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a little fix for new metric

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darkliang
2022-11-29 23:33:00 +08:00
parent 793405ad7c
commit 59c8a8505c
2 changed files with 34 additions and 29 deletions
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opengait/evaluation/evaluator.py
+32 -27
View File
@@ -16,40 +16,49 @@ def de_diag(acc, each_angle=False):
return result
def cross_view_gallery_evaluation(feature, label, seq_type, view, probe_seq_dict, gallery_seq_list, metric):
def cross_view_gallery_evaluation(feature, label, seq_type, view, dataset, metric):
'''More details can be found: More details can be found in
[A Comprehensive Study on the Evaluation of Silhouette-based Gait Recognition](https://ieeexplore.ieee.org/document/9928336).
'''
probe_seq_dict = {'CASIA-B': {'NM': ['nm-01'], 'BG': ['bg-01'], 'CL': ['cl-01']},
'OUMVLP': {'NM': ['00']}}
gallery_seq_dict = {'CASIA-B': ['nm-02', 'bg-02', 'cl-02'],
'OUMVLP': ['01']}
msg_mgr = get_msg_mgr()
acc = {}
map = {}
mean_ap = {}
view_list = sorted(np.unique(view))
for (type_, probe_seq) in probe_seq_dict.items():
for (type_, probe_seq) in probe_seq_dict[dataset].items():
acc[type_] = np.zeros(len(view_list)) - 1.
map[type_] = np.zeros(len(view_list)) - 1.
mean_ap[type_] = np.zeros(len(view_list)) - 1.
for (v1, probe_view) in enumerate(view_list):
pseq_mask = np.isin(seq_type, probe_seq) & np.isin(
view, [probe_view])
view, probe_view)
probe_x = feature[pseq_mask, :]
probe_y = label[pseq_mask]
gseq_mask = np.isin(seq_type, gallery_seq_list)
gseq_mask = np.isin(seq_type, gallery_seq_dict[dataset])
gallery_y = label[gseq_mask]
gallery_x = feature[gseq_mask, :]
dist = cuda_dist(probe_x, gallery_x, metric)
eval_results = compute_ACC_mAP(
dist.cpu().numpy(), probe_y, gallery_y, np.asarray(view)[pseq_mask], np.asarray(view)[gseq_mask])
dist.cpu().numpy(), probe_y, gallery_y, view[pseq_mask], view[gseq_mask])
acc[type_][v1] = np.round(eval_results[0] * 100, 2)
map[type_][v1] = np.round(eval_results[1] * 100, 2)
mean_ap[type_][v1] = np.round(eval_results[1] * 100, 2)
result_dict = {}
msg_mgr.log_info(
'===Cross View Gallery Evaluation (Excluded identical-view cases)===')
out_acc_str = "========= Rank@1 Acc =========\n"
out_map_str = "============= mAP ============\n"
for type_ in probe_seq_dict.keys():
for type_ in probe_seq_dict[dataset].keys():
avg_acc = np.mean(acc[type_])
avg_map = np.mean(map[type_])
avg_map = np.mean(mean_ap[type_])
result_dict[f'scalar/test_accuracy/{type_}-Rank@1'] = avg_acc
result_dict[f'scalar/test_accuracy/{type_}-mAP'] = avg_map
out_acc_str += f"{type_}:\t{acc[type_]}, mean: {avg_acc:.2f}%\n"
out_map_str += f"{type_}:\t{map[type_]}, mean: {avg_map:.2f}%\n"
out_map_str += f"{type_}:\t{mean_ap[type_]}, mean: {avg_map:.2f}%\n"
# msg_mgr.log_info(f'========= Rank@1 Acc =========')
msg_mgr.log_info(f'{out_acc_str}')
# msg_mgr.log_info(f'========= mAP =========')
@@ -59,24 +68,26 @@ def cross_view_gallery_evaluation(feature, label, seq_type, view, probe_seq_dict
# Modified From https://github.com/AbnerHqC/GaitSet/blob/master/model/utils/evaluator.py
def single_view_gallery_evaluation(feature, label, seq_type, view, probe_seq_dict, gallery_seq_list, metric):
def single_view_gallery_evaluation(feature, label, seq_type, view, dataset, metric):
probe_seq_dict = {'CASIA-B': {'NM': ['nm-05', 'nm-06'], 'BG': ['bg-01', 'bg-02'], 'CL': ['cl-01', 'cl-02']},
'OUMVLP': {'NM': ['00']}}
gallery_seq_dict = {'CASIA-B': ['nm-01', 'nm-02', 'nm-03', 'nm-04'],
'OUMVLP': ['01']}
msg_mgr = get_msg_mgr()
acc = {}
map = {}
view_list = sorted(np.unique(view))
view_num = len(view_list)
num_rank = 1
for (type_, probe_seq) in probe_seq_dict.items():
for (type_, probe_seq) in probe_seq_dict[dataset].items():
acc[type_] = np.zeros((view_num, view_num)) - 1.
map[type_] = np.zeros((view_num, view_num)) - 1.
for (v1, probe_view) in enumerate(view_list):
pseq_mask = np.isin(seq_type, probe_seq) & np.isin(
view, [probe_view])
view, probe_view)
probe_x = feature[pseq_mask, :]
probe_y = label[pseq_mask]
for (v2, gallery_view) in enumerate(view_list):
gseq_mask = np.isin(seq_type, gallery_seq_list) & np.isin(
gseq_mask = np.isin(seq_type, gallery_seq_dict[dataset]) & np.isin(
view, [gallery_view])
gallery_y = label[gseq_mask]
gallery_x = feature[gseq_mask, :]
@@ -88,7 +99,7 @@ def single_view_gallery_evaluation(feature, label, seq_type, view, probe_seq_dic
result_dict = {}
msg_mgr.log_info('===Rank-1 (Exclude identical-view cases)===')
out_str = ""
for type_ in probe_seq_dict.keys():
for type_ in probe_seq_dict[dataset].keys():
sub_acc = de_diag(acc[type_], each_angle=True)
msg_mgr.log_info(f'{type_}: {sub_acc}')
result_dict[f'scalar/test_accuracy/{type_}'] = np.mean(sub_acc)
@@ -102,20 +113,14 @@ def evaluate_indoor_dataset(data, dataset, metric='euc', cross_view_gallery=Fals
label = np.array(label)
view = np.array(view)
probe_seq_dict = {'CASIA-B': {'NM': ['nm-05', 'nm-06'], 'BG': ['bg-01', 'bg-02'], 'CL': ['cl-01', 'cl-02']},
'OUMVLP': {'NM': ['00']}}
gallery_seq_dict = {'CASIA-B': ['nm-01', 'nm-02', 'nm-03', 'nm-04'],
'OUMVLP': ['01']}
if dataset not in (probe_seq_dict or gallery_seq_dict):
if dataset not in ('CASIA-B', 'OUMVLP'):
raise KeyError("DataSet %s hasn't been supported !" % dataset)
if cross_view_gallery:
return cross_view_gallery_evaluation(
feature, label, seq_type, view, probe_seq_dict[dataset], gallery_seq_dict[dataset], metric)
feature, label, seq_type, view, dataset, metric)
else:
return single_view_gallery_evaluation(
feature, label, seq_type, view, probe_seq_dict[dataset], gallery_seq_dict[dataset], metric)
feature, label, seq_type, view, dataset, metric)
def evaluate_real_scene(data, dataset, metric='euc'):