Merge branch 'development' into graph-laplacian

tests/test_augmentation.py

@@ -9,7 +9,8 @@
 from grace.base import GraphAttrs, Annotation
 from grace.utils.augment_image import (
     RandomEdgeCrop,
-)  # , RandomImageGraphRotate
+    RandomImageGraphRotate,
+)
 from grace.utils.augment_graph import (
     find_average_annotation,
     RandomEdgeAdditionAndRemoval,
@@ -146,51 +147,48 @@ def test_augment_random_edge_crop(n, max_fraction, fraction, num_rot):
 ]
 
 
-# @pytest.mark.parametrize(
-#     "n, rot_angle, rot_center",
-#     [
-#         (0, 0, None),
-#         (1, 90, None),
-#         (2, 45, None),
-#         (3, 30, None),
-#         (4, 28, None),
-#         (5, 28, [2, 2]),
-#     ],
-# )
-# def test_augment_rotate_image_and_graph(n, rot_angle, rot_center):
-#     print ("hello-1")
-#     with patch("numpy.random.default_rng") as mock:
-#         rng = mock.return_value
-#         rng.uniform.return_value = 0
-#         rng.integers.return_value = augment_rotate_coords[n]
-#         rng.choice.return_value = [1] * 4
-#         image, graph = random_image_and_graph(rng, image_size=(6, 6))
-#     print ("hello-2")
-
-#     image = torch.tensor(image.astype("int16"))
-#     target = {"graph": graph}
-
-#     with patch("numpy.random.default_rng") as mock:
-#         rng = mock.return_value
-#         rng.uniform.return_value = rot_angle
-#         transform = RandomImageGraphRotate(rot_center=rot_center, rng=rng)
-#         image, target = transform(image, target)
-#     print ("hello-3")
-
-#     augmented_img_coords = np.where(image.squeeze().numpy())
-#     augmented_float_coords = np.array(
-#         [
-#             [f[GraphAttrs.NODE_X], f[GraphAttrs.NODE_Y]]
-#             for f in target["graph"].nodes.values()
-#         ],
-#         dtype=np.float32,
-#     )
-#     print ("hello-4")
-
-#     assert np.array_equal(expected_end_coords_img[n], augmented_img_coords)
-#     assert np.allclose(
-#         expected_end_coords_float[n], augmented_float_coords, atol=0.01
-#     )
+@pytest.mark.skip(reason="augmentations throw an error for some reason")
+@pytest.mark.parametrize(
+    "n, rot_angle, rot_center",
+    [
+        (0, 0, None),
+        (1, 90, None),
+        (2, 45, None),
+        (3, 30, None),
+        (4, 28, None),
+        (5, 28, [2, 2]),
+    ],
+)
+def test_augment_rotate_image_and_graph(n, rot_angle, rot_center):
+    with patch("numpy.random.default_rng") as mock:
+        rng = mock.return_value
+        rng.uniform.return_value = 0
+        rng.integers.return_value = augment_rotate_coords[n]
+        rng.choice.return_value = [1] * 4
+        image, graph = random_image_and_graph(rng, image_size=(6, 6))
+
+    image = torch.tensor(image.astype("int16"))
+    target = {"graph": graph}
+
+    with patch("numpy.random.default_rng") as mock:
+        rng = mock.return_value
+        rng.uniform.return_value = rot_angle
+        transform = RandomImageGraphRotate(rot_center=rot_center, rng=rng)
+        image, target = transform(image, target)
+
+    augmented_img_coords = np.where(image.squeeze().numpy())
+    augmented_float_coords = np.array(
+        [
+            [f[GraphAttrs.NODE_X], f[GraphAttrs.NODE_Y]]
+            for f in target["graph"].nodes.values()
+        ],
+        dtype=np.float32,
+    )
+
+    assert np.array_equal(expected_end_coords_img[n], augmented_img_coords)
+    assert np.allclose(
+        expected_end_coords_float[n], augmented_float_coords, atol=0.01
+    )
 
 
 @pytest.mark.parametrize(

tests/test_extractor.py

@@ -1,8 +1,8 @@
-# import math
+import math
 import torch
 import pytest
 
-from grace.base import GraphAttrs  # , Annotation
+from grace.base import GraphAttrs, Annotation
 from grace.models.feature_extractor import FeatureExtractor
 
 from conftest import random_image_and_graph
@@ -55,47 +55,48 @@ def test_feature_extractor_forward(self, bbox_size, model, vars):
             if bbox_image.shape == bbox_size:
                 assert features == model(bbox_image)
 
-    # @pytest.mark.parametrize("keep_patch_fraction", [0.3, 0.5, 0.7])
-    # def test_feature_extractor_rejects_edge_touching_boxes(
-    #     self, bbox_size, model, vars, keep_patch_fraction
-    # ):
-    #     """TODO: There's a bug here if parametrised with:
-    #         "keep_patch_fraction", [0.3, 0.5, 0.7] """
-    #     extractor = vars["extractor"]
-    #     image = vars["image"]
-    #     graph = vars["graph"]
-
-    #     setattr(extractor, "keep_patch_fraction", keep_patch_fraction)
-    #     graph.add_node(
-    #         4,
-    #         **{
-    #             GraphAttrs.NODE_X: bbox_size[0] * 0.5
-    #             + math.ceil(
-    #                 image.size(-1) - bbox_size[0] * keep_patch_fraction * 0.99
-    #             ),
-    #             GraphAttrs.NODE_Y: 0,
-    #             GraphAttrs.NODE_GROUND_TRUTH: Annotation.TRUE_POSITIVE,
-    #         },
-    #     )
-    #     graph.add_node(
-    #         5,
-    #         **{
-    #             GraphAttrs.NODE_X: 0,
-    #             GraphAttrs.NODE_Y: bbox_size[1] * 0.5
-    #             + math.floor(-bbox_size[1] * keep_patch_fraction * 1.01),
-    #             GraphAttrs.NODE_GROUND_TRUTH: Annotation.TRUE_POSITIVE,
-    #         },
-    #     )
-
-    #     _, target_out = extractor(image, {"graph": graph})
-    #     graph_out = target_out["graph"]
-    #     print(bbox_size)
-    #     print(graph_out.number_of_nodes(), graph_out.nodes(data=True))
-    #     labels = [
-    #         node_attr[GraphAttrs.NODE_GROUND_TRUTH]
-    #         for _, node_attr in graph_out.nodes(data=True)
-    #     ]
-
-    #     num_unknown = len([lab for lab in labels if lab == Annotation.UNKNOWN])
-
-    #     assert num_unknown == 2
+    @pytest.mark.skip(reason="keep patch fraction needs to be re-implemented")
+    @pytest.mark.parametrize("keep_patch_fraction", [0.3, 0.5, 0.7])
+    def test_feature_extractor_rejects_edge_touching_boxes(
+        self, bbox_size, model, vars, keep_patch_fraction
+    ):
+        """TODO: There's a bug here if parametrised with:
+        "keep_patch_fraction", [0.3, 0.5, 0.7]"""
+        extractor = vars["extractor"]
+        image = vars["image"]
+        graph = vars["graph"]
+
+        setattr(extractor, "keep_patch_fraction", keep_patch_fraction)
+        graph.add_node(
+            4,
+            **{
+                GraphAttrs.NODE_X: bbox_size[0] * 0.5
+                + math.ceil(
+                    image.size(-1) - bbox_size[0] * keep_patch_fraction * 0.99
+                ),
+                GraphAttrs.NODE_Y: 0,
+                GraphAttrs.NODE_GROUND_TRUTH: Annotation.TRUE_POSITIVE,
+            },
+        )
+        graph.add_node(
+            5,
+            **{
+                GraphAttrs.NODE_X: 0,
+                GraphAttrs.NODE_Y: bbox_size[1] * 0.5
+                + math.floor(-bbox_size[1] * keep_patch_fraction * 1.01),
+                GraphAttrs.NODE_GROUND_TRUTH: Annotation.TRUE_POSITIVE,
+            },
+        )
+
+        _, target_out = extractor(image, {"graph": graph})
+        graph_out = target_out["graph"]
+        print(bbox_size)
+        print(graph_out.number_of_nodes(), graph_out.nodes(data=True))
+        labels = [
+            node_attr[GraphAttrs.NODE_GROUND_TRUTH]
+            for _, node_attr in graph_out.nodes(data=True)
+        ]
+
+        num_unknown = len([lab for lab in labels if lab == Annotation.UNKNOWN])
+
+        assert num_unknown == 2

tests/test_run.py

@@ -1,33 +1,66 @@
-# import torch
-
-# from grace.run import run_grace
-# from grace.training.config import Config, write_config_file
-
-
-# def test_run_grace(mrc_image_and_annotations_dir, simple_extractor):
-#     tmp_data_dir = mrc_image_and_annotations_dir
-
-#     # temp extractor
-#     extractor_fn = tmp_data_dir / "extractor.pt"
-#     torch.save(simple_extractor, extractor_fn)
-
-#     config = Config(
-#         train_image_dir=tmp_data_dir,
-#         train_grace_dir=tmp_data_dir,
-#         valid_image_dir=tmp_data_dir,
-#         valid_grace_dir=tmp_data_dir,
-#         infer_image_dir=tmp_data_dir,
-#         infer_grace_dir=tmp_data_dir,
-#         log_dir=tmp_data_dir,
-#         run_dir=tmp_data_dir,
-#         extractor_fn=extractor_fn,
-#         epochs=3,
-#         batch_size=1,
-#         patch_size=(1, 1),
-#         feature_dim=2,
+import pytest
+import torch
+
+from grace.run import run_grace
+from grace.training.config import Config, write_config_file
+
+
+@pytest.mark.xfail(
+    reason="sample graph contains no node features & edge properties"
+)
+def test_run_grace(mrc_image_and_annotations_dir, simple_extractor):
+    tmp_data_dir = mrc_image_and_annotations_dir
+
+    # temp extractor
+    extractor_fn = tmp_data_dir / "extractor.pt"
+    torch.save(simple_extractor, extractor_fn)
+
+    config = Config(
+        train_image_dir=tmp_data_dir,
+        train_grace_dir=tmp_data_dir,
+        valid_image_dir=tmp_data_dir,
+        valid_grace_dir=tmp_data_dir,
+        infer_image_dir=tmp_data_dir,
+        infer_grace_dir=tmp_data_dir,
+        log_dir=tmp_data_dir,
+        run_dir=tmp_data_dir,
+        extractor_fn=extractor_fn,
+        epochs=1,
+        batch_size=1,
+        patch_size=(1, 1),
+        feature_dim=2,
+    )
+    write_config_file(config, filetype="json")
+
+    # run
+    config_fn = tmp_data_dir / "config_hyperparams.json"
+    run_grace(config_file=config_fn)
+
+
+# def test_run_grace_without_required_graph_attributes(
+#     mrc_image_and_annotations_dir,
+#     simple_extractor,
+# ):
+#     run_grace_training(
+#         mrc_image_and_annotations_dir,
+#         simple_extractor,
+#         store_graph_attributes_permanently,
 #     )
-#     write_config_file(config, filetype="json")
 
-#     # run
-#     config_fn = tmp_data_dir / "config_hyperparams.json"
-#     run_grace(config_file=config_fn)
+
+# @pytest.mark.parametrize(
+#     "store_graph_attributes_permanently",
+#     [
+#         True,
+#     ],
+# )
+# def test_run_grace_if_graph_attribute_computation_allowed(
+#     mrc_image_and_annotations_dir,
+#     simple_extractor,
+#     store_graph_attributes_permanently,
+# ):
+#     run_grace_training(
+#         mrc_image_and_annotations_dir,
+#         simple_extractor,
+#         store_graph_attributes_permanently,
+#     )