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X2Paddle
提交 2fc9ffd0 编写于 作者: S SunAhong1993
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fix fro pre-commit

上级 ec07a4c3
隐藏空白更改
内联 并排
Showing with 746 addition and 304 deletion
x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py
浏览文件 @ 2fc9ffd0
......@@ -31,7 +31,6 @@ dtype_dict = {
}
def aten_abs(mapper, graph, node):
""" 构造获取绝对值的PaddleLayer。
......@@ -49,13 +48,17 @@ def aten_abs(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%n.3
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.abs", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.abs",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -79,7 +82,8 @@ def aten_adaptive_avg_pool2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.3
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -129,13 +133,20 @@ def aten_addmm(mapper, graph, node):
current_outputs = [output_name]
# 处理输入0，即%150
mapper._check_input(
graph, inputs_node[0], inputs_name[0], current_outputs, scope_name, add_dim=True)
graph,
inputs_node[0],
inputs_name[0],
current_outputs,
scope_name,
add_dim=True)
layer_inputs["input"] = inputs_name[0]
# 处理输入1，即%input.3
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[1]
# 处理输入2，即%156
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -183,16 +194,26 @@ def aten_add(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%i.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%288
mapper._check_input(
graph, inputs_node[1], inputs_name[1], current_outputs, scope_name, add_dim=True)
graph,
inputs_node[1],
inputs_name[1],
current_outputs,
scope_name,
add_dim=True)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.add", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.add",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -216,11 +237,17 @@ def aten_add_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%output.2
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%150
mapper._check_input(
graph, inputs_node[1], inputs_name[1], current_outputs, scope_name, add_dim=True)
graph,
inputs_node[1],
inputs_name[1],
current_outputs,
scope_name,
add_dim=True)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -234,7 +261,11 @@ def aten_add_(mapper, graph, node):
current_inputs.append(inputs_name[2])
graph.add_layer(
"prim.add_", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs)
"prim.add_",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
......@@ -256,15 +287,21 @@ def aten___and__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%i.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%288
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.and", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.and",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -285,15 +322,21 @@ def aten_append(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [inputs_name[0]]
# 处理输入0，即_output_size.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["list"] = inputs_name[0]
# 处理输入1，即v.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["element"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.append", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.append",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -422,7 +465,8 @@ def aten_avg_pool2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.34
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -455,6 +499,7 @@ def aten_avg_pool2d(mapper, graph, node):
return current_inputs, current_outputs
def aten_avg_pool3d(mapper, graph, node):
""" 构造最大池化的PaddleLayer。
......@@ -480,7 +525,8 @@ def aten_avg_pool3d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.34
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -538,7 +584,8 @@ def aten_avg_pool1d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.34
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -600,7 +647,8 @@ def aten_batch_norm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.80
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
......@@ -653,16 +701,26 @@ def aten_bmm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%i.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%288
mapper._check_input(
graph, inputs_node[1], inputs_name[1], current_outputs, scope_name, add_dim=True)
graph,
inputs_node[1],
inputs_name[1],
current_outputs,
scope_name,
add_dim=True)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.bmm", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.bmm",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -685,7 +743,8 @@ def aten_cat(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -726,7 +785,8 @@ def aten_chunk(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.170
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -775,7 +835,8 @@ def aten_clamp(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -824,7 +885,8 @@ def aten_clamp_min(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -864,15 +926,21 @@ def aten___contains__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%50
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input"] = inputs_name[0]
# 处理输入1，即%name.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["element"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.contain", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.contain",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -897,7 +965,8 @@ def aten_constant_pad_nd(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input1.24
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -929,7 +998,8 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs=[inputs_name[0] + "_if", output_name],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.sub",
inputs={"y": inputs_name[0] + "_len"},
......@@ -960,10 +1030,15 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs=[output_name],
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
layer_inputs["input"] = inputs_name[0]
block.add_layer(
kernel, inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs)
kernel,
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
if_layer.inputs["input-1"] = inputs_name[0] + "_len"
......@@ -1003,12 +1078,17 @@ def aten_contiguous(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%4058
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.equal",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1037,7 +1117,8 @@ def aten_conv2d(mapper, graph, node):
# 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)
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())
......@@ -1101,18 +1182,20 @@ def aten__convolution(mapper, graph, node):
# 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)
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
weights = mapper.pytorch_params[inputs_name[1]]
mapper.paddle_params[op_name + ".weight"] = weights #np.swapaxes(weights, 0, 1)
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:]
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]]
......@@ -1135,11 +1218,11 @@ def aten__convolution(mapper, graph, node):
# 处理输入8，即%12
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]]
layer_attrs['in_channels'] = weights.shape[0] * mapper.attrs[
inputs_name[8]]
else:
layer_attrs['in_channels'] = weights.shape[1] * mapper.attrs[inputs_name[
8]]
layer_attrs['in_channels'] = weights.shape[1] * mapper.attrs[
inputs_name[8]]
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv2DTranspose",
......@@ -1183,7 +1266,8 @@ def aten_conv_transpose2d(mapper, graph, node):
# 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)
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())
......@@ -1211,8 +1295,7 @@ def aten_conv_transpose2d(mapper, graph, node):
layer_attrs["groups"] = mapper.attrs[inputs_name[6]]
# 处理输入7，即%22
layer_attrs["dilation"] = mapper.attrs[inputs_name[7]]
layer_attrs['in_channels'] = weights.shape[0] * mapper.attrs[inputs_name[
6]]
layer_attrs['in_channels'] = weights.shape[0] * mapper.attrs[inputs_name[6]]
graph.add_layer(
"paddle.nn.Conv2DTranspose",
inputs=layer_inputs,
......@@ -1239,12 +1322,17 @@ def aten_cos(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%sinusoid_inp.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.cos", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.cos",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1268,7 +1356,8 @@ def aten_cumsum(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%mask.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -1315,11 +1404,16 @@ def aten_detach(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%end.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.equal",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1340,7 +1434,11 @@ def aten_dict(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
graph.add_layer("prim.dict", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.dict",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1360,15 +1458,22 @@ def aten_dim(mapper, graph, node):
# 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)
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())
graph.add_layer(
"prim.shape", inputs=layer_inputs, outputs=[output_name], scope_name=scope_name)
"prim.shape",
inputs=layer_inputs,
outputs=[output_name],
scope_name=scope_name)
graph.add_layer(
"prim.len", inputs={"input": output_name}, outputs=[output_name], scope_name=scope_name)
"prim.len",
inputs={"input": output_name},
outputs=[output_name],
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1389,15 +1494,21 @@ def aten_div_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.div", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.div",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1419,15 +1530,21 @@ def aten_div(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.div", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.div",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1450,13 +1567,18 @@ def aten_dropout(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%119
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Dropout", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, p=0.0)
"paddle.nn.Dropout",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
p=0.0)
return current_inputs, current_outputs
......@@ -1479,13 +1601,18 @@ def aten_dropout_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%119
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Dropout", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, p=0.0)
"paddle.nn.Dropout",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
p=0.0)
return current_inputs, current_outputs
......@@ -1517,7 +1644,8 @@ def aten_embedding(mapper, graph, node):
layer_attrs["num_embeddings"] = weights.shape[0]
layer_attrs["embedding_dim"] = weights.shape[1]
# 处理输入1，即%input_ids.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["input"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -1556,18 +1684,24 @@ def aten_eq(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
x_value = list(node.inputs())[0]
x_type = x_value.type()
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
y_value = list(node.inputs())[1]
y_type = y_value.type()
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.eq", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.eq",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1588,12 +1722,17 @@ def aten_erf(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%sinusoid_inp.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.erf", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.erf",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1614,13 +1753,17 @@ def aten_exp(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.exp", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.exp",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1644,7 +1787,8 @@ def aten_expand(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%1875
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
# 处理输入1，即%51
......@@ -1656,9 +1800,9 @@ def aten_expand(mapper, graph, node):
layer_inputs["shape"] = inputs_name[1]
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.expand",
inputs=layer_inputs,
outputs=layer_outputs,
"paddle.expand",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
......@@ -1682,14 +1826,16 @@ def aten_expand_as(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%1875
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%1888
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[0]},
......@@ -1711,7 +1857,8 @@ def aten_expand_as(mapper, graph, node):
outputs=[inputs_name[0] + "_if1"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.type",
inputs={"input": inputs_name[1]},
......@@ -1724,18 +1871,23 @@ def aten_expand_as(mapper, graph, node):
scope_name=scope_name,
dtype=inputs_name[1] + "_type")
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
if_layer.inputs["input-1"] = inputs_name[1]
graph.add_layer(
"paddle.expand_as", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.expand_as",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
graph.add_layer(
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if2"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"paddle.cast",
inputs={"x": layer_outputs[0]},
......@@ -1743,20 +1895,21 @@ def aten_expand_as(mapper, graph, node):
scope_name=scope_name,
dtype=string("bool"))
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = layer_outputs[0]
# TODO(syf): check expand_as
# # 处理输入0，即%1875
# mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
# layer_inputs["x"] = inputs_name[0]
# # 处理输入1，即%1888
# mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
# layer_inputs["y"] = inputs_name[1]
# # 获取当前节点输入的list
# current_inputs = list(layer_inputs.values())
# graph.add_layer(
# "paddle.expand_as", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
# # 处理输入0，即%1875
# mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
# layer_inputs["x"] = inputs_name[0]
# # 处理输入1，即%1888
# mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
# layer_inputs["y"] = inputs_name[1]
# # 获取当前节点输入的list
# current_inputs = list(layer_inputs.values())
# graph.add_layer(
# "paddle.expand_as", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1783,7 +1936,8 @@ def aten_eye(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%49
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["num_rows"] = inputs_name[0]
if len(inputs_name) > 5:
# 处理输入1，即%_50
......@@ -1803,6 +1957,7 @@ def aten_eye(mapper, graph, node):
**layer_attrs)
return current_inputs, current_outputs
def aten_feature_dropout(mapper, graph, node):
""" 构造Dropout的PaddleLayer。
......@@ -1822,13 +1977,18 @@ def aten_feature_dropout(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%119
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Dropout", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, p=0.0)
"paddle.nn.Dropout",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
p=0.0)
return current_inputs, current_outputs
......@@ -1853,7 +2013,8 @@ def aten_flatten(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
# 处理输入1，即%4
layer_attrs["start_axis"] = mapper.attrs[inputs_name[1]]
# 处理输入2，即%20
......@@ -1888,12 +2049,17 @@ def aten_Float(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%3991
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.float", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.float",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1914,37 +2080,44 @@ def aten_floor(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%scale.18
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.type",
{'input': inputs_name[0]},
"prim.type", {'input': inputs_name[0]},
outputs=[inputs_name[0] + "_type"],
scope_name=scope_name)
graph.add_layer(
"prim.str",
{'input': inputs_name[0] + "_type"},
"prim.str", {'input': inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_type"],
scope_name=scope_name)
graph.add_layer(
"prim.startswith",
{'input': inputs_name[0] + "_type"},
"prim.startswith", {'input': inputs_name[0] + "_type"},
outputs=[inputs_name[0] + "_cond"],
scope_name=scope_name,
start_str=string("VarType"))
start_str=string("VarType"))
graph.add_layer(
"prim.if",
{'input': inputs_name[0] + "_cond"},
"prim.if", {'input': inputs_name[0] + "_cond"},
outputs=[inputs_name[0] + "_if"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer("paddle.floor", inputs=copy.deepcopy(layer_inputs), outputs=copy.deepcopy(layer_outputs), scope_name=scope_name)
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"paddle.floor",
inputs=copy.deepcopy(layer_inputs),
outputs=copy.deepcopy(layer_outputs),
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer("prim.floor", inputs=copy.deepcopy(layer_inputs), outputs=copy.deepcopy(layer_outputs), scope_name=scope_name)
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.floor",
inputs=copy.deepcopy(layer_inputs),
outputs=copy.deepcopy(layer_outputs),
scope_name=scope_name)
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[0]
if_layer.outputs.append(output_name)
......@@ -1969,15 +2142,21 @@ def aten_floordiv(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.floordiv", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.floordiv",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -1999,15 +2178,21 @@ def aten_floor_divide(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.floordiv", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.floordiv",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2035,7 +2220,8 @@ def aten_full_like(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%val_if_large.3
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -2080,20 +2266,22 @@ def aten_gather(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%18
layer_attrs["dim"] = mapper.attrs[inputs_name[1]]
# 处理输入2，即%19
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["index"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"custom_layer:Gather",
inputs=layer_inputs,
outputs=layer_outputs,
"custom_layer:Gather",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
......@@ -2119,13 +2307,17 @@ def aten_gelu(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.GELU", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.GELU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2147,15 +2339,21 @@ def aten___getitem__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%72
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["list"] = inputs_name[0]
# 处理输入1，即%88
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["index"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.getitem", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.getitem",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2177,15 +2375,21 @@ def aten_gt(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%82
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%78
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.gt", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.gt",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2218,38 +2422,47 @@ def aten_gru(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = output_names
# 处理输入0，即%input.95
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input0"] = inputs_name[0]
# 处理输入1，即%734
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["input1"] = inputs_name[1]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
# 处理输入2，即%734
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
graph.layers.pop(mapper.output2id[inputs_name[2]])
param_inputs_name, _ = mapper._get_inputs_name(inputs_node[2])
new_param_inputs_name = list()
for i, param_name in enumerate(param_inputs_name):
if i == 0:
layer_attrs["hidden_size"] = int(mapper.paddle_params[param_name].shape[0] / 3)
layer_attrs["input_size"] = int(mapper.paddle_params[param_name].shape[1])
layer_attrs["hidden_size"] = int(
mapper.paddle_params[param_name].shape[0] / 3)
layer_attrs["input_size"] = int(mapper.paddle_params[param_name]
.shape[1])
if len(mapper.paddle_params[param_name].shape) > 1:
part_name = param_name.split("_weight_")[-1]
mapper.paddle_params["{}.weight_{}".format(op_name, part_name)] = mapper.paddle_params[param_name]
new_param_inputs_name.append("{}.weight_{}".format(op_name, part_name))
mapper.paddle_params["{}.weight_{}".format(
op_name, part_name)] = mapper.paddle_params[param_name]
new_param_inputs_name.append("{}.weight_{}".format(op_name,
part_name))
else:
part_name = param_name.split("_bias_")[-1]
mapper.paddle_params["{}.bias_{}".format(op_name, part_name)] = mapper.paddle_params[param_name]
mapper.paddle_params["{}.bias_{}".format(
op_name, part_name)] = mapper.paddle_params[param_name]
mapper.paddle_params.pop(param_name)
# 处理输入3，即%526
is_bias = mapper.attrs[inputs_name[3]]
if not is_bias:
for param_name in new_param_inputs_name:
bias_name = param_name.replace("weight", "bias")
bias_shape= mapper.paddle_params[param_name].shape[:1]
mapper.paddle_params[bias_name] = np.zeros(bias_shape).astype("float32")
bias_shape = mapper.paddle_params[param_name].shape[:1]
mapper.paddle_params[bias_name] = np.zeros(bias_shape).astype(
"float32")
# 处理输入4，即%525
layer_attrs["num_layers"] = mapper.attrs[inputs_name[4]]
# 处理输入5，即%524
......@@ -2292,7 +2505,8 @@ def aten_hardtanh_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.20
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -2301,9 +2515,12 @@ def aten_hardtanh_(mapper, graph, node):
# 处理输入2，即%66
layer_attrs["max"] = mapper.attrs[inputs_name[2]]
if layer_attrs["min"] ==0 and layer_attrs["max"] == 6:
if layer_attrs["min"] == 0 and layer_attrs["max"] == 6:
graph.add_layer(
"paddle.nn.ReLU6", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.ReLU6",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
else:
graph.add_layer(
'paddle.nn.Hardtanh',
......@@ -2334,7 +2551,8 @@ def aten_index_select(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x2.3
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%320
if inputs_name[1] in mapper.attrs:
......@@ -2345,7 +2563,8 @@ def aten_index_select(mapper, graph, node):
layer_inputs["axis"] = inputs_name[1]
current_inputs.append(inputs_name[1])
# 处理输入2，即%371
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["index"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -2361,7 +2580,7 @@ def aten_index_select(mapper, graph, node):
def aten_instance_norm(mapper, graph, node):
"""构造InstanceNorm的PaddleLayer
TorchScript示例:
%res.7 : Tensor = aten::instance_norm(%res.5, %88, %85, %84, %83, %87, %91, %92, %87)
参数含义:
......@@ -2386,7 +2605,8 @@ def aten_instance_norm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.80
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
......@@ -2438,12 +2658,17 @@ def aten_Int(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%1738
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.int", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.int",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2465,15 +2690,21 @@ def aten___is__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%size.122
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%3931
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.is", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.is",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2495,15 +2726,21 @@ def aten___isnot__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%size.122
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%3931
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.isnot", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.isnot",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2531,7 +2768,8 @@ def aten_layer_norm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.6
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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、输出的list
current_inputs = list(layer_inputs.values())
......@@ -2577,15 +2815,21 @@ def aten_le(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%78
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%79
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.le", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.le",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2609,7 +2853,8 @@ def aten_leaky_relu_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -2642,12 +2887,17 @@ def aten_len(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%72
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.len", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.len",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2668,13 +2918,17 @@ def aten_log(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%786
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.log", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.log",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2708,38 +2962,47 @@ def aten_lstm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = output_names
# 处理输入0，即%input.95
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input0"] = inputs_name[0]
# 处理输入1，即%734
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["input1"] = inputs_name[1]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
# 处理输入2，即%734
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
graph.layers.pop(mapper.output2id[inputs_name[2]])
param_inputs_name, _ = mapper._get_inputs_name(inputs_node[2])
new_param_inputs_name = list()
for i, param_name in enumerate(param_inputs_name):
if i == 0:
layer_attrs["hidden_size"] = int(mapper.paddle_params[param_name].shape[0] / 4)
layer_attrs["input_size"] = int(mapper.paddle_params[param_name].shape[1])
layer_attrs["hidden_size"] = int(
mapper.paddle_params[param_name].shape[0] / 4)
layer_attrs["input_size"] = int(mapper.paddle_params[param_name]
.shape[1])
if len(mapper.paddle_params[param_name].shape) > 1:
part_name = param_name.split("_weight_")[-1]
mapper.paddle_params["{}.weight_{}".format(op_name, part_name)] = mapper.paddle_params[param_name]
new_param_inputs_name.append("{}.weight_{}".format(op_name, part_name))
mapper.paddle_params["{}.weight_{}".format(
op_name, part_name)] = mapper.paddle_params[param_name]
new_param_inputs_name.append("{}.weight_{}".format(op_name,
part_name))
else:
part_name = param_name.split("_bias_")[-1]
mapper.paddle_params["{}.bias_{}".format(op_name, part_name)] = mapper.paddle_params[param_name]
mapper.paddle_params["{}.bias_{}".format(
op_name, part_name)] = mapper.paddle_params[param_name]
mapper.paddle_params.pop(param_name)
# 处理输入3，即%526
is_bias = mapper.attrs[inputs_name[3]]
if not is_bias:
for param_name in new_param_inputs_name:
bias_name = param_name.replace("weight", "bias")
bias_shape= mapper.paddle_params[param_name].shape[:1]
mapper.paddle_params[bias_name] = np.zeros(bias_shape).astype("float32")
bias_shape = mapper.paddle_params[param_name].shape[:1]
mapper.paddle_params[bias_name] = np.zeros(bias_shape).astype(
"float32")
# 处理输入4，即%525
layer_attrs["num_layers"] = mapper.attrs[inputs_name[4]]
# 处理输入5，即%524
......@@ -2779,15 +3042,21 @@ def aten_lt(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%78
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%79
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.lt", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.lt",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -2812,7 +3081,8 @@ def aten_masked_fill_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.4
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
current_inputs.append(inputs_name[0])
graph.add_layer(
"prim.type",
......@@ -2820,7 +3090,8 @@ def aten_masked_fill_(mapper, graph, node):
outputs=[inputs_name[0] + "_type"],
scope_name=scope_name)
# 处理输入1，即%scores.2
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.logical_not",
......@@ -2846,7 +3117,8 @@ def aten_masked_fill_(mapper, graph, node):
outputs=[inputs_name[0] + "_not_mask"],
scope_name=scope_name)
# 处理输入2，即%46
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
......@@ -2872,14 +3144,16 @@ def aten_masked_fill_(mapper, graph, node):
outputs=[inputs_name[2] + "_if"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.equal",
inputs={"input": inputs_name[1] + "_mask"},
outputs=[inputs_name[2] + "_1"],
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.mul",
inputs={"x": inputs_name[1] + "_mask",
......@@ -2920,7 +3194,8 @@ def aten_masked_fill(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.4
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
current_inputs.append(inputs_name[0])
graph.add_layer(
"prim.type",
......@@ -2928,7 +3203,8 @@ def aten_masked_fill(mapper, graph, node):
outputs=[inputs_name[0] + "_type"],
scope_name=scope_name)
# 处理输入1，即%scores.2
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.logical_not",
......@@ -2954,7 +3230,8 @@ def aten_masked_fill(mapper, graph, node):
outputs=[inputs_name[0] + "_not_mask"],
scope_name=scope_name)
# 处理输入2，即%46
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
......@@ -2980,14 +3257,16 @@ def aten_masked_fill(mapper, graph, node):
outputs=[inputs_name[2] + "_if"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.equal",
inputs={"input": inputs_name[1] + "_mask"},
outputs=[inputs_name[2] + "_1"],
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.mul",
inputs={"x": inputs_name[1] + "_mask",
......@@ -3037,7 +3316,10 @@ def aten_max(mapper, graph, node):
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.maximum", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.maximum",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
else:
pass
return current_inputs, current_outputs
......@@ -3068,7 +3350,8 @@ def aten_max_pool2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.11
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -3093,7 +3376,7 @@ def aten_max_pool2d(mapper, graph, node):
# 处理输入5，即%19
layer_attrs["ceil_mode"] = mapper.attrs[inputs_name[5]]
layer_attrs_tmp["ceil_mode"] = mapper.attrs[inputs_name[5]]
graph.add_layer(
"paddle.nn.MaxPool2D",
inputs=layer_inputs,
......@@ -3121,15 +3404,21 @@ def aten_matmul(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%101
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%102
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.matmul", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.matmul",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3163,7 +3452,10 @@ def aten_min(mapper, graph, node):
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.minimum", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.minimum",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
else:
pass
return current_inputs, current_outputs
......@@ -3190,7 +3482,8 @@ def aten_mean(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
# 处理输入1，即%4967
......@@ -3236,13 +3529,18 @@ def aten_meshgrid(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["args"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = layer_inputs.values()
current_outputs = layer_outputs
graph.add_layer("paddle.meshgrid", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.meshgrid",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3264,16 +3562,22 @@ def aten_mul(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%size_prods.38
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%114
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.mul", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.mul",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3295,16 +3599,22 @@ def aten_mul_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%size_prods.38
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%114
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.mul", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.mul",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3326,15 +3636,21 @@ def aten_ne(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.ne", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.ne",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3355,12 +3671,17 @@ def aten_neg(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.neg", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.neg",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3385,7 +3706,8 @@ def aten_norm(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
# 处理输入1，即%21
......@@ -3439,12 +3761,17 @@ def aten___not__(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
graph.add_layer("prim.not", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.not",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3509,7 +3836,8 @@ def aten_permute(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%cls_confs0.2
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -3550,7 +3878,8 @@ def aten_pixel_shuffle(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.101
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
# 处理输入1，即%726
......@@ -3564,6 +3893,7 @@ def aten_pixel_shuffle(mapper, graph, node):
**layer_attrs)
return current_inputs, current_outputs
def aten_pow(mapper, graph, node):
""" 构造指数激活的PaddleLayer。
......@@ -3582,7 +3912,8 @@ def aten_pow(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%4700
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -3623,7 +3954,8 @@ def aten_prelu(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.150
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%999
weight = mapper.pytorch_params[inputs_name[1]]
......@@ -3632,10 +3964,10 @@ def aten_prelu(mapper, graph, node):
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.PReLU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
"paddle.nn.PReLU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
num_parameters=weight.shape[0])
return current_inputs, current_outputs
......@@ -3660,7 +3992,8 @@ def aten_reflection_pad1d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -3676,7 +4009,7 @@ def aten_reflection_pad1d(mapper, graph, node):
ipt_node = inputs_node[0]
layer_attrs["padding"] = list(mapper.pytorch_params[inputs_name[0]])
layer_attrs["mode"] = string("reflect")
graph.add_layer(
"paddle.nn.Pad1D",
inputs=layer_inputs,
......@@ -3706,7 +4039,8 @@ def aten_reflection_pad2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -3722,7 +4056,7 @@ def aten_reflection_pad2d(mapper, graph, node):
ipt_node = inputs_node[0]
layer_attrs["padding"] = list(mapper.pytorch_params[inputs_name[0]])
layer_attrs["mode"] = string("reflect")
graph.add_layer(
"paddle.nn.Pad2D",
inputs=layer_inputs,
......@@ -3752,13 +4086,17 @@ def aten_relu(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.ReLU", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.ReLU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3782,13 +4120,17 @@ def aten_relu_(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.ReLU", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.ReLU",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3812,13 +4154,17 @@ def aten_relu6(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.ReLU6", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.ReLU6",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3841,7 +4187,8 @@ def aten_repeat(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%699
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -3882,7 +4229,8 @@ def aten_reshape(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%4700
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -3894,7 +4242,7 @@ def aten_reshape(mapper, graph, node):
current_outputs, scope_name)
layer_inputs["shape"] = inputs_name[1]
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.reshape",
inputs=layer_inputs,
......@@ -3924,18 +4272,25 @@ def aten_rsub(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%30
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%13
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[1]
# 处理输入2，即%7
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["alpha"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.rsub", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.rsub",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -3959,14 +4314,18 @@ def aten_ScalarImplicit(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%end.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input"] = inputs_name[0]
input_type = list(node.inputs())[0].type()
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
if str(input_type) == "Tensor":
graph.add_layer(
"prim.equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"prim.equal",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
else:
raise Exception(
"The input type {} of aten::ScalarImplicit is not implemented yet!"
......@@ -3994,12 +4353,14 @@ def aten_select(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%18
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["input"] = inputs_name[0]
# 处理输入1，即%8
layer_attrs["dim"] = mapper.attrs[inputs_name[1]]
# 处理输入2，即%75
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["index"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4029,18 +4390,22 @@ def aten__set_item(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = []
# 处理输入0，即%features.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["dict"] = inputs_name[0]
# 处理输入1，即%out_name.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["key"] = inputs_name[1]
# 处理输入2，即%x.3
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["value"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.set_item", inputs=layer_inputs, outputs=[], scope_name=scope_name)
graph.add_layer(
"prim.set_item", inputs=layer_inputs, outputs=[], scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4062,13 +4427,17 @@ def aten_sigmoid(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%54
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Sigmoid", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.Sigmoid",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4089,12 +4458,17 @@ def aten_sin(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%sinusoid_inp.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.sin", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.sin",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4117,7 +4491,8 @@ def aten_size(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -4139,7 +4514,10 @@ def aten_size(mapper, graph, node):
return current_inputs, current_outputs
graph.add_layer(
"prim.shape", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"prim.shape",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4278,7 +4656,10 @@ def aten_slice(mapper, graph, node):
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.slice", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"prim.slice",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4303,7 +4684,8 @@ def aten_softmax(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.31
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4339,7 +4721,8 @@ def aten_softplus(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.31
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4375,7 +4758,8 @@ def aten_split_with_sizes(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%1446
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%1750
if inputs_name[1] in mapper.attrs:
......@@ -4422,13 +4806,17 @@ def aten_sqrt(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%786
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.sqrt", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.sqrt",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4451,7 +4839,8 @@ def aten_squeeze(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%start_logits.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4491,7 +4880,8 @@ def aten_stack(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4532,11 +4922,17 @@ def aten_sub(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%839
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%836
mapper._check_input(
graph, inputs_node[1], inputs_name[1], current_outputs, scope_name, add_dim=True)
graph,
inputs_node[1],
inputs_name[1],
current_outputs,
scope_name,
add_dim=True)
layer_inputs["y"] = inputs_name[1]
# 处理输入2，即%3
if len(inputs_node) > 2:
......@@ -4552,7 +4948,12 @@ def aten_sub(mapper, graph, node):
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.sub", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs)
graph.add_layer(
"prim.sub",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs
......@@ -4569,6 +4970,7 @@ def aten_sub_(mapper, graph, node):
"""
return aten_sub(mapper, graph, node)
def aten_t(mapper, graph, node):
""" 构造矩阵转置的PaddleLayer。
......@@ -4586,7 +4988,8 @@ def aten_t(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4618,13 +5021,17 @@ def aten_tanh(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Tanh", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.nn.Tanh",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4648,13 +5055,16 @@ def aten_split(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%159
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入2，即%723
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["axis"] = inputs_name[2]
# 处理输入1，即%135
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
input_type = list(node.inputs())[0].type()
if "[]" in str(input_type):
layer_inputs["num_or_sections"] = inputs_name[1]
......@@ -4692,16 +5102,19 @@ def aten_transpose(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.21
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%704
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
dim1 = inputs_name[1]
# 处理输入2，即%705
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
dim2 = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.shape",
inputs={"input": inputs_name[0]},
......@@ -4780,7 +5193,8 @@ def aten_to(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4817,12 +5231,14 @@ def aten_type_as(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%56
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
# 处理输入0，即%mask.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
graph.add_layer(
"prim.type",
inputs={"input": inputs_name[1]},
......@@ -4832,7 +5248,10 @@ def aten_type_as(mapper, graph, node):
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.cast", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
"paddle.cast",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -4855,7 +5274,8 @@ def aten_unsqueeze(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4898,7 +5318,8 @@ def aten_upsample_bilinear2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4922,14 +5343,16 @@ def aten_upsample_bilinear2d(mapper, graph, node):
outputs=[inputs_name[0] + "_if1"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.var2list",
inputs={"input": inputs_name[1]},
outputs=[inputs_name[1]],
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1]
# 处理输入2，即%5421
......@@ -4954,6 +5377,7 @@ def aten_upsample_bilinear2d(mapper, graph, node):
**layer_attrs)
return current_inputs, current_outputs
def aten_upsample_nearest2d(mapper, graph, node):
""" 构造使用nearest上采样的PaddleLayer。
......@@ -4975,7 +5399,8 @@ def aten_upsample_nearest2d(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.13
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......@@ -4999,14 +5424,16 @@ def aten_upsample_nearest2d(mapper, graph, node):
outputs=[inputs_name[0] + "_if1"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block.add_layer(
"prim.var2list",
inputs={"input": inputs_name[1]},
outputs=[inputs_name[1]],
scope_name=scope_name)
if_layer.add_block(block)
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
block = PaddleGraph(
source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1]
if "size" in layer_attrs and layer_attrs["size"] is None:
......@@ -5041,12 +5468,17 @@ def aten_values(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%78
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.dict2values", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"prim.dict2values",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -5075,7 +5507,8 @@ def aten_view(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%x.20
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
......@@ -5114,7 +5547,8 @@ def aten_warn(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%3
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
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())
......@@ -5155,18 +5589,25 @@ def aten_where(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%209
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["condition"] = inputs_name[0]
# 处理输入1，即%w0.2
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[1]
# 处理输入1，即%w0.2
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_inputs["y"] = inputs_name[2]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("paddle.where", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
graph.add_layer(
"paddle.where",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
......@@ -5235,7 +5676,8 @@ def aten_zeros_like(mapper, graph, node):
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%n.2
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
......
x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/instance_norm.py
浏览文件 @ 2fc9ffd0
......@@ -19,7 +19,7 @@ from paddle.fluid.initializer import Constant
class InstanceNorm(paddle.nn.Layer):
"""
This class is based class for InstanceNorm1D, 2d, 3d.
This class is based class for InstanceNorm1D, 2d, 3d.
See InstaceNorm1D, InstanceNorm2D or InstanceNorm3D for more details.
"""
......
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