# 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 import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.regularizer import L2Decay from ppdet.core.workspace import register, serializable from numbers import Integral from ..shape_spec import ShapeSpec __all__ = ['MobileNetV3'] def make_divisible(v, divisor=8, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) if new_v < 0.9 * v: new_v += divisor return new_v class ConvBNLayer(nn.Layer): def __init__(self, in_c, out_c, filter_size, stride, padding, num_groups=1, act=None, lr_mult=1., conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, name=""): super(ConvBNLayer, self).__init__() self.act = act self.conv = nn.Conv2D( in_channels=in_c, out_channels=out_c, kernel_size=filter_size, stride=stride, padding=padding, groups=num_groups, weight_attr=ParamAttr( learning_rate=lr_mult, regularizer=L2Decay(conv_decay)), bias_attr=False) norm_lr = 0. if freeze_norm else lr_mult param_attr = ParamAttr( learning_rate=norm_lr, regularizer=L2Decay(norm_decay), trainable=False if freeze_norm else True) bias_attr = ParamAttr( learning_rate=norm_lr, regularizer=L2Decay(norm_decay), trainable=False if freeze_norm else True) global_stats = True if freeze_norm else None if norm_type in ['sync_bn', 'bn']: self.bn = nn.BatchNorm2D( out_c, weight_attr=param_attr, bias_attr=bias_attr, use_global_stats=global_stats) norm_params = self.bn.parameters() if freeze_norm: for param in norm_params: param.stop_gradient = True def forward(self, x): x = self.conv(x) x = self.bn(x) if self.act is not None: if self.act == "relu": x = F.relu(x) elif self.act == "relu6": x = F.relu6(x) elif self.act == "hard_swish": x = F.hardswish(x) else: raise NotImplementedError( "The activation function is selected incorrectly.") return x class ResidualUnit(nn.Layer): def __init__(self, in_c, mid_c, out_c, filter_size, stride, use_se, lr_mult, conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, act=None, return_list=False, name=''): super(ResidualUnit, self).__init__() self.if_shortcut = stride == 1 and in_c == out_c self.use_se = use_se self.return_list = return_list self.expand_conv = ConvBNLayer( in_c=in_c, out_c=mid_c, filter_size=1, stride=1, padding=0, act=act, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_expand") self.bottleneck_conv = ConvBNLayer( in_c=mid_c, out_c=mid_c, filter_size=filter_size, stride=stride, padding=int((filter_size - 1) // 2), num_groups=mid_c, act=act, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_depthwise") if self.use_se: self.mid_se = SEModule( mid_c, lr_mult, conv_decay, name=name + "_se") self.linear_conv = ConvBNLayer( in_c=mid_c, out_c=out_c, filter_size=1, stride=1, padding=0, act=None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_linear") def forward(self, inputs): y = self.expand_conv(inputs) x = self.bottleneck_conv(y) if self.use_se: x = self.mid_se(x) x = self.linear_conv(x) if self.if_shortcut: x = paddle.add(inputs, x) if self.return_list: return [y, x] else: return x class SEModule(nn.Layer): def __init__(self, channel, lr_mult, conv_decay, reduction=4, name=""): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2D(1) mid_channels = int(channel // reduction) self.conv1 = nn.Conv2D( in_channels=channel, out_channels=mid_channels, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr( learning_rate=lr_mult, regularizer=L2Decay(conv_decay)), bias_attr=ParamAttr( learning_rate=lr_mult, regularizer=L2Decay(conv_decay))) self.conv2 = nn.Conv2D( in_channels=mid_channels, out_channels=channel, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr( learning_rate=lr_mult, regularizer=L2Decay(conv_decay)), bias_attr=ParamAttr( learning_rate=lr_mult, regularizer=L2Decay(conv_decay))) def forward(self, inputs): outputs = self.avg_pool(inputs) outputs = self.conv1(outputs) outputs = F.relu(outputs) outputs = self.conv2(outputs) outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5) return paddle.multiply(x=inputs, y=outputs) class ExtraBlockDW(nn.Layer): def __init__(self, in_c, ch_1, ch_2, stride, lr_mult, conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, name=None): super(ExtraBlockDW, self).__init__() self.pointwise_conv = ConvBNLayer( in_c=in_c, out_c=ch_1, filter_size=1, stride=1, padding='SAME', act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra1") self.depthwise_conv = ConvBNLayer( in_c=ch_1, out_c=ch_2, filter_size=3, stride=stride, padding='SAME', num_groups=int(ch_1), act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra2_dw") self.normal_conv = ConvBNLayer( in_c=ch_2, out_c=ch_2, filter_size=1, stride=1, padding='SAME', act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra2_sep") def forward(self, inputs): x = self.pointwise_conv(inputs) x = self.depthwise_conv(x) x = self.normal_conv(x) return x @register @serializable class MobileNetV3(nn.Layer): __shared__ = ['norm_type'] def __init__( self, scale=1.0, model_name="large", feature_maps=[6, 12, 15], with_extra_blocks=False, extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]], lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0], conv_decay=0.0, multiplier=1.0, norm_type='bn', norm_decay=0.0, freeze_norm=False): super(MobileNetV3, self).__init__() if isinstance(feature_maps, Integral): feature_maps = [feature_maps] if norm_type == 'sync_bn' and freeze_norm: raise ValueError( "The norm_type should not be sync_bn when freeze_norm is True") self.feature_maps = feature_maps self.with_extra_blocks = with_extra_blocks self.extra_block_filters = extra_block_filters inplanes = 16 if model_name == "large": self.cfg = [ # k, exp, c, se, nl, s, [3, 16, 16, False, "relu", 1], [3, 64, 24, False, "relu", 2], [3, 72, 24, False, "relu", 1], [5, 72, 40, True, "relu", 2], # RCNN output [5, 120, 40, True, "relu", 1], [5, 120, 40, True, "relu", 1], # YOLOv3 output [3, 240, 80, False, "hard_swish", 2], # RCNN output [3, 200, 80, False, "hard_swish", 1], [3, 184, 80, False, "hard_swish", 1], [3, 184, 80, False, "hard_swish", 1], [3, 480, 112, True, "hard_swish", 1], [3, 672, 112, True, "hard_swish", 1], # YOLOv3 output [5, 672, 160, True, "hard_swish", 2], # SSD/SSDLite/RCNN output [5, 960, 160, True, "hard_swish", 1], [5, 960, 160, True, "hard_swish", 1], # YOLOv3 output ] elif model_name == "small": self.cfg = [ # k, exp, c, se, nl, s, [3, 16, 16, True, "relu", 2], [3, 72, 24, False, "relu", 2], # RCNN output [3, 88, 24, False, "relu", 1], # YOLOv3 output [5, 96, 40, True, "hard_swish", 2], # RCNN output [5, 240, 40, True, "hard_swish", 1], [5, 240, 40, True, "hard_swish", 1], [5, 120, 48, True, "hard_swish", 1], [5, 144, 48, True, "hard_swish", 1], # YOLOv3 output [5, 288, 96, True, "hard_swish", 2], # SSD/SSDLite/RCNN output [5, 576, 96, True, "hard_swish", 1], [5, 576, 96, True, "hard_swish", 1], # YOLOv3 output ] else: raise NotImplementedError( "mode[{}_model] is not implemented!".format(model_name)) if multiplier != 1.0: self.cfg[-3][2] = int(self.cfg[-3][2] * multiplier) self.cfg[-2][1] = int(self.cfg[-2][1] * multiplier) self.cfg[-2][2] = int(self.cfg[-2][2] * multiplier) self.cfg[-1][1] = int(self.cfg[-1][1] * multiplier) self.cfg[-1][2] = int(self.cfg[-1][2] * multiplier) self.conv1 = ConvBNLayer( in_c=3, out_c=make_divisible(inplanes * scale), filter_size=3, stride=2, padding=1, num_groups=1, act="hard_swish", lr_mult=lr_mult_list[0], conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name="conv1") self._out_channels = [] self.block_list = [] i = 0 inplanes = make_divisible(inplanes * scale) for (k, exp, c, se, nl, s) in self.cfg: lr_idx = min(i // 3, len(lr_mult_list) - 1) lr_mult = lr_mult_list[lr_idx] # for SSD/SSDLite, first head input is after ResidualUnit expand_conv return_list = self.with_extra_blocks and i + 2 in self.feature_maps block = self.add_sublayer( "conv" + str(i + 2), sublayer=ResidualUnit( in_c=inplanes, mid_c=make_divisible(scale * exp), out_c=make_divisible(scale * c), filter_size=k, stride=s, use_se=se, act=nl, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, return_list=return_list, name="conv" + str(i + 2))) self.block_list.append(block) inplanes = make_divisible(scale * c) i += 1 self._update_out_channels( make_divisible(scale * exp) if return_list else inplanes, i + 1, feature_maps) if self.with_extra_blocks: self.extra_block_list = [] extra_out_c = make_divisible(scale * self.cfg[-1][1]) lr_idx = min(i // 3, len(lr_mult_list) - 1) lr_mult = lr_mult_list[lr_idx] conv_extra = self.add_sublayer( "conv" + str(i + 2), sublayer=ConvBNLayer( in_c=inplanes, out_c=extra_out_c, filter_size=1, stride=1, padding=0, num_groups=1, act="hard_swish", lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name="conv" + str(i + 2))) self.extra_block_list.append(conv_extra) i += 1 self._update_out_channels(extra_out_c, i + 1, feature_maps) for j, block_filter in enumerate(self.extra_block_filters): in_c = extra_out_c if j == 0 else self.extra_block_filters[j - 1][1] conv_extra = self.add_sublayer( "conv" + str(i + 2), sublayer=ExtraBlockDW( in_c, block_filter[0], block_filter[1], stride=2, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name='conv' + str(i + 2))) self.extra_block_list.append(conv_extra) i += 1 self._update_out_channels(block_filter[1], i + 1, 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): x = self.conv1(inputs['image']) outs = [] for idx, block in enumerate(self.block_list): x = block(x) if idx + 2 in self.feature_maps: if isinstance(x, list): outs.append(x[0]) x = x[1] else: outs.append(x) if not self.with_extra_blocks: return outs for i, block in enumerate(self.extra_block_list): idx = i + len(self.block_list) x = block(x) if idx + 2 in self.feature_maps: outs.append(x) return outs @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]