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add mask_iou #552
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add mask_iou #552
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Hakuyume
reviewed
Apr 3, 2018
| 'expected': np.zeros((0, 1)) | ||
| }, | ||
| ) | ||
| class TestmaskIou(unittest.TestCase): |
| 'mask_b': np.array([[[False, False, True], [True, True]]], dtype=np.bool) | ||
| }, | ||
| ) | ||
| class TestmaskIouInvalidShape(unittest.TestCase): |
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I add doc, thx. |
Hakuyume
reviewed
Apr 3, 2018
chainercv/utils/mask/mask_iou.py
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| n_mask_a = len(mask_a) | ||
| n_mask_b = len(mask_b) | ||
| iou = xp.zeros((n_mask_a, n_mask_b), dtype=xp.float32) |
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How about using xp.empty? Since all elements are updated in the following loop, iou does not need to be initialized here.
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thx, i will updated.
chainercv/utils/mask/mask_iou.py
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| n_mask_b = len(mask_b) | ||
| iou = xp.zeros((n_mask_a, n_mask_b), dtype=xp.float32) | ||
| for i, m_a in enumerate(mask_a): | ||
| for j, m_b in enumerate(mask_b): |
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Using n and k is consistent with the docs.
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thx, i will updated.
chainercv/utils/mask/mask_iou.py
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| n_mask_a = len(mask_a) | ||
| n_mask_b = len(mask_b) | ||
| iou = xp.zeros((n_mask_a, n_mask_b), dtype=xp.float32) | ||
| for i, m_a in enumerate(mask_a): |
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We can remove these loops by using numpy tricks.
i = xp.bitwise_and(mask_a[:, None], mask_b).sum(axis=(2,3))
u = xp.bitwise_or(mask_a[:, None], mask_b).sum(axis=(2,3))
return i / uHow do you think of it?
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A drawback is that it requires an array (N, K, H, W), where the original code requires only (H, W).
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(N, K, H, W) requires too much memory, so I write the original one.
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LGTM |
yuyu2172
approved these changes
Apr 3, 2018
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add
mask_ioufunction inutils