diff --git a/docs/inference_model_convertor/op_list.md b/docs/inference_model_convertor/op_list.md index b8e89a8691d6b387b81083876e7c3abc39a3a2a2..b7d19c2a6a15576aca801b71470214967fccca74 100755 --- a/docs/inference_model_convertor/op_list.md +++ b/docs/inference_model_convertor/op_list.md @@ -117,7 +117,7 @@ Aten: | 125 | aten::complex | 126 | aten::real | 127 | aten::imag | 128 | aten::fft\_rfftn | | 129 | aten::fft\_irfftn | 130 | aten::hardsigmoid | 131 | aten::hardswish | 132 | aten::linear | | 133 | aten::rsqrt | 134 | aten::replication\_pad1d | 135 | aten::full | 136 | aten::group\_norm | -| 137 | aten::argmax | 138 | aten::copy | | | | | +| 137 | aten::argmax | 138 | aten::copy | 139 | aten::upsample\_trilinear3d | | | Prim: | 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP | diff --git a/x2paddle/op_mapper/pytorch2paddle/aten.py b/x2paddle/op_mapper/pytorch2paddle/aten.py index 676fee6e277951048971917210cfc59cdaa04335..a5dcae494baf71b5336d8ab34ea39680bbfb93d9 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 @@ -6065,6 +6002,66 @@ def aten_upsample_bilinear2d(mapper, graph, node): return current_inputs, current_outputs +def aten_upsample_trilinear3d(mapper, graph, node): + """ + TorchScript Code: + %4997 : Tensor = aten::upsample_trilinear3d(%x.13, %4963, %5421, %4995) + Parameter meaning: + %4997 (Tensor): Output Tensor + %x.13 (Tensor): Input Tensor + %4963 (list): output_size + %5421 (bool): align_corners + %4995 (float): scale_factors + """ + scope_name = mapper.normalize_scope_name(node) + output_name = mapper._get_outputs_name(node)[0] + layer_outputs = [output_name] + layer_inputs = {} + layer_attrs = {} + inputs_name, inputs_node = mapper._get_inputs_name(node) + # Output list + current_outputs = [output_name] + # process Input Tensor + 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()) + # process output_size + if inputs_name[1] in mapper.attrs: + layer_attrs["size"] = mapper.attrs[inputs_name[1]] + else: + mapper._check_input(graph, inputs_node[1], inputs_name[1], + current_outputs, scope_name) + layer_inputs["size"] = inputs_name[1] + current_inputs.append(inputs_name[1]) + # process align_corners + if inputs_name[2] in mapper.attrs: + layer_attrs["align_corners"] = mapper.attrs[inputs_name[2]] + else: + mapper._check_input(graph, inputs_node[2], inputs_name[2], + current_outputs, scope_name) + layer_inputs["align_corners"] = inputs_name[2] + current_inputs.append(inputs_name[2]) + # process scale_factor + if inputs_name[3] in mapper.attrs: + layer_attrs["scale_factor"] = mapper.attrs[inputs_name[3]] + else: + mapper._check_input(graph, inputs_node[3], inputs_name[3], + current_outputs, scope_name) + layer_inputs["scale_factor"] = inputs_name[3] + current_inputs.append(inputs_name[3]) + layer_attrs["align_mode"] = 0 + layer_attrs["mode"] = string("trilinear") + layer_attrs["data_format"] = string("NCDHW") + graph.add_layer( + "paddle.nn.functional.interpolate", + inputs=layer_inputs, + outputs=layer_outputs, + scope_name=scope_name, + **layer_attrs) + return current_inputs, current_outputs + + def aten_upsample_nearest2d(mapper, graph, node): """ 构造使用nearest上采样的PaddleLayer。 TorchScript示例: