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fix computation of confusion matrix, pred to predn. #267

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fix computation of confusion matrix, pred to predn. #267

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8 changes: 4 additions & 4 deletions test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def test(data,
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
"domain": "pixel"} for *xyxy, conf, cls in predn.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))

Expand All @@ -164,14 +164,14 @@ def test(data,
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
for p, b in zip(predn.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': coco91class[int(p[15])] if is_coco else int(p[15]),
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})

# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
correct = torch.zeros(predn.shape[0], niou, dtype=torch.bool, device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
Expand All @@ -180,7 +180,7 @@ def test(data,
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1))
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

# Per target class
for cls in torch.unique(tcls_tensor):
Expand Down