Meet "RuntimeError: dictionary changed size during iteration " in extract_composition_features() function #738
Description
- TIA Toolbox version: 1.4.1
- Python version: 3.11
- Operating System: Ubuntu 22.04
Description
I try to run tutorial at https://tia-toolbox.readthedocs.io/en/latest/_notebooks/jnb/inference-pipelines/slide-graph.html#cell-composition-extraction (I use my wsi file rather than your example file), but meet following error. By the way I can run nucleus instance segmentor tutorial successfully.
What I Did
################ func from tutorial ############
def get_cell_compositions(
wsi_path: str,
mask_path: str,
inst_pred_path: str,
save_dir: str,
num_types: int = 6,
patch_input_shape: tuple[int] = (512, 512),
stride_shape: tuple[int] = (512, 512),
resolution: float = 0.25,
units: str = "mpp",
) -> None:
r"""
Estimates cellular composition.
"""
reader = WSIReader.open(wsi_path)
inst_pred = joblib.load(inst_pred_path)
# Convert to {key: int, value: dict}
inst_pred = {i: v for i, (_, v) in enumerate(inst_pred.items())}
inst_boxes = [v["box"] for v in inst_pred.values()]
inst_boxes = np.array(inst_boxes)
geometries = [shapely_box(*bounds) for bounds in inst_boxes]
spatial_indexer = STRtree(geometries)
# * Generate patch coordinates (in xy format)
wsi_shape = reader.slide_dimensions(resolution=resolution, units=units)
(patch_inputs, _) = PatchExtractor.get_coordinates(
image_shape=wsi_shape,
patch_input_shape=patch_input_shape,
patch_output_shape=patch_input_shape,
stride_shape=stride_shape,
)
# Filter out coords which dont lie in mask
selected_coord_indices = PatchExtractor.filter_coordinates(
WSIReader.open(mask_path),
patch_inputs,
wsi_shape=wsi_shape,
min_mask_ratio=0.5,
)
patch_inputs = patch_inputs[selected_coord_indices]
bounds_compositions = []
for bounds in patch_inputs:
bounds_ = shapely_box(*bounds)
indices = [
geo
for geo in spatial_indexer.query(bounds_)
if bounds_.contains(geometries[geo])
]
insts = [inst_pred[v]["type"] for v in indices]
uids, freqs = np.unique(insts, return_counts=True)
# A bound may not contain all types, hence, to sync
# the array and placement across all types, we create
# a holder then fill the count within.
holder = np.zeros(num_types, dtype=np.int16)
holder[uids.astype(int)] = freqs
bounds_compositions.append(holder)
bounds_compositions = np.array(bounds_compositions)
base_name = Path(wsi_path).stem
# Output in the same saving protocol for construct graph
np.save(f"{save_dir}/{base_name}.position.npy", patch_inputs)
np.save(f"{save_dir}/{base_name}.features.npy", bounds_compositions)
def extract_composition_features(
wsi_paths: list[str],
save_dir: str,
preproc_func: Callable = None,
msk_paths: list[str] = None,
) -> list:
r"""
"""
inst_segmentor = NucleusInstanceSegmentor(
pretrained_model="hovernet_fast-pannuke",
batch_size=16,
num_postproc_workers=4,
num_loader_workers=4,
auto_generate_mask=True,
)
# bigger tile shape for postprocessing performance
inst_segmentor.ioconfig.tile_shape = (4000, 4000)
# Injecting customized preprocessing functions,
# check the document or sample codes below for API
inst_segmentor.model.preproc_func = preproc_func
if Path(save_dir).is_dir():
shutil.rmtree(save_dir)
output_map_list = inst_segmentor.predict(
wsi_paths,
msk_paths,
mode="wsi",
on_gpu=torch.cuda.is_available(),
crash_on_exception=True,
save_dir=save_dir,
)
# Rename output files of toolbox
output_paths = []
for input_path, output_path in output_map_list:
input_name = Path(input_path).stem
output_parent_dir = Path(output_path).parent
src_path = Path(f"{output_path}.dat")
new_path = Path(f"{output_parent_dir}/{input_name}.dat")
src_path.rename(new_path)
output_paths.append(new_path)
# TODO(TBC): Parallelize this if possible # noqa: TD003, FIX002
for idx, path in enumerate(output_paths):
get_cell_compositions(wsi_paths[idx], msk_paths[idx], path, save_dir)
return output_paths
################## run ###################################
wsi_paths = "./experiments/CMU-1.tif"
save_dir= 'tmp/test'
meta = extract_composition_features(wsi_paths, save_dir)Error message
File "conda/lib/python3.11/site-packages/tiatoolbox/models/engine/semantic_segmentor.py", line 1328, in predict │·························
self._predict_wsi_handle_exception( │·························
File "/conda/lib/python3.11/site-packages/tiatoolbox/models/engine/semantic_segmentor.py", line 1180, in _predict_wsi_handle_exception │·························
raise err │·························
File "conda/lib/python3.11/site-packages/tiatoolbox/models/engine/semantic_segmentor.py", line 1156, in _predict_wsi_handle_exception │·························
self._predict_one_wsi(wsi_idx, ioconfig, str(wsi_save_path), mode) │·························
File "conda/lib/python3.11/site-packages/tiatoolbox/models/engine/nucleus_instance_segmentor.py", line 737, in _predict_one_wsi │·························
self._merge_post_process_results() │·························
File "conda/lib/python3.11/site-packages/tiatoolbox/models/engine/nucleus_instance_segmentor.py", line 784, in _merge_post_process_results │·························
raise future.exception() │·························
File "conda/lib/python3.11/multiprocessing/queues.py", line 244, in _feed │·························
obj = _ForkingPickler.dumps(obj) │·························
^^^^^^^^^^^^^^^^^^^^^^^^^^ │·························
File "conda/lib/python3.11/multiprocessing/reduction.py", line 51, in dumps │·························
cls(buf, protocol).dump(obj) Metadata
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