def test_load_dataset_2d_png(download_data_testing_test_files,
loader_parameters, model_parameters,
transform_parameters):
"""
Test to make sure load_dataset runs with 2D PNG files, writes corresponding NIfTI files,
and binarizes ground-truth values to 0 and 1.
"""
loader_parameters.update({LoaderParamsKW.MODEL_PARAMS: model_parameters})
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
data_lst = ['sub-rat3_ses-01_sample-data9_SEM.png']
ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
fname_png = bids_df.df[bids_df.df['filename'] ==
data_lst[0]]['path'].values[0]
fname_nii = imed_loader_utils.update_filename_to_nifti(fname_png)
assert Path(fname_nii).exists() == 1
assert ds[0]['input'].shape == (1, 756, 764)
assert ds[0]['gt'].shape == (1, 756, 764)
assert np.unique(ds[0]['gt']).tolist() == [0, 1]
def test_get_target_filename_list_multiple_raters(loader_parameters,
model_parameters,
transform_parameters):
"""
Test that all target_suffix are considered for target filename when list
"""
loader_parameters.update({LoaderParamsKW.MODEL_PARAMS: model_parameters})
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
data_lst = ['sub-rat3_ses-01_sample-data9_SEM.png']
test_ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
target_filename = test_ds.filename_pairs[0][1]
assert len(target_filename) == len(
loader_parameters[LoaderParamsKW.TARGET_SUFFIX])
assert len(target_filename[0]) == len(
loader_parameters[LoaderParamsKW.TARGET_SUFFIX][0])
assert len(target_filename[1]) == len(
loader_parameters[LoaderParamsKW.TARGET_SUFFIX][1])
def test_bids_df_microscopy_png(download_data_testing_test_files,
loader_parameters):
"""
Test for microscopy png file format
Test for _sessions.tsv and _scans.tsv files
Test for target_suffix as a nested list
Test for when no contrast_params are provided
"""
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
df_test = bids_df.df.drop(columns=['path'])
df_test = df_test.sort_values(by=['filename']).reset_index(drop=True)
csv_ref = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_ref.csv")
csv_test = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_test.csv")
df_test.to_csv(csv_test, index=False)
diff = csv_diff.compare(csv_diff.load_csv(open(csv_ref)),
csv_diff.load_csv(open(csv_test)))
assert diff == {
'added': [],
'removed': [],
'changed': [],
'columns_added': [],
'columns_removed': []
}
def test_bids_df_anat(download_data_testing_test_files, loader_parameters):
"""
Test for MRI anat nii.gz file format
Test for when no file extensions are provided
Test for multiple target_suffix
Test behavior when "roi_suffix" is not None
"""
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
df_test = bids_df.df.drop(columns=['path'])
df_test = df_test.sort_values(by=['filename']).reset_index(drop=True)
csv_ref = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_ref.csv")
csv_test = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_test.csv")
df_test.to_csv(csv_test, index=False)
diff = csv_diff.compare(csv_diff.load_csv(open(csv_ref)),
csv_diff.load_csv(open(csv_test)))
assert diff == {
'added': [],
'removed': [],
'changed': [],
'columns_added': [],
'columns_removed': []
}
def test_bids_df_no_validate(download_data_testing_test_files,
loader_parameters):
"""
Test for ct-scan nii.gz file format
Test for when validate_BIDS is set to False for the loader
"""
# Rename files so the loader won't pick them up if validate_BIDS is true
Path(loader_parameters[LoaderParamsKW.PATH_DATA][0], "sub-spleen2").rename(
Path(loader_parameters[LoaderParamsKW.PATH_DATA][0], "ssub-spleen2"))
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
df_test = bids_df.df.drop(columns=['path'])
df_test = df_test.sort_values(by=['filename']).reset_index(drop=True)
csv_ref = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_ref.csv")
csv_test = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_test.csv")
df_test.to_csv(csv_test, index=False)
diff = csv_diff.compare(csv_diff.load_csv(open(csv_ref)),
csv_diff.load_csv(open(csv_test)))
Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"ssub-spleen2").rename(
Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"sub-spleen2"))
assert diff == {
'added': [],
'removed': [],
'changed': [],
'columns_added': [],
'columns_removed': []
}
def split_dataset(initial_config):
"""
Args:
initial_config (dict): The original config file, which we use as a basis from which
to modify our hyperparameters.
.. code-block:: JSON
{
"training_parameters": {
"batch_size": 18,
"loss": {"name": "DiceLoss"}
},
"default_model": {
"name": "Unet",
"dropout_rate": 0.3,
"depth": 3
},
"model_name": "seg_tumor_t2",
"path_output": "./tmp/"
}
"""
loader_parameters = initial_config[ConfigKW.LOADER_PARAMETERS]
path_output = Path(initial_config[ConfigKW.PATH_OUTPUT])
if not path_output.is_dir():
logger.info(f'Creating output path: {path_output}')
path_output.mkdir(parents=True)
else:
logger.info(f'Output path already exists: {path_output}')
bids_df = BidsDataframe(loader_parameters,
str(path_output),
derivatives=True)
train_lst, valid_lst, test_lst = imed_loader_utils.get_new_subject_file_split(
df=bids_df.df,
data_testing=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.DATA_TESTING],
split_method=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.SPLIT_METHOD],
random_seed=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.RANDOM_SEED],
train_frac=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.TRAIN_FRACTION],
test_frac=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.TEST_FRACTION],
path_output="./",
balance=initial_config[ConfigKW.SPLIT_DATASET][SplitDatasetKW.BALANCE] \
if SplitDatasetKW.BALANCE in initial_config[ConfigKW.SPLIT_DATASET] else None
)
# save the subject distribution
split_dct = {'train': train_lst, 'valid': valid_lst, 'test': test_lst}
split_path = "./" + "common_split_datasets.joblib"
joblib.dump(split_dct, split_path)
initial_config[ConfigKW.SPLIT_DATASET][
SplitDatasetKW.FNAME_SPLIT] = split_path
return initial_config
def test_slice_filter(download_data_testing_test_files, transforms_dict,
train_lst, target_lst, roi_params, slice_filter_params):
if "ROICrop" in transforms_dict and roi_params["suffix"] is None:
return
cuda_available, device = imed_utils.define_device(GPU_ID)
loader_params = {
"transforms_params": transforms_dict,
"data_list": train_lst,
"dataset_type": "training",
"requires_undo": False,
"contrast_params": {
"contrast_lst": ['T2w'],
"balance": {}
},
"path_data": [__data_testing_dir__],
"target_suffix": target_lst,
"extensions": [".nii.gz"],
"roi_params": roi_params,
"model_params": {
"name": "Unet"
},
"slice_filter_params": slice_filter_params,
"patch_filter_params": {
"filter_empty_mask": False,
"filter_empty_input": False
},
"slice_axis": "axial",
"multichannel": False
}
# Get Training dataset
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds_train = imed_loader.load_dataset(bids_df, **loader_params)
logger.info(f"\tNumber of loaded slices: {len(ds_train)}")
train_loader = DataLoader(ds_train,
batch_size=BATCH_SIZE,
shuffle=True,
pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
cmpt_neg, cmpt_pos = _cmpt_slice(train_loader)
if slice_filter_params["filter_empty_mask"]:
assert cmpt_neg == 0
assert cmpt_pos != 0
else:
# We verify if there are still some negative slices (they are removed with our filter)
assert cmpt_neg != 0 and cmpt_pos != 0
logger.info(f"\tNumber of Neg/Pos slices in GT: {cmpt_neg/cmpt_pos}")
def test_read_png_tif(download_data_testing_test_files, loader_parameters,
model_parameters):
"""
Test to make sure all combinaitions of PNG/TIF, 8/16 bits, Grayscale/RGB/RGBA files
can be loaded without errors.
"""
metadata = {}
metadata[MetadataKW.PIXEL_SIZE] = [0.07, 0.07]
metadata[MetadataKW.PIXEL_SIZE_UNITS] = "um"
loader_parameters.update({LoaderParamsKW.MODEL_PARAMS: model_parameters})
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=False)
file_lst = bids_df.df['path'].tolist()
filename_pairs = [(file_lst, None, None,
metadata if isinstance(metadata, list) else [metadata])]
slice_axis = imed_utils.AXIS_DCT[loader_parameters[
LoaderParamsKW.SLICE_AXIS]]
ds = imed_loader_mri2dseg.MRI2DSegmentationDataset(filename_pairs,
slice_axis=slice_axis,
nibabel_cache=True,
transform=[None, None],
slice_filter_fn=None)
ds.load_filenames()
def test_bids_df_multi(download_data_testing_test_files, loader_parameters):
"""
Test for multiple folders in path_data
"""
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
df_test = bids_df.df.drop(columns=['path'])
df_test = df_test.sort_values(by=['filename']).reset_index(drop=True)
csv_ref = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_ref_multi.csv")
csv_test = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_test_multi.csv")
df_test.to_csv(csv_test, index=False)
diff = csv_diff.compare(csv_diff.load_csv(open(csv_ref)),
csv_diff.load_csv(open(csv_test)))
assert diff == {
'added': [],
'removed': [],
'changed': [],
'columns_added': [],
'columns_removed': []
}
def test_bids_df_ctscan(download_data_testing_test_files, loader_parameters):
"""
Test for ct-scan nii.gz file format
Test for when dataset_description.json is not present in derivatives folder
"""
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
df_test = bids_df.df.drop(columns=['path'])
df_test = df_test.sort_values(by=['filename']).reset_index(drop=True)
csv_ref = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_ref.csv")
csv_test = Path(loader_parameters[LoaderParamsKW.PATH_DATA][0],
"df_test.csv")
df_test.to_csv(csv_test, index=False)
diff = csv_diff.compare(csv_diff.load_csv(open(csv_ref)),
csv_diff.load_csv(open(csv_test)))
assert diff == {
'added': [],
'removed': [],
'changed': [],
'columns_added': [],
'columns_removed': []
}
def test_config_sha256(download_data_testing_test_files, initial_config):
file_lst = ["sub-unf01_T2w.nii.gz"]
loader_params = {
"transforms_params": {},
"data_list": ['sub-unf01'],
"dataset_type": "testing",
"requires_undo": True,
"contrast_params": {"contrast_lst": ['T2w'], "balance": {}},
"path_data": [__data_testing_dir__],
"target_suffix": ["_lesion-manual"],
"extensions": [".nii.gz"],
"roi_params": {"suffix": "_seg-manual", "slice_filter_roi": 10},
"slice_filter_params": {
"filter_empty_mask": False,
"filter_empty_input": True
},
"slice_axis": "axial",
"multichannel": False
}
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
generate_sha_256(initial_config, bids_df.df, file_lst)
assert(initial_config['training_sha256']['sub-unf01_T2w.nii.gz'] ==
'f020b368fea15399fa112badd28b2df69e044dba5d23b3fe1646d12d7d3d39ac')
def test_2d_patches_and_resampling(download_data_testing_test_files,
loader_parameters, model_parameters,
transform_parameters):
"""
Test that 2d patching is done properly.
Test that microscopy pixelsize and resampling are applied on the right dimensions.
"""
loader_parameters.update({LoaderParamsKW.MODEL_PARAMS: model_parameters})
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
data_lst = ['sub-rat3_ses-01_sample-data9_SEM.png']
ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
assert ds.is_2d_patch == True
assert ds[0]['input'].shape == (1, 256, 128)
assert ds[0]['input_metadata'][0].metadata[MetadataKW.INDEX_SHAPE] == (
1512, 382)
assert len(ds) == 28
def test_patch_filter(download_data_testing_test_files, transforms_dict,
train_lst, target_lst, patch_filter_params,
dataset_type):
cuda_available, device = imed_utils.define_device(GPU_ID)
loader_params = {
"transforms_params": transforms_dict,
"data_list": train_lst,
"dataset_type": dataset_type,
"requires_undo": False,
"contrast_params": {
"contrast_lst": ['SEM'],
"balance": {}
},
"path_data": [os.path.join(__data_testing_dir__, "microscopy_png")],
"bids_config": f"{path_repo_root}/ivadomed/config/config_bids.json",
"target_suffix": target_lst,
"extensions": [".png"],
"roi_params": {
"suffix": None,
"slice_filter_roi": None
},
"model_params": {
"name": "Unet",
"length_2D": [32, 32],
"stride_2D": [32, 32]
},
"slice_filter_params": {
"filter_empty_mask": False,
"filter_empty_input": False
},
"patch_filter_params": patch_filter_params,
"slice_axis": "axial",
"multichannel": False
}
# Get Training dataset
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds = imed_loader.load_dataset(bids_df, **loader_params)
logger.info(f"\tNumber of loaded patches: {len(ds)}")
loader = DataLoader(ds,
batch_size=BATCH_SIZE,
shuffle=True,
pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
logger.info("\tNumber of Neg/Pos patches in GT.")
cmpt_neg, cmpt_pos = _cmpt_slice(loader)
if patch_filter_params["filter_empty_mask"]:
if dataset_type == "testing":
# Filters on patches are not applied at testing time
assert cmpt_neg + cmpt_pos == len(ds)
else:
# Filters on patches are applied at training time
assert cmpt_neg == 0
assert cmpt_pos != 0
else:
# We verify if there are still some negative patches (they are removed with our filter)
assert cmpt_neg != 0 and cmpt_pos != 0
def test_bounding_box(download_data_testing_test_files, train_lst, target_lst, config):
# Create mask
mask_coord = [20, 40, 20, 90, 0, 25]
mx1, mx2, my1, my2, mz1, mz2 = mask_coord
mask = np.zeros((96, 96, 96))
mask[mx1:mx2 + 1, my1:my2 + 1, mz1:mz2 + 1] = 1
coord = imed_obj_detect.get_bounding_boxes(mask)
assert coord[0] == mask_coord
loader_params = {
"data_list": train_lst,
"dataset_type": "training",
"requires_undo": False,
"path_data": [__data_testing_dir__],
"target_suffix": target_lst,
"extensions": [".nii.gz"],
"slice_filter_params": {"filter_empty_mask": False, "filter_empty_input": True},
"patch_filter_params": {"filter_empty_mask": False, "filter_empty_input": False},
"slice_axis": "axial"
}
if "Modified3DUNet" in config:
config['model_params']["name"] = "Modified3DUNet"
config['model_params'].update(config["Modified3DUNet"])
bounding_box_dict = {}
bounding_box_path = Path(PATH_OUTPUT, 'bounding_boxes.json')
if not Path(PATH_OUTPUT).exists():
PATH_OUTPUT.mkdir(parents=True, exist_ok=True)
current_dir = Path.cwd()
sub = train_lst[0].split('_')[0]
contrast = config['contrast_params']['contrast_lst'][0]
bb_path = str(Path(current_dir, __data_testing_dir__, sub, "anat", sub + "_" + contrast + ".nii.gz"))
bounding_box_dict[bb_path] = coord
with open(bounding_box_path, 'w') as fp:
json.dump(bounding_box_dict, fp, indent=4)
# Update loader_params with config
loader_params.update(config)
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds = imed_loader.load_dataset(bids_df, **loader_params)
handler = ds.handlers if "Modified3DUNet" in config else ds.indexes
for index in range(len(handler)):
if "Modified3DUNet" in config:
if ds.disk_cache:
path_seg_pair, _ = handler[index]
with path_seg_pair.open('rb') as f:
seg_pair = pickle.load(f)
else:
seg_pair, _ = handler[index]
assert seg_pair['input'][0].shape[-3:] == (mx2 - mx1, my2 - my1, mz2 - mz1)
else:
if ds.disk_cache:
path_seg_pair = handler[index]
with path_seg_pair.open('rb') as f:
seg_pair, _ = pickle.load(f)
else:
seg_pair, _ = handler[index]
assert seg_pair['input'][0].shape[-2:] == (mx2 - mx1, my2 - my1)
shutil.rmtree(PATH_OUTPUT)
def test_sampler(download_data_testing_test_files, transforms_dict, train_lst,
target_lst, roi_params):
cuda_available, device = imed_utils.define_device(GPU_ID)
loader_params = {
"transforms_params": transforms_dict,
"data_list": train_lst,
"dataset_type": "training",
"requires_undo": False,
"contrast_params": {
"contrast_lst": ['T2w'],
"balance": {}
},
"path_data": [__data_testing_dir__],
"target_suffix": target_lst,
"extensions": [".nii.gz"],
"roi_params": roi_params,
"model_params": {
"name": "Unet"
},
"slice_filter_params": {
"filter_empty_mask": False,
"filter_empty_input": True
},
"patch_filter_params": {
"filter_empty_mask": False,
"filter_empty_input": False
},
"slice_axis": "axial",
"multichannel": False
}
# Get Training dataset
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds_train = imed_loader.load_dataset(bids_df, **loader_params)
logger.info("\nLoading without sampling")
train_loader = DataLoader(ds_train,
batch_size=BATCH_SIZE,
shuffle=True,
pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
neg_percent, pos_percent = _cmpt_label(train_loader)
assert abs(neg_percent - pos_percent) > 20
logger.info("\nLoading with sampling")
train_loader_balanced = DataLoader(
ds_train,
batch_size=BATCH_SIZE,
sampler=BalancedSampler(ds_train),
shuffle=False,
pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
neg_percent_bal, pos_percent_bal = _cmpt_label(train_loader_balanced)
# Check if the loader is more balanced. The actual distribution comes from a probabilistic model
# This is however not very efficient to get close to 50 %
# in the case where we have 16 slices, with 87.5 % of one class (positive sample).
assert abs(neg_percent_bal - pos_percent_bal) <= abs(neg_percent -
pos_percent)
def test_microscopy_pixelsize(download_data_testing_test_files,
loader_parameters, model_parameters):
"""
Test that PixelSize and PixelSizeUnits microscopy metadata
are handled properly for PixelSizeUnits: "mm", "um" and "nm"
"""
loader_parameters.update({LoaderParamsKW.MODEL_PARAMS: model_parameters})
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
# PixelSizeUnits: "mm"
data_lst = ['sub-rat2_sample-data5_SEM.png']
transform_parameters = {
TransformationKW.RESAMPLE: {
"wspace": 0.000093,
"hspace": 0.000093
}
}
ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
assert ds[0]['input'].shape == (1, 725, 725)
# PixelSizeUnits: "um"
data_lst = ['sub-rat3_ses-02_sample-data11_run-1_SEM.png']
transform_parameters = {
TransformationKW.RESAMPLE: {
"wspace": 0.0001,
"hspace": 0.0001
}
}
ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
assert ds[0]['input'].shape == (1, 839, 769)
# PixelSizeUnits: "nm"
data_lst = ['sub-rat3_ses-02_sample-data10_SEM.png']
transform_parameters = {
TransformationKW.RESAMPLE: {
"wspace": 0.0001,
"hspace": 0.0001
}
}
ds = imed_loader.load_dataset(
bids_df, **{
**loader_parameters,
**{
'data_list': data_lst,
'transforms_params': transform_parameters,
'dataset_type': 'training'
}
})
assert ds[0]['input'].shape == (1, 758, 737)
def test_inference_2d_microscopy(download_data_testing_test_files, transforms_dict, test_lst, target_lst, roi_params,
testing_params):
"""
This test checks if the number of NifTI predictions equals the number of test subjects on 2d microscopy data.
Used to catch a bug where the last slice of the last volume wasn't appended to the prediction
(see: https://github.com/ivadomed/ivadomed/issues/823)
Also tests the conversions to PNG predictions when source files are not Nifti and checks if the number of PNG
predictions is 2x the number of test subjects (2-class model, outputs 1 PNG per class per subject).
"""
cuda_available, device = imed_utils.define_device(GPU_ID)
model_params = {"name": "Unet", "is_2d": True, "out_channel": 3}
loader_params = {
"transforms_params": transforms_dict,
"data_list": test_lst,
"dataset_type": "testing",
"requires_undo": True,
"contrast_params": {"contrast_lst": ['SEM'], "balance": {}},
"path_data": [str(Path(__data_testing_dir__, "microscopy_png"))],
"bids_config": f"{path_repo_root}/ivadomed/config/config_bids.json",
"target_suffix": target_lst,
"extensions": [".png"],
"roi_params": roi_params,
"slice_filter_params": {"filter_empty_mask": False, "filter_empty_input": True},
"patch_filter_params": {"filter_empty_mask": False, "filter_empty_input": False},
"slice_axis": SLICE_AXIS,
"multichannel": False
}
loader_params.update({"model_params": model_params})
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
# Get Testing dataset
ds_test = imed_loader.load_dataset(bids_df, **loader_params)
test_loader = DataLoader(ds_test, batch_size=BATCH_SIZE,
shuffle=False, pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
# Undo transform
val_undo_transform = imed_transforms.UndoCompose(imed_transforms.Compose(transforms_dict))
# Update testing_params
testing_params.update({
"slice_axis": loader_params["slice_axis"],
"target_suffix": loader_params["target_suffix"],
"undo_transforms": val_undo_transform
})
# Model
model = imed_models.Unet(out_channel=model_params['out_channel'])
if cuda_available:
model.cuda()
model.eval()
if not __output_dir__.is_dir():
__output_dir__.mkdir(parents=True, exist_ok=True)
preds_npy, gt_npy = imed_testing.run_inference(test_loader=test_loader,
model=model,
model_params=model_params,
testing_params=testing_params,
ofolder=str(__output_dir__),
cuda_available=cuda_available)
assert len([x for x in __output_dir__.iterdir() if x.name.endswith(".nii.gz")]) == len(test_lst)
assert len([x for x in __output_dir__.iterdir() if x.name.endswith(".png")]) == 2*len(test_lst)
def test_inference_target_suffix(download_data_testing_test_files, transforms_dict, test_lst, target_lst, roi_params,
testing_params):
"""
This test checks if the filename(s) of the prediction(s) saved as NifTI file(s) in the pred_masks
dir conform to the target_suffix or not. Thus, independent of underscore(s) in the target_suffix. As a result,
_seg-axon-manual or _seg-axon_manual should yield the same filename(s).
(c.f: https://github.com/ivadomed/ivadomed/issues/1135)
"""
cuda_available, device = imed_utils.define_device(GPU_ID)
model_params = {"name": "Unet", "is_2d": True, "out_channel": 3}
loader_params = {
"transforms_params": transforms_dict,
"data_list": test_lst,
"dataset_type": "testing",
"requires_undo": True,
"contrast_params": {"contrast_lst": ['SEM'], "balance": {}},
"path_data": [str(Path(__data_testing_dir__, "microscopy_png"))],
"bids_config": f"{path_repo_root}/ivadomed/config/config_bids.json",
"target_suffix": target_lst,
"extensions": [".png"],
"roi_params": roi_params,
"slice_filter_params": {"filter_empty_mask": False, "filter_empty_input": True},
"patch_filter_params": {"filter_empty_mask": False, "filter_empty_input": False},
"slice_axis": SLICE_AXIS,
"multichannel": False
}
loader_params.update({"model_params": model_params})
# restructuring the dataset
gt_path = f'{loader_params["path_data"][0]}/derivatives/labels/'
for file_path in Path(gt_path).rglob('*.png'):
src_filename = file_path.resolve()
dst_filename = '_'.join(str(src_filename).rsplit('-', 1))
src_filename.rename(Path(dst_filename))
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds_test = imed_loader.load_dataset(bids_df, **loader_params)
test_loader = DataLoader(ds_test, batch_size=BATCH_SIZE,
shuffle=False, pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
# Undo transform
val_undo_transform = imed_transforms.UndoCompose(imed_transforms.Compose(transforms_dict))
# Update testing_params
testing_params.update({
"slice_axis": loader_params["slice_axis"],
"target_suffix": loader_params["target_suffix"],
"undo_transforms": val_undo_transform
})
# Model
model = imed_models.Unet(out_channel=model_params['out_channel'])
if cuda_available:
model.cuda()
model.eval()
if not __output_dir__.is_dir():
__output_dir__.mkdir(parents=True, exist_ok=True)
preds_npy, gt_npy = imed_testing.run_inference(test_loader=test_loader,
model=model,
model_params=model_params,
testing_params=testing_params,
ofolder=str(__output_dir__),
cuda_available=cuda_available)
for x in __output_dir__.iterdir():
if x.name.endswith('_pred.nii.gz'):
assert x.name.rsplit('_', 1)[0].endswith(loader_params['contrast_params']['contrast_lst'][-1]), (
'Incompatible filename(s) of the prediction(s) saved as NifTI file(s)!'
)
def test_inference(download_data_testing_test_files, transforms_dict, test_lst, target_lst, roi_params, testing_params):
cuda_available, device = imed_utils.define_device(GPU_ID)
model_params = {"name": "Unet", "is_2d": True}
loader_params = {
"transforms_params": transforms_dict,
"data_list": test_lst,
"dataset_type": "testing",
"requires_undo": True,
"contrast_params": {"contrast_lst": ['T2w'], "balance": {}},
"path_data": [__data_testing_dir__],
"target_suffix": target_lst,
"extensions": [".nii.gz"],
"roi_params": roi_params,
"slice_filter_params": {"filter_empty_mask": False, "filter_empty_input": True},
"patch_filter_params": {"filter_empty_mask": False, "filter_empty_input": False},
"slice_axis": SLICE_AXIS,
"multichannel": False
}
loader_params.update({"model_params": model_params})
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
# Get Testing dataset
ds_test = imed_loader.load_dataset(bids_df, **loader_params)
test_loader = DataLoader(ds_test, batch_size=BATCH_SIZE,
shuffle=False, pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=0)
# Undo transform
val_undo_transform = imed_transforms.UndoCompose(imed_transforms.Compose(transforms_dict))
# Update testing_params
testing_params.update({
"slice_axis": loader_params["slice_axis"],
"target_suffix": loader_params["target_suffix"],
"undo_transforms": val_undo_transform
})
# Model
model = imed_models.Unet()
if cuda_available:
model.cuda()
model.eval()
metric_fns = [imed_metrics.dice_score,
imed_metrics.hausdorff_score,
imed_metrics.precision_score,
imed_metrics.recall_score,
imed_metrics.specificity_score,
imed_metrics.intersection_over_union,
imed_metrics.accuracy_score]
metric_mgr = imed_metrics.MetricManager(metric_fns)
if not __output_dir__.is_dir():
__output_dir__.mkdir(parents=True, exist_ok=True)
preds_npy, gt_npy = imed_testing.run_inference(test_loader=test_loader,
model=model,
model_params=model_params,
testing_params=testing_params,
ofolder=str(__output_dir__),
cuda_available=cuda_available)
metric_mgr(preds_npy, gt_npy)
metrics_dict = metric_mgr.get_results()
metric_mgr.reset()
logger.debug(metrics_dict)
def test_unet_time(download_data_testing_test_files, train_lst, target_lst, config):
cuda_available, device = imed_utils.define_device(GPU_ID)
loader_params = {
"data_list": train_lst,
"dataset_type": "training",
"requires_undo": False,
"path_data": [__data_testing_dir__],
"target_suffix": target_lst,
"extensions": [".nii.gz"],
"slice_filter_params": {"filter_empty_mask": False, "filter_empty_input": True},
"patch_filter_params": {"filter_empty_mask": False, "filter_empty_input": False},
"slice_axis": "axial"
}
# Update loader_params with config
loader_params.update(config)
# Get Training dataset
bids_df = BidsDataframe(loader_params, __tmp_dir__, derivatives=True)
ds_train = imed_loader.load_dataset(bids_df, **loader_params)
# Loader
train_loader = DataLoader(ds_train,
batch_size=1 if config["model_params"]["name"] == "Modified3DUNet" else BATCH_SIZE,
shuffle=True, pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=1)
# MODEL
model_params = loader_params["model_params"]
model_params.update(MODEL_DEFAULT)
# Get in_channel from contrast_lst
if loader_params["multichannel"]:
model_params["in_channel"] = len(loader_params["contrast_params"]["contrast_lst"])
else:
model_params["in_channel"] = 1
# Get out_channel from target_suffix
model_params["out_channel"] = len(loader_params["target_suffix"])
model_class = getattr(imed_models, model_params["name"])
model = model_class(**model_params)
logger.debug(f"Training {model_params['name']}")
if cuda_available:
model.cuda()
step_scheduler_batch = False
# TODO: Add optim in pytest
optimizer = optim.Adam(model.parameters(), lr=INIT_LR)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, N_EPOCHS)
# TODO: add to pytest
loss_fct = imed_losses.DiceLoss()
load_lst, pred_lst, opt_lst, schedule_lst, init_lst, gen_lst = [], [], [], [], [], []
for epoch in tqdm(range(1, N_EPOCHS + 1), desc="Training"):
start_time = time.time()
start_init = time.time()
model.train()
tot_init = time.time() - start_init
init_lst.append(tot_init)
num_steps = 0
start_gen = 0
for i, batch in enumerate(train_loader):
if i > 0:
tot_gen = time.time() - start_gen
gen_lst.append(tot_gen)
start_load = time.time()
input_samples = imed_utils.cuda(batch["input"], cuda_available)
gt_samples = imed_utils.cuda(batch["gt"], cuda_available, non_blocking=True)
tot_load = time.time() - start_load
load_lst.append(tot_load)
start_pred = time.time()
if 'film_layers' in model_params:
preds = model(input_samples, [[0, 1]])
else:
preds = model(input_samples)
tot_pred = time.time() - start_pred
pred_lst.append(tot_pred)
start_opt = time.time()
loss = loss_fct(preds, gt_samples)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step_scheduler_batch:
scheduler.step()
num_steps += 1
tot_opt = time.time() - start_opt
opt_lst.append(tot_opt)
start_gen = time.time()
start_schedule = time.time()
if not step_scheduler_batch:
scheduler.step()
tot_schedule = time.time() - start_schedule
schedule_lst.append(tot_schedule)
end_time = time.time()
total_time = end_time - start_time
tqdm.write("Epoch {} took {:.2f} seconds.".format(epoch, total_time))
logger.info(f"Mean SD init {np.mean(init_lst)} -- {np.std(init_lst)}")
logger.info(f"Mean SD load {np.mean(load_lst)} -- {np.std(load_lst)}")
logger.info(f"Mean SD pred {np.mean(pred_lst)} -- {np.std(pred_lst)}")
logger.info(f"Mean SDopt {np.mean(opt_lst)} -- {np.std(opt_lst)}")
logger.info(f"Mean SD gen {np.mean(gen_lst)} -- {np.std(gen_lst)}")
logger.info(f"Mean SD scheduler {np.mean(schedule_lst)} -- {np.std(schedule_lst)}")
def test_image_orientation(download_data_testing_test_files,
loader_parameters):
device = torch.device("cuda:" +
str(GPU_ID) if torch.cuda.is_available() else "cpu")
cuda_available = torch.cuda.is_available()
if cuda_available:
torch.cuda.set_device(device)
logger.info(f"Using GPU ID {device}")
bids_df = BidsDataframe(loader_parameters, __tmp_dir__, derivatives=True)
contrast_params = loader_parameters["contrast_params"]
target_suffix = loader_parameters["target_suffix"]
roi_params = loader_parameters["roi_params"]
train_lst = ['sub-unf01_T1w.nii.gz']
training_transform_dict = {
"Resample": {
"wspace": 1.5,
"hspace": 1,
"dspace": 3
},
"CenterCrop": {
"size": [176, 128, 160]
},
"NormalizeInstance": {
"applied_to": ['im']
}
}
tranform_lst, training_undo_transform = imed_transforms.prepare_transforms(
training_transform_dict)
model_params = {
"name": "Modified3DUNet",
"dropout_rate": 0.3,
"bn_momentum": 0.9,
"depth": 2,
"in_channel": 1,
"out_channel": 1,
"length_3D": [176, 128, 160],
"stride_3D": [176, 128, 160],
"attention": False,
"n_filters": 8
}
for dim in ['2d', '3d']:
for slice_axis in [0, 1, 2]:
if dim == '2d':
ds = BidsDataset(bids_df=bids_df,
subject_file_lst=train_lst,
target_suffix=target_suffix,
contrast_params=contrast_params,
model_params=model_params,
metadata_choice=False,
slice_axis=slice_axis,
transform=tranform_lst,
multichannel=False)
ds.load_filenames()
else:
ds = Bids3DDataset(bids_df=bids_df,
subject_file_lst=train_lst,
target_suffix=target_suffix,
model_params=model_params,
contrast_params=contrast_params,
metadata_choice=False,
slice_axis=slice_axis,
transform=tranform_lst,
multichannel=False)
loader = DataLoader(ds,
batch_size=1,
shuffle=True,
pin_memory=True,
collate_fn=imed_loader_utils.imed_collate,
num_workers=1)
input_filename, gt_filename, roi_filename, metadata = ds.filename_pairs[
0]
segpair = SegmentationPair(input_filename,
gt_filename,
metadata=metadata,
slice_axis=slice_axis)
nib_original = nib.load(gt_filename[0])
# Get image with original, ras and hwd orientations
input_init = nib_original.get_fdata()
input_ras = nib.as_closest_canonical(nib_original).get_fdata()
img, gt = segpair.get_pair_data()
input_hwd = gt[0]
pred_tmp_lst, z_tmp_lst = [], []
for i, batch in enumerate(loader):
# batch["input_metadata"] = batch["input_metadata"][0] # Take only metadata from one input
# batch["gt_metadata"] = batch["gt_metadata"][0] # Take only metadata from one label
for smp_idx in range(len(batch['gt'])):
# undo transformations
if dim == '2d':
preds_idx_undo, metadata_idx = training_undo_transform(
batch["gt"][smp_idx],
batch["gt_metadata"][smp_idx],
data_type='gt')
# add new sample to pred_tmp_lst
pred_tmp_lst.append(preds_idx_undo[0])
z_tmp_lst.append(
int(batch['input_metadata'][smp_idx][0]
['slice_index']))
else:
preds_idx_undo, metadata_idx = training_undo_transform(
batch["gt"][smp_idx],
batch["gt_metadata"][smp_idx],
data_type='gt')
fname_ref = metadata_idx[0]['gt_filenames'][0]
if (pred_tmp_lst and i == len(loader) - 1) or dim == '3d':
# save the completely processed file as a nii
nib_ref = nib.load(fname_ref)
nib_ref_can = nib.as_closest_canonical(nib_ref)
if dim == '2d':
tmp_lst = []
for z in range(nib_ref_can.header.get_data_shape()
[slice_axis]):
tmp_lst.append(
pred_tmp_lst[z_tmp_lst.index(z)])
arr = np.stack(tmp_lst, axis=-1)
else:
arr = np.array(preds_idx_undo[0])
# verify image after transform, undo transform and 3D reconstruction
input_hwd_2 = imed_postpro.threshold_predictions(arr)
# Some difference are generated due to transform and undo transform
# (e.i. Resample interpolation)
assert imed_metrics.dice_score(input_hwd_2,
input_hwd) >= 0.8
input_ras_2 = imed_loader_utils.orient_img_ras(
input_hwd_2, slice_axis)
assert imed_metrics.dice_score(input_ras_2,
input_ras) >= 0.8
input_init_2 = imed_loader_utils.reorient_image(
input_hwd_2, slice_axis, nib_ref, nib_ref_can)
assert imed_metrics.dice_score(input_init_2,
input_init) >= 0.8
# re-init pred_stack_lst
pred_tmp_lst, z_tmp_lst = [], []
loader_params = context["loader_parameters"]
loader_params["contrast_params"]["contrast_lst"] = loader_params[
"contrast_params"]["training_validation"]
# CHOOSE TO INDEX DERIVATIVES OR NOT
# As per pybids, the indexing of derivatives works only if a "dataset_description.json" file
# is present in "derivatives" or "labels" folder with minimal content:
# {"Name": "Example dataset", "BIDSVersion": "1.0.2", "PipelineDescription": {"Name": "Example pipeline"}}
derivatives = True
# CREATE OUTPUT PATH
path_output = context["path_output"]
if not os.path.isdir(path_output):
logger.info(f"Creating output path: {path_output}")
os.makedirs(path_output)
else:
logger.warning(f"Output path already exists: {path_output}")
# CREATE BIDSDataframe OBJECT
bids_df = BidsDataframe(loader_params, path_output, derivatives)
df = bids_df.df
# DROP "path" COLUMN AND SORT BY FILENAME FOR TESTING PURPOSES WITH data-testing
df = df.drop(columns=['path'])
df = df.sort_values(by=['filename']).reset_index(drop=True)
# SAVE DATAFRAME TO CSV FILE FOR data-testing
path_csv = "test_df_new_loader.csv"
df.to_csv(path_csv, index=False)
logger.debug(df)
