def main(): global args, best_error, n_iter args = parser.parse_args() normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) train_transform = custom_transforms.Compose([ custom_transforms.RandomRotate(), custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) train_set = SequenceFolder(args.data, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length) train_loader = torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, num_workers=4, pin_memory=True, drop_last=True) print(len(train_loader))
def get_train_transforms(normalize): train_transforms = [] train_transforms.append(transforms.Scale(160)) train_transforms.append(transforms.RandomHorizontalFlip()) train_transforms.append(transforms.RandomColor(0.15)) train_transforms.append(transforms.RandomRotate(15)) train_transforms.append(transforms.RandomSizedCrop(128)) train_transforms.append(transforms.ToTensor()) train_transforms.append(normalize) train_transforms = transforms.Compose(train_transforms) return train_transforms
return sample if __name__ == '__main__': print(os.getcwd()) # from dataloaders import custom_transforms as tr import custom_transforms as tr from torch.utils.data import DataLoader from torchvision import transforms import matplotlib.pyplot as plt composed_transforms_tr = transforms.Compose([ tr.RandomHorizontalFlip(), tr.RandomScale((0.5, 0.75)), tr.RandomCrop((512, 1024)), tr.RandomRotate(5), tr.ToTensor()]) msrab_train= MSRAB(split='train',transform=composed_transforms_tr) dataloader = DataLoader(msrab_train, batch_size=2, shuffle=True, num_workers=2) for ii, sample in enumerate(dataloader): print(ii, sample["image"].size(), sample["label"].size(), type(sample["image"]), type(sample["label"])) for jj in range(sample["image"].size()[0]): img = sample['image'].numpy() gt = sample['label'].numpy() tmp = np.array(gt[jj]*255.0).astype(np.uint8) tmp = np.squeeze(tmp, axis=0) tmp = np.expand_dims(tmp, axis=2) segmap = np.concatenate((tmp,tmp,tmp), axis=2)
return _img, _target def __str__(self): return 'ISPRS(split=' + str(self.split) + ')' if __name__ == '__main__': import custom_transforms as tr from utils import decode_segmap from torch.utils.data import DataLoader from torchvision import transforms import matplotlib.pyplot as plt composed_transforms_tr = transforms.Compose([tr.RandomHorizontalFlip(), tr.RandomCrop(512), tr.RandomRotate(15), tr.ToTensor()] ) isprs_train = ISPRSSegmentation(split='train', transform=composed_transforms_tr) dataloader = DataLoader(isprs_train, batch_size=5, shuffle=True, num_workers=2) for ii, sample in enumerate(dataloader): for jj in range(sample["image"].size()[0]): img = sample['image'].numpy() gt = sample['label'].numpy() tmp = np.array(gt[jj]).astype(np.uint8) tmp = np.squeeze(tmp, axis=0) segmap = decode_segmap(tmp, dataset='ISPRS') img_tmp = np.transpose(img[jj], axes=[1, 2, 0]).astype(np.uint8)
def main(args): # parse args best_acc1 = 0.0 if args.gpu >= 0: print("Use GPU: {}".format(args.gpu)) else: print('You are using CPU for computing!', 'Yet we assume you are using a GPU.', 'You will NOT be able to switch between CPU and GPU training!') # set up the model + loss if args.use_custom_conv: print("Using custom convolutions in the network") model = default_model(conv_op=CustomConv2d, num_classes=100) else: model = default_model(num_classes=100) model_arch = "simplenet" # model_arch = "simplenet_batchnorm2d" # model_arch = "resnet18" # model_arch = "resnet34" criterion = nn.CrossEntropyLoss() # put everthing to gpu if args.gpu >= 0: model = model.cuda(args.gpu) criterion = criterion.cuda(args.gpu) # setup the optimizer optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # resume from a checkpoint? if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_acc1 = checkpoint['best_acc1'] model.load_state_dict(checkpoint['state_dict']) if args.gpu < 0: model = model.cpu() else: model = model.cuda(args.gpu) # only load the optimizer if necessary if (not args.evaluate) and (not args.attack): optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {}, acc1 {})" .format(args.resume, checkpoint['epoch'], best_acc1)) else: print("=> no checkpoint found at '{}'".format(args.resume)) # set up transforms for data augmentation normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_transforms = [] train_transforms.append(transforms.Scale(160)) train_transforms.append(transforms.RandomHorizontalFlip()) train_transforms.append(transforms.RandomColor(0.15)) train_transforms.append(transforms.RandomRotate(15)) train_transforms.append(transforms.RandomSizedCrop(128)) train_transforms.append(transforms.ToTensor()) train_transforms.append(normalize) train_transforms = transforms.Compose(train_transforms) # val transofrms val_transforms=[] val_transforms.append(transforms.Scale(160, interpolations=None)) val_transforms.append(transforms.ToTensor()) val_transforms.append(normalize) val_transforms = transforms.Compose(val_transforms) if (not args.evaluate) and (not args.attack): print("Training time data augmentations:") print(train_transforms) # setup dataset and dataloader train_dataset = MiniPlacesLoader(args.data_folder, split='train', transforms=train_transforms) val_dataset = MiniPlacesLoader(args.data_folder, split='val', transforms=val_transforms) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, sampler=None, drop_last=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=100, shuffle=False, num_workers=args.workers, pin_memory=True, sampler=None, drop_last=False) # testing only if (args.evaluate==args.attack) and args.evaluate: print("Cann't set evaluate and attack to True at the same time!") return # set up visualizer if args.vis: visualizer = default_attention(criterion) else: visualizer = None # evaluation if args.resume and args.evaluate: print("Testing the model ...") cudnn.deterministic = True validate(val_loader, model, -1, args, visualizer=visualizer) return # attack if args.resume and args.attack: print("Generating adversarial samples for the model ..") cudnn.deterministic = True validate(val_loader, model, -1, args, attacker=default_attack(criterion), visualizer=visualizer) return # enable cudnn benchmark cudnn.enabled = True cudnn.benchmark = True # warmup the training if (args.start_epoch == 0) and (args.warmup_epochs > 0): print("Warmup the training ...") for epoch in range(0, args.warmup_epochs): train(train_loader, model, criterion, optimizer, epoch, "warmup", args) # start the training print("Training the model ...") for epoch in range(args.start_epoch, args.epochs): # train for one epoch train(train_loader, model, criterion, optimizer, epoch, "train", args) # evaluate on validation set acc1 = validate(val_loader, model, epoch, args) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) save_checkpoint({ 'epoch': epoch + 1, 'model_arch': model_arch, 'state_dict': model.state_dict(), 'best_acc1': best_acc1, 'optimizer' : optimizer.state_dict(), }, is_best)
def main(): global args, best_error, n_iter args = parser.parse_args() save_path = Path(args.name) args.save_path = 'checkpoints' / save_path print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() torch.manual_seed(args.seed) normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) train_transform = custom_transforms.Compose([ custom_transforms.RandomRotate(), custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) training_writer = SummaryWriter(args.save_path) intrinsics = np.array( [542.822841, 0, 315.593520, 0, 542.576870, 237.756098, 0, 0, 1]).astype(np.float32).reshape((3, 3)) train_set = SequenceFolder(root=args.dataset_dir, intrinsics=intrinsics, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) print("=> creating model") mask_net = MaskNet6.MaskNet6().cuda() flow_net = back2future.Model(nlevels=args.nlevels).cuda() pose_net = PoseNetB6.PoseNetB6().cuda() if args.pretrained_mask: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_mask) mask_net.load_state_dict(weights['state_dict']) else: mask_net.init_weights() mask_net = torch.nn.DataParallel(mask_net) if args.pretrained_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_pose) pose_net.load_state_dict(weights['state_dict']) else: pose_net.init_weights() if args.pretrained_flow: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_flow) flow_net.load_state_dict(weights['state_dict']) else: flow_net.init_weights() print('=> setting adam solver') parameters = chain(mask_net.parameters(), pose_net.parameters(), flow_net.parameters()) optimizer = torch.optim.Adam(parameters, args.lr, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) # training best_error = 0 train_loss = 0 for epoch in tqdm(range(args.epochs)): if args.fix_flownet: for fparams in flow_net.parameters(): fparams.requires_grad = False if args.fix_masknet: for fparams in mask_net.parameters(): fparams.requires_grad = False if args.fix_posenet: for fparams in pose_net.parameters(): fparams.requires_grad = False is_best = train_loss < best_error best_error = min(best_error, train_loss) save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': mask_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_net.state_dict() }, { 'epoch': epoch + 1, 'state_dict': flow_net.state_dict() }, { 'epoch': epoch + 1, 'state_dict': optimizer.state_dict() }, is_best) train_loss = train(train_loader, mask_net, pose_net, flow_net, optimizer, args.epoch_size, training_writer)
def main(): global args, best_error, n_iter n_iter = 0 best_error = 0 args = parser.parse_args() ''' args = ['--name', 'deemo', '--FCCMnet', 'PatchWiseNetwork', '--dataset_dir', '/notebooks/FCCM/pre-process/pre-process', '--label_dir', '/notebooks/FCCM', '--batch-size', '4', '--epochs','100', '--lr', '1e-4' ] ''' save_path = Path(args.name) args.save_path = 'checkpoints'/save_path print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() train_writer = SummaryWriter(args.save_path) torch.manual_seed(args.seed) train_transform = custom_transforms.Compose([ custom_transforms.RandomRotate(), custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor() ]) train_set = Generate_train_set( root = args.dataset_dir, label_root = args.label_dir, transform=train_transform, seed=args.seed, train=True ) val_set = Generate_val_set( root = args.dataset_dir, label_root = args.label_dir, seed=args.seed ) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) print('{} samples found in {} val scenes'.format(len(val_set), len(val_set.scenes))) train_loader = torch.utils.data.DataLoader( train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) val_loader = torch.utils.data.DataLoader( val_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) print("=> creating model") if args.FCCMnet == 'VGG': FCCM_net = models_inpytorch.vgg16(num_classes=19) FCCM_net.features[0]=nn.Conv2d(1, 64, kernel_size=3, padding=1) if args.FCCMnet == 'ResNet': FCCM_net = models.resnet18(num_classes=19) if args.FCCMnet == 'CatNet': FCCM_net = models.catnet18(num_classes=19) if args.FCCMnet == 'CatNet_FCCM': FCCM_net = models.catnet1(num_classes=19) if args.FCCMnet == 'ResNet_FCCM': FCCM_net = models.resnet1(num_classes=19) if args.FCCMnet == 'ImageWise': FCCM_net = models.ImageWiseNetwork() if args.FCCMnet == 'PatchWise': FCCM_net = models.PatchWiseNetwork() if args.FCCMnet == 'Baseline': FCCM_net = models.Baseline() FCCM_net = FCCM_net.cuda() if args.pretrained_model: print("=> using pre-trained weights for net") weights = torch.load(args.pretrained_model) FCCM_net.load_state_dict(weights['state_dict']) cudnn.benchmark = True FCCM_net = torch.nn.DataParallel(FCCM_net) print('=> setting adam solver') parameters = chain(FCCM_net.parameters()) optimizer = torch.optim.Adam(parameters, args.lr, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) is_best = False best_error = float("inf") FCCM_net.train() loss = 0 for epoch in tqdm(range(args.epochs)): is_best = loss <= best_error best_error = min(best_error, loss) save_checkpoint( args.save_path, { 'epoch': epoch + 1, 'state_dict': FCCM_net.state_dict() }, { 'epoch': epoch + 1, 'state_dict': optimizer.state_dict() }, is_best) validation(val_loader, FCCM_net, epoch, train_writer) loss = train(train_loader, FCCM_net, optimizer, args.epoch_size, train_writer)
def main(): global global_vars_dict args = global_vars_dict['args'] best_error = -1 #best model choosing #mkdir timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M") args.save_path = Path('checkpoints') / Path(args.data_dir).stem / timestamp print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() torch.manual_seed(args.seed) if args.alternating: args.alternating_flags = np.array([False, False, True]) #mk writers tb_writer = SummaryWriter(args.save_path) # Data loading code flow_loader_h, flow_loader_w = 256, 832 if args.data_normalization == 'global': normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) elif args.data_normalization == 'local': normalize = custom_transforms.NormalizeLocally() if args.fix_flownet: train_transform = custom_transforms.Compose([ custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) else: train_transform = custom_transforms.Compose([ custom_transforms.RandomRotate(), custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) valid_transform = custom_transforms.Compose( [custom_transforms.ArrayToTensor(), normalize]) valid_flow_transform = custom_transforms.Compose([ custom_transforms.Scale(h=flow_loader_h, w=flow_loader_w), custom_transforms.ArrayToTensor(), normalize ]) print("=> fetching scenes in '{}'".format(args.data_dir)) #train set, loader only建立一个 if args.dataset_format == 'stacked': from datasets.stacked_sequence_folders import SequenceFolder elif args.dataset_format == 'sequential': from datasets.sequence_folders import SequenceFolder train_set = SequenceFolder( #mc data folder args.data_dir, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length, #5 target_transform=None) elif args.dataset_format == 'sequential_with_gt': # with all possible gt from datasets.sequence_mc import SequenceFolder train_set = SequenceFolder( # mc data folder args.data_dir, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length, # 5 target_transform=None) else: return if args.DEBUG: train_set.__len__ = 32 train_set.samples = train_set.samples[:32] train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) if args.epoch_size == 0: args.epoch_size = len(train_loader) #val set,loader 挨个建立 # if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping if args.val_without_gt: from datasets.sequence_folders2 import SequenceFolder #就多了一级文件夹 val_set_without_gt = SequenceFolder( #只有图 args.data_dir, transform=valid_transform, seed=None, train=False, sequence_length=args.sequence_length, target_transform=None) val_loader = torch.utils.data.DataLoader(val_set_without_gt, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True, drop_last=True) if args.val_with_depth_gt: from datasets.validation_folders2 import ValidationSet val_set_with_depth_gt = ValidationSet(args.data_dir, transform=valid_transform) val_loader_depth = torch.utils.data.DataLoader( val_set_with_depth_gt, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True, drop_last=True) if args.val_with_flow_gt: #暂时没有 from datasets.validation_flow import ValidationFlow val_flow_set = ValidationFlow(root=args.kitti_dir, sequence_length=args.sequence_length, transform=valid_flow_transform) val_flow_loader = torch.utils.data.DataLoader( val_flow_set, batch_size=1, # batch size is 1 since images in kitti have different sizes shuffle=False, num_workers=args.workers, pin_memory=True, drop_last=True) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) if args.val_without_gt: print('{} samples found in {} valid scenes'.format( len(val_set_without_gt), len(val_set_without_gt.scenes))) #1 create model print("=> creating model") #1.1 disp_net disp_net = getattr(models, args.dispnet)().cuda() output_exp = True #args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") #1.2 pose_net pose_net = getattr(models, args.posenet)(nb_ref_imgs=args.sequence_length - 1).cuda() #1.3.flow_net if args.flownet == 'SpyNet': flow_net = getattr(models, args.flownet)(nlevels=args.nlevels, pre_normalization=normalize).cuda() elif args.flownet == 'FlowNetC6': #flonwtc6 flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda() elif args.flownet == 'FlowNetS': flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda() elif args.flownet == 'Back2Future': flow_net = getattr(models, args.flownet)(nlevels=args.nlevels).cuda() # 1.4 mask_net mask_net = getattr(models, args.masknet)(nb_ref_imgs=args.sequence_length - 1, output_exp=True).cuda() #2 载入参数 #2.1 pose if args.pretrained_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_pose) pose_net.load_state_dict(weights['state_dict']) else: pose_net.init_weights() if args.pretrained_mask: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_mask) mask_net.load_state_dict(weights['state_dict']) else: mask_net.init_weights() # import ipdb; ipdb.set_trace() if args.pretrained_disp: print("=> using pre-trained weights from {}".format( args.pretrained_disp)) weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() if args.pretrained_flow: print("=> using pre-trained weights for FlowNet") weights = torch.load(args.pretrained_flow) flow_net.load_state_dict(weights['state_dict']) else: flow_net.init_weights() if args.resume: print("=> resuming from checkpoint") dispnet_weights = torch.load(args.save_path / 'dispnet_checkpoint.pth.tar') posenet_weights = torch.load(args.save_path / 'posenet_checkpoint.pth.tar') masknet_weights = torch.load(args.save_path / 'masknet_checkpoint.pth.tar') flownet_weights = torch.load(args.save_path / 'flownet_checkpoint.pth.tar') disp_net.load_state_dict(dispnet_weights['state_dict']) pose_net.load_state_dict(posenet_weights['state_dict']) flow_net.load_state_dict(flownet_weights['state_dict']) mask_net.load_state_dict(masknet_weights['state_dict']) # import ipdb; ipdb.set_trace() cudnn.benchmark = True disp_net = torch.nn.DataParallel(disp_net) pose_net = torch.nn.DataParallel(pose_net) mask_net = torch.nn.DataParallel(mask_net) flow_net = torch.nn.DataParallel(flow_net) print('=> setting adam solver') parameters = chain(disp_net.parameters(), pose_net.parameters(), mask_net.parameters(), flow_net.parameters()) optimizer = torch.optim.Adam(parameters, args.lr, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) if args.resume and (args.save_path / 'optimizer_checkpoint.pth.tar').exists(): print("=> loading optimizer from checkpoint") optimizer_weights = torch.load(args.save_path / 'optimizer_checkpoint.pth.tar') optimizer.load_state_dict(optimizer_weights['state_dict']) with open(args.save_path / args.log_summary, 'w') as csvfile: writer = csv.writer(csvfile, delimiter='\t') writer.writerow(['train_loss', 'validation_loss']) with open(args.save_path / args.log_full, 'w') as csvfile: writer = csv.writer(csvfile, delimiter='\t') writer.writerow([ 'train_loss', 'photo_cam_loss', 'photo_flow_loss', 'explainability_loss', 'smooth_loss' ]) # if args.log_terminal: logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader_depth)) logger.epoch_bar.start() else: logger = None #预先评估下 if args.pretrained_disp or args.evaluate: logger.reset_valid_bar() if args.val_without_gt: pass #val_loss = validate_without_gt(val_loader,disp_net,pose_net,mask_net,flow_net,epoch=0, logger=logger, tb_writer=tb_writer,nb_writers=3,global_vars_dict = global_vars_dict) #val_loss =0 if args.val_with_depth_gt: pass depth_errors, depth_error_names = validate_depth_with_gt( val_loader_depth, disp_net, epoch=0, logger=logger, tb_writer=tb_writer, global_vars_dict=global_vars_dict) #3. main cycle for epoch in range(1, args.epochs): #epoch 0 在第没入循环之前已经测试了. #3.1 四个子网络,训练哪几个 if args.fix_flownet: for fparams in flow_net.parameters(): fparams.requires_grad = False if args.fix_masknet: for fparams in mask_net.parameters(): fparams.requires_grad = False if args.fix_posenet: for fparams in pose_net.parameters(): fparams.requires_grad = False if args.fix_dispnet: for fparams in disp_net.parameters(): fparams.requires_grad = False if args.log_terminal: logger.epoch_bar.update(epoch) logger.reset_train_bar() #validation data flow_error_names = ['no'] flow_errors = [0] errors = [0] error_names = ['no error names depth'] print('\nepoch [{}/{}]\n'.format(epoch + 1, args.epochs)) #3.2 train for one epoch--------- #train_loss=0 train_loss = train_gt(train_loader, disp_net, pose_net, mask_net, flow_net, optimizer, logger, tb_writer, global_vars_dict) #3.3 evaluate on validation set----- if args.val_without_gt: val_loss = validate_without_gt(val_loader, disp_net, pose_net, mask_net, flow_net, epoch=0, logger=logger, tb_writer=tb_writer, nb_writers=3, global_vars_dict=global_vars_dict) if args.val_with_depth_gt: depth_errors, depth_error_names = validate_depth_with_gt( val_loader_depth, disp_net, epoch=epoch, logger=logger, tb_writer=tb_writer, global_vars_dict=global_vars_dict) if args.val_with_flow_gt: pass #flow_errors, flow_error_names = validate_flow_with_gt(val_flow_loader, disp_net, pose_net, mask_net, flow_net, epoch, logger, tb_writer) #for error, name in zip(flow_errors, flow_error_names): # training_writer.add_scalar(name, error, epoch) #---------------------- #3.4 Up to you to chose the most relevant error to measure your model's performance, careful some measures are to maximize (such as a1,a2,a3) if not args.fix_posenet: decisive_error = 0 # flow_errors[-2] # epe_rigid_with_gt_mask elif not args.fix_dispnet: decisive_error = 0 # errors[0] #depth abs_diff elif not args.fix_flownet: decisive_error = 0 # flow_errors[-1] #epe_non_rigid_with_gt_mask elif not args.fix_masknet: decisive_error = 0 #flow_errors[3] # percent outliers #3.5 log if args.log_terminal: logger.train_writer.write( ' * Avg Loss : {:.3f}'.format(train_loss)) logger.reset_valid_bar() #eopch data log on tensorboard #train loss tb_writer.add_scalar('epoch/train_loss', train_loss, epoch) #val_without_gt loss if args.val_without_gt: tb_writer.add_scalar('epoch/val_loss', val_loss, epoch) if args.val_with_depth_gt: #val with depth gt for error, name in zip(depth_errors, depth_error_names): tb_writer.add_scalar('epoch/' + name, error, epoch) #3.6 save model and remember lowest error and save checkpoint if best_error < 0: best_error = train_loss is_best = train_loss <= best_error best_error = min(best_error, train_loss) save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': mask_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': flow_net.module.state_dict() }, is_best) with open(args.save_path / args.log_summary, 'a') as csvfile: writer = csv.writer(csvfile, delimiter='\t') writer.writerow([train_loss, decisive_error]) if args.log_terminal: logger.epoch_bar.finish()