def main(): global args, best_error, n_iter, device args = parser.parse_args() save_path = save_path_formatter(args, parser) args.save_path = 'checkpoints_shifted' / save_path print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() torch.manual_seed(args.seed) training_writer = SummaryWriter(args.save_path) output_writers = [] if args.log_output: for i in range(3): output_writers.append( SummaryWriter(args.save_path / 'valid' / str(i))) # Data loading code 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.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) valid_transform = custom_transforms.Compose( [custom_transforms.ArrayToTensor(), normalize]) print("=> fetching scenes in '{}'".format(args.data)) train_set = ShiftedSequenceFolder( args.data, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length, target_displacement=args.target_displacement) # if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping if args.with_gt: from datasets.validation_folders import ValidationSet val_set = ValidationSet(args.data, transform=valid_transform) else: val_set = SequenceFolder( args.data, transform=valid_transform, seed=args.seed, train=False, sequence_length=args.sequence_length, ) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) print('{} samples found in {} valid 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) adjust_loader = torch.utils.data.DataLoader( train_set, batch_size=args.batch_size, shuffle=False, num_workers=0, pin_memory=True ) # workers is set to 0 to avoid multiple instances to be modified at the same time val_loader = torch.utils.data.DataLoader(val_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) if args.epoch_size == 0: args.epoch_size = len(train_loader) train.args = args # create model print("=> creating model") disp_net = models.DispNetS().cuda() output_exp = args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") pose_exp_net = models.PoseExpNet( nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device) if args.pretrained_exp_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_exp_pose) pose_exp_net.load_state_dict(weights['state_dict'], strict=False) else: pose_exp_net.init_weights() if args.pretrained_disp: print("=> using pre-trained weights for Dispnet") weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() cudnn.benchmark = True disp_net = torch.nn.DataParallel(disp_net) pose_exp_net = torch.nn.DataParallel(pose_exp_net) print('=> setting adam solver') parameters = chain(disp_net.parameters(), pose_exp_net.parameters()) optimizer = torch.optim.Adam(parameters, args.lr, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) 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_loss', 'explainability_loss', 'smooth_loss']) logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader)) logger.epoch_bar.start() for epoch in range(args.epochs): logger.epoch_bar.update(epoch) # train for one epoch logger.reset_train_bar() train_loss = train(args, train_loader, disp_net, pose_exp_net, optimizer, args.epoch_size, logger, training_writer) logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss)) if (epoch + 1) % 5 == 0: train_set.adjust = True logger.reset_train_bar(len(adjust_loader)) average_shifts = adjust_shifts(args, train_set, adjust_loader, pose_exp_net, epoch, logger, training_writer) shifts_string = ' '.join( ['{:.3f}'.format(s) for s in average_shifts]) logger.train_writer.write( ' * adjusted shifts, average shifts are now : {}'.format( shifts_string)) for i, shift in enumerate(average_shifts): training_writer.add_scalar('shifts{}'.format(i), shift, epoch) train_set.adjust = False # evaluate on validation set logger.reset_valid_bar() if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, epoch, logger, output_writers) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, epoch, logger, output_writers) error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors)) logger.valid_writer.write(' * Avg {}'.format(error_string)) for error, name in zip(errors, error_names): training_writer.add_scalar(name, error, epoch) # 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) decisive_error = errors[0] if best_error < 0: best_error = decisive_error # remember lowest error and save checkpoint is_best = decisive_error < best_error best_error = min(best_error, decisive_error) save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_exp_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]) logger.epoch_bar.finish()
def main(): global args, best_photo_loss, n_iter args = parser.parse_args() if args.dataset_format == 'stacked': from datasets.stacked_sequence_folders import SequenceFolder elif args.dataset_format == 'sequential': from datasets.sequence_folders import SequenceFolder save_path = Path('{}epochs{},seq{},b{},lr{},p{},m{},s{}'.format( args.epochs, ',epochSize' + str(args.epoch_size) if args.epoch_size > 0 else '', args.sequence_length, args.batch_size, args.lr, args.photo_loss_weight, args.mask_loss_weight, args.smooth_loss_weight)) timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M") args.save_path = 'checkpoints' / save_path / timestamp print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() torch.manual_seed(args.seed) train_writer = SummaryWriter(args.save_path / 'train') valid_writer = SummaryWriter(args.save_path / 'valid') output_writers = [] if args.log_output: for i in range(3): output_writers.append( SummaryWriter(args.save_path / 'valid' / str(i))) # Data loading code normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) input_transform = custom_transforms.Compose([ custom_transforms.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) print("=> fetching scenes in '{}'".format(args.data)) train_set = SequenceFolder(args.data, transform=input_transform, seed=args.seed, train=True, sequence_length=args.sequence_length) val_set = SequenceFolder(args.data, transform=custom_transforms.Compose([ custom_transforms.ArrayToTensor(), normalize ]), seed=args.seed, train=False, sequence_length=args.sequence_length) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) print('{} samples found in {} valid 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) val_loader = torch.utils.data.DataLoader(val_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) if args.epoch_size == 0: args.epoch_size = len(train_loader) # create model print("=> creating model") disp_net = models.DispNetS().cuda() output_exp = args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") pose_exp_net = models.PoseExpNet( nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).cuda() if args.pretrained_exp_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_exp_pose) pose_exp_net.load_state_dict(weights['state_dict'], strict=False) else: pose_exp_net.init_weights() if args.pretrained_disp: print("=> using pre-trained weights for Dispnet") weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() cudnn.benchmark = True disp_net = torch.nn.DataParallel(disp_net) pose_exp_net = torch.nn.DataParallel(pose_exp_net) print('=> setting adam solver') parameters = chain(disp_net.parameters(), pose_exp_net.parameters()) optimizer = torch.optim.Adam(parameters, args.lr, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) 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_loss', 'explainability_loss', 'smooth_loss']) logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader)) logger.epoch_bar.start() for epoch in range(args.epochs): logger.epoch_bar.update(epoch) # train for one epoch logger.reset_train_bar() train_loss = train(train_loader, disp_net, pose_exp_net, optimizer, args.epoch_size, logger, train_writer) logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss)) # evaluate on validation set logger.reset_valid_bar() valid_photo_loss, valid_exp_loss, valid_total_loss = validate( val_loader, disp_net, pose_exp_net, epoch, logger, output_writers) logger.valid_writer.write( ' * Avg Photo Loss : {:.3f}, Valid Loss : {:.3f}, Total Loss : {:.3f}' .format(valid_photo_loss, valid_exp_loss, valid_total_loss)) valid_writer.add_scalar( 'photometric_error', valid_photo_loss * 4, n_iter ) # Loss is multiplied by 4 because it's only one scale, instead of 4 during training valid_writer.add_scalar('explanability_loss', valid_exp_loss * 4, n_iter) valid_writer.add_scalar('total_loss', valid_total_loss * 4, n_iter) if best_photo_loss < 0: best_photo_loss = valid_photo_loss # remember lowest error and save checkpoint is_best = valid_photo_loss < best_photo_loss best_photo_loss = min(valid_photo_loss, best_photo_loss) save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_exp_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, valid_total_loss]) logger.epoch_bar.finish()
def main(): global best_error, n_iter, device args = parser.parse_args() if args.dataset_format == 'stacked': from datasets.stacked_sequence_folders import SequenceFolder elif args.dataset_format == 'sequential': from datasets.sequence_folders import SequenceFolder, StereoSequenceFolder save_path = save_path_formatter(args, parser) 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) if args.evaluate: args.epochs = 0 training_writer = SummaryWriter(args.save_path) output_writers = [] if args.log_output: for i in range(3): output_writers.append(SummaryWriter(args.save_path/'valid'/str(i))) # Data loading code 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.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize]) print("=> fetching scenes in '{}'".format(args.data)) train_set = StereoSequenceFolder( args.data, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length ) # if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping if args.with_gt: from datasets.validation_folders import ValidationSet val_set = ValidationSet( args.data, transform=valid_transform ) else: val_set = StereoSequenceFolder( args.data, transform=valid_transform, seed=args.seed, train=False, sequence_length=args.sequence_length, ) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) print('{} samples found in {} valid 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) val_loader = torch.utils.data.DataLoader( val_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) # 没有epoch_size的时候(=0),每个epoch训练train_set中所有的samples # 有epoch_size的时候,每个epoch只训练一部分train_set if args.epoch_size == 0: args.epoch_size = len(train_loader) # create model # 初始化网络结构 print("=> creating model") # disp_net = models.DispNetS().to(device) disp_net = models.DispResNet(3).to(device) output_exp = args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") # 如果有mask loss,PoseExpNet 要输出mask和pose estimation,因为两个输出共享encoder网络 # pose_exp_net = PoseExpNet(nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device) pose_exp_net = models.PoseExpNet(nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device) if args.pretrained_exp_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_exp_pose) pose_exp_net.load_state_dict(weights['state_dict'], strict=False) else: pose_exp_net.init_weights() if args.pretrained_disp: print("=> using pre-trained weights for Dispnet") weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() cudnn.benchmark = True # 并行化 disp_net = torch.nn.DataParallel(disp_net) pose_exp_net = torch.nn.DataParallel(pose_exp_net) # 训练方式:Adam print('=> setting adam solver') # 两个网络一起 optim_params = [ {'params': disp_net.parameters(), 'lr': args.lr}, {'params': pose_exp_net.parameters(), 'lr': args.lr} ] optimizer = torch.optim.Adam(optim_params, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) 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_loss', 'explainability_loss', 'smooth_loss']) # 对pretrained模型先做评估 if args.pretrained_disp or args.evaluate: if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, 0, output_writers) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, 0, output_writers) for error, name in zip(errors, error_names): training_writer.add_scalar(name, error, 0) error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names[2:9], errors[2:9])) # 正式训练 for epoch in range(args.epochs): # train for one epoch 训练一个周期 print('\n') train_loss = train(args, train_loader, disp_net, pose_exp_net, optimizer, args.epoch_size, training_writer, epoch) # evaluate on validation set print('\n') if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, epoch, output_writers) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, epoch, output_writers) error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors)) for error, name in zip(errors, error_names): training_writer.add_scalar(name, error, epoch) # 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) # 验证输出四个loss:总体final loss,warping loss以及mask正则化loss # 可自选以哪一种loss作为best model的标准 decisive_error = errors[0] if best_error < 0: best_error = decisive_error # remember lowest error and save checkpoint # 保存validation最佳model is_best = decisive_error < best_error best_error = min(best_error, decisive_error) save_checkpoint( args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_exp_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])
def main(): global best_error, n_iter, device args = parser.parse_args() if args.dataset_format == 'stacked': from datasets.stacked_sequence_folders import SequenceFolder elif args.dataset_format == 'sequential': from datasets.sequence_folders import SequenceFolder save_path = save_path_formatter(args, parser) 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) if args.evaluate: args.epochs = 0 tb_writer = SummaryWriter(args.save_path) # Data loading code 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.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) valid_transform = custom_transforms.Compose( [custom_transforms.ArrayToTensor(), normalize]) print("=> fetching scenes in '{}'".format(args.data)) train_set = SequenceFolder(args.data, transform=train_transform, seed=args.seed, train=True, sequence_length=args.sequence_length) # if no Groundtruth is avalaible, Validation set is the same type as training set to measure photometric loss from warping if args.with_gt: from datasets.validation_folders import ValidationSet val_set = ValidationSet(args.data, transform=valid_transform) else: val_set = SequenceFolder( args.data, transform=valid_transform, seed=args.seed, train=False, sequence_length=args.sequence_length, ) print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes))) print('{} samples found in {} valid 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=False, num_workers=args.workers, pin_memory=True) if args.epoch_size == 0: args.epoch_size = len(train_loader) # create model print("=> creating model") disp_net = models.DispNetS().to(device) seg_net = DeepLab(num_classes=args.nclass, backbone=args.backbone, output_stride=args.out_stride, sync_bn=args.sync_bn, freeze_bn=args.freeze_bn).to(device) if args.pretrained_seg: print("=> using pre-trained weights for seg net") weights = torch.load(args.pretrained_seg) seg_net.load_state_dict(weights, strict=False) output_exp = args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") pose_exp_net = models.PoseExpNet( nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device) if args.pretrained_exp_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_exp_pose) pose_exp_net.load_state_dict(weights['state_dict'], strict=False) else: pose_exp_net.init_weights() if args.pretrained_disp: print("=> using pre-trained weights for Dispnet") weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() cudnn.benchmark = True disp_net = torch.nn.DataParallel(disp_net) pose_exp_net = torch.nn.DataParallel(pose_exp_net) seg_net = torch.nn.DataParallel(seg_net) print('=> setting adam solver') optim_params = [{ 'params': disp_net.parameters(), 'lr': args.lr }, { 'params': pose_exp_net.parameters(), 'lr': args.lr }] optimizer = torch.optim.Adam(optim_params, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) 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_loss', 'explainability_loss', 'smooth_loss']) logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader)) logger.epoch_bar.start() if args.pretrained_disp or args.evaluate: logger.reset_valid_bar() if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, 0, logger, tb_writer) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, 0, logger, tb_writer) for error, name in zip(errors, error_names): tb_writer.add_scalar(name, error, 0) error_string = ', '.join( '{} : {:.3f}'.format(name, error) for name, error in zip(error_names[2:9], errors[2:9])) logger.valid_writer.write(' * Avg {}'.format(error_string)) for epoch in range(args.epochs): logger.epoch_bar.update(epoch) # train for one epoch logger.reset_train_bar() train_loss = train(args, train_loader, disp_net, pose_exp_net, seg_net, optimizer, args.epoch_size, logger, tb_writer) logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss)) # evaluate on validation set logger.reset_valid_bar() if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, seg_net, epoch, logger, tb_writer) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, epoch, logger, tb_writer) error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors)) logger.valid_writer.write(' * Avg {}'.format(error_string)) for error, name in zip(errors, error_names): tb_writer.add_scalar(name, error, epoch) # 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) decisive_error = errors[1] if best_error < 0: best_error = decisive_error # remember lowest error and save checkpoint is_best = decisive_error < best_error best_error = min(best_error, decisive_error) save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_exp_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]) logger.epoch_bar.finish()
def main(): global best_error, n_iter, device args = parser.parse_args() if args.dataset_format == 'stacked': from datasets.stacked_sequence_folders import SequenceFolder elif args.dataset_format == 'sequential': from datasets.sequence_folders import SequenceFolder save_path = save_path_formatter(args, parser) args.save_path = 'checkpoints' / save_path print('=> will save everything to {}'.format(args.save_path)) args.save_path.makedirs_p() #如果没有,则建立,有则啥都不干 in Path.py小工具 torch.manual_seed(args.seed) if args.evaluate: args.epochs = 0 #tensorboard SummaryWriter training_writer = SummaryWriter(args.save_path) #for tensorboard output_writers = [] #list if args.log_output: for i in range(3): output_writers.append( SummaryWriter(args.save_path / 'valid' / str(i))) # Data loading code 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.RandomHorizontalFlip(), custom_transforms.RandomScaleCrop(), custom_transforms.ArrayToTensor(), normalize ]) '''transform''' valid_transform = custom_transforms.Compose( [custom_transforms.ArrayToTensor(), normalize]) print("=> fetching scenes in '{}'".format(args.data)) train_set = SequenceFolder( args.data, #processed_data_train_sets transform=train_transform, #把几种变换函数输入进去 seed=args.seed, train=True, sequence_length=args.sequence_length) # if no Groundtruth is avalaible, Validation set is # the same type as training set to measure photometric loss from warping if args.with_gt: from datasets.validation_folders import ValidationSet val_set = ValidationSet(args.data, transform=valid_transform) else: val_set = SequenceFolder( args.data, transform=valid_transform, seed=args.seed, train=False, sequence_length=args.sequence_length, ) print('{} samples found in {} train scenes'.format( len(train_set), len(train_set.scenes))) #训练集都是序列,不用左右 print('{} samples found in {} valid scenes'.format( len(val_set), len(val_set.scenes))) #测试集也是序列,不需要左右 train_loader = torch.utils.data.DataLoader( #data(list): [tensor(B,3,H,W),list(B),(B,H,W),(b,h,w)] dataset=train_set, #sequenceFolder batch_size=args.batch_size, shuffle=True, #打乱 num_workers=args.workers, #多线程读取数据 pin_memory=True) val_loader = torch.utils.data.DataLoader( dataset=val_set, batch_size=args.batch_size, shuffle=False, #不打乱 num_workers=args.workers, pin_memory=True) if args.epoch_size == 0: args.epoch_size = len(train_loader) # create model print("=> creating model") #disp disp_net = models.DispNetS().to(device) output_exp = args.mask_loss_weight > 0 if not output_exp: print("=> no mask loss, PoseExpnet will only output pose") #pose pose_exp_net = models.PoseExpNet( nb_ref_imgs=args.sequence_length - 1, output_exp=args.mask_loss_weight > 0).to(device) #init posenet if args.pretrained_exp_pose: print("=> using pre-trained weights for explainabilty and pose net") weights = torch.load(args.pretrained_exp_pose) pose_exp_net.load_state_dict(weights['state_dict'], strict=False) else: pose_exp_net.init_weights() #init dispNet if args.pretrained_disp: print("=> using pre-trained weights for Dispnet") weights = torch.load(args.pretrained_disp) disp_net.load_state_dict(weights['state_dict']) else: disp_net.init_weights() cudnn.benchmark = True disp_net = torch.nn.DataParallel(disp_net) pose_exp_net = torch.nn.DataParallel(pose_exp_net) print('=> setting adam solver') #可以看到两个一起训练 optim_params = [{ 'params': disp_net.parameters(), 'lr': args.lr }, { 'params': pose_exp_net.parameters(), 'lr': args.lr }] optimizer = torch.optim.Adam(optim_params, betas=(args.momentum, args.beta), weight_decay=args.weight_decay) #训练结果写入csv 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_loss', 'explainability_loss', 'smooth_loss']) n_epochs = args.epochs train_size = min(len(train_loader), args.epoch_size) valid_size = len(val_loader) logger = TermLogger(n_epochs=args.epochs, train_size=min(len(train_loader), args.epoch_size), valid_size=len(val_loader)) logger.epoch_bar.start() if args.pretrained_disp or args.evaluate: logger.reset_valid_bar() if args.with_gt: errors, error_names = validate_with_gt(args, val_loader, disp_net, 0, logger, output_writers) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, 0, logger, output_writers) for error, name in zip( errors, error_names ): #validation时,对['Total loss', 'Photo loss', 'Exp loss']三个 epoch-record 指标添加记录值 training_writer.add_scalar(name, error, 0) error_string = ', '.join( '{} : {:.3f}'.format(name, error) for name, error in zip(error_names[2:9], errors[2:9])) logger.valid_writer.write(' * Avg {}'.format(error_string)) #main cycle for epoch in range(args.epochs): logger.epoch_bar.update(epoch) logger.reset_train_bar() #1. train for one epoch train_loss = train(args, train_loader, disp_net, pose_exp_net, optimizer, args.epoch_size, logger, training_writer) #其他参数都好解释, logger: SelfDefined class, logger.train_writer.write(' * Avg Loss : {:.3f}'.format(train_loss)) logger.reset_valid_bar() # 2. validate on validation set if args.with_gt: #<class 'list'>: ['Total loss', 'Photo loss', 'Exp loss'] errors, error_names = validate_with_gt(args, val_loader, disp_net, epoch, logger, output_writers) else: errors, error_names = validate_without_gt(args, val_loader, disp_net, pose_exp_net, epoch, logger, output_writers) error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors)) logger.valid_writer.write(' * Avg {}'.format(error_string)) for error, name in zip(errors, error_names): training_writer.add_scalar(name, error, epoch) #损失函数中记录epoch-record指标 # 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) # 3. remember lowest error and save checkpoint decisive_error = errors[1] if best_error < 0: best_error = decisive_error is_best = decisive_error < best_error best_error = min(best_error, decisive_error) #模型保存 save_checkpoint(args.save_path, { 'epoch': epoch + 1, 'state_dict': disp_net.module.state_dict() }, { 'epoch': epoch + 1, 'state_dict': pose_exp_net.module.state_dict() }, is_best) with open(args.save_path / args.log_summary, 'a') as csvfile: #每个epoch留下结果 writer = csv.writer(csvfile, delimiter='\t') writer.writerow([train_loss, decisive_error]) #第二个就是validataion 中的epoch-record # loss<class 'list'>: ['Total loss', 'Photo loss', 'Exp loss'] logger.epoch_bar.finish()