init
This commit is contained in:
305
main_toal_self_supervised_noadapter.py
Normal file
305
main_toal_self_supervised_noadapter.py
Normal file
@@ -0,0 +1,305 @@
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import shutil
|
||||
import time
|
||||
from clip import clip
|
||||
import numpy as np
|
||||
import torch.backends.cudnn as cudnn
|
||||
import torch.nn as nn
|
||||
import torch.optim
|
||||
from data.prepare_data_shot2 import generate_dataloader # Prepare the data and dataloader
|
||||
from opts import opts # The options for the project
|
||||
from trainer_self_supervised_noadapter import train # For the training process
|
||||
from trainer_self_supervised_noadapter import warm_train # For the training process
|
||||
from trainer_self_supervised_noadapter import validate # For the validate (test) process
|
||||
from models.DomainClassifierTarget import DClassifierForTarget
|
||||
from models.DomainClassifierSource import DClassifierForSource
|
||||
from engine import partial_model
|
||||
from clip.model import ModifiedResNet, VisionTransformer
|
||||
from datasets import build_dataset
|
||||
from datasets.utils import build_data_loader
|
||||
import torchvision.transforms as transforms
|
||||
import math
|
||||
import shutil
|
||||
|
||||
best_prec1 = 0
|
||||
|
||||
|
||||
# adapter 0.0001 text_encoder=0 89.6146011352539
|
||||
|
||||
class Weight_Adapter(nn.Module):
|
||||
def __init__(self, n_input, n_output, adapter_weights):
|
||||
super().__init__()
|
||||
self.linear1 = nn.Linear(n_input, n_output)
|
||||
self.linear1.weight.data = adapter_weights # Initialize linear layer weights
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear1(x.float())
|
||||
return x
|
||||
|
||||
|
||||
|
||||
|
||||
def zeroshot_classifier(classname, templates, CLIP_Text):
|
||||
with torch.no_grad():
|
||||
classname = classname.replace('_', ' ')
|
||||
str_prompts = [template.format(classname) for template in templates]
|
||||
prompts = torch.cat([clip.tokenize(p) for p in str_prompts]).cuda()
|
||||
features, eot_indices = CLIP_Text(prompts)
|
||||
return features, eot_indices
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
|
||||
def accuracy(output, target, topk=(1,)):
|
||||
"""Computes the precision@k for the specified values of k"""
|
||||
maxk = max(topk)
|
||||
batch_size = target.size(0)
|
||||
_, pred = output.topk(maxk, 1, True, True)
|
||||
pred = pred.t()
|
||||
_2, pred2 = output.topk(1, 1, True, True)
|
||||
a = target.view(1, -1)
|
||||
correct = pred.eq(target.view(1, -1).expand_as(pred))
|
||||
# print(correct)
|
||||
res = []
|
||||
for k in topk:
|
||||
correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
|
||||
res.append(correct_k.mul_(100.0 / batch_size))
|
||||
return res
|
||||
|
||||
|
||||
def all_classifier(classnames, templates, model):
|
||||
with torch.no_grad():
|
||||
zeroshot_weights = []
|
||||
for classname in classnames:
|
||||
classname = classname.replace('_', ' ')
|
||||
texts = [template.format(classname) for template in templates] # format with class
|
||||
texts = clip.tokenize(texts).cuda() # tokenizeclip.tokenize向量化文字
|
||||
class_embeddings = model.encode_text(texts) # embed with text encoder
|
||||
class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
|
||||
class_embedding = class_embeddings.mean(dim=0)
|
||||
class_embedding /= class_embedding.norm()
|
||||
zeroshot_weights.append(class_embedding)
|
||||
|
||||
zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
|
||||
return zeroshot_weights
|
||||
|
||||
|
||||
def main():
|
||||
seed = 2023
|
||||
random.seed(seed)
|
||||
os.environ['PYTHONHASHSEED'] = str(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
global args, best_prec1
|
||||
current_epoch = 0
|
||||
args = opts()
|
||||
clip.available_models()
|
||||
model, preprocess = clip.load(args.name)
|
||||
# model = model.cuda()
|
||||
model.float()
|
||||
|
||||
if os.path.exists(args.filename_dir):
|
||||
print('exist')
|
||||
else:
|
||||
os.makedirs(args.filename_dir)
|
||||
|
||||
filename = args.filename_dir + args.dataset_name + '.txt'
|
||||
if os.path.exists(filename):
|
||||
print(filename + " exist!")
|
||||
else:
|
||||
print("create " + filename)
|
||||
f = open(filename, "w")
|
||||
f.close()
|
||||
|
||||
epx_dir = args.savedir + args.dataset_name + '_epx/' + str(args.shot) + 'shot' + '/'
|
||||
if os.path.exists(epx_dir):
|
||||
print('epx_dir exist')
|
||||
else:
|
||||
os.makedirs(epx_dir)
|
||||
|
||||
dataset = build_dataset(args.dataset_name, args.dataset_dir, args.shot)
|
||||
classnames = dataset.classnames
|
||||
templates = dataset.template
|
||||
|
||||
# loader = build_data_loader(data_source=dataset.val, batch_size=64, is_train=False, tfm=preprocess,
|
||||
# shuffle=False)
|
||||
loader = build_data_loader(data_source=dataset.test, batch_size=64, is_train=False, tfm=preprocess,
|
||||
shuffle=False)
|
||||
|
||||
train_tranform = transforms.Compose([
|
||||
transforms.RandomResizedCrop(size=224, scale=(0.5, 1), interpolation=transforms.InterpolationMode.BICUBIC),
|
||||
transforms.RandomHorizontalFlip(p=0.5),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
|
||||
])
|
||||
train_loader_shuffle = build_data_loader(data_source=dataset.train_x, batch_size=256, tfm=train_tranform,
|
||||
is_train=True,
|
||||
shuffle=True)
|
||||
|
||||
criterion = nn.CrossEntropyLoss().cuda()
|
||||
if not os.path.isdir(args.log):
|
||||
os.makedirs(args.log)
|
||||
log = open(os.path.join(args.log, 'log.txt'), 'a')
|
||||
state = {k: v for k, v in args._get_kwargs()}
|
||||
log.write(json.dumps(state) + '\n')
|
||||
log.close()
|
||||
|
||||
cudnn.benchmark = True # Benchmark模式会提升计算速度，但是由于计算中有随机性，每次网络前馈结果略有差异
|
||||
|
||||
log = open(os.path.join(args.log, 'log.txt'), 'a')
|
||||
log.write('\n-------------------------------------------\n')
|
||||
log.write(time.asctime(time.localtime(time.time())))
|
||||
log.write('\n-------------------------------------------')
|
||||
log.close()
|
||||
|
||||
# process the data and prepare the dataloaders.
|
||||
# train_loader_shuffle, loader = generate_dataloader(args, preprocess)
|
||||
|
||||
# 拆分CLIP图像编码器
|
||||
if args.name == "ViT-B/16":
|
||||
CLIP_Text, Text_Encoder = partial_model.get_text(model, text_layer_idx=1)
|
||||
assert type(model.visual) == VisionTransformer
|
||||
CLIP_Image, Image_Encoder = partial_model.get_image_vit(model.visual, image_layer_idx=0)
|
||||
elif args.name == "ViT-B/32":
|
||||
CLIP_Text, Text_Encoder = partial_model.get_text(model, text_layer_idx=1)
|
||||
assert type(model.visual) == VisionTransformer
|
||||
CLIP_Image, Image_Encoder = partial_model.get_image_vit(model.visual, image_layer_idx=0)
|
||||
elif args.name == "RN50":
|
||||
CLIP_Text, Text_Encoder = partial_model.get_text(model, text_layer_idx=0)
|
||||
assert type(model.visual) == ModifiedResNet
|
||||
CLIP_Image, Image_Encoder = partial_model.get_image_resnet(model.visual, image_layer_idx=1)
|
||||
elif args.name == "RN101":
|
||||
CLIP_Text, Text_Encoder = partial_model.get_text(model, text_layer_idx=0)
|
||||
assert type(model.visual) == ModifiedResNet
|
||||
CLIP_Image, Image_Encoder = partial_model.get_image_resnet(model.visual, image_layer_idx=0)
|
||||
elif args.name == "RN50x16":
|
||||
CLIP_Text, Text_Encoder = partial_model.get_text(model, text_layer_idx=1)
|
||||
assert type(model.visual) == ModifiedResNet
|
||||
CLIP_Image, Image_Encoder = partial_model.get_image_resnet(model.visual, image_layer_idx=0)
|
||||
|
||||
# 1000类标签经过clip
|
||||
model = model.cuda()
|
||||
zero_weights = all_classifier(classnames, templates, model)
|
||||
CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder = CLIP_Text.cuda(), Text_Encoder.cuda(), CLIP_Image.cuda(), Image_Encoder.cuda()
|
||||
Init_Image_Encoder = Image_Encoder
|
||||
best_epoch = 0
|
||||
best_init_acc = 0
|
||||
criterion_classifier_target = DClassifierForTarget(nClass=len(classnames)).cuda()
|
||||
criterion_classifier_source = DClassifierForSource(nClass=len(classnames)).cuda()
|
||||
text_weights = zero_weights
|
||||
adapter_weights = torch.cat([text_weights, text_weights], dim=1).T
|
||||
adapter = Weight_Adapter(1024, 2 * len(classnames), adapter_weights).cuda()
|
||||
|
||||
ADAM_BETAS = (0.9, 0.999)
|
||||
if args.shot >= 18:
|
||||
optimizer = torch.optim.RMSprop([{'params': adapter.parameters(), 'lr': 0.0001},
|
||||
{'params': Image_Encoder.parameters(), 'lr': 0.00001},
|
||||
{'params': Text_Encoder.parameters(), 'lr': 0.00001}],
|
||||
eps=1e-5)
|
||||
else:
|
||||
optimizer = torch.optim.RMSprop(
|
||||
[
|
||||
{'params': adapter.parameters(), 'lr': 0.0001, 'weight_decay': 0.00001, 'betas': ADAM_BETAS},
|
||||
{'params': Image_Encoder.parameters(), 'lr': 0.0001, 'weight_decay': 0.0001, 'betas': ADAM_BETAS},
|
||||
{'params': Text_Encoder.parameters(), 'lr': 0.0001, 'weight_decay': 0.0001, 'betas': ADAM_BETAS}]
|
||||
, eps=1e-4
|
||||
)
|
||||
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs * len(train_loader_shuffle))
|
||||
source_train_loader_batch = enumerate(train_loader_shuffle)
|
||||
dir = args.savedir + args.dataset_name + '_epx/' + str(args.shot) + 'shot' + '/'
|
||||
torch.save(CLIP_Text, dir + '/CLIP_Text.pth')
|
||||
torch.save(CLIP_Image, dir + '/CLIP_Image.pth')
|
||||
while (current_epoch < args.epochs):
|
||||
if (current_epoch <= 20):
|
||||
source_train_loader_batch, current_epoch, new_epoch_flag = warm_train(classnames, templates,
|
||||
train_loader_shuffle,
|
||||
source_train_loader_batch,
|
||||
model,
|
||||
adapter,
|
||||
criterion_classifier_source,
|
||||
criterion_classifier_target,
|
||||
optimizer,
|
||||
current_epoch,
|
||||
args, scheduler, criterion, CLIP_Text,
|
||||
Text_Encoder, CLIP_Image,
|
||||
Image_Encoder,
|
||||
zero_weights
|
||||
)
|
||||
elif (current_epoch > 20):
|
||||
source_train_loader_batch, current_epoch, new_epoch_flag = train(classnames, templates,
|
||||
train_loader_shuffle,
|
||||
source_train_loader_batch,
|
||||
model,
|
||||
adapter,
|
||||
criterion_classifier_source,
|
||||
criterion_classifier_target,
|
||||
optimizer,
|
||||
current_epoch,
|
||||
args, scheduler, criterion, CLIP_Text,
|
||||
Text_Encoder, CLIP_Image, Image_Encoder,
|
||||
zero_weights)
|
||||
if new_epoch_flag:
|
||||
if (current_epoch + 1) % args.test_freq == 0 or current_epoch == 0:
|
||||
if current_epoch >= args.valepoch:
|
||||
prec1 = validate(classnames, templates, loader, model, adapter, current_epoch, args,
|
||||
zero_weights,
|
||||
criterion,
|
||||
CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder)
|
||||
# record the best prec1 and save checkpoint
|
||||
is_best = prec1 > best_prec1
|
||||
if prec1 > args.valacc:
|
||||
save_dir = dir + '/epoch_' + str(current_epoch) + '_' + str(
|
||||
prec1)
|
||||
if not os.path.isdir(save_dir):
|
||||
os.mkdir(save_dir)
|
||||
torch.save(adapter, save_dir + '/_adapter_extractor.pth')
|
||||
torch.save(Text_Encoder, save_dir + '/Text_Encoder.pth')
|
||||
torch.save(Image_Encoder, save_dir + '/Image_Encoder.pth')
|
||||
best_prec1 = max(prec1, best_prec1)
|
||||
if is_best:
|
||||
weights_path = save_dir
|
||||
best_init_acc = best_prec1
|
||||
best_epoch = current_epoch
|
||||
log = open(os.path.join(args.log, 'log.txt'), 'a')
|
||||
log.write('Best acc: %3f' % (best_prec1))
|
||||
log.close()
|
||||
|
||||
# evaluate on the val data
|
||||
|
||||
filename = args.filename_dir + args.dataset_name + '.txt'
|
||||
strr = str(args.shot) + 'shot' + ' ' + 'best_epoch' + ' ' + str(best_epoch) + ' ' + 'best_init_acc' + ' ' + str(
|
||||
best_init_acc)
|
||||
with open(filename, 'a') as f:
|
||||
f.write(strr + '\n')
|
||||
f.close()
|
||||
log = open(os.path.join(args.log, 'log.txt'), 'a')
|
||||
log.write('\n-------------------------------------------\n')
|
||||
log.write(time.asctime(time.localtime(time.time())))
|
||||
log.write('\n-------------------------------------------\n')
|
||||
log.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user