Fix Issues when running tests on the CI

Several issues relating to running tests on the CI have been fixed.

• Databases are now directly available on the CI. There is no need to load them from the ci-assets package.
• Some tests checking the model transforms were failing due to differences in the environment when testing locally vs on the CI. A statistical approach was used to handle this.

tests/conftest.py

@@ -5,11 +5,10 @@
 import pathlib
 import typing
 
+import numpy
 import pytest
 import torch
 
-from torchvision.transforms.functional import to_pil_image
-
 from mednet.data.split import JSONDatabaseSplit
 from mednet.data.typing import DatabaseSplit
 
@@ -204,7 +203,7 @@ class DatabaseCheckers:
 
     @staticmethod
     def check_image_quality(
-        datamodule, reference_histogram_file, histogram_edges_threshold=2
+        datamodule, reference_histogram_file, pearson_coeff_threshold=0.005
     ):
         ref_histogram_splits = JSONDatabaseSplit(reference_histogram_file)
 
@@ -226,19 +225,22 @@ class DatabaseCheckers:
                 image_tensor = datamodule._datasets[split_name][
                     dataset_sample_index
                 ][0]
-                img = to_pil_image(image_tensor)
-                histogram = img.histogram()
-
-                # The histograms do not exacly match due to the torch resize transform
-                # acting differently depending on the environment.
-                assert (
-                    histogram[
-                        histogram_edges_threshold:-histogram_edges_threshold
-                    ]
-                    == ref_hist_data[
-                        histogram_edges_threshold:-histogram_edges_threshold
-                    ]
+
+                image_tensor = numpy.multiply(image_tensor.numpy(), 255).astype(
+                    int
                 )
+                histogram = numpy.histogram(
+                    image_tensor, bins=256, range=(0, 256)
+                )[0].tolist()
+
+                # We cannot test if histograms are exactly equal because
+                # the torch.resize transform is inconsistent depending on the environment.
+                # assert histogram == ref_hist_data
+
+                # 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
 
 
 @pytest.fixture