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import torch
import torch.nn as nn
from torchvision.transforms import Compose, Resize, InterpolationMode, Normalize
import logging
logger = logging.getLogger(__name__)
class Dinov2Wrapper(nn.Module):
"""
Dino v2 wrapper using original implementation, hacked with modulation.
"""
def __init__(self, model_name: str, modulation_dim: int = None, freeze: bool = True):
super().__init__()
self.modulation_dim = modulation_dim
self.model = self._build_dinov2(model_name, modulation_dim=modulation_dim)
self.preprocess = Compose([
Resize(self.model.patch_embed.img_size[0], interpolation=InterpolationMode.BICUBIC),
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
if freeze:
if modulation_dim is not None:
raise ValueError("Modulated Dinov2 requires training, freezing is not allowed.")
self._freeze()
def _freeze(self):
logger.warning(f"======== Freezing Dinov2Wrapper ========")
self.model.eval()
for name, param in self.model.named_parameters():
param.requires_grad = False
@staticmethod
def _build_dinov2(model_name: str, modulation_dim: int = None, pretrained: bool = True):
from importlib import import_module
dinov2_hub = import_module(".dinov2.hub.backbones", package=__package__)
model_fn = getattr(dinov2_hub, model_name)
logger.info(f"Modulation dim for Dinov2 is {modulation_dim}.")
model = model_fn(modulation_dim=modulation_dim, pretrained=pretrained)
return model
# @torch.compile
def forward(self, image: torch.Tensor, mod: torch.Tensor = None):
# image: [N, H, W, C] -- need to be permuted!!!
# mod: [N, D] or None
assert image.shape[-1] == 3
image = image.permute(0, 3, 1, 2) / 255.
image = self.preprocess(image)
if self.modulation_dim is None:
assert mod is None, "Unexpected modulation input in dinov2 forward."
outs = self.model(image, is_training=True)
else:
assert mod is not None, "Modulation input is required in modulated dinov2 forward."
outs = self.model(image, mod=mod, is_training=True)
ret = torch.cat([
outs["x_norm_clstoken"].unsqueeze(dim=1),
outs["x_norm_patchtokens"],
], dim=1)
# ret in [B, 1370, 384]
return ret
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