Fix/da/image size bug (#135)

* fix image size bug and add test case

* fix padim inference

* docstrings

* use dummy dataset for image size tests

* detach before moving to cpu

anomalib/data/__init__.py

@@ -39,7 +39,7 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule
             # TODO: Remove config values. IAAALD-211
             root=config.dataset.path,
             category=config.dataset.category,
-            image_size=(config.dataset.image_size[0], config.dataset.image_size[0]),
+            image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
             train_batch_size=config.dataset.train_batch_size,
             test_batch_size=config.dataset.test_batch_size,
             num_workers=config.dataset.num_workers,

anomalib/deploy/inferencers/torch.py

@@ -154,7 +154,7 @@ def post_process(
         anomaly_map, pred_score = self._normalize(anomaly_map, pred_score, meta_data)
 
         if isinstance(anomaly_map, Tensor):
-            anomaly_map = anomaly_map.cpu().numpy()
+            anomaly_map = anomaly_map.detach().cpu().numpy()
 
         if "image_shape" in meta_data and anomaly_map.shape != meta_data["image_shape"]:
             anomaly_map = cv2.resize(anomaly_map, meta_data["image_shape"])

anomalib/models/padim/model.py

@@ -82,7 +82,7 @@ def __init__(
 
         n_features = DIMS[backbone]["reduced_dims"]
         patches_dims = torch.tensor(input_size) / DIMS[backbone]["emb_scale"]
-        n_patches = patches_dims.prod().int().item()
+        n_patches = patches_dims.ceil().prod().int().item()
         self.gaussian = MultiVariateGaussian(n_features, n_patches)
 
         if apply_tiling:

tests/pre_merge/datasets/test_dataset.py

@@ -3,9 +3,11 @@
 import numpy as np
 import pytest
 
+from anomalib.config import get_configurable_parameters, update_input_size_config
+from anomalib.data import get_datamodule
 from anomalib.data.mvtec import MVTecDataModule
 from anomalib.pre_processing.transforms import Denormalize, ToNumpy
-from tests.helpers.dataset import get_dataset_path
+from tests.helpers.dataset import TestDataset, get_dataset_path
 
 
 @pytest.fixture(autouse=True)
@@ -100,3 +102,30 @@ def test_one_channel_images(self, data_sample):
     def test_representation(self):
         """Test ToNumpy() representation should return string `ToNumpy()`"""
         assert str(ToNumpy()) == "ToNumpy()"
+
+
+class TestConfigToDataModule:
+    """Tests that check if the dataset parameters in the config achieve the desired effect."""
+
+    @pytest.mark.parametrize(
+        ["input_size", "effective_image_size"],
+        [
+            (512, (512, 512)),
+            ((245, 276), (245, 276)),
+            ((263, 134), (263, 134)),
+            ((267, 267), (267, 267)),
+        ],
+    )
+    @TestDataset(num_train=20, num_test=10)
+    def test_image_size(self, input_size, effective_image_size, category="shapes", path=""):
+        """Test if the image size parameter works as expected."""
+        model_name = "stfpm"
+        configurable_parameters = get_configurable_parameters(model_name)
+        configurable_parameters.dataset.path = path
+        configurable_parameters.dataset.category = category
+        configurable_parameters.dataset.image_size = input_size
+        configurable_parameters = update_input_size_config(configurable_parameters)
+
+        data_module = get_datamodule(configurable_parameters)
+        data_module.setup()
+        assert iter(data_module.train_dataloader()).__next__()["image"].shape[-2:] == effective_image_size