Merge pull request #472 from SunAhong1993/lstm

Lstm

README.md

@@ -10,7 +10,7 @@ X2Paddle在多个主流的CV模型上，测试过TensorFlow/Caffe/ONNX/PyTorch
 ## 环境依赖
 
 python == 2.7 | python >= 3.5  
-paddlepaddle 2.0-rc 或者 develop  
+paddlepaddle 2.0.0-rc1 或者 develop  
 
 **按需安装以下依赖**  
 tensorflow ： tensorflow == 1.14.0  
@@ -93,12 +93,6 @@ X2Paddle提供了工具解决如下问题，详见[tools/README.md](tools/README
 6. [X2Paddle添加内置的Caffe自定义层](./docs/user_guides/add_caffe_custom_layer.md)
 
 ## 更新历史
-2019.08.05  
-1. 统一tensorflow/caffe/onnx模型转换代码和对外接口
-2. 解决上一版caffe2fluid无法转换多分支模型的问题
-3. 解决Windows上保存模型无法加载的问题
-4. 新增optimizer，优化代码结构，合并conv、batch_norm的bias和激活函数  
-
 2020.12.09
 1. 新增PyTorch2Paddle转换方式，转换得到Paddle动态图代码，并动转静获得inference_model。
 方式一：trace方式，转换后的代码有模块划分，每个模块的功能与PyTorch相同。
@@ -107,8 +101,6 @@ X2Paddle提供了工具解决如下问题，详见[tools/README.md](tools/README
 3. 新增TensorFlow op（14个）：Neg、Greater、FloorMod、LogicalAdd、Prd、Equal、Conv3D、Ceil、AddN、DivNoNan、Where、MirrorPad、Size、TopKv2
 4. 新增Optimizer模块，主要包括op融合、op消除功能，转换后的代码可读性更强，进行预测时耗时更短。
 
-**如果你需要之前版本的tensorflow2fluid/caffe2fluid/onnx2fluid，可以继续访问release-0.9分支，获取之前版本的代码使用。**
-
 
 ## Acknowledgements
 

docs/introduction/op_list.md

@@ -61,7 +61,7 @@
 | 41 | MatMul   | 42 | Sum       | 43 | Transpose | 44 | BatchNormalization |
 | 45 | Squeeze  | 46 | Equal     | 47 | Identity  | 48 | GlobalAveragePool  |
 | 49 | MaxPool  | 50 | Conv      | 51 | Gemm      | 52 | NonZero            |
-| 53 | Abs      | 54 | Floor     |
+| 53 | Abs      | 54 | Floor     | 52 | ArgMax    |
 
 ## PyTorch
 Aten:
@@ -93,7 +93,8 @@ Aten:
 | 93 | aten::sub | 94 | aten::t |95|aten::tanh|96|aten::split|
 | 97 | aten::transpose | 98 | aten::to |99|aten::type\_as|100|aten::unsqueeze|
 | 101 | aten::upsample\_bilinear2d | 102 | aten::values |103|aten::view|104|aten::warn|
-| 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|||
+| 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|108|aten::bmm|
+| 109 | aten::sub\_ | 110 | aten:erf |111|aten::lstm|112|aten::gather|
 
 Prim:
 | 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |

docs/introduction/x2paddle_model_zoo.md

@@ -5,28 +5,28 @@
 
 ## TensorFlow
 
-| 模型 | 代码 | 备注 |
-|------|----------|------|
-| SqueezeNet | [code](https://github.com/tensorflow/tpu/blob/master/models/official/squeezenet/squeezenet_model.py)|-|
-| MobileNet_V1 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| MobileNet_V2 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ShuffleNet | [code](https://github.com/TropComplique/shufflenet-v2-tensorflow) |-|
-| mNASNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |-|
-| EfficientNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |-|
-| Inception_V3 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |-|
-| Inception_V4 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py) |-|
-| Inception_ResNet_V2 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |-|
-| VGG16 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ResNet_V1_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ResNet_V2_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| UNet | [code1](https://github.com/jakeret/tf_unet )/[code2](https://github.com/lyatdawn/Unet-Tensorflow) |-|
-| MTCNN | [code](https://github.com/AITTSMD/MTCNN-Tensorflow) |-|
-| YOLO-V3| [code](https://github.com/YunYang1994/tensorflow-yolov3) | -|
-| FALSR | [code](https://github.com/xiaomi-automl/FALSR) | 需使用参数without_data_format_optimization |
-| DCSCN | [code](https://modelzoo.co/model/dcscn-super-resolution) | 需使用参数without_data_format_optimization |
-| Bert（albert） | [code](https://github.com/google-research/albert#pre-trained-models) | 需使用参数without_data_format_optimization |
-| Bert（chinese_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 需使用参数without_data_format_optimization |
-| Bert（multi_cased_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 需使用参数without_data_format_optimization |
+| 模型 | 代码 |
+|------|----------|
+| SqueezeNet | [code](https://github.com/tensorflow/tpu/blob/master/models/official/squeezenet/squeezenet_model.py)|
+| MobileNet_V1 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| MobileNet_V2 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ShuffleNet | [code](https://github.com/TropComplique/shufflenet-v2-tensorflow) |
+| mNASNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |
+| EfficientNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| Inception_V3 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |
+| Inception_V4 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py) |
+| Inception_ResNet_V2 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |
+| VGG16 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ResNet_V1_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ResNet_V2_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| UNet | [code1](https://github.com/jakeret/tf_unet )/[code2](https://github.com/lyatdawn/Unet-Tensorflow) |
+| MTCNN | [code](https://github.com/AITTSMD/MTCNN-Tensorflow) |
+| YOLO-V3| [code](https://github.com/YunYang1994/tensorflow-yolov3) | 
+| FALSR | [code](https://github.com/xiaomi-automl/FALSR) |
+| DCSCN | [code](https://modelzoo.co/model/dcscn-super-resolution) |
+| Bert（albert） | [code](https://github.com/google-research/albert#pre-trained-models) |
+| Bert（chinese_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 
+| Bert（multi_cased_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 
 
 ## Caffe
 
@@ -72,8 +72,8 @@
 | EfficientNet | [pytorch(personal practice)](https://github.com/rwightman/gen-efficientnet-pytorch) |9|
 | SqueezeNet | [onnx official](https://s3.amazonaws.com/download.onnx/models/opset_9/squeezenet.tar.gz) |9|
 |Ultra-Light-Fast-Generic-Face-Detector-1MB| [onnx_model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/models/onnx)|9 |
-|BERT| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](FAQ.md)|
-|GPT2| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](FAQ.md)|
+|BERT| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../user_guides/FAQ.md)|
+|GPT2| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../user_guides/FAQ.md)|
 
 
 ## PyTorch
@@ -96,3 +96,6 @@
 | FlaubertModel | [code](https://huggingface.co/transformers/model_doc/flaubert.html) |只支持trace模式|
 | Roberta| [code](https://huggingface.co/transformers/model_doc/roberta.html)  |只支持trace模式|
 | XLMRobertaForTokenClassification|[code](https://huggingface.co/transformers/model_doc/xlmroberta.html)  |只支持trace模式|
+| EasyOCR_detector|[code](https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/detection.py)  |-|
+| EasyOCR_recognizer|[code](https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/recognition.py)  |-|
+

x2paddle/core/program.py

@@ -26,6 +26,7 @@ import six
 import pickle
 import numpy as np
 from os import path as osp 
+from x2paddle.core.util import *
 
 
 class PaddleLayer(object):
@@ -210,6 +211,8 @@ class PaddleGraph(object):
                         layer_id, 0) == 0 and layer.kernel != "prim.assert" \
                         and layer.kernel != "prim.exception" \
                         and layer.kernel != "prim.warnings":
+                    if layer.kernel == "paddle.to_tensor":
+                        self.inputs_info.pop(layer.outputs[0])
                     invalid_list.append(layer_id)
         for layer_id in invalid_list:
             self.layers.pop(layer_id)
@@ -272,7 +275,7 @@ class PaddleGraph(object):
                 
     def gen_dygraph_model(self, save_dir, jit_type=None):
         if jit_type == "trace":
-            from x2paddle.optimizer.code_optimizer import HierarchicalTree
+            from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree
             hierarchical_tree = HierarchicalTree(self)
             for layer_id, layer in self.layers.items():
                 hierarchical_tree.insert(layer)
@@ -280,7 +283,7 @@ class PaddleGraph(object):
             self.dump_dygraph_parameter(save_dir)
         else:
             if self.source_type == "pytorch":
-                from x2paddle.optimizer.code_optimizer import ModuleGraph
+                from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph
                 module_graph = ModuleGraph(self)
                 module_graph.save_source_files(save_dir)
                 self.dump_dygraph_parameter(save_dir)
@@ -324,12 +327,10 @@ class PaddleGraph(object):
 
         write_code(
             f, [
-                "from paddle.fluid.initializer import Constant",
-                "from paddle.fluid.param_attr import ParamAttr",
-                "import paddle.fluid as fluid", 
                 custom_import,
-                "import paddle", "import math", "",
-                
+                "import paddle", 
+                "import math", 
+                "",
             ],
             indent=0)
         if self.custom_code is not None:
@@ -346,6 +347,8 @@ class PaddleGraph(object):
             ],
             indent=1)
         for layer_id, layer in self.layers.items():
+            if layer.kernel.startswith("paddle"):
+                remove_default_attrs(layer.kernel, layer.attrs)
             edges_in = self.edges_in.get(layer_id, [])
             edges_out = self.edges_out.get(layer_id, [])
             if len(edges_in) == 0 and len(edges_out) == 0:
@@ -425,7 +428,6 @@ class PaddleGraph(object):
                     continue
                 if layer.kernel == "paddle.to_tensor":
                     data = layer.attrs["data"]
-                    if not data.startswith("params["):
                     self.inputs.append(data)
                 if len(layer.blocks) > 0:
                     for block in layer.blocks:
@@ -473,10 +475,7 @@ class PaddleGraph(object):
                 custom_import = ""
             self.head = gen_codes(
                 [
-                    "from paddle.fluid.initializer import Constant",
-                    "from paddle.fluid.param_attr import ParamAttr",
                     "import paddle",
-                    "import paddle.fluid as fluid",
                     "import math",
                     custom_import,
                     "",
@@ -548,6 +547,8 @@ class PaddleGraph(object):
             gen_head()
 
         for layer_id, layer in self.layers.items():
+            if layer.kernel.startswith("paddle"):
+                remove_default_attrs(layer.kernel, layer.attrs)
             if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel
                 ) or layer.kernel == "paddle.to_tensor" or \
                 layer.kernel.startswith("custom_layer") or \
@@ -577,6 +578,9 @@ class PaddleGraph(object):
                     line = layer.outputs[0]
                 elif len(layer.outputs) == 2:
                     line = layer.outputs[1]
+                else:
+                    if layer.kernel == "paddle.nn.LSTM":
+                        line = "{}, ({})".format(layer.outputs[1], ', '.join(layer.outputs[-2:]))
                     else:
                         line = ','.join(layer.outputs[1:])
                 if layer.kernel == "paddle.to_tensor" and layer.attrs[

x2paddle/core/util.py

+# -*- coding:UTF-8 -*-
 # Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
@@ -14,15 +15,61 @@
 import numpy
 import math
 import os
+import inspect
 
 
 def string(param):
+    """ 生成字符串。
+    """
     return "\'{}\'".format(param)
 
 def name_generator(nn_name, nn_name2id):
+    """ 生成paddle.nn类op的名字。
+    
+    Args:
+        nn_name (str): 名字。
+        nn_name2id (dict): key为名字，value为名字出现的次数-1。
+    """
     if nn_name in nn_name2id:
         nn_name2id[nn_name] += 1
     else:
         nn_name2id[nn_name] = 0
     real_nn_name = nn_name + str(nn_name2id[nn_name])
     return real_nn_name
+
+def remove_default_attrs(kernel, attrs):
+    """ 删除每个OP的默认参数。
+    
+    Args:
+        kernel (str): OP的类型名字。
+        attrs (dict): 目前该OP所包含的参数， key为参数名，value为参数值。
+    """
+    def get_default_args(func):
+        signature = inspect.signature(func)
+        return {
+            k: v.default
+            for k, v in signature.parameters.items()
+            if v.default is not inspect.Parameter.empty
+        }
+    is_func = True
+    if "paddle.nn" in kernel and "functional"not in kernel:
+        is_func = False
+    import paddle
+    obj = paddle
+    for i, part in enumerate(kernel.split(".")):
+        if i == 0:
+            continue
+        obj = getattr(obj, part)
+    if is_func:
+        func = obj
+    else:
+        func = obj.__init__ 
+    default_attrs = get_default_args(func)
+    for default_k, default_v in default_attrs.items():
+        if default_k in attrs:
+            if (isinstance(attrs[default_k], list) or isinstance(attrs[default_k], tuple)) \
+                    and not is_func:
+                if len(set(attrs[default_k])) == 1:
+                    attrs[default_k] = attrs[default_k][0]
+            if default_v == attrs[default_k]:
+                attrs.pop(default_k)
\ No newline at end of file

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

@@ -367,57 +367,46 @@ class CaffeOpMapper(OpMapper):
                     output_size=kernel)
         else:
             layer_attrs = {
-                'pool_size': kernel,
-                'pool_stride': stride,
-                'pool_padding': pad,
+                'kernel_size': kernel,
+                'stride': stride,
+                'padding': pad,
                 'ceil_mode': ceil_mode,
-                'pool_type': string(pool_type),
-                'exclusive': False,
-                'global_pooling': global_pool,
             }
+            if params.pool == 0:
+                self.paddle_graph.add_layer(
+                    "paddle.nn.MaxPool2D",
+                    inputs={"input": input.name},
+                    outputs=layer_outputs,
+                    **layer_attrs)
+            else:
                 self.paddle_graph.add_layer(
-                "paddle.fluid.dygraph.Pool2D",
+                    "paddle.nn.AvgPool2D",
                     inputs={"input": input.name},
                     outputs=layer_outputs,
                     **layer_attrs)
-#             layer_attrs = {
-#                 'kernel_size': kernel,
-#                 'stride': stride,
-#                 'padding': pad,
-#                 'ceil_mode': ceil_mode,
-#             }
-#             if params.pool == 0:
-#                 self.paddle_graph.add_layer(
-#                     "paddle.nn.MaxPool2D",
-#                     inputs={"input": input.name},
-#                     outputs=layer_outputs,
-#                     **layer_attrs)
-#             else:
-#                 layer_attrs["count_include_pad"] = True
-#                 self.paddle_graph.add_layer(
-#                     "paddle.nn.AvgPool2D",
-#                     inputs={"input": input.name},
-#                     outputs=layer_outputs,
-#                     **layer_attrs)
 
     def LRN(self, node):
+        lrn_name = name_generator("lrn", self.nn_name2id)
+        output_name = node.layer_name
+        layer_outputs = [lrn_name, output_name]
         assert len(node.inputs) == 1, "The count of LRN node\'s input is not 1."
         input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.lrn_param
         assert params.local_size % 2 == 1
         alpha = params.alpha / float(params.local_size)
         layer_attrs = {
-            "n": params.local_size,
+            "size": params.local_size, 
             "k": params.k, 
             "alpha": alpha,
-            "beta": params.beta,
+            "beta": params.beta
         }
         self.paddle_graph.add_layer(
-            "fluid.layers.lrn", 
+            "paddle.nn.LocalResponseNorm",
             inputs={"input": input.name},
-            outputs=[node.layer_name],
+            outputs=layer_outputs,
             **layer_attrs)
 
+
     def InnerProduct(self, node):
         linear_name = name_generator("linear", self.nn_name2id)
         output_name = node.layer_name
@@ -1131,7 +1120,7 @@ class CaffeOpMapper(OpMapper):
         input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.shuffle_channel_param
         self.paddle_graph.add_layer(
-            "fluid.layers.shuffle_channel",
+            "paddle.fluid.layers.shuffle_channel",
             inputs={"x": input.name},
             outputs=[node.layer_name],
             group=params.group)

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -14,8 +14,6 @@
 
 from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
 from x2paddle.core.graph import GraphNode
-from x2paddle.core.fluid_code import Layer
-from x2paddle.core.fluid_code import FluidCode
 from x2paddle.core.util import *
 from functools import reduce
 import numpy as np
@@ -86,7 +84,7 @@ class OpSet9():
     elementwise_ops = {
         'Add': 'paddle.add',
         'Div': 'paddle.divide',
-        'Sub': 'fluid.layers.elementwise_sub',
+        'Sub': 'paddle.subtract',
         'Mul': 'paddle.multiply',
         'Pow': 'paddle.pow',
     }
@@ -281,16 +279,11 @@ class OpSet9():
                     inputs={"x": var_hw},
                     outputs=[var_hw],
                     dtype=string('int32'))
-#                 inputs['size'] = var_hw
-                
-                # TODO(syf): all use 
-                inputs['out_shape'] = var_hw
-                ipt = inputs.pop("x")
-                inputs["input"] = ipt
-                mode = node.get_attr('mode', 'nearest')
-                attrs.update({"align_corners": False})
+                inputs['size'] = var_hw
+                attrs = {"align_corners": False,
+                         "mode": string(node.get_attr('mode', 'nearest'))}
                 self.paddle_graph.add_layer(
-                    kernel="fluid.layers.resize_nearest",
+                    kernel="paddle.nn.functional.interpolate",
                     inputs=inputs,
                     outputs=[node.name],
                     **attrs)
@@ -356,7 +349,7 @@ class OpSet9():
             'sampling_ratio': sampling_ratio,
         }
         self.paddle_graph.add_layer(
-            'fluid.layers.roi_align',
+            'paddle.fluid.layers.roi_align',
             inputs={'input': val_x.name,
                     'rois': val_rois.name},
             outputs=[node.name],
@@ -376,7 +369,7 @@ class OpSet9():
             'spatial_scale': spatial_scale,
         }
         self.paddle_graph.add_layer(
-            'fluid.layers.roi_pool',
+            'paddle.fluid.layers.roi_pool',
             inputs={'input': val_x.name,
                     'rois': val_rois.name},
             outputs=[node.name],
@@ -405,7 +398,7 @@ class OpSet9():
             layer_attrs['data_format'] = string('NCHW')
             layer_attrs['value'] = value
         else:
-            paddle_op = 'fluid.layers.pad'
+            paddle_op = 'paddle.fluid.layers.pad'
             layer_attrs["pad_value"] = value
         if len(pads) == 4:
             paddings = np.array(pads).reshape(
@@ -1062,40 +1055,23 @@ class OpSet9():
                                       strides[1])
             paddings = pad_h + pad_w
 
-        paddle_op = 'fluid.layers.pool{}d'.format(poolnd)
-        assert 2 <= poolnd <= 3, 'only pool2d and pool3d are supported'
+        op_name = name_generator("pool", self.nn_name2id)
+        output_name = node.name
+        layer_outputs = [op_name, output_name]
+        paddle_op = 'paddle.nn.AvgPool{}D'.format(poolnd)
+        assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
         layer_attrs = {
-            "pool_size": kernel_shape,
-            "pool_type": string('avg'),
-            "pool_stride": strides,
-            "pool_padding": paddings,
+            "kernel_size": kernel_shape,
+            "stride": strides,
+            "padding": paddings,
             "ceil_mode": ceil_mode,
             "exclusive": 'True',
-            "name": string(node.name)
         }
         self.paddle_graph.add_layer(
             paddle_op, 
-            inputs={'input': val_x if isinstance(val_x, str) else val_x.name}, 
-            outputs=[node.name], 
+            inputs={'x': val_x.name}, 
+            outputs=layer_outputs, 
             **layer_attrs)
-        # TODO(syf): op has diff
-#         op_name = name_generator("pool", self.nn_name2id)
-#         output_name = node.name
-#         layer_outputs = [op_name, output_name]
-#         paddle_op = 'paddle.nn.Pool{}D'.format(poolnd)
-#         assert 1 <= poolnd <= 3, 'only Pool1D, Pool2D and Pool3D are supported'
-#         layer_attrs = {
-#             "kernel_size": kernel_shape,
-#             "stride": strides,
-#             "padding": paddings,
-#             "ceil_mode": ceil_mode,
-#             "exclusive": 'True',
-#         }
-#         self.paddle_graph.add_layer(
-#             paddle_op, 
-#             inputs={'x': val_x.name}, 
-#             outputs=layer_outputs, 
-#             **layer_attrs)
 
     @print_mapping_info
     def Concat(self, node):

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -426,11 +426,11 @@ def aten_avg_pool2d(mapper, graph, node):
     # 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]]
+    layer_attrs["kernel_size"] = mapper.attrs[inputs_name[1]]
     # 处理输入2，即%539
-    layer_attrs["pool_stride"] = mapper.attrs[inputs_name[2]]
+    layer_attrs["stride"] = mapper.attrs[inputs_name[2]]
     # 处理输入3，即%540
-    layer_attrs["pool_padding"] = mapper.attrs[inputs_name[3]]
+    layer_attrs["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
@@ -445,22 +445,13 @@ def aten_avg_pool2d(mapper, graph, node):
         key=mapper.attrs[inputs_name[6]],
         value=None)
 
-                # TODO(syf): The op has diff.
-#         self.paddle_graph.add_layer(
-#             kernel="paddle.nn.AvgPool2D",
-#             inputs={"input": input_name},
-#             outputs=layer_outputs,
-#             kernel_size=k_size[2:4],
-#             stride=strides[2:4],
-#             padding=string(pad_mode))
-
-    layer_attrs["pool_type"] = string("avg")
     graph.add_layer(
-        "fluid.layers.pool2d",
+        kernel="paddle.nn.AvgPool2D",
         inputs=layer_inputs,
-        outputs=layer_outputs[1:],
+        outputs=layer_outputs,
         scope_name=scope_name,
         **layer_attrs)
+
     return current_inputs, current_outputs
 
 def aten_avg_pool3d(mapper, graph, node):
@@ -493,11 +484,11 @@ def aten_avg_pool3d(mapper, graph, node):
     # 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]]
+    layer_attrs["kernel_size"] = mapper.attrs[inputs_name[1]]
     # 处理输入2，即%539
-    layer_attrs["pool_stride"] = mapper.attrs[inputs_name[2]]
+    layer_attrs["stride"] = mapper.attrs[inputs_name[2]]
     # 处理输入3，即%540
-    layer_attrs["pool_padding"] = mapper.attrs[inputs_name[3]]
+    layer_attrs["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
@@ -512,20 +503,10 @@ def aten_avg_pool3d(mapper, graph, node):
         key=mapper.attrs[inputs_name[6]],
         value=None)
 
-                # TODO(syf): The op has diff.
-#         self.paddle_graph.add_layer(
-#             kernel="paddle.nn.AvgPool2D",
-#             inputs={"input": input_name},
-#             outputs=layer_outputs,
-#             kernel_size=k_size[2:4],
-#             stride=strides[2:4],
-#             padding=string(pad_mode))
-
-    layer_attrs["pool_type"] = string("avg")
     graph.add_layer(
-        "fluid.layers.pool3d",
+        kernel="paddle.nn.AvgPool3D",
         inputs=layer_inputs,
-        outputs=layer_outputs[1:],
+        outputs=layer_outputs,
         scope_name=scope_name,
         **layer_attrs)
     return current_inputs, current_outputs
@@ -561,11 +542,11 @@ def aten_avg_pool1d(mapper, graph, node):
     # 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]]
+    layer_attrs["kernel_size"] = mapper.attrs[inputs_name[1]]
     # 处理输入2，即%539
-    layer_attrs["pool_stride"] = mapper.attrs[inputs_name[2]]
+    layer_attrs["stride"] = mapper.attrs[inputs_name[2]]
     # 处理输入3，即%540
-    layer_attrs["pool_padding"] = mapper.attrs[inputs_name[3]]
+    layer_attrs["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
@@ -580,20 +561,10 @@ def aten_avg_pool1d(mapper, graph, node):
         key=mapper.attrs[inputs_name[6]],
         value=None)
 
-                # TODO(syf): The op has diff.
-#         self.paddle_graph.add_layer(
-#             kernel="paddle.nn.AvgPool2D",
-#             inputs={"input": input_name},
-#             outputs=layer_outputs,
-#             kernel_size=k_size[2:4],
-#             stride=strides[2:4],
-#             padding=string(pad_mode))
-
-    layer_attrs["pool_type"] = string("avg")
     graph.add_layer(
-        "fluid.layers.pool1d",
+        kernel="paddle.nn.AvgPool1D",
         inputs=layer_inputs,
-        outputs=layer_outputs[1:],
+        outputs=layer_outputs,
         scope_name=scope_name,
         **layer_attrs)
     return current_inputs, current_outputs
@@ -929,7 +900,7 @@ def aten_constant_pad_nd(mapper, graph, node):
             outputs=[inputs_name[0] + "_list"],
             scope_name=scope_name)
         block.add_layer(
-            "paddle.tensor.unsqueeze",
+            "paddle.unsqueeze",
             inputs={"x": inputs_name[0],
                     "axis": inputs_name[0] + "_list"},
             outputs=[inputs_name[0] + "_var"],
@@ -941,7 +912,7 @@ def aten_constant_pad_nd(mapper, graph, node):
             scope_name=scope_name,
             **layer_attrs)
         block.add_layer(
-            "paddle.tensor.squeeze",
+            "paddle.squeeze",
             inputs={"x": output_name,
                     "axis": inputs_name[0] + "_list"},
             outputs=[output_name],
@@ -1703,7 +1674,7 @@ def aten_expand_as(mapper, graph, node):
         outputs=[inputs_name[1] + "_type"],
         scope_name=scope_name)
     block.add_layer(
-        "fluid.layers.cast",
+        "paddle.cast",
         inputs={"x": inputs_name[0]},
         outputs=[inputs_name[0]],
         scope_name=scope_name,
@@ -1722,7 +1693,7 @@ def aten_expand_as(mapper, graph, node):
     if_layer = graph.layers[list(graph.layers.keys())[-1]]
     block = PaddleGraph(source_type="pytorch", parent_layer=if_layer, graph_type="dygraph")
     block.add_layer(
-        "fluid.layers.cast",
+        "paddle.cast",
         inputs={"x": layer_outputs[0]},
         outputs=copy.deepcopy(layer_outputs),
         scope_name=scope_name,
@@ -2515,6 +2486,89 @@ def aten_log(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_lstm(mapper, graph, node):
+    """ 构造长短期记忆网络（LSTM）的PaddleLayer。
+
+    TorchScript示例:
+        %input.96, %551, %552 = aten::lstm(%input.95, %734, %549, %526, %525, %524, %526, %526, %526)
+        参数含义:
+        %input.96 (Tensor): 输出，由前向和后向cell的输出拼接得到。
+        %551 (Tensor): cell state。
+        %552 (Tensor): hidden state。
+        %input.95 (Tensor): 网络输入。
+        %734 (Tensor): 网络的初始状态。
+        %549 (list): 所有权重组合成的list。
+        %526 (bool): 是否使用bias。
+        %525 (int): 网络层数。
+        %524 (float): dropout概率。
+        %526 (bool): 是否为训练阶段。
+        %526 (bool): 是否使用双向LSTM。
+        %526 (bool): 第一个维度是否为batch size。
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    op_name = name_generator("lstm", mapper.nn_name2id)
+    output_names = mapper._get_outputs_name(node)
+    layer_outputs = [op_name]
+    layer_outputs.extend(output_names)
+    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_names
+    # 处理输入0，即%input.95
+    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)
+    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)
+    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])
+        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))
+        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.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")
+    # 处理输入4，即%525
+    layer_attrs["num_layers"] = mapper.attrs[inputs_name[4]]
+    # 处理输入5，即%524
+    layer_attrs["dropout"] = mapper.attrs[inputs_name[5]]
+    # 处理输入7，即%526
+    is_bidirectional = mapper.attrs[inputs_name[7]]
+    if is_bidirectional:
+        layer_attrs["direction"] = string("bidirectional")
+    # 处理输入8，即%526
+    batch_first = mapper.attrs[inputs_name[8]]
+    if not batch_first:
+        layer_attrs["time_major"] = True
+    graph.add_layer(
+        "paddle.nn.LSTM",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten_lt(mapper, graph, node):
     """ 构造对比大小的PaddleLayer。
 
@@ -2848,15 +2902,6 @@ def aten_max_pool2d(mapper, graph, node):
     layer_attrs["ceil_mode"] = mapper.attrs[inputs_name[5]]
     layer_attrs_tmp["ceil_mode"] = mapper.attrs[inputs_name[5]]
     
-    if mapper.attrs[inputs_name[5]] == True:
-        layer_attrs["pool_type"] = string("max")
-        graph.add_layer(
-            "fluid.layers.pool2d",
-            inputs=layer_inputs,
-            outputs=layer_outputs[1:],
-            scope_name=scope_name,
-            **layer_attrs_tmp)
-    else:
     graph.add_layer(
         "paddle.nn.MaxPool2D",
         inputs=layer_inputs,
@@ -3991,7 +4036,7 @@ def aten_squeeze(mapper, graph, node):
         layer_inputs["axis"] = inputs_name[1]
         current_inputs.append(inputs_name[1])
     graph.add_layer(
-        "paddle.tensor.squeeze",
+        "paddle.squeeze",
         inputs=layer_inputs,
         outputs=layer_outputs,
         scope_name=scope_name,

x2paddle/op_mapper/dygraph/pytorch2paddle/prim.py

@@ -33,10 +33,32 @@ def prim_Constant(mapper, graph, node):
     output_type = output.type()
     if isinstance(value, str):
         value = string(value)
-    if str(output_type) == "Tensor":
+    if "Tensor" in str(output_type):
         tensor_value = value
         value = "{}".format(value)
         if "tensor" in value:
+            if isinstance(tensor_value, list) or isinstance(tensor_value, tuple):
+                name_dict = dict()
+                for i, tv in enumerate(tensor_value):
+                    output_name_i = "{}_p{}".format(output_name,i)
+                    key_i = "input{}".format(i)
+                    mapper.paddle_params[output_name_i] = tv.cpu().detach().numpy()
+                    graph.add_layer(
+                        "self.create_parameter",
+                        inputs={},
+                        outputs=[output_name_i],
+                        scope_name=scope_name,
+                        dtype=string(str(mapper.paddle_params[output_name_i].dtype)),
+                        shape = mapper.paddle_params[output_name_i].shape,
+                        default_initializer="paddle.nn.initializer.Constant(value=0.0)")
+                    name_dict[key_i] = output_name_i
+                graph.add_layer(
+                    "prim.list",
+                    inputs=name_dict,
+                    outputs=[output_name],
+                    scope_name=scope_name)
+                return [], [output_name]
+            else:
                 mapper.pytorch_params[output_name] = tensor_value.cpu().detach().numpy()
 
     if "inf" in str(value):
@@ -218,11 +240,13 @@ def prim_ListConstruct(mapper, graph, node):
     current_outputs = [output_name]
     # 处理每个输入
     for i, input_name in enumerate(inputs_name):
+        mapper._check_input(graph, inputs_node[i], input_name, current_outputs, scope_name)
         layer_inputs["input{}".format(i)] = input_name
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.list", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+    layer_id = graph.add_layer("prim.list", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+    mapper.output2id[output_name] = layer_id
     return current_inputs, current_outputs
 
 

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/gather.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import paddle
-import paddle.fluid as fluid
 from itertools import product
 import numpy as np
 

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py

@@ -37,6 +37,7 @@ class PyTorchOpMapper(OpMapper):
         self.scope_name_list = list()
         self.scope_name2id = dict()
         self.inputs_info = dict()
+        self.output2id = dict() # output名字和layer_id的关系，用于lstm去除前面的node
         # 转换
         if not self.op_checker(decoder.graph):
             raise Exception("Model is not supported yet.")
@@ -175,7 +176,7 @@ class PyTorchOpMapper(OpMapper):
                 if add_dim:
                     param = param[np.newaxis, :]
                 self.paddle_params[output_name] = param
-                graph.add_layer(
+                layer_id = graph.add_layer(
                     "self.create_parameter",
                     inputs={},
                     outputs=[output_name],
@@ -183,6 +184,7 @@ class PyTorchOpMapper(OpMapper):
                     dtype=string(str(param.dtype)),
                     shape = param.shape,
                     default_initializer="paddle.nn.initializer.Constant(value=0.0)")
+                self.output2id[output_name] = layer_id
             else:
                 if isinstance(param, dict) and "Tensor" in param and \
                 "parent_layer_id" in param:
@@ -202,7 +204,7 @@ class PyTorchOpMapper(OpMapper):
                                         if add_dim:
                                             param = param[np.newaxis, :]
                                         self.paddle_params[output_name] = param
-                                        graph.add_layer(
+                                        layer_id = graph.add_layer(
                                             "self.create_parameter",
                                             inputs={},
                                             outputs=[output_name],
@@ -211,6 +213,7 @@ class PyTorchOpMapper(OpMapper):
                                             shape = param.shape,
                                           default_initializer="paddle.nn.initializer.Constant(value=0.0)")
                                         node_outputs.append(output_name)
+                                        self.output2id[output_name] = layer_id
                                         return
                     # 若if-else外，则可直接引用if-else中的赋值结果
                     graph.add_layer(
@@ -231,7 +234,7 @@ class PyTorchOpMapper(OpMapper):
         elif node.kind() == "prim::Constant" and output_name in self.pytorch_params:
             param = self.pytorch_params[output_name]
             self.paddle_params[output_name] = param
-            graph.add_layer(
+            layer_id = graph.add_layer(
                 "self.create_parameter",
                 inputs={},
                 outputs=[output_name],
@@ -239,6 +242,7 @@ class PyTorchOpMapper(OpMapper):
                 dtype=string(str(param.dtype)),
                 shape = param.shape,
                 default_initializer="paddle.nn.initializer.Constant(value=0.0)")  
+            self.output2id[output_name] = layer_id
 
             
     def _get_inputs_name(self, node):

x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py

@@ -70,7 +70,7 @@ class TFOpMapper(OpMapper):
         'AddV2': 'paddle.add',
         'RealDiv': 'paddle.divide',
         'DivNoNan': 'paddle.divide',
-        'Sub': 'fluid.layers.elementwise_sub',
+        'Sub': 'paddle.subtract',
         'Maximum': 'paddle.maximum',
         'Minimum': 'paddle.minimum',
         'Mul': 'paddle.multiply',
@@ -346,7 +346,7 @@ class TFOpMapper(OpMapper):
             shape=[0, c, h, w])
 
         self.paddle_graph.add_layer(
-            kernel="fluid.layers.pixel_shuffle",
+            kernel="paddle.nn.functional.pixel_shuffle",
             inputs={"x": reshape_name},
             outputs=[node.name],
             upscale_factor=block_size)
@@ -858,22 +858,22 @@ class TFOpMapper(OpMapper):
         layer_outputs = [op_name, output_name]
         
         # TODO(syf): The op has diff.
-#         self.paddle_graph.add_layer(
-#             kernel="paddle.nn.AvgPool2D",
-#             inputs={"input": input_name},
-#             outputs=layer_outputs,
-#             kernel_size=k_size[2:4],
-#             stride=strides[2:4],
-#             padding=string(pad_mode))
-
         self.paddle_graph.add_layer(
-            kernel="fluid.layers.pool2d",
+            kernel="paddle.nn.AvgPool2D",
             inputs={"input": input_name},
-            outputs=[node.name],
-            pool_size=k_size[2:4],
-            pool_type=string("avg"),
-            pool_stride=strides[2:4],
-            pool_padding=string(pad_mode))
+            outputs=layer_outputs,
+            kernel_size=k_size[2:4],
+            stride=strides[2:4],
+            padding=string(pad_mode))
+
+#         self.paddle_graph.add_layer(
+#             kernel="fluid.layers.pool2d",
+#             inputs={"input": input_name},
+#             outputs=[node.name],
+#             pool_size=k_size[2:4],
+#             pool_type=string("avg"),
+#             pool_stride=strides[2:4],
+#             pool_padding=string(pad_mode))
 
         if data_format == "NHWC":
             self.paddle_graph.add_layer(
@@ -1118,14 +1118,6 @@ class TFOpMapper(OpMapper):
             begin = begin.value.tolist()
             attrs['offsets'] = begin
         else:
-            #             shape = begin.out_shapes[0]
-            #             reshape_name = gen_name("slice", "reshape")
-            #             self.paddle_graph.add_layer(
-            #                 kernel="fluid.layers.reshape",
-            #                 inputs={"x": begin.name},
-            #                 outputs=[reshape_name],
-            #                 shape=shape)
-            #             inputs['offsets'] = reshape_name
             begin = self.decoder.infer_tensor(begin, use_diff_inputs=False).tolist()
             attrs['offsets'] = begin
         if size.layer_type == "Const":
@@ -1433,7 +1425,7 @@ class TFOpMapper(OpMapper):
         y_shape = y.out_shapes[0]
         # TODO(syf)
         layer_id = self.paddle_graph.add_layer(
-            "fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name])
+            "paddle.subtract", inputs=inputs, outputs=[node.name])
         self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
 
         inputs = {"x": node.name, "y": node.name}

x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py

@@ -401,18 +401,14 @@ class CaffeOpMapper(OpMapper):
                     padding=pad,
                     ceil_mode=ceil_mode)
             else:
-                # TODO(syf): The op has diff.
                 self.paddle_graph.add_layer(
-                    kernel="fluid.layers.pool2d",
-                    inputs={"input": input.name},
+                    kernel="paddle.nn.functional.avg_pool2d",
+                    inputs={"x": input.name},
                     outputs=[node.name],
-                    pool_size=kernel,
-                    pool_type=string("avg"),
-                    pool_stride=stride,
-                    pool_padding=pad,
-                    ceil_mode=ceil_mode,
-                    exclusive=False,
-                    global_pooling=False)
+                    kernel_size=kernel,
+                    stride=stride,
+                    padding=pad,
+                    ceil_mode=ceil_mode)
 
     def LRN(self, node):
         assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.'
@@ -433,7 +429,7 @@ class CaffeOpMapper(OpMapper):
             'name': string(node.name)
         }
         self.paddle_graph.add_layer(
-            kernel="fluid.layers.lrn",
+            kernel="paddle.fluid.layers.lrn",
             inputs={"input": input.name},
             outputs=[node.name],
             **layer_attrs)
@@ -1184,7 +1180,7 @@ class CaffeOpMapper(OpMapper):
         input = self.graph.get_input_node(node, idx=0, copy=True)
         params = node.layer.shuffle_channel_param
         self.paddle_graph.add_layer(
-            "fluid.layers.shuffle_channel",
+            "paddle.fluid.layers.shuffle_channel",
             inputs={"x": input.name},
             outputs=[node.layer_name],
             group=params.group)

x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py

@@ -14,8 +14,6 @@
 
 from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
 from x2paddle.core.graph import GraphNode
-from x2paddle.core.fluid_code import Layer
-from x2paddle.core.fluid_code import FluidCode
 from x2paddle.core.util import string
 from functools import reduce
 import numpy as np
@@ -88,7 +86,7 @@ class OpSet9():
     elementwise_ops = {
         'Add': 'paddle.add',
         'Div': 'paddle.divide',
-        'Sub': 'fluid.layers.elementwise_sub',
+        'Sub': 'paddle.subtract',
         'Mul': 'paddle.multiply',
         'Pow': 'paddle.pow',
     }
@@ -271,16 +269,11 @@ class OpSet9():
                     inputs={"x": var_hw},
                     outputs=[var_hw],
                     dtype=string('int32'))
-#                 inputs['size'] = var_hw
-                
-                # TODO(syf): all use 
-                inputs['out_shape'] = var_hw
-                ipt = inputs.pop("x")
-                inputs["input"] = ipt
-                mode = node.get_attr('mode', 'nearest')
-                attrs.update({"align_corners": False})
+                inputs['size'] = var_hw
+                attrs = {"align_corners": False,
+                         "mode": string(node.get_attr('mode', 'nearest'))}
                 self.paddle_graph.add_layer(
-                    kernel="fluid.layers.resize_nearest",
+                    kernel="paddle.nn.functional.interpolate",
                     inputs=inputs,
                     outputs=[node.name],
                     **attrs)
@@ -346,7 +339,7 @@ class OpSet9():
             'sampling_ratio': sampling_ratio,
         }
         self.paddle_graph.add_layer(
-            'fluid.layers.roi_align',
+            'paddle.fluid.layers.roi_align',
             inputs={'input': val_x.name,
                     'rois': val_rois.name},
             outputs=[node.name],
@@ -365,7 +358,7 @@ class OpSet9():
             'spatial_scale': spatial_scale,
         }
         self.paddle_graph.add_layer(
-            'fluid.layers.roi_pool',
+            'paddle.fluid.layers.roi_pool',
             inputs={'input': val_x.name,
                     'rois': val_rois.name},
             outputs=[node.name],
@@ -394,7 +387,7 @@ class OpSet9():
             layer_attrs['data_format'] = string('NCHW')
             layer_attrs['value'] = value
         else:
-            paddle_op = 'fluid.layers.pad'
+            paddle_op = 'paddle.fluid.layers.pad'
             layer_attrs["pad_value"] = value
         if len(pads) == 4:
             paddings = np.array(pads).reshape(
@@ -1046,23 +1039,21 @@ class OpSet9():
                                       strides[1])
             paddings = pad_h + pad_w
 
-        paddle_op = 'fluid.layers.pool{}d'.format(poolnd)
-        assert 2 <= poolnd <= 3, 'only pool2d and pool3d are supported'
+        paddle_op = 'paddle.nn.functional.avg_pool{}d'.format(poolnd)
+        assert 1 <= poolnd <= 3, 'only avg_pool1d, avg_pool2d and avg_pool3d are supported'
         layer_attrs = {
-            "pool_size": kernel_shape,
-            "pool_type": string('avg'),
-            "pool_stride": strides,
-            "pool_padding": paddings,
+            "kernel_size": kernel_shape,
+            "stride": strides,
+            "padding": paddings,
             "ceil_mode": ceil_mode,
-            "exclusive": 'True',
+            "exclusive": True,
             "name": string(node.name)
         }
         self.paddle_graph.add_layer(
             paddle_op, 
-            inputs={'input': val_x if isinstance(val_x, str) else val_x.name}, 
+            inputs={'x': val_x if isinstance(val_x, str) else val_x.name}, 
             outputs=[node.name], 
             **layer_attrs)
-        # TODO(syf): op has diff
 
     @print_mapping_info
     def Concat(self, node):

x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py

@@ -72,7 +72,7 @@ class TFOpMapper(OpMapper):
         'RealDiv': 'paddle.divide',
         'DivNoNan': 'paddle.divide',
         # TODO (syf): replace
-        'Sub': 'fluid.layers.elementwise_sub',
+        'Sub': 'paddle.subtract',
         'Maximum': 'paddle.maximum',
         'Minimum': 'paddle.minimum',
         'Mul': 'paddle.multiply',
@@ -315,7 +315,7 @@ class TFOpMapper(OpMapper):
             shape=[0, c, h, w])
 
         self.paddle_graph.add_layer(
-            kernel="fluid.layers.pixel_shuffle",
+            kernel="paddle.nn.functional.pixel_shuffle",
             inputs={"x": reshape_name},
             outputs=[node.name],
             upscale_factor=block_size)
@@ -437,8 +437,6 @@ class TFOpMapper(OpMapper):
 
         if c == -1:
             attr = {"shape": [0, k_size[2], 0, 0]}
-            node.fluid_code.add_layer(
-                "reshape", inputs=input, output=input, param_attr=attr)
             self.paddle_graph.add_layer(
                 kernel="paddle.reshape",
                 inputs={"x": input_name},
@@ -842,13 +840,12 @@ class TFOpMapper(OpMapper):
         # TODO(syf): The op has diff.
 
         self.paddle_graph.add_layer(
-            kernel="fluid.layers.pool2d",
-            inputs={"input": input_name},
+            kernel="paddle.nn.functional.avg_pool2d",
+            inputs={"x": input_name},
             outputs=[node.name],
-            pool_size=k_size[2:4],
-            pool_type=string("avg"),
-            pool_stride=strides[2:4],
-            pool_padding=string(pad_mode))
+            kernel_size=k_size[2:4],
+            stride=strides[2:4],
+            padding=string(pad_mode))
 
         if data_format == "NHWC":
             self.paddle_graph.add_layer(
@@ -1406,7 +1403,7 @@ class TFOpMapper(OpMapper):
         y_shape = y.out_shapes[0]
         # TODO(syf)
         layer_id = self.paddle_graph.add_layer(
-            "fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name])
+            "paddle.subtract", inputs=inputs, outputs=[node.name])
         self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
 
         inputs = {"x": node.name, "y": node.name}

x2paddle/optimizer/fusion/dygraph/bn_scale_fuser.py

@@ -21,47 +21,94 @@ from x2paddle.core.util import *
 class DygraphBNScaleFuser(FuseBase):
     def __init__(self):
         super(DygraphBNScaleFuser, self).__init__(graph_type="dygraph")
+        patterns = list()
 
     def build_pattern(self):
         """ 描述需要替换的batchnorm2d图结构。
         batchnorm2d层模式python实现代码示例:
+            模式一：
             bn_conv1 = self.batchnorm0(conv1)
             scale_conv1_cparam1 = self.scale_conv1_cparam1
             scale_conv1_mul = paddle.multiply(x=bn_conv1, y=scale_conv1_cparam1, axis=1)
             scale_conv1_cparam2 = self.scale_conv1_cparam2
-            scale_conv1 = fluid.layers.elementwise_add(x=scale_conv1_mul, y=scale_conv1_cparam2, axis=1)
+            scale_conv1 = paddle.add(x=scale_conv1_mul, y=scale_conv1_cparam2, axis=1)
+            模式二：
+            bn_conv1 = self.batchnorm0(conv1)
+            scale_conv1_cparam1 = self.scale_conv1_cparam1
+            scale_conv1_mul = paddle.multiply(x=bn_conv1, y=scale_conv1_cparam1, axis=1)
+            scale_conv1_cparam2 = self.scale_conv1_cparam2
+            scale_conv1_cparam2 = paddle.reshape(x=scale_conv1_cparam2, shape=[32, 1, 1])
+            scale_conv1 = paddle.add(x=scale_conv1_mul, y=scale_conv1_cparam2, axis=1)
         """
 
         def gen_name(id):
             return "x" + str(id)
         
-        self.pattern.add_layer(
+        pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
+            "paddle.nn.BatchNorm2D",
+            inputs={"input": "bn-input-0"},
+            outputs=[gen_name(0)])
+        pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(1)])
+        inputs_dict = {}
+        inputs_dict['x'] = gen_name(0)
+        inputs_dict['y'] = gen_name(1)
+        pattern.add_layer(
+            "paddle.multiply",
+            inputs=inputs_dict,
+            outputs=[gen_name(2)])
+        pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(3)])
+        inputs_dict = {}
+        inputs_dict['x'] = gen_name(2)
+        inputs_dict['y'] = gen_name(3)
+        pattern.add_layer(
+            "paddle.add",
+            inputs=inputs_dict,
+            outputs=[gen_name(4)])
+        pattern.build(inputs={"input-0": "bn-input-0"})
+        self.patterns.append(pattern)
+        
+        pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
             "paddle.nn.BatchNorm2D",
             inputs={"input": "bn-input-0"},
             outputs=[gen_name(0)])
-        self.pattern.add_layer(
+        pattern.add_layer(
             "self.create_parameter",
             inputs={},
             outputs=[gen_name(1)])
         inputs_dict = {}
         inputs_dict['x'] = gen_name(0)
         inputs_dict['y'] = gen_name(1)
-        self.pattern.add_layer(
+        pattern.add_layer(
             "paddle.multiply",
             inputs=inputs_dict,
             outputs=[gen_name(2)])
-        self.pattern.add_layer(
+        pattern.add_layer(
             "self.create_parameter",
             inputs={},
             outputs=[gen_name(3)])
+        pattern.add_layer(
+            "paddle.reshape",
+            inputs={"x": gen_name(3)},
+            outputs=[gen_name(3)])
         inputs_dict = {}
         inputs_dict['x'] = gen_name(2)
         inputs_dict['y'] = gen_name(3)
-        self.pattern.add_layer(
-            "fluid.layers.elementwise_add",
+        pattern.add_layer(
+            "paddle.add",
             inputs=inputs_dict,
             outputs=[gen_name(4)])
-        self.pattern.build(inputs={"input-0": "bn-input-0"})
+        pattern.build(inputs={"input-0": "bn-input-0"})
+        self.patterns.append(pattern)
+        
+        
 
     def insert_new_layer(self, graph, parameters, matches):
         new_layer = self.gen_new_layer(parameters, matches)
@@ -78,7 +125,7 @@ class DygraphBNScaleFuser(FuseBase):
         layer_attrs = layer.attrs
         layer_attrs.pop("weight_attr")
         layer_attrs.pop("bias_attr")
-        layer = matches[layers_id[4]]
+        layer = matches[layers_id[-1]]
         layer_outputs = [bn_name] + layer.outputs
         layer = matches[layers_id[1]]
         data0_name = layer.outputs[0]

x2paddle/optimizer/fusion/dygraph/reshape_fuser.py

@@ -27,7 +27,7 @@ class DygraphReshapeFuser(FuseBase):
         reshape层模式python实现代码示例:
             x165 = int(x164)
             x166 = [x158, x159, x165]
-            x167 = fluid.layers.reshape(x=x157, shape=x166)
+            x167 = paddle.reshape(x=x157, shape=x166)
         """
 
         def gen_name(id):
@@ -46,7 +46,7 @@ class DygraphReshapeFuser(FuseBase):
             },
             outputs=[gen_name(1)])
         self.pattern.add_layer(
-            "fluid.layers.reshape",
+            "paddle.reshape",
             inputs={"x": "reshape-input-3",
                     "shape": gen_name(1)},
             outputs=[gen_name(2)])

x2paddle/optimizer/fusion/dygraph/trace_fc_fuser.py

@@ -49,7 +49,7 @@ class TraceFcFuser(FuseBase):
             inputs={},
             outputs=[gen_name(0)])
         pattern.add_layer(
-            "fluid.layers.transpose",
+            "paddle.transpose",
             inputs={"x": gen_name(0)},
             outputs=[gen_name(1)],
             perm=[1, 0])

x2paddle/optimizer/fusion/static/bn_scale_fuser.py

@@ -21,12 +21,14 @@ from x2paddle.core.util import *
 class Static_BNScaleFuser(FuseBase):
     def __init__(self):
         super(Static_BNScaleFuser, self).__init__(graph_type="static")
-        patterns = list()
+        self.patterns = list()
 
     def build_pattern(self):
         """ 描述需要替换的batchnorm2d图结构。
         batchnorm2d层模式python实现代码示例:
         模式一：
+        conv1_bn_mean = paddle.static.create_parameter(shape=(128,), dtype='float32', name='conv1_bn_mean')
+        conv1_bn_variance = paddle.static.create_parameter(shape=(128,), dtype='float32', name='conv1_bn_variance')
         conv1_bn = paddle.nn.functional.batch_norm(x=conv1, weight=conv1_bn_weight, bias=conv1_bn_bias, running_mean=conv1_bn_mean, running_var=conv1_bn_variance, epsilon=9.999999747378752e-06, momentum=0.9990000128746033)
         conv1_scale_cparam1 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam1')
         conv1_scale_mul = paddle.multiply(x=conv1_bn, y=conv1_scale_cparam1, axis=1)
@@ -34,6 +36,8 @@ class Static_BNScaleFuser(FuseBase):
         conv1_scale_cparam2 = paddle.reshape(x=conv1_scale_cparam2, shape=[32, 1, 1])
         conv1_scale = paddle.add(x=conv1_scale_mul, y=conv1_scale_cparam2)
         模式二：
+        conv1_bn_mean = paddle.static.create_parameter(shape=(128,), dtype='float32', name='conv1_bn_mean')
+        conv1_bn_variance = paddle.static.create_parameter(shape=(128,), dtype='float32', name='conv1_bn_variance')
         conv1_bn = paddle.nn.functional.batch_norm(x=conv1, weight=conv1_bn_weight, bias=conv1_bn_bias, running_mean=conv1_bn_mean, running_var=conv1_bn_variance, epsilon=9.999999747378752e-06, momentum=0.9990000128746033)
         conv1_scale_cparam1 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam1')
         conv1_scale_mul = paddle.multiply(x=conv1_bn, y=conv1_scale_cparam1, axis=1)
@@ -45,13 +49,21 @@ class Static_BNScaleFuser(FuseBase):
             return "x" + str(id)
         
         pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
+            "paddle.static.create_parameter",
+            inputs={},
+            outputs=[gen_name(10)])
+        pattern.add_layer(
+            "paddle.static.create_parameter",
+            inputs={},
+            outputs=[gen_name(11)])
         pattern.add_layer(
             "paddle.nn.functional.batch_norm",
             inputs={"input": "bn-input-0",
                     "weight": "bn-input-1",
                     "bias": "bn-input-2",
-                    "running_mean": "bn-input-3",
-                    "running_var": "bn-input-4",},
+                    "running_mean": gen_name(10),
+                    "running_var": gen_name(11)},
             outputs=[gen_name(0)])
         pattern.add_layer(
             "paddle.static.create_parameter",
@@ -81,19 +93,25 @@ class Static_BNScaleFuser(FuseBase):
             outputs=[gen_name(5)])
         pattern.build(inputs={"input-0": "bn-input-0",
                               "input-1": "bn-input-1",
-                              "input-2": "bn-input-2",
-                              "input-3": "bn-input-3",
-                              "input-4": "bn-input-4"})
+                              "input-2": "bn-input-2"})
         self.patterns.append(pattern)
         
         pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
+            "paddle.static.create_parameter",
+            inputs={},
+            outputs=[gen_name(10)])
+        pattern.add_layer(
+            "paddle.static.create_parameter",
+            inputs={},
+            outputs=[gen_name(11)])
         pattern.add_layer(
             "paddle.nn.functional.batch_norm",
             inputs={"input": "bn-input-0",
                     "weight": "bn-input-1",
                     "bias": "bn-input-2",
-                    "running_mean": "bn-input-3",
-                    "running_var": "bn-input-4",},
+                    "running_mean": gen_name(10),
+                    "running_var": gen_name(11),},
             outputs=[gen_name(0)])
         pattern.add_layer(
             "paddle.static.create_parameter",
@@ -119,25 +137,25 @@ class Static_BNScaleFuser(FuseBase):
             outputs=[gen_name(4)])
         pattern.build(inputs={"input-0": "bn-input-0",
                               "input-1": "bn-input-1",
-                              "input-2": "bn-input-2",
-                              "input-3": "bn-input-3",
-                              "input-4": "bn-input-4"})
+                              "input-2": "bn-input-2"})
         self.patterns.append(pattern)
 
     def insert_new_layer(self, graph, parameters, matches):
         new_layer = self.gen_new_layer(parameters, matches)
         new_layer_id = list(matches.keys())[-1]
         graph.layers[new_layer_id] = new_layer
+        matches.pop(list(matches.keys())[0])
+        matches.pop(list(matches.keys())[0])
         matches.pop(list(matches.keys())[1])
         matches.pop(list(matches.keys())[2])
         matches.pop(new_layer_id)
 
     def gen_new_layer(self, parameters, matches):
         layers_id = list(matches.keys())
-        bn_layer = matches[layers_id[0]]
-        layer = matches[layers_id[1]]
-        bn_layer.inputs["weight"] = layer.outputs[0]
+        bn_layer = matches[layers_id[2]]
         layer = matches[layers_id[3]]
+        bn_layer.inputs["weight"] = layer.outputs[0]
+        layer = matches[layers_id[5]]
         bn_layer.inputs["bias"] = layer.outputs[0]
         bn_layer.id = layers_id[-1]
         layer = matches[layers_id[-1]]

x2paddle/optimizer/pattern_matcher.py

@@ -116,7 +116,20 @@ class PatternMatcher(object):
                                     else:
                                         subgraph_id2layers.pop(layer_id)
                                         continue
-                                        
+                            else:
+                                layer_out = graph.edges_out[layer_id]
+                                pattern_layer_out = pattern.edges_out[pattern_layer_id]
+                                is_pop = False
+                                for i in range(len(layer_out)):
+                                    layer_id_out = layer_out[i]
+                                    pattern_layer_id_out = pattern_layer_out[i]
+                                    if layer_id_out != -1:
+                                        if graph_layers[layer_id_out].kernel != pattern.layers[pattern_layer_id_out].kernel:
+                                            is_pop = True
+                                            break
+                                if is_pop:
+                                    subgraph_id2layers.pop(layer_id)
+                                    continue
                     # 当为控制流时的处理
                     if layer.kernel == "prim.if" or layer.kernel == "prim.loop":
                         if len(pattern_layer.blocks) != len(layer.blocks):
@@ -161,7 +174,7 @@ class PatternMatcher(object):
 
         for i, (layer_id, layer) in enumerate(graph.layers.items()):
             match_info = get_subgraph(self.pattern, graph, i)
-            if match_info:
+            if match_info and match_info not in self.matches:
                 self.matches.append(match_info)
             for j, block in enumerate(layer.blocks):
                 if len(block.layers) > 0:
@@ -344,3 +357,4 @@ class FuseBase(object):
                     # layer_id可能是属于子图的，此时删除父layer，即删除整个子图
                     subgraph.layers.pop(layer_id)
                     
+                    
\ No newline at end of file

x2paddle/optimizer/code_optimizer/__init__.py → x2paddle/optimizer/pytorch_code_optimizer/__init__.py

@@ -13,5 +13,5 @@
 # limitations under the License.
 
 
-from x2paddle.optimizer.code_optimizer.hierachical_tree import HierarchicalTree
-from x2paddle.optimizer.code_optimizer.module_graph import ModuleGraph
+from x2paddle.optimizer.pytorch_code_optimizer.hierachical_tree import HierarchicalTree
+from x2paddle.optimizer.pytorch_code_optimizer.module_graph import ModuleGraph
\ No newline at end of file

x2paddle/optimizer/code_optimizer/hierachical_tree.py → x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py

@@ -18,10 +18,10 @@ import copy
 import os.path as osp
 from treelib import Tree
 from queue import Queue
-from x2paddle.optimizer.code_optimizer.layer_code_generator import gen_layer_code, rename_layers, NN_KERNEL_WITH_PARAMS, NN_KERNEL_NAME
-from x2paddle.optimizer.code_optimizer.subgraphs_union import  distinguish_sequential, get_inputs_outputs
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import gen_layer_code, rename_layers, NN_KERNEL_WITH_PARAMS, NN_KERNEL_NAME
+from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import  distinguish_sequential, get_inputs_outputs
 from x2paddle.core.program import PaddleLayer
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode, PamareterTree
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode, PamareterTree
 
 SEPARATOR_IN_SCOPE = "/"
 
@@ -39,6 +39,7 @@ class HierarchicalTree(Tree):
         self.identifier_idx = dict()
         self.param_tree = PamareterTree()
         self.module_name2count = dict()
+        self.scope_name_list = list()
         
     def insert(self, layer):
         """ 往层次树中插入节点。
@@ -47,6 +48,7 @@ class HierarchicalTree(Tree):
             layer (PaddleLayer): 需要插入的节点。
         """
         scope_name = layer.scope_name
+        self.scope_name_list.append(scope_name)
         if scope_name == "":
             if layer.kernel == "prim.tuple" or layer.kernel == "prim.tuple_unpack":
                 layer_id = layer.id
@@ -55,6 +57,30 @@ class HierarchicalTree(Tree):
                     layer_id_list.append(int(input_layer_id))
                 layer_id_list = list(set(layer_id_list))
                 layer_id_list.sort(reverse=True) 
+                
+                if layer.kernel == "prim.tuple":
+                    for i, input_layer_id in enumerate(layer_id_list):
+                        input_layer_id_str = str(input_layer_id)
+                        scope_name = self.pd_graph.layers[input_layer_id_str].scope_name
+                        if i == 0:
+                            min_scope_name = scope_name
+                        else:
+                            len1 = len(min_scope_name.split("/"))
+                            len2 = len(scope_name.split("/"))
+                            if scope_name not in self.scope_name_list:
+                                min_scope_name = scope_name
+                                continue
+                            if len1 > len2:
+                                min_scope_name = scope_name
+                    if min_scope_name == "":
+                        self.create_node(tag=layer.id, 
+                                 identifier="no_scope_" + layer.id, 
+                                 parent=self.pd_graph.name,
+                                 data=layer)
+                        return 
+                    layer.scope_name = min_scope_name
+                    scope_name = min_scope_name
+                else:
                     for input_layer_id in layer_id_list:
                         input_layer_id_str = str(input_layer_id)
                         if self.pd_graph.layers[input_layer_id_str].scope_name != "":
@@ -369,9 +395,6 @@ class HierarchicalTree(Tree):
         self.convert_subgraph_to_layer()
         self.update_parameters()
         import_list = ["import paddle",
-                       "import paddle.fluid as fluid",
-                       "from paddle.fluid.initializer import Constant",
-                       "from paddle.fluid.param_attr import ParamAttr",
                        "import math",
                        "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
                                  "import pytorch_custom_layer as x2paddle_nn"

x2paddle/optimizer/code_optimizer/layer_code_generator.py → x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py

@@ -14,7 +14,11 @@
 # limitations under the License.
 
 import copy
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode
+import os.path as osp
+import x2paddle
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode
+from x2paddle.core.util import *
+
 
 NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.LayerNorm": "layernorm",
@@ -22,6 +26,7 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.Embedding": "embedding",
                   "paddle.nn.Linear": "linear",
                   "paddle.nn.Conv2DTranspose": "conv",
+                  "paddle.nn.LSTM": "lstm",
                   "paddle.nn.ReLU": "relu",
                   "paddle.nn.ReLU6": "relu",
                   "paddle.nn.Softmax": "softmax",
@@ -36,7 +41,7 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.GELU": "gelu",
                   "paddle.nn.Hardtanh": "tanh",
                   "paddle.nn.LeakyReLU": "leakly_relu"}
-NN_KERNEL_WITH_PARAMS = list(NN_KERNEL_NAME.keys())[:6]
+NN_KERNEL_WITH_PARAMS = list(NN_KERNEL_NAME.keys())[:7]
 
 def rename_layers(layers, param_tree=None, is_rename_module=False):
     """ 对子模块的输入输出等进行重命名。
@@ -125,14 +130,30 @@ def rename_layers(layers, param_tree=None, is_rename_module=False):
     return layers_cp, nn_param_nodes, new_names
 
 
-def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
+def _update_attrs(layer, different_attrs):
+    if "module" in layer.kernel or "prim" in layer.kernel:
+        return
+    common_attrs = copy.deepcopy(layer.attrs)
+    special_attrs = dict()
+    for k, v in layer.attrs.items():
+        if len(layer.outputs) < 1:
+            break
+        key_name = "{}_{}".format(layer.outputs[0], k)
+        if key_name in different_attrs:
+            common_attrs.pop(k)
+            special_attrs[k] = v
+    remove_default_attrs(layer.kernel, common_attrs)
+    common_attrs.update(special_attrs)
+    layer.attrs = common_attrs
+
+def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
     """ 根据sub_layers生成对应的Module代码。
     
     Args:
         graph (x2paddle.core.program.PaddleGraph): 整个Paddle图。
         sub_layers (dict): 子图的id和其对应layer组成的字典。
         sub_layers_name (str): 子图的名字。
-        different_attrs (list): 属性列表，这些属性表明在被调用时赋予不同值。
+        different_attrs (dict/list): 属性字典/列表，这些属性表明在被调用时赋予不同值。
     """
     def gen_codes(code_list, indent=0):
         """ 根据code_list生成代码段。
@@ -157,6 +178,12 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
         # 生成Layer的头部代码
         head = gen_codes(["class {}(paddle.nn.Layer):".format(sub_layers_name)], indent=0)
         # 生成init函数的头部代码
+        diff_str_list = list()
+        if isinstance(different_attrs, dict):
+            for k, v in different_attrs.items():
+                diff_str_list.append("{}={}".format(k, v))
+            attrs_str = ", ".join(diff_str_list)
+        else:
             attrs_str = ", ".join(different_attrs)
         init_func_head = \
             gen_codes(["def __init__(self, {}):".format(attrs_str)], indent=1) + \
@@ -213,6 +240,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 outputs.append(layer.outputs[0])
     no_output_count = 0
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
+        _update_attrs(layer, different_attrs)
         if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
                 layer.kernel.startswith("custom_layer"):
             line = "self.{}".format(layer.outputs[0])
@@ -234,6 +262,9 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 line = layer.outputs[0]
             elif len(layer.outputs) == 2:
                 line = layer.outputs[1]
+            else:
+                if layer.kernel == "paddle.nn.LSTM":
+                    line = "{}, ({})".format(layer.outputs[1], ', '.join(layer.outputs[-2:]))
                 else:
                     line = ','.join(layer.outputs[1:])
             
@@ -263,7 +294,7 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                     init_func=init_func,
                     forward_func=forward_func,
                     layer_id=layer_id, 
-                    different_attrs=different_attrs)
+                    different_attrs=list(different_attrs.keys()) if isinstance(different_attrs, dict) else different_attrs)
                 cur_outputs.extend(layer.outputs)
             else:
                 raise Exception(

x2paddle/optimizer/code_optimizer/module_graph.py → x2paddle/optimizer/pytorch_code_optimizer/module_graph.py

@@ -17,9 +17,9 @@ import copy
 import os
 import os.path as osp
 from x2paddle.core.program import PaddleLayer
-from x2paddle.optimizer.code_optimizer.subgraphs_union import construct_attrs_table, get_inputs_outputs
-from x2paddle.optimizer.code_optimizer.layer_code_generator import gen_layer_code, rename_layers
-from x2paddle.optimizer.code_optimizer.parameter_tree import PamareterNode, PamareterTree
+from x2paddle.optimizer.pytorch_code_optimizer.subgraphs_union import construct_attrs_table, get_inputs_outputs
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import gen_layer_code, rename_layers
+from x2paddle.optimizer.pytorch_code_optimizer.parameter_tree import PamareterNode, PamareterTree
 
 
 NoModuleStart = ["paddle.nn.ReLU"]
@@ -179,16 +179,27 @@ class ModuleGraph(object):
     def analyze_attrs_table(self, attrs_table):
         """ 分析属性表格，哪些属性取值不一致。
         """
-        diff_attrs_column = list()
+        diff_attrs_column = dict()
         for column in list(attrs_table.columns):
             elements = list(attrs_table.get(column))
-            base = elements[0]
-            for element in elements[1:]:
-                if isinstance(base, str) and "'" not in base:
-                    break
-                if element != base:
-                    diff_attrs_column.append(column)
+            elements_list = list()
+            count_list = list()
+            for element in elements:
+                if isinstance(element, str) and "'" not in element:
                     break
+                if element not in elements_list:
+                    count_list.append(1)
+                    elements_list.append(element)
+                else:
+                    index = elements_list.index(element)
+                    count_list[index] += 1
+            if len(elements_list) > 1:
+                max_ct = 0
+                for k, v in zip(elements_list, count_list):
+                    if v > max_ct and str(k) != "nan" :
+                        max_ele = k
+                        max_ct = v
+                diff_attrs_column[column] = max_ele
         return diff_attrs_column
     
     def analyze_graph(self, sub_layers_list):
@@ -258,8 +269,10 @@ class ModuleGraph(object):
             outputs = ["{}_{}".format(mn, index)] + outputs
             node_name = "{}_{}".format(module_name, index)
             diff_attrs = dict() 
-            for column in diff_attrs_column:
-                diff_attrs[column] = attrs_table.get(column).loc[node_name]
+            for column, element in diff_attrs_column.items():
+                current_element = attrs_table.get(column).loc[node_name]
+                if current_element != element:
+                    diff_attrs[column] = current_element
             new_layer = PaddleLayer(id=list(sub_layers.keys())[-1],
                                     kernel="module",
                                     inputs=inputs_dict,
@@ -352,9 +365,6 @@ class ModuleGraph(object):
         self.convert_subgraph_to_layer(combination, combination_id)
         self.update_parameters()
         import_list = ["import paddle",
-                       "import paddle.fluid as fluid",
-                       "from paddle.fluid.initializer import Constant",
-                       "from paddle.fluid.param_attr import ParamAttr",
                        "import math",
                        "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
                                  "import pytorch_custom_layer as x2paddle_nn"

x2paddle/optimizer/code_optimizer/subgraphs_union.py → x2paddle/optimizer/pytorch_code_optimizer/subgraphs_union.py

@@ -16,7 +16,7 @@
 
 import copy
 import pandas as pd
-from x2paddle.optimizer.code_optimizer.layer_code_generator import rename_layers
+from x2paddle.optimizer.pytorch_code_optimizer.layer_code_generator import rename_layers
 
 
 def construct_attrs_table(sub_layers_list, node_name2sub_layers=None, module_name=None):