imagenet train pytorch script

imagenet_train.py

# https://github.com/pytorch/vision/blob/master/torchvision/models/__init__.py
import argparse
import os
import shutil
import time
import os, sys, pdb, shutil, time, random, datetime

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
from utils import convert_secs2time, time_string, time_file_str,AverageMeter
import torch.optim.lr_scheduler as lr_scheduler
# from models import print_log
import models
from tensorboardX import SummaryWriter
from utils import convert_model, measure_model



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 directory containing training and validation folders')
parser.add_argument('--train_dir_name', metavar='DIR', help='training set directory name')
parser.add_argument('--val_dir_name', metavar='DIR', help='validation set directory name')
parser.add_argument('--save_dir', type=str, default='./', help='Folder to save checkpoints and log.')
parser.add_argument('--arch', '-a', metavar='ARCH', default='simpnet_imgnet_5m_nodrp_safc_s1',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: simpnet_imgnet_5m_nodrp_safc_s1)')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N', help='number of data loading workers (default: 16)')
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('-b', '--batch-size', default=128, type=int, metavar='N', help='mini-batch size (default: 128)')
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-5, type=float, metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=200, type=int, metavar='N', help='print frequency (default: 100)')
parser.add_argument('--resume', default='', 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')

args = parser.parse_args()
args.prefix = time_file_str()

def main():

    best_prec1 = 0
    best_prec5 = 0

    writer = SummaryWriter()

    if not os.path.isdir(args.save_dir):
      os.makedirs(args.save_dir)
    # used for file names, etc 
    time_stamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')     
    log = open(os.path.join(args.save_dir, '{}.{}_{}.log'.format(args.arch, args.prefix, time_stamp)), 'w')

    # create model
    print_log("=> creating model '{}'".format(args.arch), log)
    model = models.__dict__[args.arch](1000)
    print_log("=> Model : {}".format(model), log)
    print_log("=> parameter : {}".format(args), log)

    if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
      model.features = torch.nn.DataParallel(model.features)
      model.cuda()
    else:
      model = torch.nn.DataParallel(model).cuda()

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()


    # optimizer = torch.optim.Adadelta(model.parameters(), weight_decay=args.weight_decay,
    #                                  lr=0.1, rho=0.9)
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                momentum=args.momentum,
                weight_decay=args.weight_decay,
                nesterov=True)


    IMAGE_SIZE=224
    print_log(summary(model, input_size=(3, IMAGE_SIZE, IMAGE_SIZE)), log)

    
    n_flops, n_params = measure_model(model, IMAGE_SIZE, IMAGE_SIZE)
    print_log('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6), log)

    ## epoch 
    milestones = [30, 60, 90, 130, 150]#[10, 20, 30, 40, 50, 60]#[15, 30, 60, 90, 110, 140]
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1)
    
    #scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print_log("=> loading checkpoint '{}'".format(args.resume), log)
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            if 'best_prec5' in checkpoint:
                best_prec5 = checkpoint['best_prec5']
            else:
                best_prec5 = 0.00
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            model.eval()
            print_log("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']), log)
        else:
            print_log("=> no checkpoint found at '{}'".format(args.resume), log)

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, args.train_dir_name) #'train')
    valdir = os.path.join(args.data, args.val_dir_name) #'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    # meanstd = {
    # mean = { 0.485, 0.456, 0.406 },
    # std = { 0.229, 0.224, 0.225 },
    #           }
    pca  = {
    'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]),
    'eigvec': torch.Tensor([
        [-0.5675,  0.7192,  0.4009],
        [-0.5808, -0.0045, -0.8140],
        [-0.5836, -0.6948,  0.4203],
    ])
}


    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomSizedCrop(224),#224
         #    transforms.ColorJitter(
         #    brightness = 0.4,
         #    contrast = 0.4,
         #    saturation = 0.4,
         # ),
        #transforms.Lighting(0.1, pca['eigval'], pca['eigvec']),
        #transforms.ColorNormalize(meanstd),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize,
        ]))

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

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Scale(256),
            #t.ColorNormalize(meanstd),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

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

    filename = os.path.join(args.save_dir, 'checkpoint.{0}.{1}_{2}.pth.tar'.format(args.arch, args.prefix, time_stamp))
    bestname = os.path.join(args.save_dir, 'best.{0}.{1}_{2}.pth.tar'.format(args.arch, args.prefix, time_stamp))

    start_time = time.time()
    epoch_time = AverageMeter()
    for epoch in range(args.start_epoch, args.epochs):

        current_learning_rate = float(scheduler.get_lr()[-1])

        scheduler.step()

        #adjust_learning_rate(optimizer, epoch)

        need_hour, need_mins, need_secs = convert_secs2time(epoch_time.val * (args.epochs-epoch))
        need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(need_hour, need_mins, need_secs)
        #print_log(' [{:s}] :: {:3d}/{:3d} ----- [{:s}] {:s}'.format(args.arch, epoch, args.epochs, time_string(), need_time), log)

        print_log('\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [learning_rate={:.6f}]'.format(time_string(), epoch, args.epochs, need_time, current_learning_rate) \
                + ' [Best : Accuracy(T1/T5)={:.2f}/{:.2f}, Error={:.2f}/{:.2f}]'.format(best_prec1, best_prec5, 100-best_prec1,100-best_prec5), log)

        # train for one epoch
        tr_prec1, tr_prec5, tr_loss = train(train_loader, model, criterion, optimizer, epoch, log)

        # evaluate on validation set
        prec1,prec5, val_loss = validate(val_loader, model, criterion, log)

        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        best_prec5 = max(prec5, best_prec5)
        
        
        writer.add_scalar('Learning rate ', current_learning_rate, epoch)
        writer.add_scalar('training/loss', tr_loss, epoch)
        writer.add_scalar('training/Top1', tr_prec1, epoch)
        writer.add_scalar('training/Top5', tr_prec5, epoch)

        writer.add_scalar('validation/loss', val_loss, epoch)
        writer.add_scalar('validation/Top1', prec1, epoch)
        writer.add_scalar('validation/Top5', prec5, epoch)

        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'best_prec5': best_prec5,
            'optimizer' : optimizer.state_dict(),
        }, is_best, filename, bestname)
        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()

    writer.close()       
    log.close()


# -- Lighting noise (AlexNet-style PCA-based noise)
# function M.Lighting(alphastd, eigval, eigvec)
#    return function(input)
#       if alphastd == 0 then
#          return input
#       end

#       local alpha = torch.Tensor(3):normal(0, alphastd)
#       local rgb = eigvec:clone()
#          :cmul(alpha:view(1, 3):expand(3, 3))
#          :cmul(eigval:view(1, 3):expand(3, 3))
#          :sum(2)
#          :squeeze()

#       input = input:clone()
#       for i=1,3 do
#          input[i]:add(rgb[i])
#       end
#       return input
#    end
# end

# class Lighting(object):
#     """Lighting noise(AlexNet - style PCA - based noise)"""

#     def __init__(self, alphastd, eigval, eigvec):
#         self.alphastd = alphastd
#         self.eigval = eigval
#         self.eigvec = eigvec

#     def __call__(self, img):
#         if self.alphastd == 0:
#             return img

#         alpha = img.new().resize_(3).normal_(0, self.alphastd)
#         rgb = self.eigvec.type_as(img).clone()\
#             .mul(alpha.view(1, 3).expand(3, 3))\
#             .mul(self.eigval.view(1, 3).expand(3, 3))\
#             .sum(1).squeeze()

#         return img.add(rgb.view(3, 1, 1).expand_as(img))

def train(train_loader, model, criterion, optimizer, epoch, log):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = 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)

        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input)
        target_var = torch.autograd.Variable(target)
        # print('i: ',i)
        # compute output
        output = model(input_var)
        # print('output: ',output.shape)
        # print('output(target_var): ',target_var.shape)
        # print('target_var: ', target_var)
        loss = criterion(output, target_var)
        
        # print('loss: ',loss.shape)  
        
        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        # print('input.size(): ', input.size(0))
        # print('loss.data[0]: ', loss.data[0])

        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.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:
            print_log('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_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(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1, top5=top5), log)
    return top1.avg, top5.avg, losses.avg


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

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input, volatile=True)
        target_var = torch.autograd.Variable(target, volatile=True)

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

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], 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_log('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), log)

    print_log(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Loss@ {error:.3f}'.format(top1=top1, top5=top5, error=losses.avg), log)

    return top1.avg, top5.avg, losses.avg


def save_checkpoint(state, is_best, filename, bestname):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, bestname)


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 print_log(print_string, log):
  print("{}".format(print_string))
  log.write('{}\n'.format(print_string))
  log.flush()

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

import torch
import torch.nn as nn
from torch.autograd import Variable

from collections import OrderedDict
def summary(model, input_size):
        def register_hook(module):
            def hook(module, input, output):
                class_name = str(module.__class__).split('.')[-1].split("'")[0]
                module_idx = len(summary)

                m_key = '%s-%i' % (class_name, module_idx+1)
                summary[m_key] = OrderedDict()
                summary[m_key]['input_shape'] = list(input[0].size())
                summary[m_key]['input_shape'][0] = -1
                if isinstance(output, (list,tuple)):
                    summary[m_key]['output_shape'] = [[-1] + list(o.size())[1:] for o in output]
                else:
                    summary[m_key]['output_shape'] = list(output.size())
                    summary[m_key]['output_shape'][0] = -1

                params = 0
                if hasattr(module, 'weight') and hasattr(module.weight, 'size'):
                    params += torch.prod(torch.LongTensor(list(module.weight.size())))
                    summary[m_key]['trainable'] = module.weight.requires_grad
                if hasattr(module, 'bias') and hasattr(module.bias, 'size'):
                    params +=  torch.prod(torch.LongTensor(list(module.bias.size())))
                summary[m_key]['nb_params'] = params
                
            if (not isinstance(module, nn.Sequential) and 
               not isinstance(module, nn.ModuleList) and 
               not (module == model)):
                hooks.append(module.register_forward_hook(hook))
                
        if torch.cuda.is_available():
            dtype = torch.cuda.FloatTensor
        else:
            dtype = torch.FloatTensor
        
        # check if there are multiple inputs to the network
        if isinstance(input_size[0], (list, tuple)):
            x = [Variable(torch.rand(2,*in_size)).type(dtype) for in_size in input_size]
        else:
            x = Variable(torch.rand(2,*input_size)).type(dtype)
            
            
        # print(type(x[0]))
        # create properties
        summary = OrderedDict()
        hooks = []
        # register hook
        model.apply(register_hook)
        # make a forward pass
        # print(x.shape)
        model(x)
        # remove these hooks
        for h in hooks:
            h.remove()

        print('----------------------------------------------------------------')
        line_new = '{:>20}  {:>25} {:>15}'.format('Layer (type)', 'Output Shape', 'Param #')
        print(line_new)
        print('================================================================')
        total_params = 0
        trainable_params = 0
        for layer in summary:
            # input_shape, output_shape, trainable, nb_params
            line_new = '{:>20}  {:>25} {:>15}'.format(layer, str(summary[layer]['output_shape']), '{0:,}'.format(summary[layer]['nb_params']))
            total_params += summary[layer]['nb_params']
            if 'trainable' in summary[layer]:
                if summary[layer]['trainable'] == True:
                    trainable_params += summary[layer]['nb_params']
            print(line_new)
        print('================================================================')
        print('Total params: {0:,}'.format(total_params))
        print('Trainable params: {0:,}'.format(trainable_params))
        print('Non-trainable params: {0:,}'.format(total_params - trainable_params))
        print('----------------------------------------------------------------')
        # return summary
if __name__ == '__main__':
    main()