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update tutorial for hid 2023

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darkliang
2023-02-17 11:46:14 +08:00
parent 76ea237749
commit 0ef6b6f5b4
4 changed files with 928 additions and 14 deletions
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opengait/evaluation/evaluator.py
+12 -7
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@@ -189,7 +189,7 @@ def GREW_submission(data, dataset, metric='euc'):
return
def HID_submission(data, dataset, metric='euc'):
def HID_submission(data, dataset, rerank=True, metric='euc'):
msg_mgr = get_msg_mgr()
msg_mgr.log_info("Evaluating HID")
feature, label, seq_type = data['embeddings'], data['labels'], data['views']
@@ -201,12 +201,16 @@ def HID_submission(data, dataset, metric='euc'):
gallery_y = label[gallery_mask]
probe_x = feature[probe_mask, :]
probe_y = seq_type[probe_mask]
feat = np.concatenate([probe_x, gallery_x])
dist = cuda_dist(feat, feat, metric).cpu().numpy()
msg_mgr.log_info("Starting Re-ranking")
re_rank = re_ranking(dist, probe_x.shape[0], k1=6, k2=6, lambda_value=0.3)
idx = np.argsort(re_rank, axis=1)
if rerank:
feat = np.concatenate([probe_x, gallery_x])
dist = cuda_dist(feat, feat, metric).cpu().numpy()
msg_mgr.log_info("Starting Re-ranking")
re_rank = re_ranking(
dist, probe_x.shape[0], k1=6, k2=6, lambda_value=0.3)
idx = np.argsort(re_rank, axis=1)
else:
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.cpu().sort(1)[1].numpy()
save_path = os.path.join(
"HID_result/"+strftime('%Y-%m%d-%H%M%S', localtime())+".csv")
@@ -226,6 +230,7 @@ def evaluate_segmentation(data, dataset):
get_msg_mgr().log_info('mIOU: %.3f' % (miou.mean()))
return {"scalar/test_accuracy/mIOU": miou}
def evaluate_Gait3D(data, conf, metric='euc'):
msg_mgr = get_msg_mgr()