# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from x2paddle.decoder.tf_decoder import TFGraph from x2paddle.core.op_mapper import OpMapper from x2paddle.core.util import * from x2paddle import program from x2paddle import gen_name import traceback import math import inspect import numpy import sys # compute padding size for SAME mode def get_same_padding(in_size, kernel_size, stride): new_size = int(math.ceil(in_size * 1.0 / stride)) pad_size = (new_size - 1) * stride + kernel_size - in_size if pad_size < 0: pad_size = 0 pad0 = int(pad_size / 2) pad1 = pad_size - pad0 return [pad0, pad1] class TFOpMapperNHWC(OpMapper): directly_map_ops = { 'Relu': ['relu'], 'Relu6': ['relu6'], 'Abs': ['abs'], 'Sigmoid': ['sigmoid'], 'Exp': ['exp'], 'Rsqrt': ['rsqrt'], 'Sqrt': ['sqrt'], 'swish_f32': ['swish'], 'Tanh': ['tanh'], 'Softplus': ['softplus'], 'LeakyRelu': ['leaky_relu', { 'alpha': 'alpha' }], 'Floor': ['floor'], 'Erf': ['erf'], 'Square': ['square'] } elementwise_ops = { 'Add': 'elementwise_add', 'AddV2': 'elementwise_add', 'RealDiv': 'elementwise_div', 'Sub': 'elementwise_sub', 'Maximum': 'elementwise_max', 'Minimum': 'elementwise_min', 'LessEqual': 'less_equal', 'GreaterEqual': 'greater_equal', 'Mul': 'elementwise_mul', 'FloorDiv': 'elementwise_floordiv' } def __init__(self, decoder): super(TFOpMapperNHWC, self).__init__() self.decoder = decoder self.graph = decoder.tf_graph self.weights = dict() self.omit_nodes = list() self.used_custom_layers = dict() program.clear() not_placeholder = list() for name in self.graph.input_nodes: if self.graph.get_node( name).layer_type != "Placeholder" and self.graph.get_node( name ).layer_type != "OneShotIterator" and self.graph.get_node( name).layer_type != "IteratorV2": not_placeholder.append(name) for name in not_placeholder: 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 unsupported_ops = set() sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort))) for i, node_name in enumerate(self.graph.topo_sort): sys.stderr.write("\rConverting node {} ... ".format(i + 1)) node = self.graph.get_node(node_name) op = node.layer_type if op in self.directly_map_ops: if len(unsupported_ops) > 0: continue self.directly_map(node) elif op in self.elementwise_ops: if len(unsupported_ops) > 0: continue self.elementwise_map(node) elif hasattr(self, op): if len(unsupported_ops) > 0: continue func = getattr(self, op) try: func(node) except Exception as e: unsupported_ops.add(op) print("\n{}\n".format(traceback.format_exc())) else: unsupported_ops.add(op) if len(unsupported_ops) > 0: print("\n========= {} OPs are not supported yet ===========".format( len(unsupported_ops))) for op in unsupported_ops: print("========== {} ============".format(op)) sys.exit(-1) sys.stderr.write("\nDone!\n") def directly_map(self, node): assert node.layer_type in self.directly_map_ops op_info = self.directly_map_ops[node.layer_type] input = self.graph.get_node(node.layer.input[0]) attr = dict() for param in op_info[1:]: tf_param_name = list(param.keys())[0] pd_param_name = list(param.values())[0] tf_param = node.get_attr(tf_param_name) attr[pd_param_name] = tf_param program.add_layer( kernel="fluid.layers.{}".format(op_info[0]), inputs={"x": input.name}, outputs=[node.name], **attr) def elementwise_map(self, node): assert node.layer_type in self.elementwise_ops op_type = self.elementwise_ops[node.layer_type] x = self.graph.get_node(node.layer.input[0]) y = self.graph.get_node(node.layer.input[1]) program.add_layer( kernel="fluid.layers.{}".format(op_type), inputs={"x": x.name, "y": y.name}, outputs=[node.name]) 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( kernel="fluid.layers.not_equal", inputs={"x": x.name, "y": y.name}, outputs=[node.name]) def Placeholder(self, node): shape = node.out_shapes[0] assert len(shape) != 0, "Unknown shape of input nodes[{}].".format( node.layer_name) dtype = node.dtype program.add_layer( kernel="fluid.data", inputs={}, outputs=[node.name], dtype=string(dtype), shape=shape, name=string(node.name)) def Const(self, node): shape = node.out_shapes[0] dtype = node.dtype value = node.value initializer = "Constant(0.0)" if len(shape) == 0: assert value.size == 1, "Unexpected situation happend" shape = [1] if value == float('inf'): value = "float('inf')" initializer = "Constant({})".format(value) program.parameters[node.name] = node.value program.add_layer( kernel="fluid.layers.create_parameter", inputs={}, outputs=[node.name], dtype=string(dtype), shape=shape, name=string(node.name), default_initializer=initializer) def Transpose(self, node): input = self.graph.get_node(node.layer.input[0]) perm = self.graph.get_node(node.layer.input[1]) assert perm.layer_type == "Const", "Perm of transpose OP should be Const" perm = perm.value.tolist() program.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[node.name], perm=perm) def Fill(self, node): dims = self.graph.get_node(node.layer.input[0]) input_value = self.graph.get_node(node.layer.input[1]) inputs = dict() attr = dict() assert input_value.layer_type == "Const", "Value of fill OP should be Const" if dims.layer_type == "Const": attr["shape"] = dims.value.tolist() else: inputs["shape"] = dims.name attr["dtype"] = string(input_value.dtype) attr["value"] = input_value.value program.add_layer( "fluid.layers.fill_constant", inputs=inputs, outputs=[node.name], **attr) def DepthToSpace(self, node): input = self.graph.get_node(node.layer.input[0]) block_size = node.get_attr("block_size") data_format = node.get_attr("data_format").decode() if data_format == "NHWC": n, h, w, c = input.out_shapes[0] else: n, c, h, w = input.out_shapes[0] input_name = input.name if data_format == "NHWC": transpose_name = gen_name("depth_to_space", "transpose") program.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name shape = [0, block_size * block_size, -1, h, w] reshape_name = gen_name("depth_to_space", "reshape") program.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( 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( kernel="fluid.layers.reshape", inputs={"x": transpose_name}, outputs=[reshape_name], shape=[0, c, h, w]) program.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( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def MaxPool(self, node): input = self.graph.get_node(node.layer.input[0]) k_size = node.get_attr("ksize") strides = node.get_attr("strides") data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() input_name = input.name if data_format == "NHWC": transpose_name = gen_name("max_pool", "transpose") program.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) strides = [strides[i] for i in [0, 3, 1, 2]] k_size = [k_size[i] for i in [0, 3, 1, 2]] input_name = transpose_name program.add_layer( kernel="fluid.layers.pool2d", inputs={"input": input_name}, outputs=[node.name], pool_size=k_size[2:4], pool_type=string("max"), pool_stride=strides[2:4], pool_padding=string(pad_mode)) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def Conv2D(self, node): input = self.graph.get_node(node.layer.input[0]) kernel = self.graph.get_node(node.layer.input[1]) k_size = kernel.out_shapes[0] strides = node.get_attr("strides") dilations = node.get_attr("dilations") data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() if data_format == "NHWC": n, h, w, c = input.out_shapes[0] else: n, c, h, w = input.out_shapes[0] if kernel.layer_type == 'Const': kernel_value = kernel.value kernel_weight_name = kernel.name.replace('/', '_') else: kernel_value = self.decoder.infer_tensor(kernel) if kernel.layer_type == 'Split': kernel_weight_name = "{}_{}_kernel".format(node.name, kernel.name) else: kernel_weight_name = kernel.name.replace('/', '_') program.parameters[kernel_weight_name] = numpy.transpose(kernel_value, (3, 2, 0, 1)) input_name = input.name if data_format == "NHWC": 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( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name if c == -1: attr = {"shape": [0, k_size[2], 0, 0]} node.fluid_code.add_layer( "reshape", inputs=input, output=input, param_attr=attr) program.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name}, outputs=[input_name], shape=[0, k_size[2], 0, 0]) program.add_layer( kernel="fluid.layers.conv2d", inputs={"input": input_name}, outputs=[node.name], bias_attr=False, param_attr=string(kernel_weight_name), num_filters=k_size[3], filter_size=k_size[0:2], stride=strides[2:4], dilation=dilations[2:4], padding=string(pad_mode)) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) 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( kernel="fluid.layers.elementwise_add", inputs={"x": input.name, "y": bias.name}, outputs=[node.name]) def FusedBatchNorm(self, node): input = self.graph.get_node(node.layer.input[0]) gamma = self.graph.get_node(node.layer.input[1]) beta = self.graph.get_node(node.layer.input[2]) moving_mean = self.graph.get_node(node.layer.input[3]) moving_var = self.graph.get_node(node.layer.input[4]) data_format = node.get_attr("data_format").decode() assert gamma.layer_type == "Const" assert beta.layer_type == "Const" assert moving_mean.layer_type == "Const" assert moving_var.layer_type == "Const" input_name = input.name if data_format == "NHWC": transpose_name = gen_name("batch_norm", "transpose") program.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( kernel="fluid.layers.batch_norm", inputs={"input": input_name}, outputs=[node.name], epsilon=node.get_attr("epsilon"), param_attr=string(gamma.name), bias_attr=string(beta.name), moving_mean_name=string(moving_mean.name), moving_variance_name=string(moving_var.name), is_test=True) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def Mean(self, node): input = self.graph.get_node(node.layer.input[0]) reduce_idx = self.graph.get_node(node.layer.input[1]) assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" dims = reduce_idx.value.tolist() keep_dims = node.get_attr("keep_dims") program.add_layer( kernel="fluid.layers.reduce_mean", inputs={"input": input.name}, outputs=[node.name], dim=dims, keep_dim=keep_dims) def Reshape(self, node): input = self.graph.get_node(node.layer.input[0]) param = self.graph.get_node(node.layer.input[1]) input_name = input.name if input.dtype == 'bool': cast_name = gen_name('reshape', 'cast') program.add_layer( kernel="fluid.layers.cast", inputs={"x": input_name}, outputs=[cast_name], dtype="'int32'") input_name = cast_name if param.layer_type == "Const": shape = param.value.tolist() program.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name}, outputs=[node.name], shape=shape) else: program.add_layer( kernel="fluid.layers.reshape", inputs={"x": input_name, "shape": param.name}, outputs=[node.name]) if param.layer_type != "Const": out_shape = numpy.array(node.out_shapes[0]) if (out_shape > 0).any(): out_shape[out_shape < 0] = 0 program.add_layer( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], shape=out_shape.tolist()) if input.dtype == 'bool': program.add_layer( kernel="fluid.layers.cast", inputs={"x": node.name}, outputs=[node.name], dtype="'bool'") def Pad(self, node): input = self.graph.get_node(node.layer.input[0]) paddings = self.graph.get_node(node.layer.input[1]) assert paddings.layer_type == "Const", "Padding should be Const" paddings = paddings.value.flatten().tolist() if len(input.out_shapes[0]) == 4: if paddings[0] + paddings[1] + paddings[6] + paddings[7] == 0: new_padding = paddings[2:6] transpose_name = gen_name("pad", "transpose") program.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) program.add_layer( kernel="fluid.layers.pad2d", inputs={"input": transpose_name}, outputs=[node.name], paddings=new_padding) program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) return program.add_layer( kernel="fluid.layers.pad", inputs={"input": input.name}, outputs=[node.name], paddings=paddings) def Squeeze(self, node): input = self.graph.get_node(node.layer.input[0]) squeeze_dims = node.get_attr('squeeze_dims') program.add_layer( kernel="fluid.layers.squeeze", inputs={"input": input.name}, outputs=[node.name], axes=squeeze_dims) def Softmax(self, node): input = self.graph.get_node(node.layer.input[0]) axis = node.get_attr("axis") program.add_layer( kernel="fluid.layers.softmax", inputs={"input": input.name}, outputs=[node.name], axis=axis) def Shape(self, node): input = self.graph.get_node(node.layer.input[0]) input_name = input.name if input.dtype == 'bool': cast_name = gen_name('shape', 'cast') program.add_layer( kernel="fluid.layers.cast", inputs={"x": input.name}, outputs=[cast_name], dtype="'int32'") input_name = cast_name program.add_layer( kernel="fluid.layers.shape", inputs={"input": input_name}, outputs=[node.name]) def ArgMax(self, node): input = self.graph.get_node(node.layer.input[0]) 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( kernel="fluid.layers.argmax", inputs={"x": input.name}, outputs=[node.name], axis=axis) def MatMul(self, node): x = self.graph.get_node(node.layer.input[0]) y = self.graph.get_node(node.layer.input[1]) transpose_a = node.get_attr('transpose_a') transpose_b = node.get_attr('transpose_b') if transpose_a is None: transpose_a = node.get_attr('adj_x') if transpose_b is None: transpose_b = node.get_attr('adj_y') program.add_layer( kernel="fluid.layers.matmul", inputs={"x": x.name, "y": y.name}, outputs=[node.name], transpose_x=transpose_a, transpose_y=transpose_b) def BatchMatMul(self, node): return self.MatMul(node) def BatchMatMulV2(self, node): return self.MatMul(node) def DepthwiseConv2dNative(self, node): input = self.graph.get_node(node.layer.input[0]) kernel = self.graph.get_node(node.layer.input[1]) assert kernel.layer_type == "Const", "Kernel of DepthwiseConv2DNative should be Const" in_shape = input.out_shapes[0] k_size = kernel.out_shapes[0] strides = node.get_attr("strides") dilations = node.get_attr("dilations") data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() program.parameters[kernel.layer_name.replace( '/', '_')] = numpy.transpose(kernel.value, (2, 3, 0, 1)) input_name = input.name if data_format == "NHWC": in_shape = [in_shape[i] for i in [0, 3, 1, 2]] 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( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name program.add_layer( kernel="fluid.layers.conv2d", inputs={"input": input_name}, outputs=[node.name], num_filters=in_shape[1], filter_size=k_size[0:2], stride=strides[2:4], dilation=dilations[2:4], groups=k_size[3] * in_shape[1], padding=string(pad_mode), param_attr=string(kernel.layer_name), bias_attr=False) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def AvgPool(self, node): input = self.graph.get_node(node.layer.input[0]) k_size = node.get_attr("ksize") strides = node.get_attr("strides") data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() input_name = input.name if data_format == "NHWC": transpose_name = gen_name("avg_pool", "transpose") program.add_layer( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) strides = [strides[i] for i in [0, 3, 1, 2]] k_size = [k_size[i] for i in [0, 3, 1, 2]] input_name = transpose_name program.add_layer( kernel="fluid.layers.pool2d", inputs={"input": input_name}, outputs=[node.name], pool_size=k_size[2:4], pool_type=string("avg"), pool_stride=strides[2:4], pool_padding=string(pad_mode)) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def Pack(self, node): 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( kernel="fluid.layers.stack", inputs={"x": input_names}, outputs=[node.name], axis=axis) if len(node.out_shapes[0]) == 1: program.add_layer( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], shape=[-1]) def Unpack(self, node): input = self.graph.get_node(node.layer.input[0]) axis = node.get_attr("axis") num = node.get_attr("num") shape = input.out_shapes[0] input_name = input.name if len(shape) == 1: if shape[0] > 0 and num == shape[0]: program.add_layer( kernel="fluid.layers.unsqueeze", inputs={"input": input.name}, outputs=[node.name], axes=[0]) input_name = node.name axis = 1 else: raise Exception("Unexpected situation happend in Unpack OP") program.add_layer( kernel="fluid.layers.unstack", inputs={"x": input_name}, outputs=["{}_p{}".format(node.layer_name, i) for i in range(num)], axis=axis, num=num) def ConcatV2(self, node): inputs = [self.graph.get_node(name) for name in node.layer.input[:-1]] axis = self.graph.get_node(node.layer.input[-1]) assert axis.layer_type == "Const", "axis for ConcatV2 must be type Const" axis = axis.value if axis < 0: axis += len(inputs[0].out_shapes[0]) input_names = [i.name for i in inputs] for i, ipt in enumerate(inputs): if node.dtype == 'bool': cast_name = gen_name('concat', 'cast') program.add_layer( kernel="fluid.layers.cast", inputs={"x": ipt.name}, outputs=[cast_name], dtype="'int32'") input_names[i] = cast_name program.add_layer( kernel="fluid.layers.concat", inputs={"input": input_names}, outputs=[node.name], axis=axis) if node.dtype == 'bool': program.add_layer( kernel="fluid.layers.cast", inputs={"x": node.name}, outputs=[node.name], dtype="'bool'") def StridedSlice(self, node): input = self.graph.get_node(node.layer.input[0]) begin = self.graph.get_node(node.layer.input[1]) end = self.graph.get_node(node.layer.input[2]) strides = self.graph.get_node(node.layer.input[3]) if strides.layer_type == "Const": strides = strides.value.tolist() else: strides = self.decoder.infer_shape_tensor(strides) if begin.layer_type == "Const": begin = begin.value.tolist() else: begin = self.decoder.infer_shape_tensor(begin) if end.layer_type == "Const": end = end.value.tolist() else: end = self.decoder.infer_shape_tensor(end) assert len(set(strides)) == 1 and strides[ 0] == 1, "Only support strides be 1 in StridedSlice OP" if len(begin) < len(input.out_shapes[0]): begin = begin + [0] * (len(input.out_shapes[0]) - len(begin)) if len(end) < len(input.out_shapes[0]): end = end + [0] * (len(input.out_shapes[0]) - len(end)) for i in range(len(end)): if end[i] == 0: end[i] = 999999 begin_mask = node.get_attr('begin_mask') end_mask = node.get_attr('end_mask') ellipsis_mask = node.get_attr('ellipsis_mask') new_axis_mask = node.get_attr('new_axis_mask') shrink_axis_mask = node.get_attr('shrink_axis_mask') assert ellipsis_mask == 0, "(OP:{} Name:{})Only support ellipsis_mask be 0[now: {}] n StridedSlice OP".format( node.layer_type, node.layer.name, ellipsis_mask) # TODO codes without validation # Use it carefully new_begin = list() new_end = list() new_axes = list() shrink_axes = list() for i, item in enumerate(begin): mask = (new_axis_mask >> i) & 1 if mask != 0: new_axes.append(i) continue mask = (shrink_axis_mask >> i) & 1 if mask != 0: shrink_axes.append(i) mask = (begin_mask >> i) & 1 if mask != 0: new_begin.append(0) else: new_begin.append(item) mask = (end_mask >> i) & 1 if mask != 0: new_end.append(999999) else: new_end.append(end[i]) program.add_layer( kernel="fluid.layers.slice", inputs={"input": input.name}, outputs=[node.name], axes=[i for i in range(len(new_begin))], starts=new_begin, ends=new_end) if len(new_axes) > 0: program.add_layer( kernel="fluid.layers.unsqueeze", inputs={"input": node.name}, outputs=[node.name], axes=new_axes) if len(shrink_axes) > 0: if len(input.out_shapes[0]) + len(new_axes) <= 1: pass else: program.add_layer( kernel="fluid.layers.squeeze", inputs={"input": node.name}, outputs=[node.name], axes=shrink_axes) def Split(self, node): dim = self.graph.get_node(node.layer.input[0]) input = self.graph.get_node(node.layer.input[1]) assert dim.layer_type == "Const" num_split = node.get_attr('num_split') dim = dim.value program.add_layer( kernel="fluid.layers.split", inputs={"input": input.name}, outputs=[ "{}_p{}".format(node.layer_name, i) for i in range(num_split) ], num_or_sections=num_split, dim=dim) def Slice(self, node): input = self.graph.get_node(node.layer.input[0]) begin = self.graph.get_node(node.layer.input[1]) size = self.graph.get_node(node.layer.input[2]) inputs = {"x": input.name} attrs = {} if begin.layer_type == "Const": begin = begin.value.tolist() attrs['offsets'] = begin else: # shape = begin.out_shapes[0] # reshape_name = gen_name("slice", "reshape") # program.add_layer( # kernel="fluid.layers.reshape", # inputs={"x": begin.name}, # outputs=[reshape_name], # shape=shape) # inputs['offsets'] = reshape_name begin = self.decoder.infer_tensor(begin).tolist() attrs['offsets'] = begin if size.layer_type == "Const": size = size.value.tolist() attrs['shape'] = size else: shape = size.out_shapes[0] reshape_name = gen_name("slice", "reshape") program.add_layer( kernel="fluid.layers.reshape", inputs={"x": size.name}, outputs=[reshape_name], shape=shape) inputs['shape'] = reshape_name program.add_layer( kernel="fluid.layers.crop_tensor", inputs=inputs, outputs=[node.name], **attrs) def ResizeNearestNeighbor(self, node): input = self.graph.get_node(node.layer.input[0]) resize_shape = self.graph.get_node(node.layer.input[1]) data_format = "NHWC" inputs = {"input": input.name} attrs = {"align_corners": node.get_attr("align_corners")} if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() attrs["out_shape"] = resize_shape else: shape = resize_shape.out_shapes[0] reshape_name = gen_name("resize_nearest", "reshape") program.add_layer( kernel="fluid.layers.reshape", inputs={"x": resize_shape.name}, outputs=[reshape_name], shape=shape) inputs["out_shape"] = reshape_name if data_format == "NHWC": transpose_name = gen_name("resize_nearest", "reshape") program.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( kernel="fluid.layers.resize_nearest", inputs=inputs, outputs=[node.name], **attrs) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def ResizeBilinear(self, node): input = self.graph.get_node(node.layer.input[0]) resize_shape = self.graph.get_node(node.layer.input[1]) data_format = "NHWC" inputs = {"input": input.name} attrs = {"align_corners": node.get_attr("align_corners")} if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() attrs["out_shape"] = resize_shape else: shape = resize_shape.out_shapes[0] reshape_name = gen_name("resize_bilinear", "reshape") program.add_layer( kernel="fluid.layers.reshape", inputs={"x": resize_shape.name}, outputs=[reshape_name], shape=shape) inputs["out_shape"] = reshape_name if data_format == "NHWC": transpose_name = gen_name("resize_bilinear", "reshape") program.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( kernel="fluid.layers.resize_bilinear", inputs=inputs, outputs=[node.name], **attrs) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def Cast(self, node): input = self.graph.get_node(node.layer.input[0]) dtype = node.dtype program.add_layer( kernel="fluid.layers.cast", inputs={"x": input.name}, outputs=[node.name], dtype=string(dtype)) def Sum(self, node): input = self.graph.get_node(node.layer.input[0]) reduce_idx = self.graph.get_node(node.layer.input[1]) 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( kernel="fluid.layers.reduce_sum", inputs={"input": input.name}, outputs=[node.name], dim=dim, keep_dim=keep_dims) def Max(self, node): input = self.graph.get_node(node.layer.input[0]) reduce_idx = self.graph.get_node(node.layer.input[1]) 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( kernel="fluid.layers.reduce_max", inputs={"input": input.name}, outputs=[node.name], dim=dim, keep_dim=keep_dims) def RandomUniform(self, node): shape = self.graph.get_node(node.layer.input[0]) if shape.layer_type == "Const": shape = shape.value.tolist() program.add_layer( kernel="fluid.layers.uniform_random", inputs={}, outputs=[node.name], shape=shape, min=0.0, max=0.9999) else: program.add_layer( kernel="fluid.layers.uniform_random", inputs={'shape': shape.name}, outputs=[node.name], min=0.0, max=0.9999) def Conv2DBackpropInput(self, node): out_shape = self.graph.get_node(node.layer.input[0]) kernel = self.graph.get_node(node.layer.input[1]) input = self.graph.get_node(node.layer.input[2]) assert kernel.layer_type == "Const", "Kernel of Conv2DBackpropInput should be Const" if out_shape.layer_type == "Const": out_shape = out_shape.value.tolist() else: out_shape = self.decoder.infer_shape_tensor(out_shape, node.out_shapes[0]) in_shape = input.out_shapes[0] if in_shape.count(-1) > 2: in_shape = self.decoder.infer_tensor(input).shape k_size = kernel.out_shapes[0] if k_size.count(-1) > 2: k_size = self.decoder.infer_tensor(kernel).shape pad_mode = node.get_attr("padding").decode() strides = node.get_attr("strides") dilations = node.get_attr("dilations") data_format = node.get_attr("data_format").decode() program.parameters[kernel.layer_name.replace( '/', '_')] = numpy.transpose(kernel.value, (3, 2, 0, 1)) input_name = input.name if data_format == "NHWC": in_shape = [in_shape[i] for i in [0, 3, 1, 2]] 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( kernel="fluid.layers.transpose", inputs={"x": input.name}, outputs=[transpose_name], perm=[0, 3, 1, 2]) input_name = transpose_name program.add_layer( kernel="fluid.layers.conv2d_transpose", inputs={"input": input_name}, outputs=[node.name], bias_attr=False, param_attr=string(kernel.layer_name), num_filters=k_size[2], filter_size=k_size[0:2], stride=strides[2:4], dilation=dilations[2:4], padding=string(pad_mode), output_size=out_shape[1:3]) if data_format == "NHWC": program.add_layer( kernel="fluid.layers.transpose", inputs={"x": node.name}, outputs=[node.name], perm=[0, 2, 3, 1]) def Tile(self, node): input = self.graph.get_node(node.layer.input[0]) expand_times = self.graph.get_node(node.layer.input[1]) inputs = {"x": input.name} attr = dict() if expand_times.layer_type == "Const": expand_times = expand_times.value.tolist() attr["expand_times"] = expand_times else: inputs["expand_times"] = expand_times.name program.add_layer( kernel="fluid.layers.expand", inputs=inputs, outputs=[node.name], **attr) def Range(self, node): start = self.graph.get_node(node.layer.input[0]) limit = self.graph.get_node(node.layer.input[1]) delta = self.graph.get_node(node.layer.input[2]) inputs = dict() attr = dict() if start.layer_type == "Const": attr["start"] = start.value else: inputs["start"] = start.name if limit.layer_type == "Const": attr["end"] = limit.value else: inputs["end"] = limit.name if delta.layer_type == "Const": attr["step"] = delta.value else: inputs["step"] = delta.name attr["dtype"] = string(node.dtype) program.add_layer( kernel="fluid.layers.range", inputs=inputs, outputs=[node.name], **attr) def SquaredDifference(self, node): x = self.graph.get_node(node.layer.input[0]) y = self.graph.get_node(node.layer.input[1]) inputs = {"x": x.name, "y": y.name} program.add_layer( "fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name]) inputs = {"x": node.name, "y": node.name} program.add_layer( "fluid.layers.elementwise_mul", inputs=inputs, outputs=[node.name]) def OneHot(self, node): input = self.graph.get_node(node.layer.input[0]) depth = self.graph.get_node(node.layer.input[1]) on_value = self.graph.get_node(node.layer.input[2]) off_value = self.graph.get_node(node.layer.input[3]) assert depth.layer_type == 'Const', 'Parameter depth should be Const in OneHot' assert on_value.layer_type == 'Const', 'Parameter on_value should be Const in OneHot' assert off_value.layer_type == 'Const', 'Parameter off_value should be Const in OneHot' attr = {'depth': depth.value} on_value = on_value.value off_value = off_value.value assert math.fabs(on_value - 1.0) < 1e-06, "on_value should be 1 in OneHot" assert math.fabs(off_value - 0.0) < 1e-06, "off_value should be 0 in OneHot" program.add_layer( "fluid.one_hot", inputs={"input": input.name}, outputs=[node.name], depth=depth.value) def Pow(self, node): x = self.graph.get_node(node.layer.input[0]) factor = self.graph.get_node(node.layer.input[1]) inputs = {"x": x.name} attr = dict() if factor.layer_type == 'Const': attr["factor"] = factor.value.tolist() else: inputs["factor"] = factor.name program.add_layer( "fluid.layers.pow", inputs=inputs, outputs=[node.name], **attr) def All(self, node): input = self.graph.get_node(node.layer.input[0]) reduce_idx = self.graph.get_node(node.layer.input[1]) assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" attr = dict() attr["dim"] = reduce_idx.value.tolist() attr["keep_dim"] = node.get_attr("keep_dims") program.add_layer( "fluid.layers.reduce_all", inputs={"input": input.name}, outputs=[node.name], **attr) node.layer.attr['dtype'].type = 10 def GatherV2(self, node): embeddings = self.graph.get_node(node.layer.input[0]) index = self.graph.get_node(node.layer.input[1]) axis = self.graph.get_node(node.layer.input[2]) assert axis.layer_type == 'Const', "Only support Const parameter[axis]" axis = axis.value.tolist() assert axis == 0, "Only support axis=0 in GatherV2 OP" index_name = index.name if len(index.out_shapes[0]) != 1: reshape_name = gen_name("gather", "reshape") index_name = reshape_name program.add_layer( "fluid.layers.reshape", inputs={"x": index.name}, outputs=[reshape_name], shape=[-1]) inputs = {'input': embeddings.name, 'index': index_name} program.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( kernel="fluid.layers.reshape", inputs={"x": node.name}, outputs=[node.name], shape=out_shape) def ExpandDims(self, node): x = self.graph.get_node(node.layer.input[0], copy=True) y = self.graph.get_node(node.layer.input[1], copy=True) inputs = {"input": x.name} attr = dict() if y.layer_type == 'Const': dim = y.value.tolist() if not isinstance(dim, list): dim = [dim] attr['axes'] = dim else: inputs['axes'] = y.name program.add_layer( "fluid.layers.unsqueeze", inputs=inputs, outputs=[node.name], **attr)