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b612017 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """PathAGG Hugging Face loaders (trust_remote_code).
Usage:
from transformers import AutoModel
model = AutoModel.from_pretrained("luoxd96/PathAGG", subfolder="vitb", trust_remote_code=True)
cls = model(images) # [B, D]
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
import torch
import torch.nn as nn
from transformers import PretrainedConfig, PreTrainedModel
from transformers.utils import cached_file
class PathAGGConfig(PretrainedConfig):
model_type = "pathagg"
def __init__(
self,
variant: str = "vitb",
img_size: int = 224,
patch_size: int = 14,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
num_register_tokens: int = 4,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
init_values: float | None = None,
no_embed_class: bool = False,
return_patch: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.variant = variant
self.img_size = img_size
self.patch_size = patch_size
self.embed_dim = embed_dim
self.depth = depth
self.num_heads = num_heads
self.num_register_tokens = num_register_tokens
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.init_values = init_values
self.no_embed_class = no_embed_class
self.return_patch = return_patch
def _build_backbone(config: PathAGGConfig) -> nn.Module:
from timm.models.vision_transformer import VisionTransformer
kwargs: dict[str, Any] = dict(
img_size=config.img_size,
patch_size=config.patch_size,
in_chans=3,
num_classes=0,
global_pool="",
embed_dim=config.embed_dim,
depth=config.depth,
num_heads=config.num_heads,
mlp_ratio=config.mlp_ratio,
qkv_bias=config.qkv_bias,
reg_tokens=config.num_register_tokens,
)
if config.init_values is not None:
kwargs["init_values"] = config.init_values
if config.no_embed_class:
kwargs["no_embed_class"] = True
return VisionTransformer(**kwargs)
class PathAGGModel(PreTrainedModel):
"""Pathology multi-teacher KD student. Forward returns CLS ``[B, D]`` by default."""
config_class = PathAGGConfig
base_model_prefix = "pathagg"
_no_split_modules = ["Block"]
def __init__(self, config: PathAGGConfig) -> None:
super().__init__(config)
self.return_patch = bool(config.return_patch)
self.model = _build_backbone(config)
# Do not call post_init() random re-init; weights come from checkpoint.
@property
def embed_dim(self) -> int:
return int(self.model.embed_dim)
@property
def num_register_tokens(self) -> int:
return int(getattr(self.model, "reg_tokens", getattr(self.model, "num_register_tokens", 4)))
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
return self.model.forward_features(x)
def forward(
self,
pixel_values: torch.Tensor | None = None,
x: torch.Tensor | None = None,
return_patch: bool | None = None,
**kwargs: Any,
):
if pixel_values is None and x is None:
raise ValueError("provide pixel_values=... or x=...")
images = pixel_values if pixel_values is not None else x
tokens = self.forward_features(images)
cls = tokens[:, 0]
use_patch = self.return_patch if return_patch is None else bool(return_patch)
if not use_patch:
return cls
num_prefix = int(getattr(self.model, "num_prefix_tokens", 1 + self.num_register_tokens))
patch = tokens[:, num_prefix:]
return cls, patch
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
"""Load config + weights without transformers gamma↔weight remapping (LayerScale)."""
subfolder = kwargs.pop("subfolder", "")
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
cache_dir = kwargs.pop("cache_dir", None)
token = kwargs.pop("token", None)
if token is None:
token = kwargs.pop("use_auth_token", None)
kwargs.pop("trust_remote_code", None)
kwargs.pop("torch_dtype", None)
kwargs.pop("device_map", None)
kwargs.pop("low_cpu_mem_usage", None)
return_patch = kwargs.pop("return_patch", None)
config = kwargs.pop("config", None)
if config is None:
config = PathAGGConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder=subfolder,
local_files_only=local_files_only,
revision=revision,
cache_dir=cache_dir,
token=token,
**kwargs,
)
if return_patch is not None:
config.return_patch = bool(return_patch)
model = cls(config)
weight_file = cached_file(
pretrained_model_name_or_path,
"pytorch_model.bin",
subfolder=subfolder,
local_files_only=local_files_only,
revision=revision,
cache_dir=cache_dir,
token=token,
)
if weight_file is None:
raise FileNotFoundError(
f"pytorch_model.bin not found under {pretrained_model_name_or_path!r} (subfolder={subfolder!r})"
)
try:
state = torch.load(weight_file, map_location="cpu", weights_only=True)
except TypeError:
state = torch.load(weight_file, map_location="cpu")
# transformers may have renamed LayerScale gamma→weight in some pipelines; normalize back.
fixed = {}
for k, v in state.items():
if k.endswith(".ls1.weight") or k.endswith(".ls2.weight"):
fixed[k[: -len(".weight")] + ".gamma"] = v
else:
fixed[k] = v
missing, unexpected = model.load_state_dict(fixed, strict=True)
if missing or unexpected:
raise RuntimeError(f"load failed: missing={missing}, unexpected={unexpected}")
model.eval()
return model
def get_preprocess(img_size: int = 224):
"""ImageNet normalize preprocess (PIL → tensor)."""
from torchvision import transforms
return transforms.Compose(
[
transforms.Resize(img_size, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
]
)
__all__ = [
"PathAGGConfig",
"PathAGGModel",
"get_preprocess",
]
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