main_eval_visda.py

import argparse
import os
import random
import shutil
import time
import warnings
import copy 

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 torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

from visda2017 import VisDA17
from pruning_utils import *

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('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')
parser.add_argument('--epochs', default=20, 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: 256), this is the total '
                        'batch size of all GPUs on the current node when '
                        'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--pretrained', default=None, type=str, help='pretrained weight of Ticket')
parser.add_argument('--dict_key', default=None, type=str, help='key of pretrained file')
parser.add_argument('--mask_dir', default=None, type=str, help='mask direction of Ticket')
parser.add_argument('--conv1', action="store_true", help="whether pruning conv1")
parser.add_argument('--reverse_mask', action="store_true", help="whether using reverse mask")


parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=50, 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('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--save_dir', default='results/', type=str)





best_acc1 = 0
best_epoch = 0

def main():
    global best_acc1, best_epoch    
    args = parser.parse_args()

    os.makedirs(args.save_dir, exist_ok=True)

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                    'This will turn on the CUDNN deterministic setting, '
                    'which can slow down your training considerably! '
                    'You may see unexpected behavior when restarting '
                    'from checkpoints.')
    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    # create model
    print("=> using model '{}'".format(args.arch))
    model = models.__dict__[args.arch](pretrained=False)
    ch = model.fc.in_features
    model.fc = nn.Linear(ch,12)

    load_ticket_vis(model, args)

    if args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        print('mode dataparallel')
        model = torch.nn.DataParallel(model).cuda()


    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(args.gpu)
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    cudnn.benchmark = True

    # Data loading code
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                    std=[0.229, 0.224, 0.225])

    train_trans = transforms.Compose([
            transforms.RandomResizedCrop(size=224, scale=(0.75, 1.33)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ])

    val_trans = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            normalize,
        ])

    train_dataset = VisDA17(txt_file=os.path.join(args.data, "train/image_list.txt"), 
                        root_dir=os.path.join(args.data, "train"), transform=train_trans)
    val_dataset = VisDA17(txt_file=os.path.join(args.data, "validation/image_list.txt"), 
                        root_dir=os.path.join(args.data, "validation"), transform=val_trans)

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=True,
        num_workers=args.workers, pin_memory=True)

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

    for epoch in range(args.start_epoch, args.epochs):

        print(optimizer.state_dict()['param_groups'][0]['lr'])
        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_acc1': best_acc1,
            'optimizer' : optimizer.state_dict(),
        }, is_best, checkpoint=args.save_dir, best_name='model_best.pth.tar')

    print('best TA = ', best_acc1)


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()
    wp_steps = len(train_loader)

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        adjust_learning_rate(optimizer, epoch, args, i+1, steps_for_one_epoch=wp_steps)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        if i % args.print_freq == 0:
            progress.display(i)

def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

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

            if i % args.print_freq == 0:
                progress.display(i)

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg

def load_ticket_vis(model, args):

    # weight 
    if args.pretrained:

        initalization = torch.load(args.pretrained, map_location = 'cpu')
        if args.dict_key:
            print('loading from {}'.format(args.dict_key))
            initalization = initalization[args.dict_key]

        loading_weight = extract_main_weight(initalization, fc=False, conv1=True)

        for key in loading_weight.keys():
            assert key in model.state_dict().keys()

        print('*number of loading weight={}'.format(len(loading_weight.keys())))
        print('*number of model weight={}'.format(len(model.state_dict().keys())))
        model.load_state_dict(loading_weight, strict=False)

    # mask 
    if args.mask_dir:

        current_mask_weight = torch.load(args.mask_dir, map_location = 'cpu')
        if 'state_dict' in current_mask_weight.keys():
            current_mask_weight = current_mask_weight['state_dict']
        current_mask = extract_mask(current_mask_weight)

        if args.reverse_mask:
            current_mask = reverse_mask(current_mask)
        prune_model_custom(model, current_mask, conv1=args.conv1)
        check_sparsity(model, conv1=args.conv1)

def save_checkpoint(state, is_best, checkpoint, filename='checkpoint.pth.tar', best_name='model_best.pth.tar'):
    filepath = os.path.join(checkpoint, filename)
    torch.save(state, filepath)
    if is_best:
        shutil.copyfile(filepath, os.path.join(checkpoint, best_name))

def adjust_learning_rate(optimizer, epoch, args, iterations, steps_for_one_epoch):

    max_lr = args.lr

    if epoch < 10:
        lr = max_lr
    else:
        lr = max_lr*0.1

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        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 __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'

def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        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()