#in the name of God, The most compassionate the most merciful # 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 import torchvision.transforms.functional as TF 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_drpall', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: simpnet_imgnet_drpall)') parser.add_argument('-j', '--workers', default=16, type=int, metavar='N', help='number of data loading workers (default: 16)') parser.add_argument('--smode','--stridemode', default=1, type=int, metavar='N', help='stride mode (1 to 5 (deault: 1)') parser.add_argument('--netidx', '--network-index', default=0, type=int, metavar='N', help='The index used for selecting 5mil or 8mil network (default: 0)') parser.add_argument('--scale', default=1.0, type=float, metavar='S', help='scale factor for the architecture width(common values: 1, 0.75, 1.44 (default:1.0)') 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=0.045, type=float, metavar='LR', help='initial learning rate') #0.045 parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('--weight-decay', '--wd', default=0.00004, 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') parser.add_argument('--gpus', default=None, help='List of GPUs used for training - e.g 0,1,3') args = parser.parse_args() args.prefix = time_file_str() __imagenet_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], ]) } # Lighting data augmentation take from here - https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py 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 #img = TF.to_tensor(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 main(): best_prec1 = 0 best_prec5 = 0 if args.gpus is not None: args.gpus = [int(i) for i in args.gpus.split(',')] device = 'cuda:' + str(args.gpus[0]) print('device is ', device) else: device = 'cpu' 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, '{}_s{}_{}.{}_{}.log'.format(args.arch, args.smode, args.scale, args.prefix, time_stamp)), 'w') writer = SummaryWriter('runs/{}_s{}_{}.{}_{}'.format(args.arch, args.smode, args.scale, args.prefix, time_stamp)) # create model print_log("=> creating model '{}_s{}_{}'".format(args.arch, args.smode, args.scale), log) if 'simpnet_imgnet_drpall' in args.arch: model = models.__dict__[args.arch](classes=1000, scale=args.scale, network_idx=args.netidx, mode=args.smode) else: model = models.__dict__[args.arch]() 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() model = torch.nn.DataParallel(model, args.gpus).cuda() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda() optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, alpha=0.9, eps=1, weight_decay= 0.00004, momentum=0.9, centered=False) IMAGE_SIZE=224 print_log(summary(model, input_size=(3, IMAGE_SIZE, IMAGE_SIZE)), log) print_log("=> optimizer : {}'".format(str(optimizer)), 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) milestones = [30, 60, 90, 130, 150] scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.98, last_epoch=-1) # if we are resuming from a model that did not save the scheduler state.(used only for old snapshots) no_scheduler_stat = False if(no_scheduler_stat == True): print(scheduler.state_dict()) tmp = scheduler.state_dict() tmp['last_epoch']=137 print(tmp) # 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) if args.start_epoch ==0 : 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']) #loading scheduler state if no_scheduler_stat: scheduler.load_state_dict(tmp) # else: # if args.start_epoch ==0 : scheduler.load_state_dict(checkpoint['scheduler']) 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]) train_dataset = datasets.ImageFolder( traindir, transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.RandomCrop(224), 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) input_size = 224 val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, transforms.Compose([ transforms.Resize(256), 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,log) return filename = os.path.join(args.save_dir, 'chkpt_{0}_{1}.pth.tar'.format(args.arch, time_stamp)) bestname = os.path.join(args.save_dir, 'best_{0}_{1}.pth.tar'.format(args.arch, time_stamp)) bestname_weight = os.path.join(args.save_dir, 'bestweight_{0}_{1}.pth'.format(args.arch, time_stamp)) start_time = time.time() epoch_time = AverageMeter() for epoch in range(args.start_epoch, args.epochs): # for step based exp scheduler # for param_group in optimizer.param_groups: # current_learning_rate = param_group['lr'] current_learning_rate = float(scheduler.get_lr()[-1]) ###for exp decay 2 epochs per if (epoch%2 ==0 and epoch >0): scheduler.step() ###current_learning_rate = 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) if (epoch %100 == 0 and epoch >0 ): 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(), 'scheduler' : scheduler.state_dict() }, False, os.path.join(args.save_dir,'chkpt_{0}_epc{1}_{2}.pth.tar'.format(args.arch, epoch, time_stamp)), 'bst_void') # save only the weights if is_best: save_checkpoint({ 'state_dict': model.state_dict() }, True, filename, bestname_weight) 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(), 'scheduler' : scheduler.state_dict() }, is_best, filename, bestname) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() writer.close() log.close() 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) #for step based exp scheduler #learning_rate = exp_lr_scheduler(optimizer,iter=i,lr_decay_iter=6400) target = target.cuda(async=True) input_var = torch.autograd.Variable(input) target_var = torch.autograd.Variable(target) # 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.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.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() with torch.no_grad(): for i, (input, target) in enumerate(val_loader): target = target.cuda(async=True) input_var = torch.autograd.Variable(input) target_var = torch.autograd.Variable(target) # 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.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_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 like the pattern below 30 60 90 130 150 """ if(epoch <=90): lr = args.lr * (0.1 ** (epoch // 30)) elif(epoch>90 and epoch<=130): lr = args.lr * (0.1 ** (epoch // 50)) else: lr = args.lr * (0.1 ** (epoch // 20)) 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) # create properties summary = OrderedDict() hooks = [] # register hook model.apply(register_hook) # make a forward pass model(x) # remove these hooks for h in hooks: h.remove() inf_str_summery = '-' * 64 #print('----------------------------------------------------------------') line_new = '{:>20} {:>25} {:>15}'.format('Layer (type)', 'Output Shape', 'Param #') #print(line_new) inf_str_summery += '\n{0}'.format(line_new) inf_str_summery += '\n{0}'.format('='*64) #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) inf_str_summery += '\n{0}'.format(line_new) inf_str_summery += '\n{0}'.format('=' * 64) inf_str_summery += '\nTotal params: {0:,}'.format(total_params) inf_str_summery += '\nTrainable params: {0:,}'.format(trainable_params) inf_str_summery += '\nNon-trainable params: {0:,}'.format(total_params - trainable_params) inf_str_summery += '\n{0}'.format('-' * 64) # 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('----------------------------------------------------------------') print(inf_str_summery) return inf_str_summery if __name__ == '__main__': main()