def get_transformv2(opt): transform_list = [] # Transforms in opt.preprocess if 'fixsize' in opt.preprocess: transform_list.append(tr.Resize(286, 286, interpolation=2, p=1)) if 'resize' in opt.preprocess: transform_list.append( tr.Resize(opt.load_size, opt.load_size, interpolation=2, p=1)) if 'crop' in opt.preprocess: transform_list.append(tr.RandomCrop(opt.crop_size, opt.crop_size, p=1)) # Transforms in colorspace if 'color' in opt.preprocess: transform_list.extend([ tr.RandomContrast(limit=0.2, p=0.5), tr.RandomBrightness(limit=0.2, p=0.5), tr.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5), # tr.ISONoise() ]) # Necessary transforms transform_list.extend([ tr.HorizontalFlip(p=0.5), tr.VerticalFlip(p=0.5), tr.Normalize(p=1.0), ToTensorV2(p=1) ]) return Compose(transform_list, additional_targets={'imageB': 'image'})
def __init__( self, prob=0.7, blur_prob=0.7, jitter_prob=0.7, rotate_prob=0.7, flip_prob=0.7, ): super().__init__() self.prob = prob self.blur_prob = blur_prob self.jitter_prob = jitter_prob self.rotate_prob = rotate_prob self.flip_prob = flip_prob self.transforms = al.Compose( [ transforms.RandomRotate90(), transforms.Flip(), transforms.HueSaturationValue(), transforms.RandomBrightnessContrast(), transforms.Transpose(), OneOf([ transforms.RandomCrop(220, 220, p=0.5), transforms.CenterCrop(220, 220, p=0.5) ], p=0.5), # transforms.Resize(352,352), # transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ], p=self.prob)
def augmentation(mode, target_size, prob=0.5, aug_m=2): ''' description: augmentation mode: 'train' 'test' target_size: int or list, the shape of image , aug_m: Strength of transform ''' high_p = prob low_p = high_p / 2.0 M = aug_m first_size = [int(x / 0.7) for x in target_size] if mode == 'train': return composition.Compose([ transforms.Resize(first_size[0], first_size[1], interpolation=3), transforms.Flip(p=0.5), composition.OneOf([ RandomCenterCut(scale=0.1 * M), transforms.ShiftScaleRotate(shift_limit=0.05 * M, scale_limit=0.1 * M, rotate_limit=180, border_mode=cv2.BORDER_CONSTANT, value=0), albumentations.imgaug.transforms.IAAAffine( shear=(-10 * M, 10 * M), mode='constant') ], p=high_p), transforms.RandomBrightnessContrast( brightness_limit=0.1 * M, contrast_limit=0.03 * M, p=high_p), transforms.HueSaturationValue(hue_shift_limit=5 * M, sat_shift_limit=15 * M, val_shift_limit=10 * M, p=high_p), transforms.OpticalDistortion(distort_limit=0.03 * M, shift_limit=0, border_mode=cv2.BORDER_CONSTANT, value=0, p=low_p), composition.OneOf([ transforms.Blur(blur_limit=7), albumentations.imgaug.transforms.IAASharpen(), transforms.GaussNoise(var_limit=(2.0, 10.0), mean=0), transforms.ISONoise() ], p=low_p), transforms.Resize(target_size[0], target_size[1], interpolation=3), transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), max_pixel_value=255.0) ], p=1) else: return composition.Compose([ transforms.Resize(target_size[0], target_size[1], interpolation=3), transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), max_pixel_value=255.0) ], p=1)
def HueSaturationValue(hue_shift_limit=10, sat_shift_limit=30, val_shift_limit=20, p=0.5): return transforms.HueSaturationValue(hue_shift_limit=hue_shift_limit, sat_shift_limit=sat_shift_limit, val_shift_limit=val_shift_limit, p=p)
def get_presize_combine_transforms_V4(): transforms_presize = A.Compose([ transforms.PadIfNeeded(600, 800), geometric.Perspective( scale=[0, .1], pad_mode=cv2.BORDER_REFLECT, interpolation=cv2.INTER_AREA, p = .3), transforms.Flip(), geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.95, scale_limit=0.0), crops.RandomResizedCrop( TARGET_SIZE, TARGET_SIZE, scale=(config['rrc_scale_min'], config['rrc_scale_max']), ratio=(.70, 1.4), interpolation=cv2.INTER_CUBIC, p=1.0), transforms.Transpose() #rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99), ]) transforms_postsize = A.Compose([ #imgaug.IAAPiecewiseAffine(), transforms.CoarseDropout(), transforms.CLAHE(p=.1), transforms.RandomToneCurve(scale=.1, p=0.2), transforms.RandomBrightnessContrast( brightness_limit=.1, contrast_limit=0.4, p=.8), transforms.HueSaturationValue( hue_shift_limit=20, sat_shift_limit=50, val_shift_limit=0, p=0.5), transforms.Equalize(p=0.05), transforms.FancyPCA(p=0.05), transforms.RandomGridShuffle(p=0.1), A.OneOf([ transforms.MotionBlur(blur_limit=(3, 9)), transforms.GaussianBlur(), transforms.MedianBlur() ], p=0.1), transforms.ISONoise(p=.2), transforms.GaussNoise(var_limit=127., p=.3), A.OneOf([ transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5), transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3), ], p=.3), geometric.ElasticTransform(alpha=4, sigma=4, alpha_affine=4, interpolation=cv2.INTER_AREA, p=0.3), transforms.CoarseDropout(), transforms.Normalize(), ToTensorV2() ]) return transforms_presize, transforms_postsize
def get_presize_combine_tune_transforms(): transforms_presize = A.Compose([ transforms.Transpose(), transforms.Flip(), #transforms.PadIfNeeded(600, 800), crops.RandomResizedCrop( TARGET_SIZE, TARGET_SIZE, scale=(.75, 1), interpolation=cv2.INTER_CUBIC, p=1.0), rotate.Rotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99), ]) transforms_postsize = A.Compose([ transforms.CoarseDropout(), # transforms.CLAHE(p=.1), transforms.RandomToneCurve(scale=.1), transforms.RandomBrightnessContrast( brightness_limit=.1, contrast_limit=0.2, p=.7), transforms.HueSaturationValue( hue_shift_limit=20, sat_shift_limit=60, val_shift_limit=0, p=0.6), #transforms.Equalize(p=0.1), #transforms.FancyPCA(p=0.05), #transforms.RandomGridShuffle(p=0.1), #A.OneOf([ # transforms.MotionBlur(blur_limit=(3, 9)), # transforms.GaussianBlur(), # transforms.MedianBlur() # ], p=0.2), transforms.ISONoise(p=.3), transforms.GaussNoise(var_limit=255., p=.3), #A.OneOf([ # transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5), # transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3), # ], p=.3), geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3), transforms.CoarseDropout(), transforms.Normalize(), ToTensorV2() ]) return transforms_presize, transforms_postsize
def get_augmentations(): """Get a list of 'major' and 'minor' augmentation functions for the pipeline in a dictionary.""" return { "major": { "shift-scale-rot": trans.ShiftScaleRotate( shift_limit=0.05, rotate_limit=35, border_mode=cv2.BORDER_REPLICATE, always_apply=True, ), "crop": trans.RandomResizedCrop(100, 100, scale=(0.8, 0.95), ratio=(0.8, 1.2), always_apply=True), # "elastic": trans.ElasticTransform( # alpha=0.8, # alpha_affine=10, # sigma=40, # border_mode=cv2.BORDER_REPLICATE, # always_apply=True, # ), "distort": trans.OpticalDistortion(0.2, always_apply=True), }, "minor": { "blur": trans.GaussianBlur(7, always_apply=True), "noise": trans.GaussNoise((20.0, 40.0), always_apply=True), "bright-contrast": trans.RandomBrightnessContrast(0.4, 0.4, always_apply=True), "hsv": trans.HueSaturationValue(30, 40, 50, always_apply=True), "rgb": trans.RGBShift(always_apply=True), "flip": trans.HorizontalFlip(always_apply=True), }, }
def get_train_transforms(): return A.Compose([ transforms.PadIfNeeded(600, 800), geometric.ShiftScaleRotate(interpolation=cv2.INTER_LANCZOS4, p = 0.99, scale_limit=0.8), geometric.Perspective(pad_mode=cv2.BORDER_REFLECT,interpolation=cv2.INTER_AREA), crops.RandomResizedCrop( TARGET_SIZE, TARGET_SIZE, scale=(config['rrc_scale_min'], config['rrc_scale_max']), interpolation=cv2.INTER_CUBIC, p=1.0), transforms.Transpose(), transforms.Flip(), transforms.CoarseDropout(), transforms.CLAHE(p=.1), transforms.RandomToneCurve(scale=.1), transforms.RandomBrightnessContrast( brightness_limit=.1, contrast_limit=0.3, p=.7), transforms.HueSaturationValue( hue_shift_limit=20, sat_shift_limit=60, val_shift_limit=0, p=0.6), transforms.RandomGridShuffle(p=0.1), A.OneOf([ transforms.MotionBlur(blur_limit=(3, 9)), transforms.GaussianBlur(), transforms.MedianBlur() ], p=0.2), transforms.ISONoise(p=.3), transforms.GaussNoise(var_limit=255., p=.3), A.OneOf([ transforms.GridDistortion(interpolation=cv2.INTER_AREA, distort_limit=[0.7, 0.7], p=0.5), transforms.OpticalDistortion(interpolation=cv2.INTER_AREA, p=.3), ], p=.3), geometric.ElasticTransform(alpha=4, sigma=100, alpha_affine=100, interpolation=cv2.INTER_AREA, p=0.3), transforms.CoarseDropout(), transforms.Normalize(), ToTensorV2() ])
def __init__(self, im_paths=None, labels=None, phase=None, resize=False): """ Args: im_paths (numpy): image_data y (numpy): label data transform: pytorch transforms for transforms and tensor conversion """ self.im_paths = im_paths self.labels = labels self.resize = resize self.albumentations_transform = { 'train': Compose([ ab_transforms.HorizontalFlip(p=0.2), ab_transforms.VerticalFlip(p=0.2), ab_transforms.Rotate(limit=180, p=0.2), ab_transforms.HueSaturationValue(p=0.1), ab_transforms.RandomContrast(p=0.1), ab_transforms.GaussianBlur(blur_limit=3, p=0.2), ab_transforms.GaussNoise(p=0.05), ab_transforms.CLAHE(p=0.2), ab_transforms.Normalize(mean=[0.5944, 0.4343, 0.5652], std=[0.2593, 0.2293, 0.2377]), ToTensor() ]), 'val': Compose([ ab_transforms.Normalize(mean=[0.5944, 0.4343, 0.5652], std=[0.2593, 0.2293, 0.2377]), ToTensor() ]), } if phase == 'train': self.transform = self.albumentations_transform['train'] else: self.transform = self.albumentations_transform['val']
def __init__( self, prob=0, Flip_prob=0, HueSaturationValue_prob=0, RandomBrightnessContrast_prob=0, crop_prob=0, randomrotate90_prob=0, elastictransform_prob=0, gridistortion_prob=0, opticaldistortion_prob=0, verticalflip_prob=0, horizontalflip_prob=0, randomgamma_prob=0, CoarseDropout_prob=0, RGBShift_prob=0, MotionBlur_prob=0, MedianBlur_prob=0, GaussianBlur_prob=0, GaussNoise_prob=0, ChannelShuffle_prob=0, ColorJitter_prob=0, ): super().__init__() self.prob = prob self.randomrotate90_prob = randomrotate90_prob self.elastictransform_prob = elastictransform_prob self.transforms = al.Compose( [ transforms.RandomRotate90(p=randomrotate90_prob), transforms.Flip(p=Flip_prob), transforms.HueSaturationValue(p=HueSaturationValue_prob), transforms.RandomBrightnessContrast( p=RandomBrightnessContrast_prob), transforms.Transpose(), OneOf( [ transforms.RandomCrop(220, 220, p=0.5), transforms.CenterCrop(220, 220, p=0.5), ], p=crop_prob, ), ElasticTransform( p=elastictransform_prob, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03, ), GridDistortion(p=gridistortion_prob), OpticalDistortion(p=opticaldistortion_prob, distort_limit=2, shift_limit=0.5), VerticalFlip(p=verticalflip_prob), HorizontalFlip(p=horizontalflip_prob), RandomGamma(p=randomgamma_prob), RGBShift(p=RGBShift_prob), MotionBlur(p=MotionBlur_prob, blur_limit=7), MedianBlur(p=MedianBlur_prob, blur_limit=9), GaussianBlur(p=GaussianBlur_prob, blur_limit=9), GaussNoise(p=GaussNoise_prob), ChannelShuffle(p=ChannelShuffle_prob), CoarseDropout(p=CoarseDropout_prob, max_holes=8, max_height=32, max_width=32), ColorJitter(p=ColorJitter_prob) # transforms.Resize(352, 352), # transforms.Normalize( # mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) # ), ], p=self.prob, )
from glob import glob train_img_paths = [] train_mask_paths = [] train_data_path = ["data/kvasir-seg/TrainDataset"] for i in train_data_path: train_img_paths.extend(glob(os.path.join(i, "images", "*"))) train_mask_paths.extend(glob(os.path.join(i, "masks", "*"))) train_img_paths.sort() train_mask_paths.sort() transforms = al.Compose( [ transforms.RandomRotate90(), transforms.Flip(), transforms.HueSaturationValue(), transforms.RandomBrightnessContrast(), transforms.Transpose(), OneOf( [ transforms.RandomCrop(220, 220, p=0.5), transforms.CenterCrop(220, 220, p=0.5), ], p=1, ), # transforms.Resize(352,352), # transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ], p=1, ) dataset = KvasirDataset(train_img_paths,
def main(): # config = vars(parse_args_func()) #config_file = "../configs/config_v1.json" args = vars(parse_args_func()) config_file = args['config'] config_dict = json.loads(open(config_file, 'rt').read()) # config_dict = json.loads(open(sys.argv[1], 'rt').read()) file_dict = config_dict['file_path'] config = config_dict['opt_config'] input_folder = file_dict['input_path'] # '../inputs' checkpoint_folder = file_dict['checkpoint_path'] # '../checkpoint' model_folder = file_dict['model_path'] # '../models' if 'False' in config['deep_supervision']: config['deep_supervision'] = False else: config['deep_supervision'] = True if 'False' in config['nesterov']: config['nesterov'] = False else: config['nesterov'] = True if 'None' in config['name']: config['name'] = None if config['name'] is None: config['name'] = '%s_%s_segmodel' % (config['dataset'], config['arch']) os.makedirs(os.path.join(model_folder, '%s' % config['name']), exist_ok=True) print('-' * 20) for key in config: print('%s: %s' % (key, config[key])) print('-' * 20) with open(os.path.join(model_folder, '%s/config.yml' % config['name']), 'w') as f: yaml.dump(config, f) # define loss function (criterion) if config['loss'] == 'BCEWithLogitsLoss': criterion = nn.BCEWithLogitsLoss().cuda() else: criterion = losses.__dict__[config['loss']]().cuda() cudnn.benchmark = True # create model if 'False' in config['resume']: config['resume'] = False else: config['resume'] = True # Data loading code img_ids = glob( os.path.join(input_folder, config['dataset'], 'images', 'training', '*' + config['img_ext'])) train_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] img_ids = glob( os.path.join(input_folder, config['val_dataset'], 'images', 'validation', '*' + config['img_ext'])) val_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] img_ids = glob( os.path.join(input_folder, config['val_dataset'], 'images', 'test', '*' + config['img_ext'])) test_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] train_transform = Compose([ # transforms.RandomScale ([config['scale_min'], config['scale_max']]), # transforms.RandomRotate90(), transforms.Rotate([config['rotate_min'], config['rotate_max']], value=mean, mask_value=0), # transforms.GaussianBlur (), transforms.Flip(), # transforms.HorizontalFlip (), transforms.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10), transforms.RandomBrightnessContrast(brightness_limit=0.10, contrast_limit=0.10, brightness_by_max=True), transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(mean=mean, std=std), ]) val_transform = Compose([ transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(mean=mean, std=std), ]) train_dataset = Dataset(img_ids=train_img_ids, img_dir=os.path.join(input_folder, config['dataset'], 'images', 'training'), mask_dir=os.path.join(input_folder, config['dataset'], 'annotations', 'training'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=train_transform) val_dataset = Dataset(img_ids=val_img_ids, img_dir=os.path.join(input_folder, config['dataset'], 'images', 'validation'), mask_dir=os.path.join(input_folder, config['dataset'], 'annotations', 'validation'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=val_transform) test_dataset = Dataset(img_ids=val_img_ids, img_dir=os.path.join(input_folder, config['dataset'], 'images', 'test'), mask_dir=os.path.join(input_folder, config['dataset'], 'annotations', 'test'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=val_transform) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=config['num_workers'], drop_last=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=1, # config['batch_size'], shuffle=False, num_workers=config['num_workers'], drop_last=False) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=1, # config['batch_size'], shuffle=False, num_workers=config['num_workers'], drop_last=False) log = OrderedDict([ ('epoch', []), ('lr', []), ('loss', []), ('iou', []), ('dice', []), ('val_loss', []), ('val_iou', []), ('val_dice', []), ]) if not os.path.isdir(checkpoint_folder): os.mkdir(checkpoint_folder) # create generator model #val_config = config_dict['config'] generator_name = config['generator_name'] with open(os.path.join(model_folder, '%s/config.yml' % generator_name), 'r') as f: g_config = yaml.load(f, Loader=yaml.FullLoader) generator = Generator(g_config) generator.initialize_with_srresnet(model_folder, g_config) lr = config['gan_lr'] # Initialize generator's optimizer optimizer_g = torch.optim.Adam(params=filter(lambda p: p.requires_grad, generator.parameters()), lr=lr) #params = filter(lambda p: p.requires_grad, generator.parameters()) #optimizer_g, scheduler_g = optimizer_scheduler(params, config) # Discriminator # Discriminator parameters num_classes = config['num_classes'] kernel_size_d = 3 # kernel size in all convolutional blocks n_channels_d = 64 # number of output channels in the first convolutional block, after which it is doubled in every 2nd block thereafter n_blocks_d = 8 # number of convolutional blocks fc_size_d = 1024 # size of the first fully connected layer discriminator = Discriminator(num_classes, kernel_size=kernel_size_d, n_channels=n_channels_d, n_blocks=n_blocks_d, fc_size=fc_size_d) # Initialize discriminator's optimizer optimizer_d = torch.optim.Adam(params=filter(lambda p: p.requires_grad, discriminator.parameters()), lr=lr) #params = filter(lambda p: p.requires_grad, discriminator.parameters()) #optimizer_d, scheduler_d = optimizer_scheduler(params, config) adversarial_loss_criterion = nn.BCEWithLogitsLoss() content_loss_criterion = nn.MSELoss() generator = generator.cuda() discriminator = discriminator.cuda() #truncated_vgg19 = truncated_vgg19.cuda() content_loss_criterion = content_loss_criterion.cuda() adversarial_loss_criterion = adversarial_loss_criterion.cuda() generator = torch.nn.DataParallel(generator) discriminator = torch.nn.DataParallel(discriminator) if not os.path.isdir(checkpoint_folder): os.mkdir(checkpoint_folder) log_name = config['name'] log_dir = os.path.join(checkpoint_folder, log_name) writer = SummaryWriter(logdir=log_dir) best_iou = 0 trigger = 0 Best_dice = 0 iou_AtBestDice = 0 start_epoch = 0 for epoch in range(start_epoch, config['epochs']): print('Epoch [%d/%d]' % (epoch, config['epochs'])) # train for one epoch train_log = train(epoch, config, train_loader, generator, discriminator, criterion, adversarial_loss_criterion, content_loss_criterion, optimizer_g, optimizer_d) # evaluate on validation set val_log = validate(config, val_loader, generator, criterion) test_log = validate(config, test_loader, generator, criterion) if Best_dice < test_log['dice']: Best_dice = test_log['dice'] iou_AtBestDice = test_log['iou'] print( 'loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f - test_iou %.4f - test_dice %.4f - Best_dice %.4f - iou_AtBestDice %.4f' % (train_log['loss'], train_log['iou'], train_log['dice'], val_log['loss'], val_log['iou'], val_log['dice'], test_log['iou'], test_log['dice'], Best_dice, iou_AtBestDice)) save_tensorboard(writer, train_log, val_log, test_log, epoch) log['epoch'].append(epoch) log['lr'].append(config['lr']) log['loss'].append(train_log['loss']) log['iou'].append(train_log['iou']) log['dice'].append(train_log['dice']) log['val_loss'].append(val_log['loss']) log['val_iou'].append(val_log['iou']) log['val_dice'].append(val_log['dice']) pd.DataFrame(log).to_csv(os.path.join(model_folder, '%s/log.csv' % config['name']), index=False) trigger += 1 if test_log['iou'] > best_iou: torch.save( generator.state_dict(), os.path.join(model_folder, '%s/model.pth' % config['name'])) best_iou = test_log['iou'] print("=> saved best model") trigger = 0 # early stopping if config['early_stopping'] >= 0 and trigger >= config[ 'early_stopping']: print("=> early stopping") break torch.cuda.empty_cache()
def main(): config = vars(parse_args()) if config['name'] is None: config['name'] = '%s_%s' % (config['arch'], datetime.now().strftime('%m%d%H')) config['num_filters'] = [int(n) for n in config['num_filters'].split(',')] if not os.path.exists('models/detection/%s' % config['name']): os.makedirs('models/detection/%s' % config['name']) if config['resume']: with open('models/detection/%s/config.yml' % config['name'], 'r') as f: config = yaml.load(f, Loader=yaml.FullLoader) config['resume'] = True with open('models/detection/%s/config.yml' % config['name'], 'w') as f: yaml.dump(config, f) print('-' * 20) for key in config.keys(): print('- %s: %s' % (key, str(config[key]))) print('-' * 20) cudnn.benchmark = True df = pd.read_csv('inputs/train.csv') img_paths = np.array('inputs/train_images/' + df['ImageId'].values + '.jpg') mask_paths = np.array('inputs/train_masks/' + df['ImageId'].values + '.jpg') labels = np.array( [convert_str_to_labels(s) for s in df['PredictionString']]) test_img_paths = None test_mask_paths = None test_outputs = None if config['pseudo_label'] is not None: test_df = pd.read_csv('inputs/sample_submission.csv') test_img_paths = np.array('inputs/test_images/' + test_df['ImageId'].values + '.jpg') test_mask_paths = np.array('inputs/test_masks/' + test_df['ImageId'].values + '.jpg') ext = os.path.splitext(config['pseudo_label'])[1] if ext == '.pth': test_outputs = torch.load('outputs/raw/test/%s' % config['pseudo_label']) elif ext == '.csv': test_labels = pd.read_csv('outputs/submissions/test/%s' % config['pseudo_label']) null_idx = test_labels.isnull().any(axis=1) test_img_paths = test_img_paths[~null_idx] test_mask_paths = test_mask_paths[~null_idx] test_labels = test_labels.dropna() test_labels = np.array([ convert_str_to_labels( s, names=['pitch', 'yaw', 'roll', 'x', 'y', 'z', 'score']) for s in test_labels['PredictionString'] ]) print(test_labels) else: raise NotImplementedError if config['resume']: checkpoint = torch.load('models/detection/%s/checkpoint.pth.tar' % config['name']) heads = OrderedDict([ ('hm', 1), ('reg', 2), ('depth', 1), ]) if config['rot'] == 'eular': heads['eular'] = 3 elif config['rot'] == 'trig': heads['trig'] = 6 elif config['rot'] == 'quat': heads['quat'] = 4 else: raise NotImplementedError if config['wh']: heads['wh'] = 2 criterion = OrderedDict() for head in heads.keys(): criterion[head] = losses.__dict__[config[head + '_loss']]().cuda() train_transform = Compose([ transforms.ShiftScaleRotate(shift_limit=config['shift_limit'], scale_limit=0, rotate_limit=0, border_mode=cv2.BORDER_CONSTANT, value=0, p=config['shift_p']) if config['shift'] else NoOp(), OneOf([ transforms.HueSaturationValue(hue_shift_limit=config['hue_limit'], sat_shift_limit=config['sat_limit'], val_shift_limit=config['val_limit'], p=config['hsv_p']) if config['hsv'] else NoOp(), transforms.RandomBrightness( limit=config['brightness_limit'], p=config['brightness_p'], ) if config['brightness'] else NoOp(), transforms.RandomContrast( limit=config['contrast_limit'], p=config['contrast_p'], ) if config['contrast'] else NoOp(), ], p=1), transforms.ISONoise(p=config['iso_noise_p'], ) if config['iso_noise'] else NoOp(), transforms.CLAHE(p=config['clahe_p'], ) if config['clahe'] else NoOp(), ], keypoint_params=KeypointParams( format='xy', remove_invisible=False)) val_transform = None folds = [] best_losses = [] # best_scores = [] kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41) for fold, (train_idx, val_idx) in enumerate(kf.split(img_paths)): print('Fold [%d/%d]' % (fold + 1, config['n_splits'])) if (config['resume'] and fold < checkpoint['fold'] - 1) or ( not config['resume'] and os.path.exists('models/%s/model_%d.pth' % (config['name'], fold + 1))): log = pd.read_csv('models/detection/%s/log_%d.csv' % (config['name'], fold + 1)) best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss'] # best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values folds.append(str(fold + 1)) best_losses.append(best_loss) # best_scores.append(best_score) continue train_img_paths, val_img_paths = img_paths[train_idx], img_paths[ val_idx] train_mask_paths, val_mask_paths = mask_paths[train_idx], mask_paths[ val_idx] train_labels, val_labels = labels[train_idx], labels[val_idx] if config['pseudo_label'] is not None: train_img_paths = np.hstack((train_img_paths, test_img_paths)) train_mask_paths = np.hstack((train_mask_paths, test_mask_paths)) train_labels = np.hstack((train_labels, test_labels)) # train train_set = Dataset( train_img_paths, train_mask_paths, train_labels, input_w=config['input_w'], input_h=config['input_h'], transform=train_transform, lhalf=config['lhalf'], hflip=config['hflip_p'] if config['hflip'] else 0, scale=config['scale_p'] if config['scale'] else 0, scale_limit=config['scale_limit'], # test_img_paths=test_img_paths, # test_mask_paths=test_mask_paths, # test_outputs=test_outputs, ) train_loader = torch.utils.data.DataLoader( train_set, batch_size=config['batch_size'], shuffle=True, num_workers=config['num_workers'], # pin_memory=True, ) val_set = Dataset(val_img_paths, val_mask_paths, val_labels, input_w=config['input_w'], input_h=config['input_h'], transform=val_transform, lhalf=config['lhalf']) val_loader = torch.utils.data.DataLoader( val_set, batch_size=config['batch_size'], shuffle=False, num_workers=config['num_workers'], # pin_memory=True, ) # create model model = get_model(config['arch'], heads=heads, head_conv=config['head_conv'], num_filters=config['num_filters'], dcn=config['dcn'], gn=config['gn'], ws=config['ws'], freeze_bn=config['freeze_bn']) model = model.cuda() if config['load_model'] is not None: model.load_state_dict( torch.load('models/detection/%s/model_%d.pth' % (config['load_model'], fold + 1))) params = filter(lambda p: p.requires_grad, model.parameters()) if config['optimizer'] == 'Adam': optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'AdamW': optimizer = optim.AdamW(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'RAdam': optimizer = RAdam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'SGD': optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'], nesterov=config['nesterov'], weight_decay=config['weight_decay']) else: raise NotImplementedError if config['apex']: amp.initialize(model, optimizer, opt_level='O1') if config['scheduler'] == 'CosineAnnealingLR': scheduler = lr_scheduler.CosineAnnealingLR( optimizer, T_max=config['epochs'], eta_min=config['min_lr']) elif config['scheduler'] == 'ReduceLROnPlateau': scheduler = lr_scheduler.ReduceLROnPlateau( optimizer, factor=config['factor'], patience=config['patience'], verbose=1, min_lr=config['min_lr']) elif config['scheduler'] == 'MultiStepLR': scheduler = lr_scheduler.MultiStepLR( optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma']) else: raise NotImplementedError log = { 'epoch': [], 'loss': [], # 'score': [], 'val_loss': [], # 'val_score': [], } best_loss = float('inf') # best_score = float('inf') start_epoch = 0 if config['resume'] and fold == checkpoint['fold'] - 1: model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) scheduler.load_state_dict(checkpoint['scheduler']) start_epoch = checkpoint['epoch'] log = pd.read_csv( 'models/detection/%s/log_%d.csv' % (config['name'], fold + 1)).to_dict(orient='list') best_loss = checkpoint['best_loss'] for epoch in range(start_epoch, config['epochs']): print('Epoch [%d/%d]' % (epoch + 1, config['epochs'])) # train for one epoch train_loss = train(config, heads, train_loader, model, criterion, optimizer, epoch) # evaluate on validation set val_loss = validate(config, heads, val_loader, model, criterion) if config['scheduler'] == 'CosineAnnealingLR': scheduler.step() elif config['scheduler'] == 'ReduceLROnPlateau': scheduler.step(val_loss) print('loss %.4f - val_loss %.4f' % (train_loss, val_loss)) # print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f' # % (train_loss, train_score, val_loss, val_score)) log['epoch'].append(epoch) log['loss'].append(train_loss) # log['score'].append(train_score) log['val_loss'].append(val_loss) # log['val_score'].append(val_score) pd.DataFrame(log).to_csv('models/detection/%s/log_%d.csv' % (config['name'], fold + 1), index=False) if val_loss < best_loss: torch.save( model.state_dict(), 'models/detection/%s/model_%d.pth' % (config['name'], fold + 1)) best_loss = val_loss # best_score = val_score print("=> saved best model") state = { 'fold': fold + 1, 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_loss': best_loss, 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), } torch.save( state, 'models/detection/%s/checkpoint.pth.tar' % config['name']) print('val_loss: %f' % best_loss) # print('val_score: %f' % best_score) folds.append(str(fold + 1)) best_losses.append(best_loss) # best_scores.append(best_score) results = pd.DataFrame({ 'fold': folds + ['mean'], 'best_loss': best_losses + [np.mean(best_losses)], # 'best_score': best_scores + [np.mean(best_scores)], }) print(results) results.to_csv('models/detection/%s/results.csv' % config['name'], index=False) del model torch.cuda.empty_cache() del train_set, train_loader del val_set, val_loader gc.collect() if not config['cv']: break
def main(): config = vars(parse_args()) if config['name'] is None: if config['deep_supervision']: config['name'] = '%s_%s_wDS' % (config['dataset'], config['arch']) else: config['name'] = '%s_%s_woDS' % (config['dataset'], config['arch']) os.makedirs('models/%s' % config['name'], exist_ok=True) print('-' * 20) for key in config: print('%s: %s' % (key, config[key])) print('-' * 20) with open('models/%s/config.yml' % config['name'], 'w') as f: yaml.dump(config, f) # define loss function (criterion) if config['loss'] == 'BCEWithLogitsLoss': criterion = nn.BCEWithLogitsLoss().cuda() else: criterion = losses.__dict__[config['loss']]().cuda() cudnn.benchmark = True # create model print("=> creating model %s" % config['arch']) model = archs.__dict__[config['arch']](config['num_classes'], config['input_channels'], config['deep_supervision']) model = model.cuda() params = filter(lambda p: p.requires_grad, model.parameters()) if config['optimizer'] == 'Adam': optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'SGD': optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'], nesterov=config['nesterov'], weight_decay=config['weight_decay']) else: raise NotImplementedError if config['scheduler'] == 'CosineAnnealingLR': scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'], eta_min=config['min_lr']) elif config['scheduler'] == 'ReduceLROnPlateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, factor=config['factor'], patience=config['patience'], verbose=1, min_lr=config['min_lr']) elif config['scheduler'] == 'MultiStepLR': scheduler = lr_scheduler.MultiStepLR( optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma']) elif config['scheduler'] == 'ConstantLR': scheduler = None else: raise NotImplementedError # Data loading code img_ids = glob( os.path.join('inputs', config['dataset'], 'images', '*' + config['img_ext'])) img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] train_img_ids, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41) ####################### train_img_ids = img_ids ####################### train_transform = Compose([ transforms.RandomRotate90(), transforms.Flip(), OneOf([ transforms.HueSaturationValue(), transforms.RandomBrightness(), transforms.RandomContrast(), ], p=1), transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(), ]) val_transform = Compose([ transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(), ]) train_dataset = Dataset(img_ids=train_img_ids, img_dir=os.path.join('inputs', config['dataset'], 'images'), mask_dir=os.path.join('inputs', config['dataset'], 'masks'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], transform=train_transform) val_dataset = Dataset(img_ids=val_img_ids, img_dir=os.path.join('inputs', config['dataset'], 'images'), mask_dir=os.path.join('inputs', config['dataset'], 'masks'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], transform=val_transform) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=config['num_workers'], drop_last=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=config['batch_size'], shuffle=False, num_workers=config['num_workers'], drop_last=False) log = OrderedDict([ ('epoch', []), ('lr', []), ('loss', []), ('iou', []), ('val_loss', []), ('val_iou', []), ]) best_iou = 0 trigger = 0 for epoch in range(config['epochs']): print('Epoch [%d/%d]' % (epoch, config['epochs'])) # train for one epoch train_log = train(config, train_loader, model, criterion, optimizer) # evaluate on validation set val_log = validate(config, val_loader, model, criterion) if config['scheduler'] == 'CosineAnnealingLR': scheduler.step() elif config['scheduler'] == 'ReduceLROnPlateau': scheduler.step(val_log['loss']) print('loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f' % (train_log['loss'], train_log['iou'], val_log['loss'], val_log['iou'])) log['epoch'].append(epoch) log['lr'].append(config['lr']) log['loss'].append(train_log['loss']) log['iou'].append(train_log['iou']) log['val_loss'].append(val_log['loss']) log['val_iou'].append(val_log['iou']) pd.DataFrame(log).to_csv('models/%s/log.csv' % config['name'], index=False) trigger += 1 if val_log['iou'] > best_iou: torch.save(model.state_dict(), 'models/%s/model.pth' % config['name']) best_iou = val_log['iou'] print("=> saved best model") trigger = 0 # early stopping if config['early_stopping'] >= 0 and trigger >= config[ 'early_stopping']: print("=> early stopping") break torch.cuda.empty_cache()
def main(): config = vars(parse_args()) if config['name'] is None: config['name'] = '%s_%s' % (config['arch'], datetime.now().strftime('%m%d%H')) if not os.path.exists('models/pose/%s' % config['name']): os.makedirs('models/pose/%s' % config['name']) if config['resume']: with open('models/pose/%s/config.yml' % config['name'], 'r') as f: config = yaml.load(f, Loader=yaml.FullLoader) config['resume'] = True with open('models/pose/%s/config.yml' % config['name'], 'w') as f: yaml.dump(config, f) print('-'*20) for key in config.keys(): print('- %s: %s' % (key, str(config[key]))) print('-'*20) cudnn.benchmark = True df = pd.read_csv('inputs/train.csv') img_ids = df['ImageId'].values pose_df = pd.read_csv('processed/pose_train.csv') pose_df['img_path'] = 'processed/pose_images/train/' + pose_df['img_path'] if config['resume']: checkpoint = torch.load('models/pose/%s/checkpoint.pth.tar' % config['name']) if config['rot'] == 'eular': num_outputs = 3 elif config['rot'] == 'trig': num_outputs = 6 elif config['rot'] == 'quat': num_outputs = 4 else: raise NotImplementedError if config['loss'] == 'L1Loss': criterion = nn.L1Loss().cuda() elif config['loss'] == 'MSELoss': criterion = nn.MSELoss().cuda() else: raise NotImplementedError train_transform = Compose([ transforms.ShiftScaleRotate( shift_limit=config['shift_limit'], scale_limit=0, rotate_limit=0, border_mode=cv2.BORDER_CONSTANT, value=0, p=config['shift_p'] ) if config['shift'] else NoOp(), OneOf([ transforms.HueSaturationValue( hue_shift_limit=config['hue_limit'], sat_shift_limit=config['sat_limit'], val_shift_limit=config['val_limit'], p=config['hsv_p'] ) if config['hsv'] else NoOp(), transforms.RandomBrightness( limit=config['brightness_limit'], p=config['brightness_p'], ) if config['brightness'] else NoOp(), transforms.RandomContrast( limit=config['contrast_limit'], p=config['contrast_p'], ) if config['contrast'] else NoOp(), ], p=1), transforms.ISONoise( p=config['iso_noise_p'], ) if config['iso_noise'] else NoOp(), transforms.CLAHE( p=config['clahe_p'], ) if config['clahe'] else NoOp(), transforms.Resize(config['input_w'], config['input_h']), transforms.Normalize(), ToTensor(), ]) val_transform = Compose([ transforms.Resize(config['input_w'], config['input_h']), transforms.Normalize(), ToTensor(), ]) folds = [] best_losses = [] kf = KFold(n_splits=config['n_splits'], shuffle=True, random_state=41) for fold, (train_idx, val_idx) in enumerate(kf.split(img_ids)): print('Fold [%d/%d]' %(fold + 1, config['n_splits'])) if (config['resume'] and fold < checkpoint['fold'] - 1) or (not config['resume'] and os.path.exists('pose_models/%s/model_%d.pth' % (config['name'], fold+1))): log = pd.read_csv('models/pose/%s/log_%d.csv' %(config['name'], fold+1)) best_loss = log.loc[log['val_loss'].values.argmin(), 'val_loss'] # best_loss, best_score = log.loc[log['val_loss'].values.argmin(), ['val_loss', 'val_score']].values folds.append(str(fold + 1)) best_losses.append(best_loss) # best_scores.append(best_score) continue train_img_ids, val_img_ids = img_ids[train_idx], img_ids[val_idx] train_img_paths = [] train_labels = [] for img_id in train_img_ids: tmp = pose_df.loc[pose_df.ImageId == img_id] img_path = tmp['img_path'].values train_img_paths.append(img_path) yaw = tmp['yaw'].values pitch = tmp['pitch'].values roll = tmp['roll'].values roll = rotate(roll, np.pi) if config['rot'] == 'eular': label = np.array([ yaw, pitch, roll ]).T elif config['rot'] == 'trig': label = np.array([ np.cos(yaw), np.sin(yaw), np.cos(pitch), np.sin(pitch), np.cos(roll), np.sin(roll), ]).T elif config['rot'] == 'quat': raise NotImplementedError else: raise NotImplementedError train_labels.append(label) train_img_paths = np.hstack(train_img_paths) train_labels = np.vstack(train_labels) val_img_paths = [] val_labels = [] for img_id in val_img_ids: tmp = pose_df.loc[pose_df.ImageId == img_id] img_path = tmp['img_path'].values val_img_paths.append(img_path) yaw = tmp['yaw'].values pitch = tmp['pitch'].values roll = tmp['roll'].values roll = rotate(roll, np.pi) if config['rot'] == 'eular': label = np.array([ yaw, pitch, roll ]).T elif config['rot'] == 'trig': label = np.array([ np.cos(yaw), np.sin(yaw), np.cos(pitch), np.sin(pitch), np.cos(roll), np.sin(roll), ]).T elif config['rot'] == 'quat': raise NotImplementedError else: raise NotImplementedError val_labels.append(label) val_img_paths = np.hstack(val_img_paths) val_labels = np.vstack(val_labels) # train train_set = PoseDataset( train_img_paths, train_labels, transform=train_transform, ) train_loader = torch.utils.data.DataLoader( train_set, batch_size=config['batch_size'], shuffle=True, num_workers=config['num_workers'], # pin_memory=True, ) val_set = PoseDataset( val_img_paths, val_labels, transform=val_transform, ) val_loader = torch.utils.data.DataLoader( val_set, batch_size=config['batch_size'], shuffle=False, num_workers=config['num_workers'], # pin_memory=True, ) # create model model = get_pose_model(config['arch'], num_outputs=num_outputs, freeze_bn=config['freeze_bn']) model = model.cuda() params = filter(lambda p: p.requires_grad, model.parameters()) if config['optimizer'] == 'Adam': optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'AdamW': optimizer = optim.AdamW(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'RAdam': optimizer = RAdam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'SGD': optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'], nesterov=config['nesterov'], weight_decay=config['weight_decay']) else: raise NotImplementedError if config['scheduler'] == 'CosineAnnealingLR': scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'], eta_min=config['min_lr']) elif config['scheduler'] == 'ReduceLROnPlateau': scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, factor=config['factor'], patience=config['patience'], verbose=1, min_lr=config['min_lr']) elif config['scheduler'] == 'MultiStepLR': scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma']) else: raise NotImplementedError log = { 'epoch': [], 'loss': [], # 'score': [], 'val_loss': [], # 'val_score': [], } best_loss = float('inf') # best_score = float('inf') start_epoch = 0 if config['resume'] and fold == checkpoint['fold'] - 1: model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) scheduler.load_state_dict(checkpoint['scheduler']) start_epoch = checkpoint['epoch'] log = pd.read_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1)).to_dict(orient='list') best_loss = checkpoint['best_loss'] for epoch in range(start_epoch, config['epochs']): print('Epoch [%d/%d]' % (epoch + 1, config['epochs'])) # train for one epoch train_loss = train(config, train_loader, model, criterion, optimizer, epoch) # evaluate on validation set val_loss = validate(config, val_loader, model, criterion) if config['scheduler'] == 'CosineAnnealingLR': scheduler.step() elif config['scheduler'] == 'ReduceLROnPlateau': scheduler.step(val_loss) print('loss %.4f - val_loss %.4f' % (train_loss, val_loss)) # print('loss %.4f - score %.4f - val_loss %.4f - val_score %.4f' # % (train_loss, train_score, val_loss, val_score)) log['epoch'].append(epoch) log['loss'].append(train_loss) # log['score'].append(train_score) log['val_loss'].append(val_loss) # log['val_score'].append(val_score) pd.DataFrame(log).to_csv('models/pose/%s/log_%d.csv' % (config['name'], fold+1), index=False) if val_loss < best_loss: torch.save(model.state_dict(), 'models/pose/%s/model_%d.pth' % (config['name'], fold+1)) best_loss = val_loss # best_score = val_score print("=> saved best model") state = { 'fold': fold + 1, 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_loss': best_loss, 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), } torch.save(state, 'models/pose/%s/checkpoint.pth.tar' % config['name']) print('val_loss: %f' % best_loss) # print('val_score: %f' % best_score) folds.append(str(fold + 1)) best_losses.append(best_loss) # best_scores.append(best_score) results = pd.DataFrame({ 'fold': folds + ['mean'], 'best_loss': best_losses + [np.mean(best_losses)], # 'best_score': best_scores + [np.mean(best_scores)], }) print(results) results.to_csv('models/pose/%s/results.csv' % config['name'], index=False) del model torch.cuda.empty_cache() del train_set, train_loader del val_set, val_loader gc.collect() if not config['cv']: break
def augment(im, params=None): """ Perform data augmentation on some image using the albumentations package. Parameters ---------- im : Numpy array params : dict or None Contains the data augmentation parameters Mandatory keys: - h_flip ([0,1] float): probability of performing an horizontal left-right mirroring. - v_flip ([0,1] float): probability of performing an vertical up-down mirroring. - rot ([0,1] float): probability of performing a rotation to the image. - rot_lim (int): max degrees of rotation. - stretch ([0,1] float): probability of randomly stretching an image. - crop ([0,1] float): randomly take an image crop. - zoom ([0,1] float): random zoom applied to crop_size. --> Therefore the effective crop size at each iteration will be a random number between 1 and crop*(1-zoom). For example: * crop=1, zoom=0: no crop of the image * crop=1, zoom=0.1: random crop of random size between 100% image and 90% of the image * crop=0.9, zoom=0.1: random crop of random size between 90% image and 80% of the image * crop=0.9, zoom=0: random crop of always 90% of the image Image size refers to the size of the shortest side. - blur ([0,1] float): probability of randomly blurring an image. - pixel_noise ([0,1] float): probability of randomly adding pixel noise to an image. - pixel_sat ([0,1] float): probability of randomly using HueSaturationValue in the image. - cutout ([0,1] float): probability of using cutout in the image. Returns ------- Numpy array """ ## 1) Crop the image effective_zoom = np.random.rand() * params['zoom'] crop = params['crop'] - effective_zoom ly, lx, channels = im.shape crop_size = int(crop * min([ly, lx])) rand_x = np.random.randint(low=0, high=lx - crop_size + 1) rand_y = np.random.randint(low=0, high=ly - crop_size + 1) crop = transforms.Crop(x_min=rand_x, y_min=rand_y, x_max=rand_x + crop_size, y_max=rand_y + crop_size) im = crop(image=im)['image'] ## 2) Now add the transformations for augmenting the image pixels transform_list = [] # Add random stretching if params['stretch']: transform_list.append( imgaug_transforms.IAAPerspective(scale=0.1, p=params['stretch'])) # Add random rotation if params['rot']: transform_list.append( transforms.Rotate(limit=params['rot_lim'], p=params['rot'])) # Add horizontal flip if params['h_flip']: transform_list.append(transforms.HorizontalFlip(p=params['h_flip'])) # Add vertical flip if params['v_flip']: transform_list.append(transforms.VerticalFlip(p=params['v_flip'])) # Add some blur to the image if params['blur']: transform_list.append( albumentations.OneOf([ transforms.MotionBlur(blur_limit=7, p=1.), transforms.MedianBlur(blur_limit=7, p=1.), transforms.Blur(blur_limit=7, p=1.), ], p=params['blur'])) # Add pixel noise if params['pixel_noise']: transform_list.append( albumentations.OneOf( [ transforms.CLAHE(clip_limit=2, p=1.), imgaug_transforms.IAASharpen(p=1.), imgaug_transforms.IAAEmboss(p=1.), transforms.RandomBrightnessContrast(contrast_limit=0, p=1.), transforms.RandomBrightnessContrast(brightness_limit=0, p=1.), transforms.RGBShift(p=1.), transforms.RandomGamma(p=1.) #, # transforms.JpegCompression(), # transforms.ChannelShuffle(), # transforms.ToGray() ], p=params['pixel_noise'])) # Add pixel saturation if params['pixel_sat']: transform_list.append( transforms.HueSaturationValue(p=params['pixel_sat'])) # Remove randomly remove some regions from the image if params['cutout']: ly, lx, channels = im.shape scale_low, scale_high = 0.05, 0.25 # min and max size of the squares wrt the full image scale = np.random.uniform(scale_low, scale_high) transform_list.append( transforms.Cutout(num_holes=8, max_h_size=int(scale * ly), max_w_size=int(scale * lx), p=params['cutout'])) # Compose all image transformations and augment the image augmentation_fn = albumentations.Compose(transform_list) im = augmentation_fn(image=im)['image'] return im
def augment(im, params=None): """ Perform data augmentation on some image using the albumentations package. Parameters ---------- im : Numpy array params : dict or None Contains the data augmentation parameters Mandatory keys: - h_flip ([0,1] float): probability of performing an horizontal left-right mirroring. - v_flip ([0,1] float): probability of performing an vertical up-down mirroring. - rot ([0,1] float): probability of performing a rotation to the image. - rot_lim (int): max degrees of rotation. - stretch ([0,1] float): probability of randomly stretching an image. - expand ([True, False] bool): whether to pad the image to a square shape with background color canvas. - crop ([0,1] float): randomly take an image crop. - invert_col ([0, 1] float): randomly invert the colors of the image. p=1 -> invert colors (VPR) - zoom ([0,1] float): random zoom applied to crop_size. --> Therefore the effective crop size at each iteration will be a random number between 1 and crop*(1-zoom). For example: * crop=1, zoom=0: no crop of the image * crop=1, zoom=0.1: random crop of random size between 100% image and 90% of the image * crop=0.9, zoom=0.1: random crop of random size between 90% image and 80% of the image * crop=0.9, zoom=0: random crop of always 90% of the image Image size refers to the size of the shortest side. - blur ([0,1] float): probability of randomly blurring an image. - pixel_noise ([0,1] float): probability of randomly adding pixel noise to an image. - pixel_sat ([0,1] float): probability of randomly using HueSaturationValue in the image. - cutout ([0,1] float): probability of using cutout in the image. Returns ------- Numpy array """ ## 1) Expand the image by padding it with bg-color canvas if params["expand"]: desired_size = max(im.shape) # check bg if np.argmax(im.shape) > 0: bgcol = tuple(np.repeat(int(np.mean(im[[0, -1], :, :])), 3)) else: bgcol = tuple(np.repeat(int(np.mean(im[:, [0, -1], :])), 3)) im = Image.fromarray(im) old_size = im.size # old_size[0] is in (width, height) format ratio = float(desired_size) / max(old_size) new_size = tuple([int(x * ratio) for x in old_size]) im = im.resize(new_size, Image.ANTIALIAS) # create a new image and paste the resized on it new_im = Image.new("RGB", (desired_size, desired_size), color=bgcol) new_im.paste(im, ((desired_size - new_size[0]) // 2, (desired_size - new_size[1]) // 2)) im = np.array(new_im) ## 2) Crop the image if params["crop"] and params["crop"] != 1: effective_zoom = np.random.rand() * params['zoom'] crop = params['crop'] - effective_zoom ly, lx, channels = im.shape crop_size = int(crop * min([ly, lx])) rand_x = np.random.randint(low=0, high=lx - crop_size + 1) rand_y = np.random.randint(low=0, high=ly - crop_size + 1) crop = transforms.Crop(x_min=rand_x, y_min=rand_y, x_max=rand_x + crop_size, y_max=rand_y + crop_size) im = crop(image=im)['image'] if params["enhance"]: im = Image.fromarray(im) enhancer = ImageEnhance.Contrast(im) im = np.array(enhancer.enhance(params["enhance"])) ## 3) Now add the transformations for augmenting the image pixels transform_list = [] if params['invert_col']: transform_list.append(transforms.InvertImg(p=params['invert_col'])) # Add random stretching if params['stretch']: transform_list.append( imgaug_transforms.IAAPerspective(scale=0.1, p=params['stretch'])) # Add random rotation if params['rot']: transform_list.append( transforms.Rotate(limit=params['rot_lim'], p=params['rot'])) # Add horizontal flip if params['h_flip']: transform_list.append(transforms.HorizontalFlip(p=params['h_flip'])) # Add vertical flip if params['v_flip']: transform_list.append(transforms.VerticalFlip(p=params['v_flip'])) # Add some blur to the image if params['blur']: transform_list.append( albumentations.OneOf([ transforms.MotionBlur(blur_limit=7, p=1.), transforms.MedianBlur(blur_limit=7, p=1.), transforms.Blur(blur_limit=7, p=1.), ], p=params['blur'])) # Add pixel noise if params['pixel_noise']: transform_list.append( albumentations.OneOf( [ transforms.CLAHE(clip_limit=2, p=1.), imgaug_transforms.IAASharpen(p=1.), imgaug_transforms.IAAEmboss(p=1.), transforms.RandomBrightnessContrast(contrast_limit=0, p=1.), transforms.RandomBrightnessContrast(brightness_limit=0, p=1.), transforms.RGBShift(p=1.), transforms.RandomGamma(p=1.) #, # transforms.JpegCompression(), # transforms.ChannelShuffle(), # transforms.ToGray() ], p=params['pixel_noise'])) # Add pixel saturation if params['pixel_sat']: transform_list.append( transforms.HueSaturationValue(p=params['pixel_sat'])) # Remove randomly remove some regions from the image if params['cutout']: ly, lx, channels = im.shape scale_low, scale_high = 0.05, 0.25 # min and max size of the squares wrt the full image scale = np.random.uniform(scale_low, scale_high) transform_list.append( transforms.Cutout(num_holes=8, max_h_size=int(scale * ly), max_w_size=int(scale * lx), p=params['cutout'])) # Compose all image transformations and augment the image augmentation_fn = albumentations.Compose(transform_list) im = augmentation_fn(image=im)['image'] return im
def main(): config = vars(parse_args()) now = datetime.datetime.now() if config["name"] is None: if config["deep_supervision"]: config["name"] = "%s_%s_wDS_%s" % ( config["dataset"], config["arch"], now.strftime("%Y%m%d_%H%M%S"), ) else: config["name"] = "%s_%s_woDS_%s" % ( config["dataset"], config["arch"], now.strftime("%Y%m%d_%H%M%S"), ) output_path = os.path.join(cfg.UNET_RESULTS_DIR, config["name"]) try: os.makedirs(output_path, exist_ok=True) except Exception as e: print(e) models_path = os.path.join(output_path, "models") os.mkdir(models_path) with open(os.path.join(models_path, "config.yml"), "w") as f: yaml.dump(config, f) print("-" * 20) for key in config: print("%s: %s" % (key, config[key])) print("-" * 20) # Tensorboad 用のログを記録するディレクトリパス log_dir = os.path.join(output_path, "log") os.mkdir(log_dir) writer = SummaryWriter(log_dir=log_dir) # define loss function(criterion) if config["loss"] == "BCEWithLogitsLoss": criterion = nn.BCEWithLogitsLoss().cuda() else: criterion = losses.__dict__[config["loss"]]().cuda() cudnn.benchmark = True # create model print("=> creating model %s" % config["arch"]) model = archs.__dict__[config["arch"]](config["num_classes"], config["input_channels"], config["deep_supervision"]) model = model.cuda() # モデルを TensorBorad で表示するため,ログに保存 # image = torch.randn(1, 3, 2224, 224) # writer.add_graph(model, image) params = filter(lambda p: p.requires_grad, model.parameters()) if config["optimizer"] == "Adam": optimizer = optim.Adam(params, lr=config["lr"], weight_decay=config["weight_decay"]) elif config["optimizer"] == "SGD": optimizer = optim.SGD( params, lr=config["lr"], momentum=config["momentum"], nesterov=config["nesterov"], weight_decay=config["weight_decay"], ) else: raise NotImplementedError # scheduler if config["scheduler"] == "CosineAnnealingLR": scheduler = lr_scheduler.CosineAnnealingLR(optimizer=optimizer, T_max=config["epochs"], eta_min=config["min_lr"]) elif config["scheduler"] == "ReduceLROnPlateau": scheduler = lr_scheduler( optimizer=optimizer, factor=config["factor"], patience=config["patience"], verbose=1, min_lr=config["min_lr"], ) elif config["scheduler"] == "MultiStepLR": scheduler = lr_scheduler.MultiStepLR( optimizer=optimizer, milestones=[int(e) for e in config["milestones"].split(",")], gamma=config["gamma"], ) elif config["scheduler"] == "ConstantLR": scheduler = None else: raise NotImplementedError # Data loading code if config["dataset"] == "dsb2018_96": input_dir = cfg.DSB2018_96_DIR img_ids = glob( os.path.join(input_dir, "images", "*" + config["img_ext"])) img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] train_img_ids, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41) train_transform = Compose([ transforms.RandomRotate90(), transforms.Flip(), OneOf( [ transforms.HueSaturationValue(), transforms.RandomBrightness(), transforms.RandomContrast(), ], p=1, ), transforms.Resize(config["input_h"], config["input_w"]), transforms.Normalize(), ]) val_transform = Compose([ transforms.Resize(config["input_h"], config["input_w"]), transforms.Normalize(), ]) train_dataset = Dataset( img_ids=train_img_ids, img_dir=os.path.join(input_dir, "images"), mask_dir=os.path.join(input_dir, "masks"), img_ext=config["img_ext"], mask_ext=config["mask_ext"], num_classes=config["num_classes"], transform=train_transform, ) val_dataset = Dataset( img_ids=val_img_ids, img_dir=os.path.join(input_dir, "images"), mask_dir=os.path.join(input_dir, "masks"), img_ext=config["img_ext"], mask_ext=config["mask_ext"], num_classes=config["num_classes"], transform=val_transform, ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config["batch_size"], shuffle=True, num_workers=config["num_workers"], drop_last=True, ) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=config["batch_size"], shuffle=False, num_workers=config["num_workers"], drop_last=False, ) log = OrderedDict([ ("epoch", []), ("lr", []), ("loss", []), ("iou", []), ("val_loss", []), ("val_iou", []), ]) best_iou = 0 trigger = 0 for epoch in range(config["epochs"] + 1): print("Epoch [%d/%d]" % (epoch, config["epochs"])) # train for one epoch train_log = train(config, train_loader, model, criterion, optimizer) # evaluate on validation set val_log = validate(config, val_loader, model, criterion) if config["scheduler"] == "CosineAnnealingLR": scheduler.step() elif config["scheduler"] == "ReduceLROnPlateau": scheduler.step(val_log["loss"]) print("loss %.4f - iou %.4f - val_loss %.4f - val_iou %.4f" % (train_log["loss"], train_log["iou"], val_log["loss"], val_log["iou"])) log["epoch"].append(epoch) log["lr"].append(config["lr"]) log["loss"].append(train_log["loss"]) log["iou"].append(train_log["iou"]) log["val_loss"].append(val_log["loss"]) log["val_iou"].append(val_log["iou"]) # Tensorboard用のデータ writer.add_scalar("training loss", train_log["loss"], epoch) writer.add_scalar("validation loss", val_log["loss"], epoch) pd.DataFrame(log).to_csv("%s/log.csv" % (log_dir), index=False) if epoch == 0: best_loss = val_log["loss"] trigger += 1 # Best Model Save # if val_log['iou'] > best_iou: if (val_log["iou"] > best_iou) and (val_log["loss"] <= best_loss): torch.save(model.state_dict(), "%s/model.pth" % (models_path)) best_iou = val_log["iou"] best_loss = val_log["loss"] print("=> saved best model") trigger = 0 # early stopping if (config["early_stopping"] >= 0 and trigger >= config["early_stopping"]) or val_log["loss"] < 1e-4: print("=> early stopping") break torch.cuda.empty_cache() # summary writer を必要としない場合,close()メソッドを呼び出す writer.close()
import pathlib from dataset import TRAFFIC_LABELS_TO_NUM alb_transforms = [ alb.IAAAdditiveGaussianNoise(p=1), alb.GaussNoise(p=1), alb.MotionBlur(p=1), alb.MedianBlur(blur_limit=3, p=1), alb.Blur(blur_limit=3, p=1), alb.OpticalDistortion(p=1), alb.GridDistortion(p=1), alb.IAAPiecewiseAffine(p=1), aat.CLAHE(clip_limit=2, p=1), alb.IAASharpen(p=1), alb.IAAEmboss(p=1), aat.HueSaturationValue(p=0.3), aat.HorizontalFlip(p=1), aat.RGBShift(), aat.RandomBrightnessContrast(), aat.RandomGamma(p=1), aat.Cutout(2, 10, 10, p=1), aat.Equalize(mode='cv', p=1), aat.FancyPCA(p=1), aat.RandomFog(p=1), aat.RandomRain(blur_value=3, p=1), albumentations.IAAAffine(p=1), albumentations.ShiftScaleRotate(rotate_limit=15, p=1) ] def one_by_one():
fig, axs = plt.subplots(1, 3, constrained_layout=True, figsize=(20, 20)) [axs[i].set_axis_off() for i in range(3)] # axs[0].imshow(image) # axs[0].set_title("Original",fontsize=30) # image_ = transforms.RandomRotate90(always_apply=True)(image=image)["image"] # axs[1].imshow(image_) # axs[1].set_title("Rotate",fontsize=30) # image_ = transforms.Flip(always_apply=True)(image=image)["image"] # axs[2].imshow(image_) # axs[2].set_title("Flip",fontsize=30) image_ = transforms.HueSaturationValue(always_apply=True)(image=image)["image"] axs[0].imshow(image_) axs[0].set_title("Saturation", fontsize=30) image_ = transforms.RandomBrightnessContrast(always_apply=True)( image=image)["image"] axs[1].imshow(image_) axs[1].set_title("Brightness", fontsize=30) image_ = OneOf( [ transforms.RandomCrop(204, 250, p=1), transforms.CenterCrop(204, 250, p=1), ], p=1, )(image=image)["image"]
def main(): args = vars(parse_args_func()) #config_file = "../configs/config_SN7.json" config_file = args['config'] # "../configs/config_v1.json" config_dict = json.loads(open(config_file, 'rt').read()) #config_dict = json.loads(open(sys.argv[1], 'rt').read()) file_dict = config_dict['file_path'] config = config_dict['opt_config'] input_folder = file_dict['input_path'] # '../inputs' checkpoint_folder = file_dict['checkpoint_path'] # '../checkpoint' model_folder = file_dict['model_path'] # '../models' if 'False' in config['deep_supervision']: config['deep_supervision'] = False else: config['deep_supervision'] = True if 'False' in config['nesterov']: config['nesterov'] = False else: config['nesterov'] = True if 'None' in config['name']: config['name'] = None if config['name'] is None: config['name'] = '%s_%s_segmodel' % (config['dataset'], config['arch']) os.makedirs(os.path.join(model_folder, '%s' % config['name']), exist_ok=True) if not os.path.isdir(checkpoint_folder): os.mkdir(checkpoint_folder) log_name = config['name'] log_dir = os.path.join(checkpoint_folder, log_name) writer = SummaryWriter(logdir=log_dir) print('-' * 20) for key in config: print('%s: %s' % (key, config[key])) print('-' * 20) with open(os.path.join(model_folder, '%s/config.yml' % config['name']), 'w') as f: yaml.dump(config, f) # define loss function (criterion) if config['loss'] == 'BCEWithLogitsLoss': criterion = nn.BCEWithLogitsLoss().cuda() else: criterion = losses.__dict__[config['loss']]().cuda() cudnn.benchmark = True # create model print("=> creating model %s" % config['arch']) model = archs.__dict__[config['arch']](config['num_classes'], config['input_channels'], config['deep_supervision']) if 'False' in config['resume']: config['resume'] = False else: config['resume'] = True resume_flag = False if resume_flag == True: save_path = os.path.join(model_folder, config['name'], 'model.pth') weights = torch.load(save_path) model.load_state_dict(weights) name_yaml = config['name'] with open(os.path.join(model_folder, '%s/config.yml' % name_yaml), 'r') as f: config = yaml.load(f, Loader=yaml.FullLoader) #start_epoch = config['epochs'] start_epoch = 0 else: start_epoch = 0 model = model.cuda() if 'effnet' in config['arch']: eff_flag = True else: eff_flag = False if eff_flag == True: cnn_subs = list(model.encoder.eff_conv.children())[1:] #cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs] #cnn_params = [item for sublist in cnn_params for item in sublist] summary(model, (config['input_channels'], config['input_w'], config['input_h'])) params = filter(lambda p: p.requires_grad, model.parameters()) if eff_flag == True: params = list(params) + list(model.encoder.conv_a.parameters()) model = torch.nn.DataParallel(model) if config['optimizer'] == 'Adam': optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay']) elif config['optimizer'] == 'SGD': optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'], nesterov=config['nesterov'], weight_decay=config['weight_decay']) else: raise NotImplementedError if eff_flag == True: cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs] cnn_params = [item for sublist in cnn_params for item in sublist] cnn_optimizer = torch.optim.Adam(cnn_params, lr=0.001, weight_decay=config['weight_decay']) #cnn_optimizer = None else: cnn_optimizer = None if config['optimizer'] == 'SGD': if config['scheduler'] == 'CosineAnnealingLR': scheduler = lr_scheduler.CosineAnnealingLR( optimizer, T_max=config['epochs'], eta_min=config['min_lr']) elif config['scheduler'] == 'ReduceLROnPlateau': scheduler = lr_scheduler.ReduceLROnPlateau( optimizer, factor=config['factor'], patience=config['patience'], verbose=1, min_lr=config['min_lr']) elif config['scheduler'] == 'MultiStepLR': scheduler = lr_scheduler.MultiStepLR( optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma']) elif config['scheduler'] == 'ConstantLR': scheduler = None else: raise NotImplementedError else: scheduler = None # Data loading code img_ids = glob( os.path.join(input_folder, config['dataset'], 'images', 'training', '*' + config['img_ext'])) train_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] #img_dir = os.path.join(input_folder, config['dataset'], 'images', 'training') #mask_dir = os.path.join(input_folder, config['dataset'], 'annotations', 'training') #train_image_mask = image_to_afile(img_dir, mask_dir, None, train_img_ids, config) img_ids = glob( os.path.join(input_folder, config['val_dataset'], 'images', 'validation', '*' + config['img_ext'])) val_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] img_ids = glob( os.path.join(input_folder, config['val_dataset'], 'images', 'test', '*' + config['img_ext'])) test_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids] mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] train_transform = Compose([ #transforms.RandomScale ([config['scale_min'], config['scale_max']]), #transforms.RandomRotate90(), transforms.Rotate([config['rotate_min'], config['rotate_max']], value=mean, mask_value=0), transforms.Flip(), #transforms.HorizontalFlip (), transforms.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10), transforms.RandomBrightnessContrast(brightness_limit=0.10, contrast_limit=0.10, brightness_by_max=True), transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(mean=mean, std=std), ]) val_transform = Compose([ transforms.Resize(config['input_h'], config['input_w']), transforms.Normalize(mean=mean, std=std), ]) train_dataset = Dataset(img_ids=train_img_ids, img_dir=os.path.join(input_folder, config['dataset'], 'images', 'training'), mask_dir=os.path.join(input_folder, config['dataset'], 'annotations', 'training'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=train_transform, from_file=None) val_dataset = Dataset(img_ids=val_img_ids, img_dir=os.path.join(input_folder, config['val_dataset'], 'images', 'validation'), mask_dir=os.path.join(input_folder, config['val_dataset'], 'annotations', 'validation'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=val_transform, from_file=None) test_dataset = Dataset(img_ids=test_img_ids, img_dir=os.path.join(input_folder, config['val_dataset'], 'images', 'test'), mask_dir=os.path.join(input_folder, config['val_dataset'], 'annotations', 'test'), img_ext=config['img_ext'], mask_ext=config['mask_ext'], num_classes=config['num_classes'], input_channels=config['input_channels'], transform=val_transform, from_file=None) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=config['num_workers'], drop_last=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=1, #config['batch_size'], shuffle=False, num_workers=config['num_workers'], drop_last=False) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=1, #config['batch_size'], shuffle=False, num_workers=config['num_workers'], drop_last=False) log = OrderedDict([ ('epoch', []), ('lr', []), ('loss', []), ('iou', []), ('dice', []), ('val_loss', []), ('val_iou', []), ('val_dice', []), ]) best_iou = 0 trigger = 0 Best_dice = 0 iou_AtBestDice = 0 for epoch in range(start_epoch, config['epochs']): print('{:s} Epoch [{:d}/{:d}]'.format(config['arch'], epoch, config['epochs'])) # train for one epoch train_log = train(epoch, config, train_loader, model, criterion, optimizer, cnn_optimizer) if config['optimizer'] == 'SGD': if config['scheduler'] == 'CosineAnnealingLR': scheduler.step() elif config['scheduler'] == 'ReduceLROnPlateau': scheduler.step(val_log['loss']) elif config['scheduler'] == 'MultiStepLR': scheduler.step() # evaluate on validation set val_log = validate(config, val_loader, model, criterion) test_log = validate(config, test_loader, model, criterion) if Best_dice < test_log['dice']: Best_dice = test_log['dice'] iou_AtBestDice = test_log['iou'] print( 'loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f - test_iou %.4f - test_dice %.4f - Best_dice %.4f - iou_AtBestDice %.4f' % (train_log['loss'], train_log['iou'], train_log['dice'], val_log['loss'], val_log['iou'], val_log['dice'], test_log['iou'], test_log['dice'], Best_dice, iou_AtBestDice)) save_tensorboard(writer, train_log, val_log, test_log, epoch) log['epoch'].append(epoch) log['lr'].append(config['lr']) log['loss'].append(train_log['loss']) log['iou'].append(train_log['iou']) log['dice'].append(train_log['dice']) log['val_loss'].append(val_log['loss']) log['val_iou'].append(val_log['iou']) log['val_dice'].append(val_log['dice']) pd.DataFrame(log).to_csv(os.path.join(model_folder, '%s/log.csv' % config['name']), index=False) trigger += 1 if val_log['iou'] > best_iou: torch.save( model.state_dict(), os.path.join(model_folder, '%s/model.pth' % config['name'])) best_iou = val_log['iou'] print("=> saved best model") trigger = 0 # early stopping if config['early_stopping'] >= 0 and trigger >= config[ 'early_stopping']: print("=> early stopping") break torch.cuda.empty_cache()