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import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from transformers import PretrainedConfig, PreTrainedModel
class LinearProbe(nn.Module):
def __init__(self, input_dim, num_classes, normalize_inputs=False):
super().__init__()
self.linear = nn.Linear(input_dim, num_classes)
self.normalize_inputs = normalize_inputs
def forward(self, x: torch.Tensor, **kwargs):
if self.normalize_inputs:
x = F.normalize(x, p=2, dim=1)
return self.linear(x)
class CLIPEncoder(nn.Module):
def __init__(self, model_name="openai/clip-vit-large-patch14"):
super().__init__()
from transformers import CLIPModel, CLIPProcessor
try:
self._preprocess = CLIPProcessor.from_pretrained(model_name)
except Exception:
self._preprocess = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
clip: CLIPModel = CLIPModel.from_pretrained(model_name)
# take vision model from CLIP, maps image to vision_embed_dim
self.vision_model = clip.vision_model
self.model_name = model_name
self.features_dim = self.vision_model.config.hidden_size
# take visual_projection, maps vision_embed_dim to projection_dim
self.visual_projection = clip.visual_projection
def preprocess(self, image: Image) -> torch.Tensor:
return self._preprocess(images=image, return_tensors="pt")["pixel_values"][0]
def forward(self, preprocessed_images: torch.Tensor) -> torch.Tensor:
return self.vision_model(preprocessed_images).pooler_output
def get_features_dim(self):
return self.features_dim
class DINOEncoder(nn.Module):
def __init__(self, model_name="facebook/dinov2-with-registers-base"):
super().__init__()
from transformers import AutoImageProcessor, AutoModel, Dinov2Model, Dinov2WithRegistersModel
self._preprocess = AutoImageProcessor.from_pretrained(model_name)
self.backbone: Dinov2Model | Dinov2WithRegistersModel = AutoModel.from_pretrained(model_name)
self.features_dim = self.backbone.config.hidden_size
def preprocess(self, image: Image) -> torch.Tensor:
return self._preprocess(images=image, return_tensors="pt")["pixel_values"][0]
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return self.backbone(inputs).last_hidden_state[:, 0]
def get_features_dim(self) -> int:
return self.features_dim
class PerceptionEncoder(nn.Module):
def __init__(self, model_name="vit_pe_core_large_patch14_336", img_size: None | int = None):
super().__init__()
if img_size is not None:
dynamic_img_size = True
import timm
from timm.models.eva import Eva
self.backbone: Eva = timm.create_model(
model_name,
pretrained=True,
dynamic_img_size=dynamic_img_size,
)
# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(self.backbone)
if img_size is not None:
data_config["input_size"] = (3, img_size, img_size)
self._preprocess = timm.data.create_transform(**data_config, is_training=False)
# Remove head
self.backbone.head = nn.Identity()
self.features_dim = self.backbone.num_features
def preprocess(self, image: Image.Image) -> torch.Tensor:
return self._preprocess(image)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return self.backbone(inputs)
def get_features_dim(self) -> int:
return self.features_dim
class GenDConfig(PretrainedConfig):
model_type = "GenD"
def __init__(self, backbone: str = "openai/clip-vit-large-patch14", head: str = "linear", **kwargs):
super().__init__(**kwargs)
self.backbone = backbone
self.head = head
class GenD(PreTrainedModel):
config_class = GenDConfig
def __init__(self, config):
super().__init__(config)
self.head = config.head
self.backbone = config.backbone
self.config = config
self._init_feature_extractor()
self._init_head()
def _init_feature_extractor(self):
backbone = self.backbone
backbone_lowercase = backbone.lower()
if "clip" in backbone_lowercase:
self.feature_extractor = CLIPEncoder(backbone)
elif "vit_pe" in backbone_lowercase:
from src.encoders.perception_encoder import PerceptionEncoder
self.feature_extractor = PerceptionEncoder(backbone, self.config.backbone_args.img_size)
elif "dino" in backbone_lowercase:
from src.encoders.dino_encoder import DINOEncoder
if self.config.backbone_args is not None:
merge_cls_token_with_patches = self.config.backbone_args.merge_cls_token_with_patches
else:
merge_cls_token_with_patches = None
self.feature_extractor = DINOEncoder(backbone, merge_cls_token_with_patches)
else:
raise ValueError(f"Unknown backbone: {backbone}")
def _init_head(self):
features_dim = self.feature_extractor.get_features_dim()
match self.head:
case "linear":
self.model = LinearProbe(features_dim, 2)
case "LinearNorm":
self.model = LinearProbe(features_dim, 2, True)
case _:
raise ValueError(f"Unknown head: {self.head}")
def forward(self, inputs: torch.Tensor):
features = self.feature_extractor(inputs)
outputs = self.model.forward(features)
return outputs
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