Merge pull request #516 from SunAhong1993/develop

fix one caffe bug, one tf bug, one pytorch bug and two onnx bug

x2paddle/core/program.py

@@ -508,9 +508,9 @@ class PaddleGraph(object):
                 ],
                 indent=0)
             comment_list = list()
-            comment_list.append("# 共{}个输入".format(len(self.inputs_info)))
+            comment_list.append("# There are {} inputs.".format(len(self.inputs_info)))
             for k, v in self.inputs_info.items():
-                comment_list.append("# {}: 形状为{}，类型为{}。".format(k, v[0], v[1]))
+                comment_list.append("# {}: shape-{}，type-{}。".format(k, v[0], v[1]))
             self.run_func.extend(
                 gen_codes(
                     comment_list,

x2paddle/op_mapper/dygraph/caffe2paddle/caffe_op_mapper.py

@@ -571,7 +571,7 @@ class CaffeOpMapper(OpMapper):
         if params.HasField('negative_slope') and params.negative_slope != 0:
             negative_slope = float(params.negative_slope)
 
-            layer_attrs = {'alpha': negative_slope}
+            layer_attrs = {'negative_slope': negative_slope}
             self.paddle_graph.add_layer(
                 "paddle.nn.LeakyReLU",
                 inputs={"input": input.name},

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

@@ -445,6 +445,8 @@ class OpSet9():
         layer_outputs = [nn_op_name, output_name]
         if is_pads_attr:
             paddings = []
+            if len(pads) == 10 and sum(pads) == 0:
+                pads = pads[0: 6]
             if len(pads) in [2, 4, 6]:
                 if data_shape:
                     assume_pad |= data_shape and 2 * (len(data_shape) - 2) == len(pads) # NCHW
@@ -728,11 +730,14 @@ class OpSet9():
                 inputs={'x': name_trans,
                         'index': indices.name},
                 outputs=[node.name])
+            new_perm = [0] * len(perm)
+            for i in range(len(perm)):
+                new_perm[perm[i]] = i
             self.paddle_graph.add_layer(
                 'paddle.transpose', 
                 inputs={"x": node.name}, 
                 outputs=[node.name], 
-                perm=perm)
+                perm=new_perm)
             if len(indices_shape) < 1:
                 self.paddle_graph.add_layer(
                     'paddle.squeeze',
@@ -809,11 +814,15 @@ class OpSet9():
                         'index': indices_reshape},
                 outputs=[node.name])
             input_transpose = node.name + '_transpose'
+            new_perm = [0] * len(perm)
+            for i in range(len(perm)):
+                new_perm[perm[i]] = i
             self.paddle_graph.add_layer(
                 'paddle.transpose',
                 inputs={"x": node.name},
                 outputs=[input_transpose],
-                perm=perm)
+                perm=new_perm)
+            perm = new_perm
             val_x_shape = val_x.out_shapes[0]
             reshaped_shape = []
             for i in perm:

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -3353,6 +3353,42 @@ def aten_pow(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_prelu(mapper, graph, node):
+    """ 构造prelu激活的PaddleLayer。
+
+    TorchScript示例:
+        %result.3 : aten::prelu(%input.150, %999)
+        参数含义:
+        %result.3 (Tensor): 输出，prelu后的结果。
+        %input.150 (Tensor): 需要prelu的Tensor。
+        %999 (Tnsor): 权重。
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    op_name = name_generator("relu", mapper.nn_name2id)
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [op_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%result.150
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
+    layer_inputs["x"] = inputs_name[0]
+    # 处理输入1，即%999
+    weight = mapper.pytorch_params[inputs_name[1]]
+    mapper.paddle_params[op_name + "._weight"] = weight
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer(
+        "paddle.nn.PReLU", 
+        inputs=layer_inputs, 
+        outputs=layer_outputs, 
+        scope_name=scope_name, 
+        num_parameters=weight.shape[0])
+    return current_inputs, current_outputs
+
+
 def aten_relu(mapper, graph, node):
     """ 构造ReLU激活的PaddleLayer。
 

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

@@ -407,6 +407,8 @@ class OpSet9():
         if is_pads_attr:
             paddings = []
             paddle_op = 'paddle.nn.functional.pad'
+            if len(pads) == 10 and sum(pads) == 0:
+                pads = pads[0: 6]
             if len(pads) in [2, 4, 6]:
                 if data_shape:
                     assume_pad |= data_shape and 2 * (len(data_shape) - 2) == len(pads) # NCHW
@@ -424,7 +426,7 @@ class OpSet9():
                         (2, -1)).transpose().astype("int32")
                     paddings = np.flip(paddings, axis=0).flatten().tolist()
                     layer_attrs['pad'] = paddings
-                    layer_attrs['data_format'] = data_format
+                    layer_attrs['data_format'] = string(data_format)
                 else:
                     if data_shape:
                         assume_pad |= data_shape and 2 * len(data_shape) == len(pads) # NCHW
@@ -694,11 +696,14 @@ class OpSet9():
                 inputs={'x': name_trans,
                         'index': indices.name},
                 outputs=[node.name])
+            new_perm = [0] * len(perm)
+            for i in range(len(perm)):
+                new_perm[perm[i]] = i
             self.paddle_graph.add_layer(
                 'paddle.transpose', 
                 inputs={"x": node.name}, 
                 outputs=[node.name], 
-                perm=perm)
+                perm=new_perm)
             if len(indices_shape) < 1:
                 self.paddle_graph.add_layer(
                     'paddle.squeeze',
@@ -770,11 +775,15 @@ class OpSet9():
                         'index': indices_reshape},
                 outputs=[node.name])
             input_transpose = node.name + '_transpose'
+            new_perm = [0] * len(perm)
+            for i in range(len(perm)):
+                new_perm[perm[i]] = i
             self.paddle_graph.add_layer(
                 'paddle.transpose',
                 inputs={"x": node.name},
                 outputs=[input_transpose],
-                perm=perm)
+                perm=new_perm)
+            perm = new_perm
             val_x_shape = val_x.out_shapes[0]
             reshaped_shape = []
             for i in perm:

x2paddle/optimizer/optimizer.py

@@ -60,7 +60,7 @@ class GraphOptimizer(object):
     def optimize(self, graph):
         for pass_name in self.passes:
             pass_ = PassManager.lookup(pass_name)()
-            if pass_name.endswith("_eliminate_pass"):
+            if pass_name.endswith("_eliminate_pass") or pass_name.endswith("_conv2d_add_fuse_pass"):
                 pass_.apply(graph)
             else:
                 while True:

x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py

@@ -383,9 +383,9 @@ class HierarchicalTree(Tree):
             input_data_name = ', '.join(self.pd_graph.inputs)
             run_func_list = list()
             run_func_list.append("def main({}):".format(input_data_name))
-            run_func_list.append("    # 共{}个输入".format(len(self.pd_graph.inputs_info)))
+            run_func_list.append("    # There are {} inputs.".format(len(self.pd_graph.inputs_info)))
             for k, v in self.pd_graph.inputs_info.items():
-                run_func_list.append("    # {}: 形状为{}，类型为{}。".format(k, v[0], v[1]))
+                run_func_list.append("    # {}: shape-{}，type-{}。".format(k, v[0], v[1]))
             run_func_list.extend(
                 ["    paddle.disable_static()",
                  "    params = paddle.load('{}/model.pdparams')".format(osp.abspath(save_dir)),

x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py

@@ -27,6 +27,7 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.Linear": "linear",
                   "paddle.nn.Conv2DTranspose": "conv",
                   "paddle.nn.LSTM": "lstm",
+                  "paddle.nn.PReLU": "prelu",
                   "paddle.nn.ReLU": "relu",
                   "paddle.nn.ReLU6": "relu",
                   "paddle.nn.Softmax": "softmax",
@@ -41,7 +42,7 @@ 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())[:7]
+NN_KERNEL_WITH_PARAMS = list(NN_KERNEL_NAME.keys())[:8]
 
 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。

x2paddle/optimizer/pytorch_code_optimizer/module_graph.py

@@ -349,9 +349,9 @@ class ModuleGraph(object):
             input_data_name = ', '.join(self.pd_graph.inputs)
             run_func_list = list()
             run_func_list.append("def main({}):".format(input_data_name))
-            run_func_list.append("    # 共{}个输入".format(len(self.pd_graph.inputs_info)))
+            run_func_list.append("    # There are {} inputs.".format(len(self.pd_graph.inputs_info)))
             for k, v in self.pd_graph.inputs_info.items():
-                run_func_list.append("    # {}: 形状为{}，类型为{}。".format(k, v[0], v[1]))
+                run_func_list.append("    # {}: shape-{}，type-{}.".format(k, v[0], v[1]))
             run_func_list.extend(
                 ["    paddle.disable_static()",
                  "    params = paddle.load('{}/model.pdparams')".format(osp.abspath(save_dir)),