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Support new Dataset: GREW

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This commit is contained in:
Noah
2022-03-21 18:49:09 +08:00
parent 9c1aed2c87
commit 0071833d3f
5 changed files with 322 additions and 11 deletions
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lib/utils/evaluation.py
+115 -5
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@@ -122,11 +122,13 @@ def identification_real_scene(data, dataset, metric='euc'):
label = np.array(label)
gallery_seq_type = {'0001-1000': ['1', '2'],
"HID2021": ['0'], '0001-1000-test': ['0']}
"HID2021": ['0'], '0001-1000-test': ['0'],
'GREW': ['01']}
probe_seq_type = {'0001-1000': ['3', '4', '5', '6'],
"HID2021": ['1'], '0001-1000-test': ['1']}
"HID2021": ['1'], '0001-1000-test': ['1'],
'GREW': ['02']}
num_rank = 5
num_rank = 20
acc = np.zeros([num_rank]) - 1.
gseq_mask = np.isin(seq_type, gallery_seq_type[dataset])
gallery_x = feature[gseq_mask, :]
@@ -143,8 +145,46 @@ def identification_real_scene(data, dataset, metric='euc'):
msg_mgr.log_info('%.3f' % (np.mean(acc[0])))
msg_mgr.log_info('==Rank-5==')
msg_mgr.log_info('%.3f' % (np.mean(acc[4])))
msg_mgr.log_info('==Rank-10==')
msg_mgr.log_info('%.3f' % (np.mean(acc[9])))
msg_mgr.log_info('==Rank-20==')
msg_mgr.log_info('%.3f' % (np.mean(acc[19])))
return {"scalar/test_accuracy/Rank-1": np.mean(acc[0]), "scalar/test_accuracy/Rank-5": np.mean(acc[4])}
def identification_GREW_submission(data, dataset, metric='euc'):
msg_mgr = get_msg_mgr()
feature, label, seq_type, view = data['embeddings'], data['labels'], data['types'], data['views']
label = np.array(label)
view = np.array(view)
gallery_seq_type = {'GREW': ['01','02']}
probe_seq_type = {'GREW': ['03']}
num_rank = 20
acc = np.zeros([num_rank]) - 1.
gseq_mask = np.isin(seq_type, gallery_seq_type[dataset])
gallery_x = feature[gseq_mask, :]
gallery_y = label[gseq_mask]
pseq_mask = np.isin(seq_type, probe_seq_type[dataset])
probe_x = feature[pseq_mask, :]
probe_y = view[pseq_mask]
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.cpu().sort(1)[1].numpy()
import os
from time import strftime, localtime
save_path = os.path.join(
"GREW_result/"+strftime('%Y-%m%d-%H%M%S', localtime())+".csv")
os.makedirs("GREW_result", exist_ok=True)
with open(save_path, "w") as f:
f.write("videoId,rank1,rank2,rank3,rank4,rank5,rank6,rank7,rank8,rank9,rank10,rank11,rank12,rank13,rank14,rank15,rank16,rank17,rank18,rank19,rank20\n")
for i in range(len(idx)):
r_format = [int(idx) for idx in gallery_y[idx[i, 0:20]]]
output_row = '{}'+',{}'*20+'\n'
f.write(output_row.format(probe_y[i], *r_format))
print("GREW result saved to {}/{}".format(os.getcwd(), save_path))
return
def evaluate_HID(data, dataset, metric='euc'):
msg_mgr = get_msg_mgr()
@@ -168,6 +208,76 @@ def evaluate_HID(data, dataset, metric='euc'):
with open(save_path, "w") as f:
f.write("videoID,label\n")
for i in range(len(idx)):
f.write("{},{}\n".format(probe_y[i], gallery_y[idx[i, 0]]))
print("HID result saved to {}/{}".format(os.getcwd(), save_path))
f.write("{},{},\n".format(probe_y[i], gallery_y[idx[i, 0]]))
print("GREW result saved to {}/{}".format(os.getcwd(), save_path))
return
def evaluate_GREW(data, dataset, metric='euc'):
msg_mgr = get_msg_mgr()
msg_mgr.log_info("Evaluating GREW")
feature, label, seq_type, view = data['embeddings'], data['labels'], data['types'], data['views']
label = np.array(label)
if dataset not in (probe_seq_dict or gallery_seq_dict):
raise KeyError("DataSet %s hasn't been supported !" % dataset)
num_rank = 5
acc = np.zeros([len(probe_seq_dict[dataset]),
view_num, view_num, num_rank]) - 1.
for (p, probe_seq) in enumerate(probe_seq_dict[dataset]):
for gallery_seq in gallery_seq_dict[dataset]:
for (v1, probe_view) in enumerate(view_list):
for (v2, gallery_view) in enumerate(view_list):
gseq_mask = np.isin(seq_type, gallery_seq) & np.isin(
view, [gallery_view])
gallery_x = feature[gseq_mask, :]
gallery_y = label[gseq_mask]
pseq_mask = np.isin(seq_type, probe_seq) & np.isin(
view, [probe_view])
probe_x = feature[pseq_mask, :]
probe_y = label[pseq_mask]
dist = cuda_dist(probe_x, gallery_x, metric)
idx = dist.sort(1)[1].cpu().numpy()
acc[p, 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 = {}
np.set_printoptions(precision=3, suppress=True)
if 'OUMVLP' not in dataset:
for i in range(1):
msg_mgr.log_info(
'===Rank-%d (Include identical-view cases)===' % (i + 1))
msg_mgr.log_info('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
np.mean(acc[0, :, :, i]),
np.mean(acc[1, :, :, i]),
np.mean(acc[2, :, :, i])))
for i in range(1):
msg_mgr.log_info(
'===Rank-%d (Exclude identical-view cases)===' % (i + 1))
msg_mgr.log_info('NM: %.3f,\tBG: %.3f,\tCL: %.3f' % (
de_diag(acc[0, :, :, i]),
de_diag(acc[1, :, :, i]),
de_diag(acc[2, :, :, i])))
result_dict["scalar/test_accuracy/NM"] = de_diag(acc[0, :, :, i])
result_dict["scalar/test_accuracy/BG"] = de_diag(acc[1, :, :, i])
result_dict["scalar/test_accuracy/CL"] = de_diag(acc[2, :, :, i])
np.set_printoptions(precision=2, floatmode='fixed')
for i in range(1):
msg_mgr.log_info(
'===Rank-%d of each angle (Exclude identical-view cases)===' % (i + 1))
msg_mgr.log_info('NM: {}'.format(de_diag(acc[0, :, :, i], True)))
msg_mgr.log_info('BG: {}'.format(de_diag(acc[1, :, :, i], True)))
msg_mgr.log_info('CL: {}'.format(de_diag(acc[2, :, :, i], True)))
else:
msg_mgr.log_info('===Rank-1 (Include identical-view cases)===')
msg_mgr.log_info('NM: %.3f ' % (np.mean(acc[0, :, :, 0])))
msg_mgr.log_info('===Rank-1 (Exclude identical-view cases)===')
msg_mgr.log_info('NM: %.3f ' % (de_diag(acc[0, :, :, 0])))
msg_mgr.log_info(
'===Rank-1 of each angle (Exclude identical-view cases)===')
msg_mgr.log_info('NM: {}'.format(de_diag(acc[0, :, :, 0], True)))
result_dict["scalar/test_accuracy/NM"] = de_diag(acc[0, :, :, 0])
return result_dict