diff --git a/x2paddle/convert.py b/x2paddle/convert.py index 618778b188afc064748f764220c4698a713e1d90..857856fcd50de4ed63fdf337e77fafcda55b8f5b 100644 --- a/x2paddle/convert.py +++ b/x2paddle/convert.py @@ -103,6 +103,7 @@ def tf2paddle(model_path, save_dir, without_data_format_optimization=False, define_input_shape=False, + paddle_type="dygraph", params_merge=False): # check tensorflow installation and version try: @@ -120,25 +121,28 @@ def tf2paddle(model_path, "[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"." ) return - + from x2paddle.decoder.tf_decoder import TFDecoder - from x2paddle.op_mapper.tf_op_mapper import TFOpMapper + if paddle_type == "dygraph": + from x2paddle.op_mapper.dygraph.tf2paddle.tf_op_mapper import TFOpMapper + else: + from x2paddle.op_mapper.static.tf2paddle.tf_op_mapper import TFOpMapper + from x2paddle.optimizer.tensorflow.bias import BiasOpt from x2paddle.optimizer.tensorflow.transpose import TransposeOpt from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt - print("Now translating model from tensorflow to paddle.") model = TFDecoder(model_path, define_input_shape=define_input_shape) - mapper = TFOpMapper(model) - program.build() + mapper.paddle_graph.build() bias_opt = BiasOpt() transpose_opt = TransposeOpt() batch_norm_opt = BatchNormOpt() bias_opt.run(program) batch_norm_opt.run(program) transpose_opt.run(program) - program.gen_model(save_dir) + mapper.paddle_graph.gen_model(save_dir) + def caffe2paddle(proto, weight, save_dir, caffe_proto, @@ -293,7 +297,7 @@ def main(): if args.params_merge: params_merge = True tf2paddle(args.model, args.save_dir, without_data_format_optimization, - define_input_shape, params_merge) + define_input_shape, args.paddle_type, params_merge) elif args.framework == "caffe": assert args.prototxt is not None and args.weight is not None, "--prototxt and --weight should be defined while translating caffe model" diff --git a/x2paddle/core/program.py b/x2paddle/core/program.py index 70f3bd99256e6293d3f0d2ffbfc744cff130a97a..c9d23cceb669a50f3d916d2032c7744bb54e06b0 100644 --- a/x2paddle/core/program.py +++ b/x2paddle/core/program.py @@ -76,6 +76,7 @@ class PaddleGraph(object): self.custom_code = None self.inputs_info = None + def set_name(self, name): self.name = name.replace("-", "_").replace("/", "_") @@ -285,8 +286,8 @@ class PaddleGraph(object): for input_name in self.inputs: input_shapes.append(self.inputs_info[input_name][0]) input_types.append(self.inputs_info[input_name][1]) - # 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]] - self.dygraph2static(save_dir, input_shapes, input_types) + # 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]] + self.dygraph2static(save_dir, input_shapes, input_types) def gen_static_code(self, code_dir): def write_code(f, code_list, indent=0): @@ -446,6 +447,8 @@ class PaddleGraph(object): if self.source_type == "caffe": custom_import = "from x2paddle.op_mapper.dygraph.caffe2paddle " + \ "import caffe_custom_layer as x2paddle_nn" + else: + custom_import = "" self.head = gen_codes( [ "from paddle.fluid.initializer import Constant", @@ -618,7 +621,10 @@ class PaddleGraph(object): paddle.disable_static() restore, _ = fluid.load_dygraph(osp.join(save_dir, "model")) model = getattr(x2paddle_code, self.name)() - model.set_dict(restore) + if self.source_type == "tf": + model.set_dict(restore, use_structured_name=False) + else: + model.set_dict(restore) model.eval() static_model = paddle.jit.to_static(model, input_spec=sepc_list) paddle.jit.save(static_model, osp.join(save_dir, "inference_model/model")) \ No newline at end of file diff --git a/x2paddle/decoder/tf_decoder.py b/x2paddle/decoder/tf_decoder.py index e55f5c05b4a36d54d26cac3aadc034ec5b347b3b..3b017f137ebcbadb91af9fc912a02bec7b734d44 100644 --- a/x2paddle/decoder/tf_decoder.py +++ b/x2paddle/decoder/tf_decoder.py @@ -132,6 +132,7 @@ class TFGraph(Graph): self.identity_map = dict() self.multi_out_ops = ['Split', 'SplitV', 'IteratorV2'] self.tf_data_format = data_format + self.graph_name = "TFModel" def build(self): for layer in self.model.node: diff --git a/x2paddle/op_mapper/dygraph/prim2code.py b/x2paddle/op_mapper/dygraph/prim2code.py index fb151a9d9b9561080d1a4e0c78cd36375ae4eac3..f0d53006f52aeba94a7c82b7cf2d21d150d11ea8 100644 --- a/x2paddle/op_mapper/dygraph/prim2code.py +++ b/x2paddle/op_mapper/dygraph/prim2code.py @@ -277,7 +277,11 @@ def prim_list(layer, indent=1, init_func=[], forward_func=[], layer_id=None, dif inputs_str = ', '.join(inputs_list) line = "{} = [{}]".format(layer.outputs[0], inputs_str) forward_func.extend(gen_codes([line], indent=indent)) - + +def prim_list_mul(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None): + line = "{} = [a*b for a,b in zip({}, {})]".format(layer.outputs[0], + get_value(layer, "list0", different_attrs), + get_value(layer, "list1", different_attrs)) def prim_list_unpack(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None): line = "{} = {}".format(", ".join(layer.outputs), get_value(layer, "input", different_attrs)) diff --git a/x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py b/x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py index 1fd0a4ebe0222765090d7f860dc03ec71dbd5de5..f06eb071e085cc9ff7c9684e7c2dfc7ee6d8d443 100644 --- a/x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py +++ b/x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py @@ -13,6 +13,7 @@ # limitations under the License. from x2paddle.decoder.tf_decoder import TFGraph +from x2paddle.core.program import PaddleGraph from x2paddle.core.op_mapper import OpMapper from x2paddle.core.util import * from x2paddle import program @@ -82,10 +83,8 @@ class TFOpMapper(OpMapper): super(TFOpMapper, self).__init__() self.decoder = decoder self.graph = decoder.tf_graph - self.weights = dict() - self.omit_nodes = list() - self.used_custom_layers = dict() - program.clear() + self.params = dict() + self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="static", source_type="tf") not_placeholder = list() for name in self.graph.input_nodes: @@ -99,8 +98,8 @@ class TFOpMapper(OpMapper): idx = self.graph.input_nodes.index(name) del self.graph.input_nodes[idx] - program.inputs = self.graph.input_nodes - program.outputs = self.graph.output_nodes + self.paddle_graph.inputs = self.graph.input_nodes + self.paddle_graph.outputs = self.graph.output_nodes unsupported_ops = set() sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort))) @@ -134,6 +133,8 @@ class TFOpMapper(OpMapper): print("========== {} ============".format(op)) sys.exit(-1) sys.stderr.write("\nDone!\n") + self.paddle_graph.set_name(self.graph.graph_name) + self.paddle_graph.set_parameters(self.params) def directly_map(self, node): assert node.layer_type in self.directly_map_ops @@ -146,7 +147,7 @@ class TFOpMapper(OpMapper): tf_param = node.get_attr(tf_param_name) attr[pd_param_name] = tf_param - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.{}".format(op_info[0]), inputs={"x": input.name}, outputs=[node.name], @@ -159,18 +160,18 @@ class TFOpMapper(OpMapper): y = self.graph.get_node(node.layer.input[1]) x_shape = x.out_shapes[0] y_shape = y.out_shapes[0] - layer_id = program.add_layer( + layer_id = self.paddle_graph.add_layer( kernel="fluid.layers.{}".format(op_type), inputs={"x": x.name, "y": y.name}, outputs=[node.name]) - program.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} + self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} def NotEqual(self, node): x = self.graph.get_node(node.layer.input[0]) y = self.graph.get_node(node.layer.input[1]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.not_equal", inputs={"x": x.name, "y": y.name}, @@ -181,7 +182,7 @@ class TFOpMapper(OpMapper): assert len(shape) != 0, "Unknown shape of input nodes[{}].".format( node.layer_name) dtype = node.dtype - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.data", inputs={}, outputs=[node.name], @@ -199,7 +200,7 @@ class TFOpMapper(OpMapper): shape = [1] if value == float('inf'): value = "float('inf')" - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.fill_constant", inputs={}, outputs=[node.name], @@ -208,8 +209,8 @@ class TFOpMapper(OpMapper): value=value) return - program.parameters[node.name] = node.value - program.add_layer( + self.params[node.name] = node.value + self.paddle_graph.add_layer( kernel="fluid.layers.create_parameter", inputs={}, outputs=[node.name], @@ -224,7 +225,7 @@ class TFOpMapper(OpMapper): assert perm.layer_type == "Const", "Perm of transpose OP should be Const" perm = perm.value.tolist() - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[node.name], @@ -243,7 +244,7 @@ class TFOpMapper(OpMapper): attr["dtype"] = string(input_value.dtype) attr["value"] = input_value.value - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.fill_constant", inputs=inputs, outputs=[node.name], @@ -262,7 +263,7 @@ class TFOpMapper(OpMapper): input_name = input.name if data_format == "NHWC": transpose_name = gen_name("depth_to_space", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], @@ -271,34 +272,34 @@ class TFOpMapper(OpMapper): shape = [0, block_size * block_size, -1, h, w] reshape_name = gen_name("depth_to_space", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name}, outputs=[reshape_name], shape=shape) transpose_name = gen_name("depth_to_space", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": reshape_name}, outputs=[transpose_name], perm=[0, 2, 1, 3, 4]) reshape_name = gen_name("depth_to_space", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": transpose_name}, outputs=[reshape_name], shape=[0, c, h, w]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.pixel_shuffle", inputs={"x": reshape_name}, outputs=[node.name], upscale_factor=block_size) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -315,7 +316,7 @@ class TFOpMapper(OpMapper): input_name = input.name if data_format == "NHWC": transpose_name = gen_name("max_pool", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], @@ -324,7 +325,7 @@ class TFOpMapper(OpMapper): k_size = [k_size[i] for i in [0, 3, 1, 2]] input_name = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.pool2d", inputs={"input": input_name}, outputs=[node.name], @@ -334,7 +335,7 @@ class TFOpMapper(OpMapper): pool_padding=string(pad_mode)) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -364,7 +365,7 @@ class TFOpMapper(OpMapper): kernel.name) else: kernel_weight_name = kernel.name.replace('/', '_') - program.parameters[kernel_weight_name] = numpy.transpose(kernel_value, + self.params[kernel_weight_name] = numpy.transpose(kernel_value, (3, 2, 0, 1)) input_name = input.name @@ -372,7 +373,7 @@ class TFOpMapper(OpMapper): strides = [strides[i] for i in [0, 3, 1, 2]] dilations = [dilations[i] for i in [0, 3, 1, 2]] transpose_name = gen_name("conv2d", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], @@ -383,13 +384,13 @@ class TFOpMapper(OpMapper): attr = {"shape": [0, k_size[2], 0, 0]} node.fluid_code.add_layer( "reshape", inputs=input, output=input, param_attr=attr) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name}, outputs=[input_name], shape=[0, k_size[2], 0, 0]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.conv2d", inputs={"input": input_name}, outputs=[node.name], @@ -402,7 +403,7 @@ class TFOpMapper(OpMapper): padding=string(pad_mode)) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -411,7 +412,7 @@ class TFOpMapper(OpMapper): def BiasAdd(self, node): input = self.graph.get_node(node.layer.input[0]) bias = self.graph.get_node(node.layer.input[1]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.elementwise_add", inputs={"x": input.name, "y": bias.name}, @@ -433,14 +434,14 @@ class TFOpMapper(OpMapper): input_name = input.name if data_format == "NHWC": transpose_name = gen_name("batch_norm", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.batch_norm", inputs={"input": input_name}, outputs=[node.name], @@ -452,7 +453,7 @@ class TFOpMapper(OpMapper): is_test=True) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -465,7 +466,7 @@ class TFOpMapper(OpMapper): dims = reduce_idx.value.tolist() keep_dims = node.get_attr("keep_dims") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reduce_mean", inputs={"input": input.name}, outputs=[node.name], @@ -479,7 +480,7 @@ class TFOpMapper(OpMapper): input_name = input.name if input.dtype == 'bool': cast_name = gen_name('reshape', 'cast') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": input_name}, outputs=[cast_name], @@ -488,13 +489,13 @@ class TFOpMapper(OpMapper): if param.layer_type == "Const": shape = param.value.tolist() - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name}, outputs=[node.name], shape=shape) else: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name, "shape": param.name}, @@ -503,14 +504,14 @@ class TFOpMapper(OpMapper): out_shape = numpy.array(node.out_shapes[0]) if (out_shape > 0).any(): out_shape[out_shape < 0] = 0 - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], shape=out_shape.tolist()) if input.dtype == 'bool': - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": node.name}, outputs=[node.name], @@ -526,24 +527,24 @@ class TFOpMapper(OpMapper): if paddings[0] + paddings[1] + paddings[6] + paddings[7] == 0: new_padding = paddings[2:6] transpose_name = gen_name("pad", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.pad2d", inputs={"input": transpose_name}, outputs=[node.name], paddings=new_padding) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) return - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.pad", inputs={"input": input.name}, outputs=[node.name], @@ -552,7 +553,7 @@ class TFOpMapper(OpMapper): def Squeeze(self, node): input = self.graph.get_node(node.layer.input[0]) squeeze_dims = node.get_attr('squeeze_dims') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.squeeze", inputs={"input": input.name}, outputs=[node.name], @@ -561,7 +562,7 @@ class TFOpMapper(OpMapper): def Softmax(self, node): input = self.graph.get_node(node.layer.input[0]) axis = node.get_attr("axis") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.softmax", inputs={"input": input.name}, outputs=[node.name], @@ -572,13 +573,13 @@ class TFOpMapper(OpMapper): input_name = input.name if input.dtype == 'bool': cast_name = gen_name('shape', 'cast') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": input.name}, outputs=[cast_name], dtype="'int32'") input_name = cast_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.shape", inputs={"input": input_name}, outputs=[node.name]) @@ -588,7 +589,7 @@ class TFOpMapper(OpMapper): axis = self.graph.get_node(node.layer.input[1]) assert axis.layer_type == "Const", "ArgMax only support Const parameter" axis = axis.value - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.argmax", inputs={"x": input.name}, outputs=[node.name], @@ -603,7 +604,7 @@ class TFOpMapper(OpMapper): transpose_a = node.get_attr('adj_x') if transpose_b is None: transpose_b = node.get_attr('adj_y') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.matmul", inputs={"x": x.name, "y": y.name}, @@ -629,7 +630,7 @@ class TFOpMapper(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() - program.parameters[kernel.layer_name.replace( + self.params[kernel.layer_name.replace( '/', '_')] = numpy.transpose(kernel.value, (2, 3, 0, 1)) input_name = input.name @@ -638,14 +639,14 @@ class TFOpMapper(OpMapper): strides = [strides[i] for i in [0, 3, 1, 2]] dilations = [dilations[i] for i in [0, 3, 1, 2]] transpose_name = gen_name('depthwise_conv2d', 'transpose') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.conv2d", inputs={"input": input_name}, outputs=[node.name], @@ -659,7 +660,7 @@ class TFOpMapper(OpMapper): bias_attr=False) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -676,7 +677,7 @@ class TFOpMapper(OpMapper): input_name = input.name if data_format == "NHWC": transpose_name = gen_name("avg_pool", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], @@ -685,7 +686,7 @@ class TFOpMapper(OpMapper): k_size = [k_size[i] for i in [0, 3, 1, 2]] input_name = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.pool2d", inputs={"input": input_name}, outputs=[node.name], @@ -695,7 +696,7 @@ class TFOpMapper(OpMapper): pool_padding=string(pad_mode)) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -705,13 +706,13 @@ class TFOpMapper(OpMapper): inputs = [self.graph.get_node(name) for name in node.layer.input] input_names = [i.name for i in inputs] axis = node.get_attr("axis") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.stack", inputs={"x": input_names}, outputs=[node.name], axis=axis) if len(node.out_shapes[0]) == 1: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], @@ -725,7 +726,7 @@ class TFOpMapper(OpMapper): input_name = input.name if len(shape) == 1: if shape[0] > 0 and num == shape[0]: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.unsqueeze", inputs={"input": input.name}, outputs=[node.name], @@ -734,7 +735,7 @@ class TFOpMapper(OpMapper): axis = 1 else: raise Exception("Unexpected situation happend in Unpack OP") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.unstack", inputs={"x": input_name}, outputs=["{}_p{}".format(node.layer_name, i) for i in range(num)], @@ -753,19 +754,19 @@ class TFOpMapper(OpMapper): for i, ipt in enumerate(inputs): if ipt.dtype == 'bool': cast_name = gen_name('concat', 'cast') - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": ipt.name}, outputs=[cast_name], dtype="'int32'") input_names[i] = cast_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.concat", inputs={"input": input_names}, outputs=[node.name], axis=axis) if node.dtype == 'bool': - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": node.name}, outputs=[node.name], @@ -838,7 +839,7 @@ class TFOpMapper(OpMapper): else: new_end.append(end[i]) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.slice", inputs={"input": input.name}, outputs=[node.name], @@ -846,7 +847,7 @@ class TFOpMapper(OpMapper): starts=new_begin, ends=new_end) if len(new_axes) > 0: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.unsqueeze", inputs={"input": node.name}, outputs=[node.name], @@ -855,7 +856,7 @@ class TFOpMapper(OpMapper): if len(input.out_shapes[0]) + len(new_axes) <= 1: pass else: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.squeeze", inputs={"input": node.name}, outputs=[node.name], @@ -868,7 +869,7 @@ class TFOpMapper(OpMapper): num_split = node.get_attr('num_split') dim = dim.value - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.split", inputs={"input": input.name}, outputs=[ @@ -890,7 +891,7 @@ class TFOpMapper(OpMapper): else: # shape = begin.out_shapes[0] # reshape_name = gen_name("slice", "reshape") - # program.add_layer( + # self.paddle_graph.add_layer( # kernel="fluid.layers.reshape", # inputs={"x": begin.name}, # outputs=[reshape_name], @@ -904,13 +905,13 @@ class TFOpMapper(OpMapper): else: shape = size.out_shapes[0] reshape_name = gen_name("slice", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": size.name}, outputs=[reshape_name], shape=shape) inputs['shape'] = reshape_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.crop_tensor", inputs=inputs, outputs=[node.name], @@ -929,7 +930,7 @@ class TFOpMapper(OpMapper): else: shape = resize_shape.out_shapes[0] reshape_name = gen_name("resize_nearest", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": resize_shape.name}, outputs=[reshape_name], @@ -938,21 +939,21 @@ class TFOpMapper(OpMapper): if data_format == "NHWC": transpose_name = gen_name("resize_nearest", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) inputs["input"] = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.resize_nearest", inputs=inputs, outputs=[node.name], **attrs) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -971,7 +972,7 @@ class TFOpMapper(OpMapper): else: shape = resize_shape.out_shapes[0] reshape_name = gen_name("resize_bilinear", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": resize_shape.name}, outputs=[reshape_name], @@ -980,21 +981,21 @@ class TFOpMapper(OpMapper): if data_format == "NHWC": transpose_name = gen_name("resize_bilinear", "reshape") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) inputs["input"] = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.resize_bilinear", inputs=inputs, outputs=[node.name], **attrs) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -1003,7 +1004,7 @@ class TFOpMapper(OpMapper): def Cast(self, node): input = self.graph.get_node(node.layer.input[0]) dtype = node.dtype - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.cast", inputs={"x": input.name}, outputs=[node.name], @@ -1016,7 +1017,7 @@ class TFOpMapper(OpMapper): keep_dims = node.get_attr("keep_dims") dim = reduce_idx.value.tolist() - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reduce_sum", inputs={"input": input.name}, outputs=[node.name], @@ -1029,7 +1030,7 @@ class TFOpMapper(OpMapper): assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" keep_dims = node.get_attr("keep_dims") dim = reduce_idx.value.tolist() - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reduce_max", inputs={"input": input.name}, outputs=[node.name], @@ -1040,7 +1041,7 @@ class TFOpMapper(OpMapper): shape = self.graph.get_node(node.layer.input[0]) if shape.layer_type == "Const": shape = shape.value.tolist() - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.uniform_random", inputs={}, outputs=[node.name], @@ -1048,7 +1049,7 @@ class TFOpMapper(OpMapper): min=0.0, max=0.9999) else: - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.uniform_random", inputs={'shape': shape.name}, outputs=[node.name], @@ -1080,7 +1081,7 @@ class TFOpMapper(OpMapper): dilations = node.get_attr("dilations") data_format = node.get_attr("data_format").decode() - program.parameters[kernel.layer_name.replace( + self.params[kernel.layer_name.replace( '/', '_')] = numpy.transpose(kernel.value, (3, 2, 0, 1)) input_name = input.name @@ -1089,14 +1090,14 @@ class TFOpMapper(OpMapper): strides = [strides[i] for i in [0, 3, 1, 2]] dilations = [dilations[i] for i in [0, 3, 1, 2]] transpose_name = gen_name("conv2dbackpropinput", "transpose") - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.conv2d_transpose", inputs={"input": input_name}, outputs=[node.name], @@ -1110,7 +1111,7 @@ class TFOpMapper(OpMapper): output_size=out_shape[1:3]) if data_format == "NHWC": - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], @@ -1127,7 +1128,7 @@ class TFOpMapper(OpMapper): else: inputs["expand_times"] = expand_times.name - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.expand", inputs=inputs, outputs=[node.name], @@ -1162,7 +1163,7 @@ class TFOpMapper(OpMapper): node.set_dtype(dtype) attr["dtype"] = string(node.dtype) - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.range", inputs=inputs, outputs=[node.name], @@ -1174,16 +1175,16 @@ class TFOpMapper(OpMapper): inputs = {"x": x.name, "y": y.name} x_shape = x.out_shapes[0] y_shape = y.out_shapes[0] - layer_id = program.add_layer( + layer_id = self.paddle_graph.add_layer( "fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name]) - program.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} + self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} inputs = {"x": node.name, "y": node.name} x_shape = node.out_shapes[0] y_shape = node.out_shapes[0] - layer_id = program.add_layer( + layer_id = self.paddle_graph.add_layer( "fluid.layers.elementwise_mul", inputs=inputs, outputs=[node.name]) - program.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} + self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape} def OneHot(self, node): input = self.graph.get_node(node.layer.input[0]) @@ -1202,7 +1203,7 @@ class TFOpMapper(OpMapper): assert math.fabs(off_value - 0.0) < 1e-06, "off_value should be 0 in OneHot" - program.add_layer( + self.paddle_graph.add_layer( "fluid.one_hot", inputs={"input": input.name}, outputs=[node.name], @@ -1217,7 +1218,7 @@ class TFOpMapper(OpMapper): attr["factor"] = factor.value.tolist() else: inputs["factor"] = factor.name - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.pow", inputs=inputs, outputs=[node.name], **attr) def All(self, node): @@ -1231,12 +1232,12 @@ class TFOpMapper(OpMapper): input_name = input.name if input.dtype != "bool": input_name = gen_name("all", "cast") - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.cast", inputs={"x": input.name}, outputs=[input_name], dtype=string("bool")) - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.reduce_all", inputs={"input": input_name}, outputs=[node.name], @@ -1255,20 +1256,20 @@ class TFOpMapper(OpMapper): if len(index.out_shapes[0]) != 1: reshape_name = gen_name("gather", "reshape") index_name = reshape_name - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.reshape", inputs={"x": index.name}, outputs=[reshape_name], shape=[-1]) inputs = {'input': embeddings.name, 'index': index_name} - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.gather", inputs=inputs, outputs=[node.name], overwrite=False) if len(index.out_shapes[0]) != 1: out_shape = node.out_shapes[0] - program.add_layer( + self.paddle_graph.add_layer( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], @@ -1286,7 +1287,7 @@ class TFOpMapper(OpMapper): attr['axes'] = dim else: inputs['axes'] = y.name - program.add_layer( + self.paddle_graph.add_layer( "fluid.layers.unsqueeze", inputs=inputs, outputs=[node.name], diff --git a/x2paddle/optimizer/fusion/static/__pycache__/__init__.cpython-37.pyc b/x2paddle/optimizer/fusion/static/__pycache__/__init__.cpython-37.pyc index d7cc64fdc58c4e2aca8dd49161865807a388b3b0..d4db8729c8fa58efb4ce4bb5234aaf9e32484da7 100644 Binary files a/x2paddle/optimizer/fusion/static/__pycache__/__init__.cpython-37.pyc and b/x2paddle/optimizer/fusion/static/__pycache__/__init__.cpython-37.pyc differ diff --git a/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuse_pass.cpython-37.pyc b/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuse_pass.cpython-37.pyc index 7bd0bb9093ce6b88dd647c2cd96456587d69bee3..f1e8c0a1b8fe4f1237a3d6f875837d1c99cf870c 100644 Binary files a/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuse_pass.cpython-37.pyc and b/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuse_pass.cpython-37.pyc differ diff --git a/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuser.cpython-37.pyc b/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuser.cpython-37.pyc index bfbeb57fa1edb40bda0bcd7943edd9c32ed1a47f..92a440a463a1e94dd81e488c73fc1c1c8c99f0e5 100644 Binary files a/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuser.cpython-37.pyc and b/x2paddle/optimizer/fusion/static/__pycache__/bn_scale_fuser.cpython-37.pyc differ