train_rp_indoor_vgg_vgg.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 realistic_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 Indoor Training')
parser.add_argument('-d', '--dataset', default='indoor', help='dataset name')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet50',
                    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=1, type=int, metavar='N',
                    help='number of data loading workers (default: 1)')
parser.add_argument('--gpu', default='0,4,6,7', help='index of gpus to use')
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=32, 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='10', 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='./indoor/checkpoint_vgg_vgg2.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('--per', '--percentage', default=0.95, type=float,
                    metavar='PER', help='remaining percentage')

best_prec1 = 0


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

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

    # data loader
    assert callable(realistic_datasets.__dict__[args.dataset])
    get_dataset = getattr(realistic_datasets, args.dataset)
    num_classes = realistic_datasets._NUM_CLASSES[args.dataset]
    train_loader, val_loader = get_dataset(
        batch_size=args.batch_size, percentage=args.per, thres_type=0, net_type=0, num_workers=args.workers)

    # create model
    model_main = torch_models.vgg16(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()

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()
    optimizer_m = torch.optim.SGD(model_main.parameters(), lr=0.0001, momentum=0.9, weight_decay=1e-4)

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

    cudnn.benchmark = True

    if args.evaluate:
        validate(val_loader, model_main, criterion)
        return

    lr_step = list(map(int, args.lr_step.split(',')))

    for epoch in range(args.start_epoch, args.epochs):
        if epoch in lr_step:
            for param_group in optimizer_m.param_groups:
                param_group['lr'] *= 0.1

        # train for one epoch
        train(train_loader, model_main, optimizer_m, epoch, criterion)

        # evaluate on validation set
        prec1, prec5 = validate(val_loader, model_main, criterion)


def train(train_loader, model_main, optimizer_m, epoch, criterion):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses_m = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model_main.train()

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

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

        # compute output
        predicted_labels = model_main(input)
        loss_m = criterion(predicted_labels, target)

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

        # compute gradient and do SGD step
        optimizer_m.zero_grad()
        loss_m.backward(retain_graph=True)
        optimizer_m.step()

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

        if i % args.print_freq == 0:
            curr_lr_m = optimizer_m.param_groups[0]['lr']
            print('Epoch: [{0}/{1}][{2}/{3}]\t'
                  'LR: [{4}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss_m {loss_m.val:.4f} ({loss_m.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                epoch, args.epochs, i, len(train_loader), curr_lr_m,
                batch_time=batch_time, data_time=data_time, loss_m=losses_m, top1=top1, top5=top5))


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

    # switch to evaluate mode
    model_main.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):

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

        # compute output
        output = model_main(input)
        loss = criterion(output, target)

        # 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))

    return top1.avg, top5.avg


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()
    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()