人脸关键点检测网络：HighResolutionNet 类详解
这是一个用于人脸关键点检测的网络实现，具体来说：
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定义了一个名为 'HighResolutionNet' 的类，继承自 'nn.Module' 类，用于定义整个网络的结构。
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在 'init' 方法中，定义了网络的各个组件，包括卷积层 ('nn.Conv2d')、BatchNorm 层 ('BatchNorm2d')、ReLU 层 ('nn.ReLU')、Softmax 层 ('nn.Softmax')、残差块等。
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定义了 '_make_transition_layer' 方法，用于构建过渡层，将不同分支的特征图进行融合。
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定义了 '_make_layer' 方法，用于构建残差块。
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定义了 '_make_stage' 方法，用于构建整个阶段的模块。
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在 'forward' 方法中，将输入的图像依次经过各个组件，得到最终的输出。
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定义了 'init_weights' 方法，用于初始化网络的权重。
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最后，定义了 'get_face_alignment_net' 方法，用于创建 'HighResolutionNet' 类的实例，并初始化其权重。
代码示例：
class HighResolutionNet(nn.Module):
def __init__(self, config, **kwargs):
self.inplanes = 64
extra = config.MODEL.EXTRA
super(HighResolutionNet, self).__init__()
# stem net
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.sf = nn.Softmax(dim=1)
self.layer1 = self._make_layer(Bottleneck, 64, 64, 4)
self.stage2_cfg = extra['STAGE2']
num_channels = self.stage2_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage2_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition1 = self._make_transition_layer(
[256], num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
self.stage3_cfg = extra['STAGE3']
num_channels = self.stage3_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage3_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition2 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
self.stage4_cfg = extra['STAGE4']
num_channels = self.stage4_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage4_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition3 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multi_scale_output=True)
final_inp_channels = sum(pre_stage_channels)
self.head = nn.Sequential(
nn.Conv2d(
in_channels=final_inp_channels,
out_channels=final_inp_channels,
kernel_size=1,
stride=1,
padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0),
BatchNorm2d(final_inp_channels, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True),
nn.Conv2d(
in_channels=final_inp_channels,
out_channels=config.MODEL.NUM_JOINTS,
kernel_size=extra.FINAL_CONV_KERNEL,
stride=1,
padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0)
)
def _make_transition_layer(
self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(nn.Sequential(
nn.Conv2d(num_channels_pre_layer[i],
num_channels_cur_layer[i],
3,
1,
1,
bias=False),
BatchNorm2d(
num_channels_cur_layer[i], momentum=BN_MOMENTUM),
nn.ReLU(inplace=True)))
else:
transition_layers.append(None)
else:
conv3x3s = []
for j in range(i + 1 - num_branches_pre):
inchannels = num_channels_pre_layer[-1]
outchannels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else inchannels
conv3x3s.append(nn.Sequential(
nn.Conv2d(
inchannels, outchannels, 3, 2, 1, bias=False),
BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True)))
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(block(inplanes, planes, stride, downsample))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(inplanes, planes))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, num_inchannels,
multi_scale_output=True):
num_modules = layer_config['NUM_MODULES']
num_branches = layer_config['NUM_BRANCHES']
num_blocks = layer_config['NUM_BLOCKS']
num_channels = layer_config['NUM_CHANNELS']
block = blocks_dict[layer_config['BLOCK']]
fuse_method = layer_config['FUSE_METHOD']
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
if not multi_scale_output and i == num_modules - 1:
reset_multi_scale_output = False
else:
reset_multi_scale_output = True
modules.append(
HighResolutionModule(num_branches,
block,
num_blocks,
num_inchannels,
num_channels,
fuse_method,
reset_multi_scale_output)
)
num_inchannels = modules[-1].get_num_inchannels()
return nn.Sequential(*modules), num_inchannels
def forward(self, x):
# h, w = x.size(2), x.size(3)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.layer1(x)
x_list = []
for i in range(self.stage2_cfg['NUM_BRANCHES']):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
x_list.append(x)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.stage3_cfg['NUM_BRANCHES']):
if self.transition2[i] is not None:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.stage4_cfg['NUM_BRANCHES']):
if self.transition3[i] is not None:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
x = self.stage4(x_list)
# Head Part
height, width = x[0].size(2), x[0].size(3)
x1 = F.interpolate(x[1], size=(height, width), mode='bilinear', align_corners=False)
x2 = F.interpolate(x[2], size=(height, width), mode='bilinear', align_corners=False)
x3 = F.interpolate(x[3], size=(height, width), mode='bilinear', align_corners=False)
x = torch.cat([x[0], x1, x2, x3], 1)
x = self.head(x)
return x
def init_weights(self, pretrained=''):
logger.info('=> init weights from normal distribution')
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.normal_(m.weight, std=0.001)
# nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if os.path.isfile(pretrained):
pretrained_dict = torch.load(pretrained)
logger.info('=> loading pretrained model {}'.format(pretrained))
model_dict = self.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()}
for k, _ in pretrained_dict.items():
logger.info(
'=> loading {} pretrained model {}'.format(k, pretrained))
model_dict.update(pretrained_dict)
self.load_state_dict(model_dict)
def get_face_alignment_net(config, **kwargs):
model = HighResolutionNet(config, **kwargs)
pretrained = config.MODEL.PRETRAINED if config.MODEL.INIT_WEIGHTS else ''
model.init_weights(pretrained=pretrained)
return model
说明：
- 代码中使用了 PyTorch 框架。
- 'HighResolutionNet' 类定义了整个网络结构，包括卷积层、BatchNorm 层、ReLU 层、Softmax 层、残差块等。
- '_make_transition_layer'、'_make_layer' 和 '_make_stage' 方法用于构建网络的各个模块。
- 'forward' 方法定义了网络的前向传播过程。
- 'init_weights' 方法用于初始化网络的权重。
- 'get_face_alignment_net' 方法用于创建 'HighResolutionNet' 类的实例，并初始化其权重。
注意：
- 代码中缺少一些关键部分，例如 'Bottleneck' 类、'blocks_dict' 字典、'HighResolutionModule' 类、'BatchNorm2d' 类、'F.interpolate' 函数等。这些部分需要根据具体的实现进行补充。
- 代码中使用了 'logger' 对象，需要根据具体的实现进行定义。
- 代码中使用了 'config' 对象，需要根据具体的实现进行定义。
- 代码中使用了 'os' 模块，需要根据具体的实现进行导入。
- 代码中使用了 'torch' 模块，需要根据具体的实现进行导入。
希望这份解析能够帮助您理解人脸关键点检测网络的代码实现。
原文地址: https://www.cveoy.top/t/topic/fZwi 著作权归作者所有。请勿转载和采集!