From 192a17f97df99ea66824ae7a87b3da27d959e456 Mon Sep 17 00:00:00 2001 From: wjj19950828 Date: Wed, 22 Jun 2022 15:01:32 +0800 Subject: [PATCH] rewrite aten::constant_pad_nd --- x2paddle/op_mapper/pytorch2paddle/aten.py | 125 ++++++---------------- 1 file changed, 31 insertions(+), 94 deletions(-) diff --git a/x2paddle/op_mapper/pytorch2paddle/aten.py b/x2paddle/op_mapper/pytorch2paddle/aten.py index 26951a7..a5dcae4 100755 --- a/x2paddle/op_mapper/pytorch2paddle/aten.py +++ b/x2paddle/op_mapper/pytorch2paddle/aten.py @@ -1189,129 +1189,66 @@ def aten___contains__(mapper, graph, node): def aten_constant_pad_nd(mapper, graph, node): - """ 构造填充固定值的PaddleLayer。 - TorchScript示例: + """ + TorchScript Code: %58 : Tensor = aten::constant_pad_nd(%input1.24, %4876, %42) - 参数含义: - %58 (Tensor): 输出，填充后的Tensor。 - %input1.24 (Tensor): 需要填充的Tensor。 - %4876 (list): 填充大小。 - %42 (-): 填充值。 + Parameter meaning: + %58 (Tensor): Output Tensor + %input1.24 (Tensor): Input Tensor + %4876 (list): pad + %42 (-): value """ scope_name = mapper.normalize_scope_name(node) op_name = name_generator("pad", mapper.nn_name2id) output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [op_name, output_name] layer_inputs = {} layer_attrs = {} inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list + # Output list current_outputs = [output_name] - # 处理输入0，即%input1.24 + # process Input Tensor 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%4876 - is_padding_tensor = False + # process pad + padding_attr = None if inputs_name[1] in mapper.attrs: - layer_attrs["padding"] = mapper.attrs[inputs_name[1]] + padding_attr = mapper.attrs[inputs_name[1]] else: mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name) layer_inputs["pad"] = inputs_name[1] - is_padding_tensor = True - # 获取当前节点输入的list - current_inputs = list(layer_inputs.values()) - # 处理输入2，即%42 + # process value layer_attrs["value"] = mapper.attrs[inputs_name[2]] - if not is_padding_tensor: - graph.add_layer( - "prim.shape", - inputs={"input": inputs_name[0]}, - outputs=[inputs_name[0] + "_shape"], - scope_name=scope_name) - graph.add_layer( - "prim.len", - inputs={"input": inputs_name[0] + "_shape"}, - outputs=[inputs_name[0] + "_len"], - scope_name=scope_name) - - def add_pad_layers(kernel, dim): - graph.add_layer( - "prim.ne", - inputs={"x": inputs_name[0] + "_len"}, - outputs=[inputs_name[0] + "_cond"], - scope_name=scope_name, - y=dim) + if padding_attr is not None: + layer_inputs["x"] = inputs_name[0] + kernel_name = "paddle.nn.functional.pad" + if len(padding_attr) == 2: + layer_attrs["pad"] = [0, 0, 0, 0, 0, 0] + padding_attr + elif len(padding_attr) == 4: + layer_inputs["x"] = inputs_name[0] + layer_attrs["pad"] = [0, 0, 0, 0] + padding_attr + elif len(padding_attr) == 6: + layer_inputs["x"] = inputs_name[0] + layer_attrs["pad"] = [0, 0] + padding_attr + else: + layer_inputs["x"] = inputs_name[0] + layer_attrs["pad"] = padding_attr graph.add_layer( - "prim.if", {'input': inputs_name[0] + "_cond"}, - 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) - block.add_layer( - "prim.sub", - inputs={"y": inputs_name[0] + "_len"}, - outputs=[inputs_name[0] + "_len0"], - scope_name=scope_name, - alpha=1.0, - x=dim) - block.add_layer( - "prim.len2list", - inputs={"len": inputs_name[0] + "_len0"}, - outputs=[inputs_name[0] + "_list"], - scope_name=scope_name) - block.add_layer( - "paddle.unsqueeze", - inputs={"x": inputs_name[0], - "axis": inputs_name[0] + "_list"}, - outputs=[inputs_name[0] + "_var"], - scope_name=scope_name) - block.add_layer( - kernel, - inputs={"input": inputs_name[0] + "_var"}, - outputs=copy.deepcopy(layer_outputs), - scope_name=scope_name, - **layer_attrs) - block.add_layer( - "paddle.squeeze", - inputs={"x": output_name, - "axis": inputs_name[0] + "_list"}, - outputs=[output_name], - scope_name=scope_name) - if_layer.add_block(block) - block = PaddleGraph(source_type="pytorch", parent_layer=if_layer) - layer_inputs["input"] = inputs_name[0] - block.add_layer( - kernel, + kernel_name, inputs=layer_inputs, - outputs=layer_outputs, + outputs=[output_name], 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" - - if not is_padding_tensor: - if len(layer_attrs["padding"]) == 2: - layer_outputs[0] = layer_outputs[0].replace("pad", "pad1d") - add_pad_layers("paddle.nn.Pad1D", 3) - elif len(layer_attrs["padding"]) == 4: - layer_outputs[0] = layer_outputs[0].replace("pad", "pad2d") - add_pad_layers("paddle.nn.Pad2D", 4) - elif len(layer_attrs["padding"]) == 6: - layer_outputs[0] = layer_outputs[0].replace("pad", "pad3d") - add_pad_layers("paddle.nn.Pad3D", 5) - else: - raise Exception("The lenght of padding list must be 2, 4 or 6!") else: + layer_inputs["input"] = inputs_name[0] graph.add_layer( "custom_layer:Pad", inputs=layer_inputs, outputs=[output_name], scope_name=scope_name, **layer_attrs) + current_inputs = list(layer_inputs.values()) return current_inputs, current_outputs -- GitLab