image_pretrained.py

import argparse
import os, sys
import os.path as osp
import torchvision
from torchvision import transforms
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import network, loss
from torch.utils.data import DataLoader
from data_list import ImageList, ImageList_idx
import random, pdb, math, copy
from tqdm import tqdm
from scipy.spatial.distance import cdist
from sklearn.metrics import confusion_matrix

def op_copy(optimizer):
    for param_group in optimizer.param_groups:
        param_group['lr0'] = param_group['lr']
    return optimizer

def lr_scheduler(optimizer, iter_num, max_iter, gamma=10, power=0.75):
    decay = (1 + gamma * iter_num / max_iter) ** (-power)
    for param_group in optimizer.param_groups:
        param_group['lr'] = param_group['lr0'] * decay
        param_group['weight_decay'] = 1e-3 
        param_group['momentum'] = 0.9
        param_group['nesterov'] = True
    return optimizer

def image_train(resize_size=256, crop_size=224, alexnet=False):
  if not alexnet:
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225])
  else:
    normalize = Normalize(meanfile='./ilsvrc_2012_mean.npy')
  return  transforms.Compose([
        transforms.Resize((resize_size, resize_size)),
        transforms.RandomCrop(crop_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize
    ])

def image_test(resize_size=256, crop_size=224, alexnet=False):
  if not alexnet:
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225])
  else:
    normalize = Normalize(meanfile='./ilsvrc_2012_mean.npy')
  return  transforms.Compose([
        transforms.Resize((resize_size, resize_size)),
        transforms.CenterCrop(crop_size),
        transforms.ToTensor(),
        normalize
    ])

def data_load(args): 
    dsets = {}
    dset_loaders = {}
    train_bs = args.batch_size
    txt_tar = open(args.t_dset_path).readlines()
    txt_test = open(args.test_dset_path).readlines()

    dsets["target"] = ImageList_idx(txt_tar, transform=image_train())
    dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, shuffle=True, num_workers=args.worker, drop_last=False)
    dsets["test"] = ImageList_idx(txt_test, transform=image_test())
    dset_loaders["test"] = DataLoader(dsets["test"], batch_size=train_bs*3, shuffle=False, num_workers=args.worker, drop_last=False)

    return dset_loaders

def cal_acc(loader, net, flag=False):
    start_test = True
    with torch.no_grad():
        iter_test = iter(loader)
        for i in range(len(loader)):
            data = iter_test.next()
            inputs = data[0]
            labels = data[1]
            inputs = inputs.cuda()
            _, outputs = net(inputs)
            if start_test:
                all_output = outputs.float().cpu()
                all_label = labels.float()
                start_test = False
            else:
                all_output = torch.cat((all_output, outputs.float().cpu()), 0)
                all_label = torch.cat((all_label, labels.float()), 0)
    _, predict = torch.max(all_output, 1)
    all_output = nn.Softmax(dim=1)(all_output)
    ent = torch.sum(-all_output * torch.log(all_output + args.epsilon), dim=1) / np.log(all_output.size(1))
    accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
    mean_ent = torch.mean(loss.Entropy(nn.Softmax(dim=1)(all_output))).cpu().data.item() 
    
    return accuracy, mean_ent

def train_target(args):
    dset_loaders = data_load(args)  
    netF = network.Res50().cuda()

    param_group = []  
    for k, v in netF.named_parameters():
        if k.__contains__("fc"):
            v.requires_grad = False
        else:
            param_group += [{'params': v, 'lr': args.lr*args.lr_decay1}]

    optimizer = optim.SGD(param_group)
    optimizer = op_copy(optimizer)

    max_iter = args.max_epoch * len(dset_loaders["target"])
    interval_iter = max_iter // args.interval
    iter_num = 0

    netF.train()
    while iter_num < max_iter:
        try:
            inputs_test, _, tar_idx = iter_test.next()
        except:
            iter_test = iter(dset_loaders["target"])
            inputs_test, _, tar_idx = iter_test.next()

        if inputs_test.size(0) == 1:
            continue

        if iter_num % interval_iter == 0 and args.cls_par > 0:
            netF.eval()
            mem_label = obtain_label(dset_loaders['test'], netF, args)
            mem_label = torch.from_numpy(mem_label).cuda()
            netF.train()

        inputs_test = inputs_test.cuda()
        iter_num += 1
        lr_scheduler(optimizer, iter_num=iter_num, max_iter=max_iter)

        features_test, outputs_test = netF(inputs_test)

        if args.cls_par > 0:
            pred = mem_label[tar_idx]
            classifier_loss = nn.CrossEntropyLoss()(outputs_test, pred)
            classifier_loss *= args.cls_par
        else:
            classifier_loss = torch.tensor(0.0).cuda()

        if args.ent:
            softmax_out = nn.Softmax(dim=1)(outputs_test)
            entropy_loss = torch.mean(loss.Entropy(softmax_out))
            if args.gent:
                msoftmax = softmax_out.mean(dim=0)
                gentropy_loss = torch.sum(-msoftmax * torch.log(msoftmax + args.epsilon))
                entropy_loss -= gentropy_loss
            classifier_loss += entropy_loss * args.ent_par

        optimizer.zero_grad()
        classifier_loss.backward()
        optimizer.step()

        if iter_num % interval_iter == 0 or iter_num == max_iter:
            netF.eval()
            acc, ment = cal_acc(dset_loaders['test'], netF)
            log_str = 'Task: {}, Iter:{}/{}; Accuracy = {:.2f}%'.format(args.dset, iter_num, max_iter, acc*100)
            args.out_file.write(log_str + '\n')
            args.out_file.flush()
            print(log_str+'\n')
            netF.train()
    
    if args.issave:
        torch.save(netF.state_dict(), osp.join(args.output_dir, "target" + args.savename + ".pt"))

    return netF

def print_args(args):
    s = "==========================================\n"
    for arg, content in args.__dict__.items():
        s += "{}:{}\n".format(arg, content)
    return s

def obtain_label(loader, net, args):
    start_test = True
    with torch.no_grad():
        iter_test = iter(loader)
        for _ in range(len(loader)):
            data = iter_test.next()
            inputs = data[0]
            labels = data[1]
            inputs = inputs.cuda()
            feas, outputs = net(inputs)
            if start_test:
                all_fea = feas.float().cpu()
                all_output = outputs.float().cpu()
                all_label = labels.float()
                start_test = False
            else:
                all_fea = torch.cat((all_fea, feas.float().cpu()), 0)
                all_output = torch.cat((all_output, outputs.float().cpu()), 0)
                all_label = torch.cat((all_label, labels.float()), 0)

    all_output = nn.Softmax(dim=1)(all_output)
    ent = torch.sum(-all_output * torch.log(all_output + args.epsilon), dim=1)
    unknown_weight = 1 - ent / np.log(args.class_num)
    _, predict = torch.max(all_output, 1)

    accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
    if args.distance == 'cosine':
        all_fea = torch.cat((all_fea, torch.ones(all_fea.size(0), 1)), 1)
        all_fea = (all_fea.t() / torch.norm(all_fea, p=2, dim=1)).t()

    all_fea = all_fea.float().cpu().numpy()
    K = all_output.size(1)
    aff = all_output.float().cpu().numpy()
    initc = aff.transpose().dot(all_fea)
    initc = initc / (1e-8 + aff.sum(axis=0)[:,None])
    cls_count = np.eye(K)[predict].sum(axis=0)
    labelset = np.where(cls_count>args.threshold)
    labelset = labelset[0]
    # print(labelset)

    dd = cdist(all_fea, initc[labelset], args.distance)
    pred_label = dd.argmin(axis=1)
    pred_label = labelset[pred_label]

    for round in range(1):
        aff = np.eye(K)[pred_label]
        initc = aff.transpose().dot(all_fea)
        initc = initc / (1e-8 + aff.sum(axis=0)[:,None])
        dd = cdist(all_fea, initc[labelset], args.distance)
        pred_label = dd.argmin(axis=1)
        pred_label = labelset[pred_label]

    acc = np.sum(pred_label == all_label.float().numpy()) / len(all_fea)
    log_str = 'Accuracy = {:.2f}% -> {:.2f}%'.format(accuracy*100, acc*100)

    args.out_file.write(log_str + '\n')
    args.out_file.flush()
    print(log_str+'\n')

    return pred_label.astype('int') #, labelset


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='SHOT')
    parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run")
    parser.add_argument('--max_epoch', type=int, default=15, help="max iterations")
    parser.add_argument('--interval', type=int, default=15, help="max iterations")
    parser.add_argument('--batch_size', type=int, default=64, help="batch_size")
    parser.add_argument('--worker', type=int, default=4, help="number of workers")
    parser.add_argument('--distance', type=str, default='cosine', choices=["euclidean", "cosine"])
    parser.add_argument('--dset', type=str, default='imagenet_caltech', choices=['imagenet_caltech'])
    parser.add_argument('--lr', type=float, default=1e-2, help="learning rate")
    parser.add_argument('--net', type=str, default='resnet50', help="vgg16, resnet50, resnet101")
    parser.add_argument('--seed', type=int, default=2019, help="random seed")
    parser.add_argument('--epsilon', type=float, default=1e-5)  
    parser.add_argument('--gent', type=bool, default=False)
    parser.add_argument('--ent', type=bool, default=True)
    parser.add_argument('--threshold', type=int, default=30)

    parser.add_argument('--cls_par', type=float, default=0.3)
    parser.add_argument('--ent_par', type=float, default=1.0)
    parser.add_argument('--output', type=str, default='seed')
    parser.add_argument('--da', type=str, default='pda', choices=['pda'])
    parser.add_argument('--issave', type=bool, default=True)
    parser.add_argument('--lr_decay1', type=float, default=0.1)

    args = parser.parse_args()        

    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
    SEED = args.seed
    torch.manual_seed(SEED)
    torch.cuda.manual_seed(SEED)
    np.random.seed(SEED)
    random.seed(SEED)
    # torch.backends.cudnn.deterministic = True

    args.class_num = 1000
    folder = './data/'
    if args.da == 'pda':
        args.t_dset_path = folder + args.dset + '/' + 'caltech_84' + '_list.txt'
    args.test_dset_path = args.t_dset_path

    args.output_dir = osp.join(args.output, args.da, args.dset)
    args.name = args.dset

    if not osp.exists(args.output_dir):
        os.system('mkdir -p ' + args.output_dir)
    if not osp.exists(args.output_dir):
        os.mkdir(args.output_dir)

    args.savename = 'par_' + str(args.cls_par)
    if args.da == 'pda':
        args.savename = 'par_' + str(args.cls_par) + '_thr' + str(args.threshold)
    args.out_file = open(osp.join(args.output_dir, 'log_' + args.savename + '.txt'), 'w')
    args.out_file.write(print_args(args)+'\n')
    args.out_file.flush()
    train_target(args)