diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst index 45e0827cf9..88fbea7ff0 100644 --- a/docs/source/metrics.rst +++ b/docs/source/metrics.rst @@ -98,7 +98,6 @@ Metrics `Hausdorff distance` -------------------- .. autofunction:: compute_hausdorff_distance -.. autofunction:: compute_percent_hausdorff_distance .. autoclass:: HausdorffDistanceMetric :members: diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py index 721c0db489..d92a79a16a 100644 --- a/monai/apps/deepgrow/transforms.py +++ b/monai/apps/deepgrow/transforms.py @@ -441,8 +441,8 @@ def __call__(self, data): if np.all(np.less(current_size, self.spatial_size)): cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size) - box_start = np.array([s.start for s in cropper.slices]) - box_end = np.array([s.stop for s in cropper.slices]) + box_start = [s.start for s in cropper.slices] + box_end = [s.stop for s in cropper.slices] else: cropper = SpatialCrop(roi_start=box_start, roi_end=box_end) diff --git a/monai/bundle/scripts.py b/monai/bundle/scripts.py index dbfc449edb..2873997290 100644 --- a/monai/bundle/scripts.py +++ b/monai/bundle/scripts.py @@ -31,7 +31,6 @@ from monai._version import get_versions from monai.apps.utils import _basename, download_url, extractall, get_logger -from monai.bundle.config_item import ConfigComponent from monai.bundle.config_parser import ConfigParser from monai.bundle.utils import DEFAULT_INFERENCE, DEFAULT_METADATA, merge_kv from monai.bundle.workflows import BundleWorkflow, ConfigWorkflow @@ -48,7 +47,6 @@ from monai.utils import ( IgniteInfo, check_parent_dir, - deprecated_arg, ensure_tuple, get_equivalent_dtype, min_version, @@ -629,9 +627,6 @@ def download( _check_monai_version(bundle_dir_, name_) -@deprecated_arg("net_name", since="1.2", removed="1.5", msg_suffix="please use ``model`` instead.") -@deprecated_arg("net_kwargs", since="1.2", removed="1.5", msg_suffix="please use ``model`` instead.") -@deprecated_arg("return_state_dict", since="1.2", removed="1.5") def load( name: str, model: torch.nn.Module | None = None, @@ -650,10 +645,7 @@ def load( workflow_name: str | BundleWorkflow | None = None, args_file: str | None = None, copy_model_args: dict | None = None, - return_state_dict: bool = True, net_override: dict | None = None, - net_name: str | None = None, - **net_kwargs: Any, ) -> object | tuple[torch.nn.Module, dict, dict] | Any: """ Load model weights or TorchScript module of a bundle. @@ -699,12 +691,7 @@ def load( workflow_name: specified bundle workflow name, should be a string or class, default to "ConfigWorkflow". args_file: a JSON or YAML file to provide default values for all the args in "download" function. copy_model_args: other arguments for the `monai.networks.copy_model_state` function. - return_state_dict: whether to return state dict, if True, return state_dict, else a corresponding network - from `_workflow.network_def` will be instantiated and load the achieved weights. net_override: id-value pairs to override the parameters in the network of the bundle, default to `None`. - net_name: if not `None`, a corresponding network will be instantiated and load the achieved weights. - This argument only works when loading weights. - net_kwargs: other arguments that are used to instantiate the network class defined by `net_name`. Returns: 1. If `load_ts_module` is `False` and `model` is `None`, @@ -719,9 +706,6 @@ def load( when `model` and `net_name` are all `None`. """ - if return_state_dict and (model is not None or net_name is not None): - warnings.warn("Incompatible values: model and net_name are all specified, return state dict instead.") - bundle_dir_ = _process_bundle_dir(bundle_dir) net_override = {} if net_override is None else net_override copy_model_args = {} if copy_model_args is None else copy_model_args @@ -757,11 +741,8 @@ def load( warnings.warn(f"the state dictionary from {full_path} should be a dictionary but got {type(model_dict)}.") model_dict = get_state_dict(model_dict) - if return_state_dict: - return model_dict - _workflow = None - if model is None and net_name is None: + if model is None: bundle_config_file = bundle_dir_ / name / "configs" / f"{workflow_type}.json" if bundle_config_file.is_file(): _net_override = {f"network_def#{key}": value for key, value in net_override.items()} @@ -781,10 +762,6 @@ def load( return model_dict else: model = _workflow.network_def - elif net_name is not None: - net_kwargs["_target_"] = net_name - configer = ConfigComponent(config=net_kwargs) - model = configer.instantiate() # type: ignore model.to(device) # type: ignore diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py index 75cf7b0b09..e045e82ec5 100644 --- a/monai/bundle/workflows.py +++ b/monai/bundle/workflows.py @@ -27,7 +27,7 @@ from monai.bundle.properties import InferProperties, MetaProperties, TrainProperties from monai.bundle.utils import DEFAULT_EXP_MGMT_SETTINGS, EXPR_KEY, ID_REF_KEY, ID_SEP_KEY from monai.config import PathLike -from monai.utils import BundleProperty, BundlePropertyConfig, deprecated_arg, ensure_tuple +from monai.utils import BundleProperty, BundlePropertyConfig, ensure_tuple __all__ = ["BundleWorkflow", "ConfigWorkflow"] @@ -45,10 +45,6 @@ class BundleWorkflow(ABC): or "infer", "inference", "eval", "evaluation" for a inference workflow, other unsupported string will raise a ValueError. default to `None` for only using meta properties. - workflow: specifies the workflow type: "train" or "training" for a training workflow, - or "infer", "inference", "eval", "evaluation" for a inference workflow, - other unsupported string will raise a ValueError. - default to `None` for common workflow. properties_path: the path to the JSON file of properties. If `workflow_type` is specified, properties will be loaded from the file based on the provided `workflow_type` and meta. If no `workflow_type` is specified, properties will default to loading from "meta". If `properties_path` is None, default properties @@ -65,17 +61,9 @@ class BundleWorkflow(ABC): supported_train_type: tuple = ("train", "training") supported_infer_type: tuple = ("infer", "inference", "eval", "evaluation") - @deprecated_arg( - "workflow", - since="1.2", - removed="1.5", - new_name="workflow_type", - msg_suffix="please use `workflow_type` instead.", - ) def __init__( self, workflow_type: str | None = None, - workflow: str | None = None, properties_path: PathLike | None = None, meta_file: str | Sequence[str] | None = None, logging_file: str | None = None, @@ -102,7 +90,6 @@ def __init__( ) meta_file = None - workflow_type = workflow if workflow is not None else workflow_type if workflow_type is not None: if workflow_type.lower() in self.supported_train_type: workflow_type = "train" @@ -403,10 +390,6 @@ class ConfigWorkflow(BundleWorkflow): or "infer", "inference", "eval", "evaluation" for a inference workflow, other unsupported string will raise a ValueError. default to `None` for common workflow. - workflow: specifies the workflow type: "train" or "training" for a training workflow, - or "infer", "inference", "eval", "evaluation" for a inference workflow, - other unsupported string will raise a ValueError. - default to `None` for common workflow. properties_path: the path to the JSON file of properties. If `workflow_type` is specified, properties will be loaded from the file based on the provided `workflow_type` and meta. If no `workflow_type` is specified, properties will default to loading from "train". If `properties_path` is None, default properties @@ -419,13 +402,6 @@ class ConfigWorkflow(BundleWorkflow): """ - @deprecated_arg( - "workflow", - since="1.2", - removed="1.5", - new_name="workflow_type", - msg_suffix="please use `workflow_type` instead.", - ) def __init__( self, config_file: str | Sequence[str], @@ -436,11 +412,9 @@ def __init__( final_id: str = "finalize", tracking: str | dict | None = None, workflow_type: str | None = "train", - workflow: str | None = None, properties_path: PathLike | None = None, **override: Any, ) -> None: - workflow_type = workflow if workflow is not None else workflow_type if config_file is not None: _config_files = ensure_tuple(config_file) config_root_path = Path(_config_files[0]).parent diff --git a/monai/metrics/__init__.py b/monai/metrics/__init__.py index 7176f3311f..ae20903cfd 100644 --- a/monai/metrics/__init__.py +++ b/monai/metrics/__init__.py @@ -19,7 +19,7 @@ from .fid import FIDMetric, compute_frechet_distance from .froc import compute_fp_tp_probs, compute_fp_tp_probs_nd, compute_froc_curve_data, compute_froc_score from .generalized_dice import GeneralizedDiceScore, compute_generalized_dice -from .hausdorff_distance import HausdorffDistanceMetric, compute_hausdorff_distance, compute_percent_hausdorff_distance +from .hausdorff_distance import HausdorffDistanceMetric, compute_hausdorff_distance from .loss_metric import LossMetric from .meandice import DiceHelper, DiceMetric, compute_dice from .meaniou import MeanIoU, compute_iou diff --git a/monai/metrics/generalized_dice.py b/monai/metrics/generalized_dice.py index 516021949b..05eb94af48 100644 --- a/monai/metrics/generalized_dice.py +++ b/monai/metrics/generalized_dice.py @@ -14,7 +14,7 @@ import torch from monai.metrics.utils import do_metric_reduction, ignore_background -from monai.utils import MetricReduction, Weight, deprecated_arg, deprecated_arg_default, look_up_option +from monai.utils import MetricReduction, Weight, deprecated_arg, look_up_option from .metric import CumulativeIterationMetric @@ -37,6 +37,8 @@ class GeneralizedDiceScore(CumulativeIterationMetric): reduction: Define mode of reduction to the metrics. Available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``, ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction. + Default value is changed from `MetricReduction.MEAN_BATCH` to `MetricReduction.MEAN` in v1.5.0. + Old versions computed `mean` when `mean_batch` was provided due to bug in reduction. weight_type: {``"square"``, ``"simple"``, ``"uniform"``}. Type of function to transform ground truth volume into a weight factor. Defaults to ``"square"``. @@ -44,21 +46,10 @@ class GeneralizedDiceScore(CumulativeIterationMetric): ValueError: When the `reduction` is not one of MetricReduction enum. """ - @deprecated_arg_default( - "reduction", - old_default=MetricReduction.MEAN_BATCH, - new_default=MetricReduction.MEAN, - since="1.4.0", - replaced="1.5.0", - msg_suffix=( - "Old versions computed `mean` when `mean_batch` was provided due to bug in reduction， " - "If you want to retain the old behavior (calculating the mean), please explicitly set the parameter to 'mean'." - ), - ) def __init__( self, include_background: bool = True, - reduction: MetricReduction | str = MetricReduction.MEAN_BATCH, + reduction: MetricReduction | str = MetricReduction.MEAN, weight_type: Weight | str = Weight.SQUARE, ) -> None: super().__init__() diff --git a/monai/metrics/hausdorff_distance.py b/monai/metrics/hausdorff_distance.py index d727eb0567..1b83c93e5b 100644 --- a/monai/metrics/hausdorff_distance.py +++ b/monai/metrics/hausdorff_distance.py @@ -17,18 +17,12 @@ import numpy as np import torch -from monai.metrics.utils import ( - do_metric_reduction, - get_edge_surface_distance, - get_surface_distance, - ignore_background, - prepare_spacing, -) -from monai.utils import MetricReduction, convert_data_type, deprecated +from monai.metrics.utils import do_metric_reduction, get_edge_surface_distance, ignore_background, prepare_spacing +from monai.utils import MetricReduction, convert_data_type from .metric import CumulativeIterationMetric -__all__ = ["HausdorffDistanceMetric", "compute_hausdorff_distance", "compute_percent_hausdorff_distance"] +__all__ = ["HausdorffDistanceMetric", "compute_hausdorff_distance"] class HausdorffDistanceMetric(CumulativeIterationMetric): @@ -216,31 +210,3 @@ def _compute_percentile_hausdorff_distance( if 0 <= percentile <= 100: return torch.quantile(surface_distance, percentile / 100) raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.") - - -@deprecated(since="1.3.0", removed="1.5.0") -def compute_percent_hausdorff_distance( - edges_pred: np.ndarray, - edges_gt: np.ndarray, - distance_metric: str = "euclidean", - percentile: float | None = None, - spacing: int | float | np.ndarray | Sequence[int | float] | None = None, -) -> float: - """ - This function is used to compute the directed Hausdorff distance. - """ - - surface_distance: np.ndarray = get_surface_distance( # type: ignore - edges_pred, edges_gt, distance_metric=distance_metric, spacing=spacing - ) - - # for both pred and gt do not have foreground - if surface_distance.shape == (0,): - return np.nan - - if not percentile: - return surface_distance.max() # type: ignore[no-any-return] - - if 0 <= percentile <= 100: - return np.percentile(surface_distance, percentile) # type: ignore[no-any-return] - raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.") diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py index 96d60c9098..972ec0061e 100644 --- a/monai/metrics/utils.py +++ b/monai/metrics/utils.py @@ -30,7 +30,6 @@ convert_to_numpy, convert_to_tensor, deprecated_arg, - deprecated_arg_default, ensure_tuple_rep, look_up_option, optional_import, @@ -131,9 +130,6 @@ def do_metric_reduction( return f, not_nans -@deprecated_arg_default( - name="always_return_as_numpy", since="1.3.0", replaced="1.5.0", old_default=True, new_default=False -) @deprecated_arg( name="always_return_as_numpy", since="1.5.0", @@ -146,7 +142,7 @@ def get_mask_edges( label_idx: int = 1, crop: bool = True, spacing: Sequence | None = None, - always_return_as_numpy: bool = True, + always_return_as_numpy: bool = False, ) -> tuple[NdarrayTensor, NdarrayTensor]: """ Compute edges from binary segmentation masks. This @@ -175,6 +171,7 @@ def get_mask_edges( otherwise `scipy`'s binary erosion is used to calculate the edges. always_return_as_numpy: whether to a numpy array regardless of the input type. If False, return the same type as inputs. + The default value is changed from `True` to `False` in v1.5.0. """ # move in the funciton to avoid using all the GPUs cucim_binary_erosion, has_cucim_binary_erosion = optional_import("cucim.skimage.morphology", name="binary_erosion") diff --git a/monai/networks/nets/swin_unetr.py b/monai/networks/nets/swin_unetr.py index 22e1e6f659..4566a96856 100644 --- a/monai/networks/nets/swin_unetr.py +++ b/monai/networks/nets/swin_unetr.py @@ -25,7 +25,6 @@ from monai.networks.blocks import PatchEmbed, UnetOutBlock, UnetrBasicBlock, UnetrUpBlock from monai.networks.layers import DropPath, trunc_normal_ from monai.utils import ensure_tuple_rep, look_up_option, optional_import -from monai.utils.deprecate_utils import deprecated_arg rearrange, _ = optional_import("einops", name="rearrange") @@ -50,16 +49,8 @@ class SwinUNETR(nn.Module): " """ - @deprecated_arg( - name="img_size", - since="1.3", - removed="1.5", - msg_suffix="The img_size argument is not required anymore and " - "checks on the input size are run during forward().", - ) def __init__( self, - img_size: Sequence[int] | int, in_channels: int, out_channels: int, patch_size: int = 2, @@ -83,10 +74,6 @@ def __init__( ) -> None: """ Args: - img_size: spatial dimension of input image. - This argument is only used for checking that the input image size is divisible by the patch size. - The tensor passed to forward() can have a dynamic shape as long as its spatial dimensions are divisible by 2**5. - It will be removed in an upcoming version. in_channels: dimension of input channels. out_channels: dimension of output channels. patch_size: size of the patch token. @@ -113,13 +100,13 @@ def __init__( Examples:: # for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48. - >>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48) + >>> net = SwinUNETR(in_channels=1, out_channels=4, feature_size=48) # for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage. - >>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2)) + >>> net = SwinUNETR(in_channels=4, out_channels=3, depths=(2,4,2,2)) # for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing. - >>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2) + >>> net = SwinUNETR(in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2) """ @@ -130,12 +117,9 @@ def __init__( self.patch_size = patch_size - img_size = ensure_tuple_rep(img_size, spatial_dims) patch_sizes = ensure_tuple_rep(self.patch_size, spatial_dims) window_size = ensure_tuple_rep(window_size, spatial_dims) - self._check_input_size(img_size) - if not (0 <= drop_rate <= 1): raise ValueError("dropout rate should be between 0 and 1.") @@ -1109,7 +1093,7 @@ def filter_swinunetr(key, value): from monai.networks.utils import copy_model_state from monai.networks.nets.swin_unetr import SwinUNETR, filter_swinunetr - model = SwinUNETR(img_size=(96, 96, 96), in_channels=1, out_channels=3, feature_size=48) + model = SwinUNETR(in_channels=1, out_channels=3, feature_size=48) resource = ( "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/ssl_pretrained_weights.pth" ) diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py index d5ca876e98..fc0bafbcc4 100644 --- a/monai/transforms/croppad/array.py +++ b/monai/transforms/croppad/array.py @@ -51,7 +51,6 @@ TransformBackends, convert_data_type, convert_to_tensor, - deprecated_arg_default, ensure_tuple, ensure_tuple_rep, fall_back_tuple, @@ -809,13 +808,12 @@ def threshold_at_one(x): """ - @deprecated_arg_default("allow_smaller", old_default=True, new_default=False, since="1.2", replaced="1.5") def __init__( self, select_fn: Callable = is_positive, channel_indices: IndexSelection | None = None, margin: Sequence[int] | int = 0, - allow_smaller: bool = True, + allow_smaller: bool = False, return_coords: bool = False, k_divisible: Sequence[int] | int = 1, mode: str = PytorchPadMode.CONSTANT, @@ -830,7 +828,8 @@ def __init__( margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims. allow_smaller: when computing box size with `margin`, whether to allow the image edges to be smaller than the final box edges. If `False`, part of a padded output box might be outside of the original image, if `True`, - the image edges will be used as the box edges. Default to `True`. + the image edges will be used as the box edges. Default to `False`. + The default value is changed from `True` to `False` in v1.5.0. return_coords: whether return the coordinates of spatial bounding box for foreground. k_divisible: make each spatial dimension to be divisible by k, default to 1. if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions. diff --git a/monai/transforms/croppad/dictionary.py b/monai/transforms/croppad/dictionary.py index cea11d9676..462412686a 100644 --- a/monai/transforms/croppad/dictionary.py +++ b/monai/transforms/croppad/dictionary.py @@ -50,7 +50,7 @@ from monai.transforms.traits import LazyTrait, MultiSampleTrait from monai.transforms.transform import LazyTransform, MapTransform, Randomizable from monai.transforms.utils import is_positive -from monai.utils import MAX_SEED, Method, PytorchPadMode, deprecated_arg_default, ensure_tuple_rep +from monai.utils import MAX_SEED, Method, PytorchPadMode, ensure_tuple_rep __all__ = [ "Padd", @@ -716,7 +716,6 @@ class CropForegroundd(Cropd): for more information. """ - @deprecated_arg_default("allow_smaller", old_default=True, new_default=False, since="1.2", replaced="1.5") def __init__( self, keys: KeysCollection, @@ -724,7 +723,7 @@ def __init__( select_fn: Callable = is_positive, channel_indices: IndexSelection | None = None, margin: Sequence[int] | int = 0, - allow_smaller: bool = True, + allow_smaller: bool = False, k_divisible: Sequence[int] | int = 1, mode: SequenceStr = PytorchPadMode.CONSTANT, start_coord_key: str | None = "foreground_start_coord", @@ -744,7 +743,8 @@ def __init__( margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims. allow_smaller: when computing box size with `margin`, whether to allow the image edges to be smaller than the final box edges. If `False`, part of a padded output box might be outside of the original image, if `True`, - the image edges will be used as the box edges. Default to `True`. + the image edges will be used as the box edges. Default to `False`. + The default value is changed from `True` to `False` in v1.5.0. k_divisible: make each spatial dimension to be divisible by k, default to 1. if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions. mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 1ff0abc27c..be5fdd512b 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -58,7 +58,6 @@ SplineMode, TraceKeys, TraceStatusKeys, - deprecated_arg_default, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, @@ -1067,13 +1066,12 @@ def _create_translate( return array_func(affine) # type: ignore -@deprecated_arg_default("allow_smaller", old_default=True, new_default=False, since="1.2", replaced="1.5") def generate_spatial_bounding_box( img: NdarrayOrTensor, select_fn: Callable = is_positive, channel_indices: IndexSelection | None = None, margin: Sequence[int] | int = 0, - allow_smaller: bool = True, + allow_smaller: bool = False, ) -> tuple[list[int], list[int]]: """ Generate the spatial bounding box of foreground in the image with start-end positions (inclusive). @@ -1093,8 +1091,9 @@ def generate_spatial_bounding_box( of image. if None, select foreground on the whole image. margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims. allow_smaller: when computing box size with `margin`, whether to allow the image edges to be smaller than the - final box edges. If `True`, the bounding boxes edges are aligned with the input image edges, if `False`, - the bounding boxes edges are aligned with the final box edges. Default to `True`. + final box edges. If `True`, the bounding boxes edges are aligned with the input image edges, if `False`, + the bounding boxes edges are aligned with the final box edges. Default to `False`. + The default value is changed from `True` to `False` in v1.5.0. """ check_non_lazy_pending_ops(img, name="generate_spatial_bounding_box") diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 866ca26f40..369333f122 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -546,7 +546,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "4c18daf") + return os.environ.get("MONAI_ALGO_HASH", "21ed8e5") @staticmethod def trace_transform() -> str | None: diff --git a/tests/networks/nets/test_swin_unetr.py b/tests/networks/nets/test_swin_unetr.py index 2c4532ecc4..d321c777ba 100644 --- a/tests/networks/nets/test_swin_unetr.py +++ b/tests/networks/nets/test_swin_unetr.py @@ -51,7 +51,6 @@ "spatial_dims": len(img_size), "in_channels": in_channels, "out_channels": out_channels, - "img_size": img_size, "feature_size": feature_size, "depths": depth, "norm_name": norm_name, @@ -67,7 +66,7 @@ TEST_CASE_FILTER = [ [ - {"img_size": (96, 96, 96), "in_channels": 1, "out_channels": 14, "feature_size": 48, "use_checkpoint": True}, + {"in_channels": 1, "out_channels": 14, "feature_size": 48, "use_checkpoint": True}, "swinViT.layers1.0.blocks.0.norm1.weight", torch.tensor([0.9473, 0.9343, 0.8566, 0.8487, 0.8065, 0.7779, 0.6333, 0.5555]), ] @@ -85,30 +84,13 @@ def test_shape(self, input_param, input_shape, expected_shape): def test_ill_arg(self): with self.assertRaises(ValueError): - SwinUNETR( - in_channels=1, - out_channels=3, - img_size=(128, 128, 128), - feature_size=24, - norm_name="instance", - attn_drop_rate=4, - ) + SwinUNETR(spatial_dims=1, in_channels=1, out_channels=2, feature_size=48, norm_name="instance") with self.assertRaises(ValueError): - SwinUNETR(in_channels=1, out_channels=2, img_size=(96, 96), feature_size=48, norm_name="instance") + SwinUNETR(in_channels=1, out_channels=4, feature_size=50, norm_name="instance") with self.assertRaises(ValueError): - SwinUNETR(in_channels=1, out_channels=4, img_size=(96, 96, 96), feature_size=50, norm_name="instance") - - with self.assertRaises(ValueError): - SwinUNETR( - in_channels=1, - out_channels=3, - img_size=(85, 85, 85), - feature_size=24, - norm_name="instance", - drop_rate=0.4, - ) + SwinUNETR(in_channels=1, out_channels=3, feature_size=24, norm_name="instance", drop_rate=-1) def test_patch_merging(self): dim = 10