# copyright (c) 2021 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.nn import Conv2D, MaxPool2D, AdaptiveAvgPool2D from paddle.nn.initializer import KaimingNormal from paddle.regularizer import L2Decay from ppdet.core.workspace import register, serializable from numbers import Integral from ..shape_spec import ShapeSpec from ppdet.modeling.ops import channel_shuffle from ppdet.modeling.backbones.shufflenet_v2 import ConvBNLayer __all__ = ['ESNet'] def make_divisible(v, divisor=16, 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 SEModule(nn.Layer): def __init__(self, channel, reduction=4): super(SEModule, self).__init__() self.avg_pool = AdaptiveAvgPool2D(1) self.conv1 = Conv2D( in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr(), bias_attr=ParamAttr()) self.conv2 = Conv2D( in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr(), bias_attr=ParamAttr()) 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) return paddle.multiply(x=inputs, y=outputs) class InvertedResidual(nn.Layer): def __init__(self, in_channels, mid_channels, out_channels, stride, act="relu"): super(InvertedResidual, self).__init__() self._conv_pw = ConvBNLayer( in_channels=in_channels // 2, out_channels=mid_channels // 2, kernel_size=1, stride=1, padding=0, groups=1, act=act) self._conv_dw = ConvBNLayer( in_channels=mid_channels // 2, out_channels=mid_channels // 2, kernel_size=3, stride=stride, padding=1, groups=mid_channels // 2, act=None) self._se = SEModule(mid_channels) self._conv_linear = ConvBNLayer( in_channels=mid_channels, out_channels=out_channels // 2, kernel_size=1, stride=1, padding=0, groups=1, act=act) def forward(self, inputs): x1, x2 = paddle.split( inputs, num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2], axis=1) x2 = self._conv_pw(x2) x3 = self._conv_dw(x2) x3 = paddle.concat([x2, x3], axis=1) x3 = self._se(x3) x3 = self._conv_linear(x3) out = paddle.concat([x1, x3], axis=1) return channel_shuffle(out, 2) class InvertedResidualDS(nn.Layer): def __init__(self, in_channels, mid_channels, out_channels, stride, act="relu"): super(InvertedResidualDS, self).__init__() # branch1 self._conv_dw_1 = ConvBNLayer( in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=stride, padding=1, groups=in_channels, act=None) self._conv_linear_1 = ConvBNLayer( in_channels=in_channels, out_channels=out_channels // 2, kernel_size=1, stride=1, padding=0, groups=1, act=act) # branch2 self._conv_pw_2 = ConvBNLayer( in_channels=in_channels, out_channels=mid_channels // 2, kernel_size=1, stride=1, padding=0, groups=1, act=act) self._conv_dw_2 = ConvBNLayer( in_channels=mid_channels // 2, out_channels=mid_channels // 2, kernel_size=3, stride=stride, padding=1, groups=mid_channels // 2, act=None) self._se = SEModule(mid_channels // 2) self._conv_linear_2 = ConvBNLayer( in_channels=mid_channels // 2, out_channels=out_channels // 2, kernel_size=1, stride=1, padding=0, groups=1, act=act) self._conv_dw_mv1 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, groups=out_channels, act="hard_swish") self._conv_pw_mv1 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, groups=1, act="hard_swish") def forward(self, inputs): x1 = self._conv_dw_1(inputs) x1 = self._conv_linear_1(x1) x2 = self._conv_pw_2(inputs) x2 = self._conv_dw_2(x2) x2 = self._se(x2) x2 = self._conv_linear_2(x2) out = paddle.concat([x1, x2], axis=1) out = self._conv_dw_mv1(out) out = self._conv_pw_mv1(out) return out @register @serializable class ESNet(nn.Layer): def __init__(self, scale=1.0, act="hard_swish", feature_maps=[4, 11, 14], channel_ratio=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]): super(ESNet, self).__init__() self.scale = scale if isinstance(feature_maps, Integral): feature_maps = [feature_maps] self.feature_maps = feature_maps stage_repeats = [3, 7, 3] stage_out_channels = [ -1, 24, make_divisible(128 * scale), make_divisible(256 * scale), make_divisible(512 * scale), 1024 ] self._out_channels = [] self._feature_idx = 0 # 1. conv1 self._conv1 = ConvBNLayer( in_channels=3, out_channels=stage_out_channels[1], kernel_size=3, stride=2, padding=1, act=act) self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1) self._feature_idx += 1 # 2. bottleneck sequences self._block_list = [] arch_idx = 0 for stage_id, num_repeat in enumerate(stage_repeats): for i in range(num_repeat): channels_scales = channel_ratio[arch_idx] mid_c = make_divisible( int(stage_out_channels[stage_id + 2] * channels_scales), divisor=8) if i == 0: block = self.add_sublayer( name=str(stage_id + 2) + '_' + str(i + 1), sublayer=InvertedResidualDS( in_channels=stage_out_channels[stage_id + 1], mid_channels=mid_c, out_channels=stage_out_channels[stage_id + 2], stride=2, act=act)) else: block = self.add_sublayer( name=str(stage_id + 2) + '_' + str(i + 1), sublayer=InvertedResidual( in_channels=stage_out_channels[stage_id + 2], mid_channels=mid_c, out_channels=stage_out_channels[stage_id + 2], stride=1, act=act)) self._block_list.append(block) arch_idx += 1 self._feature_idx += 1 self._update_out_channels(stage_out_channels[stage_id + 2], self._feature_idx, self.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): y = self._conv1(inputs['image']) y = self._max_pool(y) outs = [] for i, inv in enumerate(self._block_list): y = inv(y) if i + 2 in self.feature_maps: outs.append(y) return outs @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]