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import torch
import torch.nn as nn
from src.core import register
__all__ = ['Classification', 'ClassHead']
@register
class Classification(nn.Module):
__inject__ = ['backbone', 'head']
def __init__(self, backbone: nn.Module, head: nn.Module=None):
super().__init__()
self.backbone = backbone
self.head = head
def forward(self, x):
x = self.backbone(x)
if self.head is not None:
x = self.head(x)
return x
@register
class ClassHead(nn.Module):
def __init__(self, hidden_dim, num_classes):
super().__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
self.proj = nn.Linear(hidden_dim, num_classes)
def forward(self, x):
x = x[0] if isinstance(x, (list, tuple)) else x
x = self.pool(x)
x = x.reshape(x.shape[0], -1)
x = self.proj(x)
return x
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