# 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.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode from x2paddle.core.graph import GraphNode from x2paddle.core.fluid_code import Layer from x2paddle.core.fluid_code import FluidCode from x2paddle.core.util import string from x2paddle.op_mapper.onnx2paddle.opset9.custom_layer import * from functools import reduce 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 import math import os import shutil _logger = _logging.getLogger(__name__) def _const_weight_or_none(node, necessary=False): if 'Constant' in node.layer_type: return node.value if isinstance(node, ONNXGraphDataNode): return node.weight if necessary: assert '{} should be an initializer or Constant operator.'.format( node.layer_name) 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] def print_mapping_info(func): def run_mapping(*args, **kwargs): node = args[1] try: res = func(*args, **kwargs) except: print("convert failed node:{}, op_type is {}".format( node.layer_name[9:], node.layer_type)) raise else: #print("convert successfully node:{}, op_type is {}".format( # node.layer_name[9:], node.layer_type)) return res return run_mapping class OpSet9(): elementwise_ops = { 'Add': 'elementwise_add', 'Div': 'elementwise_div', 'Sub': 'elementwise_sub', 'Mul': 'elementwise_mul', 'Pow': 'elementwise_pow', } default_op_mapping_field_values = OrderedDict() default_op_mapping_field_values['FLUID_OP'] = '' default_op_mapping_field_values['FLUID_INPUT_ARGS'] = None default_op_mapping_field_values['FLUID_OUTPUT_ARGS'] = None default_op_mapping_field_values['ATTR_MAPPING'] = dict() default_op_mapping_field_values['DEFAULTS'] = dict() default_op_mapping_field_values['INPUT_PERM'] = None default_op_mapping_field_values['OUTPUT_PERM'] = None default_op_mapping_field_values['FILL_NAME_FIELD'] = True default_op_mapping = { 'Shape': ['shape', ['X'], ['Out']], 'Clip': [ 'clip', ['X'], ['Out'], dict(), dict( min=(np.asarray( [255, 255, 127, 255], dtype=np.uint8).view(np.float32)[0]), max=(np.asarray( [255, 255, 127, 127], dtype=np.uint8).view(np.float32)[0]), ) ], 'Erf': ['erf', ['X'], ['Out']], 'Ceil': ['ceil', ['X'], ['Out']], 'ReduceMean': [ 'reduce_mean', ['X'], ['Out'], dict( axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceSum': [ 'reduce_sum', ['X'], ['Out'], dict( axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceMin': [ 'reduce_min', ['X'], ['Out'], dict( axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceMax': [ 'reduce_max', ['X'], ['Out'], dict( axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], #active function 'Relu': ['relu', ['X'], ['Out']], 'LeakyRelu': ['leaky_relu', ['X'], ['Out'], dict(), dict(alpha=.01)], 'Elu': ['elu', ['X'], ['Out'], dict(), dict(alpha=1.)], 'ThresholdedRelu': [ 'thresholded_relu', ['X'], ['Out'], dict(alpha='threshold'), dict(alpha=1.) ], 'Tanh': ['tanh', ['X'], ['Out']], 'Sigmoid': ['sigmoid', ['X'], ['Out']], 'HardSigmoid': [ 'hard_sigmoid', ['X'], ['Out'], dict( alpha='slope', beta='offset'), dict( slope=.2, offset=.5) ], 'Softsign': ['softsign', ['X'], ['Out']], 'Softplus': ['softplus', ['X'], ['Out']], 'Exp': ['exp', ['X'], ['Out']], 'Softmax': ['softmax', ['X'], ['Out'], dict(), dict(axis=1)], 'Sqrt': ['sqrt', ['X'], ['Out']], 'Floor': ['floor', ['X'], ['Out']], 'Abs': ['abs', ['X'], ['Out']], } default_ioa_constraint = {} def __init__(self, decoder): super(OpSet9, self).__init__() self.graph = decoder.graph self.input_shapes = [] self.weights = dict() self.omit_nodes = list() self.used_custom_layers = dict() @print_mapping_info 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 = self.default_op_mapping[op_type] info.extend( list(self.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 self.default_ioa_constraint: for predicate, message in self.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', 'erf']: attr['name'] = string(node.layer_name) node.fluid_code.add_layer( fluid_op, inputs=val_inps[0], output=val_outs[0], param_attr=attr) if fluid_op in ['shape']: node.fluid_code.add_layer( 'cast', inputs=val_outs[0], output=val_outs[0], param_attr={'dtype': string('int64')}) @print_mapping_info 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 @print_mapping_info 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) < len(val_y_shape): val_x, val_y = val_y, val_x val_y_shape, val_x_shape = val_x_shape, val_y_shape str_y_shape = ','.join(str(e) for e in val_y_shape) str_x_shape = ','.join(str(e) for e in val_x_shape) slice_idx = 0 if str_y_shape not in str_x_shape: for dim in val_y_shape: if dim == 1: slice_idx += 1 else: break attr = {"name": string(node.layer_name)} if slice_idx < len(val_y_shape) and slice_idx > 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) @print_mapping_info 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) @print_mapping_info def create_parameter(self, node, parameter=None): if parameter is not None: node = parameter dtype = node.dtype shape = node.out_shapes[0] if len(node.weight.shape) == 0: shape = [1] self.weights[node.layer_name] = node.weight attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'default_initializer': 'Constant(0.0)' } if dtype == 'bool': attr['dtype'] = string('int64') node.fluid_code.add_layer( "create_parameter", inputs=None, output=node, param_attr=attr) node.fluid_code.add_layer( "cast", inputs=node, output=node, param_attr={'dtype': string('bool')}) elif dtype == 'uint8': attr['dtype'] = string('float32') node.fluid_code.add_layer( "create_parameter", inputs=None, output=node, param_attr=attr) else: 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) inputs = {'input': val_x} if node.layer_type == 'Resize': if len(node.layer.input) == 2: # opset 10 val_scales = self.graph.get_input_node(node, idx=1, copy=True) inputs['scale'] = val_scales elif len(node.layer.input) == 3: # opset 11 val_scales = self.graph.get_input_node(node, idx=2, copy=True) inputs['scale'] = val_scales elif len(node.layer.input) == 4: # opset 11 val_sizes = self.graph.get_input_node(node, idx=3, copy=True) var_nc, var_hw = val_sizes.layer_name + '_nc', val_sizes.layer_name + '_hw' node.fluid_code.add_layer( 'split', inputs=val_sizes, output=var_nc + ',' + var_hw, param_attr={ 'dim': 0, 'num_or_sections': [2, 2], }) node.fluid_code.add_layer( "cast", inputs=var_hw, output=var_hw, param_attr={'dtype': string('int32')}) inputs['out_shape'] = var_hw elif node.layer_type == 'Upsample': val_scales = self.graph.get_input_node(node, idx=1, copy=True) inputs['scale'] = val_scales attr = {'name': string(node.layer_name)} 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' node.fluid_code.add_layer( fluid_op, inputs=inputs, output=node, param_attr=attr) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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' @print_mapping_info def Unsqueeze(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)} if len(val_x.out_shapes[0]) == 0: if node.layer_name: node.fluid_code.add_layer( 'reshape', inputs=val_x, output=node, param_attr={'shape': [1]}) else: node.fluid_code.add_layer( 'unsqueeze', inputs=val_x, output=node, param_attr=attr) @print_mapping_info 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 Greater(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( 'greater_than', inputs={'x': val_x, 'y': val_y}, output=node, param_attr=None) @print_mapping_info 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: if dtype.name == 'uint8': dtype = 'int64' value = np.reshape(value, shape) self.weights[node.layer_name] = value attr = { 'dtype': string(dtype), 'shape': shape, 'name': string(node.layer_name), 'default_initializer': 'Constant(0.0)' } node.fluid_code.add_layer( "create_parameter", inputs=None, output=node, param_attr=attr) @print_mapping_info def Resize(self, node): self._interpolate(node) @print_mapping_info def Upsample(self, node): self._interpolate(node) @print_mapping_info def InstanceNormalization(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) epsilon = node.get_attr('epsilon', 1e-5) attr = { 'epsilon': epsilon, 'param_attr': string(val_scale.layer_name), 'bias_attr': string(val_b.layer_name) } node.fluid_code.add_layer( "instance_norm", inputs=val_x, output=node, param_attr=attr) @print_mapping_info 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) @print_mapping_info 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 axis == 0 and len(indices_shape) > 1: if val_x.out_shapes[0] is not None and isinstance( val_x, ONNXGraphDataNode): if indices.dtype != 'int64': node.fluid_code.add_layer( 'cast', inputs=indices, output=indices, param_attr={'dtype': string('int64')}) node.fluid_code.add_layer( 'embedding', inputs=indices, output=node, use_fluid=True, param_attr={ 'param_attr': string(val_x.layer_name), 'size': val_x.out_shapes[0] }) else: from functools import reduce #indices_shape = [1,7] reshape_shape = reduce(lambda x, y: x * y, indices_shape) indices_reshape = indices.layer_name + '_shape' node.fluid_code.add_layer( 'reshape', inputs=indices, output=indices_reshape, param_attr={'shape': [reshape_shape, ]}) perm = list(range(len(val_x.out_shapes[0]))) node.fluid_code.add_layer( 'gather', inputs={'input': val_x, 'index': indices_reshape}, output=node, param_attr=None) 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}) elif axis > 0 and len(indices_shape) > 1: from functools import reduce reshape_shape = reduce(lambda x, y: x * y, indices_shape) indices_reshape = indices.layer_name + '_shape' node.fluid_code.add_layer( 'reshape', inputs=indices, output=indices_reshape, 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_reshape}, 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}) @print_mapping_info def Range(self, node): val_start = self.graph.get_input_node(node, idx=0, copy=True) val_limit = self.graph.get_input_node(node, idx=1, copy=True) val_delta = self.graph.get_input_node(node, idx=2, copy=True) dtype = val_start.dtype inputs = {'start': val_start, 'end': val_limit, 'step': val_delta} node.fluid_code.add_layer( 'range', inputs=inputs, output=node, param_attr={'dtype': string(dtype)}) @print_mapping_info def Slice(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) starts, ends, axes, steps = None, None, None, None attr = {} 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) axes = _const_weight_or_none(axes, necessary=True) if len(node.inputs) > 4: steps = self.graph.get_input_node(node, idx=4, copy=True) steps = _const_weight_or_none(steps) if steps is not None: assert steps == 1, "Only support convert op:Slice, which attribute:steps == 1" attr = { "axes": axes, "starts": starts.layer_name, "ends": ends.layer_name } starts_value = _const_weight_or_none(starts) ends_value = _const_weight_or_none(ends) if starts_value is not None and ends_value is not None: self.omit_nodes.append(starts.layer_name) self.omit_nodes.append(ends.layer_name) ends_value = ends_value.copy() for idx in range(len(ends_value)): if ends_value[idx] > 2**31 - 1: ends_value[idx] = 2**31 - 1 attr = { "axes": axes, "starts": starts_value, "ends": ends_value } else: if starts.dtype != 'int32': node.fluid_code.add_layer( 'cast', inputs=starts, output=starts, param_attr={'dtype': string('int32')}) if ends.dtype != 'int32': node.fluid_code.add_layer( 'cast', inputs=ends, output=ends, param_attr={'dtype': string('int32')}) else: starts = node.get_attr('starts') ends = node.get_attr('ends') axes = node.get_attr('axes') for idx in range(len(ends)): if ends[idx] > 2**31 - 1: ends[idx] = 2**31 - 1 attr = {"axes": axes, "starts": starts, "ends": ends} node.fluid_code.add_layer( 'slice', inputs=val_x, output=node, param_attr=attr) @print_mapping_info 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) value = node.get_attr('value') dtype = value.dtype value = value.tolist() assert len(value) == 1, ('given value not Scalar, shape of value > 1, ' 'this is not supported') if len(value) == 1: value = value[0] if dtype.name == 'int64': dtype = 'int32' attr = { 'shape': val_shape.layer_name, 'dtype': string(dtype), 'value': value } node.fluid_code.add_layer( 'fill_constant', inputs=None, output=node, param_attr=attr) @print_mapping_info 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) @print_mapping_info 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) attr = {} shape_value = _const_weight_or_none(val_shape) shape_dims = len(val_shape.out_shapes[0]) if shape_value is not None: node.fluid_code.add_layer( 'reshape', inputs={'x': val_x}, output=node, param_attr={'shape': shape_value.tolist()}) elif 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')}) # shape may be [], come form Gather by scalar indices if len(val_shape.out_shapes[0]) > 0: node.fluid_code.add_layer( 'reshape', inputs=val_shape_cast, output=val_shape_cast, param_attr={'shape': val_shape.out_shapes[0]}) node.fluid_code.add_layer( 'reshape', inputs={'x': val_x, 'shape': val_shape_cast}, output=node, param_attr=attr) else: # shape may be [], come form Gather by scalar indices if len(val_shape.out_shapes[0]) > 0: node.fluid_code.add_layer( 'reshape', inputs=val_shape, output=val_shape, param_attr={'shape': val_shape.out_shapes[0]}) node.fluid_code.add_layer( 'reshape', inputs={'x': val_x, 'shape': val_shape}, output=node, param_attr=attr) @print_mapping_info 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) @print_mapping_info 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]) paddings = pad_h + pad_w 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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) @print_mapping_info 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)} if len(val_x.out_shapes[0]) == 1: node.fluid_code.add_layer( "cast", inputs=val_x, output=node, param_attr={'dtype': string(val_x.dtype)}) else: node.fluid_code.add_layer( "squeeze", inputs=val_x, output=node, param_attr=attr) @print_mapping_info 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) @print_mapping_info def Greater(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( "greater_than", inputs={'x': val_x, 'y': val_y}, output=node, param_attr=None) @print_mapping_info 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) @print_mapping_info def NonZero(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) val_x_dim = len(val_x.out_shapes[0]) if val_x_dim == 1: node.fluid_code.add_layer("nonzero", inputs=val_x, output=val_x) node.fluid_code.add_layer( "transpose", inputs=val_x, output=node, param_attr={'perm': [1, 0]}) if val_x_dim > 1: node.fluid_code.add_layer("nonzero", inputs=val_x, output=val_x) node.fluid_code.add_layer( "split", inputs=val_x, output=val_x, param_attr={'num_or_sections': 1, 'dim': val_x_dim}) node.fluid_code.add_layer("concat", inputs=val_x, output=node) @print_mapping_info 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) @print_mapping_info 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) if repeats is None: repeats = val_repeats.layer_name elif 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) @print_mapping_info 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]) paddings = pad_h + pad_w 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) fluid_op = 'pool2d' 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) @print_mapping_info def GlobalMaxPool(self, node): self._global_pool(node) @print_mapping_info def GlobalAveragePool(self, node): self._global_pool(node) @print_mapping_info 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] fluid_op = 'conv{}d'.format(convnd) num_groups = node.get_attr('group', 1) strides = node.get_attr('strides', [1] * convnd) dilations = node.get_attr('dilations', [1] * convnd) pads = node.get_attr('pads', [0] * (convnd * 2)) 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]) paddings = pad_h + pad_w 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) @print_mapping_info 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)