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shaneahmed merged 2 commits into from
Mar 18, 2022
Merged

TST: Make Stain Norm CLI Tests Use Temp Dir #319

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a160284
TST: Make Stain Norm CLI Tests Use Temp Dir
John-P Mar 18, 2022
8162ce7
DOC: Update micronet.py docstring
shaneahmed Mar 18, 2022
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4 changes: 2 additions & 2 deletions tests/models/test_arch_micronet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def test_functionality(remote_sample, tmp_path):
patch = MicroNet.preproc(patch)
batch = torch.from_numpy(patch)[None]
model = MicroNet()
fetch_pretrained_weights("micronet_hovernet-consep", f"{tmp_path}/weights.pth")
fetch_pretrained_weights("micronet-consep", f"{tmp_path}/weights.pth")
map_location = utils.misc.select_device(utils.env_detection.has_gpu())
pretrained = torch.load(f"{tmp_path}/weights.pth", map_location=map_location)
model.load_state_dict(pretrained)
Expand All @@ -70,7 +70,7 @@ def test_micronet_output(remote_sample, tmp_path):
"""Tests the output of MicroNet."""
svs_1_small = pathlib.Path(remote_sample("svs-1-small"))
micronet_output = pathlib.Path(remote_sample("micronet-output"))
pretrained_model = "micronet_hovernet-consep"
pretrained_model = "micronet-consep"
batch_size = 5
num_loader_workers = 0
num_postproc_workers = 0
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22 changes: 18 additions & 4 deletions tests/test_stainnorm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ def test_vahadane_normalize(source_image, norm_vahadane):
# -------------------------------------------------------------------------------------


def test_command_line_stainnorm(source_image):
def test_command_line_stainnorm(source_image, tmp_path):
"""Test for the stain normalization CLI."""
source_img = pathlib.Path(source_image)
target_img = _local_sample_path("target_image.png")
Expand All @@ -165,6 +165,8 @@ def test_command_line_stainnorm(source_image):
source_img,
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"reinhard",
],
Expand All @@ -180,6 +182,8 @@ def test_command_line_stainnorm(source_image):
source_img,
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"ruifrok",
],
Expand All @@ -195,6 +199,8 @@ def test_command_line_stainnorm(source_image):
source_img,
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"macenko",
],
Expand All @@ -210,6 +216,8 @@ def test_command_line_stainnorm(source_image):
source_img,
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"vahadane",
],
Expand All @@ -218,7 +226,7 @@ def test_command_line_stainnorm(source_image):
assert stainnorm_result.exit_code == 0


def test_cli_stainnorm_dir(source_image):
def test_cli_stainnorm_dir(source_image, tmp_path):
"""Test directory input for the stain normalization CLI."""
source_img = source_image.parent
target_img = _local_sample_path("target_image.png")
Expand All @@ -231,6 +239,8 @@ def test_cli_stainnorm_dir(source_image):
str(source_img),
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_ouput"),
"--method",
"vahadane",
],
Expand All @@ -239,7 +249,7 @@ def test_cli_stainnorm_dir(source_image):
assert stainnorm_result.exit_code == 0


def test_cli_stainnorm_file_not_found_error(source_image):
def test_cli_stainnorm_file_not_found_error(source_image, tmp_path):
"""Test file not found error for the stain normalization CLI."""
source_img = pathlib.Path(source_image)
target_img = stain_norm_target()
Expand All @@ -252,6 +262,8 @@ def test_cli_stainnorm_file_not_found_error(source_image):
str(source_img)[:-1],
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"vahadane",
],
Expand All @@ -262,7 +274,7 @@ def test_cli_stainnorm_file_not_found_error(source_image):
assert isinstance(stainnorm_result.exception, FileNotFoundError)


def test_cli_stainnorm_method_not_supported(source_image):
def test_cli_stainnorm_method_not_supported(source_image, tmp_path):
"""Test method not supported for the stain normalization CLI."""
source_img = pathlib.Path(source_image)
target_img = stain_norm_target()
Expand All @@ -275,6 +287,8 @@ def test_cli_stainnorm_method_not_supported(source_image):
str(source_img),
"--target-input",
target_img,
"--output-path",
str(tmp_path / "stainnorm_output"),
"--method",
"Test",
],
Expand Down
4 changes: 2 additions & 2 deletions tiatoolbox/data/pretrained_model.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -726,8 +726,8 @@ hovernetplus-oed:
stride_shape: [164, 164]
save_resolution: {'units': 'mpp', 'resolution': 0.50}

micronet_hovernet-consep:
url: https://tiatoolbox.dcs.warwick.ac.uk/models/seg/micronet_hovernet-consep.pth
micronet-consep:
url: https://tiatoolbox.dcs.warwick.ac.uk/models/seg/micronet-consep.pth
architecture:
class: micronet.MicroNet
kwargs:
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12 changes: 6 additions & 6 deletions tiatoolbox/models/architecture/micronet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -334,7 +334,7 @@ def out_arch_branch(in_ch, num_class=2):
class MicroNet(ModelABC):
"""Initialise MicroNet [1].
The following models have been included in tiatoolbox.
1. `micronet_hovernet-consep`: This is trained on
1. `micronet-consep`: This is trained on
`ConSep dataset <https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/>`_
The model is retrained in torch as the original model with results on ConSep [2]
was trained in TensorFlow.
Expand Down Expand Up @@ -367,12 +367,12 @@ class MicroNet(ModelABC):
num_class (int): Number of output channels. default=2.

References:
[1] Raza, SEA et al., “Micro-Net: A unified model for segmentation of
various objects in microscopy images,” Medical Image Analysis,
Dec. 2018, vol. 52, p. 160–173.
[1] Raza, Shan E Ahmed, et al. "Micro-Net: A unified model for
segmentation of various objects in microscopy images."
Medical image analysis 52 (2019): 160-173.

[2] Graham, Simon, et al. "Hover-net: Simultaneous segmentation and
classification of nuclei in multi-tissue histology images."
[2] Graham, Simon, et al. "Hover-net: Simultaneous segmentation
and classification of nuclei in multi-tissue histology images."
Medical Image Analysis 58 (2019): 101563.

"""
Expand Down