# 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 numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout from paddle.fluid.initializer import MSRA import math __all__ = [ "MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1" ] class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, num_channels, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='relu', use_cudnn=True, name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=num_groups, act=None, use_cudnn=use_cudnn, param_attr=ParamAttr( initializer=MSRA(), name=name + "_weights"), bias_attr=False) self._batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(name + "_bn_scale"), bias_attr=ParamAttr(name + "_bn_offset"), moving_mean_name=name + "_bn_mean", moving_variance_name=name + "_bn_variance") def forward(self, inputs): y = self._conv(inputs) y = self._batch_norm(y) return y class DepthwiseSeparable(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters1, num_filters2, num_groups, stride, scale, name=None): super(DepthwiseSeparable, self).__init__() self._depthwise_conv = ConvBNLayer( num_channels=num_channels, num_filters=int(num_filters1 * scale), filter_size=3, stride=stride, padding=1, num_groups=int(num_groups * scale), use_cudnn=False, name=name + "_dw") self._pointwise_conv = ConvBNLayer( num_channels=int(num_filters1 * scale), filter_size=1, num_filters=int(num_filters2 * scale), stride=1, padding=0, name=name + "_sep") def forward(self, inputs): y = self._depthwise_conv(inputs) y = self._pointwise_conv(y) return y class MobileNet(fluid.dygraph.Layer): def __init__(self, scale=1.0, class_dim=1000): super(MobileNet, self).__init__() self.scale = scale self.block_list = [] self.conv1 = ConvBNLayer( num_channels=3, filter_size=3, channels=3, num_filters=int(32 * scale), stride=2, padding=1, name="conv1") conv2_1 = self.add_sublayer( "conv2_1", sublayer=DepthwiseSeparable( num_channels=int(32 * scale), num_filters1=32, num_filters2=64, num_groups=32, stride=1, scale=scale, name="conv2_1")) self.block_list.append(conv2_1) conv2_2 = self.add_sublayer( "conv2_2", sublayer=DepthwiseSeparable( num_channels=int(64 * scale), num_filters1=64, num_filters2=128, num_groups=64, stride=2, scale=scale, name="conv2_2")) self.block_list.append(conv2_2) conv3_1 = self.add_sublayer( "conv3_1", sublayer=DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=128, num_groups=128, stride=1, scale=scale, name="conv3_1")) self.block_list.append(conv3_1) conv3_2 = self.add_sublayer( "conv3_2", sublayer=DepthwiseSeparable( num_channels=int(128 * scale), num_filters1=128, num_filters2=256, num_groups=128, stride=2, scale=scale, name="conv3_2")) self.block_list.append(conv3_2) conv4_1 = self.add_sublayer( "conv4_1", sublayer=DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=256, num_groups=256, stride=1, scale=scale, name="conv4_1")) self.block_list.append(conv4_1) conv4_2 = self.add_sublayer( "conv4_2", sublayer=DepthwiseSeparable( num_channels=int(256 * scale), num_filters1=256, num_filters2=512, num_groups=256, stride=2, scale=scale, name="conv4_2")) self.block_list.append(conv4_2) for i in range(5): conv5 = self.add_sublayer( "conv5_" + str(i + 1), sublayer=DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=512, num_groups=512, stride=1, scale=scale, name="conv5_" + str(i + 1))) self.block_list.append(conv5) conv5_6 = self.add_sublayer( "conv5_6", sublayer=DepthwiseSeparable( num_channels=int(512 * scale), num_filters1=512, num_filters2=1024, num_groups=512, stride=2, scale=scale, name="conv5_6")) self.block_list.append(conv5_6) conv6 = self.add_sublayer( "conv6", sublayer=DepthwiseSeparable( num_channels=int(1024 * scale), num_filters1=1024, num_filters2=1024, num_groups=1024, stride=1, scale=scale, name="conv6")) self.block_list.append(conv6) self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True) self.out = Linear( int(1024 * scale), class_dim, param_attr=ParamAttr( initializer=MSRA(), name="fc7_weights"), bias_attr=ParamAttr(name="fc7_offset")) def forward(self, inputs): y = self.conv1(inputs) for block in self.block_list: y = block(y) y = self.pool2d_avg(y) y = fluid.layers.reshape(y, shape=[-1, int(1024 * self.scale)]) y = self.out(y) return y def MobileNetV1_x0_25(**args): model = MobileNet(scale=0.25, **args) return model def MobileNetV1_x0_5(**args): model = MobileNet(scale=0.5, **args) return model def MobileNetV1_x0_75(**args): model = MobileNet(scale=0.75, **args) return model def MobileNetV1(**args): model = MobileNet(scale=1.0, **args) return model