597 lines
16 KiB
Python
597 lines
16 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import nn
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from paddle import ParamAttr
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from paddle.nn.functional import upsample
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from paddle.nn.initializer import Uniform
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from ppcls.arch.backbone.base.theseus_layer import TheseusLayer, Identity
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MODEL_URLS = {
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"HRNet_W18_C": "",
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"HRNet_W30_C": "",
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"HRNet_W32_C": "",
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"HRNet_W40_C": "",
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"HRNet_W44_C": "",
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"HRNet_W48_C": "",
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"HRNet_W60_C": "",
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"HRNet_W64_C": "",
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"SE_HRNet_W18_C": "",
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"SE_HRNet_W30_C": "",
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"SE_HRNet_W32_C": "",
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"SE_HRNet_W40_C": "",
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"SE_HRNet_W44_C": "",
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"SE_HRNet_W48_C": "",
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"SE_HRNet_W60_C": "",
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"SE_HRNet_W64_C": "",
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}
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__all__ = list(MODEL_URLS.keys())
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class ConvBNLayer(TheseusLayer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act="relu"):
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super(ConvBNLayer, self).__init__()
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self.conv = nn.Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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bias_attr=False)
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self.bn = nn.BatchNorm(
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num_filters,
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act=None)
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self.act = create_act(act)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.act(x)
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return x
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def create_act(act):
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if act == 'hardswish':
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return nn.Hardswish()
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elif act == 'relu':
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return nn.ReLU()
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elif act is None:
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return Identity()
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else:
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raise RuntimeError(
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'The activation function is not supported: {}'.format(act))
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class BottleneckBlock(TheseusLayer):
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def __init__(self,
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num_channels,
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num_filters,
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has_se,
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stride=1,
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downsample=False):
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super(BottleneckBlock, self).__init__()
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self.has_se = has_se
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self.downsample = downsample
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self.conv1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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act="relu")
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act="relu")
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self.conv3 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None)
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if self.downsample:
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self.conv_down = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None)
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if self.has_se:
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self.se = SELayer(
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num_channels=num_filters * 4,
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num_filters=num_filters * 4,
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reduction_ratio=16)
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self.relu = nn.ReLU()
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def forward(self, x, res_dict=None):
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residual = x
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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if self.downsample:
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residual = self.conv_down(residual)
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if self.has_se:
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x = self.se(x)
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x = paddle.add(x=residual, y=x)
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x = self.relu(x)
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return x
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class BasicBlock(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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has_se=False):
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super(BasicBlock, self).__init__()
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self.has_se = has_se
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self.conv1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=3,
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stride=1,
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act="relu")
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=1,
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act=None)
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if self.has_se:
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self.se = SELayer(
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num_channels=num_filters,
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num_filters=num_filters,
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reduction_ratio=16)
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self.relu = nn.ReLU()
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def forward(self, x):
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residual = x
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x = self.conv1(x)
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x = self.conv2(x)
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if self.has_se:
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x = self.se(x)
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x = paddle.add(x=residual, y=x)
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x = self.relu(x)
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return x
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class SELayer(TheseusLayer):
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def __init__(self, num_channels, num_filters, reduction_ratio):
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super(SELayer, self).__init__()
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self.pool2d_gap = nn.AdaptiveAvgPool2D(1)
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self._num_channels = num_channels
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med_ch = int(num_channels / reduction_ratio)
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stdv = 1.0 / math.sqrt(num_channels * 1.0)
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self.fc_squeeze = nn.Linear(
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num_channels,
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med_ch,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv)))
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self.relu = nn.ReLU()
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stdv = 1.0 / math.sqrt(med_ch * 1.0)
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self.fc_excitation = nn.Linear(
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med_ch,
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num_filters,
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weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
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self.sigmoid = nn.Sigmoid()
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def forward(self, x, res_dict=None):
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residual = x
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x = self.pool2d_gap(x)
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x = paddle.squeeze(x, axis=[2, 3])
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x = self.fc_squeeze(x)
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x = self.relu(x)
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x = self.fc_excitation(x)
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x = self.sigmoid(x)
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x = paddle.unsqueeze(x, axis=[2, 3])
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x = residual * x
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return x
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class Stage(TheseusLayer):
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def __init__(self,
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num_modules,
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num_filters,
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has_se=False):
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super(Stage, self).__init__()
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self._num_modules = num_modules
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self.stage_func_list = nn.LayerList()
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for i in range(num_modules):
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self.stage_func_list.append(
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HighResolutionModule(
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num_filters=num_filters,
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has_se=has_se))
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def forward(self, x, res_dict=None):
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x = x
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for idx in range(self._num_modules):
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x = self.stage_func_list[idx](x)
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return x
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class HighResolutionModule(TheseusLayer):
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def __init__(self,
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num_filters,
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has_se=False):
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super(HighResolutionModule, self).__init__()
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self.basic_block_list = nn.LayerList()
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for i in range(len(num_filters)):
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self.basic_block_list.append(
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nn.Sequential(*[
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BasicBlock(
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num_channels=num_filters[i],
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num_filters=num_filters[i],
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has_se=has_se) for j in range(4)]))
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self.fuse_func = FuseLayers(
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in_channels=num_filters,
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out_channels=num_filters)
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def forward(self, x, res_dict=None):
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out = []
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for idx, xi in enumerate(x):
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basic_block_list = self.basic_block_list[idx]
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for basic_block_func in basic_block_list:
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xi = basic_block_func(xi)
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out.append(xi)
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out = self.fuse_func(out)
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return out
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class FuseLayers(TheseusLayer):
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def __init__(self,
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in_channels,
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out_channels):
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super(FuseLayers, self).__init__()
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self._actual_ch = len(in_channels)
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self._in_channels = in_channels
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self.residual_func_list = nn.LayerList()
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self.relu = nn.ReLU()
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for i in range(len(in_channels)):
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for j in range(len(in_channels)):
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if j > i:
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self.residual_func_list.append(
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ConvBNLayer(
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num_channels=in_channels[j],
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num_filters=out_channels[i],
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filter_size=1,
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stride=1,
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act=None))
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elif j < i:
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pre_num_filters = in_channels[j]
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for k in range(i - j):
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if k == i - j - 1:
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self.residual_func_list.append(
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ConvBNLayer(
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num_channels=pre_num_filters,
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num_filters=out_channels[i],
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filter_size=3,
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stride=2,
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act=None))
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pre_num_filters = out_channels[i]
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else:
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self.residual_func_list.append(
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ConvBNLayer(
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num_channels=pre_num_filters,
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num_filters=out_channels[j],
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filter_size=3,
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stride=2,
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act="relu"))
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pre_num_filters = out_channels[j]
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def forward(self, x, res_dict=None):
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out = []
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residual_func_idx = 0
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for i in range(len(self._in_channels)):
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residual = x[i]
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for j in range(len(self._in_channels)):
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if j > i:
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xj = self.residual_func_list[residual_func_idx](x[j])
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residual_func_idx += 1
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xj = upsample(xj, scale_factor=2**(j - i), mode="nearest")
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residual = paddle.add(x=residual, y=xj)
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elif j < i:
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xj = x[j]
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for k in range(i - j):
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xj = self.residual_func_list[residual_func_idx](xj)
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residual_func_idx += 1
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residual = paddle.add(x=residual, y=xj)
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residual = self.relu(residual)
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out.append(residual)
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return out
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class LastClsOut(TheseusLayer):
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def __init__(self,
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num_channel_list,
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has_se,
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num_filters_list=[32, 64, 128, 256]):
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super(LastClsOut, self).__init__()
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self.func_list = nn.LayerList()
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for idx in range(len(num_channel_list)):
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self.func_list.append(
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BottleneckBlock(
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num_channels=num_channel_list[idx],
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num_filters=num_filters_list[idx],
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has_se=has_se,
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downsample=True))
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def forward(self, x, res_dict=None):
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out = []
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for idx, xi in enumerate(x):
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xi = self.func_list[idx](xi)
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out.append(xi)
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return out
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class HRNet(TheseusLayer):
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"""
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HRNet
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Args:
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width: int=18. Base channel number of HRNet.
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has_se: bool=False. If 'True', add se module to HRNet.
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class_num: int=1000. Output num of last fc layer.
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"""
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def __init__(self, width=18, has_se=False, class_num=1000):
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super(HRNet, self).__init__()
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self.width = width
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self.has_se = has_se
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self._class_num = class_num
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channels_2 = [self.width, self.width * 2]
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channels_3 = [self.width, self.width * 2, self.width * 4]
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channels_4 = [self.width, self.width * 2, self.width * 4, self.width * 8]
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self.conv_layer1_1 = ConvBNLayer(
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num_channels=3,
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num_filters=64,
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filter_size=3,
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stride=2,
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act='relu')
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self.conv_layer1_2 = ConvBNLayer(
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num_channels=64,
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num_filters=64,
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filter_size=3,
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stride=2,
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act='relu')
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self.layer1 = nn.Sequential(*[
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BottleneckBlock(
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num_channels=64 if i == 0 else 256,
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num_filters=64,
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has_se=has_se,
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stride=1,
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downsample=True if i == 0 else False)
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for i in range(4)
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])
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self.conv_tr1_1 = ConvBNLayer(
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num_channels=256,
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num_filters=width,
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filter_size=3)
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self.conv_tr1_2 = ConvBNLayer(
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num_channels=256,
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num_filters=width * 2,
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filter_size=3,
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stride=2
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)
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self.st2 = Stage(
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num_modules=1,
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num_filters=channels_2,
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has_se=self.has_se)
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self.conv_tr2 = ConvBNLayer(
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num_channels=width * 2,
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num_filters=width * 4,
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filter_size=3,
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stride=2
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)
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self.st3 = Stage(
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num_modules=4,
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num_filters=channels_3,
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has_se=self.has_se)
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self.conv_tr3 = ConvBNLayer(
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num_channels=width * 4,
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num_filters=width * 8,
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filter_size=3,
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stride=2
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)
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self.st4 = Stage(
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num_modules=3,
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num_filters=channels_4,
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has_se=self.has_se)
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# classification
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num_filters_list = [32, 64, 128, 256]
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self.last_cls = LastClsOut(
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num_channel_list=channels_4,
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has_se=self.has_se,
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num_filters_list=num_filters_list)
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last_num_filters = [256, 512, 1024]
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self.cls_head_conv_list = nn.LayerList()
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for idx in range(3):
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self.cls_head_conv_list.append(
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ConvBNLayer(
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num_channels=num_filters_list[idx] * 4,
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num_filters=last_num_filters[idx],
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filter_size=3,
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stride=2))
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self.conv_last = ConvBNLayer(
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num_channels=1024,
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num_filters=2048,
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filter_size=1,
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stride=1)
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self.avg_pool = nn.AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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self.fc = nn.Linear(
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2048,
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class_num,
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weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
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def forward(self, x, res_dict=None):
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x = self.conv_layer1_1(x)
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x = self.conv_layer1_2(x)
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x = self.layer1(x)
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tr1_1 = self.conv_tr1_1(x)
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tr1_2 = self.conv_tr1_2(x)
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x = self.st2([tr1_1, tr1_2])
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tr2 = self.conv_tr2(x[-1])
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x.append(tr2)
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x = self.st3(x)
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tr3 = self.conv_tr3(x[-1])
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x.append(tr3)
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x = self.st4(x)
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x = self.last_cls(x)
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y = x[0]
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for idx in range(3):
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y = paddle.add(x[idx + 1], self.cls_head_conv_list[idx](y))
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y = self.conv_last(y)
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y = self.avg_pool(y)
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y = paddle.reshape(y, shape=[-1, y.shape[1]])
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y = self.fc(y)
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return y
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def HRNet_W18_C(**args):
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model = HRNet(width=18, **args)
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return model
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def HRNet_W30_C(**args):
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model = HRNet(width=30, **args)
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return model
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def HRNet_W32_C(**args):
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model = HRNet(width=32, **args)
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return model
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def HRNet_W40_C(**args):
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model = HRNet(width=40, **args)
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return model
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def HRNet_W44_C(**args):
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model = HRNet(width=44, **args)
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return model
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def HRNet_W48_C(**args):
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model = HRNet(width=48, **args)
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return model
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def HRNet_W60_C(**args):
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model = HRNet(width=60, **args)
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return model
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def HRNet_W64_C(**args):
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model = HRNet(width=64, **args)
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return model
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|
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def SE_HRNet_W18_C(**args):
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model = HRNet(width=18, has_se=True, **args)
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return model
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|
|
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def SE_HRNet_W30_C(**args):
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model = HRNet(width=30, has_se=True, **args)
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return model
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|
|
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def SE_HRNet_W32_C(**args):
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model = HRNet(width=32, has_se=True, **args)
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return model
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|
|
|
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def SE_HRNet_W40_C(**args):
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model = HRNet(width=40, has_se=True, **args)
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return model
|
|
|
|
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def SE_HRNet_W44_C(**args):
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|
model = HRNet(width=44, has_se=True, **args)
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|
return model
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|
|
|
|
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def SE_HRNet_W48_C(**args):
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|
model = HRNet(width=48, has_se=True, **args)
|
|
return model
|
|
|
|
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def SE_HRNet_W60_C(**args):
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|
model = HRNet(width=60, has_se=True, **args)
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|
return model
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|
|
|
|
|
def SE_HRNet_W64_C(**args):
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|
model = HRNet(width=64, has_se=True, **args)
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|
return model
|