#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #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 absolute_import from __future__ import division from __future__ import print_function import collections import re import math import copy import paddle.fluid as fluid from .layers import conv2d, init_batch_norm_layer, init_fc_layer __all__ = [ 'EfficientNet', 'EfficientNetB0', 'EfficientNetB1', 'EfficientNetB2', 'EfficientNetB3', 'EfficientNetB4', 'EfficientNetB5', 'EfficientNetB6', 'EfficientNetB7' ] GlobalParams = collections.namedtuple('GlobalParams', [ 'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'num_classes', 'width_coefficient', 'depth_coefficient', 'depth_divisor', 'min_depth', 'drop_connect_rate', ]) BlockArgs = collections.namedtuple('BlockArgs', [ 'kernel_size', 'num_repeat', 'input_filters', 'output_filters', 'expand_ratio', 'id_skip', 'stride', 'se_ratio' ]) GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields) BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields) def efficientnet_params(model_name): """ Map EfficientNet model name to parameter coefficients. """ params_dict = { # Coefficients: width,depth,resolution,dropout 'efficientnet-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-b6': (1.8, 2.6, 528, 0.5), 'efficientnet-b7': (2.0, 3.1, 600, 0.5), } return params_dict[model_name] def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2, drop_connect_rate=0.2): """ Get block arguments according to parameter and coefficients. """ blocks_args = [ 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', 'r1_k3_s11_e6_i192_o320_se0.25', ] blocks_args = BlockDecoder.decode(blocks_args) global_params = GlobalParams( batch_norm_momentum=0.99, batch_norm_epsilon=1e-3, dropout_rate=dropout_rate, drop_connect_rate=drop_connect_rate, num_classes=1000, width_coefficient=width_coefficient, depth_coefficient=depth_coefficient, depth_divisor=8, min_depth=None) return blocks_args, global_params def get_model_params(model_name, override_params): """ Get the block args and global params for a given model """ if model_name.startswith('efficientnet'): w, d, _, p = efficientnet_params(model_name) blocks_args, global_params = efficientnet( width_coefficient=w, depth_coefficient=d, dropout_rate=p) else: raise NotImplementedError('model name is not pre-defined: %s' % model_name) if override_params: global_params = global_params._replace(**override_params) return blocks_args, global_params def round_filters(filters, global_params): """ Calculate and round number of filters based on depth multiplier. """ multiplier = global_params.width_coefficient if not multiplier: return filters divisor = global_params.depth_divisor min_depth = global_params.min_depth filters *= multiplier min_depth = min_depth or divisor new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor) if new_filters < 0.9 * filters: # prevent rounding by more than 10% new_filters += divisor return int(new_filters) def round_repeats(repeats, global_params): """ Round number of filters based on depth multiplier. """ multiplier = global_params.depth_coefficient if not multiplier: return repeats return int(math.ceil(multiplier * repeats)) class EfficientNet(): def __init__(self, name='b0', padding_type='SAME', override_params=None, is_test=False, use_se=True): valid_names = ['b' + str(i) for i in range(8)] assert name in valid_names, 'efficient name should be in b0~b7' model_name = 'efficientnet-' + name self._blocks_args, self._global_params = get_model_params( model_name, override_params) self._bn_mom = self._global_params.batch_norm_momentum self._bn_eps = self._global_params.batch_norm_epsilon self.is_test = is_test self.padding_type = padding_type self.use_se = use_se def net(self, input, class_dim=1000, is_test=False): conv = self.extract_features(input, is_test=is_test) out_channels = round_filters(1280, self._global_params) conv = self.conv_bn_layer( conv, num_filters=out_channels, filter_size=1, bn_act='swish', bn_mom=self._bn_mom, bn_eps=self._bn_eps, padding_type=self.padding_type, name='', conv_name='_conv_head', bn_name='_bn1') pool = fluid.layers.pool2d( input=conv, pool_type='avg', global_pooling=True, use_cudnn=False) if self._global_params.dropout_rate: pool = fluid.layers.dropout( pool, self._global_params.dropout_rate, dropout_implementation='upscale_in_train') param_attr, bias_attr = init_fc_layer(class_dim, '_fc') out = fluid.layers.fc(pool, class_dim, name='_fc', param_attr=param_attr, bias_attr=bias_attr) return out def _drop_connect(self, inputs, prob, is_test): if is_test: return inputs keep_prob = 1.0 - prob inputs_shape = fluid.layers.shape(inputs) random_tensor = keep_prob + fluid.layers.uniform_random( shape=[inputs_shape[0], 1, 1, 1], min=0., max=1.) binary_tensor = fluid.layers.floor(random_tensor) output = inputs / keep_prob * binary_tensor return output def _expand_conv_norm(self, inputs, block_args, is_test, name=None): # Expansion phase oup = block_args.input_filters * block_args.expand_ratio # number of output channels if block_args.expand_ratio != 1: conv = self.conv_bn_layer( inputs, num_filters=oup, filter_size=1, bn_act=None, bn_mom=self._bn_mom, bn_eps=self._bn_eps, padding_type=self.padding_type, name=name, conv_name=name + '_expand_conv', bn_name='_bn0') return conv def _depthwise_conv_norm(self, inputs, block_args, is_test, name=None): k = block_args.kernel_size s = block_args.stride if isinstance(s, list) or isinstance(s, tuple): s = s[0] oup = block_args.input_filters * block_args.expand_ratio # number of output channels conv = self.conv_bn_layer( inputs, num_filters=oup, filter_size=k, stride=s, num_groups=oup, bn_act=None, padding_type=self.padding_type, bn_mom=self._bn_mom, bn_eps=self._bn_eps, name=name, use_cudnn=False, conv_name=name + '_depthwise_conv', bn_name='_bn1') return conv def _project_conv_norm(self, inputs, block_args, is_test, name=None): final_oup = block_args.output_filters conv = self.conv_bn_layer( inputs, num_filters=final_oup, filter_size=1, bn_act=None, padding_type=self.padding_type, bn_mom=self._bn_mom, bn_eps=self._bn_eps, name=name, conv_name=name + '_project_conv', bn_name='_bn2') return conv def conv_bn_layer(self, input, filter_size, num_filters, stride=1, num_groups=1, padding_type="SAME", conv_act=None, bn_act='swish', use_cudnn=True, use_bn=True, bn_mom=0.9, bn_eps=1e-05, use_bias=False, name=None, conv_name=None, bn_name=None): conv = conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, groups=num_groups, act=conv_act, padding_type=padding_type, use_cudnn=use_cudnn, name=conv_name, use_bias=use_bias) if use_bn == False: return conv else: bn_name = name + bn_name param_attr, bias_attr = init_batch_norm_layer(bn_name) return fluid.layers.batch_norm( input=conv, act=bn_act, momentum=bn_mom, epsilon=bn_eps, name=bn_name, moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance', param_attr=param_attr, bias_attr=bias_attr) def _conv_stem_norm(self, inputs, is_test): out_channels = round_filters(32, self._global_params) bn = self.conv_bn_layer( inputs, num_filters=out_channels, filter_size=3, stride=2, bn_act=None, bn_mom=self._bn_mom, padding_type=self.padding_type, bn_eps=self._bn_eps, name='', conv_name='_conv_stem', bn_name='_bn0') return bn def mb_conv_block(self, inputs, block_args, is_test=False, drop_connect_rate=None, name=None): # Expansion and Depthwise Convolution oup = block_args.input_filters * block_args.expand_ratio # number of output channels has_se = self.use_se and (block_args.se_ratio is not None) and ( 0 < block_args.se_ratio <= 1) id_skip = block_args.id_skip # skip connection and drop connect conv = inputs if block_args.expand_ratio != 1: conv = fluid.layers.swish( self._expand_conv_norm(conv, block_args, is_test, name)) conv = fluid.layers.swish( self._depthwise_conv_norm(conv, block_args, is_test, name)) # Squeeze and Excitation if has_se: num_squeezed_channels = max( 1, int(block_args.input_filters * block_args.se_ratio)) conv = self.se_block(conv, num_squeezed_channels, oup, name) conv = self._project_conv_norm(conv, block_args, is_test, name) # Skip connection and drop connect input_filters, output_filters = block_args.input_filters, block_args.output_filters if id_skip and block_args.stride == 1 and input_filters == output_filters: if drop_connect_rate: conv = self._drop_connect(conv, drop_connect_rate, self.is_test) conv = fluid.layers.elementwise_add(conv, inputs) return conv def se_block(self, inputs, num_squeezed_channels, oup, name): x_squeezed = fluid.layers.pool2d( input=inputs, pool_type='avg', global_pooling=True, use_cudnn=False) x_squeezed = conv2d( x_squeezed, num_filters=num_squeezed_channels, filter_size=1, use_bias=True, padding_type=self.padding_type, act='swish', name=name + '_se_reduce') x_squeezed = conv2d( x_squeezed, num_filters=oup, filter_size=1, use_bias=True, padding_type=self.padding_type, name=name + '_se_expand') se_out = inputs * fluid.layers.sigmoid(x_squeezed) return se_out def extract_features(self, inputs, is_test): """ Returns output of the final convolution layer """ conv = fluid.layers.swish( self._conv_stem_norm( inputs, is_test=is_test)) block_args_copy = copy.deepcopy(self._blocks_args) idx = 0 block_size = 0 for block_arg in block_args_copy: block_arg = block_arg._replace( input_filters=round_filters(block_arg.input_filters, self._global_params), output_filters=round_filters(block_arg.output_filters, self._global_params), num_repeat=round_repeats(block_arg.num_repeat, self._global_params)) block_size += 1 for _ in range(block_arg.num_repeat - 1): block_size += 1 for block_args in self._blocks_args: # Update block input and output filters based on depth multiplier. block_args = block_args._replace( input_filters=round_filters(block_args.input_filters, self._global_params), output_filters=round_filters(block_args.output_filters, self._global_params), num_repeat=round_repeats(block_args.num_repeat, self._global_params)) # The first block needs to take care of stride and filter size increase. drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: drop_connect_rate *= float(idx) / block_size conv = self.mb_conv_block(conv, block_args, is_test, drop_connect_rate, '_blocks.' + str(idx) + '.') idx += 1 if block_args.num_repeat > 1: block_args = block_args._replace( input_filters=block_args.output_filters, stride=1) for _ in range(block_args.num_repeat - 1): drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: drop_connect_rate *= float(idx) / block_size conv = self.mb_conv_block(conv, block_args, is_test, drop_connect_rate, '_blocks.' + str(idx) + '.') idx += 1 return conv def shortcut(self, input, data_residual): return fluid.layers.elementwise_add(input, data_residual) class BlockDecoder(object): """ Block Decoder for readability, straight from the official TensorFlow repository """ @staticmethod def _decode_block_string(block_string): """ Gets a block through a string notation of arguments. """ assert isinstance(block_string, str) ops = block_string.split('_') options = {} for op in ops: splits = re.split(r'(\d.*)', op) if len(splits) >= 2: key, value = splits[:2] options[key] = value # Check stride assert ( ('s' in options and len(options['s']) == 1) or (len(options['s']) == 2 and options['s'][0] == options['s'][1])) return BlockArgs( kernel_size=int(options['k']), num_repeat=int(options['r']), input_filters=int(options['i']), output_filters=int(options['o']), expand_ratio=int(options['e']), id_skip=('noskip' not in block_string), se_ratio=float(options['se']) if 'se' in options else None, stride=[int(options['s'][0])]) @staticmethod def _encode_block_string(block): """Encodes a block to a string.""" args = [ 'r%d' % block.num_repeat, 'k%d' % block.kernel_size, 's%d%d' % (block.strides[0], block.strides[1]), 'e%s' % block.expand_ratio, 'i%d' % block.input_filters, 'o%d' % block.output_filters ] if 0 < block.se_ratio <= 1: args.append('se%s' % block.se_ratio) if block.id_skip is False: args.append('noskip') return '_'.join(args) @staticmethod def decode(string_list): """ Decodes a list of string notations to specify blocks inside the network. :param string_list: a list of strings, each string is a notation of block :return: a list of BlockArgs namedtuples of block args """ assert isinstance(string_list, list) blocks_args = [] for block_string in string_list: blocks_args.append(BlockDecoder._decode_block_string(block_string)) return blocks_args @staticmethod def encode(blocks_args): """ Encodes a list of BlockArgs to a list of strings. :param blocks_args: a list of BlockArgs namedtuples of block args :return: a list of strings, each string is a notation of block """ block_strings = [] for block in blocks_args: block_strings.append(BlockDecoder._encode_block_string(block)) return block_strings def EfficientNetB0(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b0', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB1(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b1', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB2(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b2', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB3(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b3', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB4(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b4', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB5(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b5', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB6(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b6', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model def EfficientNetB7(is_test=False, padding_type='SAME', override_params=None, use_se=True): model = EfficientNet( name='b7', is_test=is_test, padding_type=padding_type, override_params=override_params, use_se=use_se) return model