Spaces:
Paused
Paused
File size: 1,279 Bytes
4716563 24ea486 4716563 24ea486 4716563 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as tvm
class ResNetItemEmbedder(nn.Module):
def __init__(self, embedding_dim: int = 512, backbone: str = "resnet50", pretrained: bool = True) -> None:
super().__init__()
if backbone == "resnet50":
model = tvm.resnet50(weights=tvm.ResNet50_Weights.DEFAULT if pretrained else None)
feat_dim = 2048
elif backbone == "resnet101":
model = tvm.resnet101(weights=tvm.ResNet101_Weights.DEFAULT if pretrained else None)
feat_dim = 2048
else:
raise ValueError(f"Unsupported backbone: {backbone}")
# Remove classifier, keep global average pooling output
modules = list(model.children())[:-1] # drop fc
self.backbone = nn.Sequential(*modules)
self.proj = nn.Linear(feat_dim, embedding_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: (B, 3, H, W)
feats = self.backbone(x) # (B, C, 1, 1)
feats = feats.flatten(1) # (B, C)
emb = self.proj(feats) # (B, D)
# Apply L2 normalization as specified in requirements
emb = F.normalize(emb, p=2, dim=1)
return emb
|