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| # Copyright (c) 2021 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 copy | ||
| import collections | ||
| import itertools | ||
| import six | ||
| import math | ||
| import sys | ||
| import warnings | ||
| from functools import partial, reduce | ||
|
|
||
| import numpy as np | ||
| import paddle | ||
| import paddle.fluid as fluid | ||
| from paddle import framework | ||
| from paddle.device import get_device, get_cudnn_version | ||
| from paddle.nn import initializer as I | ||
| from paddle.nn import Layer, LayerList | ||
| from paddle.fluid.layers import utils | ||
| from paddle.fluid.layer_helper import LayerHelper | ||
| from paddle.fluid.layers.utils import map_structure, flatten, pack_sequence_as | ||
| from paddle.fluid.data_feeder import convert_dtype | ||
| from paddle.fluid.param_attr import ParamAttr | ||
| from paddle import _C_ops | ||
| __all__ = ['resnet_unit', 'ResNetUnit'] | ||
|
|
||
|
|
||
| def resnet_unit(x, filter_x, scale_x, bias_x, mean_x, var_x, z, filter_z, | ||
| scale_z, bias_z, mean_z, var_z, stride, stride_z, padding, | ||
| dilation, groups, momentum, eps, data_format, fuse_add, | ||
| has_shortcut, use_global_stats, is_test, act): | ||
|
|
||
| helper = LayerHelper('resnet_unit', **locals()) | ||
| bn_param_dtype = fluid.core.VarDesc.VarType.FP32 | ||
| bit_mask_dtype = fluid.core.VarDesc.VarType.INT32 | ||
| out = helper.create_variable_for_type_inference(x.dtype) | ||
| bit_mask = helper.create_variable_for_type_inference( | ||
| dtype=bit_mask_dtype, stop_gradient=True) | ||
| # intermediate_out for x | ||
| conv_x = helper.create_variable_for_type_inference( | ||
| dtype=x.dtype, stop_gradient=True) | ||
| saved_mean_x = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) | ||
| saved_invstd_x = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) | ||
| running_mean_x = mean_x | ||
| running_var_x = var_x | ||
| # intermediate_out for z | ||
| conv_z = helper.create_variable_for_type_inference( | ||
| dtype=x.dtype, stop_gradient=True) | ||
| saved_mean_z = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) | ||
| saved_invstd_z = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) | ||
| running_mean_z = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) if mean_z is None else mean_z | ||
| running_var_z = helper.create_variable_for_type_inference( | ||
| dtype=bn_param_dtype, stop_gradient=True) if var_z is None else var_z | ||
|
|
||
| inputs = { | ||
| 'X': x, | ||
| 'FilterX': filter_x, | ||
| 'ScaleX': scale_x, | ||
| 'BiasX': bias_x, | ||
| 'MeanX': mean_x, | ||
| 'VarX': var_x, | ||
| 'Z': z, | ||
| 'FilterZ': filter_z, | ||
| 'ScaleZ': scale_z, | ||
| 'BiasZ': bias_z, | ||
| 'MeanZ': mean_z, | ||
| 'VarZ': var_z | ||
| } | ||
|
|
||
| attrs = { | ||
| 'stride': stride, | ||
| 'stride_z': stride_z, | ||
| 'padding': padding, | ||
| 'dilation': dilation, | ||
| 'group': groups, | ||
| 'momentum': momentum, | ||
| 'epsilon': eps, | ||
| 'data_format': data_format, | ||
| 'fuse_add': fuse_add, | ||
| 'has_shortcut': has_shortcut, | ||
| 'use_global_stats': use_global_stats, | ||
| 'is_test': is_test, | ||
| 'act_type': act | ||
| } | ||
|
|
||
| outputs = { | ||
| 'Y': out, | ||
| 'BitMask': bit_mask, | ||
| 'ConvX': conv_x, | ||
| 'SavedMeanX': saved_mean_x, | ||
| 'SavedInvstdX': saved_invstd_x, | ||
| 'RunningMeanX': running_mean_x, | ||
| 'RunningVarX': running_var_x, | ||
| 'ConvZ': conv_z, | ||
| 'SavedMeanZ': saved_mean_z, | ||
| 'SavedInvstdZ': saved_invstd_z, | ||
| 'RunningMeanZ': running_mean_z, | ||
| 'RunningVarZ': running_var_z, | ||
| } | ||
|
|
||
| helper.append_op( | ||
| type='resnet_unit', inputs=inputs, outputs=outputs, attrs=attrs) | ||
|
|
||
| return out | ||
|
|
||
|
|
||
| class ResNetUnit(Layer): | ||
| r""" | ||
| ******Temporary version******. | ||
| ResNetUnit is designed for optimize the performence by using cudnnv8 API. | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| num_channels_x, | ||
| num_filters, | ||
| filter_size, | ||
| stride=1, | ||
| momentum=0.9, | ||
| eps=1e-5, | ||
| data_format='NHWC', | ||
| act='relu', | ||
| fuse_add=False, | ||
| has_shortcut=False, | ||
| use_global_stats=False, | ||
| is_test=False, | ||
| filter_x_attr=None, | ||
| scale_x_attr=None, | ||
| bias_x_attr=None, | ||
| moving_mean_x_name=None, | ||
| moving_var_x_name=None, | ||
| num_channels_z=1, | ||
| stride_z=1, | ||
| filter_z_attr=None, | ||
| scale_z_attr=None, | ||
| bias_z_attr=None, | ||
| moving_mean_z_name=None, | ||
| moving_var_z_name=None): | ||
| super(ResNetUnit, self).__init__() | ||
| self._stride = stride | ||
| self._stride_z = stride_z | ||
| self._dilation = 1 | ||
| self._kernel_size = utils.convert_to_list(filter_size, 2, 'kernel_size') | ||
| self._padding = (filter_size - 1) // 2 | ||
| self._groups = 1 | ||
| self._momentum = momentum | ||
| self._eps = eps | ||
| self._data_format = data_format | ||
| self._act = act | ||
| self._fuse_add = fuse_add | ||
| self._has_shortcut = has_shortcut | ||
| self._use_global_stats = use_global_stats | ||
| self._is_test = is_test | ||
|
|
||
| # check format | ||
| valid_format = {'NHWC'} | ||
| if data_format not in valid_format: | ||
| raise ValueError( | ||
| "conv_format must be one of {}, but got conv_format='{}'". | ||
| format(valid_format, data_format)) | ||
|
|
||
| def _get_default_param_initializer(channels): | ||
| filter_elem_num = np.prod(self._kernel_size) * channels | ||
| std = (2.0 / filter_elem_num)**0.5 | ||
| return I.Normal(0.0, std) | ||
|
|
||
| # initial filter | ||
| bn_param_dtype = fluid.core.VarDesc.VarType.FP32 | ||
| bn_param_shape = [1, 1, 1, num_filters] | ||
| filter_x_shape = [num_filters, filter_size, filter_size, num_channels_x] | ||
| filter_z_shape = [num_filters, filter_size, filter_size, num_channels_z] | ||
|
|
||
| self.filter_x = self.create_parameter( | ||
| shape=filter_x_shape, | ||
| attr=filter_x_attr, | ||
| default_initializer=_get_default_param_initializer(num_channels_x)) | ||
| self.scale_x = self.create_parameter( | ||
| shape=bn_param_shape, | ||
| attr=scale_x_attr, | ||
| dtype=bn_param_dtype, | ||
| default_initializer=I.Constant(1.0)) | ||
| self.bias_x = self.create_parameter( | ||
| shape=bn_param_shape, | ||
| attr=bias_x_attr, | ||
| dtype=bn_param_dtype, | ||
| is_bias=True) | ||
| self.mean_x = self.create_parameter( | ||
| attr=ParamAttr( | ||
| name=moving_mean_x_name, | ||
| initializer=I.Constant(0.0), | ||
| trainable=False), | ||
| shape=bn_param_shape, | ||
| dtype=bn_param_dtype) | ||
| self.mean_x.stop_gradient = True | ||
| self.var_x = self.create_parameter( | ||
| attr=ParamAttr( | ||
| name=moving_var_x_name, | ||
| initializer=I.Constant(1.0), | ||
| trainable=False), | ||
| shape=bn_param_shape, | ||
| dtype=bn_param_dtype) | ||
| self.var_x.stop_gradient = True | ||
| if has_shortcut: | ||
| self.filter_z = self.create_parameter( | ||
| shape=filter_z_shape, | ||
| attr=filter_z_attr, | ||
| default_initializer=_get_default_param_initializer( | ||
| num_channels_z)) | ||
| self.scale_z = self.create_parameter( | ||
| shape=bn_param_shape, | ||
| attr=scale_z_attr, | ||
| dtype=bn_param_dtype, | ||
| default_initializer=I.Constant(1.0)) | ||
| self.bias_z = self.create_parameter( | ||
| shape=bn_param_shape, | ||
| attr=bias_z_attr, | ||
| dtype=bn_param_dtype, | ||
| is_bias=True) | ||
| self.mean_z = self.create_parameter( | ||
| attr=ParamAttr( | ||
| name=moving_mean_z_name, | ||
| initializer=I.Constant(0.0), | ||
| trainable=False), | ||
| shape=bn_param_shape, | ||
| dtype=bn_param_dtype) | ||
| self.mean_z.stop_gradient = True | ||
| self.var_z = self.create_parameter( | ||
| attr=ParamAttr( | ||
| name=moving_var_z_name, | ||
| initializer=I.Constant(1.0), | ||
| trainable=False), | ||
| shape=bn_param_shape, | ||
| dtype=bn_param_dtype) | ||
| self.var_z.stop_gradient = True | ||
| else: | ||
| self.filter_z = None | ||
| self.scale_z = None | ||
| self.bias_z = None | ||
| self.mean_z = None | ||
| self.var_z = None | ||
|
|
||
| def forward(self, x, z=None): | ||
| if self._fuse_add and z is None: | ||
| raise ValueError("z can not be None") | ||
|
|
||
| out = resnet_unit( | ||
| x, self.filter_x, self.scale_x, self.bias_x, self.mean_x, | ||
| self.var_x, z, self.filter_z, self.scale_z, self.bias_z, | ||
| self.mean_z, self.var_z, self._stride, self._stride_z, | ||
| self._padding, self._dilation, self._groups, self._momentum, | ||
| self._eps, self._data_format, self._fuse_add, self._has_shortcut, | ||
| self._use_global_stats, self._is_test, self._act) | ||
| return out |