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chuanfushen
2023-07-15 17:12:13 +08:00
parent 3795705313
commit 66971ea129
8 changed files with 1725 additions and 17 deletions
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opengait/evaluation/evaluator.py
+26 -13
View File
@@ -74,46 +74,59 @@ def single_view_gallery_evaluation(feature, label, seq_type, view, dataset, metr
'CASIA-E': {'NM': ['H-scene2-nm-1', 'H-scene2-nm-2', 'L-scene2-nm-1', 'L-scene2-nm-2', 'H-scene3-nm-1', 'H-scene3-nm-2', 'L-scene3-nm-1', 'L-scene3-nm-2', 'H-scene3_s-nm-1', 'H-scene3_s-nm-2', 'L-scene3_s-nm-1', 'L-scene3_s-nm-2', ],
'BG': ['H-scene2-bg-1', 'H-scene2-bg-2', 'L-scene2-bg-1', 'L-scene2-bg-2', 'H-scene3-bg-1', 'H-scene3-bg-2', 'L-scene3-bg-1', 'L-scene3-bg-2', 'H-scene3_s-bg-1', 'H-scene3_s-bg-2', 'L-scene3_s-bg-1', 'L-scene3_s-bg-2'],
'CL': ['H-scene2-cl-1', 'H-scene2-cl-2', 'L-scene2-cl-1', 'L-scene2-cl-2', 'H-scene3-cl-1', 'H-scene3-cl-2', 'L-scene3-cl-1', 'L-scene3-cl-2', 'H-scene3_s-cl-1', 'H-scene3_s-cl-2', 'L-scene3_s-cl-1', 'L-scene3_s-cl-2']
}
},
'SUSTech1K': {'Normal': ['01-nm'], 'Bag': ['bg'], 'Clothing': ['cl'], 'Carrying':['cr'], 'Umberalla': ['ub'], 'Uniform': ['uf'], 'Occlusion': ['oc'],'Night': ['nt'], 'Overall': ['01','02','03','04']}
}
gallery_seq_dict = {'CASIA-B': ['nm-01', 'nm-02', 'nm-03', 'nm-04'],
'OUMVLP': ['01'],
'CASIA-E': ['H-scene1-nm-1', 'H-scene1-nm-2', 'L-scene1-nm-1', 'L-scene1-nm-2']}
'CASIA-E': ['H-scene1-nm-1', 'H-scene1-nm-2', 'L-scene1-nm-1', 'L-scene1-nm-2'],
'SUSTech1K': ['00-nm'],}
msg_mgr = get_msg_mgr()
acc = {}
view_list = sorted(np.unique(view))
num_rank = 1
if dataset == 'CASIA-E':
view_list.remove("270")
if dataset == 'SUSTech1K':
num_rank = 5
view_num = len(view_list)
num_rank = 1
for (type_, probe_seq) in probe_seq_dict[dataset].items():
acc[type_] = np.zeros((view_num, view_num)) - 1.
acc[type_] = np.zeros((view_num, view_num, num_rank)) - 1.
for (v1, probe_view) in enumerate(view_list):
pseq_mask = np.isin(seq_type, probe_seq) & np.isin(
view, probe_view)
pseq_mask = pseq_mask if 'SUSTech1K' not in dataset else np.any(np.asarray(
[np.char.find(seq_type, probe)>=0 for probe in probe_seq]), axis=0
) & np.isin(view, probe_view) # For SUSTech1K only
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_dict[dataset]) & np.isin(
view, [gallery_view])
gseq_mask = gseq_mask if 'SUSTech1K' not in dataset else np.any(np.asarray(
[np.char.find(seq_type, gallery)>=0 for gallery in gallery_seq_dict[dataset]]), axis=0
) & np.isin(view, [gallery_view]) # For SUSTech1K only
gallery_y = label[gseq_mask]
gallery_x = feature[gseq_mask, :]
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.topk(num_rank, largest=False)[1].cpu().numpy()
acc[type_][v1, v2] = np.round(np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx], 1) > 0,
acc[type_][v1, v2, :] = np.round(np.sum(np.cumsum(np.reshape(probe_y, [-1, 1]) == gallery_y[idx[:, 0:num_rank]], 1) > 0,
0) * 100 / dist.shape[0], 2)
result_dict = {}
msg_mgr.log_info('===Rank-1 (Exclude identical-view cases)===')
out_str = ""
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)
out_str += f"{type_}: {np.mean(sub_acc):.2f}%\t"
msg_mgr.log_info(out_str)
for rank in range(num_rank):
out_str = ""
for type_ in probe_seq_dict[dataset].keys():
sub_acc = de_diag(acc[type_][:,:,rank], each_angle=True)
if rank == 0:
msg_mgr.log_info(f'{type_}@R{rank+1}: {sub_acc}')
result_dict[f'scalar/test_accuracy/{type_}@R{rank+1}'] = np.mean(sub_acc)
out_str += f"{type_}@R{rank+1}: {np.mean(sub_acc):.2f}%\t"
msg_mgr.log_info(out_str)
return result_dict
@@ -122,7 +135,7 @@ def evaluate_indoor_dataset(data, dataset, metric='euc', cross_view_gallery=Fals
label = np.array(label)
view = np.array(view)
if dataset not in ('CASIA-B', 'OUMVLP', 'CASIA-E'):
if dataset not in ('CASIA-B', 'OUMVLP', 'CASIA-E', 'SUSTech1K'):
raise KeyError("DataSet %s hasn't been supported !" % dataset)
if cross_view_gallery:
return cross_view_gallery_evaluation(