# 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.core.graph import GraphNode from x2paddle.core.op_mapper import OpMapper from x2paddle.core.fluid_code import Layer from x2paddle.core.fluid_code import FluidCode from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode from x2paddle.op_mapper.onnx_directly_map import default_op_mapping_field_values from x2paddle.op_mapper.onnx_directly_map import default_op_mapping from x2paddle.op_mapper.onnx_directly_map import default_ioa_constraint from x2paddle.op_mapper.onnx_custom_layer import * from x2paddle.core.util import string import numpy as np import onnx import onnx.numpy_helper as numpy_helper from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE import logging as _logging from collections import OrderedDict as _dict import math import os import shutil _logger = _logging.getLogger(__name__) def _const_weight_or_none(node): if 'Constant' in node.layer_type: return node.value if isinstance(node, ONNXGraphDataNode): return node.weight return None 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 pad0 = int(pad_size / 2) pad1 = pad_size - pad0 return [pad0, pad1] class ONNXOpMapper(OpMapper): elementwise_ops = { 'Add': 'elementwise_add', 'Div': 'elementwise_div', 'Sub': 'elementwise_sub', 'Mul': 'elementwise_mul', 'Pow': 'elementwise_pow',} def __init__(self, decoder, save_dir): super(ONNXOpMapper, self).__init__() self.decoder = decoder self.graph = decoder.onnx_graph self.input_shapes = [] self.weights = dict() self.omit_nodes = list() self.used_custom_layers = dict() self.is_inference = False self.tmp_data_dir = os.path.join(save_dir, 'tmp_data') self.get_output_shapes() if not self.op_checker(): raise Exception("Model are not supported yet.") #mapping op print("Total nodes: {}".format( sum([ isinstance(node, ONNXGraphNode) for name, node in self.graph.node_map.items() ]))) 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 default_op_mapping: self.directly_map(node) elif op in custom_layers: self.deal_custom_layer(node) elif op in self.elementwise_ops: self.elementwise_map(node) self.remove_tmp_data() 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 default_op_mapping and \ op not in custom_layers and \ op not in self.elementwise_ops: unsupported_ops.add(op) if len(unsupported_ops) == 0: return True else: print("There are {} ops not supported yet, list as below".format( len(unsupported_ops))) for op in unsupported_ops: print(op) return False def get_results_of_inference(self, model, value_infos, data_nodes): if not os.path.exists(self.tmp_data_dir): os.makedirs(self.tmp_data_dir) for data_node in data_nodes: value_info = value_infos[data_node] shape = value_info['shape'] for i, dim_shape in enumerate(shape): if dim_shape==0 and i==0: shape[i]=1 if dim_shape==0 and i!=0: assert 'shape of input is not assigned' ipt = np.random.random(shape).astype( value_info['dtype']) np.save(os.path.join(self.tmp_data_dir, data_node), ipt) model = onnx.shape_inference.infer_shapes(model) outputs = [] for value_info in model.graph.value_info: outputs.append(value_info) model.graph.ClearField('output') model.graph.output.MergeFrom(outputs) onnx.save(model, os.path.join(self.tmp_data_dir, 'onnx_model_infer.onnx')) os.system('onnx_infer --save_dir=' + self.tmp_data_dir) return def get_dynamic_shape(self, layer): """ get dynamic shape from infer_result """ path = os.path.join(self.tmp_data_dir, layer + '.npy') if not os.path.exists(path): return [None, None, None] output = np.load(path) return output.tolist(), output.dtype, output.shape def get_output_shapes(self): """ build topo_sort of ONNX model """ nodes = self.decoder.model.graph.node node_map = self.decoder.onnx_graph.node_map value_infos = self.decoder.onnx_graph.value_infos onnx_model = self.decoder.model for layer in nodes: node = node_map[layer.name] for opt in layer.output: if opt in value_infos: value_info = value_infos[opt] if len(value_info['shape'] ) == 0 or value_info['dtype'] is None or 0 in value_info['shape']: if self.is_inference == False: self.get_results_of_inference( onnx_model, value_infos, self.decoder.onnx_graph.place_holder_nodes) self.is_inference = True _, dtype, shape = self.get_dynamic_shape(opt) node.out_shapes.append(shape) node.dtype = dtype else: node.dtype = value_info['dtype'] node.out_shapes.append(value_info['shape']) else: if self.is_inference == False: self.get_results_of_inference( onnx_model, value_infos, self.decoder.onnx_graph.place_holder_nodes) self.is_inference = True _, dtype, shape = self.get_dynamic_shape(opt) node.dtype = dtype node.out_shapes.append(shape) def remove_tmp_data(self): """ remove temporarily generated file """ if os.path.exists(self.tmp_data_dir): import shutil shutil.rmtree(self.tmp_data_dir) def directly_map(self, node, name='', *args, **kwargs): inputs = node.layer.input outputs = node.layer.output op_type = node.layer_type attrs = node.attr_map info = default_op_mapping[op_type] info.extend(list(default_op_mapping_field_values.values())[len(info):]) ( fluid_op, fluid_input_args, fluid_output_args, attr_mapping, default_attrs, input_perm, output_perm, fill_name_field, ) = info if fluid_op in default_ioa_constraint: for predicate, message in default_ioa_constraint[fluid_op]: assert predicate(inputs, outputs, attrs), message mapped_attrs = { attr_mapping.get(key, key): value for key, value in attrs.items() } if '' in mapped_attrs: mapped_attrs.pop('') if '_' in mapped_attrs: mapped_attrs.pop('_') fluid_attrs = default_attrs.copy() fluid_attrs.update(mapped_attrs) inputs = inputs if input_perm is None else list( map(lambda i: inputs[i], input_perm)) val_inps = [] for idx, ipt in enumerate(inputs): val_inps.append(self.graph.get_input_node(node, idx=idx, copy=True)) val_outs = outputs if output_perm is None else list( map(lambda i: outputs[i], output_perm)) attr = fluid_attrs assert len(val_inps) == 1, 'directly_map error with multi inputs' if fluid_op not in ['shape']: attr['name'] = string(node.layer_name) node.fluid_code.add_layer(fluid_op, inputs=val_inps[0], output=val_outs[0], param_attr=attr) def deal_custom_layer(self, node): op = node.layer_type custom_code, func = make_custom_layer(node) child_func_code, child_func = make_custom_child_func(node) params = get_params(node.layer, node.layer_type) arg_names, kwargs = set_args(func, params) kwargs['name'] = string(node.layer_name) node.fluid_code.add_layer(func.__code__.co_name, inputs=node.inputs, output=node, param_attr=kwargs, is_custom_layer=True) if op not in self.used_custom_layers: self.used_custom_layers[op] = custom_code if op + '_child_func' not in self.used_custom_layers: if child_func_code is not None: self.used_custom_layers[op + '_child_func'] = child_func_code def elementwise_map(self, node): assert node.layer_type in self.elementwise_ops op_type = self.elementwise_ops[node.layer_type] val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_input_node(node, idx=1, copy=True) val_y_shape = val_y.out_shapes[0] val_x_shape = val_x.out_shapes[0] if len(val_x_shape) 0: val_y_reshaped = val_y_shape[slice_idx:] var_y_reshaped = val_y.layer_name + '_reshaped' attr_reshaped = { 'shape': val_y_reshaped, 'name': string(var_y_reshaped) } node.fluid_code.add_layer('reshape', inputs=val_y, output=var_y_reshaped, param_attr=attr_reshaped) inputs = {'x': val_x, 'y': var_y_reshaped} node.fluid_code.add_layer(op_type, inputs=inputs, output=node, param_attr=attr) else: inputs = {'x': val_x, 'y': val_y} node.fluid_code.add_layer(op_type, inputs=inputs, output=node, param_attr=attr) def place_holder(self, node): self.input_shapes.append(node.out_shapes[0]) shape = node.out_shapes[0] for i, dim_shape in enumerate(shape): if dim_shape==0 and i==0: shape[i]=1 if dim_shape==0 and i!=0: assert 'shape of input is not assigned' attr = { "dtype": string(node.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 create_parameter(self, node, parameter=None): if parameter is not None: node = parameter dtype = node.dtype shape = node.out_shapes[0] self.weights[node.layer_name] = node.weight attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'attr': string(node.layer_name), 'default_initializer': 'Constant(0.0)' } node.fluid_code.add_layer("create_parameter", inputs=None, output=node, param_attr=attr) def _pad_if_asymmetric(self, node, pads, val_name): # pads: SSEE assert len(pads) & 1 == 0 symmetric = True ndims = len(pads) // 2 for idx_dim in range(ndims): if pads[idx_dim] != pads[ndims + idx_dim]: symmetric = False break if symmetric: return pads[:ndims], val_name val_padded = self.Pad(node, op_independent=False) return [0] * ndims, val_padded def _interpolate(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_scales = self.graph.get_input_node(node, idx=1, copy=True) val_y = self.graph.get_node(node.layer.output[0], copy=True) out_shape = val_y.out_shapes[0] if out_shape is not None: assert len(out_shape) == 4, 'only 4-D Tensor as X and Y supported' out_shape = out_shape[2:] scales = _const_weight_or_none(val_scales) if isinstance(val_scales, ONNXGraphNode): scales, _, _ = self.get_dynamic_shape(val_scales.layer_name) attr = { 'name': string(node.layer_name)} use_scales = True if scales is not None: try: assert len(scales) == 4, 'only 4-D Tensor as X and Y supported' assert scales[0] == 1 and scales[ 1] == 1, 'only scale on (NC)HW supported' assert scales[2] == scales[ 3], 'only aspect-ratio-invariant scale supported' except: use_scales=False scale = scales[2] if scales else None if scale is None: assert out_shape, 'neither scales nor output shape is available' else: if out_shape is None: in_shape = val_x.out_shapes[0] assert in_shape is not None, 'out_shape required but not inferrable' assert len( in_shape) == 4, 'only 4-D Tensor as X and Y supported' out_shape = [in_shape[2] * scale, in_shape[3] * scale] mode = node.get_attr('mode', 'nearest') fluid_op = 'resize_{}'.format(mode) if 'linear' in mode: print('Warnning: paddle not support op:resize wiht mode: linear, we use bilinear replace linear') fluid_op = 'resize_bilinear' if use_scales and scale is not None: attr['scale'] = scale else: attr['out_shape'] = out_shape node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def RoiAlign(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_rois = self.graph.get_input_node(node, idx=1, copy=True) pooled_height = node.get_attr('output_height') pooled_width = node.get_attr('output_width') spatial_scale = node.get_attr('spatial_scale') sampling_ratio = node.get_attr('sampling_ratio') attr = { 'pooled_height': pooled_height, 'pooled_width': pooled_width, 'spatial_scale': spatial_scale, 'sampling_ratio':sampling_ratio, } node.fluid_code.add_layer('roi_align', inputs={'input':val_x,'rois':val_rois}, output=node, param_attr=attr) def MaxRoiPool(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_rois = self.graph.get_input_node(node, idx=1, copy=True) spatial_scale = node.get_attr('spatial_scale') pooled_height, pooled_width = node.get_attr('pooled_shape') attr = { 'pooled_height': pooled_height, 'pooled_width': pooled_width, 'spatial_scale': spatial_scale, } node.fluid_code.add_layer('roi_pool', inputs={'input':val_x,'rois':val_rois}, output=node, param_attr=attr) def Pad(self, node, op_independent=True): val_x = self.graph.get_input_node(node, idx=0, copy=True) pads = node.get_attr('pads') mode = node.get_attr('mode', 'constant') value = node.get_attr('value', 0.) data_shape = val_x.out_shapes[0] output_shape = node.out_shapes[0] assume_pad2d = False attr = {} if len(pads) == 4: assume_pad2d |= mode != 'constant' if data_shape: assume_pad2d |= data_shape and len(data_shape) == 4 # NCHW if output_shape: assume_pad2d |= output_shape and len(output_shape) == 4 # NCHW if assume_pad2d: fluid_op = 'pad2d' attr['data_format'] = string('NCHW') attr['mode'] = string(mode) else: attr = {'pad_value': value} fluid_op = 'pad' if len(pads) == 4: paddings = np.array(pads).reshape( (-1, 2)).transpose().flatten().tolist() # SSEE -> SESE elif len(pads) == 8: paddings = np.array(pads).reshape( (-1, 4)).transpose().flatten().tolist() # SSEE -> SESE if sum(paddings[:4]) == 0: fluid_op = 'pad2d' paddings = paddings[4:] attr['mode'] = string(mode) attr['paddings'] = paddings if op_independent: attr['name'] = string(node.layer_name) node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) else: attr['name'] = string(node.layer_name + '_paded') node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node.layer_name + '_paded', param_attr=attr) return node.layer_name + '_paded' def Unsqueeze(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) axes = node.get_attr('axes') print(val_x.outputs) if len(val_x.out_shapes[0])==0: node.fluid_code.add_layer('assign', inputs=val_x, output=node, param_attr=None) else: attr = {'axes': axes, 'name': string(node.layer_name)} node.fluid_code.add_layer('unsqueeze', inputs=val_x, output=node, param_attr=attr) def Shrink(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) bias = node.get_attr('bias') lambd = node.get_attr('lambd') assert bias == 0.0, 'not support bias!=0' attr = {'threshold': lambd, 'name': node.layer_name} node.fluid_code.add_layer('hard_shrink', inputs=val_x, output=node, param_attr=attr) def Constant(self, node): val_output = self.graph.get_node(node.layer.output[0], copy=True) value = node.get_attr('value') dtype = np.dtype(value.dtype) output_dtype = val_output.dtype if output_dtype: assert dtype == output_dtype, 'tensor dtype unmatches storage dtype' shape = node.get_attr('shape', None) if shape is None: shape = val_output.out_shapes[0] if shape is None: shape = list(value.shape) _logger.warning( 'in (Constant -> %s): ' 'attribute "shape" of %s not inferred, ' 'using value as 1-D tensor may lead to fails', val_output.layer_name, val_output.layer_name) if len(value) == 1: value = value.tolist() shape = [1] value = value[0] if dtype.name == 'int64': dtype = 'int32' attr = {'shape': shape, 'dtype': string(dtype), 'value': value} node.fluid_code.add_layer('fill_constant', inputs=None, output=node, param_attr=attr) else: value = np.reshape(value, shape) self.weights[node.layer_name] = value attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'attr': string(node.layer_name), 'default_initializer': 'Constant(0.0)' } node.fluid_code.add_layer("create_parameter", inputs=None, output=node, param_attr=attr) def Resize(self, node): self._interpolate(node) def Upsample(self, node): self._interpolate(node) def Expand(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_shape = self.graph.get_input_node(node, idx=1, copy=True) if len(val_shape.outputs)==1: self.omit_nodes.append(val_shape.layer_name) val_y = self.graph.get_node(node.layer.output[0], copy=True) out_shape = node.out_shapes[0] val_x_dtype = val_x.dtype name_ones= node.layer_name + '_ones' attr_ones = { 'shape':out_shape, 'dtype':string(val_x_dtype) } node.fluid_code.add_layer('ones', inputs=None, output=name_ones, param_attr=attr_ones) inputs = {'x':name_ones,'y':val_x} attr = {'name':string(node.layer_name)} node.fluid_code.add_layer('elementwise_mul', inputs=inputs, output=node.layer_name, param_attr=attr ) def Gather(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) indices = self.graph.get_input_node(node, idx=1, copy=True) indices_shape = indices.out_shapes[0] axis = node.get_attr('axis', 0) assert len( indices_shape) <= 2, "Gather op don't support dim of indice >2 " if axis==0 and len(indices_shape)<=1: node.fluid_code.add_layer('gather', inputs={ 'input': val_x, 'index': indices }, output=node, param_attr=None) elif axis > 0 and len(indices_shape) <= 1: perm = list(range(len(val_x.out_shapes[0]))) perm = [axis] + perm[:axis] + perm[axis + 1:] attr_trans = {'perm': perm} name_trans = val_x.layer_name + '_trans' node.fluid_code.add_layer('transpose', inputs=val_x, output=name_trans, param_attr=attr_trans) node.fluid_code.add_layer('gather', inputs={ 'input': name_trans, 'index': indices }, output=node, param_attr=None) node.fluid_code.add_layer('transpose', inputs=node, output=node, param_attr=attr_trans) elif len(indices_shape)>1: from functools import reduce reshape_shape = reduce(lambda x,y:x*y, indices_shape) node.fluid_code.add_layer('reshape', inputs=indices, output=indices, param_attr={'shape':[reshape_shape,]}) perm = list(range(len(val_x.out_shapes[0]))) perm = [axis] + perm[:axis] + perm[axis + 1:] attr_trans = {'perm': perm} name_trans = val_x.layer_name + '_trans' node.fluid_code.add_layer('transpose', inputs=val_x, output=name_trans, param_attr=attr_trans) node.fluid_code.add_layer('gather', inputs={ 'input': name_trans, 'index': indices }, output=node, param_attr=None) node.fluid_code.add_layer('transpose', inputs=node, output=node, param_attr=attr_trans) val_x_shape = val_x.out_shapes[0] reshaped_shape = [] for i in perm: reshaped_shape.append(indices_shape[i]) for i in val_x_shape[:axis] + val_x_shape[axis + 1:]: reshaped_shape.append(i) node.fluid_code.add_layer('reshape', inputs=node, output=node, param_attr={'shape':reshaped_shape}) def Slice(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) starts, ends, axes, steps = None, None, None, None if len(node.inputs) > 1: starts = self.graph.get_input_node(node, idx=1, copy=True) ends = self.graph.get_input_node(node, idx=2, copy=True) if len(node.inputs)>3: axes = self.graph.get_input_node(node, idx=3, copy=True) self.omit_nodes.append(axes.layer_name) axes = _const_weight_or_none(axes) if len(node.inputs)>4: steps = self.graph.get_input_node(node, idx=4, copy=True) self.omit_nodes.append(steps.layer_name) steps = _const_weight_or_none(steps) self.omit_nodes.append(starts.layer_name) self.omit_nodes.append(ends.layer_name) starts = _const_weight_or_none(starts) ends = _const_weight_or_none(ends) else: starts = node.get_attr('starts') ends = node.get_attr('ends') axes = node.get_attr('axes') val_y = self.graph.get_node(node.layer.output[0], copy=True) shape = val_x.out_shapes[0] if shape is not None: for idx, value in enumerate(starts): if value > shape[axes[idx]]: starts[idx] = shape[axes[idx]] for idx, value in enumerate(ends): if value > shape[axes[idx]]: ends[idx] = shape[axes[idx]] attr = {"axes": axes, "starts": starts, "ends": ends} node.fluid_code.add_layer('slice', inputs=val_x, output=node, param_attr=attr) def ConstantOfShape(self, node): val_shape = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_node(node.layer.output[0], copy=True) shape = _const_weight_or_none(val_shape) if shape is None: shape = node.out_shapes[0] assert shape is not None, ( 'given shape is neither const value nor deductible from output, ' 'this is not supported') value = node.get_attr('value') dtype = value.dtype value = value.tolist() if len(value) == 1: shape = [1] value = value[0] if dtype.name == 'int64': dtype = 'int32' attr = {'shape': shape, 'dtype': string(dtype), 'value': value} node.fluid_code.add_layer('fill_constant', inputs=None, output=node, param_attr=attr) def Split(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_node(node.layer.output[0], copy=True) fluid_op = 'split' split = node.get_attr('split') axis = node.get_attr('axis', 0) attr = { 'num_or_sections': split, 'dim': axis, 'name': string(node.layer_name) } node.fluid_code.add_layer('split', inputs=val_x, output=val_y, param_attr=attr) def Reshape(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_shape = self.graph.get_input_node(node, idx=1, copy=True) val_reshaped = self.graph.get_node(node.layer.output[0], copy=True) shape = None if isinstance(val_shape, ONNXGraphDataNode): self.omit_nodes.append(val_shape.layer_name) attr = {'name': string(node.layer_name)} # catch dynamic graph shape if isinstance(val_shape, ONNXGraphNode): shape, _, _ = self.get_dynamic_shape(val_shape.layer_name) if val_shape.dtype == 'int64': val_shape_cast = val_shape.layer_name+'_cast' node.fluid_code.add_layer('cast', inputs=val_shape, output=val_shape_cast, param_attr={'dtype':string('int32')}) attr['actual_shape'] = val_shape_cast else: attr['actual_shape'] = val_shape if shape is None: shape = val_reshaped.out_shapes[0] if shape is None: shape = [1, -1] _logger.warning( 'in %s(%s -> Reshape -> %s): ' 'input "shape" not inferred, use [1, -1] as dummy value, ' 'the behavior of Paddle fluid maybe undefined', node.layer_name, val_x.layer_name, val_reshaped.layer_name) attr['shape'] = shape node.fluid_code.add_layer('reshape', inputs=val_x, output=node, param_attr=attr) def Cast(self, node): val_input = self.graph.get_input_node(node, idx=0, copy=True) val_output = self.graph.get_node(node.layer.output[0], copy=True) dtype = node.get_attr('to') if not isinstance(dtype, np.dtype): dtype = TENSOR_TYPE_TO_NP_TYPE[dtype] output_dtype = val_output.dtype if output_dtype: assert dtype == output_dtype, 'dtype of to unmatches output' attr = {'dtype': string(dtype)} node.fluid_code.add_layer('cast', inputs=val_input, output=node, param_attr=attr) def AveragePool(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) auto_pad = node.get_attr('auto_pad', 'NOTSET') kernel_shape = node.get_attr("kernel_shape") poolnd = len(kernel_shape) strides = node.get_attr("strides") pad_mode = node.get_attr("pads") ceil_mode = bool(node.get_attr('ceil_mode', 0)) pads = node.get_attr('pads', [0] * (poolnd * 2)) fluid_op = 'pool{}d'.format(poolnd) assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' paddings, val_x = self._pad_if_asymmetric(node, pads, val_x) if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER": input_shape = val_x.out_shapes[0] pad_h = get_same_padding(input_shape[2], kernel_shape[0], strides[0]) pad_w = get_same_padding(input_shape[3], kernel_shape[1], strides[1]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} attr = { "pool_size": kernel_shape, "pool_type": string('avg'), "pool_stride": strides, "pool_padding": paddings, "ceil_mode": ceil_mode, "exclusive": 'True', "name": string(node.layer_name) } node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def Concat(self, node): inputs = [] for i in range(len(node.layer.input)): ipt = self.graph.get_input_node(node, idx=i, copy=True) if isinstance(ipt, str): inputs.append(ipt) else: inputs.append(ipt.layer_name) axis = node.get_attr('axis') attr = {'axis': axis} node.fluid_code.add_layer('concat', inputs=inputs, output=node, param_attr=attr) def Flatten(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) axis = node.get_attr('axis', 1) attr = {"axis": str(axis), "name": string(node.layer_name)} node.fluid_code.add_layer('flatten', inputs=val_x, output=node, param_attr=attr) def Gemm(self, node): val_a = self.graph.get_input_node(node, idx=0, copy=True) val_b = self.graph.get_input_node(node, idx=1, copy=True) val_c = self.graph.get_input_node(node, idx=2, copy=True) alpha = node.get_attr('alpha', 1.) # optional beta = node.get_attr('beta', 1.) # optional trans_a = bool(node.get_attr('transA', 0)) # optional trans_b = bool(node.get_attr('transB', 0)) # optional val_mm = node.layer_name + '_mm' matmul_inputs = {"x": val_a, "y": val_b} attr_matmul = { "transpose_x": trans_a, "transpose_y": trans_b, "alpha": alpha, "name": string(val_mm) } node.fluid_code.add_layer('matmul', inputs=matmul_inputs, output=val_mm, param_attr=attr_matmul) if beta != 0: if beta == 1.: add_inputs = {"x": val_mm, "y": val_c} attr = {"name": string(node.layer_name)} node.fluid_code.add_layer("elementwise_add", inputs=add_inputs, output=node, param_attr=attr) else: var_beta = node.layer_name + '_beta' matmul_beta_inputs = {"x": val_c, "y": var_beta} node.fluid_code.add_layer("Constant", inputs=matmul_beta_inputs, output=var_beta, param_attr={'value': beta}) add_inputs = {"x": val_mm, "y": var_beta} attr = {"name": string(node.layer_name)} node.fluid_code.add_layer("elementwise_add", inputs=add_inputs, output=node, param_attr=attr) def Sum(self, node): val_inps = node.layer.input inputs = { "x": self.graph.get_input_node(node, idx=0, copy=True), "y": self.graph.get_input_node(node, idx=1, copy=True), } node.fluid_code.add_layer("elementwise_add", inputs=inputs, output=node) for idx, ipt in enumerate(val_inps[2:]): y = self.graph.get_input_node(node, idx=idx, copy=True) inputs = { "x": node.layer_name, "y": y, } node.fluid_code.add_layer("elementwise_add", inputs=inputs, output=node) def MatMul(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_input_node(node, idx=1, copy=True) inputs = {"x": val_x, "y": val_y} attr = {"name": string(node.layer_name)} node.fluid_code.add_layer("matmul", inputs=inputs, output=node, param_attr=attr) def BatchNormalization(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_scale = self.graph.get_input_node(node, idx=1, copy=True) val_b = self.graph.get_input_node(node, idx=2, copy=True) val_mean = self.graph.get_input_node(node, idx=3, copy=True) val_var = self.graph.get_input_node(node, idx=4, copy=True) self.omit_nodes.append(val_scale.layer_name) self.omit_nodes.append(val_b.layer_name) self.omit_nodes.append(val_mean.layer_name) self.omit_nodes.append(val_var.layer_name) momentum = node.get_attr('momentum', .9) epsilon = node.get_attr('epsilon', 1e-5) # Attribute: spatial is used in BatchNormalization-1,6,7 spatial = bool(node.get_attr('spatial')) attr = { "momentum": momentum, "epsilon": epsilon, "data_layout": string('NCHW'), "is_test": True, "param_attr": string(val_scale.layer_name), "bias_attr": string(val_b.layer_name), "moving_mean_name": string(val_mean.layer_name), "moving_variance_name": string(val_var.layer_name), "use_global_stats": spatial, "name": string(node.layer_name) } node.fluid_code.add_layer("batch_norm", inputs=val_x, output=node, param_attr=attr) def Transpose(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) perm = node.get_attr('perm') attr = {'perm': perm, "name": string(node.layer_name)} node.fluid_code.add_layer("transpose", inputs=val_x, output=node, param_attr=attr) def Relu(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) attr = {"name": string(node.layer_name)} node.fluid_code.add_layer("relu", inputs=val_x, output=node, param_attr=attr) def PRelu(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_slope = self.graph.get_input_node(node, idx=1, copy=True) mode = 'channel' shape_slope = val_slope.out_shapes[0] if len(shape_slope) == 1: mode = 'all' elif len(shape_slope) > 2: mode = 'element' attr = { "param_attr": string(val_slope.layer_name), 'mode': string(mode) } node.fluid_code.add_layer("prelu", inputs=val_x, output=node, param_attr=attr) def Squeeze(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) axes = node.get_attr('axes') attr = {'axes': axes, "name": string(node.layer_name)} node.fluid_code.add_layer("squeeze", inputs=val_x, output=node, param_attr=attr) def Equal(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_input_node(node, idx=1, copy=True) node.fluid_code.add_layer("equal", inputs={'x':val_x, 'y':val_y}, output=node, param_attr=None) def Where(self, node): condition = self.graph.get_input_node(node, idx=0, copy=True) val_x = self.graph.get_input_node(node, idx=1, copy=True) val_y = self.graph.get_input_node(node, idx=2, copy=True) not_condition = condition.layer_name + '_not' node.fluid_code.add_layer("logical_not", inputs=condition, output=not_condition, param_attr=None) cast_not_condition = not_condition+'_cast' node.fluid_code.add_layer("cast", inputs=not_condition, output=cast_not_condition, param_attr={'dtype':string(val_x.dtype)}) cast_condition = condition.layer_name + '_cast' node.fluid_code.add_layer("cast", inputs=condition, output=cast_condition, param_attr={'dtype':string(val_x.dtype)}) mul_val_x = val_x.layer_name + '_mul' node.fluid_code.add_layer("elementwise_mul", inputs={'x':val_x,'y':cast_condition}, output=mul_val_x, param_attr=None) mul_val_y = val_y.layer_name + '_mul' node.fluid_code.add_layer("elementwise_mul", inputs={'x':val_y,'y':cast_not_condition}, output=mul_val_y, param_attr=None) node.fluid_code.add_layer("elementwise_add", inputs={'x':mul_val_x,'y':mul_val_y}, output=node, param_attr=None) def Identity(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) node.fluid_code.add_layer("assign", inputs=val_x, output=node) def Tile(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_repeats = self.graph.get_input_node(node, idx=1, copy=True) repeats = _const_weight_or_none(val_repeats) assert repeats is not None, 'for OP:Tile, only const repeats supported' if isinstance(repeats, int): repeats = [repeats] attr = { 'expand_times':repeats, "name": string(node.layer_name), } node.fluid_code.add_layer("expand", inputs=val_x, output=node, param_attr=attr) def MaxPool(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) auto_pad = node.get_attr('auto_pad', 'NOTSET') assert node.get_attr( "dilations") is None, 'only dilations = 0 is supported' # optional kernel_shape = node.get_attr("kernel_shape") poolnd = len(kernel_shape) strides = node.get_attr("strides") pad_mode = node.get_attr("pads") ceil_mode = bool(node.get_attr('ceil_mode', 0)) # optional pads = node.get_attr('pads', [0] * (poolnd * 2)) # optional fluid_op = 'pool{}d'.format(poolnd) assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' paddings, val_x = self._pad_if_asymmetric(node, pads, val_x) if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER": input_shape = val_x.out_shapes[0] pad_h = get_same_padding(input_shape[2], kernel_shape[0], strides[0]) pad_w = get_same_padding(input_shape[3], kernel_shape[1], strides[1]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} attr = { "pool_size": kernel_shape, "pool_type": string("max"), "pool_stride": strides, "pool_padding": paddings, "ceil_mode": ceil_mode, "name": string(node.layer_name), "exclusive": False } node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def _global_pool(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_y = self.graph.get_node(node.layer.output[0], copy=True) input_shape = val_x.out_shapes[0] output_shape = val_y.out_shapes[0] assert input_shape is not None or output_shape is not None, 'poolnd not inferred' # N if input_shape: poolnd = len(input_shape) - 2 # NC... elif output_shape: poolnd = len(output_shape) - 2 # NC... assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' fluid_op = 'pool{}d'.format(poolnd) pool_type = None if node.layer.op_type == 'GlobalMaxPool': pool_type = 'max' elif node.layer.op_type == 'GlobalAveragePool': pool_type = 'avg' attr = { "pool_type": string(pool_type), "global_pooling": True, "name": string(node.layer_name) } node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def GlobalMaxPool(self, node): self._global_pool(node) def GlobalAveragePool(self, node): self._global_pool(node) def Conv(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_w = self.graph.get_input_node(node, idx=1, copy=True) val_y = self.graph.get_node(node.layer.output[0], copy=True) self.omit_nodes.append(val_w.layer_name) has_bias = len(node.layer.input) == 3 if has_bias: val_b = self.graph.get_input_node(node, idx=2, copy=True) self.omit_nodes.append(val_b.layer_name) auto_pad = node.get_attr('auto_pad', 'NOTSET') kernel_shape = node.get_attr('kernel_shape') convnd = len(kernel_shape) assert 2 <= convnd <= 3, 'only conv2d and conv3d is supported' num_out_channels = val_w.out_shapes[0][0] # OI... fluid_op = 'conv{}d'.format(convnd) num_groups = node.get_attr('group', 1) strides = node.get_attr('strides', [1] * convnd) # optional dilations = node.get_attr('dilations', [1] * convnd) # optional pads = node.get_attr('pads', [0] * (convnd * 2)) # optional input_shape = val_x.out_shapes[0] paddings, val_x = self._pad_if_asymmetric(node, pads, val_x) if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER": pad_h = get_same_padding(input_shape[2], kernel_shape[0], strides[0]) pad_w = get_same_padding(input_shape[3], kernel_shape[1], strides[1]) attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} attr = { "num_filters": num_out_channels, "filter_size": kernel_shape, "stride": strides, "padding": paddings, "dilation": dilations, "groups": num_groups, 'param_attr': string(val_w.layer_name), "name": string(node.layer_name) } if has_bias: attr["bias_attr"] = string(val_b.layer_name) else: attr["bias_attr"] = False node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def ConvTranspose(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_w = self.graph.get_input_node(node, idx=1, copy=True) val_b = None if len(node.layer.input)>2: val_b = self.graph.get_input_node(node, idx=2, copy=True) self.omit_nodes.append(val_b.layer_name) self.omit_nodes.append(val_w.layer_name) val_y = self.graph.get_node(node.layer.output[0], copy=True) auto_pad = node.get_attr('auto_pad', 'NOTSET') out_padding = node.get_attr('output_padding', [0, 0]) kernel_shape = node.get_attr('kernel_shape') assert kernel_shape, 'kernel_shape not inferred' convnd = len(kernel_shape) assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose supported' num_out_channels = val_w.out_shapes[0][1] fluid_op = 'conv{}d_transpose'.format(convnd) num_groups = node.get_attr('group', 1) strides = node.get_attr('strides', [1] * convnd) dilations = node.get_attr('dilations', [1] * convnd) output_size = node.get_attr('output_shape', []) pads = node.get_attr('pads', [0] * (convnd * 2)) paddings, var_x = self._pad_if_asymmetric(node, pads, val_x) output_size = [0, 0] output_size[0] = (val_x.out_shapes[0][2] - 1) * strides[0] - 2 * paddings[0] + dilations[0] * ( kernel_shape[0] - 1) + 1 + out_padding[0] output_size[1] = (val_x.out_shapes[0][3] - 1) * strides[1] - 2 * paddings[1] + dilations[1] * ( kernel_shape[1] - 1) + 1 + out_padding[1] attr = { 'num_filters': num_out_channels, 'output_size': output_size or None, 'filter_size': kernel_shape, 'padding': paddings, 'stride': strides, 'dilation': dilations, 'groups': num_groups, 'param_attr': string(val_w.layer_name), 'bias_attr': None if val_b is None else string(val_b.layer_name), 'name': string(node.layer_name), } node.fluid_code.add_layer(fluid_op, inputs=val_x, output=node, param_attr=attr) def GRU(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_w = self.graph.get_input_node(node, idx=1, copy=True) val_r = self.graph.get_input_node(node, idx=2, copy=True) val_b = None val_len = None val_xh = None miss_arg_num = 0 num_ipt = len(node.layer.input) if num_ipt>3 and node.layer.input[3] != '': val_b = self.graph.get_input_node(node, idx=3, copy=True) else: miss_arg_num += 1 if num_ipt>4 and node.layer.input[4] != '': val_len = self.graph.get_input_node(node, idx=4-miss_arg_num, copy=True) else: miss_arg_num += 1 if num_ipt>5 and node.layer.input[5] != '': val_xh = self.graph.get_input_node(node, idx=5-miss_arg_num, copy=True) data, dtype, shape = self.get_dynamic_shape(val_x.layer_name) x_shape = val_x.out_shapes[0] assert x_shape[1] == 1, 'only X with batch_size = 1 supported' assert node.get_attr('clip', None) is None, 'clipping not supported' hidden_size = node.get_attr('hidden_size', None) if hidden_size is None: r_shape = val_r.out_shapes[0] if r_shape: hidden_size = r_shape[-1] if hidden_size is None: w_shape = var_w.out_shapes[0] if w_shape: hidden_size = w_shape[-2] // 3 if hidden_size is None and val_b: b_shape = val_b.out_shapes[0] if b_shape: hidden_size = b_shape[-1] // 6 if hidden_size is None and val_xh: xh_shape = val_xh.out_shapes[0] if xh_shape: hidden_size = xh_shape[-1] direction = node.get_attr('direction', 'forward') assert direction != 'bidirectional', 'direction = bidirectional not supported' activations = node.get_attr('activations', ['Sigmoid', 'Tanh']) assert len(activations) == 2, 'bidirectional operation not supported' assert node.get_attr( 'linear_before_reset', 0) == 0, 'only linear_before_reset = 0 supported' activations = [s.lower() for s in activations] gate_activation, candidate_activation = activations is_reverse = direction == 'reverse' var_x0 = node.layer_name + '_x0' node.fluid_code.add_layer('squeeze', inputs=val_x, output=var_x0, param_attr={'axes': [1],'name':string(var_x0)}) var_w0 = node.layer_name + '_w0' node.fluid_code.add_layer('squeeze', inputs=val_w, output=var_w0, param_attr={'axes': [0],'name':string(var_w0)}) var_fc = node.layer_name + '_fc' var_mm = (node.layer_name + '_mm') if val_b else var_fc node.fluid_code.add_layer('matmul', inputs={'x':var_x0, 'y':var_w0}, output=var_mm, param_attr={'transpose_x': 0,'transpose_y': 1,'name':string(var_mm)}) var_r0 = node.layer_name + '_r0' node.fluid_code.add_layer('squeeze', inputs=val_r, output=var_r0, param_attr={'axes': [0],'name':string(var_r0)}) var_r0t = node.layer_name + '_r0t' node.fluid_code.add_layer('transpose', inputs=var_r0, output=var_r0t, param_attr={'perm': [1, 0],'name':string(var_r0t)}) if val_b: var_bi = node.layer_name + '_bi' var_bh = node.layer_name + '_bh' node.fluid_code.add_layer('split', inputs=val_b, output=var_bi+','+var_bh, param_attr={'axis': 1, 'split': [hidden_size * 3, hidden_size * 3], 'name':string(node.layer_name+'.b/split')}) var_bi0 = node.layer_name + '_bi0' node.fluid_code.add_layer('squeeze', inputs=var_bi, output=var_bi0, param_attr={'axes': [0],'name':string(var_bi0)}) node.fluid_code.add_layer('elmentwise_add', inputs=[var_mm, var_bi0], output=var_fc, param_attr={'axes': 1,'name':string(node.layer_name+'.i/bias')}) if val_xh: var_xh0 = node.layer_name + '_xh0' node.fluid_code.add_layer('squeeze', inputs=val_xh, output=var_xh0, param_attr={'axes': [1],'name':string(var_xh0)}) var_y00 = node.layer_name + '_y00' attr={ 'origin_mode':True, 'h_0': var_xh0 if val_xh else None, 'is_reverse':is_reverse, 'gate_activation':string(gate_activation), 'candidate_activation':string(candidate_activation), 'param_attr':string(var_r0t), 'bias_attr':string(var_bh) if val_b else False, } node.fluid_code.add_layer('dynamic_gru', inputs=var_fc +','+ str(hidden_size), output=var_y00, param_attr=attr) num_opt = len(node.layer.output) if num_opt>0 and node.layer.output[0] != '': node.fluid_code.add_layer('unsqueeze', inputs=var_y00, output=node.layer.output[0], param_attr={'axes': [1, 1],'name':string(node.layer.output[0])}) if num_opt>1 and node.layer.output[1] != '': node.fluid_code.add_layer('unsqueeze', inputs=var_y00, output=node.layer.output[1], param_attr={'axes': [1, 1],'name':string(node.layer.output[1])})