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difftpt_utils.py
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difftpt_utils.py
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import os
import torch
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
import torchvision.transforms as transforms
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
# from data.imagenet_variants import thousand_k_to_200, imagenet_a_mask, imagenet_r_mask, imagenet_v_mask, imagenet_r_fold
imagenet_r_fold = ['n01443537', 'n01484850', 'n01494475', 'n01498041', 'n01514859', 'n01518878', 'n01531178', 'n01534433', 'n01614925', 'n01616318', 'n01630670', 'n01632777', 'n01644373', 'n01677366', 'n01694178', 'n01748264', 'n01770393', 'n01774750', 'n01784675', 'n01806143', 'n01820546', 'n01833805', 'n01843383', 'n01847000', 'n01855672', 'n01860187', 'n01882714', 'n01910747', 'n01944390', 'n01983481', 'n01986214', 'n02007558', 'n02009912', 'n02051845', 'n02056570', 'n02066245', 'n02071294', 'n02077923', 'n02085620', 'n02086240', 'n02088094', 'n02088238', 'n02088364', 'n02088466', 'n02091032', 'n02091134', 'n02092339', 'n02094433', 'n02096585', 'n02097298', 'n02098286', 'n02099601', 'n02099712', 'n02102318', 'n02106030', 'n02106166', 'n02106550', 'n02106662', 'n02108089', 'n02108915', 'n02109525', 'n02110185', 'n02110341', 'n02110958', 'n02112018', 'n02112137', 'n02113023', 'n02113624', 'n02113799', 'n02114367', 'n02117135', 'n02119022', 'n02123045', 'n02128385', 'n02128757', 'n02129165', 'n02129604', 'n02130308', 'n02134084', 'n02138441', 'n02165456', 'n02190166', 'n02206856', 'n02219486', 'n02226429', 'n02233338', 'n02236044', 'n02268443', 'n02279972', 'n02317335', 'n02325366', 'n02346627', 'n02356798', 'n02363005', 'n02364673', 'n02391049', 'n02395406', 'n02398521', 'n02410509', 'n02423022', 'n02437616', 'n02445715', 'n02447366', 'n02480495', 'n02480855', 'n02481823', 'n02483362', 'n02486410', 'n02510455', 'n02526121', 'n02607072', 'n02655020', 'n02672831', 'n02701002', 'n02749479', 'n02769748', 'n02793495', 'n02797295', 'n02802426', 'n02808440', 'n02814860', 'n02823750', 'n02841315', 'n02843684', 'n02883205', 'n02906734', 'n02909870', 'n02939185', 'n02948072', 'n02950826', 'n02951358', 'n02966193', 'n02980441', 'n02992529', 'n03124170', 'n03272010', 'n03345487', 'n03372029', 'n03424325', 'n03452741', 'n03467068', 'n03481172', 'n03494278', 'n03495258', 'n03498962', 'n03594945', 'n03602883', 'n03630383', 'n03649909', 'n03676483', 'n03710193', 'n03773504', 'n03775071', 'n03888257', 'n03930630', 'n03947888', 'n04086273', 'n04118538', 'n04133789', 'n04141076', 'n04146614', 'n04147183', 'n04192698', 'n04254680', 'n04266014', 'n04275548', 'n04310018', 'n04325704', 'n04347754', 'n04389033', 'n04409515', 'n04465501', 'n04487394', 'n04522168', 'n04536866', 'n04552348', 'n04591713', 'n07614500', 'n07693725', 'n07695742', 'n07697313', 'n07697537', 'n07714571', 'n07714990', 'n07718472', 'n07720875', 'n07734744', 'n07742313', 'n07745940', 'n07749582', 'n07753275', 'n07753592', 'n07768694', 'n07873807', 'n07880968', 'n07920052', 'n09472597', 'n09835506', 'n10565667', 'n12267677']
class diffuData(Dataset):
def __init__(self, data_root, img_list, label_list, trainsform, augmode='difftpt', diffu_ratio=0.5, view_num=63):
self.data_root = data_root
self.img_list = img_list
self.label_list = label_list
self.trainsform = trainsform
self.augmode = augmode
self.diffu_ratio = diffu_ratio
self.view_num = view_num
def __getitem__(self, item):
if self.augmode == 'tpt':
img = Image.open(self.img_list[item]).convert('RGB')
imgs = self.trainsform[0](img)
# return imgs, self.label_list[item], self.img_list[item]
return imgs, self.label_list[item]
elif self.augmode == 'difftpt':
# diffu 64 + aug 64
img = Image.open(self.img_list[item]).convert('RGB')
imgs = self.trainsform[0](img) # imgs[ori，augx63]
diffu_img = [] # 63 diffu imgs
for i in range(self.view_num):
data_name = ((self.img_list[item]).split('/'))[-1]
data_fold = ((self.img_list[item]).split('/'))[-2]
img_i = Image.open(os.path.join(self.data_root, data_fold, data_name[:-4]+'_'+str(i)+data_name[-4:])).convert('RGB')
img_i = self.trainsform[1](img_i)
diffu_img.append(img_i)
# return imgs+diffu_img, self.label_list[item], self.img_list[item]
return imgs+diffu_img, self.label_list[item]
# [ori, tpt, diffu]
else:
print('self.augmode error! tpt or difftpt')
exit()
def __len__(self):
return len(self.img_list)
def get_data_loader(data_transform, args=None):
normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711])
data_transform_diffu = transforms.Compose([
transforms.Resize(args.resolution, interpolation=BICUBIC),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
normalize,
])
img_list = []
label_list = []
with open(os.path.join(args.diff_root, 'selected_data_list.txt'), 'r') as f:
for line in f.readlines():
read_img = line.strip()
img_list.append(read_img)
label_list.append(imagenet_r_fold.index(read_img.split('/')[-2]))
tpt_dataset = diffuData(data_root=args.diff_root, img_list=img_list, label_list=label_list, \
trainsform=[data_transform, data_transform_diffu], augmode=args.aug_mode, view_num=args.batch_size-1)
return tpt_dataset
def avg_entropy(outputs):
logits = outputs - outputs.logsumexp(dim=-1, keepdim=True) # logits = outputs.log_softmax(dim=1) [N, 1000]
avg_logits = logits.logsumexp(dim=0) - np.log(logits.shape[0]) # avg_logits = logits.mean(0) [1, 1000]
min_real = torch.finfo(avg_logits.dtype).min
avg_logits = torch.clamp(avg_logits, min=min_real)
return -(avg_logits * torch.exp(avg_logits)).sum(dim=-1)
def select_confident_samples_cosine(logits, selection_cosine, selection_selfentro):
cosine_distan = [torch.nn.CosineSimilarity(dim=0)(logits[0], logits[i]) for i in range(1, logits.shape[0])]
cosine_distan = torch.stack(cosine_distan)
idx_cosine = torch.argsort(cosine_distan, descending=True)[:int(cosine_distan.size()[0] * selection_cosine)]
# idx
for i in range(idx_cosine.shape[0]):
idx_cosine[i] +=1
logits_cos = logits[idx_cosine]
logits = torch.cat((logits[0, :].unsqueeze(0), logits_cos), dim=0)
batch_entropy = -(logits.softmax(1) * logits.log_softmax(1)).sum(1)
idx = torch.argsort(batch_entropy, descending=False)[:int(batch_entropy.size()[0] * selection_selfentro)]
return logits[idx], [idx_cosine, idx], cosine_distan
def test_time_tuning_difftpt(model, inputs, optimizer, scaler, args):
if args.cocoop:
image_feature, pgen_ctx = inputs
pgen_ctx.requires_grad = True
optimizer = torch.optim.AdamW([pgen_ctx], args.lr)
selected_idx = None
batch_entropy = None
logit_out = None
for j in range(args.tta_steps):
with torch.cuda.amp.autocast():
if args.cocoop:
output = model((image_feature, pgen_ctx))
else:
output = model(inputs)
if selected_idx is not None:
logits_cos = output[selected_idx[0]]
logits = torch.cat((output[0, :].unsqueeze(0), logits_cos), dim=0)
output = logits[selected_idx[1]]
else:
output, selected_idx, batch_entropy = select_confident_samples_cosine(output, args.selection_cosine, args.selection_selfentro)
loss = avg_entropy(output)
optimizer.zero_grad()
# compute gradient and do SGD step
scaler.scale(loss).backward()
# Unscales the gradients of optimizer's assigned params in-place
scaler.step(optimizer)
scaler.update()
if args.cocoop:
return pgen_ctx
return