# 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. import numbers import copy import numpy as np from x2paddle.decoder.caffe_decoder import CaffeGraph, CaffeGraphNode from x2paddle.core.op_mapper import OpMapper from x2paddle.core.util import * from x2paddle.core.program import PaddleGraph def _adjust_parameters(node): data = node.data # When using the protobuf-backend, each parameter initially has four dimensions. # In certain cases (like FC layers), we want to eliminate the singleton dimensions. # This implementation takes care of the common cases. However, it does leave the # potential for future issues. # The Caffe-backend does not suffer from this problem. data = list(data) squeeze_indices = [1] # Squeeze biases. if node.layer_type == 'InnerProduct': squeeze_indices.append(0) # Squeeze FC. for idx in squeeze_indices: if idx >= len(data): continue d = data[idx] assert len( d.shape ) == 4, 'invalid shape[%s] from caffe when adjust_parameters' % ( str(d.shape)) shape_old = d.shape sq_axis = None if idx == 0: sq_axis = (0, 1) elif idx == 1: sq_axis = (0, 1, 2) else: continue data[idx] = np.squeeze(d, axis=sq_axis) shape_new = data[idx].shape return data def _get_kernel_parameters(kind, params): assert kind in ["Convolution", "Pooling", "Deconvolution", "ConvolutionDepthwise"] [k_h, k_w] = [1, 1] if isinstance(params.kernel_size, numbers.Number): [k_h, k_w] = [params.kernel_size] * 2 elif len(params.kernel_size) > 0: k_h = params.kernel_h if params.kernel_h > 0 else params.kernel_size[ 0] k_w = params.kernel_w if params.kernel_w > 0 else params.kernel_size[ len(params.kernel_size) - 1] elif params.kernel_h > 0 or params.kernel_w > 0: k_h = params.kernel_h k_w = params.kernel_w [s_h, s_w] = [1, 1] if isinstance(params.stride, numbers.Number): [s_h, s_w] = [params.stride] * 2 elif len(params.stride) > 0: s_h = params.stride_h if params.stride_h > 0 else params.stride[0] s_w = params.stride_w if params.stride_w > 0 else params.stride[len( params.stride) - 1] elif params.stride_h > 0 or params.stride_w > 0: s_h = params.stride_h s_w = params.stride_w [p_h, p_w] = [0, 0] if isinstance(params.pad, numbers.Number): [p_h, p_w] = [params.pad] * 2 elif len(params.pad) > 0: p_h = params.pad_h if params.pad_h > 0 else params.pad[0] p_w = params.pad_w if params.pad_w > 0 else params.pad[len( params.pad) - 1] elif params.pad_h > 0 or params.pad_w > 0: p_h = params.pad_h p_w = params.pad_w dila_h = dila_w = 1 group = 1 c_o = 1 if kind in ["Convolution", "Deconvolution", "ConvolutionDepthwise"]: if kind in ["Convolution", "Deconvolution"]: c_o = params.num_output dila_len = len(params.dilation) if dila_len == 2: dila_h = params.dilation[0] dila_w = params.dilation[1] elif dila_len == 1: dila_h = dila_w = params.dilation[0] else: assert dila_len == 0, "invalid length[%s] of dilation in convolution" % ( dila_len) if kind in ['Convolution', 'Deconvolution']: group = params.group kernel = [k_h, k_w] stride = [s_h, s_w] pad = [p_h, p_w] dilation = [dila_h, dila_w] return c_o, kernel, stride, pad, dilation, group class CaffeOpMapper(OpMapper): directly_map_ops = { 'AbsVal': 'paddle.abs', 'Sigmoid': 'paddle.nn.functional.sigmoid', 'TanH': 'paddle.tanh', } def __init__(self, decoder): super(CaffeOpMapper, self).__init__() self.graph = decoder.caffe_graph self.params = dict() resolver = decoder.resolver self.used_custom_layers = {} self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="static", source_type="caffe") self.paddle_graph.inputs = self.graph.input_nodes self.paddle_graph.outputs = self.graph.output_nodes print("Total nodes: {}".format( sum([ isinstance(node, CaffeGraphNode) for name, node in self.graph.node_map.items() ]))) print("Nodes converting ...") for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) op = node.layer_type if hasattr(self, op): func = getattr(self, op) func(node) elif op in self.directly_map_ops: self.directly_map(node) print("\nNodes converted.") self.paddle_graph.set_parameters(self.params) self.paddle_graph.set_custom(self.used_custom_layers) def op_checker(self): unsupported_ops = set() for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) op = node.layer_type if not hasattr(self, op) and \ op not in self.directly_map_ops and \ op not in self.elementwise_ops: unsupported_ops.add(op) if len(unsupported_ops) == 0: return True else: if len(unsupported_ops) > 0: print("\n========= {} OPs are not supported yet ===========".format( len(unsupported_ops))) for op in unsupported_ops: print("========== {} ============".format(op)) return False 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_input_node(node, idx=0, copy=True) self.paddle_graph.add_layer( kernel=op_info, inputs={"x": input.name}, outputs=[node.name]) def Input(self, node): shape = list(node.layer.input_param.shape[0].dim)[1:] dtype = 'float32' layer_attrs = { "dtype": string(dtype), "shape": [-1] + shape, "name": string(node.name) } self.paddle_graph.add_layer( kernel="paddle.static.data", inputs={}, outputs=[node.name], **layer_attrs) def Convolution(self, node): data = node.data params = node.layer.convolution_param channel, kernel, stride, pad, dilation, group = _get_kernel_parameters( node.layer_type, params) if data is None: data = [] print( "The parameter of {} (type is {}) is not set. So we set the parameters as 0" .format(node.name, node.layer_type)) input_c = node.in_shapes[0][1] output_c = channel data.append( np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype( 'float32')) data.append(np.zeros([output_c, ]).astype('float32')) else: data = _adjust_parameters(node) kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, dtype=string(str(self.params[kernel_weight_name].dtype)), name=string(kernel_weight_name)) if len(data) == 2: kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, dtype=string(str(self.params[kernel_bias_name].dtype)), name=string(kernel_bias_name)) assert len(node.inputs ) == 1, 'The count of Convolution node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) layer_inputs = {"x": input.name, "weight": kernel_weight_name} layer_attrs = {'stride': stride, 'padding': pad, 'dilation': dilation, 'groups': group} if len(data) == 2: layer_inputs["bias"] = kernel_bias_name else: layer_attrs["bias"] = None self.paddle_graph.add_layer( kernel="paddle.nn.functional.conv2d", inputs=layer_inputs, outputs=[node.name], **layer_attrs) def Deconvolution(self, node): data = node.data params = node.layer.convolution_param channel, kernel, stride, pad, dilation, group = _get_kernel_parameters( node.layer_type, params) if data is None: data = [] print( 'The parameter of {} (type is {}) is not set. So we set the parameters as 0' .format(node.name, node.layer_type)) input_c = node.in_shapes[0][1] output_c = channel data.append( np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype( 'float32')) data.append(np.zeros([output_c, ]).astype('float32')) else: data = _adjust_parameters(node) kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, dtype=string(str(self.params[kernel_weight_name].dtype)), name=string(kernel_weight_name)) if len(data) == 2: kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, dtype=string(str(self.params[kernel_bias_name].dtype)), name=string(kernel_bias_name)) assert len(node.inputs ) == 1, 'The count of Deconvolution node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) layer_inputs = {"x": input.name, "weight": kernel_weight_name} layer_attrs = {'stride': stride, 'padding': pad, 'dilation': dilation, 'groups': group} if len(data) == 2: layer_inputs["bias"] = kernel_bias_name else: layer_attrs["bias"] = None self.paddle_graph.add_layer( kernel="paddle.nn.functional.conv2d_transpose", inputs=layer_inputs, outputs=[node.name], **layer_attrs) def DepthwiseConvolution(self, node): node.layer_type = "ConvolutionDepthwise" self.ConvolutionDepthwise(node) def ConvolutionDepthwise(self, node): data = node.data params = node.layer.convolution_param out_channel, kernel, stride, pad, dilation, group = _get_kernel_parameters( node.layer_type, params) out_channel = params.num_output if params.num_output is not None else node.in_shapes[0][1] in_channel = node.in_shapes[0][1] group = int(in_channel / (in_channel / out_channel)) if in_channel > out_channel else int(in_channel / (out_channel / in_channel)) if data is None: data = [] print( "The parameter of {} (type is {}) is not set. So we set the parameters as 0" .format(node.layer_name, node.layer_type)) data.append( np.zeros([out_channel, node.in_shapes[0][1], kernel[0], kernel[1]]).astype( 'float32')) data.append(np.zeros([out_channel, ]).astype('float32')) else: data = _adjust_parameters(node) kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, dtype=string(str(self.params[kernel_weight_name].dtype)), name=string(kernel_weight_name)) if len(data) == 2: kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, dtype=string(str(self.params[kernel_bias_name].dtype)), name=string(kernel_bias_name)) assert len(node.inputs ) == 1, "The count of Deconvolution node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) layer_inputs = {"x": input.name, "weight": kernel_weight_name} layer_attrs = {'stride': stride, 'padding': pad, 'dilation': dilation, 'groups': group} if len(data) == 2: layer_inputs["bias"] = kernel_bias_name else: layer_attrs["bias"] = None self.paddle_graph.add_layer( kernel="paddle.nn.functional.conv2d", inputs=layer_inputs, outputs=[node.name], **layer_attrs) def Pooling(self, node): params = node.layer.pooling_param ceil_mode = getattr(params, 'ceil_mode', True) global_pool = getattr(params, 'global_pooling', False) kernel_default = [1, 1] channel, kernel, stride, pad, dilation, group = _get_kernel_parameters( node.layer_type, params) assert len( node.inputs) == 1, 'The count of Pooling node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) if global_pool: if kernel[0] == 0: kernel = [1, 1] if params.pool == 0: self.paddle_graph.add_layer( "paddle.nn.functional.adaptive_max_pool2d", inputs={"x": input.name}, outputs=layer_outputs, output_size=kernel) else: self.paddle_graph.add_layer( "paddle.nn.functional.adaptive_avg_pool2d", inputs={"x": input.name}, outputs=[node.name], output_size=kernel) else: if params.pool == 0: self.paddle_graph.add_layer( kernel="paddle.nn.functional.max_pool2d", inputs={"x": input.name}, outputs=[node.name], kernel_size=kernel, stride=stride, padding=pad, ceil_mode=ceil_mode) else: # TODO(syf): The op has diff. self.paddle_graph.add_layer( kernel="fluid.layers.pool2d", inputs={"input": input.name}, outputs=[node.name], pool_size=kernel, pool_type=string("avg"), pool_stride=stride, pool_padding=pad, ceil_mode=ceil_mode, exclusive=False, global_pooling=False) def LRN(self, node): assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.' params = node.layer.lrn_param # The window size must be an odd value. For a window # size of (2*n+1), Paddle defines depth_radius = n. assert params.local_size % 2 == 1 # Caffe scales by (alpha/(2*n+1)), whereas Paddle # just scales by alpha (as does Krizhevsky's paper). # We'll account for that here. alpha = params.alpha / float(params.local_size) input = self.graph.get_input_node(node, idx=0, copy=True) layer_attrs = { 'n': params.local_size, 'k': params.k, 'alpha': alpha, 'beta': params.beta, 'name': string(node.name) } self.paddle_graph.add_layer( kernel="fluid.layers.lrn", inputs={"input": input.name}, outputs=[node.name], **layer_attrs) def InnerProduct(self, node): data = node.data params = node.layer.inner_product_param if data is None: print( 'The parameter of {} (type is {}) is not set. So we set the parameters as 0.' .format(node.layer_name, node.layer_type)) input_c = node.in_shapes[0][1] output_c = params.num_output data = [] data.append( np.zeros([input_c, output_c]).astype('float32').astype( 'float32')) data.append( np.zeros([output_c]).astype('float32').astype('float32')) else: data = _adjust_parameters(node) # Reshape the parameters to Paddle's ordering transpose_order = (1, 0) w = data[0] fc_shape = w.shape output_channels = fc_shape[0] w = w.reshape((output_channels, -1)) w = w.transpose(transpose_order) data[0] = w kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, dtype=string(str(self.params[kernel_weight_name].dtype)), name=string(kernel_weight_name)) if len(data) == 2: kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, dtype=string(str(self.params[kernel_bias_name].dtype)), name=string(kernel_bias_name)) assert len(node.inputs ) == 1, 'The count of InnerProduct node\'s input is not 1.' #params = node.layer.inner_product_param assert params.axis == 1 assert params.bias_term == True input = self.graph.get_input_node(node, idx=0, copy=True) layer_inputs = {"x": input.name, "weight": kernel_weight_name} layer_attrs = dict() if len(data) == 2: layer_inputs["bias"] = kernel_bias_name else: layer_attrs["bias"] = None if node.in_shapes[0][-1] != data[0].shape[0]: self.paddle_graph.add_layer( "paddle.reshape", inputs={"x": input.name}, outputs=[input.name], shape=[-1, data[0].shape[0]]) self.paddle_graph.add_layer( kernel="paddle.nn.functional.linear", inputs=layer_inputs, outputs=[node.name], **layer_attrs) else: self.paddle_graph.add_layer( kernel="paddle.nn.functional.linear", inputs=layer_inputs, outputs=[node.name], **layer_attrs) def Softmax(self, node): assert len( node.inputs) == 1, 'The count of Softmax node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.softmax_param axis = params.axis shape = node.in_shapes[0] dims = len(shape) axis = axis + dims if axis < 0 else axis layer_attrs = {'axis': axis, 'name': string(node.layer_name + '_softmax')} self.paddle_graph.add_layer( kernel="paddle.nn.functional.softmax", inputs={"x": input.name}, outputs=[node.layer_name], **layer_attrs) def Slice(self, node): assert len( node.inputs) == 1, "The count of Slice node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) top_len = len(node.layer.top) params = node.layer.slice_param axis = params.axis slice_dim = params.slice_dim if slice_dim != 1 and axis == 1: axis = slice_dim output_shape = node.out_shapes sections_list = list() outputs_list = list() for i, s in enumerate(output_shape): sections_list.append(s[axis]) outputs_list.append("{}_p{}".format(node.layer_name, i)) layer_attrs = { 'num_or_sections': sections_list, 'axis': axis, } self.paddle_graph.add_layer( "paddle.split", inputs={"x": input.name}, outputs=outputs_list, **layer_attrs) def Concat(self, node): assert len( node.inputs ) >= 1, 'The count of Concat node\'s input is not more than 1.' inputs_list = [] for i in range(len(node.inputs)): input = self.graph.get_input_node(node, idx=i, copy=True) inputs_list.append(input.name) params = node.layer.concat_param axis = params.axis layer_attrs = {'axis': axis, 'name': string(node.name)} self.paddle_graph.add_layer( kernel="paddle.concat", inputs={"x": inputs_list}, outputs=[node.name], **layer_attrs) def ReLU(self, node): """ :param node: :return: """ assert len( node.inputs) == 1, 'The count of ReLU node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.relu_param if params.HasField('negative_slope') and params.negative_slope != 0: negative_slope = float(params.negative_slope) self.paddle_graph.add_layer( kernel="paddle.nn.functional.leaky_relu", inputs={"x": input.name}, outputs=[node.name], negative_slope=negative_slope) else: self.paddle_graph.add_layer( kernel="paddle.nn.functional.relu", inputs={"x": input.name}, outputs=[node.name]) def PReLU(self, node): assert len( node.inputs) == 1, 'The count of PReLU node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.prelu_param mode_bool = params.channel_shared output_shape = node.out_shapes[0] if mode_bool: num_parameters = 1 else: num_parameters = output_shape[1] data = node.data assert data is not None, 'The parameter of {} (type is {}) is not set. You need to use python package of caffe to set the default value.'.format( node.name, node.layer_type) kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = np.squeeze(data[0]) self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=[num_parameters], dtype=string(str(self.params[kernel_weight_name].dtype)), name=string(kernel_weight_name)) self.paddle_graph.add_layer( kernel="paddle.nn.functional.prelu", inputs={"x": input.name, "weight": kernel_weight_name}, outputs=[node.name]) def Eltwise(self, node): assert len( node.inputs) == 2, "The count of Eltwise node\'s input is not 2." params = node.layer.eltwise_param mode = params.operation inputs = [] input0 = self.graph.get_input_node(node, idx=0, copy=True) input1 = self.graph.get_input_node(node, idx=1, copy=True) input0_name = input0.name input1_name = input1.name if mode == 0: inputs_dict = {} inputs_dict['x'] = input0_name inputs_dict['y'] = input1_name self.paddle_graph.add_layer( "paddle.multiply", inputs=inputs_dict, outputs=[node.name]) elif mode == 1: if hasattr(params, 'coeff') and len(params.coeff) == 2: coeff = params.coeff self.paddle_graph.add_layer( "paddle.scale", inputs={"x": input0_name}, outputs=[node.name + '_mul0'], scale=coeff[0]) self.paddle_graph.add_layer( "paddle.scale", inputs={"x": input1_name}, outputs=[node.name + '_mul1'], scale=coeff[2]) inputs_dict = {} inputs_dict['x'] = node.name + '_mul0' inputs_dict['y'] = node.name + '_mul1' self.paddle_graph.add_layer( "paddle.add", inputs=inputs_dict, outputs=[node.name]) else: inputs_dict = {} inputs_dict['x'] = input0_name inputs_dict['y'] = input1_name self.paddle_graph.add_layer( "paddle.add", inputs=inputs_dict, outputs=[node.name]) else: inputs_dict = {} inputs_dict['x'] = input0_name inputs_dict['y'] = input1_name self.paddle_graph.add_layer( "paddle.max", inputs=inputs_dict, outputs=[node.name]) def BatchNorm(self, node): assert len( node.inputs) == 1, 'The count of BatchNorm node\'s input is not 1.' input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.batch_norm_param if hasattr(params, 'eps'): eps = params.eps else: eps = 1e-5 if hasattr(params, 'moving_average_fraction'): momentum = params.moving_average_fraction else: momentum = 0.9 if node.data is None or len(node.data) != 3: print( 'The parameter of {} (type is {}) is not set. So we set the parameters as 0' .format(node.layer_name, node.layer_type)) input_c = node.in_shapes[0][1] mean = np.zeros([input_c, ]).astype('float32') variance = np.zeros([input_c, ]).astype('float32') scale = 0 else: node.data = [np.squeeze(i).astype('float32') for i in node.data] mean, variance, scale = node.data # Prescale the stats scaling_factor = 1.0 / scale if scale != 0 else 0 mean *= scaling_factor variance *= scaling_factor weight_name = node.name + '_weight' self.paddle_graph.add_layer( kernel="paddle.ones", inputs={}, outputs=[weight_name], shape=mean.shape, dtype=string("float32")) bias_name = node.name + '_bias' self.paddle_graph.add_layer( kernel="paddle.zeros", inputs={}, outputs=[bias_name], shape=mean.shape, dtype=string("float32")) mean_name = node.name + '_mean' self.params[mean_name] = mean self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[mean_name], shape=self.params[mean_name].shape, dtype=string(str(self.params[mean_name].dtype)), name=string(mean_name)) variance_name = node.name + '_variance' self.params[variance_name] = variance self.paddle_graph.add_layer( kernel="paddle.static.nn.create_parameter", inputs={}, outputs=[variance_name], shape=self.params[variance_name].shape, dtype=string(str(self.params[variance_name].dtype)), name=string(variance_name)) layer_attrs = { 'epsilon': eps, 'momentum': momentum } self.paddle_graph.add_layer( kernel="paddle.nn.functional.batch_norm", inputs={"x": input.name, "weight": weight_name, "bias": bias_name, "running_mean": mean_name, "running_var": variance_name,}, outputs=[node.name], **layer_attrs) def Scale(self, node): if node.data is None: print( "The parameter of {} (type is {}) is not set. So we set the parameters as 0" .format(node.name, node.layer_type)) self.params[node.name + "_cparam1"] = np.zeros([ node.in_shapes[0][1], ]).astype("float32") self.params[node.name + "_cparam2"] = np.zeros([ node.in_shapes[0][1], ]).astype("float32") else: self.params[node.name + "_cparam1"] = np.squeeze(node.data[ 0]).astype("float32") self.params[node.name + "_cparam2"] = np.squeeze(node.data[ 1]).astype("float32") params = node.layer.scale_param axis = params.axis inputs = [] if len(node.inputs) == 2: input0 = self.graph.get_input_node(node, idx=0, copy=True) input1 = self.graph.get_input_node(node, idx=1, copy=True) input0_name = input0.name input1_name = input1.name inputs_dict = {} inputs_dict['x'] = input0_name inputs_dict['y'] = input1_name self.paddle_graph.add_layer( "paddle.multiply", inputs=inputs_dict, outputs=[node.name + "_mul"], axis=1) else: self.paddle_graph.add_layer( "paddle.static.nn.create_parameter", inputs={}, outputs=[node.name + "_cparam1"], shape=self.params[node.name + "_cparam1"].shape, dtype=string(str(self.params[node.name + "_cparam1"].dtype)), name=string(node.name + "_cparam1")) input0 = self.graph.get_input_node(node, idx=0, copy=True) input0_name = input0.name inputs_dict = {} inputs_dict['x'] = input0_name inputs_dict['y'] = node.name + "_cparam1" self.paddle_graph.add_layer( "paddle.multiply", inputs=inputs_dict, outputs=[node.name + "_mul"], axis=axis) self.paddle_graph.add_layer( "paddle.static.nn.create_parameter", inputs={}, outputs=[node.name + "_cparam2"], shape=self.params[node.name + "_cparam2"].shape, dtype=string(str(self.params[node.name + "_cparam2"].dtype)), name=string(node.name + "_cparam2")) inputs_dict = {} inputs_dict['x'] = node.name + "_mul" inputs_dict['y'] = node.name + "_cparam2" output_shape = node.out_shapes[0] if axis == -1: self.paddle_graph.add_layer( "paddle.add", inputs=inputs_dict, outputs=[node.name]) else: if axis < 0: axis = axis + len(output_shape) param2_shape = self.params[node.name + "_cparam2"].shape param2_shape_len = len(param2_shape) diff_len = len(output_shape) - axis - param2_shape_len new_shape = list(param2_shape) + [1] * diff_len self.paddle_graph.add_layer( "paddle.reshape", inputs={"x": node.name + "_cparam2"}, outputs=[node.name + "_cparam2"], shape=new_shape) self.paddle_graph.add_layer( "paddle.add", inputs=inputs_dict, outputs=[node.name]) def Reshape(self, node): input = self.graph.get_input_node(node, idx=0, copy=True) output_shape = node.out_shapes[0] self.paddle_graph.add_layer( "paddle.reshape", inputs={"x": input.name}, outputs=[node.name], shape=output_shape) def ArgMax(self, node): assert len(node.inputs) == 1 and len( node.outputs ) == 1, "The count of ArgMax node\'s input and output is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) in_shapes = node.in_shapes[0] params = node.layer.argmax_param out_max_val = params.out_max_val if hasattr(params, out_max_val) else False top_k = params.top_k if hasattr(params, top_k) else 1 axis = parmas.axis if hasattr(params, axis) else -1 if axis < 0: axis += len(in_shapes) if out_max_val is True: self.paddle_graph.add_layer( "paddle.topk", inputs={"x": input.name}, outputs=[node.name + "_topk_var", node.name + "_index_var"], k=top_k) self.paddle_graph.add_layer( "paddle.cast", inputs={"x": node.name + "_index_var"}, outputs=[node.name + "_index_var"], dtype="{}_topk_var.dtype".format(node.name)) self.paddle_graph.add_layer( "paddle.concat", inputs={"x": [node.name + "_topk_var", node.name + "_index_var"]}, outputs=[node.name], axis=axis) else: self.paddle_graph.add_layer( "paddle.topk", inputs={"x": input.name}, outputs=["_", node.name], k=top_k) def Crop(self, node): assert len( node.inputs) == 2, "The count of Crop node\'s input is not 2." input = self.graph.get_input_node(node, idx=0, copy=True) example = self.graph.get_input_node(node, idx=1, copy=True) params = node.layer.crop_param axis = params.axis in_shapes = node.in_shapes[0] if axis < 0: axis += len(in_shapes) offset_real = [0] * len(in_shapes) if hasattr(params, "offset") and len(params.offset) > 0: offset = list(params.offset) assert (len(in_shapes) - axis ) == len(offset), "invalid offset[%s] in crop layer" % ( str(offset)) offset_real = [0] * axis + offset self.paddle_graph.add_layer( "paddle.crop", inputs={"x": input.name}, outputs=[node.name], shape=node.in_shapes[1], offsets=list(offset_real)) def Flatten(self, node): assert len( node. inputs) == 1, "The count of DetectionOutput node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) self.paddle_graph.add_layer( "paddle.reshape", inputs={"x": input.name}, outputs=[node.name], shape=node.out_shapes[0]) def Power(self, node): assert len( node.inputs) == 1, "The count of Permute node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.power_param layer_attrs = { 'scale': params.scale, 'bias': params.shift, 'bias_after_scale': True } self.paddle_graph.add_layer( "paddle.scale", inputs={"x": input.name}, outputs=[node.name], **layer_attrs) self.paddle_graph.add_layer( "paddle.pow", inputs={"x": node.name}, outputs=[node.name], exponent=params.power) def Reduction(self, node): assert len( node.inputs) == 1, "The count of Reduction node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.reduction_param operation = params.operation axis = params.axis coeff = params.coeff assert operation >= 1 and operation <= 4, "reduction reduction [%s] error" % ( operation) input_len = len(node.in_shapes[0]) if axis < 0: axis += input_len + 1 dim = list(range(input_len)) # operation = SUM if operation == 1: layer_attrs = { "dim": dim[axis:], "keep_dim": False, } self.paddle_graph.add_layer( "paddle.sum", inputs={"input": input.name}, outputs=[node.name], **layer_attrs) # operation = ASUM elif operation == 2: self.paddle_graph.add_layer( "paddle.abs", inputs={"x": input.name}, outputs=[node.name]) layer_attrs = { "dim": dim[axis:], "keep_dim": False, } self.paddle_graph.add_layer( "paddle.sum", inputs={"input": node.name}, outputs=[node.name], **layer_attrs) # operation = SUMSQ elif operation == 3: self.paddle_graph.add_layer( "paddle.pow", inputs={"x": input.name}, outputs=[node.name], exponent=2.0) layer_attrs = { "dim": dim[axis:], "keep_dim": False, } self.paddle_graph.add_layer( "paddle.sum", inputs={"input": node.name}, outputs=[node.name], **layer_attrs) # operation = MEAN else: layer_attrs = { "dim": dim[axis:], "keep_dim": False, } self.paddle_graph.add_layer( "paddle.mean", inputs={"input": input.name}, outputs=[node.name], **layer_attrs) self.paddle_graph.add_layer( "paddle.scale", inputs={"x": node.name}, outputs=[node.name], scale=coeff) def Axpy(self, node): assert len(node.inputs) == 1 and len( node.outputs ) == 1, "The count of Axpy node\'s input and output is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.axpy_param input0 = self.graph.get_input_node(node, idx=0, copy=True) input1 = self.graph.get_input_node(node, idx=1, copy=True) input2 = self.graph.get_input_node(node, idx=2, copy=True) input0_name = input0.name input1_name = input1.name input2_name = input2.name inputs_dict = {} inputs_dict['x'] = input1_name inputs_dict['y'] = input0_name self.paddle_graph.add_layer( "paddle.multiply", inputs=inputs_dict, outputs=[node.name + "_mul"], axis=0) inputs_dict = {} inputs_dict['x'] = node.name + "_mul" inputs_dict['y'] = input2_name self.paddle_graph.add_layer( "paddle.add", inputs=inputs_dict, outputs=[node.name + "_mul"]) def DetectionOutput(self, node): assert len( node.inputs) == 3, "The count of DetectionOutput node\'s input is not 3." inputs_dict = dict() for i in range(len(node.inputs)): input = self.graph.get_input_node(node, idx=i, copy=True) if i == 1: input = self.graph.get_input_node(node, idx=i, copy=True) while input is not None \ and input.layer_type != 'Softmax' \ and input.layer_type != 'Sigmoid': input = self.graph.get_input_node(input, idx=0, copy=True) assert input is not None, 'This kind of DetectionOutput is not supported!' input = self.graph.get_input_node(input, idx=0, copy=True) inputs_dict["x{}".format(i)] = input.name params = node.layer.detection_output_param nms_param = params.nms_param nms_param_dict = dict() nms_param_dict["nms_threshold"] = nms_param.nms_threshold nms_param_dict["top_k"] = nms_param.top_k nms_param_dict["eta"] = nms_param.eta if nms_param is None: nms_param_dict = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0} default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0} fields = ["eta", "top_k", "nms_threshold"] for f in default.keys(): if f not in nms_param_dict: nms_param_dict[f] = default[f] layer_attrs = { "background_label": params.background_label_id, "nms_threshold": nms_param_dict["nms_threshold"], "nms_top_k": nms_param_dict["top_k"], "keep_top_k": params.keep_top_k, "score_threshold": params.confidence_threshold, "nms_eta": nms_param_dict["eta"]} self.paddle_graph.add_layer( kernel="custom_layer:detectionoutput", inputs=inputs_dict, outputs=[node.name], **layer_attrs) def Normalize(self, node): assert len( node.inputs) == 1, "The count of Normalize node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.norm_param scale_name = node.name + "_scale" if node.data is None or len(node.data) != 1: print( "The parameter of {} (type is {}) is not set. So we set the parameters as 0" .format(scale_name, node.layer_type)) self.parmas[scale_name] = \ np.zeros([1] if params.channel_shared else [1, 1, 1, node.in_shapes[0][1]]).astype("float32") else: self.parmas[scale_name] = _adjust_parameters(node)[0] layer_attrs = { "axis": -1 if params.channel_shared else 1, "param_name": scale_name, "param_shape": self.parmas[scale_name].shape, "param_dtype": str(self.parmas[scale_name].dtype)} self.pd_pdgraph.add_layer( "custom_layer:normalize", inputs={"x": input.name}, outputs=[node.name], **layer_attrs) def Permute(self, node): assert len( node.inputs) == 1, "The count of Permute node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.permute_param order = list(params.order) self.paddle_graph.add_layer( "paddle.transpose", inputs={"x": input.name}, outputs=[node.name], perm=order) def PriorBox(self, node): assert len( node.inputs) == 2, "The count of PriorBox node\'s input is not 2." input0 = self.graph.get_input_node(node, idx=0, copy=True) input1 = self.graph.get_input_node(node, idx=1, copy=True) inputs_dict = {} inputs_dict["x0"] = input0.name inputs_dict["x1"] = input1.name params = node.layer.prior_box_param steps = tuple(params.step) if type(params.step) \ is list or type(params.step) is tuple \ else (params.step, params.step) layer_attrs = { "min_sizes": params.min_size, "max_sizes": params.max_size, "aspect_ratios": params.aspect_ratio, "variance": params.variance, "flip": params.flip, "clip": params.clip, "steps": steps, "offset": params.offset, "min_max_aspect_ratios_order": True} self.paddle_graph.add_layer( "custom_layer:priorbox", inputs=inputs_dict, outputs=[node.name], **layer_attrs) def ReLU6(self, node): assert len( node.inputs) == 1, "The count of RelU6 node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) self.paddle_graph.add_layer( "paddle.nn.functional.relu6", inputs={"x": input.name}, outputs=[node.name]) def ROIPooling(self, node): assert len( node.inputs) == 2, "The count of ROIPooling node\'s input is not 2." input0 = self.graph.get_input_node(node, idx=0, copy=True) input1 = self.graph.get_input_node(node, idx=1, copy=True) inputs_dict = {} inputs_dict["x0"] = input0.name inputs_dict["x1"] = input1.name params = node.layer.roi_pooling_param layer_attrs = { "pooled_height": params.pooled_h, "pooled_width": params.pooled_w, "spatial_scale": params.spatial_scale} self.paddle_graph.add_layer( "custom_layer:ROIPooling", inputs=inputs_dict, outputs=[node.name], **layer_attrs) def ShuffleChannel(self, node): assert len( node.inputs) == 1, "The count of ShuffleChannel node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.shuffle_channel_param self.paddle_graph.add_layer( "fluid.layers.shuffle_channel", inputs={"x": input.name}, outputs=[node.layer_name], group=params.group) def Upsample(self, node): assert len( node.inputs) == 1, "The count of Upsample node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) params = node.layer.upsample_param layer_attrs = { "align_corners": False, "scale_factor": params.scale, "mode": "nearest"} self.paddle_graph.add_layer( "paddle.nn.functioanl.interpolate", inputs={"input": input.name}, outputs=[node.layer_name], **layer_attrs) def Select(self, node): assert len( node.inputs) == 1, "The count of Select node\'s input is not 1." input = self.graph.get_input_node(node, idx=0, copy=True) in_shapes = node.in_shapes[0] params = node.layer.select_param layer_attrs = { "in_shapes": in_shapes, "point": params.slice_point, "axis": params.axis} self.paddle_graph.add_layer( "custom_layer:select", inputs={"x": input.name}, outputs=[node.name], **layer_attrs)