Fixes for empty detections with decorators

scoutbot/__init__.py

@@ -55,7 +55,7 @@ log = utils.init_logging()
 
 from scoutbot import agg, loc, tile, wic  # NOQA
 
-VERSION = '0.1.12'
+VERSION = '0.1.13'
 version = VERSION
 __version__ = VERSION
 

scoutbot/agg/__init__.py

@@ -177,31 +177,34 @@ def compute(
     log.info(f'Aggregating {len(tile_grids)} tiles onto {img_shape} canvas')
 
     if len(tile_grids) == 0:
-        return []
-
-    # Demosaic tile detection results and aggregate across the image
-    detects = demosaic(img_shape, tile_grids, loc_outputs)
-
-    # Filter low-confidence detections
-    detects = [detect for detect in detects if detect['c'] >= agg_thresh]
-
-    # Run NMS on aggregated detections
-    coords = np.vstack(
-        [
-            [
-                detect['x'],
-                detect['y'],
-                detect['x'] + detect['w'],
-                detect['y'] + detect['h'],
-            ]
-            for detect in detects
-        ]
-    )
-    confs = np.array([detect['c'] for detect in detects])
-
-    keeps = py_cpu_nms(coords, confs, nms_thresh)
-    final = ut.take(detects, keeps)
-    final.sort(key=lambda val: val['c'], reverse=True)
+        final = []
+    else:
+        # Demosaic tile detection results and aggregate across the image
+        detects = demosaic(img_shape, tile_grids, loc_outputs)
+
+        # Filter low-confidence detections
+        detects = [detect for detect in detects if detect['c'] >= agg_thresh]
+
+        if len(detects) == 0:
+            final = []
+        else:
+            # Run NMS on aggregated detections
+            coords = np.vstack(
+                [
+                    [
+                        detect['x'],
+                        detect['y'],
+                        detect['x'] + detect['w'],
+                        detect['y'] + detect['h'],
+                    ]
+                    for detect in detects
+                ]
+            )
+            confs = np.array([detect['c'] for detect in detects])
+
+            keeps = py_cpu_nms(coords, confs, nms_thresh)
+            final = ut.take(detects, keeps)
+            final.sort(key=lambda val: val['c'], reverse=True)
 
     log.info(f'Found {len(final)} detections')
 

scoutbot/loc/__init__.py

@@ -102,7 +102,8 @@ def pre(inputs):
             - - list of transformed image data.
             - - list of each tile's original size.
     """
-    assert len(inputs) > 0
+    if len(inputs) == 0:
+        return []
 
     log.info(f'Preprocessing {len(inputs)} LOC inputs in batches of {BATCH_SIZE}')
 

scoutbot/wic/__init__.py

@@ -82,14 +82,15 @@ def pre(inputs, batch_size=BATCH_SIZE):
         generator ( list ( list ( list ( list ( float ) ) ) ) ) : generator ->
         list of transformed image data
     """
-    assert len(inputs) > 0
+    if len(inputs) == 0:
+        return []
 
     log.info(f'Preprocessing {len(inputs)} WIC inputs in batches of {batch_size}')
 
     transform = _init_transforms()
     dataset = ImageFilePathList(inputs, transform=transform)
     dataloader = torch.utils.data.DataLoader(
-        dataset, batch_size=batch_size, num_workers=8, pin_memory=False
+        dataset, batch_size=batch_size, num_workers=0, pin_memory=False
     )
 
     for (data,) in dataloader:

tests/test_loc.py

@@ -102,3 +102,8 @@ def test_loc_onnx_pipeline():
                 assert abs(output.get(key) - target.get(key)) < 1e-2
             else:
                 assert abs(output.get(key) - target.get(key)) < 3
+
+    data = pre([])
+    preds = predict(data)
+    outputs = post(preds)
+    assert len(outputs) == 0

tests/test_wic.py

@@ -52,3 +52,8 @@ def test_wic_onnx_pipeline():
     assert abs(output['positive'] - 0.99998497) < 1e-4
     assert isinstance(output['negative'], float)
     assert isinstance(output['positive'], float)
+
+    data = pre([])
+    preds = predict(data)
+    outputs = post(preds)
+    assert len(outputs) == 0