Merge pull request #524 from SunAhong1993/develop

fix the parameter path string

x2paddle/core/program.py

@@ -510,7 +510,7 @@ class PaddleGraph(object):
             comment_list = list()
             comment_list.append("# There are {} inputs.".format(len(self.inputs_info)))
             for k, v in self.inputs_info.items():
-                comment_list.append("# {}: shape-{}，type-{}。".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,
@@ -518,7 +518,7 @@ class PaddleGraph(object):
             use_structured_name = False if self.source_type in ["tf"] else True
             self.run_func.extend(
                 gen_codes(["paddle.disable_static()",
-                           "params = paddle.load('{}/model.pdparams')".format(osp.abspath(code_dir)),
+                           "params = paddle.load('{}')".format(osp.join(osp.abspath(code_dir), "model.pdparams")),
                            "model = {}()".format(self.name),
                            "model.set_dict(params, use_structured_name={})".format(use_structured_name),
                            "model.eval()",

x2paddle/decoder/pytorch_decoder.py

@@ -64,6 +64,5 @@ class TraceDecoder(Decoder):
                 print(e)
                 exit(0)
         self.graph = self._optimize_graph(self.script.inlined_graph)
-        self.input_examples = input_examples
-        
+        self.input_examples = input_examples        
             

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

@@ -2043,3 +2043,50 @@ class OpSet9():
             inputs={"x": val_x.name}, 
             outputs=layer_outputs, 
             **layer_attrs)
+        
+    @print_mapping_info
+    def DepthToSpace(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        blocksize = node.get_attr('blocksize')
+        mode = node.get_attr('mode', "DCR")
+        val_x_shape = val_x.out_shapes[0]
+        b, c, h, w = val_x_shape
+        if mode == "DCR":
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": val_x.name},
+                outputs=[node.name],
+                shape=[b, blocksize, blocksize, c // (blocksize**2), h, w]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                perm=[0, 3, 4, 1, 5, 2]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize**2), h * blocksize, w * blocksize]
+                )
+        else:
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": val_x.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize ** 2), blocksize, blocksize, h, w]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                perm=[0, 1, 4, 2, 5, 3]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize ** 2), h * blocksize, w * blocksize]
+                )
+                
\ No newline at end of file

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -2174,12 +2174,16 @@ def aten_hardtanh_(mapper, graph, node):
     # 处理输入2，即%66
     layer_attrs["max"] = mapper.attrs[inputs_name[2]]
 
-    graph.add_layer(
-        'paddle.nn.Hardtanh',
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name,
-        **layer_attrs)
+    if layer_attrs["min"] ==0 and layer_attrs["max"] == 6:
+        graph.add_layer(
+        "paddle.nn.ReLU6", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+    else:
+        graph.add_layer(
+            'paddle.nn.Hardtanh',
+            inputs=layer_inputs,
+            outputs=layer_outputs,
+            scope_name=scope_name,
+            **layer_attrs)
     return current_inputs, current_outputs
 
 

x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py

@@ -1526,3 +1526,21 @@ class TFOpMapper(OpMapper):
             inputs=inputs,
             outputs=[node.name],
             **attr)
+        
+    def ReverseV2(self, node):
+        x = self.graph.get_input_node(node, 0)
+        axis = self.graph.get_input_node(node, 1)
+        inputs = {"x": x.name}
+        attr = dict()
+        if axis.layer_type == 'Const':
+            axis = axis.value.tolist()
+            if not isinstance(axis, list):
+                axis = [axis]
+            attr['axis'] = axis
+        else:
+            inputs['axis'] = axis.name
+        self.paddle_graph.add_layer(
+            "paddle.flip",
+            inputs=inputs,
+            outputs=[node.name],
+            **attr)

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

@@ -1810,3 +1810,50 @@ class OpSet9():
             inputs={"x": val_x.name}, 
             outputs=[node.name], 
             **layer_attrs)
+
+    @print_mapping_info
+    def DepthToSpace(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        blocksize = node.get_attr('blocksize')
+        mode = node.get_attr('mode', "DCR")
+        val_x_shape = val_x.out_shapes[0]
+        b, c, h, w = val_x_shape
+        if mode == "DCR":
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": val_x.name},
+                outputs=[node.name],
+                shape=[b, blocksize, blocksize, c // (blocksize**2), h, w]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                perm=[0, 3, 4, 1, 5, 2]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize**2), h * blocksize, w * blocksize]
+                )
+        else:
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": val_x.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize ** 2), blocksize, blocksize, h, w]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.transpose',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                perm=[0, 1, 4, 2, 5, 3]
+                )
+            self.paddle_graph.add_layer(
+                'paddle.reshape',
+                inputs={"x": node.name},
+                outputs=[node.name],
+                shape=[b, c // (blocksize ** 2), h * blocksize, w * blocksize]
+                )
+                
\ No newline at end of file

x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py

@@ -1509,3 +1509,22 @@ class TFOpMapper(OpMapper):
             inputs=inputs,
             outputs=[node.name],
             **attr)
+        
+    def ReverseV2(self, node):
+        x = self.graph.get_input_node(node, 0)
+        axis = self.graph.get_input_node(node, 1)
+        inputs = {"x": x.name}
+        attr = dict()
+        if axis.layer_type == 'Const':
+            axis = axis.value.tolist()
+            if not isinstance(axis, list):
+                axis = [axis]
+            attr['axis'] = axis
+        else:
+            inputs['axis'] = axis.name
+        self.paddle_graph.add_layer(
+            "paddle.flip",
+            inputs=inputs,
+            outputs=[node.name],
+            **attr)
+

x2paddle/optimizer/pytorch_code_optimizer/hierachical_tree.py

@@ -336,8 +336,23 @@ class HierarchicalTree(Tree):
                     else:
                         module_name = module._get_name()
                     if module_name in module_name2sub_layers:
-                        module_name2sub_layers[module_name].append(sub_layers)
-                        module_name2sub_identifiers[module_name].append(sub_identifiers)
+                        if len(sub_layers[list(sub_layers.keys())[-1]].outputs) != \
+                                    len(module_name2sub_layers[module_name][0][list(module_name2sub_layers[module_name][0].keys())[-1]].outputs):
+                            while module_name in module_name2sub_layers:
+                                module_name = module_name + "__tmp"
+                                if module_name in module_name2sub_layers and \
+                                        len(sub_layers[list(sub_layers.keys())[-1]].outputs) == \
+                                        len(module_name2sub_layers[module_name][0][list(module_name2sub_layers[module_name][0].keys())[-1]].outputs):
+                                    break
+                            if module_name not in module_name2sub_layers:
+                                module_name2sub_layers[module_name] = [sub_layers]
+                                module_name2sub_identifiers[module_name] = [sub_identifiers]
+                            else:
+                                module_name2sub_layers[module_name].append(sub_layers)
+                                module_name2sub_identifiers[module_name].append(sub_identifiers)
+                        else:
+                            module_name2sub_layers[module_name].append(sub_layers)
+                            module_name2sub_identifiers[module_name].append(sub_identifiers)
                     else:
                         module_name2sub_layers[module_name] = [sub_layers]
                         module_name2sub_identifiers[module_name] = [sub_identifiers]
@@ -385,10 +400,10 @@ class HierarchicalTree(Tree):
             run_func_list.append("def main({}):".format(input_data_name))
             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("    # {}: shape-{}，type-{}。".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)),
+                 "    params = paddle.load('{}')".format(osp.join(osp.abspath(save_dir), "model.pdparams")),
                  "    model = {}()".format(self.pd_graph.name),
                  "    model.set_dict(params)",
                  "    model.eval()",

x2paddle/optimizer/pytorch_code_optimizer/module_graph.py

@@ -351,10 +351,10 @@ class ModuleGraph(object):
             run_func_list.append("def main({}):".format(input_data_name))
             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("    # {}: shape-{}，type-{}.".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)),
+                 "    params = paddle.load('{}')".format(osp.join(osp.abspath(save_dir), "model.pdparams")),
                  "    model = {}()".format(self.pd_graph.name),
                  "    model.set_dict(params)",
                  "    model.eval()",