| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from lib.models.tools.module_helper import ModuleHelper |
| from lib.utils.tools.logger import Logger as Log |
|
|
|
|
| class ProjectionHead(nn.Module): |
| def __init__(self, dim_in, proj_dim=256, proj='convmlp', bn_type='torchsyncbn'): |
| super(ProjectionHead, self).__init__() |
|
|
| Log.info('proj_dim: {}'.format(proj_dim)) |
|
|
| if proj == 'linear': |
| self.proj = nn.Conv2d(dim_in, proj_dim, kernel_size=1) |
| elif proj == 'convmlp': |
| self.proj = nn.Sequential( |
| nn.Conv2d(dim_in, dim_in, kernel_size=1), |
| ModuleHelper.BNReLU(dim_in, bn_type=bn_type), |
| nn.Conv2d(dim_in, proj_dim, kernel_size=1) |
| ) |
|
|
| def forward(self, x): |
| return F.normalize(self.proj(x), p=2, dim=1) |
