clip-symnets/new_test.py

import os
import time
from clip import clip
import torch.nn as nn
import numpy as np
import torch.optim
import random
from opts import opts  # The options for the project
# from trainer import validate  # For the validate (test) process
from models.DomainClassifierTarget import DClassifierForTarget
from models.DomainClassifierSource import DClassifierForSource
from utils.loss_utils import TargetDiscrimLoss, ConcatenatedCELoss
from utils.utils import prepare_directories, set_seed, get_dataset_loader, configure_clip_encoders, save_model, \
    set_adapter_weights, get_text_feature, AverageMeter, accuracy, calculate_zeroshot_weights, gpt_clip_classifier, \
    calculate_zeroshot_weights_GPT,calculate_zero
from Adapter import Weight_Adapter
import logging
import torch.nn.functional as F
import yaml
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import glob
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


class Feature_Extractor(nn.Module):
    def __init__(self, n_input, n_output):
        super().__init__(),
        self.linear1 = nn.Linear(n_input, n_output)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.linear1(x.float())
        x = self.relu(x)
        return x


class Weight_Adapter(nn.Module):
    def __init__(self, n_input, n_output):
        super().__init__()
        self.linear1 = nn.Linear(n_input, n_output)

    def forward(self, x):
        x = self.linear1(x.float())
        return x


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 validate(classnames, templates,val_loader, model, args, zero_shots, criterion,
                   optimizer, scheduler, alpha, beta, gama):
    global best_target_acc
    Compu1_acc = AverageMeter()
    losses = AverageMeter()
    model.eval()
    logit_scale = 4.60517
    # logit_scale = math.exp(logit_scale)
    # switch to evaluate mode

    for i, (image, label, _) in enumerate(val_loader):
        image = image.cuda()
        label = label.cuda()
        input_target = image.cuda()
        target_target = label.cuda()
        target_source = label.cuda()

        input_target_clip = model.encode_image(input_target)

        # 2
        logits2 = 100.*input_target_clip.float() @ zero_shots.float()


        # compu1:1-2+3:
        compu1 = logits2

        compu1_acc = accuracy(compu1, target_target, topk=(1, 5))
        loss = criterion(compu1, target_target)
        Compu1_acc.update(compu1_acc[0].item(), image.size(0))
        losses.update(loss.item(), image.size(0))
        print('loss:', loss.item())
        print(i, '/', len(val_loader))
        print('Compu1_acc:', Compu1_acc.val, 'alpha:', alpha.item(), 'beta:', beta.item(), 'gama:', gama.item())

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        scheduler.step()

    print('Compu1_acc.avg', Compu1_acc.avg, 'alpha:', alpha.item(), 'beta:', beta.item(), 'gama:', gama.item(),
          'losses.avg', losses.avg)
    return Compu1_acc.avg, alpha.item(), beta.item(), gama.item()


def main():
    args = opts()
    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 best_prec1
    model, preprocess = clip.load(args.name)
    model.eval()
    classnames, templates, loader, train_loader,_ = get_dataset_loader(args, preprocess)
    loader=_

    criterion = nn.CrossEntropyLoss().cuda()


    alpha = nn.Parameter(torch.ones([]), requires_grad=True)
    beta = nn.Parameter(torch.ones([]), requires_grad=True)# 91.35902633202728
    gama = nn.Parameter(torch.ones([]), requires_grad=True)
    #best_top1 93.46855981296748 best_a 1.0 best_b 6.64284086227417 best_c 0.8092490434646606

    zero_weights = all_classifier(classnames, templates, model)
    optimizer = torch.optim.AdamW(
        [{'params': beta, 'lr': 0.1}, {'params': gama, 'lr': 0.1}],
        eps=1e-4)
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 40 * len(loader))


    validate(classnames, templates,loader, model, args, zero_weights,
                                       criterion, optimizer, scheduler, alpha, beta, gama)

    #
    #
    #
    #



if __name__ == '__main__':
    main()