main.py

'''

'''

import os
import cv2
import argparse
import time
import yaml
import torch
import random
import numpy as np
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
from utils.profile import count_params
from utils.data_aug import ColorAugmentation
from torch.autograd.variable import Variable
# sklearn libs
from sklearn.metrics import confusion_matrix
import pickle
import roc
import models
from read_data import CASIA
from losses import *
from tools.benchmark import compute_speed, stat
model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='models architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: resnet18)')
parser.add_argument('--config', default='cfgs/local_test.yaml')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=200, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument("--random-seed", type=int, default=14,
                        help='Seed to provide (near-)reproducibility.')
parser.add_argument('--gpus', type=str, default='0', help='use gpus training eg.--gups 0,1')
parser.add_argument('-b', '--batch-size', default=32, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--val', '--evaluate', dest='evaluate', default=False, type=bool,
                    help='evaluate models on validation set')
parser.add_argument('--val-save', default=False, type=bool,
                    help='whether to save evaluate result')
parser.add_argument('--phase-test', default=False, type=bool,
                    help='whether testing in test dataset ')
parser.add_argument('--train_image_list', default='', type=str, help='path to train image list')
parser.add_argument('--input_size', default=224, type=int, help='img crop size')
parser.add_argument('--image_size', default=224, type=int, help='ori img size')
parser.add_argument('--model_name', default='', type=str, help='name of the models')
parser.add_argument('--speed','--speed-test', default=False, type=bool,
                    help='whether to speed test')
parser.add_argument('--summary', default=False, type=bool,
                    help='whether to analysis network complexity')
parser.add_argument('--every-decay', default=40, type=int, help='how many epoch decay the lr')
parser.add_argument('--fl-gamma', default=3, type=int, help='gamma for Focal Loss')
parser.add_argument('--phase-ir', default=0, type=int, help='phare for IR')

best_prec1 = 0

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

USE_GPU = torch.cuda.is_available()

def main():
    global args, best_prec1, USE_GPU,device
    args = parser.parse_args()

    with open(args.config) as f:
        config = yaml.load(f)

    for k, v in config['common'].items():
        setattr(args, k, v)

    ## Set random seeds ##
    torch.manual_seed(args.random_seed)
    np.random.seed(args.random_seed)
    random.seed(args.random_seed)

    # create models
    if args.input_size != 224 or args.image_size != 256:
        image_size = args.image_size
        input_size = args.input_size
    else:
        image_size = 256
        input_size = 224
    print("Input image size: {}, test size: {}".format(image_size, input_size))

    if "model" in config.keys():
        model = models.__dict__[args.arch](**config['model'])
    else:
        model = models.__dict__[args.arch]()
    device = torch.device('cuda:' + str(args.gpus[0]) if torch.cuda.is_available() else "cpu")
    str_input_size = '1x3x224x224'
    if args.summary: 
        input_size = tuple(int(x) for x in str_input_size.split('x'))
        stat(model,input_size)
        return
    if USE_GPU:
        cudnn.benchmark = True
        torch.cuda.manual_seed_all(args.random_seed)
        args.gpus = [int(i) for i in args.gpus.split(',')]
        model = torch.nn.DataParallel(model,device_ids=args.gpus)
        model.to(device)

    
    count_params(model)
    pytorch_total_params = sum(p.numel() for p in model.parameters())
    print('total_params',pytorch_total_params)

    # define loss function (criterion) and optimizer
    criterion = FocalLoss(device,2,gamma=args.fl_gamma)

    optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum,
                                weight_decay=args.weight_decay)
    
    if args.speed:
        input_size = tuple(int(x) for x in str_input_size.split('x') )
        iteration = 1000
        compute_speed(model,input_size,device,iteration)
        return
    

    # optionally resume from a checkpoint
    if args.resume:
        print(os.getcwd())
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    

    # Data loading code
    normalize = transforms.Normalize(mean=[0.14300402, 0.1434545, 0.14277956],  ##accorcoding to casia-surf val to commpute
                                     std=[0.10050353, 0.100842826, 0.10034215])
    img_size = args.input_size

    ratio = 224.0 / float(img_size)
    train_dataset = CASIA(
        transforms.Compose([
            transforms.RandomResizedCrop(img_size),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            ColorAugmentation(),
            normalize,
        ]),phase_train=True)
    val_dataset = CASIA( transforms.Compose([
        transforms.Resize(int(256 * ratio)),
        transforms.CenterCrop(img_size),
        transforms.ToTensor(),
        normalize,
    ]),phase_train=False,phase_test=args.phase_test)

    train_sampler = None
    val_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=(train_sampler is None), sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False,
                                             num_workers=args.workers, pin_memory=False, sampler=val_sampler)

    if args.evaluate:
        validate(val_loader, model, criterion,args.start_epoch)
        return
    else:
        print(model)

    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)
        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)
        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion,epoch)
        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        if is_best:
            print('epoch: {} The best is {} last best is {}'.format(epoch,prec1,best_prec1))
        best_prec1 = max(prec1, best_prec1)
        
        if not os.path.exists(args.save_path):
            os.mkdir(args.save_path)
        save_name = '{}/{}_{}_best.pth.tar'.format(args.save_path, args.model_name, epoch) if is_best else\
            '{}/{}_{}.pth.tar'.format(args.save_path, args.model_name, epoch)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'optimizer': optimizer.state_dict(),
        }, filename=save_name)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)
        input_var = Variable(input).float().to(device)
        target_var = Variable(target).long().to(device)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)
        prec1,prec2 = accuracy(output.data, target_var,topk=(1,2))

        # measure accuracy and record loss
        reduced_prec1 = prec1.clone()

        top1.update(reduced_prec1[0])

        reduced_loss = loss.data.clone()
        losses.update(reduced_loss)

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        #  check whether the network is well connected
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()
        lr = optimizer.param_groups[0]['lr']

        if i % args.print_freq == 0:
            with open('logs/{}_{}.log'.format(time_stp, args.arch), 'a+') as flog:
                line = 'Epoch: [{0}][{1}/{2}]\t lr:{3:.5f}\t' \
                       'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
                       'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \
                       'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' \
                       .format(epoch, i, len(train_loader),lr,
                        batch_time=batch_time, loss=losses, top1=top1)
                print(line)
                flog.write('{}\n'.format(line))


def validate(val_loader, model, criterion,epoch):
    global time_stp
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    result_list = []
    label_list = []
    predicted_list = []

    # switch to evaluate mode
    model.eval()

    end = time.time()
    with torch.no_grad():
        for i, (input, target,depth_dirs) in enumerate(val_loader):
            with torch.no_grad():
                input_var = Variable(input).float().to(device)
                target_var = Variable(target).long().to(device)

                # compute output
                output = model(input_var)
                loss = criterion(output, target_var)

                # measure accuracy and record loss
                prec1,prec2 = accuracy(output.data, target_var,topk=(1,2))
                losses.update(loss.data, input.size(0))
                top1.update(prec1[0], input.size(0))

                soft_output = torch.softmax(output,dim=-1)
                preds = soft_output.to('cpu').detach().numpy()
                label = target.to('cpu').detach().numpy()
                _,predicted = torch.max(soft_output.data, 1)
                predicted = predicted.to('cpu').detach().numpy()

                for i_batch in range(preds.shape[0]):
                        result_list.append(preds[i_batch,1])
                        label_list.append(label[i_batch])
                        predicted_list.append(predicted[i_batch])
                        if args.val_save:
                            f = open('submission/{}_{}_{}_submission.txt'.format(time_stp, args.arch, epoch), 'a+')
                            depth_dir = depth_dirs[i_batch].replace(os.getcwd() + '/data/','')
                            rgb_dir = depth_dir.replace('depth','color')
                            ir_dir = depth_dir.replace('depth','ir')
                            f.write(rgb_dir + ' ' + depth_dir + ' '+ir_dir+' ' + str(preds[i_batch,1]) +'\n')

                # measure elapsed time
                batch_time.update(time.time() - end)
                end = time.time()
                if i % args.print_freq == 0:
                    line = 'Test: [{0}/{1}]\t' \
                           'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
                           'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \
                           'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'.format(i, len(val_loader), batch_time=batch_time,
                                    loss=losses, top1=top1)

                    with open('logs/{}_{}.log'.format(time_stp, args.arch), 'a+') as flog:
                        flog.write('{}\n'.format(line))
                        print(line)
    tn, fp, fn, tp = confusion_matrix(label_list, predicted_list).ravel()
    apcer = fp/(tn + fp)
    npcer = fn/(fn + tp)
    acer = (apcer + npcer)/2
    metric =roc.cal_metric(label_list, result_list)
    eer = metric[0]
    tprs = metric[1]
    auc = metric[2]
    xy_dic = metric[3]
#     tpr1 = tprs['TPR@FPR=10E-2']
#     logger.info('eer: {}\t'
#                 'tpr1: {}\t'
#                 'auc: {}\t'
#                 'acer: {}\t'
#                 'accuracy: {top1.avg:.3f} ({top1.avg:.3f})'
#           .format(eer,tpr1,auc,acer,top1=top1))
#     pickle.dump(xy_dic, open('xys/xy_{}_{}_{}.pickle'.format(time_stp, args.arch,epoch),'wb'))
    with open('logs/val_result_{}_{}.txt'.format(time_stp,args.arch),'a+') as f_result:
        result_line = 'epoch: {} EER: {:.6f} TPR@FPR=10E-2: {:.6f} TPR@FPR=10E-3: {:.6f} APCER:{:.6f} NPCER:{:.6f} AUC: {:.8f} Acc:{:.3f} TN:{} FP : {} FN:{} TP:{}  ACER:{:.8f} '.format(epoch,eer, tprs["TPR@FPR=10E-2"], tprs["TPR@FPR=10E-3"],apcer,npcer,auc, top1.avg, tn, fp, fn,tp,acer)
        f_result.write('{}\n'.format(result_line))
        print(result_line)
    return top1.avg


def save_checkpoint(state, filename='checkpoint.pth.tar'):
    torch.save(state, filename)


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 adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // args.every_decay))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


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()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


if __name__ == '__main__':
    time_stp = time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime())
    main()