def main(args): datadir = get_data_dir(args.db) outputdir = get_output_dir(args.db) logger = None if args.tensorboard: # One should create folder for storing logs loggin_dir = os.path.join(outputdir, 'runs', 'pretraining') if not os.path.exists(loggin_dir): os.makedirs(loggin_dir) loggin_dir = os.path.join(loggin_dir, '%s' % (args.id)) if args.clean_log: remove_files_in_dir(loggin_dir) logger = Logger(loggin_dir) use_cuda = torch.cuda.is_available() # Set the seed for reproducing the results random.seed(args.manualSeed) np.random.seed(args.manualSeed) torch.manual_seed(args.manualSeed) if use_cuda: torch.cuda.manual_seed_all(args.manualSeed) torch.backends.cudnn.enabled = True cudnn.benchmark = True kwargs = {'num_workers': 0, 'pin_memory': True} if use_cuda else {} trainset = DCCPT_data(root=datadir, train=True, h5=args.h5) testset = DCCPT_data(root=datadir, train=False, h5=args.h5) nepoch = int( np.ceil( np.array(args.niter * args.batchsize, dtype=float) / len(trainset))) step = int( np.ceil( np.array(args.step * args.batchsize, dtype=float) / len(trainset))) trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batchsize, shuffle=True, **kwargs) testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=True, **kwargs) return pretrain( args, outputdir, { 'nlayers': 4, 'dropout': 0.2, 'reluslope': 0.0, 'nepoch': nepoch, 'lrate': [args.lr], 'wdecay': [0.0], 'step': step }, use_cuda, trainloader, testloader, logger)
def main(args, net=None): global oldassignment datadir = get_data_dir(args.db) outputdir = get_output_dir(args.db) logger = None if args.tensorboard: # One should create folder for storing logs loggin_dir = os.path.join(outputdir, 'runs', 'DCC') if not os.path.exists(loggin_dir): os.makedirs(loggin_dir) loggin_dir = os.path.join(loggin_dir, '%s' % (args.id)) if args.clean_log: remove_files_in_dir(loggin_dir) logger = Logger(loggin_dir) use_cuda = torch.cuda.is_available() # Set the seed for reproducing the results random.seed(args.manualSeed) np.random.seed(args.manualSeed) torch.manual_seed(args.manualSeed) if use_cuda: torch.cuda.manual_seed_all(args.manualSeed) torch.backends.cudnn.enabled = True cudnn.benchmark = True startepoch = 0 kwargs = {'num_workers': 5, 'pin_memory': True} if use_cuda else {} # setting up dataset specific objects trainset = DCCPT_data(root=datadir, train=True, h5=args.h5) testset = DCCPT_data(root=datadir, train=False, h5=args.h5) numeval = len(trainset) + len(testset) # extracting training data from the pretrained.mat file data, labels, pairs, Z, sampweight = makeDCCinp(args) # For simplicity, I have created placeholder for each datasets and model load_pretraining = True if net is None else False if net is None: net = dp.load_predefined_extract_net(args) # reshaping data for some datasets if args.db == 'cmnist': data = data.reshape((-1, 1, 28, 28)) elif args.db == 'ccoil100': data = data.reshape((-1, 3, 128, 128)) elif args.db == 'cytf': data = data.reshape((-1, 3, 55, 55)) elif args.db == 'cyale': data = data.reshape((-1, 1, 168, 192)) totalset = torch.utils.data.ConcatDataset([trainset, testset]) # computing and initializing the hyperparams _sigma1, _sigma2, _lambda, _delta, _delta1, _delta2, lmdb, lmdb_data = computeHyperParams(pairs, Z) oldassignment = np.zeros(len(pairs)) stopping_threshold = int(math.ceil(cfg.STOPPING_CRITERION * float(len(pairs)))) # Create dataset and random batch sampler for Finetuning stage trainset = DCCFT_data(pairs, data, sampweight) batch_sampler = DCCSampler(trainset, shuffle=True, batch_size=args.batchsize) # copying model params from Pretrained (SDAE) weights file if load_pretraining: load_weights(args, outputdir, net) # creating objects for loss functions, U's are initialized to Z here # Criterion1 corresponds to reconstruction loss criterion1 = DCCWeightedELoss(size_average=True) # Criterion2 corresponds to sum of pairwise and data loss terms criterion2 = DCCLoss(Z.shape[0], Z.shape[1], Z, size_average=True) if use_cuda: net.cuda() criterion1 = criterion1.cuda() criterion2 = criterion2.cuda() # setting up data loader for training and testing phase trainloader = torch.utils.data.DataLoader(trainset, batch_sampler=batch_sampler, **kwargs) testloader = torch.utils.data.DataLoader(totalset, batch_size=args.batchsize, shuffle=False, **kwargs) # setting up optimizer - the bias params should have twice the learning rate w.r.t. weights params bias_params = filter(lambda x: ('bias' in x[0]), net.named_parameters()) bias_params = list(map(lambda x: x[1], bias_params)) nonbias_params = filter(lambda x: ('bias' not in x[0]), net.named_parameters()) nonbias_params = list(map(lambda x: x[1], nonbias_params)) optimizer = optim.Adam([{'params': bias_params, 'lr': 2*args.lr}, {'params': nonbias_params}, {'params': criterion2.parameters(), 'lr': args.lr}, ], lr=args.lr, betas=(0.99, 0.999)) # this is needed for WARM START if args.resume: filename = outputdir+'/FTcheckpoint_%d.pth.tar' % args.level if os.path.isfile(filename): print("==> loading checkpoint '{}'".format(filename)) checkpoint = torch.load(filename) net.load_state_dict(checkpoint['state_dict']) criterion2.load_state_dict(checkpoint['criterion_state_dict']) startepoch = checkpoint['epoch'] optimizer.load_state_dict(checkpoint['optimizer']) _sigma1 = checkpoint['sigma1'] _sigma2 = checkpoint['sigma2'] _lambda = checkpoint['lambda'] _delta = checkpoint['delta'] _delta1 = checkpoint['delta1'] _delta2 = checkpoint['delta2'] else: print("==> no checkpoint found at '{}'".format(filename)) raise ValueError # This is the actual Algorithm flag = 0 for epoch in range(startepoch, args.nepoch): if logger: logger.log_value('sigma1', _sigma1, epoch) logger.log_value('sigma2', _sigma2, epoch) logger.log_value('lambda', _lambda, epoch) train(trainloader, net, optimizer, criterion1, criterion2, epoch, use_cuda, _sigma1, _sigma2, _lambda, logger) Z, U, change_in_assign, assignment = test(testloader, net, criterion2, epoch, use_cuda, _delta, pairs, numeval, flag, logger) if flag: # As long as the change in label assignment < threshold, DCC continues to run. # Note: This condition is always met in the very first epoch after the flag is set. # This false criterion is overwritten by checking for the condition twice. if change_in_assign > stopping_threshold: flag += 1 if flag == 4: break if((epoch+1) % args.M == 0): _sigma1 = max(_delta1, _sigma1 / 2) _sigma2 = max(_delta2, _sigma2 / 2) if _sigma2 == _delta2 and flag == 0: # Start checking for stopping criterion flag = 1 # Save checkpoint index = (epoch // args.M) * args.M save_checkpoint({'epoch': epoch+1, 'state_dict': net.state_dict(), 'criterion_state_dict': criterion2.state_dict(), 'optimizer': optimizer.state_dict(), 'sigma1': _sigma1, 'sigma2': _sigma2, 'lambda': _lambda, 'delta': _delta, 'delta1': _delta1, 'delta2': _delta2, }, index, filename=outputdir) output = {'Z': Z, 'U': U, 'gtlabels': labels, 'w': pairs, 'cluster':assignment} sio.savemat(os.path.join(outputdir, 'features'), output)