raw_dataset = BinaryGT(BraTS2013(BRATS_PATH), positive_classes=CONFIG['positive_classes']) dataset = cache_methods(ZooOfSpacings( apply(CropToBrain(raw_dataset), load_image=partial(min_max_scale, axes=0)), slice_spacings=CONFIG['slice_spacings'] )) # cross validation split = load_json(SPLIT_PATH) train_ids, val_ids, test_ids = split[int(FOLD)] # batch iterator batch_iter = Infinite( load_by_random_id(dataset.load_image, dataset.load_gt, ids=train_ids), unpack_args(get_random_slice), random_apply(.5, unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))), apply_at(1, prepend_dims), apply_at(1, np.float32), batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch'], combiner=combine_pad ) # model model = nn.Sequential( nn.Conv2d(dataset.n_modalities, 8, kernel_size=3, padding=1), layers.FPN( layers.ResBlock2d, downsample=nn.MaxPool2d(2, ceil_mode=True), upsample=nn.Identity, merge=lambda left, down: torch.cat(layers.interpolate_to_left(left, down, 'bilinear'), dim=1), structure=[ [[8, 8, 8], [16, 8, 8]], [[8, 16, 16], [32, 16, 8]], [[16, 32, 32], [64, 32, 16]], [[32, 64, 64], [128, 64, 32]],
# cross validation split = load_json(SPLIT_PATH) train_ids, val_ids, test_ids = split[int(FOLD)] # batch iterator batch_iter = Infinite( load_by_random_id(train_dataset.load_image, train_dataset.load_spacing, train_dataset.load_gt, ids=train_ids), unpack_args(get_random_slices, n_slices=CONFIG['n_slices']), random_apply( .5, unpack_args(lambda image, spacing, gt: (np.flip(image, 1), spacing, np.flip(gt, 0)))), apply_at(1, np.float32), batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch'], combiner=combine_pad) # model unet_2d = nn.Sequential( nn.Conv2d(dataset.n_modalities, 8, kernel_size=3, padding=1), layers.FPN(layers.ResBlock2d, downsample=nn.MaxPool2d(2, ceil_mode=True), upsample=nn.Identity, merge=lambda left, down: torch.cat( layers.interpolate_to_left(left, down, 'bilinear'), dim=1), structure=[[[8, 8, 8], [16, 8, 8]], [[8, 16, 16], [32, 16, 8]], [[16, 32, 32], [64, 32, 16]], [[32, 64, 64], [128, 64, 32]],
# cross validation split = load_json(SPLIT_PATH) train_ids, val_ids, test_ids = split[int(FOLD)] # batch iterator batch_iter = Infinite( load_by_random_id(dataset.load_image, dataset.load_spacing, dataset.load_gt, ids=train_ids), unpack_args(get_random_slices, n_slices=CONFIG['n_slices']), random_apply( .5, unpack_args(lambda image, spacing, gt: (np.flip(image, 1), spacing, np.flip(gt, 0)))), apply_at(2, prepend_dims), apply_at([1, 2], np.float32), batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch'], combiner=combine_pad) # model unet_2d = nn.Sequential( nn.Conv2d(dataset.n_modalities, 8, kernel_size=3, padding=1), layers.FPN(layers.ResBlock2d, downsample=nn.MaxPool2d(2, ceil_mode=True), upsample=nn.Identity, merge=lambda left, down: torch.cat( layers.interpolate_to_left(left, down, 'bilinear'), dim=1), structure=[[[8, 8, 8], [16, 8, 8]], [[8, 16, 16], [32, 16, 8]], [[16, 32, 32], [64, 32, 16]],
def wrapper(binary_gt_and_spacing, proba): return metric( apply_at(0, np.bool_)(binary_gt_and_spacing), proba >= .5)