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updates

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Glenn Jocher
2018-11-17 12:51:22 +01:00
parent 07f15b68d3
commit dd7c3d2455
2 changed files with 19 additions and 18 deletions
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utils/utils.py
+7 -6
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@@ -259,12 +259,12 @@ def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG
ty[b, a, gj, gi] = gy - gj.float()
# Width and height (yolo method)
tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])
# tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0])
# th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1])
# Width and height (power method)
# tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
# th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
# One-hot encoding of label
tcls[b, a, gj, gi, tc] = 1
@@ -436,8 +436,9 @@ def plot_results():
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 8))
s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
for f in ('results.txt',):
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
for f in ('results.txt',
):
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T # column 16 is mAP
for i in range(9):
plt.subplot(2, 5, i + 1)
plt.plot(results[i, :250], marker='.', label=f)