diff --git a/x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py index b3f7bca3a7a07ef55c870f7b8dbbfebe411351e8..e20f6faf3c130a874be4ec90dcabec0c0968808b 100644 --- a/x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py +++ b/x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py @@ -119,19 +119,19 @@ class OpSet9(): # reduce function 'ReduceMean': ['paddle.mean', dict(axes='axis', keepdims='keepdim'), - dict(keepdims=1)], + dict(axes=None, keepdims=1)], 'ReduceSum': ['paddle.sum', dict(axes='axis', keepdims='keepdim'), - dict(keepdims=1)], + dict(axes=None, keepdims=1)], 'ReduceMin': ['paddle.min', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], 'ReduceMax': ['paddle.max', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], 'ReduceProd': ['paddle.prod', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], # active function 'Relu': ['paddle.nn.ReLU'], 'LeakyRelu': ['paddle.nn.LeakyReLU', @@ -150,6 +150,7 @@ class OpSet9(): dict(threshold='threshold'), dict(threshold=float(sys.maxsize))], 'Exp': ['paddle.exp'], + 'Log': ['paddle.log'], 'LogSoftmax': ['paddle.nn.functional.log_softmax', dict(axis='axis'), dict(axis=1)], @@ -320,8 +321,15 @@ class OpSet9(): return elif node.layer_type == 'Upsample': val_scales = self.graph.get_input_node(node, idx=1, copy=True) - inputs['scale_factor'] = val_scales - + self.paddle_graph.add_layer( + "paddle.slice", + inputs={"input": val_scales.name}, + outputs=[val_scales.name], + axes=[0], + starts=[2], + ends=[4]) + inputs['scale_factor'] = val_scales.name + mode = node.get_attr('mode', 'nearest') attrs.update({"align_corners": False, "mode": string(mode), @@ -1013,13 +1021,12 @@ class OpSet9(): if len(value) == 1: value = value[0] layer_attrs = { - 'shape': val_shape.name, 'dtype': string(dtype), 'fill_value': value } self.paddle_graph.add_layer( "paddle.full", - inputs={}, + inputs={'shape': val_shape.name}, outputs=[node.name], **layer_attrs) @@ -1072,8 +1079,11 @@ class OpSet9(): } outputs_list = list() if isinstance(split, list) or isinstance(split, tuple): - for i in range(len(split)): - outputs_list.append("{}_p{}".format(node.layer_name, i)) + if len(split) == 1: + outputs_list.append(node.name) + else: + for i in range(len(split)): + outputs_list.append("{}_p{}".format(node.layer_name, i)) else: outputs_list.append(node.name) self.paddle_graph.add_layer( @@ -1415,6 +1425,18 @@ class OpSet9(): else: if mode == 'channel': slope_data = _const_weight_or_none(val_slope) + if slope_data is None: + self.paddle_graph.add_layer( + "paddle.reshape", + inputs={"x": val_slope.name}, + outputs=[val_slope.name], + shape=[shape_slope[0]]) + self.paddle_graph.add_layer( + "paddle.nn.functional.prelu", + inputs={"x": val_x.name, + "weight": val_slope.name}, + outputs=[node.name]) + return _rename_or_remove_weight(self.weights, val_slope.name) if len(shape_slope) > 1: self.weights[op_name+'._weight'] = np.reshape(slope_data, shape_slope[0]) @@ -1464,7 +1486,7 @@ class OpSet9(): "paddle.greater_than", inputs={'x': val_x.name, 'y': val_y.name}, - outputs=node, + outputs=[node.name], param_attr=None) @print_mapping_info @@ -1521,7 +1543,7 @@ class OpSet9(): self.paddle_graph.add_layer( "paddle.transpose", inputs={"x": val_x.name}, - outputs=[node.layer_naem], + outputs=[node.layer_name], perm=[1, 0]) if val_x_dim > 1: self.paddle_graph.add_layer( @@ -1977,3 +1999,18 @@ class OpSet9(): outputs=[y_out], perm=[0,2,1,3] ) + + @print_mapping_info + def TopK(self, node): + val_x = self.graph.get_input_node(node, idx=0, copy=True) + val_k = self.graph.get_input_node(node, idx=1, copy=True) + layer_attrs = dict() + layer_attrs["axis"] = node.get_attr('axis', -1) + layer_attrs["largest"] = True if node.get_attr('largest', 1) == 1 else False + layer_attrs["sorted"] = True if node.get_attr('sorted', 1) == 1 else False + self.paddle_graph.add_layer( + "paddle.topk", + inputs={"x": val_x.name, + "k": val_k.name}, + outputs=["{}_p{}".format(node.layer_name, 0), "{}_p{}".format(node.layer_name, 1)], + **layer_attrs) diff --git a/x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py index cd2be216883a599243cc730b73bdf1fd562529d9..9289b4c0b1ed286b7f1dffd0292a9ca95633738f 100644 --- a/x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py +++ b/x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py @@ -96,19 +96,19 @@ class OpSet9(): # reduce function 'ReduceMean': ['paddle.mean', dict(axes='axis', keepdims='keepdim'), - dict(keepdims=1)], + dict(axes=None, keepdims=1)], 'ReduceSum': ['paddle.sum', dict(axes='axis', keepdims='keepdim'), - dict(keepdims=1)], + dict(axes=None, keepdims=1)], 'ReduceMin': ['paddle.min', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], 'ReduceMax': ['paddle.max', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], 'ReduceProd': ['paddle.prod', dict(axes='axis', keepdims='keepdim'), - dict(keepdim=1)], + dict(axes=None, keepdim=1)], # active function 'Relu': ['paddle.nn.functional.relu'], 'LeakyRelu': ['paddle.nn.functional.leaky_relu', @@ -127,6 +127,7 @@ class OpSet9(): dict(threshold='threshold'), dict(threshold=float(sys.maxsize))], 'Exp': ['paddle.exp'], + 'Log': ['paddle.log'], 'Softmax': ['paddle.nn.functional.softmax', dict(axis='axis'), dict(axis=1)], @@ -283,7 +284,14 @@ class OpSet9(): return elif node.layer_type == 'Upsample': val_scales = self.graph.get_input_node(node, idx=1, copy=True) - inputs['scale'] = val_scales + self.paddle_graph.add_layer( + "paddle.slice", + inputs={"input": val_scales.name}, + outputs=[val_scales.name], + axes=[0], + starts=[2], + ends=[4]) + inputs['scale_factor'] = val_scales.name mode = node.get_attr('mode', 'nearest') attrs.update({"align_corners": False, @@ -977,13 +985,12 @@ class OpSet9(): if len(value) == 1: value = value[0] layer_attrs = { - 'shape': val_shape.name, 'dtype': string(dtype), 'fill_value': value } self.paddle_graph.add_layer( "paddle.full", - inputs={}, + inputs={'shape': val_shape.name}, outputs=[node.name], **layer_attrs) @@ -1035,8 +1042,11 @@ class OpSet9(): } outputs_list = list() if isinstance(split, list) or isinstance(split, tuple): - for i in range(len(split)): - outputs_list.append("{}_p{}".format(node.layer_name, i)) + if len(split) == 1: + outputs_list.append(node.name) + else: + for i in range(len(split)): + outputs_list.append("{}_p{}".format(node.layer_name, i)) else: outputs_list.append(node.name) self.paddle_graph.add_layer( @@ -1391,7 +1401,7 @@ class OpSet9(): "paddle.greater_than", inputs={'x': val_x.name, 'y': val_y.name}, - outputs=node, + outputs=[node.name], param_attr=None) @print_mapping_info @@ -1448,7 +1458,7 @@ class OpSet9(): self.paddle_graph.add_layer( "paddle.transpose", inputs={"x": val_x.name}, - outputs=[node.layer_naem], + outputs=[node.layer_name], perm=[1, 0]) if val_x_dim > 1: self.paddle_graph.add_layer( @@ -1758,3 +1768,18 @@ class OpSet9(): "paddle.reciprocal", inputs={"x": val_x.name}, outputs=[node.name]) + + @print_mapping_info + def TopK(self, node): + val_x = self.graph.get_input_node(node, idx=0, copy=True) + val_k = self.graph.get_input_node(node, idx=1, copy=True) + layer_attrs = dict() + layer_attrs["axis"] = node.get_attr('axis', -1) + layer_attrs["largest"] = True if node.get_attr('largest', 1) == 1 else False + layer_attrs["sorted"] = True if node.get_attr('sorted', 1) == 1 else False + self.paddle_graph.add_layer( + "paddle.topk", + inputs={"x": val_x.name, + "k": val_k.name}, + outputs=["{}_p{}".format(node.layer_name, 0), "{}_p{}".format(node.layer_name, 1)], + **layer_attrs) \ No newline at end of file