# 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 paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.regularizer import L2Decay from paddle.nn.initializer import KaimingNormal from ppdet.core.workspace import register, serializable from numbers import Integral from ..shape_spec import ShapeSpec __all__ = ['MobileNet'] class ConvBNLayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, num_groups=1, act='relu', conv_lr=1., conv_decay=0., norm_decay=0., norm_type='bn', name=None): super(ConvBNLayer, self).__init__() self.act = act self._conv = nn.Conv2D( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=num_groups, weight_attr=ParamAttr( learning_rate=conv_lr, initializer=KaimingNormal(), regularizer=L2Decay(conv_decay)), bias_attr=False) param_attr = ParamAttr(regularizer=L2Decay(norm_decay)) bias_attr = ParamAttr(regularizer=L2Decay(norm_decay)) if norm_type == 'sync_bn': self._batch_norm = nn.SyncBatchNorm( out_channels, weight_attr=param_attr, bias_attr=bias_attr) else: self._batch_norm = nn.BatchNorm( out_channels, act=None, param_attr=param_attr, bias_attr=bias_attr, use_global_stats=False) def forward(self, x): x = self._conv(x) x = self._batch_norm(x) if self.act == "relu": x = F.relu(x) elif self.act == "relu6": x = F.relu6(x) return x class DepthwiseSeparable(nn.Layer): def __init__(self, in_channels, out_channels1, out_channels2, num_groups, stride, scale, conv_lr=1., conv_decay=0., norm_decay=0., norm_type='bn', name=None): super(DepthwiseSeparable, self).__init__() self._depthwise_conv = ConvBNLayer( in_channels, int(out_channels1 * scale), kernel_size=3, stride=stride, padding=1, num_groups=int(num_groups * scale), conv_lr=conv_lr, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name=name + "_dw") self._pointwise_conv = ConvBNLayer( int(out_channels1 * scale), int(out_channels2 * scale), kernel_size=1, stride=1, padding=0, conv_lr=conv_lr, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name=name + "_sep") def forward(self, x): x = self._depthwise_conv(x) x = self._pointwise_conv(x) return x class ExtraBlock(nn.Layer): def __init__(self, in_channels, out_channels1, out_channels2, num_groups=1, stride=2, conv_lr=1., conv_decay=0., norm_decay=0., norm_type='bn', name=None): super(ExtraBlock, self).__init__() self.pointwise_conv = ConvBNLayer( in_channels, int(out_channels1), kernel_size=1, stride=1, padding=0, num_groups=int(num_groups), act='relu6', conv_lr=conv_lr, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name=name + "_extra1") self.normal_conv = ConvBNLayer( int(out_channels1), int(out_channels2), kernel_size=3, stride=stride, padding=1, num_groups=int(num_groups), act='relu6', conv_lr=conv_lr, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name=name + "_extra2") def forward(self, x): x = self.pointwise_conv(x) x = self.normal_conv(x) return x @register @serializable class MobileNet(nn.Layer): __shared__ = ['norm_type'] def __init__(self, norm_type='bn', norm_decay=0., conv_decay=0., scale=1, conv_learning_rate=1.0, feature_maps=[4, 6, 13], with_extra_blocks=False, extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]]): super(MobileNet, self).__init__() if isinstance(feature_maps, Integral): feature_maps = [feature_maps] self.feature_maps = feature_maps self.with_extra_blocks = with_extra_blocks self.extra_block_filters = extra_block_filters self._out_channels = [] self.conv1 = ConvBNLayer( in_channels=3, out_channels=int(32 * scale), kernel_size=3, stride=2, padding=1, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv1") self.dwsl = [] dws21 = self.add_sublayer( "conv2_1", sublayer=DepthwiseSeparable( in_channels=int(32 * scale), out_channels1=32, out_channels2=64, num_groups=32, stride=1, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv2_1")) self.dwsl.append(dws21) self._update_out_channels(int(64 * scale), len(self.dwsl), feature_maps) dws22 = self.add_sublayer( "conv2_2", sublayer=DepthwiseSeparable( in_channels=int(64 * scale), out_channels1=64, out_channels2=128, num_groups=64, stride=2, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv2_2")) self.dwsl.append(dws22) self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps) # 1/4 dws31 = self.add_sublayer( "conv3_1", sublayer=DepthwiseSeparable( in_channels=int(128 * scale), out_channels1=128, out_channels2=128, num_groups=128, stride=1, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv3_1")) self.dwsl.append(dws31) self._update_out_channels(int(128 * scale), len(self.dwsl), feature_maps) dws32 = self.add_sublayer( "conv3_2", sublayer=DepthwiseSeparable( in_channels=int(128 * scale), out_channels1=128, out_channels2=256, num_groups=128, stride=2, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv3_2")) self.dwsl.append(dws32) self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps) # 1/8 dws41 = self.add_sublayer( "conv4_1", sublayer=DepthwiseSeparable( in_channels=int(256 * scale), out_channels1=256, out_channels2=256, num_groups=256, stride=1, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv4_1")) self.dwsl.append(dws41) self._update_out_channels(int(256 * scale), len(self.dwsl), feature_maps) dws42 = self.add_sublayer( "conv4_2", sublayer=DepthwiseSeparable( in_channels=int(256 * scale), out_channels1=256, out_channels2=512, num_groups=256, stride=2, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv4_2")) self.dwsl.append(dws42) self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps) # 1/16 for i in range(5): tmp = self.add_sublayer( "conv5_" + str(i + 1), sublayer=DepthwiseSeparable( in_channels=int(512 * scale), out_channels1=512, out_channels2=512, num_groups=512, stride=1, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv5_" + str(i + 1))) self.dwsl.append(tmp) self._update_out_channels(int(512 * scale), len(self.dwsl), feature_maps) dws56 = self.add_sublayer( "conv5_6", sublayer=DepthwiseSeparable( in_channels=int(512 * scale), out_channels1=512, out_channels2=1024, num_groups=512, stride=2, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv5_6")) self.dwsl.append(dws56) self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps) # 1/32 dws6 = self.add_sublayer( "conv6", sublayer=DepthwiseSeparable( in_channels=int(1024 * scale), out_channels1=1024, out_channels2=1024, num_groups=1024, stride=1, scale=scale, conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv6")) self.dwsl.append(dws6) self._update_out_channels(int(1024 * scale), len(self.dwsl), feature_maps) if self.with_extra_blocks: self.extra_blocks = [] for i, block_filter in enumerate(self.extra_block_filters): in_c = 1024 if i == 0 else self.extra_block_filters[i - 1][1] conv_extra = self.add_sublayer( "conv7_" + str(i + 1), sublayer=ExtraBlock( in_c, block_filter[0], block_filter[1], conv_lr=conv_learning_rate, conv_decay=conv_decay, norm_decay=norm_decay, norm_type=norm_type, name="conv7_" + str(i + 1))) self.extra_blocks.append(conv_extra) self._update_out_channels( block_filter[1], len(self.dwsl) + len(self.extra_blocks), feature_maps) def _update_out_channels(self, channel, feature_idx, feature_maps): if feature_idx in feature_maps: self._out_channels.append(channel) def forward(self, inputs): outs = [] y = self.conv1(inputs['image']) for i, block in enumerate(self.dwsl): y = block(y) if i + 1 in self.feature_maps: outs.append(y) if not self.with_extra_blocks: return outs y = outs[-1] for i, block in enumerate(self.extra_blocks): idx = i + len(self.dwsl) y = block(y) if idx + 1 in self.feature_maps: outs.append(y) return outs @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]