputs_name[2]]
if bias is not None:
mapper.paddle_params[op_name + ".bias"] = bias
else:
layer_attrs["bias_attr"] = False
else:
layer_attrs["bias_attr"] = False
# 处理输入3，即%19
# deal with stride
layer_attrs["stride"] = mapper.attrs[inputs_name[3]]
# 处理输入4，即%20
# deal with padding
layer_attrs["padding"] = mapper.attrs[inputs_name[4]]
# 处理输入5，即%21
# deal with dilation
layer_attrs["dilation"] = mapper.attrs[inputs_name[5]]
# 处理输入6，即%13
if mapper.attrs[inputs_name[6]]:
# 处理输入7，即%22
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
# 处理输入8，即%12
# deal with groups
layer_attrs["groups"] = mapper.attrs[inputs_name[8]]
if mapper.attrs[inputs_name[6]]:
layer_attrs['in_channels'] = weights.shape[0] * mapper.attrs[
inputs_name[8]]
# for weight is nn.functional.conv2d input
if inputs_name[1] not in mapper.pytorch_params:
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
current_outputs = [output_name]
# input
mapper._check_input(graph, inputs_node[0], inputs_name[0],
current_outputs, scope_name)
layer_inputs["x"] = inputs_name[0]
# weight
mapper._check_input(graph, inputs_node[1], inputs_name[1],
current_outputs, scope_name)
layer_inputs["weight"] = inputs_name[1]
layer_attrs["bias"] = mapper.attrs[inputs_name[2]]
current_inputs = list(layer_inputs.values())
# Determine whether it is conv or convtranspose according to the attribute
if len(layer_attrs["stride"]) == 1:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.functional.conv1d_transpose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.functional.conv1d",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
elif len(layer_attrs["stride"]) == 2:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.functional.conv2d_transpose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.functional.conv2d",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
elif len(layer_attrs["stride"]) == 3:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.functional.conv3d_transpose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.functional.conv3d",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
else:
layer_attrs['in_channels'] = weights.shape[1] * mapper.attrs[
inputs_name[8]]
if len(weights.shape) == 3:
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv1DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
weights = mapper.pytorch_params[inputs_name[1]]
if len(weights.shape) == 3:
op_name = name_generator("conv1d", mapper.nn_name2id)
elif len(weights.shape) == 4:
op_name = name_generator("conv2d", mapper.nn_name2id)
else:
graph.add_layer(
"paddle.nn.Conv1D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
elif len(weights.shape) == 4:
op_name = name_generator("conv3d", mapper.nn_name2id)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [op_name, output_name]
layer_inputs = {}
current_outputs = [output_name]
mapper._check_input(graph, inputs_node[0], inputs_name[0],
current_outputs, scope_name)
layer_inputs["input"] = inputs_name[0]
current_inputs = list(layer_inputs.values())
mapper.paddle_params[op_name +
".weight"] = weights #np.swapaxes(weights, 0, 1)
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv2DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
layer_attrs["out_channels"] = weights.shape[1]
else:
graph.add_layer(
"paddle.nn.Conv2D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
layer_attrs["out_channels"] = weights.shape[0]
layer_attrs["kernel_size"] = weights.shape[2:]
# deal with bias
if inputs_name[2] in mapper.pytorch_params:
bias = mapper.pytorch_params[inputs_name[2]]
if bias is not None:
mapper.paddle_params[op_name + ".bias"] = bias
else:
layer_attrs["bias_attr"] = False
else:
layer_attrs["bias_attr"] = False
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv3DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
layer_attrs['in_channels'] = weights.shape[0] * mapper.attrs[
inputs_name[8]]
else:
graph.add_layer(
"paddle.nn.Conv3D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
layer_attrs['in_channels'] = weights.shape[1] * mapper.attrs[
inputs_name[8]]
if len(weights.shape) == 3:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.Conv1DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.Conv1D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
elif len(weights.shape) == 4:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.Conv2DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.Conv2D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
if mapper.attrs[inputs_name[6]]:
# only convtranspose have output_padding attr
layer_attrs["output_padding"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"paddle.nn.Conv3DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.Conv3D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
def aten_conv_transpose2d(mapper, graph, node):
......@@ -4385,6 +4483,88 @@ def aten_prelu(mapper, graph, node):
return current_inputs, current_outputs
def aten_rand(mapper, graph, node):
"""
TorchScript Code:
%input.49 : Tensor = aten::rand(%23, %8, %6, %24, %5)
Parameter meaning:
%input.49 (Tensor): output tensor
%23 (list): input shape list
%8 (int): dtype。
%6 (int): layout。
%4995 (int): device
%4995 (bool): requires_grad
"""
scope_name = mapper.normalize_scope_name(node)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
layer_attrs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# outputs list
current_outputs = [output_name]
current_inputs = []
# deal with shape
if inputs_name[0] in mapper.attrs:
layer_attrs["shape"] = mapper.attrs[inputs_name[0]]
else:
mapper._check_input(graph, inputs_node[0], inputs_name[0],
current_outputs, scope_name)
layer_inputs["shape"] = inputs_name[0]
current_inputs.append(inputs_name[0])
# deal with dtype
layer_attrs["dtype"] = dtype_dict[mapper.attrs[inputs_name[1]]]
graph.add_layer(
"paddle.rand",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
def aten_randn(mapper, graph, node):
"""
TorchScript Code:
%input.49 : Tensor = aten::randn(%23, %8, %6, %24, %5)
Parameter meaning:
%input.49 (Tensor): output tensor
%23 (list): input shape list
%8 (int): dtype。
%6 (int): layout。
%4995 (int): device
%4995 (bool): requires_grad
"""
scope_name = mapper.normalize_scope_name(node)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
layer_inputs = {}
layer_attrs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# outputs list
current_outputs = [output_name]
current_inputs = []
# deal with shape
if inputs_name[0] in mapper.attrs:
layer_attrs["shape"] = mapper.attrs[inputs_name[0]]
else:
mapper._check_input(graph, inputs_node[0], inputs_name[0],
current_outputs, scope_name)
layer_inputs["shape"] = inputs_name[0]
current_inputs.append(inputs_name[0])
# deal with dtype
layer_attrs["dtype"] = dtype_dict[mapper.attrs[inputs_name[1]]]
graph.add_layer(
"paddle.randn",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
def aten_real(mapper, graph, node):
"""
TorchScript示例:
......