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1cc0e74 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
from open_clip import create_model_from_pretrained, get_tokenizer
import os
from open_clip_patch import patch_encode_text
from timm_vit_return_attn_patch import patch_timm_vit_return_attn_scores
from bert_modeling_bert_self_attn_patch import patch_bert_self_attn
from loralib.utils import apply_lora
from loss import CLIPLossACE_HGAT
from PIL import Image
import torch.nn.functional as F
from prompt_templates import prompt_templates
from torchmetrics.classification import BinaryAUROC, BinaryAccuracy
import pandas as pd
from tqdm import tqdm
import pydicom
from safetensors.torch import save_file, load_file
def load_config_to_args(args_obj, config_dict):
for key, value in config_dict.items():
setattr(args_obj, key, value)
return args_obj
class _Args:
pass
class ACE_LoRA_Model(
nn.Module,
PyTorchModelHubMixin,
repo_url="https://github.com/icon-lab/ACE-LoRA",
pipeline_tag="zero-shot-classification",
license="mit",
):
def __init__(self, config: dict):
super().__init__()
self.config = config
base_model_name: str = config.get("base_model_name", "hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224")
feature_dim: int = config.get("feature_dim", 512)
self.context_length: int = config.get("context_length", 256)
self.clip_model, self.preprocess = create_model_from_pretrained(base_model_name)
self.tokenizer = get_tokenizer(base_model_name)
patch_encode_text()
patch_timm_vit_return_attn_scores()
patch_bert_self_attn()
args = _Args()
load_config_to_args(args, config)
self.lora_layers = apply_lora(args, self.clip_model)
self.lora_params = nn.ParameterList([p for group in self.lora_layers for p in group.parameters()])
logit_scale = self.clip_model.state_dict()["logit_scale"].exp()
self.loss_fn = CLIPLossACE_HGAT(args, logit_scale, feature_dim)
self.logit_scale = nn.Parameter(self.clip_model.state_dict()["logit_scale"].clone(), requires_grad=False)
def _save_pretrained(self, save_directory: str):
os.makedirs(save_directory, exist_ok=True)
payload = {
**{k: v for k, v in self.clip_model.state_dict().items() if "lora" in k.lower()},
**{f"loss_fn.{k}": v for k, v in self.loss_fn.state_dict().items()},
"logit_scale": self.logit_scale.data,
}
payload = {k: v.contiguous() for k, v in payload.items()}
save_file(payload, os.path.join(save_directory, "model.safetensors"))
@classmethod
def _from_pretrained(cls, *, model_id, revision=None, cache_dir=None,
force_download=False, proxies=None, resume_download=False,
local_files_only=False, token=None, map_location="cpu",
strict=False, config=None, **kwargs):
model = cls(config=config or {})
local_ckpt = os.path.join(model_id, "model.safetensors")
if os.path.isfile(local_ckpt):
ckpt_path = local_ckpt
else:
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(
repo_id=model_id, filename="model.safetensors",
revision=revision, cache_dir=cache_dir,
force_download=force_download, proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only, token=token,
)
state = load_file(ckpt_path, device=map_location)
lora_state = {k: v for k, v in state.items() if "lora" in k.lower()}
clip_sd = model.clip_model.state_dict()
clip_sd.update(lora_state)
model.clip_model.load_state_dict(clip_sd, strict=True)
model.lora_params = nn.ParameterList([p for group in model.lora_layers for p in group.parameters()])
ace_state = {k.replace("loss_fn.", ""): v for k, v in state.items() if k.startswith("loss_fn.")}
model.loss_fn.load_state_dict(ace_state, strict=True)
if "logit_scale" in state:
model.logit_scale.data.copy_(state["logit_scale"])
model.loss_fn.logit_scale.data.copy_(state["logit_scale"])
return model
@staticmethod
def _apply_ace_hgat(loss_fn, features, attn_weights, encoder="img"):
if encoder == "img":
edge_adapter = loss_fn.img_edge_adapter
node_adapter = loss_fn.img_node_adapter
elif encoder == "text":
edge_adapter = loss_fn.text_edge_adapter
node_adapter = loss_fn.text_node_adapter
else:
raise ValueError(f"encoder must be 'img' or 'text', got {encoder!r}")
B, N, D = features.shape
patches_norm = F.normalize(features[:, 1:, :], p=2, dim=-1)
sim = torch.zeros(B, N, N, device=features.device)
patch_sim = torch.bmm(patches_norm, patches_norm.transpose(1, 2))
sim[:, 1:, 1:] = patch_sim
sim[:, 0, 1:] = attn_weights
eye = torch.eye(N, device=features.device).bool().unsqueeze(0).repeat(B, 1, 1)
mask = eye.clone()
mask[:, 1:, 0] = True
sim = sim.masked_fill(mask, float("-inf"))
topk_vals, topk_idx = torch.topk(sim, k=5, dim=-1)
sparse = torch.full_like(sim, float("-inf"))
sparse.scatter_(-1, topk_idx, topk_vals)
A = F.softmax(sparse, dim=-1)
A = A.masked_fill(eye, 1.0)
A[:, 1:, 0] = A[:, 0, 1:]
H_edges = edge_adapter(torch.matmul(A, features))
H_context = node_adapter(torch.matmul(A.transpose(1, 2), H_edges))
return H_context
@torch.no_grad()
def encode_texts(self, class_names: list[str]) -> torch.Tensor:
device = self.logit_scale.device
feats = []
for name in class_names:
tokens = self.tokenizer([t(name) for t in prompt_templates], context_length=self.context_length).to(device)
feat, attn = self.clip_model.encode_text(tokens, normalize=True, output_attentions=True, output_tokens=True)
feat = feat / feat.norm(dim=-1, keepdim=True)
feat = feat.mean(dim=0)
attn_w = attn[-1].mean(dim=1).mean(dim=0, keepdim=True)[:, 0, 1:]
feat = self._apply_ace_hgat(self.loss_fn, feat.unsqueeze(0), attn_w, encoder="text")
feat = F.normalize(feat, dim=-1)
feats.append(feat)
return torch.cat(feats, dim=0)
@torch.no_grad()
def encode_image(self, pil_image: Image.Image) -> torch.Tensor:
device = self.logit_scale.device
old_pool = self.clip_model.visual.trunk.global_pool
self.clip_model.visual.trunk.global_pool = ""
img_features, attn = self.clip_model.visual.trunk.get_attn_scores(self.preprocess(pil_image).unsqueeze(0).to(device))
img_features = F.normalize(self.clip_model.visual.head(img_features), dim=-1)
attn_w = attn.mean(dim=1)[:, 0, 1:]
img_features = self._apply_ace_hgat(self.loss_fn, img_features, attn_w, encoder="img")
img_features = F.normalize(img_features, dim=-1)
self.clip_model.visual.trunk.global_pool = old_pool
return img_features
def forward(
self,
image: Image.Image,
class_names: list[str],
) -> torch.Tensor:
logit_scale = self.logit_scale
text_feats = self.encode_texts(class_names)
image_feats = self.encode_image(image)
logits = (logit_scale * image_feats[:, 0] @ text_feats[:, 0].t())
return logits.squeeze(0).softmax(dim=-1)
if __name__ == "__main__":
model = ACE_LoRA_Model.from_pretrained("aydnarda/ACE-LoRA", force_download=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
auc_metric = BinaryAUROC(thresholds=None)
acc_metric = BinaryAccuracy().to(device)
model = model.to(device)
model.eval()
TEST_CSV_PATH = './RSNA/test.csv'
df = pd.read_csv(TEST_CSV_PATH)
test_paths = df['Path'].tolist()
classes = ['No Finding', 'pneumonia']
logits_list = []
label_list = []
for index in tqdm(range(len(df))):
img_path = test_paths[index]
img_data = pydicom.dcmread(img_path).pixel_array
image = Image.fromarray(img_data)
label = torch.zeros(len(classes), dtype=torch.int8, device=device)
label[df['Target'][index]] = 1
pred = torch.zeros(len(classes), dtype=torch.int8, device=device)
logits = model(image, classes).unsqueeze(0)
logits_list.append(logits)
label_list.append(label.argmax())
logits_all = torch.cat(logits_list, dim=0) # (N, C)
labels_all = torch.stack(label_list)
auc = auc_metric(logits_all[:, 1], labels_all)
acc = acc_metric(logits_all[:, 1], labels_all)
print("ACC: ", acc)
print("AUC: ", auc) |