# 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 * import numpy class TFOpMapper(OpMapper): directly_map_ops = { 'Relu': ['relu'], 'Relu6': ['relu6'], 'Shape': ['shape'], 'Abs': ['abs'], 'Sigmoid': ['sigmoid'], 'Exp': ['exp'], 'Rsqrt': ['rsqrt'], 'Squeeze': ['squeeze', { 'squeeze_dims': 'axes' }], 'Softmax': ['softmax', { 'axis': 'axis' }], } elementwise_ops = { 'Add': 'elementwise_add', 'RealDiv': 'elementwise_div', 'BiasAdd': 'elementwise_add', 'Sub': 'elementwise_sub', 'Maximum': 'elementwise_max', 'Mul': 'elementwise_mul' } def __init__(self, decoder): super(TFOpMapper, self).__init__() self.decoder = decoder self.graph = decoder.tf_graph self.weights = dict() self.omit_nodes = list() def run(self): print("Total nodes: {}".format(len(self.graph.topo_sort))) # check if ops in model are all supported # TODO for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) op = node.layer_type if op in self.directly_map_ops: self.directly_map(node) elif op in self.elementwise_ops: self.elementwise_map(node) elif hasattr(self, op): func = getattr(self, op) func(node) else: raise Exception("OP: [{}] not support yet".format(op)) for i in range(len(self.graph.topo_sort)): node_name = self.graph.topo_sort[i] if node_name in self.omit_nodes: continue node = self.graph.get_node(node_name) self.net_code += node.fluid_code.gen_codes() 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], copy=True) 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 node.fluid_code.add_layer(op_info[0], inputs=input, output=node, param_attr=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], copy=True) y = self.graph.get_node(node.layer.input[1], copy=True) x_shape = x.out_shapes[0] y_shape = y.out_shapes[0] # incomplement broadcasting support for paddle x_input = x y_input = y if len(x_shape) < len(y_shape): unrevertable_ops = [ "elementwise_sub", "elementwise_div", "elementwise_floordiv", "elementwise_mod", "elementwise_pow" ] if op_type not in unrevertable_ops: x_input = y y_input = x x_shape = y.out_shapes[0] y_shape = x.out_shapes[0] else: raise Exception("Unexpected situation happend") is_sub_seq = True for i in range(len(y_shape)): index = -1 * i - 1 if y_shape[index] != x_shape[index]: is_sub_seq = False if not is_sub_seq: x_expand_times = [1] * len(x_shape) y_expand_times = [1] * len(y_shape) x_need_expand = False y_need_expand = False for i in range(len(y_shape)): index = -1 * i - 1 if y_shape[index] != x_shape[index]: if y_shape[index] == 1: y_expand_times[index] = x_shape[index] y_need_expand = True elif x_shape[index] == 1: x_expand_times[index] = y_shape[index] x_need_expand = True else: raise Exception("Unexpected situation happend") if x_need_expand: attr = {"expand_times": x_expand_times} node.fluid_code.add_layer("expand", inputs=x_input, output="x_tmp", param_attr=attr) x_input = "x_tmp" if y_need_expand: attr = {"expand_times": y_expand_times} node.fluid_code.add_layer("expand", inputs=y_input, output="y_tmp", param_attr=attr) y_input = "y_tmp" inputs = {"x": x_input, "y": y_input} node.fluid_code.add_layer(op_type, inputs=inputs, output=node, param_attr=None) 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 attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'append_batch_size': False } node.fluid_code.add_layer("data", inputs=None, output=node, param_attr=attr) 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] initializer = "Constant({})".format(value) attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'default_initializer': initializer } node.fluid_code.add_layer("create_parameter", inputs=None, output=node, param_attr=attr) self.weights[node.layer_name.replace('/', '_')] = node.value def Transpose(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) perm = self.graph.get_node(node.layer.input[1], copy=True) assert perm.layer_type == "Const", "Perm of transpose OP should be Const" del self.weights[perm.layer_name.replace('/', '_')] perm.fluid_code.clear() perm = perm.value.tolist() attr = {'perm': perm} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) def MaxPool(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) in_shape = input.out_shapes[0] if in_shape.count(-1) > 2: in_shape = self.decoder.infer_tensor(input).shape 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() channel_first = data_format == "NCHW" if not channel_first: attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) in_shape = [in_shape[i] for i in [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]] if pad_mode == "SAME": pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) pad_h = pad_h[0] + pad_h[1] pad_w = pad_w[0] + pad_w[1] attr = {"paddings": [0, pad_h, 0, pad_w], "pad_value": -10000.0} if pad_h + pad_w != 0: node.fluid_code.add_layer( "pad2d", inputs=input if channel_first else node, output=node, param_attr=attr) attr = { "pool_size": k_size[2:4], "pool_type": string("max"), "pool_stride": strides[2:4] } node.fluid_code.add_layer( "pool2d", inputs=input if channel_first and pad_mode != "SAME" else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def Conv2D(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) kernel = self.graph.get_node(node.layer.input[1], copy=True) assert kernel.layer_type == "Const", "Kernel of Conv2D should be Const" self.omit_nodes.append(kernel.layer_name) node.fluid_code.add_note("#{} : {}".format(node.layer.name, node.layer_name)) 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 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() channel_first = data_format == "NCHW" if not channel_first: self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (3, 2, 0, 1)) attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) 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]] if pad_mode == "SAME": pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} if pad_h[0] + pad_h[1] + pad_w[0] + pad_w[1] != 0: node.fluid_code.add_layer( "pad2d", inputs=input if channel_first else node, output=node, param_attr=attr) attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), "num_filters": k_size[3], "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4] } node.fluid_code.add_layer( "conv2d", inputs=input if channel_first and pad_mode != "SAME" else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def FusedBatchNorm(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) gamma = self.graph.get_node(node.layer.input[1], copy=True) beta = self.graph.get_node(node.layer.input[2], copy=True) moving_mean = self.graph.get_node(node.layer.input[3], copy=True) moving_var = self.graph.get_node(node.layer.input[4], copy=True) data_format = node.get_attr("data_format").decode() channel_first = data_format == "NCHW" assert gamma.layer_type == "Const" assert beta.layer_type == "Const" assert moving_mean.layer_type == "Const" assert moving_var.layer_type == "Const" self.omit_nodes.append(gamma.layer_name) self.omit_nodes.append(beta.layer_name) self.omit_nodes.append(moving_mean.layer_name) self.omit_nodes.append(moving_var.layer_name) if not channel_first: attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) attr = { "epsilon": node.get_attr("epsilon"), "param_attr": string(gamma.layer_name), # "data_layout": string(node.get_attr("data_format").decode()), "bias_attr": string(beta.layer_name), "moving_mean_name": string(moving_mean.layer_name), "moving_variance_name": string(moving_var.layer_name), "is_test": True } node.fluid_code.add_layer("batch_norm", inputs=input if channel_first else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def DepthwiseConv2dNative(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) kernel = self.graph.get_node(node.layer.input[1], copy=True) assert kernel.layer_type == "Const", "Kernel of DepthwiseConv2DNative should be Const" self.omit_nodes.append(kernel.layer_name) node.fluid_code.add_note("#{} : {}".format(node.layer.name, node.layer_name)) 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 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() channel_first = data_format == "NCHW" if not channel_first: self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (2, 3, 0, 1)) attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) 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]] if pad_mode == "SAME": pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} if pad_h[0] + pad_h[1] + pad_w[0] + pad_w[1] != 0: node.fluid_code.add_layer("pad2d", inputs=input if channel_first and pad_mode != "SAME" else node, output=node, param_attr=attr) attr = { "bias_attr": False, "param_attr": string(kernel.layer_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] } node.fluid_code.add_layer("conv2d", inputs=input if channel_first else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def Reshape(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) param = self.graph.get_node(node.layer.input[1], copy=True) if param.layer_type == "Const": attr = {"shape": param.value.tolist()} self.omit_nodes.append(param.layer_name) else: # Here is a trick method to solove tensor parameter in tensorflow shape = self.decoder.infer_shape_tensor(param, node.out_shapes[0]) if shape.count(-1) <= 1: attr = {"shape": shape} self.omit_nodes.append(param.layer_name) else: assert len(param.out_shapes[0] ) == 1, "Unexpected situation of shape parameter" attr = {"shape": [-1]} node.fluid_code.add_layer("reshape", inputs=param, output="shape_param", param_attr=attr) attr = {"num_or_sections": param.out_shapes[0][0], "dim": 0} node.fluid_code.add_layer("split", inputs="shape_param", output=node, param_attr=attr) new_param = "[" for i in range(param.out_shapes[0][0]): new_param += (node.layer_name + "[{}]".format(i) + ", ") new_param = new_param.strip(", ") + "]" attr = {"shape": new_param} node.fluid_code.add_layer("reshape", inputs=input, output=node, param_attr=attr) def AvgPool(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) in_shape = input.out_shapes[0] if in_shape.count(-1) > 2: in_shape = self.decoder.infer_tensor(input).shape 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() channel_first = data_format == "NCHW" if not channel_first: attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) in_shape = [in_shape[i] for i in [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]] attr = { "pool_size": k_size[2:4], "pool_type": string("avg"), "pool_stride": strides[2:4] } if pad_mode == "SAME": pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) assert pad_h[0] == pad_h[1] and pad_w[0] == pad_w[ 1], "Cannot map AvgPool" attr["pool_padding"] = [pad_h[0], pad_w[0]] node.fluid_code.add_layer("pool2d", inputs=input if channel_first else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def SplitV(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) num_sections = self.graph.get_node(node.layer.input[1], copy=True) dim = self.graph.get_node(node.layer.input[2], copy=True) assert num_sections.layer_type == "Const" assert dim.layer_type == "Const" self.omit_nodes.append(num_sections.layer_name) self.omit_nodes.append(dim.layer_name) attr = { "num_or_sections": num_sections.value.tolist(), "dim": dim.value } node.fluid_code.add_layer("split", inputs=input, output=node, param_attr=attr) def ConcatV2(self, node): inputs = [ self.graph.get_node(name, copy=True) for name in node.layer.input[:-1] ] axis = self.graph.get_node(node.layer.input[-1], copy=True) assert axis.layer_type == "Const" self.omit_nodes.append(axis.layer_name) attr = {"axis": axis.value} node.fluid_code.add_layer("concat", inputs=inputs, output=node, param_attr=attr) def Tile(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) expand_times = self.graph.get_node(node.layer.input[1], copy=True) assert expand_times.layer_type == "Const" self.omit_nodes.append(expand_times.layer_name) attr = {"expand_times": expand_times.value.tolist()} node.fluid_code.add_layer("expand", inputs=input, output=node, param_attr=attr) def Pack(self, node): inputs = [ self.graph.get_node(name, copy=True) for name in node.layer.input ] attr = {"axis": node.get_attr("axis")} node.fluid_code.add_layer("stack", inputs=inputs, output=node, param_attr=attr) def Pad(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) paddings = self.graph.get_node(node.layer.input[1], copy=True) assert paddings.layer_type == "Const", "Padding should be Const" self.omit_nodes.append(paddings.layer_name) attr = {"paddings": paddings.value.tolist()} node.fluid_code.add_layer("pad", inputs=input, output=node, param_attr=attr) def Range(self, node): start = self.graph.get_node(node.layer.input[0], copy=True) limit = self.graph.get_node(node.layer.input[1], copy=True) delta = self.graph.get_node(node.layer.input[2], copy=True) if start.layer_type == "Const": self.omit_nodes.append(start.layer_name) start = start.value if limit.layer_type == "Const": self.omit_nodes.append(limit.layer_name) limit = limit.value if delta.layer_type == "Const": self.omit_nodes.append(delta.layer_name) delta = delta.value inputs = {"start": start, "end": limit, "step": delta} attr = {"dtype": string(node.dtype)} node.fluid_code.append("range", inputs=inputs, output=node, param_attr=None) def swish_f32(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) node.fluid_code.add_layer("sigmoid", inputs=input, output=node, param_attr=None) inputs = {"x": input, "y": node} node.fluid_code.add_layer("elementwise_mul", inputs=inputs, output=node, param_attr=None) def Mean(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) reduce_idx = self.graph.get_node(node.layer.input[1], copy=True) assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" keep_dims = node.get_attr("keep_dims") attr = {"dim": reduce_idx.value.tolist(), "keep_dim": keep_dims} node.fluid_code.add_layer("reduce_mean", inputs=input, output=node, param_attr=attr) def MatMul(self, node): x = self.graph.get_node(node.layer.input[0], copy=True) y = self.graph.get_node(node.layer.input[1], copy=True) transpose_a = node.get_attr('transpose_a') transpose_b = node.get_attr('transpose_b') inputs = {"x": x, "y": y} # fix paddle shape infer problem # should be removed after paddle 1.6 if x.out_shapes[0][-1] < 0 and y.out_shapes[0][0] > 0: shape = x.out_shapes[0] shape[-1] = y.out_shapes[0][0] attr = {"shape": shape} node.fluid_code.add_layer("reshape", inputs=x, output=x, param_attr=attr) attr = {"transpose_x": transpose_a, "transpose_y": transpose_b} node.fluid_code.add_layer("matmul", inputs=inputs, output=node, param_attr=attr) def ArgMax(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) axis = self.graph.get_node(node.layer.input[1], copy=True) assert axis.layer_type == "Const", "ArgMax only support Const parameter" self.omit_nodes.append(axis.layer_name) attr = {"axis": axis.value} node.fluid_code.add_layer("argmax", inputs=input, output=node, param_attr=attr) def StridedSlice(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) begin = self.graph.get_node(node.layer.input[1], copy=True) end = self.graph.get_node(node.layer.input[2], copy=True) strides = self.graph.get_node(node.layer.input[3], copy=True) assert begin.layer_type == "Const" assert end.layer_type == "Const" assert strides.layer_type == "Const" self.omit_nodes.append(begin.layer_name) self.omit_nodes.append(end.layer_name) self.omit_nodes.append(strides.layer_name) strides = strides.value.tolist() assert len(set(strides)) == 1 and strides[0] == 1 attr = { "axes": range(len(strides)), "starts": begin.value.tolist(), "ends": end.value.tolist() } node.fluid_code.add_layer("slice", inputs=input, output=node, param_attr=attr) def Slice(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) begin = self.graph.get_node(node.layer.input[1], copy=True) size = self.graph.get_node(node.layer.input[2], copy=True) # assert begin.layer_type == "Const" # assert size.layer_type == "Const" self.omit_nodes.append(begin.layer_name) self.omit_nodes.append(size.layer_name) if begin.layer_type == "Const": begin = begin.value.tolist() else: begin = self.decoder.infer_tensor(begin).tolist() if size.layer_type == "const": size = size.value.tolist() else: size = self.decoder.infer_tensor(size).tolist() attr = {"shape": size, "offsets": begin} node.fluid_code.add_layer("crop", inputs=input, output=node, param_attr=attr) def Conv2DBackpropInput(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) kernel = self.graph.get_node(node.layer.input[1], copy=True) assert kernel.layer_type == "Const", "Kernel of Conv2DBackpropInput should be Const" self.omit_nodes.append(kernel.layer_name) node.fluid_code.add_note("#{} : {}".format(node.layer.name, node.layer_name)) 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 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() channel_first = data_format == "NCHW" if not channel_first: self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (3, 2, 0, 1)) attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer("transpose", inputs=input, output=node, param_attr=attr) 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]] if pad_mode == "SAME": pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} if pad_h[0] + pad_h[1] + pad_w[0] + pad_w[1] != 0: node.fluid_code.add_layer( "pad2d", inputs=input if channel_first else node, output=node, param_attr=attr) attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), "num_filters": k_size[3], "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4] } node.fluid_code.add_layer( "conv2d_transpose", inputs=input if channel_first and pad_mode != "SAME" else node, output=node, param_attr=attr) if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", inputs=node, output=node, param_attr=attr) def Max(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) reduce_idx = self.graph.get_node(node.layer.input[1], copy=True) assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" keep_dims = node.get_attr("keep_dims") attr = {"dim": reduce_idx.value.tolist(), "keep_dim": keep_dims} node.fluid_code.add_layer("reduce_max", inputs=input, output=node, param_attr=attr) def Sum(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) reduce_idx = self.graph.get_node(node.layer.input[1], copy=True) assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]" keep_dims = node.get_attr("keep_dims") attr = {"dim": reduce_idx.value.tolist(), "keep_dim": keep_dims} node.fluid_code.add_layer("reduce_sum", inputs=input, output=node, param_attr=attr) def Cast(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) dtype = node.dtype_map[node.get_attr('DstT')] attr = {"dtype": string(dtype)} node.fluid_code.add_layer("cast", inputs=input, output=node, param_attr=attr) def FloorDiv(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 = {'x': x, 'y': y} node.fluid_code.add_layer("elementwise_div", inputs=inputs, output=node, param_attr=None) node.fluid_code.add_layer("floor", inputs=node, output=node, param_attr=None) def Split(self, node): dim = self.graph.get_node(node.layer.input[0], copy=True) input = self.graph.get_node(node.layer.input[1], copy=True) assert dim.layer_type == "Const" self.omit_nodes.append(dim.layer_name) num_split = node.get_attr('num_split') attr = {"num_or_sections": num_split, "dim": dim.value} node.fluid_code.add_layer("split", inputs=input, output=node, param_attr=attr)