# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from x2paddle.core.util import * def aten_adaptive_avg_pool2d(mapper, graph, node): """ 构造average adaptive pool2d的PaddleLayer。 TorchScript示例: %x.5 : Tensor = aten::adaptive_avg_pool2d(%x.3, %_output_size.1) 参数含义: %x.5 (Tensor): 池化后结果Tensor。 %x.3 (Tensor): 输入Tensor。 %_output_size.1 (list): 自适应池化后的Tensor的宽、高大小。 """ 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) # 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) 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%_output_size.1 if inputs_name[1] in mapper.attrs: layer_attrs["pool_size"] = mapper.attrs[inputs_name[1]] else: mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_attrs["pool_size"] = inputs_name[1] current_inputs.append(inputs_name[1]) layer_attrs["pool_type"] = string("avg") graph.add_layer( "fluid.layers.adaptive_pool2d", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_addmm(mapper, graph, node): """ 构造addmm的PaddleLayer，该节点实现out = alpha ∗ x ∗ y + beta ∗ input。 TorchScript示例: %ret.2 : Tensor = aten::addmm(%150, %input.3, %156, %151, %152) 参数含义: %ret.2 (Tensor): addmm结果Tensor。 %150 (Tensor): 输入Tensor input。 %input.3 (Tensor): 输入Tensor x。 %156 (Tensor): 输入Tensor y。 %151 (int/float): 输入alpha。 %152 (int/float): 输入beta。 """ 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) # 获取当前节点输出的list current_outputs = [output_name] # 处理输入0，即%150 mapper._check_input( graph, inputs_node[0], inputs_name[0], current_outputs, 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) 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) layer_inputs["y"] = inputs_name[2] # 获取当前节点输入的list current_inputs = list(layer_inputs.values()) # 处理输入3，即%152 if inputs_name[3] in mapper.attrs: layer_attrs["beta"] = mapper.attrs[inputs_name[3]] else: mapper._check_input(graph, inputs_node[3], inputs_name[3], current_outputs) layer_attrs["beta"] = inputs_name[3] current_inputs.append(inputs_name[3]) # 处理输入4，即%151 if inputs_name[4] in mapper.attrs: layer_attrs["alpha"] = mapper.attrs[inputs_name[4]] else: mapper._check_input(graph, inputs_node[4], inputs_name[4], current_outputs) layer_attrs["alpha"] = inputs_name[4] current_inputs.append(inputs_name[4]) graph.add_layer( "fluid.layers.addmm", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_add(mapper, graph, node): """ 构造数值相加的PaddleLayer，该节点实现out = x + y。 TorchScript示例: %296 : int = aten::add(%i.12, %288) 参数含义: %296 (-): 相加结果。 %i.12 (-): 输入数值 x。 %288 (-): 输入数值 y。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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, 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) return current_inputs, current_outputs def aten_add_(mapper, graph, node): """ 构造数值相加的PaddleLayer，该节点实现out = x + alpha * y。 TorchScript示例: %137 : Tensor = aten::add(%136, %130, %130) 参数含义: %output.5 (Tensor): add结果Tensor。 %output.2 (Tensor): 输入Tensor x。 %150 (Tensor): 输入Tensor y。 %151 (int/float): 输入alpha。 """ 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) # 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) 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, 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()) # 处理输入2，即%151 if inputs_name[2] in mapper.attrs: layer_attrs["alpha"] = mapper.attrs[inputs_name[2]] else: mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs) layer_attrs["alpha"] = inputs_name[2] current_inputs.append(inputs_name[2]) graph.add_layer( "prim.add_", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten___and__(mapper, graph, node): """ 构造与计算的PaddleLayer。 TorchScript示例: %361 : bool = aten::__and__(%360, %358) 参数含义: %361 (bool): 输出，与计算结果。 %360 (-): 输入 x。 %358 (-): 输入 y。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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, 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.and", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_append(mapper, graph, node): """ 构造对list进行append的PaddleLayer。 TorchScript示例: %90 : int[] = aten::append(%_output_size.1, %v.1) 参数含义: %90 (list): 输出，append后的list。 %_output_size.1 (list): 需要进行append的list。 %v.1 (-): append的元素。 """ layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(node) layer_outputs = [inputs_name[0]] # 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) 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) 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) return current_inputs, current_outputs def aten_avg_pool2d(mapper, graph, node): """ 构造最大池化的PaddleLayer。 TorchScript示例: %branch_pool.2 : Tensor = aten::avg_pool2d(%x.43, %538, %539, %540, %273, %272, %271) 参数含义: %branch_pool.2 (Tensor): 输出，池化后的结果。 %x.43 (Tensor): 需要池化的Tensor。 %538 (list): 池化kernel的大小。 %539 (list): 步长大小。 %540 (list): 填充大小。 %273 (bool): 是否用ceil函数计算输出高度和宽度。 %272 (bool): 是否在平均池化模式不忽略填充值，False为忽略。 %271 (int): 如果指定，它将用作除数，否则将使用池化区域的大小。 """ if "pool" in mapper.dygraph_name_id: mapper.dygraph_name_id["pool"] += 1 else: mapper.dygraph_name_id["pool"] = 0 pool_name = "pool" + str(mapper.dygraph_name_id["pool"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [pool_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 current_outputs = [output_name] # 处理输入0，即%x.34 mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) 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%538 layer_attrs["pool_size"] = mapper.attrs[inputs_name[1]] # 处理输入2，即%539 layer_attrs["pool_stride"] = mapper.attrs[inputs_name[2]] # 处理输入3，即%540 layer_attrs["pool_padding"] = mapper.attrs[inputs_name[3]] # 处理输入4，即%273 layer_attrs["ceil_mode"] = mapper.attrs[inputs_name[4]] # 处理输入5，即%272 layer_attrs["exclusive"] = not mapper.attrs[inputs_name[5]] # 处理输入6，即%271 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[6]], type="eq", key=mapper.attrs[inputs_name[6]], value=None) layer_attrs["pool_type"] = string("avg") graph.add_layer( "fluid.dygraph.Pool2D", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_batch_norm(mapper, graph, node): """ 构造BatchNorm的PaddleLayer。 TorchScript示例: %input.81 : Tensor = aten::batch_norm(%input.80, %778, %779, %776, %777, %780, %exponential_average_factor.23, %766, %781) 参数含义: %input.81 (Tensor): 输出，批处理后的结果。 %input.80 (Tensor): 需要进行批处理的特征层。 %778 (Tensor): weights。 %779 (Tensor): bias。 %776 (Tensor): 全局均值。 %777 (Tensor): 全局方差。 %780 (bool): 是否训练。 %exponential_average_factor.23 (float): 用于计算均值和方差的比例。 %766 (float): 为了数值稳定加在分母上的值。 %781 (bool): 是否启用cudnn。 """ if "batchnorm" in mapper.dygraph_name_id: mapper.dygraph_name_id["batchnorm"] += 1 else: mapper.dygraph_name_id["batchnorm"] = 0 batchnorm_name = "batchnorm" + str(mapper.dygraph_name_id["batchnorm"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [batchnorm_name, output_name] layer_inputs = {} layer_attrs = {} layer_attrs["is_test"] = True inputs_name, inputs_node = mapper._get_inputs_name(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) layer_inputs["input"] = inputs_name[0] # 获取当前节点输入、输出的list current_inputs = list(layer_inputs.values()) # 处理输入1，即%778 weights = mapper.pytorch_params[inputs_name[1]] mapper.paddle_params[batchnorm_name + ".weight"] = weights layer_attrs['num_channels'] = weights.shape[0] # 处理输入2，即%779 if inputs_name[2] in mapper.pytorch_params: bias = mapper.pytorch_params[inputs_name[2]] if bias is not None: mapper.paddle_params[batchnorm_name + ".bias"] = bias else: mapper.paddle_params[batchnorm_name + ".bias"] = False # 处理输入3，即%776 mean = mapper.pytorch_params[inputs_name[3]] mapper.paddle_params[batchnorm_name + "._mean"] = mean # 处理输入4，即%777 var = mapper.pytorch_params[inputs_name[4]] mapper.paddle_params[batchnorm_name + "._variance"] = var # 处理输入6，即%exponential_average_factor.23 layer_attrs["momentum"] = mapper.attrs[inputs_name[6]] # 处理输入7，即%766 layer_attrs["epsilon"] = mapper.attrs[inputs_name[7]] graph.add_layer( "fluid.dygraph.BatchNorm", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_cat(mapper, graph, node): """ 构造连接Tensor的PaddleLayer。 TorchScript示例: %x.222 : Tensor = aten::cat(%32, %7) 参数含义: %x.222 (Tensor): 输出，连接后的结果。 %i.12 (list): 需要连接的Tensor组成的list。 %7 (int): 连接的轴。 """ 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) # 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) 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%12 if inputs_name[1] in mapper.attrs: layer_attrs["axis"] = mapper.attrs[inputs_name[1]] else: mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_attrs["axis"] = inputs_name[1] current_inputs.append(inputs_name[1]) graph.add_layer( "fluid.layers.concat", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_conv2d(mapper, graph, node): """ 构造conv2d的PaddleLayer。 TorchScript示例: %input.10 : Tensor = aten::conv2d(%input.8, %25, %27, %28, %29, %30, %26) 参数含义: %input.10 (Tensor): 输出，卷积后的结果。 %input.8 (Tensor): 需要进行卷积的特征层。 %25 (Tensor): weights。 %27 (Tensor): bias。 %28 (int): 步长大小。 %29 (int): 填充大小。 %30 (int): 膨胀系数大小。 %26 (int): 卷积的组数。 """ if "conv" in mapper.dygraph_name_id: mapper.dygraph_name_id["conv"] += 1 else: mapper.dygraph_name_id["conv"] = 0 conv2d_name = "conv" + str(mapper.dygraph_name_id["conv"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [conv2d_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 current_outputs = [output_name] # 处理输入0，即%input.8 mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) 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%25 weights = mapper.pytorch_params[inputs_name[1]] mapper.paddle_params[conv2d_name + ".weight"] = weights layer_attrs["num_filters"] = weights.shape[0] layer_attrs["filter_size"] = weights.shape[2:] # 处理输入2，即%27 if inputs_name[2] in mapper.pytorch_params: bias = mapper.pytorch_params[inputs_name[2]] if bias is not None: mapper.paddle_params[conv2d_name + ".bias"] = bias else: layer_attrs["bias_attr"] = False else: layer_attrs["bias_attr"] = False # 处理输入3，即%28 layer_attrs["stride"] = mapper.attrs[inputs_name[3]] # 处理输入4，即%29 layer_attrs["padding"] = mapper.attrs[inputs_name[4]] # 处理输入5，即%30 layer_attrs["dilation"] = mapper.attrs[inputs_name[5]] # 处理输入6，即%26 layer_attrs["groups"] = mapper.attrs[inputs_name[6]] layer_attrs['num_channels'] = weights.shape[1] * mapper.attrs[inputs_name[ 6]] graph.add_layer( "fluid.dygraph.Conv2D", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_dim(mapper, graph, node): """ 构造获取维度的PaddleLayer。 TorchScript示例: %106 : int = aten::dim(%101) 参数含义: %106 (int): 输出，Tensor的维度。 %101 (Tensor): 输入的Tensor。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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=layer_outputs) graph.add_layer( "prim.len", inputs={"input": output_name}, outputs=layer_outputs) return current_inputs, current_outputs def aten_dropout(mapper, graph, node): """ 构造Dropout的PaddleLayer。 TorchScript示例: %119 : Tensor = aten::dropout(%result.3, %117, %118) 参数含义: %119 (Tensor): Dropout后的Tensor。 %result.3 (Tensor): 输入Tensor。 %118 (bool): 是否是训练阶段。 """ if "dropout" in mapper.dygraph_name_id: mapper.dygraph_name_id["dropout"] += 1 else: mapper.dygraph_name_id["dropout"] = 0 dropout_name = "dropout" + str(mapper.dygraph_name_id["dropout"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [dropout_name, output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.dygraph.Dropout", inputs=layer_inputs, outputs=layer_outputs, p=0.0) return current_inputs, current_outputs def aten_dropout_(mapper, graph, node): """ 构造Dropout的PaddleLayer。 TorchScript示例: %119 : Tensor = aten::dropout_(%result.3, %117, %118) 参数含义: %119 (Tensor): Dropout后的Tensor。 %result.3 (Tensor): 输入Tensor。 %118 (bool): 是否是训练阶段。 """ if "dropout" in mapper.dygraph_name_id: mapper.dygraph_name_id["dropout"] += 1 else: mapper.dygraph_name_id["dropout"] = 0 dropout_name = "dropout" + str(mapper.dygraph_name_id["dropout"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [dropout_name, output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.dygraph.Dropout", inputs=layer_inputs, outputs=layer_outputs, p=0.0) return current_inputs, current_outputs def aten_eq(mapper, graph, node): """ 构造判断数值是否相等的PaddleLayer。 TorchScript示例: %125 : bool = aten::eq(%124, %123) 参数含义: %125 (bool): 对比后结果。 %124 (-): 需对比的输入1。 %123 (-): 需对比的输入2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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.eq", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_flatten(mapper, graph, node): """ 构造flatten的PaddleLayer。 TorchScript示例: %x.8 : Tensor = aten::flatten(%x, %4, %2) 参数含义: %x.8 (Tensor): flatten后结果。 %x (Tensor): 输入Tensor。 %4 (int): flatten的开始维度。 %2 (int): flatten的结束维度。 注意：目前flatten只支持第一维的flatten """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(node) # 获取当前节点输出的list current_outputs = [output_name] # 处理输入1，即%4 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[1]], type='eq', key=mapper.attrs[inputs_name[1]], value=1) # 处理输入2，即%2 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[2]], type='eq', key=mapper.attrs[inputs_name[2]], value=-1) # 处理输入0，即%x mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) 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( "fluid.layers.flatten", inputs=layer_inputs, outputs=layer_outputs, axis=1) return current_inputs, current_outputs def aten___getitem__(mapper, graph, node): """ 构造获取list中元素的PaddleLayer。 TorchScript示例: %v.1 : int = aten::__getitem__(%72, %88) 参数含义: %v.1 (-): 输出，list中的元素。 %72 (list): 需要获取元素的list。 %88 (int): 索引。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_gt(mapper, graph, node): """ 构造对比大小的PaddleLayer。 TorchScript示例: %83 : bool = aten::gt(%82, %78) 参数含义: %83 (bool): 输出，第一个元素是否大于第二个元素。 %82 (-): 需对比的输入1。 %78 (-): 需对比的输入2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_hardtanh_(mapper, graph, node): """ 构造hardtanh激活的PaddleLayer。 TorchScript示例: %result.9 : Tensor = aten::hardtanh_(%input.20, %67, %66) 参数含义: %result.9 (Tensor): 输出，hardtanh激活后的Tensor。 %input.20 (Tensor): 需要hardtanh激活的Tensor。 %67 (float): hardtanh激活的最小阈值。 %66 (float): hardtanh激活的最大阈值。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(node) # 获取当前节点输出的list current_outputs = [output_name] # 处理输入1，即%67 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[1]], type='eq', key=mapper.attrs[inputs_name[1]], value=0.0) # 处理输入2，即%66 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[2]], type='eq', key=mapper.attrs[inputs_name[2]], value=6.0) # 处理输入0，即%input.20 mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) 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( 'fluid.layers.relu6', inputs=layer_inputs, outputs=layer_outputs, threshold=6.0) return current_inputs, current_outputs def aten_le(mapper, graph, node): """ 构造对比大小的PaddleLayer。 TorchScript示例: %80 : bool = aten::le(%78, %79) 参数含义: %80 (bool): 输出，第一个元素是否小于等于第二个元素。 %78 (-): 需对比的输入1。 %79 (-): 需对比的输入2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_len(mapper, graph, node): """ 构造获取list长度的PaddleLayer。 TorchScript示例: %85 : int = aten::len(%83) 参数含义: %85 (int): 输出，list的长度。 %72 (list): 需要获取长度的list。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) return current_inputs, current_outputs def aten_lt(mapper, graph, node): """ 构造对比大小的PaddleLayer。 TorchScript示例: %80 : bool = aten::lt(%78, %79) 参数含义: %80 (bool): 输出，第一个元素是否小于第二个元素。 %78 (-): 需对比的输入1。 %79 (-): 需对比的输入2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_max_pool2d(mapper, graph, node): """ 构造最大池化的PaddleLayer。 TorchScript示例: %input.8 : Tensor = aten::max_pool2d(%result.11, %20, %23, %21, %22, %19) 参数含义: %input.8 (Tensor): 输出，池化后的结果。 %result.11 (Tensor): 需要池化的Tensor。 %20 (list): 池化kernel的大小。 %23 (list): 步长大小。 %21 (list): 填充大小。 %22 (list): 膨胀系数大小。 %19 (bool): 是否用ceil函数计算输出高度和宽度。 """ if "pool" in mapper.dygraph_name_id: mapper.dygraph_name_id["pool"] += 1 else: mapper.dygraph_name_id["pool"] = 0 pool_name = "pool" + str(mapper.dygraph_name_id["pool"]) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [pool_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 current_outputs = [output_name] # 处理输入0，即%result.11 mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) 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%20 layer_attrs["pool_size"] = mapper.attrs[inputs_name[1]] # 处理输入2，即%23 layer_attrs["pool_stride"] = mapper.attrs[inputs_name[2]] # 处理输入3，即%21 layer_attrs["pool_padding"] = mapper.attrs[inputs_name[3]] # 处理输入4，即%22 graph.add_layer( "prim.assert", inputs={}, outputs=[inputs_name[4]], type="eq", key=mapper.attrs[inputs_name[4]], value=[1, [1, 1]]) # 处理输入5，即%19 layer_attrs["ceil_mode"] = mapper.attrs[inputs_name[5]] layer_attrs["pool_type"] = string("max") graph.add_layer( "fluid.dygraph.Pool2D", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_matmul(mapper, graph, node): """ 构造矩阵相乘的PaddleLayer。 TorchScript示例: %output.2 : Tensor = aten::matmul(%101, %111) 参数含义: %output.2 (Tensor): 输出，相乘后的结果。 %101 (Tensor): 矩阵1。 %102 (Tensor): 矩阵2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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( "fluid.layers.matmul", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_mul(mapper, graph, node): """ 构造数值相乘的PaddleLayer。 TorchScript示例: %size_prods.39 : int = aten::mul(%size_prods.38, %114) 参数含义: %size_prods.39 (Tensor): 输出，相乘后的结果。 %size_prods.38 (-): 数值1。 %114 (-): 数值2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_ne(mapper, graph, node): """ 构造判断数值是否不相等的PaddleLayer。 TorchScript示例: %134 : bool = aten::ne(%133, %132) 参数含义: %134 (bool): 对比后结果。 %133 (-): 需对比的输入1。 %132 (-): 需对比的输入2。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) 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) return current_inputs, current_outputs def aten_neg(mapper, graph, node): """ 构造对数值取负的PaddleLayer。 TorchScript示例: %909 : int = aten::neg(%908) 参数含义: %909 (int): 取负后结果。 %908 (int): 需取负的输入。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) return current_inputs, current_outputs def aten___not__(mapper, graph, node): """ 构造对bool型取负的PaddleLayer。 TorchScript示例: %4498 : bool = aten::__not__(%aux_defined.2) 参数含义: %4498 (bool): 取负后结果。 %aux_defined.2 (bool): 需取负的输入。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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) return current_inputs, current_outputs def aten_relu(mapper, graph, node): """ 构造ReLU激活的PaddleLayer。 TorchScript示例: %result.3 : Tensor = aten::relu(%input.5) 参数含义: %result.3 (Tensor): 输出，ReLU后的结果。 %result.5 (Tensor): 需要ReLU的Tensor。 注意: inplace这个参数在paddle中未实现 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.layers.relu", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_relu_(mapper, graph, node): """ 构造ReLU激活的PaddleLayer。 TorchScript示例: %result.3 : Tensor = aten::relu_(%input.5) 参数含义: %result.3 (Tensor): 输出，ReLU后的结果。 %result.5 (Tensor): 需要ReLU的Tensor。 注意: inplace这个参数在paddle中未实现 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.layers.relu", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_relu6(mapper, graph, node): """ 构造ReLU6激活的PaddleLayer。 TorchScript示例: %result.3 : Tensor = aten::relu6(%input.5) 参数含义: %result.3 (Tensor): 输出，ReLU6后的结果。 %result.5 (Tensor): 需要ReLU6的Tensor。 注意: inplace这个参数在paddle中未实现 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.layers.relu6", inputs=layer_inputs, outputs=layer_outputs, threshold=6.0) return current_inputs, current_outputs def aten_reshape(mapper, graph, node): """ 构造调整大小的PaddleLayer。 TorchScript示例: %x.6 : Tensor = aten::reshape(%4700, %4703) 参数含义: %x.6 (Tensor): 输出，reshape后的Tensor。 %4700 (Tensor): 需要reshape的Tensor。 %4703 (list): 形状大小组成的list。 """ 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) # 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) layer_inputs["x"] = inputs_name[0] # 处理输入1，即%4703 mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_inputs["shape"] = inputs_name[1] # 获取当前节点输入、输出的list current_inputs = list(layer_inputs.values()) graph.add_layer( "fluid.layers.reshape", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_select(mapper, graph, node): """ 构造选取特定维度Variable的PaddleLayer。 TorchScript示例: %19 : Tensor = aten::select(%18, %8, %7) 参数含义: %19 (Tensor): 输出，选取的Tensor。 %18 (Tensor): 需要选取的Tensor。 %8 (int): select的维度。 %7 (int): select的第n个向量。 """ 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) # 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) 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) 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( "prim.select", inputs=layer_inputs, outputs=current_outputs, **layer_attrs) return current_inputs, current_outputs def aten_size(mapper, graph, node): """ 构造获取shape的PaddleLayer。 TorchScript示例: %73 : int[] = aten::size(%x.12) 参数含义: %73 (list): 输出，shape的list。 %x.12 (Tensor): 需要获取shape的Tensor。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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=layer_outputs) return current_inputs, current_outputs def aten_slice(mapper, graph, node): """ 构造切分list或Variable的PaddleLayer。 TorchScript示例: %83 : int[] = aten::slice(%73, %82, %75, %77) 参数含义: %83 (list/Tensor): 输出，切分后的list。 %73 (list/Tensor): 需要切分的list。 %82 (int): 切分的开始索引。 %75 (int): 切分的结束索引。 %77 (int): 切分的步长。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(node) # 获取当前节点输出的list current_outputs = [output_name] # 处理输入0，即%73 mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs) layer_inputs["input"] = inputs_name[0] # 处理输入1，即%82 mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_inputs["start"] = inputs_name[1] # 处理输入2，即%75 mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs) layer_inputs["end"] = inputs_name[2] # 处理输入3，即%77 mapper._check_input(graph, inputs_node[3], inputs_name[3], current_outputs) layer_inputs["step"] = inputs_name[3] # 获取当前节点输入的list current_inputs = list(layer_inputs.values()) graph.add_layer("prim.slice", inputs=layer_inputs, outputs=current_outputs) return current_inputs, current_outputs def aten_sub(mapper, graph, node): """ 构造数值相减的PaddleLayer。 TorchScript示例: %840 : int = aten::sub(%839, %836) 参数含义: %840 (-): 相减结果。 %839 (-): 输入数值 x。 %836 (-): 输入数值 y。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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, 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.sub", inputs=layer_inputs, outputs=layer_outputs) return current_inputs, current_outputs def aten_t(mapper, graph, node): """ 构造矩阵转置的PaddleLayer。 TorchScript示例: %840 : int = aten::sub(%839, %836) 参数含义: %109 (Tensor): 输出，转置后的矩阵。 %102 (Tensor): 需要转置的Tensor。 """ output_name = mapper._get_outputs_name(node)[0] layer_outputs = [output_name] layer_inputs = {} inputs_name, inputs_node = mapper._get_inputs_name(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) 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( "fluid.layers.transpose", inputs=layer_inputs, outputs=layer_outputs, perm=[1, 0]) return current_inputs, current_outputs def aten_unsqueeze(mapper, graph, node): """ 构造插入维度的PaddleLayer。 TorchScript示例: %13 : Tensor = aten::unsqueeze(%12, %7) 参数含义: %13 (Tensor): 输出，插入维度后的Tensor。 %12 (Tensor): 需要插入维度的Tensor。 %7 (int): 维度。 """ 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) # 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) 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%12 if inputs_name[1] in mapper.attrs: layer_attrs["axes"] = mapper.attrs[inputs_name[1]] else: mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_attrs["axes"] = inputs_name[1] current_inputs.append(inputs_name[1]) graph.add_layer( "fluid.layers.unsqueeze", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs def aten_warn(mapper, graph, node): """ 构造warning的PaddleLayer。 TorchScript示例: = aten::warn(%3, %2) 参数含义: %3 (str): warning的提示字符串。 %2 (int): warning的stacklevel。 """ 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) # 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) 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%2 if inputs_name[1] in mapper.attrs: layer_attrs["stacklevel"] = mapper.attrs[inputs_name[1]] else: mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs) layer_attrs["stacklevel"] = inputs_name[1] current_inputs.append(inputs_name[1]) graph.add_layer( "prim.warnings", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs) return current_inputs, current_outputs