# Copyright (c) 2018 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 __future__ import print_function from six.moves import reduce from .. import core from ..layers import utils from . import layers from ..framework import Variable, OpProtoHolder from ..param_attr import ParamAttr from ..initializer import Normal, Constant __all__ = [ 'Conv2D', 'Pool2D', 'FC', 'BatchNorm', ] class Conv2D(layers.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=None, use_cudnn=True, act=None, param_attr=None, bias_attr=None, name=None, dtype=core.VarDesc.VarType.FP32): assert param_attr is not False, "param_attr should not be False here." super(Conv2D, self).__init__(name=name, dtype=dtype) from ..layer_helper import LayerHelper self._helper = LayerHelper( type(self).__name__, param_attr=param_attr, bias_attr=bias_attr, dtype=dtype, name=name) self._groups = groups self._stride = utils.convert_to_list(stride, 2, 'stride') self._padding = utils.convert_to_list(padding, 2, 'padding') self._dilation = utils.convert_to_list(dilation, 2, 'dilation') if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") self._use_cudnn = use_cudnn self._num_channels = num_channels if (self._num_channels == self._groups and num_filters % self._num_channels == 0 and not self._use_cudnn): self._l_type = 'depthwise_conv2d' else: self._l_type = 'conv2d' if groups is None: num_filter_channels = num_channels else: if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels // groups filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') filter_shape = [num_filters, int(num_filter_channels)] + filter_size def _get_default_param_initializer(): filter_elem_num = filter_size[0] * filter_size[1] * num_channels std = (2.0 / filter_elem_num)**0.5 return Normal(0.0, std, 0) self._filter_param = self._helper.create_parameter( attr=self._helper.param_attr, shape=filter_shape, dtype=self._dtype, default_initializer=_get_default_param_initializer()) if self._use_cudnn: self._helper.create_variable( name="kCUDNNFwdAlgoCache", persistable=True, type=core.VarDesc.VarType.RAW) self._helper.create_variable( name="kCUDNNBwdDataAlgoCache", persistable=True, type=core.VarDesc.VarType.RAW) self._helper.create_variable( name="kCUDNNBwdFilterAlgoCache", persistable=True, type=core.VarDesc.VarType.RAW) self._bias_param = self._helper.create_parameter( attr=self._helper.bias_attr, shape=[num_filters], dtype=self._dtype, is_bias=True) def forward(self, input): pre_bias = self._helper.create_variable_for_type_inference( dtype=self._dtype) self._helper.append_op( type=self._l_type, inputs={ 'Input': input, 'Filter': self._filter_param, }, outputs={"Output": pre_bias}, attrs={ 'strides': self._stride, 'paddings': self._padding, 'dilations': self._dilation, 'groups': self._groups, 'use_cudnn': self._use_cudnn, 'use_mkldnn': False, }) pre_act = self._helper.create_variable_for_type_inference( dtype=self._dtype) self._helper.append_op( type='elementwise_add', inputs={'X': [pre_bias], 'Y': [self._bias_param]}, outputs={'Out': [pre_act]}, attrs={'axis': 1}) return self._helper.append_activation(pre_act) class Pool2D(layers.Layer): def __init__(self, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, exclusive=True, name=None, dtype=core.VarDesc.VarType.FP32): if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", str(pool_type)) if global_pooling is False and pool_size == -1: raise ValueError( "When the global_pooling is False, pool_size must be passed " "and be a valid value. Received pool_size: " + str(pool_size)) if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") super(Pool2D, self).__init__(name=name, dtype=dtype) from ..layer_helper import LayerHelper self._helper = LayerHelper(type(self).__name__, dtype=dtype, name=name) self._pool_type = pool_type self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size') self._pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding') self._pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride') self._global_pooling = global_pooling self._use_cudnn = use_cudnn self._ceil_mode = ceil_mode self._exclusive = exclusive self._l_type = 'pool2d' def forward(self, input): pool_out = self._helper.create_variable_for_type_inference(self._dtype) self._helper.append_op( type=self._l_type, inputs={"X": input}, outputs={"Out": pool_out}, attrs={ "pooling_type": self._pool_type, "ksize": self._pool_size, "global_pooling": self._global_pooling, "strides": self._pool_stride, "paddings": self._pool_padding, "use_cudnn": self._use_cudnn, "ceil_mode": self._ceil_mode, "use_mkldnn": False, "exclusive": self._exclusive, }) return pool_out class FC(layers.Layer): def __init__(self, size, param_attr=None, bias_attr=None, dtype=core.VarDesc.VarType.FP32, num_flatten_dims=1, act=None, is_test=False, name=None): super(FC, self).__init__() self._size = size self._num_flatten_dims = num_flatten_dims self._dtype = dtype from ..layer_helper import LayerHelper self._helper = LayerHelper( 'FC', param_attr=param_attr, bias_attr=bias_attr, act=act, name=name) def _build_once(self, input): input_shape = input.shape param_shape = [ reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1) ] + [self._size] self._w = self._helper.create_parameter( attr=self._helper.param_attr, shape=param_shape, dtype=self._dtype, is_bias=False) print("create param: ", self._w.name, self._w.stop_gradient) if self._helper.bias_attr: size = list([self._size]) self._b = self._helper.create_parameter( attr=self._helper.bias_attr, shape=size, dtype=self._dtype, is_bias=True) else: self._b = None def forward(self, input): tmp = self._helper.create_variable_for_type_inference(self._dtype) self._helper.append_op( type="mul", inputs={"X": input, "Y": self._w}, outputs={"Out": tmp}, attrs={ "x_num_col_dims": self._num_flatten_dims, "y_num_col_dims": 1 }) pre_bias = self._helper.create_variable_for_type_inference(self._dtype) self._helper.append_op( type="sum", inputs={"X": [tmp]}, outputs={"Out": pre_bias}, attrs={"use_mkldnn": False}) if self._b: pre_activation = self._helper.create_variable_for_type_inference( dtype=self._dtype) self._helper.append_op( type='elementwise_add', inputs={'X': [pre_bias], 'Y': [self._b]}, outputs={'Out': [pre_activation]}, attrs={'axis': self._num_flatten_dims}) else: pre_activation = pre_bias return self._helper.append_activation(pre_activation) class BatchNorm(layers.Layer): def __init__(self, num_channels, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, dtype=core.VarDesc.VarType.FP32, data_layout='NCHW', in_place=False, name=None, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, fuse_with_relu=False, use_global_stats=False): super(BatchNorm, self).__init__() assert bias_attr is not False, "bias_attr should not be False in batch_norm." from ..layer_helper import LayerHelper self._helper = LayerHelper( 'batch_norm', param_attr=param_attr, bias_attr=bias_attr, name=name) if dtype == core.VarDesc.VarType.FP16: self._dtype = core.VarDesc.VarType.FP32 else: self._dtype = dtype param_shape = [num_channels] # create parameter self._scale = self._helper.create_parameter( attr=self._helper.param_attr, shape=param_shape, dtype=self._dtype, default_initializer=Constant(1.0)) # setting stop_gradient=True to reduce computation if use_global_stats and self._helper.param_attr.learning_rate == 0.: self._scale.stop_gradient = True self._bias = self._helper.create_parameter( attr=self._helper.bias_attr, shape=param_shape, dtype=self._dtype, is_bias=True) # setting stop_gradient=True to reduce computation if use_global_stats and self._helper.bias_attr.learning_rate == 0.: self._bias.stop_gradient = True self._mean = self._helper.create_parameter( attr=ParamAttr( name=moving_mean_name, initializer=Constant(0.0), trainable=False, do_model_average=do_model_average_for_mean_and_var), shape=param_shape, dtype=self._dtype) self._mean.stop_gradient = True self._variance = self._helper.create_parameter( attr=ParamAttr( name=moving_variance_name, initializer=Constant(1.0), trainable=False, do_model_average=do_model_average_for_mean_and_var), shape=param_shape, dtype=self._dtype) self._variance.stop_gradient = True self._in_place = in_place self._momentum = momentum self._epsilon = epsilon self._is_test = is_test self._fuse_with_relu = fuse_with_relu self._use_global_stats = use_global_stats def _build_once(self, input): pass def forward(self, input): # create output # mean and mean_out share the same memory mean_out = self._mean # variance and variance out share the same memory variance_out = self._variance saved_mean = self._helper.create_variable_for_type_inference( dtype=self._dtype, stop_gradient=True) saved_variance = self._helper.create_variable_for_type_inference( dtype=self._dtype, stop_gradient=True) batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference( self._dtype) self._helper.append_op( type="batch_norm", inputs={ "X": input, "Scale": self._scale, "Bias": self._bias, "Mean": self._mean, "Variance": self._variance }, outputs={ "Y": batch_norm_out, "MeanOut": mean_out, "VarianceOut": variance_out, "SavedMean": saved_mean, "SavedVariance": saved_variance }, attrs={ "momentum": self._momentum, "epsilon": self._epsilon, "is_test": self._is_test, "use_mkldnn": False, "fuse_with_relu": self._fuse_with_relu, "use_global_stats": self._use_global_stats }) return self._helper.append_activation(batch_norm_out)