text stringlengths 1 93.6k |
|---|
y_true = matches[i, valid]
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y_score = -distmat[i][indices[i]][valid]
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# discard nan
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y_score[np.isnan(y_score)] = 0
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if not np.any(y_true): continue
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aps.append(average_precision_score(y_true, y_score))
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else:
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for i in range(m):
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valid = ((gallery_ids[indices[i]] != query_ids[i]) |
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(gallery_cams[indices[i]] != query_cams[i]))
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y_true = matches[i, valid]
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y_score = -distmat[i][indices[i]][valid]
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# discard nan
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# y_score = np.nan_to_num(y_score)
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if not np.any(y_true): continue
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aps.append(average_precision_score(y_true, y_score))
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if len(aps) == 0:
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raise RuntimeError("No valid query")
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return np.mean(aps)
|
def metrics(X, y, t_X, t_y):
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# compute Euclidean distance
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if dataset != 'CASIA_B':
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a, b = torch.from_numpy(t_X), torch.from_numpy(X)
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m, n = a.size(0), b.size(0)
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a = a.view(m, -1)
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b = b.view(n, -1)
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dist_m = torch.pow(a, 2).sum(dim=1, keepdim=True).expand(m, n) + \
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torch.pow(b, 2).sum(dim=1, keepdim=True).expand(n, m).t()
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dist_m.addmm_(1, -2, a, b.t())
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dist_m = (dist_m.clamp(min=1e-12)).sqrt()
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mAP = mean_ap(distmat=dist_m.numpy(), query_ids=t_y, gallery_ids=y)
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_, dist_sort = dist_m.sort(1)
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dist_sort = dist_sort.numpy()
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else:
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X = np.array(X)
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t_X = np.array(t_X)
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dist_m = [(np.linalg.norm(X - i, axis=1)).tolist() for i in t_X]
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dist_m = np.array(dist_m)
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mAP = mean_ap(distmat=dist_m, query_ids=t_y, gallery_ids=y)
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dist_sort = [np.argsort(np.linalg.norm(X - i, axis=1)).tolist() for i in t_X]
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dist_sort = np.array(dist_sort)
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top_1 = top_5 = top_10 = 0
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probe_num = dist_sort.shape[0]
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if (FLAGS.probe_type == 'nm.nm' or
|
FLAGS.probe_type == 'cl.cl' or FLAGS.probe_type == 'bg.bg'):
|
for i in range(probe_num):
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if t_y[i] in y[dist_sort[i, 1:2]]:
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top_1 += 1
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if t_y[i] in y[dist_sort[i, 1:6]]:
|
top_5 += 1
|
if t_y[i] in y[dist_sort[i, 1:11]]:
|
top_10 += 1
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else:
|
for i in range(probe_num):
|
if t_y[i] in y[dist_sort[i, :1]]:
|
top_1 += 1
|
if t_y[i] in y[dist_sort[i, :5]]:
|
top_5 += 1
|
if t_y[i] in y[dist_sort[i, :10]]:
|
top_10 += 1
|
return mAP, top_1 / probe_num, top_5 / probe_num, top_10 / probe_num
|
mAP_int, top_1_int, top_5_int, top_10_int = metrics(X_int, y, t_X_int, t_y)
|
mAP, top_1, top_5, top_10 = metrics(X, y, t_X, t_y)
|
mAP_P, top_1_P, top_5_P, top_10_P = metrics(X_P, y, t_X_P, t_y)
|
mAP_B, top_1_B, top_5_B, top_10_B = metrics(X_B, y, t_X_B, t_y)
|
del X, y, t_X, t_y, X_P, t_X_P, X_B, t_X_B, pro_labels_all, pro_features_all
|
gc.collect()
|
return mAP_int, top_1_int, top_5_int, top_10_int, \
|
mAP, top_1, top_5, top_10, \
|
mAP_P, top_1_P, top_5_P, top_10_P, mAP_B, top_1_B, top_5_B, top_10_B
|
max_acc_1 = 0
|
max_acc_2 = 0
|
top_5_max = 0
|
top_10_max = 0
|
max_acc_1_int = 0
|
max_acc_2_int = 0
|
top_5_max_int = 0
|
top_10_max_int = 0
|
max_acc_1_P = 0
|
max_acc_2_P = 0
|
top_5_max_P = 0
|
top_10_max_P = 0
|
max_acc_1_B = 0
|
max_acc_2_B = 0
|
top_5_max_B = 0
|
top_10_max_B = 0
|
best_cluster_info_1 = [0, 0]
|
best_cluster_info_2 = [0, 0]
|
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