def getscore(ground_truth_filename, prediction_filename,
tiou_thr=0.5,subset='validation', verbose=True, check_status=True):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset=subset, tiou_thr=tiou_thr,
verbose=verbose, check_status=True)
ap = anet_detection.evaluate()
return ap
def main(
ground_truth_filename, prediction_filename, subset="validation", tiou_thr=0.5, verbose=True, check_status=True
):
anet_detection = ANETdetection(
ground_truth_filename, prediction_filename, subset=subset, tiou_thr=tiou_thr, verbose=verbose, check_status=True
)
anet_detection.evaluate()
def main(ground_truth_filename, prediction_filename,
subset='test', tiou_thresholds=np.linspace(0.1, 0.9, 9),
verbose=True, check_status=True):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset=subset, tiou_thresholds=tiou_thresholds,
verbose=verbose, check_status=True)
anet_detection.evaluate()
def main(ground_truth_filename, prediction_filename,
subset='validation', tiou_thresholds=np.linspace(0.5, 0.95, 10),
verbose=True, check_status=True):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset=subset, tiou_thresholds=tiou_thresholds,
verbose=verbose, check_status=True)
anet_detection.evaluate()
def main(ground_truth_filename, prediction_filename,
subset='validation', tiou_thresholds=np.linspace(0.5, 0.95, 10),
verbose=True, check_status=True):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset=subset, tiou_thresholds=tiou_thresholds,
verbose=verbose, check_status=True)
res = anet_detection.evaluate()
return res
def main(ground_truth_filename, prediction_filename,
subset='testing', tiou_thresholds=np.linspace(0.5, 0.95, 10),
verbose=False, check_status=True):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset=subset, tiou_thresholds=tiou_thresholds,
verbose=verbose, check_status=False)
anet_detection.evaluate()
return anet_detection.mAP.mean()
def evaluation_detection_testset():
app = ANETdetection(
ground_truth_filename="./activity_net_test.v1-3.min.json",
prediction_filename="./output/result_detect_cuhk_100_t1.json",
subset='test',
verbose=True,
check_status=False)
app.evaluate()
def test(itr, dataset, args, model, logger, device):
model.eval()
done = False
instance_logits_stack = []
element_logits_stack = []
labels_stack = []
print("TESTING")
while not done:
features, labels, done = dataset.load_data(is_training=False)
features = torch.from_numpy(features).float().to(device)
features = features.unsqueeze(0)
_, element_logits = model(features)
element_logits = element_logits.squeeze(0)
tmp = (F.softmax(
torch.mean(
torch.topk(element_logits,
k=int(np.ceil(len(features) / args.topk)),
dim=0)[0],
dim=0,
),
dim=0,
).cpu().data.numpy())
element_logits = element_logits.cpu().data.numpy()
instance_logits_stack.append(tmp)
element_logits_stack.append(element_logits)
labels_stack.append(labels)
instance_logits_stack = np.array(instance_logits_stack)
labels_stack = np.array(labels_stack)
iou = [0.1, 0.3, 0.5, 0.7]
dmap_detect = ANETdetection(dataset.path_to_annotations, iou, args=args)
dmap_detect._import_prediction(element_logits_stack)
dmap = dmap_detect.evaluate()
if args.dataset_name == "Thumos14":
test_set = sio.loadmat("test_set_meta.mat")["test_videos"][0]
for i in range(np.shape(labels_stack)[0]):
if test_set[i]["background_video"] == "YES":
labels_stack[i, :] = np.zeros_like(labels_stack[i, :])
cmap = cmAP(instance_logits_stack, labels_stack)
print("Classification map %f" % cmap)
for k in range(len(iou)):
print("Detection map @ %f = %f" % (iou[k], dmap[k] * 100))
return (dmap[-1] + dmap[-2])
def evaluation_detection(args, eval_file):
ground_truth_filename = './Evaluation/data/thumos14.json'
anet_detection = ANETdetection(ground_truth_filename,
eval_file,
tiou_thresholds=np.linspace(0.1, 0.9, 9),
subset='test',
verbose=True,
check_status=False)
anet_detection.evaluate()
ap = anet_detection.ap
mAP = anet_detection.mAP
return mAP
def eval_mAP(ground_truth_filename,
prediction_filename,
subset='test',
tiou_thresholds=np.linspace(0.1, 0.9, 9),
verbose=True,
check_status=True):
anet_detection = ANETdetection(
ground_truth_filename,
prediction_filename,
subset=subset,
tiou_thresholds=tiou_thresholds,
verbose=verbose,
check_status=(config.DATASET_NAME == 'ActivityNet12'))
return anet_detection.evaluate()
def main(ground_truth_filename,
prediction_filename,
subset='validation',
tiou_thresholds=np.linspace(0.5, 0.95, 10),
verbose=True,
check_status=True):
anet_detection = ANETdetection(ground_truth_filename,
prediction_filename,
subset=subset,
tiou_thresholds=tiou_thresholds,
verbose=verbose,
check_status=False)
tt = time.time()
anet_detection.evaluate()
print("It takes {}s".format(time.time() - tt))
def main(ground_truth_filename,
prediction_filename,
subset='test',
tiou_thresholds='THUMOS14',
verbose=True,
check_status=True):
tiou_thresholds = tiou_thre_dict[tiou_thresholds]
anet_detection = ANETdetection(
ground_truth_filename,
prediction_filename,
subset=subset,
tiou_thresholds=tiou_thresholds,
verbose=verbose,
check_status=(config.DATASET_NAME == 'ActivityNet12'))
anet_detection.evaluate()
def evaluation_detection(opt):
app = ANETdetection(
ground_truth_filename="./Evaluation/data/activity_net_1_3_new.json",
prediction_filename=os.path.join(opt['output'],
"result_detect_cuhk_100_t1.json"),
subset='validation',
verbose=True,
check_status=False)
app.evaluate()
parent_path, run_id = os.path.split(os.path.normpath(opt['output']))
mAP_at_tIoU = [
f'mAP@{t:.2f} {mAP*100:.3f}'
for t, mAP in zip(app.tiou_thresholds, app.mAP)
]
results = f'[{run_id}|Detection] average-mAP {app.average_mAP*100:.3f} {" ".join(mAP_at_tIoU)}'
print(results)
with open(os.path.join(parent_path, 'results.txt'), 'a') as fobj:
fobj.write(f'{results}\n')
def test_net(tdcnn_demo, dataloader, args, split, max_per_video=0, thresh=0):
np.random.seed(cfg.RNG_SEED)
total_video_num = len(dataloader) * args.batch_size
all_twins = [[[] for _ in range(total_video_num)]
for _ in range(args.num_classes)
] # class_num,video_num,proposal_num
tdcnn_demo.eval()
empty_array = np.transpose(np.array([[], [], []]), (1, 0))
for data_idx, (support_data, video_data, gt_twins, num_gt,
video_info) in tqdm(enumerate(dataloader),
desc="evaluation"):
if is_debug and data_idx > fast_eval_samples:
break
video_data = video_data.cuda()
for i in range(args.shot):
support_data[i] = support_data[i].cuda()
gt_twins = gt_twins.cuda()
batch_size = video_data.shape[0]
rois, cls_prob, twin_pred = tdcnn_demo(
video_data, gt_twins, support_data
) ##torch.Size([1, 300, 3]),torch.Size([1, 300, 2]),torch.Size([1, 300, 4])
scores_all = cls_prob.data
twins = rois.data[:, :, 1:3]
if cfg.TEST.TWIN_REG: # True
# Apply bounding-twin regression deltas
twin_deltas = twin_pred.data
if cfg.TRAIN.TWIN_NORMALIZE_TARGETS_PRECOMPUTED: # True
# Optionally normalize targets by a precomputed mean and stdev
twin_deltas = twin_deltas.view(-1, 2) * torch.FloatTensor(
cfg.TRAIN.TWIN_NORMALIZE_STDS
).type_as(twin_deltas) + torch.FloatTensor(
cfg.TRAIN.TWIN_NORMALIZE_MEANS).type_as(twin_deltas)
twin_deltas = twin_deltas.view(
batch_size, -1,
2 * args.num_classes) # torch.Size([1, 300, 4])
pred_twins_all = twin_transform_inv(
twins, twin_deltas, batch_size) # torch.Size([1, 300, 4])
pred_twins_all = clip_twins(pred_twins_all, cfg.TRAIN.LENGTH[0],
batch_size) # torch.Size([1, 300, 4])
else:
# Simply repeat the twins, once for each class
pred_twins_all = np.tile(twins, (1, scores_all.shape[1]))
for b in range(batch_size):
if is_debug:
logger.info(video_info)
scores = scores_all[b] # scores.squeeze()
pred_twins = pred_twins_all[b] # .squeeze()
# skip j = 0, because it's the background class
for j in range(1, args.num_classes):
inds = torch.nonzero(scores[:, j] > thresh).view(-1)
# if there is det
if inds.numel() > 0:
cls_scores = scores[:, j][inds]
_, order = torch.sort(cls_scores, 0, True)
cls_twins = pred_twins[inds][:, j * 2:(j + 1) * 2]
cls_dets = torch.cat((cls_twins, cls_scores.unsqueeze(1)),
1)
# cls_dets = torch.cat((cls_twins, cls_scores), 1)
cls_dets = cls_dets[order]
keep = nms_cpu(cls_dets.cpu(), args.test_nms)
if (len(keep) > 0):
if is_debug:
print("after nms, keep {}".format(len(keep)))
cls_dets = cls_dets[keep.view(-1).long()]
else:
print(
"warning, after nms, none of the rois is kept!!!")
all_twins[j][data_idx * batch_size +
b] = cls_dets.cpu().numpy()
else:
all_twins[j][data_idx * batch_size + b] = empty_array
# Limit to max_per_video detections *over all classes*, useless code here, default max_per_video = 0
if max_per_video > 0:
video_scores = np.hstack([
all_twins[j][data_idx * batch_size + b][:, -1]
for j in range(1, args.num_classes)
])
if len(video_scores) > max_per_video:
video_thresh = np.sort(video_scores)[-max_per_video]
for j in range(1, args.num_classes):
keep = np.where(
all_twins[j][data_idx * batch_size +
b][:, -1] >= video_thresh)[0]
all_twins[j][data_idx * batch_size +
b] = all_twins[j][data_idx * batch_size +
b][keep, :]
# logger.info('im_detect: {:d}/{:d}'.format(i * batch_size + b + 1, len(dataloader)))
pred = dict()
pred['external_data'] = ''
pred['version'] = ''
pred['results'] = dict()
for i_video in tqdm(range(total_video_num),
desc="generating prediction json.."):
if is_debug and i_video > fast_eval_samples * batch_size - 2:
break
item_pre = []
for j_roi in range(
0, len(all_twins[1][i_video])
): # binary class problem, here we only consider class_num=1, ignoring background class
_d = dict()
_d['score'] = all_twins[1][i_video][j_roi][2].item()
_d['label'] = 'c1'
_d['segment'] = [
all_twins[1][i_video][j_roi][0].item(),
all_twins[1][i_video][j_roi][1].item()
]
item_pre.append(_d)
pred['results']["query_%05d" % i_video] = item_pre
predict_filename = os.path.join(logger.get_logger_dir(),
'{}_pred.json'.format(split))
ground_truth_filename = os.path.join('preprocess/{}'.format(args.dataset),
'{}_gt.json'.format(split))
with open(predict_filename, 'w') as f:
json.dump(pred, f)
logger.info('dump pred.json complete..')
sys.path.insert(0, "evaluation")
from eval_detection import ANETdetection
anet_detection = ANETdetection(ground_truth_filename,
predict_filename,
subset="test",
tiou_thresholds=tiou_thresholds,
verbose=True,
check_status=False)
anet_detection.evaluate()
ap = anet_detection.mAP
mAP = ap[0]
return mAP, ap
classified_proposals = nms_all(classified_proposals,
topK=20,
nms_thor=1,
remove_background=True)
# test on offical evaluation code
id2name = {}
for name, ids in names.iteritems():
id2name[ids['class']] = name
output = gen_json(classified_proposals, id2name)
with open('prediction.json', 'w') as f:
json.dump(output, f)
eval1 = ANETdetection('activity_net.v1-3.min.json',
'prediction.json',
subset='validation',
tiou_thr=0.5,
verbose=True,
check_status=False)
eval1.evaluate()
eval2 = ANETdetection('activity_net.v1-3.min.json',
'prediction.json',
subset='validation',
tiou_thr=0.75,
verbose=True,
check_status=False)
eval2.evaluate()
eval3 = ANETdetection('activity_net.v1-3.min.json',
'prediction.json',
subset='validation',
tiou_thr=0.95,
verbose=True,
def detect(prediction_filename, ground_truth_filename = 'activity_net.v1-3.min.json',output_log=''):
anet_detection = ANETdetection(ground_truth_filename, prediction_filename,
subset='validation', tiou_thresholds=np.linspace(0.5, 0.95, 10),
verbose=True, check_status=True)
anet_detection.evaluate(output_log)
return anet_detection.mAP
def evaluate(itr,
dataset,
model,
logger,
groundtruth_filename,
prediction_filename,
device=torch.device('cuda'),
background=True,
subset='test',
fps=25,
stride=16,
threshold_type='mean',
frame_type='max',
adjust_mean=1.0,
act_weight=1.0,
tiou_thresholds=np.linspace(0.1, 0.7, 7),
use_anchor=False):
'''
generate proposals and evaluate
'''
with open(groundtruth_filename, 'r') as fr:
gt_info = json.load(fr)['database']
save_dict = {'version': dataset.dataset_name, 'external_data': 'None'}
frame_dict = {'version': dataset.dataset_name, 'external_data': 'abc'}
rs = defaultdict(list)
rs2 = defaultdict(list)
instance_logits_stack = []
labels_stack = []
inds = dataset.get_testidx()
classlist = dataset.get_classlist()
tps = [0] * len(classlist)
aps = [0] * len(classlist)
res = [0] * len(classlist)
one_hots = np.eye(len(classlist))
for idx in inds:
feat = dataset.get_feature(idx)
vname = dataset.get_vname(idx)
duration = dataset.get_duration(idx)
feat = torch.from_numpy(np.expand_dims(feat,
axis=0)).float().to(device)
frame_label = dataset.get_gt_frame_label(idx)
video_label = dataset.get_video_label(idx)
if len(video_label) < 1:
continue
with torch.no_grad():
_, logits_f, _, logits_r, tcam, att_logits_f, att_logits_r, att_logits = model(
Variable(feat), device, is_training=False)
logits_f, logits_r, tcam = logits_f[0], logits_r[0], tcam[0]
topk = int(np.ceil(len(feat[0]) / 8))
tmp = F.softmax(torch.mean(torch.topk(logits_f, k=topk, dim=0)[0],
dim=0),
dim=0).cpu().data.numpy()
tmp += F.softmax(torch.mean(torch.topk(logits_r, k=topk, dim=0)[0],
dim=0),
dim=0).cpu().data.numpy()
tmp += F.softmax(torch.mean(torch.topk(tcam, k=topk, dim=0)[0], dim=0),
dim=0).cpu().data.numpy()
if background:
tcam = tcam[:, 1:]
tmp = tmp[1:]
instance_logits_stack.append(tmp)
labels_stack.append(np.sum(one_hots[video_label], axis=0))
tcam = tcam.cpu().data.numpy()
pred_label = np.argmax(tcam, axis=-1)
assert len(pred_label) == len(frame_label)
for gt in frame_label:
for g in gt:
res[g - 1] += 1
score = np.zeros((len(tcam), 1))
if use_anchor:
att = att_logits[0].cpu().data.numpy()
score = att.squeeze()
segments, frames = generate_segment(tcam,
score,
threshold_type=threshold_type,
frame_type=frame_type,
act_weight=act_weight,
adjust_mean=adjust_mean)
fps = gt_info[vname].get('fps', fps)
for frame in frames:
aps[frame[0]] += 1
if frame[0] in frame_label[frame[1]]:
tps[frame[0]] += 1
rs2[vname] += [{
'score': float(frame[2] / 100.0),
'label': classlist[frame[0]].decode('utf-8'),
'frame': float(frame[1] * stride / fps)
}]
for seg in segments:
rs[vname] += [{
'score':
float(seg[3] / 100.0),
'label':
str(classlist[seg[0]].decode('utf-8')),
'segment':
[float(seg[1] * stride / fps),
float(seg[2] * stride / fps)]
}]
save_dict['results'] = rs
frame_dict['results'] = rs2
with open(prediction_filename, 'w') as fw:
json.dump(save_dict, fw)
frame_detection = FrameDetection(groundtruth_filename,
frame_dict,
subset=subset,
verbose=True,
check_status=False)
frame_detection.evaluate()
anet_detection = ANETdetection(groundtruth_filename,
save_dict,
subset=subset,
tiou_thresholds=tiou_thresholds,
verbose=True,
check_status=False)
dmap = anet_detection.evaluate()
for i in range(len(dmap)):
logger.log_value('mAP/IoU@%s' % (str(tiou_thresholds[i])), dmap[i],
itr)
labels_stack = np.array(labels_stack)
instance_logits_stack = np.array(instance_logits_stack)
cmap = cmAP(instance_logits_stack, labels_stack)
print(cmap)
tp = np.sum(tps)
ap = np.sum(aps)
recall = np.sum(res)
print(
'All act frames %d, predict all frames : %d, right frames: %d, AP: %0.5f, Recall: %0.5f'
% (recall, ap, tp, tp / ap, tp / recall))
acc = dmap[-3]
return np.mean(acc)
results_json = os.path.join(args.results_path, 'action_segments.json')
results_npy = os.path.join(args.results_path, 'pred.npy')
pred_labels = dataset.get_actions_labels_from_json(results_json, mode='pred')
# load the predicted data
pred_data = np.load(results_npy, allow_pickle=True).item()
pred_labels = pred_data['pred_labels']
logits = pred_data['logits']
alpha = np.array([
0.1, 0.2, 0.3, 0.4, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95
])
# compute action localization mAP
anet_detection = ANETdetection(gt_json_path,
results_json,
subset='testing',
tiou_thresholds=alpha,
verbose=True,
check_status=True)
anet_detection.evaluate()
localization_score_str = "Action localization scores: \n" \
"Average mAP= {} \n".format(anet_detection.average_mAP) + \
"alpha = " + " & ".join(str(alpha).split()) + "\n " \
"mAP scores =" +\
" & ".join(str(np.around(anet_detection.mAP, 2)).split()) + "\n "
# Compute classification mAP
anet_classification = ANETclassification(gt_json_path,
results_json,
subset='testing',
verbose=True)
