Spaces:
Running
on
Zero
Running
on
Zero
""" | |
Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) | |
Copyright(c) 2023 lyuwenyu. All Rights Reserved. | |
""" | |
import torch | |
import torch.nn as nn | |
from ...core import register | |
__all__ = ["Classification", "ClassHead"] | |
class Classification(torch.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 | |
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 | |