create_student_results_ade.py

import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import numpy as np
import datasets
import models as models
import matplotlib.pyplot as plt
import torchvision.models as torch_models
from extra_setting import *
import scipy.io as sio



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 ade')
parser.add_argument('-d', '--dataset', default='ade', help='dataset name')
parser.add_argument('--arch', '-a', metavar='ARCH', default='keras',
                    choices=model_names,
                    help='model architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: resnet50)')
parser.add_argument('-c', '--channel', type=int, default=16,
                    help='first conv channel (default: 16)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 1)')
parser.add_argument('--gpu', default='3', help='index of gpus to use')
parser.add_argument('--epochs', default=40, 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('-b', '--batch-size', default=64, type=int,
                    metavar='N', help='mini-batch size (default: 200)')
parser.add_argument('--lr', '--learning-rate', default=0.001, 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('--lr_step', default='20', help='decreasing strategy')
parser.add_argument('--print-freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='./ade/checkpoint_alexnet.pth.tar', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--first_epochs', default=80, type=int, metavar='N',
                    help='number of first stage epochs to run')

best_prec1 = 0
def main():
    global args, best_prec1
    args = parser.parse_args()

    # training multiple times

    # select gpus
    args.gpu = args.gpu.split(',')
    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(args.gpu)

    # data loader
    assert callable(datasets.__dict__[args.dataset])
    get_dataset = getattr(datasets, args.dataset)
    num_classes = datasets._NUM_CLASSES[args.dataset]
    train_loader, val_loader = get_dataset(
        batch_size=args.batch_size, num_workers=args.workers)

    # create model
    model_main = models.__dict__['alexnet'](pretrained=True)
    model_main.classifier[-1] = nn.Linear(model_main.classifier[-1].in_features, num_classes)
    model_main = torch.nn.DataParallel(model_main, device_ids=range(len(args.gpu))).cuda()
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            model_main.module.load_state_dict(checkpoint['state_dict_m'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    # generate predicted hardness score
    criterion = nn.CrossEntropyLoss().cuda()
    criterion_f = nn.CrossEntropyLoss(reduce=False).cuda()
    prec1, prec5, all_correct_te, all_predicted_te, all_class_dis_te, all_gt_target_te = validate(val_loader,
                                                                                                  model_main, criterion,
                                                                                                  criterion_f)
    all_predicted_te = all_predicted_te.astype(int)
    np.save('./ade/all_correct_alexnet_te.npy', all_correct_te)
    np.save('./ade/all_predicted_alexnet_te.npy', all_predicted_te)
    np.save('./ade/all_class_dis_alexnet_te.npy', all_class_dis_te)
    np.save('./ade/all_gt_target_alexnet_te.npy', all_gt_target_te)


def validate(val_loader, model_main, criterion, criterion_f):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model_main.eval()
    end = time.time()

    all_correct_te = []
    all_predicted_te = []
    all_class_dis = np.zeros((1, 1040))
    all_gt_target = []
    for i, (input, target, index) in enumerate(val_loader):

        all_gt_target = np.concatenate((all_gt_target, target), axis=0)

        input = input.cuda()
        target = target.cuda(async=True)

        # compute output
        output = model_main(input)
        class_dis = F.softmax(output, dim=1)
        class_dis = class_dis.data.cpu().numpy()
        all_class_dis = np.concatenate((all_class_dis, class_dis), axis=0)

        loss = criterion(output, target)

        p_i_m = torch.max(output, dim=1)[1]
        all_predicted_te = np.concatenate((all_predicted_te, p_i_m), axis=0)
        p_i_m = p_i_m.long()
        p_i_m[p_i_m - target == 0] = -1
        p_i_m[p_i_m > -1] = 0
        p_i_m[p_i_m == -1] = 1
        correct = p_i_m.float()
        all_correct_te = np.concatenate((all_correct_te, correct), axis=0)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('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'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                i, len(val_loader), batch_time=batch_time, loss=losses,
                top1=top1, top5=top5))

    all_class_dis = all_class_dis[1:, :]
    return top1.avg, top5.avg, all_correct_te, all_predicted_te, all_class_dis, all_gt_target


def save_checkpoint(state, filename='checkpoint_res.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 // 30))
    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__':
    main()