num_classes = 2 #################### Dataflow #################### assert "INPUT_PATH" in os.environ data_path = os.path.join(os.environ['INPUT_PATH'], "train_tiles") csv_path = os.path.join(data_path, "tile_stats.csv") train_folds = [0, 1, 3] val_folds = [ 2, ] train_ds, val_ds = get_trainval_datasets(data_path, csv_path, train_folds=train_folds, val_folds=val_folds) batch_size = 32 num_workers = 12 mean = (0.0, 0.0, 0.0) std = (5.0, 5.0, 5.0) max_value = 1.0 transforms = A.Compose( [A.Normalize(mean=mean, std=std, max_pixel_value=max_value), ToTensorV2()]) _, data_loader, _ = get_train_val_loaders( train_ds,
start_by_validation = False #################### Dataflow #################### assert "INPUT_PATH" in os.environ data_path = os.path.join(os.environ['INPUT_PATH'], "train_tiles") csv_path = os.path.join(data_path, "tile_stats.csv") train_folds = [0, 1, 3] val_folds = [ 2, ] train_ds, val_ds = get_trainval_datasets( data_path, csv_path, train_folds=train_folds, val_folds=val_folds, read_img_mask_fn=read_img_5b_in_db_with_mask) train_sampler = get_train_sampler(train_ds, weight_per_class=(0.5, 0.5)) # ! This wont work in distributed ! # mean, std = get_train_mean_std(train_ds, unique_id="3b_in_db") # print("Computed mean/std: {} / {}".format(mean, std)) mean = [ -17.704988005545587, -10.33310725243658, -12.422949109368183, 213.3866453581477, 0.4748089840110086 ] std = [ 6.5437130712772795, 6.033536195001276, 6.063934363438651, 245.40096009414592, 238.8577452846451 ]
start_by_validation = False #################### Dataflow #################### assert "INPUT_PATH" in os.environ data_path = os.path.join(os.environ['INPUT_PATH'], "train_tiles") csv_path = os.path.join(data_path, "tile_stats.csv") train_folds = [0, 1, 3] val_folds = [ 2, ] train_ds, val_ds = get_trainval_datasets(data_path, csv_path, train_folds=train_folds, val_folds=val_folds, read_img_mask_fn=read_nimg_sqrt_mask) train_sampler = get_train_sampler(train_ds, weight_per_class=(0.7, 0.3)) batch_size = 22 num_workers = 12 val_batch_size = 20 # According to https://arxiv.org/pdf/1906.06423.pdf # For example: Train size: 224 -> Test size: 320 = max accuracy on ImageNet with ResNet-50 val_img_size = 512 train_img_size = 480 train_transforms = A.Compose([