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4687353 | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | from __future__ import annotations
from typing import List
import torch
from PIL import Image
from .encoders import ImageBackbone
class BaselineClassifier(torch.nn.Module):
def __init__(
self,
clip_backbone: ImageBackbone | None,
dino_backbone: ImageBackbone | None,
feature_dim: int,
hidden_dims: List[int],
dropout: float,
num_labels: int,
) -> None:
super().__init__()
self.clip_backbone = clip_backbone
self.dino_backbone = dino_backbone
layers: List[torch.nn.Module] = []
input_dim = feature_dim
for hidden in hidden_dims:
layers.append(torch.nn.Linear(input_dim, hidden))
layers.append(torch.nn.ReLU())
layers.append(torch.nn.Dropout(dropout))
input_dim = hidden
layers.append(torch.nn.Linear(input_dim, num_labels))
self.classifier = torch.nn.Sequential(*layers)
def encode(self, images: List[Image.Image], device: torch.device) -> torch.Tensor:
features = []
if self.clip_backbone is not None:
features.append(self.clip_backbone.forward_pil(images, device=device))
if self.dino_backbone is not None:
features.append(self.dino_backbone.forward_pil(images, device=device))
if len(features) == 1:
return features[0]
return torch.cat(features, dim=1)
def forward(self, images: List[Image.Image], device: torch.device) -> torch.Tensor:
feats = self.encode(images=images, device=device)
return self.classifier(feats)
def freeze_backbones(self) -> None:
for backbone in (self.clip_backbone, self.dino_backbone):
if backbone is None:
continue
for param in backbone.parameters():
param.requires_grad = False
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