diff --git a/x2paddle/core/program.py b/x2paddle/core/program.py old mode 100644 new mode 100755 index 1841c7ce97b553bfd88343c86c1f5a77ff4595c1..759d072d345b59329fa20f6674e385dfbbee6a7a --- a/x2paddle/core/program.py +++ b/x2paddle/core/program.py @@ -27,22 +27,23 @@ from x2paddle.core.util import * class PaddleLayer(object): def __init__(self, id, kernel, inputs, outputs, scope_name="", **kwargs): - assert isinstance( - inputs, - dict), "parameter 'inputs' for PaddleLayer should be type of dict" + assert isinstance(inputs, ( + dict, list + )), "parameter 'inputs' for PaddleLayer should be type of dict or list" assert isinstance( outputs, list), "parameter 'outputs' for PaddleLayer should be type of list" - for k, v in inputs.items(): - if isinstance(v, (list, tuple)): - for i in v: - assert isinstance( - i, six.string_types + if isinstance(inputs, dict): + for k, v in inputs.items(): + if isinstance(v, (list, tuple)): + for i in v: + assert isinstance( + i, six.string_types + ), "value in inputs should be type of string or list of string" + else: + assert isinstance(v, six.string_types) or isinstance( + v, list ), "value in inputs should be type of string or list of string" - else: - assert isinstance(v, six.string_types) or isinstance( - v, list - ), "value in inputs should be type of string or list of string" for v in outputs: assert isinstance( v, six. @@ -164,11 +165,31 @@ class PaddleGraph(object): self.clear_edges() outputs_from_nodes = dict() for layer_id, layer in self.layers.items(): - for input_key, input_var in layer.inputs.items(): - vs = input_var - if not isinstance(vs, (list, tuple)): - vs = [vs] - for v in vs: + if isinstance(layer.inputs, dict): + for input_key, input_var in layer.inputs.items(): + vs = input_var + if not isinstance(vs, (list, tuple)): + vs = [vs] + for v in vs: + assert v in outputs_from_nodes or ( + inputs is not None and v in list(inputs.values()) + ) or ( + outputs is not None and v in outputs + ), "Couldn't find {} in previous layers, the layers should be make by topological sort".format( + v) + if v in outputs_from_nodes: + in_layer_id = outputs_from_nodes[v] + else: + in_layer_id = -1 + if in_layer_id not in self.edges_out: + self.edges_out[in_layer_id] = list() + self.edges_out[in_layer_id].append(layer_id) + + if layer_id not in self.edges_in: + self.edges_in[layer_id] = list() + self.edges_in[layer_id].append(in_layer_id) + else: + for v in layer.inputs: assert v in outputs_from_nodes or ( inputs is not None and v in list(inputs.values()) ) or ( @@ -186,6 +207,7 @@ class PaddleGraph(object): if layer_id not in self.edges_in: self.edges_in[layer_id] = list() self.edges_in[layer_id].append(in_layer_id) + for output in layer.outputs: outputs_from_nodes[output] = layer_id @@ -359,6 +381,7 @@ class PaddleGraph(object): "class {}(paddle.nn.Layer):".format(self.name), ], indent=0) + print(self.inputs) input_data_name = ', '.join(self.inputs) self.init_func.extend(gen_codes(["def __init__(self):"], indent=1)) self.init_func.extend( @@ -496,16 +519,20 @@ class PaddleGraph(object): else: line = ','.join(layer.outputs) line += " = {}(".format(layer.kernel) - for k, v in layer.inputs.items(): - if isinstance(v, list): - line += "{}=[{}], ".format(k, ", ".join(v)) - elif isinstance(v, tuple): - line += "{}=({}), ".format(k, ", ".join(v)) - else: - if k == "args": - line += v + if isinstance(layer.inputs, dict): + for k, v in layer.inputs.items(): + if isinstance(v, list): + line += "{}=[{}], ".format(k, ", ".join(v)) + elif isinstance(v, tuple): + line += "{}=({}), ".format(k, ", ".join(v)) else: - line += "{}={}, ".format(k, v) + if k == "args": + line += v + else: + line += "{}={}, ".format(k, v) + else: + line += "{}".format(", ".join(layer.inputs)) + for k, v in layer.attrs.items(): line += "{}={}, ".format(k, v) line = line.strip(", ") diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py index 5fa97e28828f1cb88527271215a31dc324960419..f8c55bb3b096921954c58ba4cc063a9ae9b60338 100755 --- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py +++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py @@ -69,8 +69,6 @@ def _rename_or_remove_weight(weights, if target_name is not None: # rename weight weights[target_name] = data - if "x2paddle_297" in weights.keys(): - print("keep") def _is_static_shape(shape): @@ -233,8 +231,6 @@ class OpSet9(): @print_mapping_info def place_holder(self, node): - if node.name in ["297", "x2paddle_297"]: - print("!!!!!!!find! 1123") shape = node.out_shapes[0] for i, dim_shape in enumerate(shape): if dim_shape == 0 and i == 0: @@ -264,7 +260,6 @@ class OpSet9(): shape=[1], fill_value=node.weight) else: - print("test point:", node.name) self.weights[node.name] = node.weight self.paddle_graph.add_layer( "self.create_parameter", @@ -405,9 +400,6 @@ class OpSet9(): inputs['scale_factor'] = val_scales.name else: val_scales = node.get_attr('scales')[2:] - print(type(val_scales)) - print(val_scales) - # inputs['scale_factor'] = val_scales mode = node.get_attr('mode', 'nearest') attrs.update({ @@ -733,8 +725,6 @@ class OpSet9(): @print_mapping_info def Constant(self, node): - if node.name in ["297", "x2paddle_297"]: - print("!!!!!!!find!") val_output = self.graph.get_node(node.layer.output[0], copy=True) value = node.get_attr('value') @@ -829,8 +819,6 @@ class OpSet9(): 'fill_value': 1 } else: - print("test:", type(shape_values)) - print(shape_values.tolist()) attr_ones = { 'shape': shape_values.tolist(), 'dtype': string(val_x_dtype), @@ -855,8 +843,6 @@ class OpSet9(): val_x = self.graph.get_input_node(node, idx=0, copy=True) indices = self.graph.get_input_node(node, idx=1, copy=True) indices_shape = indices.out_shapes[0] - print("indices_shape:", node.name, " ", indices_shape, " ", - val_x.out_shapes[0]) axis = node.get_attr('axis', 0) #assert len( # indices_shape) <= 2, "Gather op don't support dim of indice >2 " @@ -1180,7 +1166,6 @@ class OpSet9(): if axes is None: axes = [i for i in range(len(starts))] - print("axes:", axes) for idx in range(len(ends)): if ends[idx] > 2**31 - 1: ends[idx] = 2**31 - 1 @@ -1221,7 +1206,6 @@ class OpSet9(): @print_mapping_info def GatherND(self, node): - print(len(node.inputs), node.inputs) val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_input_node(node, idx=1, copy=True) self.paddle_graph.add_layer( @@ -1387,7 +1371,6 @@ class OpSet9(): @print_mapping_info def GatherND(self, node): - print(len(node.inputs), node.inputs) val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_input_node(node, idx=1, copy=True) self.paddle_graph.add_layer( @@ -2037,7 +2020,6 @@ class OpSet9(): def SpaceToDepth(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) blocksize = node.get_attr('blocksize') - print(blocksize) val_x_shape = val_x.out_shapes[0] b, c, h, w = val_x_shape self.paddle_graph.add_layer( @@ -2060,12 +2042,91 @@ class OpSet9(): def GatherElements(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) indices = self.graph.get_input_node(node, idx=1, copy=True) - dtype = np.dtype(val_x.dtype) + axis = node.get_attr('axis') + val_x_shape = val_x.out_shapes[0] + indices_shape = indices.out_shapes[0] + axis = axis if axis >= 0 else axis + len(val_x_shape) + if axis == 0: + axis_perm = [i for i in range(len(val_x_shape))] + data_swaped = val_x.name + index_swaped = indices.name + else: + axis_perm = [i for i in range(len(val_x_shape))] + axis_perm[axis] = 0 + axis_perm[0] = axis + data_swaped = val_x.name + "_transpose" + self.paddle_graph.add_layer( + "paddle.transpose", + inputs={'x': val_x.name}, + perm=axis_perm, + outputs=[data_swaped]) + index_swaped = indices.name + "_transpose" + self.paddle_graph.add_layer( + "paddle.transpose", + inputs={'x': indices.name}, + perm=axis_perm, + outputs=[index_swaped]) + temp = indices_shape[0] + indices_shape[0] = indices_shape[axis] + indices_shape[axis] = temp + + idx_tensors_per_axis_pre = [ + indices_shape[i] for i in range(len(indices_shape)) + ] + name_list = list() + for i in range(len(idx_tensors_per_axis_pre)): + tensor_name = val_x.name + "_meshgrid_" + str(i) + self.paddle_graph.add_layer( + kernel="paddle.linspace", + inputs={}, + outputs=[tensor_name], + start=0, + stop=idx_tensors_per_axis_pre[i] - 1, + num=idx_tensors_per_axis_pre[i]) + name_list.append(tensor_name) + self.paddle_graph.add_layer( - "paddle.gather", - inputs={'x': val_x.name, - 'index': indices.name}, - axis=node.get_attr('axis'), + "paddle.meshgrid", inputs=name_list, outputs=name_list) + + self.paddle_graph.add_layer( + "paddle.cast", + inputs={"x": index_swaped}, + outputs=[index_swaped], + dtype=string("float32")) + import copy + copy_name_list = copy.copy(name_list) + copy_name_list[0] = index_swaped + new_name_list = list() + for i in range(len(copy_name_list)): + unsqueeze_name = copy_name_list[i] + "_unsqueeze" + self.paddle_graph.add_layer( + "paddle.unsqueeze", + inputs={"x": copy_name_list[i]}, + axis=-1, + outputs=[unsqueeze_name]) + new_name_list.append(unsqueeze_name) + concat_name = val_x.name + "_concated_layer" + self.paddle_graph.add_layer( + "paddle.concat", + inputs={'x': new_name_list}, + axis=-1, + outputs=[concat_name]) + self.paddle_graph.add_layer( + "paddle.cast", + inputs={"x": concat_name}, + outputs=[concat_name], + dtype=string("int32")) + gather_nd_name = "gather_nd_layer" + self.paddle_graph.add_layer( + "paddle.gather_nd", + inputs={'x': data_swaped, + "index": concat_name}, + outputs=[gather_nd_name]) + + self.paddle_graph.add_layer( + "paddle.transpose", + inputs={'x': gather_nd_name}, + perm=axis_perm, outputs=[node.name]) @print_mapping_info