Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,903 Bytes
e4c8837 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
import re
import types
from typing import List, Tuple, Union
import timm
import timm.data
import torch
import torch.nn.functional as F
from einops import rearrange
from timm.models.vision_transformer import VisionTransformer
from torch import nn
from torchvision import transforms
# We provide a list of timm model names, more are available on their official repo.
MODEL_LIST = [
# DINO
"vit_base_patch16_224.dino",
# DINOv2
"vit_base_patch14_dinov2.lvd142m",
# DINOv2-R
"vit_base_patch14_reg4_dinov2",
# Franca
"franca_vitb14",
# DINOv3-ViT
"vit_base_patch16_dinov3.lvd1689m",
"vit_large_patch16_dinov3.lvd1689m",
"vit_7b_patch16_dinov3.lvd1689m",
# SigLIP2
"vit_base_patch16_siglip_512.v2_webli",
# PE Core
"vit_pe_core_small_patch16_384.fb",
# PE Spatial
"vit_pe_spatial_tiny_patch16_512.fb",
# RADIO
"radio_v2.5-b",
# CAPI
"capi_vitl14_lvd",
# MAE
"vit_large_patch16_224.mae",
]
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
class PretrainedViTWrapper(nn.Module):
def __init__(
self,
name,
norm: bool = True,
dynamic_img_size: bool = True,
dynamic_img_pad: bool = False,
**kwargs,
):
super().__init__()
# comment out the following line to test the models not in the list
self.name = name
load_weights = False
if "dvt_" == name[:4]:
load_weights = True
load_tag = "dvt"
name = name.replace("dvt_", "")
if "fit3d_" == name[:6]:
load_weights = True
load_tag = "fit3d"
name = name.replace("fit3d_", "")
# Set patch size
try:
self.patch_size = int(re.search(r"patch(\d+)", name).group(1))
except:
self.patch_size = 16
if "franca" in name or "capi" in name:
self.patch_size = 14
if "convnext" in name:
self.patch_size = 32
name, self.patch_size
self.dynamic_img_size = dynamic_img_size
self.dynamic_img_pad = dynamic_img_pad
self.model, self.config = self.create_model(name, **kwargs)
self.config["ps"] = self.patch_size
self.embed_dim = self.model.embed_dim
self.norm = norm
if load_weights:
ckpt = torch.load(f"/home/lchambon/workspace/JAFAR/ckpts/{load_tag}_{name}.pth", map_location="cpu")
if load_tag == "dvt":
self.load_state_dict(ckpt["model"], strict=True)
elif load_tag == "fit3d":
self.model.load_state_dict(ckpt, strict=True)
def create_model(self, name: str, **kwargs) -> Tuple[VisionTransformer, transforms.Compose]:
if "radio" in self.name:
model = torch.hub.load(
"NVlabs/RADIO",
"radio_model",
version=name,
progress=True,
skip_validation=True,
)
data_config = {
"mean": torch.tensor([0.0, 0.0, 0.0]),
"std": torch.tensor([1.0, 1.0, 1.0]),
"input_size": (3, 512, 512),
}
elif "franca" in self.name:
model = torch.hub.load("valeoai/Franca", name, use_rasa_head=True)
data_config = {"mean": IMAGENET_DEFAULT_MEAN, "std": IMAGENET_DEFAULT_STD, "input_size": (3, 448, 448)}
elif "capi" in self.name:
model = torch.hub.load("facebookresearch/capi:main", name, force_reload=False)
data_config = {"mean": IMAGENET_DEFAULT_MEAN, "std": IMAGENET_DEFAULT_STD, "input_size": (3, 448, 448)}
else:
timm_kwargs = dict(
pretrained=True,
num_classes=0,
patch_size=self.patch_size,
)
if "sam" not in self.name and "convnext" not in self.name:
timm_kwargs["dynamic_img_size"] = self.dynamic_img_size
timm_kwargs["dynamic_img_pad"] = self.dynamic_img_pad
timm_kwargs.update(kwargs)
model = timm.create_model(name, **timm_kwargs)
data_config = timm.data.resolve_model_data_config(model=model)
model = model.eval()
return model, data_config
def forward(
self,
x: torch.Tensor,
n: Union[int, List[int], Tuple[int]] = 1,
return_prefix_tokens: bool = False,
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
"""Intermediate layer accessor inspired by DINO / DINOv2 interface.
Args:
x: Input tensor.
n: Take last n blocks if int, all if None, select matching indices if sequence
reshape: Whether to reshape the output.
"""
common_kwargs = dict(
norm=self.norm,
output_fmt="NCHW",
intermediates_only=True,
)
if "sam" not in self.name and return_prefix_tokens:
common_kwargs["return_prefix_tokens"] = return_prefix_tokens
elif "franca" in self.name:
B, C, H, W = x.shape
feats = self.model.forward_features(x, use_rasa_head=True)
out = feats["patch_token_rasa"]
out = rearrange(out, "b (h w) c -> b c h w", h=H // self.patch_size, w=W // self.patch_size)
elif "capi" in self.name:
*_, out = self.model(x)
out = out.permute(0, 3, 1, 2)
else:
out = self.model.forward_intermediates(x, n, **common_kwargs)
# "sam" models return feats only, others may return (feats, prefix)
if not isinstance(out, list) and not isinstance(out, tuple):
out = [out]
return out[0]
else:
assert len(out) == 1, f"Out contains {len(out)} elements, expected 1."
return out[0]
|