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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- '''
- Alphapose network
- '''
- import mindspore
- import mindspore.ops as ops
- import mindspore.nn as nn
- from src.SE_module import SELayer
-
- class MPReverse(nn.Cell):
- '''
- MPReverse
- '''
- def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
- super(MPReverse, self).__init__()
- self.maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, pad_mode=pad_mode)
- self.reverse = ops.ReverseV2(axis=[2, 3])
-
- def construct(self, x):
- x = self.reverse(x)
- x = self.maxpool(x)
- x = self.reverse(x)
- return x
-
- class Bottleneck(nn.Cell):
- '''
- model part of network
- '''
- expansion = 4
- def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, has_bias=False)
- self.bn1 = nn.BatchNorm2d(planes, momentum=0.1)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- pad_mode='pad',padding=1, has_bias=False)
- self.bn2 = nn.BatchNorm2d(planes, momentum=0.1)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, has_bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4, momentum=0.1)
- if reduction:
- self.se = SELayer(planes * 4)
-
- self.relu = nn.ReLU()
- self.reduc = reduction
- self.down_sample_layer = downsample
- self.stride = stride
-
- def construct(self, x):
- '''
- construct
- '''
- residual = x
- out = self.relu(self.bn1(self.conv1(x)))
- out = self.relu(self.bn2(self.conv2(out)))
- out = self.conv3(out)
- out = self.bn3(out)
- if self.reduc:
- out = self.se(out)
- if self.down_sample_layer is not None:
- residual = self.down_sample_layer(x)
- out = out+residual
- out = self.relu(out)
- return out
-
- class SEResnet(nn.Cell):
- '''
- model part of network
- '''
- def __init__(self, architecture):
- super(SEResnet, self).__init__()
- assert architecture in ["resnet50", "resnet101"]
- self.inplanes = 64
- self.layers = [3, 4, {"resnet50": 6, "resnet101": 23}[architecture], 3]
- self.block = Bottleneck
-
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
- stride=2, pad_mode='pad',padding=3, has_bias=False)
-
- self.bn1 = nn.BatchNorm2d(64, eps=1e-5, momentum=0.1)
- self.relu = nn.ReLU()
- self.maxpool = MPReverse(kernel_size=3, stride=2, pad_mode='same')
- self.layer1 = self.make_layer(self.block, 64, self.layers[0])
- self.layer2 = self.make_layer(
- self.block, 128, self.layers[1], stride=2)
- self.layer3 = self.make_layer(
- self.block, 256, self.layers[2], stride=2)
-
- self.layer4 = self.make_layer(
- self.block, 512, self.layers[3], stride=2)
-
- def construct(self, x):
- '''
- construct
- '''
- x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) # 64 * h/4 * w/4
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- return x
- def stages(self):
- return [self.layer1, self.layer2, self.layer3, self.layer4]
-
- def make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.SequentialCell([nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, has_bias=False),
- nn.BatchNorm2d(planes * block.expansion, momentum=0.1)])
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
- return nn.SequentialCell(layers)
-
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