diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index c9ae0016c90b88658cbab481bf880ef729a38ce1..0ad8ee2de5c592a4bb1e6fab2600b5e4a2841d17 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -10,6 +10,7 @@ include:
variables:
GIT_SUBMODULE_STRATEGY: normal
GIT_SUBMODULE_DEPTH: 1
+ XDG_CONFIG_HOME: $CI_PROJECT_DIR/tests/data
documentation:
before_script:
@@ -24,3 +25,9 @@ tests:
# for pytorch quirks on linux
- if [ "$TAG" == "docker" ]; then export OMP_NUM_THREADS=1; fi # Set the number of threads used to 1
- !reference [.snippets, test-prepare]
+
+conda-package:
+ before_script:
+ # for opencv-python dependence
+ - apt-get update && apt-get install -y libgl1-mesa-glx > /dev/null
+ - !reference [.snippets, conda-prepare]
diff --git a/.reuse/dep5 b/.reuse/dep5
index 5314ea6e597528ba571ecdaa3e5686b76102efe5..fb27b959e3ce20f1d74e7f560f752367575d2924 100644
--- a/.reuse/dep5
+++ b/.reuse/dep5
@@ -21,5 +21,6 @@ Files:
tests/data/*.csv
tests/data/*.json
tests/data/*.png
+ tests/*.toml
Copyright: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
License: GPL-3.0-or-later
diff --git a/conda/meta.yaml b/conda/meta.yaml
index adcb300518eff7c3ac52da07d9de606dd6f7f0e1..8653e89aaa42a91c462870bd6db0c4e7408fc0b3 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -43,6 +43,7 @@ requirements:
run:
- python >=3.10
- {{ pin_compatible('click') }}
+ - {{ pin_compatible('grad-cam', max_pin='x.x') }}
- {{ pin_compatible('matplotlib') }}
- {{ pin_compatible('numpy') }}
- {{ pin_compatible('pillow') }}
diff --git a/src/mednet/config/data/hivtb/datamodule.py b/src/mednet/config/data/hivtb/datamodule.py
index 7a5617330e86b9c8bdd2f57a0a20ed3902cafd90..68a7b7a3bdfea302ecd41bc750355b354b85c5f2 100644
--- a/src/mednet/config/data/hivtb/datamodule.py
+++ b/src/mednet/config/data/hivtb/datamodule.py
@@ -12,7 +12,7 @@ import os
import PIL.Image
-from torchvision.transforms.functional import center_crop, to_tensor
+from torchvision.transforms.functional import to_tensor
from ....data.datamodule import CachingDataModule
from ....data.image_utils import remove_black_borders
@@ -58,7 +58,6 @@ class RawDataLoader(_BaseRawDataLoader):
)
image = remove_black_borders(image)
tensor = to_tensor(image)
- tensor = center_crop(tensor, min(*tensor.shape[1:]))
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
diff --git a/src/mednet/config/data/montgomery/datamodule.py b/src/mednet/config/data/montgomery/datamodule.py
index e19f32009e7a3ea7935a1fa3aea325c493728310..86e9fdb7058c3e25351212b93be7a8167c9ecc03 100644
--- a/src/mednet/config/data/montgomery/datamodule.py
+++ b/src/mednet/config/data/montgomery/datamodule.py
@@ -11,7 +11,7 @@ import os
import PIL.Image
-from torchvision.transforms.functional import center_crop, to_tensor
+from torchvision.transforms.functional import to_tensor
from ....data.datamodule import CachingDataModule
from ....data.image_utils import remove_black_borders
@@ -57,7 +57,6 @@ class RawDataLoader(_BaseRawDataLoader):
image = PIL.Image.open(os.path.join(self.datadir, sample[0]))
image = remove_black_borders(image)
tensor = to_tensor(image)
- tensor = center_crop(tensor, min(*tensor.shape[1:]))
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
diff --git a/src/mednet/config/data/padchest/datamodule.py b/src/mednet/config/data/padchest/datamodule.py
index 94743f68edec801d8e73c5badb69f28f45ae8beb..778d505f96813f28b348b9a251d91f4213871d77 100644
--- a/src/mednet/config/data/padchest/datamodule.py
+++ b/src/mednet/config/data/padchest/datamodule.py
@@ -12,7 +12,7 @@ import os
import numpy
import PIL.Image
-from torchvision.transforms.functional import center_crop, to_tensor
+from torchvision.transforms.functional import to_tensor
from ....data.datamodule import CachingDataModule
from ....data.image_utils import remove_black_borders
@@ -59,7 +59,6 @@ class RawDataLoader(_BaseRawDataLoader):
image = remove_black_borders(image)
array = numpy.array(image).astype(numpy.float32) / 65535
tensor = to_tensor(array)
- tensor = center_crop(tensor, min(*tensor.shape[1:]))
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
diff --git a/src/mednet/config/data/shenzhen/datamodule.py b/src/mednet/config/data/shenzhen/datamodule.py
index 4b04f871849abc6e3c2e997300c4349f42cb0725..6853ebe56718c38db9e1e1d72a2c08ded11627dd 100644
--- a/src/mednet/config/data/shenzhen/datamodule.py
+++ b/src/mednet/config/data/shenzhen/datamodule.py
@@ -11,7 +11,7 @@ import os
import PIL.Image
-from torchvision.transforms.functional import center_crop, to_tensor
+from torchvision.transforms.functional import to_tensor
from ....data.datamodule import CachingDataModule
from ....data.image_utils import remove_black_borders
@@ -68,7 +68,6 @@ class RawDataLoader(_BaseRawDataLoader):
)
image = remove_black_borders(image)
tensor = to_tensor(image)
- tensor = center_crop(tensor, min(*tensor.shape[1:]))
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
diff --git a/src/mednet/config/data/tbpoc/datamodule.py b/src/mednet/config/data/tbpoc/datamodule.py
index e9d55cef980f681c078c29603a2f50906a2e316c..67846f6c7c0842c18f24d83a4018beb1ccc908b9 100644
--- a/src/mednet/config/data/tbpoc/datamodule.py
+++ b/src/mednet/config/data/tbpoc/datamodule.py
@@ -7,7 +7,7 @@ import os
import PIL.Image
-from torchvision.transforms.functional import center_crop, to_tensor
+from torchvision.transforms.functional import to_tensor
from ....data.datamodule import CachingDataModule
from ....data.image_utils import remove_black_borders
@@ -53,7 +53,6 @@ class RawDataLoader(_BaseRawDataLoader):
image = PIL.Image.open(os.path.join(self.datadir, sample[0]))
image = remove_black_borders(image)
tensor = to_tensor(image)
- tensor = center_crop(tensor, min(*tensor.shape[1:]))
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
diff --git a/src/mednet/engine/saliency/viewer.py b/src/mednet/engine/saliency/viewer.py
index 6e383801d10f0d0f4dc9875f85cc78cee0515084..0047c4e1ad7ce6ee7f9a3cdd6482a234afb11756 100644
--- a/src/mednet/engine/saliency/viewer.py
+++ b/src/mednet/engine/saliency/viewer.py
@@ -63,7 +63,7 @@ def _overlay_saliency_map(
Parameters
----------
image
- The input image that will be overlayed with the saliency map.
+ The input image that will be overlaid with the saliency map.
saliencies
The saliency map that will be overlaid on the (raw) image.
colormap
@@ -118,7 +118,7 @@ def _overlay_bounding_box(
Parameters
----------
image
- The input image that will be overlayed with the saliency map.
+ The input image that will be overlaid with the saliency map.
bbox
The bounding box to draw on the input image.
color
@@ -149,13 +149,13 @@ def _process_sample(
saliencies: numpy.typing.NDArray[numpy.double],
ground_truth: BoundingBoxes,
) -> PIL.Image.Image:
- """Generate an overlayed representation of the original sample and saliency
+ """Generate an overlaid representation of the original sample and saliency
maps.
Parameters
----------
raw_data
- The raw data representing the input sample that will be overlayed with
+ The raw data representing the input sample that will be overlaid with
saliency maps and annotations.
saliencies
The saliency map recovered from the model, that will be imprinted on
@@ -166,7 +166,7 @@ def _process_sample(
Returns
-------
PIL.Image.Image
- An image with the original raw data overlayed with the different
+ An image with the original raw data overlaid with the different
elements as selected by the user.
"""
@@ -226,6 +226,7 @@ def run(
for sample in tqdm(
dataset_loader, desc="batches", leave=False, disable=None
):
+ # WARNING: following code assumes a batch size of 1. Will break if not the case.
name = str(sample[1]["name"][0])
label = int(sample[1]["label"].item())
data = sample[0][0]
@@ -243,7 +244,9 @@ def run(
# regions of interest. We need to abstract from this to support more
# datasets and other ways to annotate.
if show_groundtruth:
- ground_truth = sample[1].get("bounding_boxes", BoundingBoxes())
+ ground_truth = sample[1].get("bounding_boxes", BoundingBoxes())[
+ 0
+ ]
else:
ground_truth = BoundingBoxes()
diff --git a/src/mednet/models/alexnet.py b/src/mednet/models/alexnet.py
index 0b2d9e180f0333da40f2d568ca2202b3ddb43b9b..d2635970f35497f6274b5ea87488f61e8c6a3a30 100644
--- a/src/mednet/models/alexnet.py
+++ b/src/mednet/models/alexnet.py
@@ -15,7 +15,7 @@ import torchvision.transforms
from ..data.typing import TransformSequence
from .separate import separate
-from .transforms import RGB
+from .transforms import RGB, SquareCenterPad
from .typing import Checkpoint
logger = logging.getLogger(__name__)
@@ -74,6 +74,7 @@ class Alexnet(pl.LightningModule):
self.num_classes = num_classes
self.model_transforms = [
+ SquareCenterPad(),
torchvision.transforms.Resize(512, antialias=True),
RGB(),
]
diff --git a/src/mednet/models/densenet.py b/src/mednet/models/densenet.py
index 2bd6a2dd01ecf8389b1f32a4e783b0c4315af26e..91f7c336457d4bda68342be49cb77bb2b22d47c3 100644
--- a/src/mednet/models/densenet.py
+++ b/src/mednet/models/densenet.py
@@ -15,7 +15,7 @@ import torchvision.transforms
from ..data.typing import TransformSequence
from .separate import separate
-from .transforms import RGB
+from .transforms import RGB, SquareCenterPad
from .typing import Checkpoint
logger = logging.getLogger(__name__)
@@ -71,8 +71,8 @@ class Densenet(pl.LightningModule):
self.name = "densenet-121"
self.num_classes = num_classes
- # image is probably large, resize first to get memory usage down
self.model_transforms = [
+ SquareCenterPad(),
torchvision.transforms.Resize(512, antialias=True),
RGB(),
]
diff --git a/src/mednet/models/pasa.py b/src/mednet/models/pasa.py
index 9007ab1b8d7e5c79b29bc65d4a996783bfc7889e..e285bf7baa36a9ab548de01451622ee61ae1c111 100644
--- a/src/mednet/models/pasa.py
+++ b/src/mednet/models/pasa.py
@@ -15,7 +15,7 @@ import torchvision.transforms
from ..data.typing import TransformSequence
from .separate import separate
-from .transforms import Grayscale
+from .transforms import Grayscale, SquareCenterPad
from .typing import Checkpoint
logger = logging.getLogger(__name__)
@@ -72,10 +72,14 @@ class Pasa(pl.LightningModule):
self.name = "pasa"
self.num_classes = num_classes
- # image is probably large, resize first to get memory usage down
self.model_transforms = [
- torchvision.transforms.Resize(512, antialias=True),
Grayscale(),
+ SquareCenterPad(),
+ torchvision.transforms.Resize(
+ 512,
+ antialias=True,
+ interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
+ ),
]
self._train_loss = train_loss
diff --git a/src/mednet/models/transforms.py b/src/mednet/models/transforms.py
index 4ee73548a5b458ed6e5e043fb5922dcb752cb126..2a4f933ae4f541ea71015176e0a048e99bdfd4d8 100644
--- a/src/mednet/models/transforms.py
+++ b/src/mednet/models/transforms.py
@@ -3,11 +3,44 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""A transform that turns grayscale images to RGB."""
+import numpy
import torch
import torch.nn
import torchvision.transforms.functional
+def square_center_pad(img: torch.Tensor) -> torch.Tensor:
+ """Return a squared version of the image, centered on a canvas padded with
+ zeros.
+
+ Parameters
+ ----------
+
+ img
+ The tensor to be transformed. Expected to be in the form: ``[...,
+ [1,3], H, W]`` (i.e. arbitrary number of leading dimensions).
+
+ Returns
+ -------
+
+ Transformed tensor, guaranteed to be square (ie. equal height and
+ width).
+ """
+
+ height, width = img.shape[-2:]
+ maxdim = numpy.max([height, width])
+
+ # padding
+ left = (maxdim - width) // 2
+ top = (maxdim - height) // 2
+ right = maxdim - width - left
+ bottom = maxdim - height - top
+
+ return torchvision.transforms.functional.pad(
+ img, [left, top, right, bottom], 0, "constant"
+ )
+
+
def grayscale_to_rgb(img: torch.Tensor) -> torch.Tensor:
"""Convert an image in grayscale to RGB.
@@ -94,6 +127,16 @@ def rgb_to_grayscale(img: torch.Tensor) -> torch.Tensor:
return torchvision.transforms.functional.rgb_to_grayscale(img)
+class SquareCenterPad(torch.nn.Module):
+ """Transform to a squared version of the image, centered on a canvas padded with zeros."""
+
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, img: torch.Tensor) -> torch.Tensor:
+ return square_center_pad(img)
+
+
class RGB(torch.nn.Module):
"""Wrapper class around :py:func:`.grayscale_to_rgb` to be used as a model transform."""
diff --git a/src/mednet/scripts/experiment.py b/src/mednet/scripts/experiment.py
index 38d88fd94c32fee58c8959ea3ed767c59ff0fd2f..275bc8b8bb91417a6467bcc62b4b7af708253533 100644
--- a/src/mednet/scripts/experiment.py
+++ b/src/mednet/scripts/experiment.py
@@ -67,7 +67,6 @@ def experiment(
save_sh_command(output_folder / "command.sh")
- # training
logger.info("Started training")
from .train import train
@@ -139,3 +138,35 @@ def experiment(
)
logger.info("Ended evaluating")
+
+ logger.info("Started generating saliencies")
+
+ from .saliency.generate import generate
+
+ saliencies_gen_folder = output_folder / "gradcam" / "saliencies"
+
+ ctx.invoke(
+ generate,
+ model=model,
+ datamodule=datamodule,
+ weight=train_output_folder,
+ output_folder=saliencies_gen_folder,
+ )
+
+ logger.info("Ended generating saliencies")
+
+ logger.info("Started viewing saliencies")
+
+ from .saliency.view import view
+
+ saliencies_view_folder = output_folder / "gradcam" / "visualizations"
+
+ ctx.invoke(
+ view,
+ model=model,
+ datamodule=datamodule,
+ input_folder=saliencies_gen_folder,
+ output_folder=saliencies_view_folder,
+ )
+
+ logger.info("Ended viewing saliencies")
diff --git a/src/mednet/scripts/saliency/interpretability.py b/src/mednet/scripts/saliency/interpretability.py
index 0d1b84f7692a1c218cca603d7e9306b617a8d1c5..14a99040b87798863d2d97d69b97b46d14bceaae 100644
--- a/src/mednet/scripts/saliency/interpretability.py
+++ b/src/mednet/scripts/saliency/interpretability.py
@@ -23,10 +23,19 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
.. code:: sh
- mednet saliency interpretability -vv tbx11k-v1-healthy-vs-atb --input-folder=parent-folder/saliencies/ --output-json=path/to/interpretability-scores.json
+ mednet saliency interpretability -vv pasa tbx11k-v1-healthy-vs-atb --input-folder=parent-folder/saliencies/ --output-json=path/to/interpretability-scores.json
""",
)
+@click.option(
+ "--model",
+ "-m",
+ help="""A lightning module instance implementing the network architecture
+ (not the weights, necessarily) to be used for inference. Currently, only
+ supports pasa and densenet models.""",
+ required=True,
+ cls=ResourceOption,
+)
@click.option(
"--datamodule",
"-d",
@@ -78,6 +87,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
)
@verbosity_option(logger=logger, cls=ResourceOption, expose_value=False)
def interpretability(
+ model,
datamodule,
input_folder,
target_label,
@@ -114,7 +124,7 @@ def interpretability(
from ...engine.saliency.interpretability import run
- datamodule.model_transforms = []
+ datamodule.model_transforms = model.transforms
datamodule.prepare_data()
datamodule.setup(stage="predict")
diff --git a/tests/conftest.py b/tests/conftest.py
index b4f92331138424dc528900644bcef76e8f4c26ce..b51eafa681ca9c95be145a2c5eedea0b1fe5a5b8 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2,16 +2,14 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-import os
import pathlib
-import tempfile
import typing
-import zipfile
+import numpy
import pytest
-import tomli_w
import torch
+from mednet.data.split import JSONDatabaseSplit
from mednet.data.typing import DatabaseSplit
@@ -97,55 +95,6 @@ def temporary_basedir(tmp_path_factory):
return tmp_path_factory.mktemp("test-cli")
-def pytest_sessionstart(session: pytest.Session) -> None:
- """Preset the session start to ensure the Montgomery dataset is always available.
-
- Parameters
- ----------
- session
- The session to use.
- """
-
- from mednet.utils.rc import load_rc
-
- rc = load_rc()
-
- database_dir = rc.get("datadir.montgomery")
- if database_dir is not None:
- # if the user downloaded it, use that copy
- return
-
- # else, we must extract the LFS component (we are likely on the CI)
- archive = (
- pathlib.Path(__file__).parents[0] / "data" / "lfs" / "test-database.zip"
- )
- assert archive.exists(), (
- f"Neither datadir.montgomery is set on the global configuration, "
- f"(typically ~/.config/mednet.toml), or it is possible to detect "
- f"the presence of {archive}' (did you git submodule init --update "
- f"this submodule?)"
- )
-
- montgomery_tempdir = tempfile.TemporaryDirectory()
- rc.setdefault("datadir.montgomery", montgomery_tempdir.name)
-
- with zipfile.ZipFile(archive) as zf:
- zf.extractall(montgomery_tempdir.name)
-
- config_filename = "mednet.toml"
- with open(
- os.path.join(montgomery_tempdir.name, config_filename), "wb"
- ) as f:
- tomli_w.dump(rc.data, f)
- f.flush()
-
- os.environ["XDG_CONFIG_HOME"] = montgomery_tempdir.name
-
- # stash the newly created temporary directory so we can erase it when the
- key = pytest.StashKey[tempfile.TemporaryDirectory]()
- session.stash[key] = montgomery_tempdir
-
-
class DatabaseCheckers:
"""Helpers for database tests."""
@@ -156,13 +105,14 @@ class DatabaseCheckers:
prefixes: typing.Sequence[str],
possible_labels: typing.Sequence[int],
):
- """Run a simple consistence check on the data split.
+ """Run a simple consistency check on the data split.
Parameters
----------
split
An instance of DatabaseSplit.
lengths
+
A dictionary that contains keys matching those of the split (this will
be checked). The values of the dictionary should correspond to the
sizes of each of the datasets in the split.
@@ -197,12 +147,13 @@ class DatabaseCheckers:
color_planes: int,
prefixes: typing.Sequence[str],
possible_labels: typing.Sequence[int],
+ expected_num_labels: int,
+ expected_image_shape: typing.Optional[tuple[int, ...]] = None,
):
- """Check the consistence of an individual (loaded) batch.
+ """Check the consistency of an individual (loaded) batch.
Parameters
----------
-
batch
The loaded batch to be checked.
batch_size
@@ -214,14 +165,22 @@ class DatabaseCheckers:
prefixes.
possible_labels
These are the list of possible labels contained in any split.
+ expected_num_labels
+ The expected number of labels each sample should have.
+ expected_image_shape
+ The expected shape of the image (num_channels, width, height).
"""
assert len(batch) == 2 # data, metadata
assert isinstance(batch[0], torch.Tensor)
assert batch[0].shape[0] == batch_size # mini-batch size
- assert batch[0].shape[1] == color_planes # grayscale images
- assert batch[0].shape[2] == batch[0].shape[3] # image is square
+ assert batch[0].shape[1] == color_planes
+
+ if expected_image_shape:
+ assert all(
+ [data.shape == expected_image_shape for data in batch[0]]
+ )
assert isinstance(batch[1], dict) # metadata
assert len(batch[1]) == 2 # label and name
@@ -229,6 +188,9 @@ class DatabaseCheckers:
assert "label" in batch[1]
assert all([k in possible_labels for k in batch[1]["label"]])
+ if expected_num_labels:
+ assert len(batch[1]["label"]) == expected_num_labels
+
assert "name" in batch[1]
assert all(
[any([k.startswith(j) for j in prefixes]) for k in batch[1]["name"]]
@@ -239,6 +201,56 @@ class DatabaseCheckers:
# to_pil_image(batch[0][0]).show()
# __import__("pdb").set_trace()
+ @staticmethod
+ def check_image_quality(
+ datamodule,
+ reference_histogram_file,
+ compare_type="equal",
+ pearson_coeff_threshold=0.005,
+ ):
+ ref_histogram_splits = JSONDatabaseSplit(reference_histogram_file)
+
+ for split_name in ref_histogram_splits:
+ raw_samples = datamodule.splits[split_name][0][0]
+
+ # It is not possible to get a sample from a Dataset by name/path, only by index.
+ # This creates a dict of sample name to dataset index.
+ raw_samples_indices = {}
+ for idx, rs in enumerate(raw_samples):
+ raw_samples_indices[rs[0]] = idx
+
+ for ref_hist_path, ref_hist_data in ref_histogram_splits[
+ split_name
+ ]:
+ # Get index in the dataset that will return the data corresponding to the specified sample name
+ dataset_sample_index = raw_samples_indices[ref_hist_path]
+
+ image_tensor = datamodule._datasets[split_name][
+ dataset_sample_index
+ ][0]
+
+ histogram = []
+ for color_channel in image_tensor:
+ color_channel = numpy.multiply(
+ color_channel.numpy(), 255
+ ).astype(int)
+ histogram.extend(
+ numpy.histogram(
+ color_channel, bins=256, range=(0, 256)
+ )[0].tolist()
+ )
+
+ if compare_type == "statistical":
+ # Compute pearson coefficients between histogram and reference
+ # and check the similarity within a certain threshold
+ pearson_coeffs = numpy.corrcoef(histogram, ref_hist_data)
+ assert (
+ 1 - pearson_coeff_threshold <= pearson_coeffs[0][1] <= 1
+ )
+
+ else:
+ assert histogram == ref_hist_data
+
@pytest.fixture
def database_checkers():
diff --git a/tests/data/histograms/models/histograms_alexnet_montgomery_default.json b/tests/data/histograms/models/histograms_alexnet_montgomery_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..1bd79552b3632299535c127e8b53cffafbfb498a
--- /dev/null
+++ b/tests/data/histograms/models/histograms_alexnet_montgomery_default.json
@@ -0,0 +1,23 @@
+{
+ "train": [
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+ ],
+ "test": [
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+ ]
+}
diff --git a/tests/data/histograms/models/histograms_densenet-121_montgomery_default.json b/tests/data/histograms/models/histograms_densenet-121_montgomery_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..1bd79552b3632299535c127e8b53cffafbfb498a
--- /dev/null
+++ b/tests/data/histograms/models/histograms_densenet-121_montgomery_default.json
@@ -0,0 +1,23 @@
+{
+ "train": [
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+ ],
+ "test": [
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+ ]
+}
diff --git a/tests/data/histograms/models/histograms_pasa_montgomery_default.json b/tests/data/histograms/models/histograms_pasa_montgomery_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..35a810e71a1fce56032fde91603484fb2b920b60
--- /dev/null
+++ b/tests/data/histograms/models/histograms_pasa_montgomery_default.json
@@ -0,0 +1,146 @@
+{
+ "train": [
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+ ],
+ "test": [
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_hivtb_fold_0.json b/tests/data/histograms/raw_data/histograms_hivtb_fold_0.json
new file mode 100644
index 0000000000000000000000000000000000000000..0ec977bf5c2e7979f936eb6483aa8c9cf9809a43
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_hivtb_fold_0.json
@@ -0,0 +1,23 @@
+{
+ "train": [
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+ ["HIV-TB_Algorithm_study_X-rays/454.BMP", [0, 0, 0, 0, 1331269, 3, 7, 10, 3, 2, 16, 1, 817, 441, 591, 2760, 3780, 3369, 2987, 2697, 2187, 1052, 1295, 2070, 2047, 1997, 1824, 1987, 2395, 2119, 2089, 2310, 2303, 2426, 2589, 2936, 2458, 2432, 2385, 2566, 2351, 2452, 2331, 2236, 1901, 1923, 1865, 2056, 1879, 2031, 1785, 1896, 1852, 1949, 2072, 2104, 1900, 1962, 1945, 2098, 2442, 3217, 3297, 3313, 2478, 2168, 2251, 2133, 2412, 2549, 2898, 3112, 3328, 3561, 4663, 5072, 5880, 7198, 9476, 9308, 9995, 10801, 11992, 12769, 13408, 12462, 16846, 14388, 17835, 16531, 20467, 22202, 21232, 22316, 27499, 25508, 30467, 28069, 28528, 34728, 31912, 33417, 34946, 36871, 38617, 40015, 46548, 41495, 41946, 35897, 48026, 41363, 40981, 35094, 47066, 36147, 42977, 43846, 44771, 45192, 44920, 44501, 42916, 36120, 41822, 41677, 42405, 42110, 41515, 41173, 35614, 41436, 40952, 40720, 40208, 39158, 31929, 34417, 31980, 30186, 29213, 29124, 28674, 27821, 22411, 27598, 19190, 21989, 21362, 21330, 20580, 19611, 19204, 21020, 17782, 16941, 16311, 15686, 15628, 15600, 18113, 16434, 16156, 18679, 16730, 18708, 16550, 16477, 17997, 17698, 14784, 15875, 14139, 15683, 15771, 15865, 15106, 14633, 14768, 15331, 16121, 16900, 19649, 17308, 16002, 16899, 14509, 16180, 14100, 17839, 15045, 17430, 19696, 18336, 18541, 18785, 21135, 18535, 18044, 16865, 16643, 17180, 18525, 19338, 22672, 24171, 21718, 23257, 23095, 20776, 20597, 23265, 22594, 22121, 22775, 20578, 23356, 19330, 17073, 17147, 19915, 17618, 14573, 12111, 9137, 6227, 4210, 3478, 2785, 1899, 1157, 302, 83, 3, 3, 1, 13, 6, 0, 5, 90, 4, 3, 1, 15, 2, 7, 2, 264, 2, 4, 5, 22, 5, 6, 4847]]
+ ],
+ "test": [
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+ ["HIV-TB_Algorithm_study_X-rays/346.BMP", [0, 0, 0, 0, 959152, 4, 2, 12, 2, 1, 5, 1, 691, 1024, 5533, 13138, 15865, 12742, 10430, 11683, 14908, 9390, 4608, 3029, 1942, 1180, 1038, 1174, 1205, 1152, 1009, 1313, 1299, 1403, 1573, 1788, 1881, 2056, 2292, 2364, 2568, 2809, 3091, 3069, 3081, 2734, 2650, 2859, 2243, 2599, 2446, 2877, 2727, 2906, 3054, 3407, 2781, 3301, 3550, 3244, 4046, 3714, 3710, 4519, 4132, 4188, 4292, 4387, 3920, 4724, 4682, 4323, 5265, 4752, 6123, 5803, 6345, 7218, 9231, 8960, 10089, 10796, 11654, 12637, 13720, 14746, 15234, 15945, 13245, 16324, 16681, 17107, 14452, 18481, 19120, 16465, 20179, 16947, 20325, 17040, 19881, 16378, 19516, 16004, 16105, 19227, 15924, 19144, 15460, 15701, 18290, 15047, 15091, 15032, 18134, 15236, 15568, 15880, 19216, 16206, 16734, 16785, 16813, 16878, 20136, 17214, 17115, 17115, 17221, 20547, 17522, 17543, 17903, 18210, 18349, 22547, 19177, 19263, 19591, 19562, 19811, 24010, 19743, 19557, 19693, 23468, 19129, 19159, 19514, 23824, 20008, 20797, 25206, 21823, 26693, 22913, 23521, 29184, 25093, 31001, 26250, 31579, 27024, 32416, 26608, 31951, 31826, 26024, 31851, 31969, 32753, 27593, 33909, 34177, 34880, 35244, 36804, 37045, 38586, 39201, 39517, 40209, 47974, 40380, 39577, 43701, 35422, 39346, 31553, 35034, 33768, 28926, 37718, 27578, 31422, 31488, 35399, 30632, 30740, 30300, 34623, 34259, 29676, 33760, 33316, 33029, 32136, 36270, 31350, 35329, 30798, 32377, 33679, 27911, 26986, 26614, 30418, 28120, 28699, 27920, 31105, 30982, 28873, 29431, 33961, 36965, 40165, 37423, 37733, 31673, 24016, 17021, 13892, 9606, 5400, 2425, 804, 167, 17, 6, 88, 4, 3, 1, 9, 2, 4, 2, 267, 1, 3, 3, 10, 10, 7, 4459]],
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_indian_default.json b/tests/data/histograms/raw_data/histograms_indian_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c711a97186166c70c34bd23c740ac909e0de06c
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_indian_default.json
@@ -0,0 +1,23 @@
+{
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+ ],
+ "validation": [
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+ ],
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_montgomery_default.json b/tests/data/histograms/raw_data/histograms_montgomery_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e3a3dd28b51d80dc00fa0880ba923a7a0bf7fd1
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_montgomery_default.json
@@ -0,0 +1,146 @@
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_nih_cxr14_default.json b/tests/data/histograms/raw_data/histograms_nih_cxr14_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..fe06e26eecade3d0f0f122d7c7191f9e22743c63
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_nih_cxr14_default.json
@@ -0,0 +1,23 @@
+{
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_padchest_idiap.json b/tests/data/histograms/raw_data/histograms_padchest_idiap.json
new file mode 100644
index 0000000000000000000000000000000000000000..6572735024736e39a9b0bb7f963b3bd0c9c865aa
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_padchest_idiap.json
@@ -0,0 +1,9 @@
+{
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_shenzhen_default.json b/tests/data/histograms/raw_data/histograms_shenzhen_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..9eb1c2431f590aaa45e3f878da38578960a57711
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_shenzhen_default.json
@@ -0,0 +1,23 @@
+{
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+ ],
+ "test": [
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_tbpoc_fold_0.json b/tests/data/histograms/raw_data/histograms_tbpoc_fold_0.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec0ca5ada5fdf14161e20ecd2ac2f78862801092
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_tbpoc_fold_0.json
@@ -0,0 +1,23 @@
+{
+ "train": [
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+ ],
+ "validation": [
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+ ],
+ "test": [
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_tbx11k_v1_fold_0.json b/tests/data/histograms/raw_data/histograms_tbx11k_v1_fold_0.json
new file mode 100644
index 0000000000000000000000000000000000000000..98b893c3025ca34a66ad9f1515e4603f4297b4db
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_tbx11k_v1_fold_0.json
@@ -0,0 +1,23 @@
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+ ]
+}
diff --git a/tests/data/histograms/raw_data/histograms_tbx11k_v2_fold_0.json b/tests/data/histograms/raw_data/histograms_tbx11k_v2_fold_0.json
new file mode 100644
index 0000000000000000000000000000000000000000..92c0513e69e2e5452023ba16697e78661a839727
--- /dev/null
+++ b/tests/data/histograms/raw_data/histograms_tbx11k_v2_fold_0.json
@@ -0,0 +1,23 @@
+{
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+ ]
+}
diff --git a/tests/data/lfs b/tests/data/lfs
index 05344d20182ad4169fc5b9c38052d629aded30ed..fe158e393b7904c6b381b0d9862895bf05c6cf7a 160000
--- a/tests/data/lfs
+++ b/tests/data/lfs
@@ -1 +1 @@
-Subproject commit 05344d20182ad4169fc5b9c38052d629aded30ed
+Subproject commit fe158e393b7904c6b381b0d9862895bf05c6cf7a
diff --git a/tests/data/mednet.toml b/tests/data/mednet.toml
new file mode 100644
index 0000000000000000000000000000000000000000..1eccba371435f50739c65227ae46bbae4d33375a
--- /dev/null
+++ b/tests/data/mednet.toml
@@ -0,0 +1,5 @@
+[datadir]
+montgomery = "/idiap/resource/database/MontgomeryXraySet"
+shenzhen = "/idiap/resource/database/ShenzhenXraySet"
+indian = "/idiap/resource/database/TBXpredict"
+tbx11k = "/idiap/resource/database/tbx11k"
diff --git a/tests/data/test_visualization_images/indirect-model/tbx11k/ablationcam/targeted_class/test/tb0004.png b/tests/data/test_visualization_images/indirect-model/tbx11k/ablationcam/targeted_class/test/tb0004.png
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diff --git a/tests/data/test_visualization_images/indirect-model/tbx11k/fullgrad/targeted_class/test/tb0004.png b/tests/data/test_visualization_images/indirect-model/tbx11k/fullgrad/targeted_class/test/tb0004.png
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Binary files a/tests/data/test_visualization_images/indirect-model/tbx11k/scorecam/targeted_class/train/tb0005.png and /dev/null differ
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 4828e1d09ca51d1b06c5cb3c29fb1f8b62ddccd0..3382c88f4022e15378e8a830e40ef829667b975f 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -436,108 +436,97 @@ def test_evaluate_pasa_montgomery(temporary_basedir):
)
-# This script does not work anymore, either fix or remove the script + this test
-# def test_evaluatevis(temporary_basedir):
-# import pandas as pd
-
-# from mednet.scripts.evaluatevis import evaluatevis
-
-# runner = CliRunner()
-
-# # Create a sample directory structure and CSV files
-# input_folder = temporary_basedir / "camutils_cli" / "gradcam"
-# input_folder.mkdir(parents=True, exist_ok=True)
-# class1_dir = input_folder / "class1"
-# class1_dir.mkdir(parents=True, exist_ok=True)
-# class2_dir = input_folder / "class2"
-# class2_dir.mkdir(parents=True, exist_ok=True)
-
-# data = {
-# "MoRF": [1, 2, 3],
-# "LeRF": [2, 4, 6],
-# "Combined Score ((LeRF-MoRF) / 2)": [1.5, 3, 4.5],
-# "IoU": [1, 2, 3],
-# "IoDA": [2, 4, 6],
-# "propEnergy": [1.5, 3, 4.5],
-# "ASF": [1, 2, 3],
-# }
-# df = pd.DataFrame(data)
-# df.to_csv(class1_dir / "file1.csv", index=False)
-# df.to_csv(class2_dir / "file1.csv", index=False)
-# df.to_csv(class1_dir / "file2.csv", index=False)
-# df.to_csv(class2_dir / "file2.csv", index=False)
-
-# result = runner.invoke(evaluatevis, ["-vv", "-i", str(input_folder)])
-
-# assert result.exit_code == 0
-
-# assert (input_folder / "file1_summary.csv").exists()
-# assert (input_folder / "file2_summary.csv").exists()
-
-
-# Not enough RAM available to do this test
-# @pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
-# def test_predict_densenetrs_montgomery(temporary_basedir, datadir):
-
-# from mednet.scripts.predict import predict
-
-# runner = CliRunner()
-
-# with stdout_logging() as buf:
-
-# output_folder = str(temporary_basedir / "predictions")
-# result = runner.invoke(
-# predict,
-# [
-# "densenet_rs",
-# "montgomery_f0_rgb",
-# "-vv",
-# "--batch-size=1",
-# f"--weight={str(datadir / 'lfs' / 'models' / 'densenetrs.pth')}",
-# f"--output-folder={output_folder}",
-# "--grad-cams"
-# ],
-# )
-# _assert_exit_0(result)
-
-# # check predictions are there
-# predictions_file1 = os.path.join(output_folder, "train/predictions.csv")
-# predictions_file2 = os.path.join(output_folder, "validation/predictions.csv")
-# predictions_file3 = os.path.join(output_folder, "test/predictions.csv")
-# assert os.path.exists(predictions_file1)
-# assert os.path.exists(predictions_file2)
-# assert os.path.exists(predictions_file3)
-# # check some grad cams are there
-# cam1 = os.path.join(output_folder, "train/cams/MCUCXR_0002_0_cam.png")
-# cam2 = os.path.join(output_folder, "train/cams/MCUCXR_0126_1_cam.png")
-# cam3 = os.path.join(output_folder, "train/cams/MCUCXR_0275_1_cam.png")
-# cam4 = os.path.join(output_folder, "validation/cams/MCUCXR_0399_1_cam.png")
-# cam5 = os.path.join(output_folder, "validation/cams/MCUCXR_0113_1_cam.png")
-# cam6 = os.path.join(output_folder, "validation/cams/MCUCXR_0013_0_cam.png")
-# cam7 = os.path.join(output_folder, "test/cams/MCUCXR_0027_0_cam.png")
-# cam8 = os.path.join(output_folder, "test/cams/MCUCXR_0094_0_cam.png")
-# cam9 = os.path.join(output_folder, "test/cams/MCUCXR_0375_1_cam.png")
-# assert os.path.exists(cam1)
-# assert os.path.exists(cam2)
-# assert os.path.exists(cam3)
-# assert os.path.exists(cam4)
-# assert os.path.exists(cam5)
-# assert os.path.exists(cam6)
-# assert os.path.exists(cam7)
-# assert os.path.exists(cam8)
-# assert os.path.exists(cam9)
-
-# keywords = {
-# r"^Loading checkpoint from.*$": 1,
-# r"^Total time:.*$": 3,
-# r"^Grad cams folder:.*$": 3,
-# }
-# buf.seek(0)
-# logging_output = buf.read()
-
-# for k, v in keywords.items():
-# assert _str_counter(k, logging_output) == v, (
-# f"Count for string '{k}' appeared "
-# f"({_str_counter(k, logging_output)}) "
-# f"instead of the expected {v}:\nOutput:\n{logging_output}"
-# )
+@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
+def test_experiment(temporary_basedir):
+ from mednet.scripts.experiment import experiment
+
+ runner = CliRunner()
+
+ output_folder = str(temporary_basedir / "experiment")
+ num_epochs = 2
+ result = runner.invoke(
+ experiment,
+ [
+ "-vv",
+ "pasa",
+ "montgomery",
+ f"--epochs={num_epochs}",
+ f"--output-folder={output_folder}",
+ ],
+ )
+ _assert_exit_0(result)
+
+ assert os.path.exists(os.path.join(output_folder, "command.sh"))
+ assert os.path.exists(os.path.join(output_folder, "predictions.json"))
+ assert os.path.exists(os.path.join(output_folder, "model", "command.sh"))
+ assert os.path.exists(os.path.join(output_folder, "model", "constants.csv"))
+ assert os.path.exists(
+ os.path.join(
+ output_folder, "model", f"model-at-epoch={num_epochs-1}.ckpt"
+ )
+ )
+ # Need to glob because we cannot be sure of the checkpoint with lowest validation loss
+ assert (
+ len(
+ glob.glob(
+ os.path.join(
+ output_folder,
+ "model",
+ "model-at-lowest-validation-loss-epoch=*.ckpt",
+ )
+ )
+ )
+ == 1
+ )
+ assert os.path.exists(
+ os.path.join(output_folder, "model", "model-summary.txt")
+ )
+ assert os.path.exists(os.path.join(output_folder, "model", "trainlog.pdf"))
+ assert (
+ len(
+ glob.glob(
+ os.path.join(
+ output_folder, "model", "logs", "events.out.tfevents.*"
+ )
+ )
+ )
+ == 1
+ )
+ assert os.path.exists(
+ os.path.join(output_folder, "evaluation", "plots.pdf")
+ )
+ assert os.path.exists(
+ os.path.join(output_folder, "evaluation", "summary.rst")
+ )
+ assert os.path.exists(os.path.join(output_folder, "gradcam", "saliencies"))
+ assert (
+ len(
+ glob.glob(
+ os.path.join(
+ output_folder,
+ "gradcam",
+ "saliencies",
+ "CXR_png",
+ "MCUCXR_*.npy",
+ )
+ )
+ )
+ == 138
+ )
+ assert os.path.exists(
+ os.path.join(output_folder, "gradcam", "visualizations")
+ )
+ assert (
+ len(
+ glob.glob(
+ os.path.join(
+ output_folder,
+ "gradcam",
+ "visualizations",
+ "CXR_png",
+ "MCUCXR_*.png",
+ )
+ )
+ )
+ == 58
+ )
diff --git a/tests/test_hivtb.py b/tests/test_hivtb.py
index 00b7a484beda022cebde19a71cf272a16b1cb12c..44c1cca3f702dc8776395c2702bc7c0b6533f105 100644
--- a/tests/test_hivtb.py
+++ b/tests/test_hivtb.py
@@ -87,5 +87,22 @@ def test_loading(database_checkers, name: str, dataset: str):
color_planes=1,
prefixes=("HIV-TB_Algorithm_study_X-rays",),
possible_labels=(0, 1),
+ expected_num_labels=1,
)
limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.hivtb")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_hivtb_fold_0.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".fold_0", "mednet.config.data.hivtb"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_indian.py b/tests/test_indian.py
index 66ffdef3c5e0cbc570fd8b5020279cc8ef755c4f..3a959e60783c4f6534c55352b0b933095ed9c093 100644
--- a/tests/test_indian.py
+++ b/tests/test_indian.py
@@ -92,5 +92,22 @@ def test_loading(database_checkers, name: str, dataset: str):
color_planes=1,
prefixes=("DatasetA/Training", "DatasetA/Testing"),
possible_labels=(0, 1),
+ expected_num_labels=1,
)
limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.indian")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_indian_default.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".default", "mednet.config.data.indian"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_montgomery.py b/tests/test_montgomery.py
index 6933e9825c7398bb0c6b65b7f37794a98aa11cc7..4ce6258064a64507ccdc2714b9aa16c957961a36 100644
--- a/tests/test_montgomery.py
+++ b/tests/test_montgomery.py
@@ -89,5 +89,63 @@ def test_loading(database_checkers, name: str, dataset: str):
color_planes=1,
prefixes=("CXR_png/MCUCXR_0",),
possible_labels=(0, 1),
+ expected_num_labels=1,
)
limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
+def test_raw_transforms_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_montgomery_default.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".default", "mednet.config.data.montgomery"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
+@pytest.mark.parametrize(
+ "model_name",
+ [
+ "alexnet",
+ "densenet",
+ "pasa",
+ ],
+)
+def test_model_transforms_image_quality(database_checkers, datadir, model_name):
+ # Densenet's model.name is "densenet-212" and does not correspond to its module name.
+ if model_name == "densenet":
+ reference_histogram_file = str(
+ datadir
+ / "histograms/models/histograms_densenet-121_montgomery_default.json"
+ )
+ else:
+ reference_histogram_file = str(
+ datadir
+ / f"histograms/models/histograms_{model_name}_montgomery_default.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".default", "mednet.config.data.montgomery"
+ ).datamodule
+
+ model = importlib.import_module(
+ f".{model_name}", "mednet.config.models"
+ ).model
+
+ datamodule.model_transforms = model.model_transforms
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(
+ datamodule,
+ reference_histogram_file,
+ compare_type="statistical",
+ pearson_coeff_threshold=0.005,
+ )
diff --git a/tests/test_nih_cxr14.py b/tests/test_nih_cxr14.py
index 8188c7cb3d3650b71a445fba484b40369c283f52..1790dbc77007c77a320745251ea0b93b3ffcd3a8 100644
--- a/tests/test_nih_cxr14.py
+++ b/tests/test_nih_cxr14.py
@@ -35,22 +35,18 @@ def test_protocol_consistency(
)
+testdata = [
+ ("default", "train", 14),
+ ("default", "validation", 14),
+ ("default", "test", 14),
+ ("cardiomegaly", "train", 14),
+ ("cardiomegaly", "validation", 14),
+]
+
+
@pytest.mark.skip_if_rc_var_not_set("datadir.nih_cxr14")
-@pytest.mark.parametrize(
- "dataset",
- [
- "train",
- "validation",
- "test",
- ],
-)
-@pytest.mark.parametrize(
- "name",
- [
- "default",
- ],
-)
-def test_loading(database_checkers, name: str, dataset: str):
+@pytest.mark.parametrize("name,dataset,num_labels", testdata)
+def test_loading(database_checkers, name: str, dataset: str, num_labels: int):
datamodule = importlib.import_module(
f".{name}", "mednet.config.data.nih_cxr14"
).datamodule
@@ -70,9 +66,23 @@ def test_loading(database_checkers, name: str, dataset: str):
color_planes=1,
prefixes=("images/000",),
possible_labels=(0, 1),
+ expected_num_labels=num_labels,
+ expected_image_shape=(1, 1024, 1024),
)
limit -= 1
-# TODO: check size 1024x1024
-# TODO: check there are 14 binary labels (0, 1)
+@pytest.mark.skip_if_rc_var_not_set("datadir.nih_cxr14")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_nih_cxr14_default.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".default", "mednet.config.data.nih_cxr14"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_padchest.py b/tests/test_padchest.py
index dfa491c879880ec01126dc11e4a112f775abaac0..68e24077725172230bc7ecd76acf367eb1ffde0d 100644
--- a/tests/test_padchest.py
+++ b/tests/test_padchest.py
@@ -40,21 +40,18 @@ def test_protocol_consistency(
)
-# TODO: Improve this to include other protocols
+testdata = [
+ ("idiap", "train", 193),
+ ("idiap", "test", 1),
+ ("tb_idiap", "train", 1),
+ ("no_tb_idiap", "train", 14),
+ ("cardiomegaly_idiap", "train", 14),
+]
+
+
@pytest.mark.skip_if_rc_var_not_set("datadir.padchest")
-@pytest.mark.parametrize(
- "dataset",
- [
- "train",
- ],
-)
-@pytest.mark.parametrize(
- "name",
- [
- "idiap",
- ],
-)
-def test_loading(database_checkers, name: str, dataset: str):
+@pytest.mark.parametrize("name,dataset,num_labels", testdata)
+def test_loading(database_checkers, name: str, dataset: str, num_labels: int):
datamodule = importlib.import_module(
f".{name}", "mednet.config.data.padchest"
).datamodule
@@ -62,22 +59,35 @@ def test_loading(database_checkers, name: str, dataset: str):
datamodule.model_transforms = [] # should be done before setup()
datamodule.setup("predict") # sets up all datasets
- loader = datamodule.predict_dataloader()[dataset]
-
- limit = 3 # limit load checking
- for batch in loader:
- if limit == 0:
- break
- database_checkers.check_loaded_batch(
- batch,
- batch_size=1,
- color_planes=1,
- prefixes=("",),
- possible_labels=(0, 1),
- )
- limit -= 1
-
-
-# TODO: check size 1024x1024
-# TODO: check there are 14 binary labels (0, 1) (in some cases, in others much
-# more)...
+ if dataset in datamodule.predict_dataloader():
+ loader = datamodule.predict_dataloader()[dataset]
+
+ limit = 3 # limit load checking
+ for batch in loader:
+ if limit == 0:
+ break
+ database_checkers.check_loaded_batch(
+ batch,
+ batch_size=1,
+ color_planes=1,
+ prefixes=("",),
+ possible_labels=(0, 1),
+ expected_num_labels=num_labels,
+ )
+ limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.padchest")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_padchest_idiap.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".idiap", "mednet.config.data.padchest"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_saliencymap_evaluator.py b/tests/test_saliencymap_interpretability.py
similarity index 100%
rename from tests/test_saliencymap_evaluator.py
rename to tests/test_saliencymap_interpretability.py
diff --git a/tests/test_shenzhen.py b/tests/test_shenzhen.py
index 6658831bdcb973aaa095385af28418d9ec4b2297..42b23ce16b50d7303bdd1e14bfe6d0199c57623e 100644
--- a/tests/test_shenzhen.py
+++ b/tests/test_shenzhen.py
@@ -89,5 +89,22 @@ def test_loading(database_checkers, name: str, dataset: str):
color_planes=1,
prefixes=("CXR_png/CHNCXR_0",),
possible_labels=(0, 1),
+ expected_num_labels=1,
)
limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.shenzhen")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_shenzhen_default.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".default", "mednet.config.data.shenzhen"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_tbpoc.py b/tests/test_tbpoc.py
index a2704fa71c1006bc42a28c3facd0843cc4698ba7..44e742789684bf823cccdaeca539e0ee0f2f0482 100644
--- a/tests/test_tbpoc.py
+++ b/tests/test_tbpoc.py
@@ -93,5 +93,22 @@ def test_loading(database_checkers, name: str, dataset: str):
"TBPOC_CXR/tbpoc-",
),
possible_labels=(0, 1),
+ expected_num_labels=1,
)
limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.tbpoc")
+def test_loaded_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_tbpoc_fold_0.json"
+ )
+
+ datamodule = importlib.import_module(
+ ".fold_0", "mednet.config.data.tbpoc"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
diff --git a/tests/test_tbx11k.py b/tests/test_tbx11k.py
index b1bbcc1f9f9a1d6473fd380a7f360fa298ae2183..5d1f584c1c79e0b420ec813d6dcc71d11ca3ecc1 100644
--- a/tests/test_tbx11k.py
+++ b/tests/test_tbx11k.py
@@ -151,29 +151,42 @@ def test_protocol_consistency(
def check_loaded_batch(
batch,
batch_size: int,
+ color_planes: int,
prefixes: typing.Sequence[str],
+ possible_labels: typing.Sequence[int],
+ expected_num_labels: int,
+ expected_image_shape: typing.Optional[tuple[int, ...]] = None,
):
- """Check the consistence of an individual (loaded) batch.
+ """Check the consistency of an individual (loaded) batch.
Parameters
----------
-
batch
The loaded batch to be checked.
batch_size
The mini-batch size.
+ color_planes
+ The number of color planes in the images.
prefixes
Each file named in a split should start with at least one of these
prefixes.
+ possible_labels
+ These are the list of possible labels contained in any split.
+ expected_num_labels
+ The expected number of labels each sample should have.
+ expected_image_shape
+ The expected shape of the image (num_channels, width, height).
"""
assert len(batch) == 2 # data, metadata
assert isinstance(batch[0], torch.Tensor)
assert batch[0].shape[0] == batch_size # mini-batch size
- assert batch[0].shape[1] == 3 # grayscale images
+ assert batch[0].shape[1] == color_planes
assert batch[0].shape[2] == batch[0].shape[3] # image is square
- assert batch[0].shape[2] == 512 # image is 512 pixels large
+
+ if expected_image_shape:
+ assert all([data.shape == expected_image_shape for data in batch[0]])
assert isinstance(batch[1], dict) # metadata
assert (
@@ -181,7 +194,10 @@ def check_loaded_batch(
) # label, name and radiological sign bounding-boxes
assert "label" in batch[1]
- assert all([k in (0, 1) for k in batch[1]["label"]])
+ assert all([k in possible_labels for k in batch[1]["label"]])
+
+ if expected_num_labels:
+ assert len(batch[1]["label"]) == expected_num_labels
assert "name" in batch[1]
assert all(
@@ -268,6 +284,33 @@ def test_loading(name: str, dataset: str, prefixes: typing.Sequence[str]):
check_loaded_batch(
batch,
batch_size=1,
+ color_planes=3,
prefixes=prefixes,
+ possible_labels=(0, 1),
+ expected_num_labels=1,
+ expected_image_shape=(3, 512, 512),
)
limit -= 1
+
+
+@pytest.mark.parametrize(
+ "split",
+ [
+ "v1_fold_0",
+ "v2_fold_0",
+ ],
+)
+@pytest.mark.skip_if_rc_var_not_set("datadir.tbx11k")
+def test_loaded_image_quality(database_checkers, datadir, split):
+ reference_histogram_file = str(
+ datadir / f"histograms/raw_data/histograms_tbx11k_{split}.json"
+ )
+
+ datamodule = importlib.import_module(
+ f".{split}", "mednet.config.data.tbx11k"
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)