def frameAP(opt, print_info=True):
redo = opt.redo
th = opt.th
split = 'val'
model_name = opt.model_name
Dataset = get_dataset(opt.dataset)
dataset = Dataset(opt, split)
inference_dirname = opt.inference_dir
print('inference_dirname is ', inference_dirname)
print('threshold is ', th)
vlist = dataset._test_videos[opt.split - 1]
# load per-frame detections
frame_detections_file = os.path.join(inference_dirname,
'frame_detections.pkl')
if os.path.isfile(frame_detections_file) and not redo:
with open(frame_detections_file, 'rb') as fid:
alldets = pickle.load(fid)
else:
alldets = load_frame_detections(opt, dataset, opt.K, vlist,
inference_dirname)
try:
with open(frame_detections_file, 'wb') as fid:
pickle.dump(alldets, fid, protocol=4)
except:
print(
"OverflowError: cannot serialize a bytes object larger than 4 GiB"
)
results = {}
# compute AP for each class
for ilabel, label in enumerate(dataset.labels):
# detections of this class
detections = alldets[alldets[:, 2] == ilabel, :]
# load ground-truth of this class
gt = {}
for iv, v in enumerate(vlist):
tubes = dataset._gttubes[v]
if ilabel not in tubes:
continue
for tube in tubes[ilabel]:
for i in range(tube.shape[0]):
k = (iv, int(tube[i, 0]))
if k not in gt:
gt[k] = []
gt[k].append(tube[i, 1:5].tolist())
for k in gt:
gt[k] = np.array(gt[k])
# pr will be an array containing precision-recall values
pr = np.empty((detections.shape[0] + 1, 2),
dtype=np.float32) # precision,recall
pr[0, 0] = 1.0
pr[0, 1] = 0.0
fn = sum([g.shape[0] for g in gt.values()]) # false negatives
fp = 0 # false positives
tp = 0 # true positives
for i, j in enumerate(np.argsort(-detections[:, 3])):
k = (int(detections[j, 0]), int(detections[j, 1]))
box = detections[j, 4:8]
ispositive = False
if k in gt:
ious = iou2d(gt[k], box)
amax = np.argmax(ious)
if ious[amax] >= th:
ispositive = True
gt[k] = np.delete(gt[k], amax, 0)
if gt[k].size == 0:
del gt[k]
if ispositive:
tp += 1
fn -= 1
else:
fp += 1
pr[i + 1, 0] = float(tp) / float(tp + fp)
pr[i + 1, 1] = float(tp) / float(tp + fn)
results[label] = pr
# display results
ap = 100 * np.array([pr_to_ap(results[label]) for label in dataset.labels])
frameap_result = np.mean(ap)
if print_info:
log_file = open(os.path.join(opt.root_dir, 'result', opt.exp_id), 'a+')
log_file.write('\nTask_{} frameAP_{}\n'.format(model_name, th))
print('Task_{} frameAP_{}\n'.format(model_name, th))
log_file.write("\n{:20s} {:8.2f}\n\n".format("mAP", frameap_result))
log_file.close()
print("{:20s} {:8.2f}".format("mAP", frameap_result))
return frameap_result
def videoAP(opt, print_info=True):
th = opt.th
model_name = opt.model_name
split = 'val'
Dataset = get_dataset(opt.dataset)
dataset = Dataset(opt, split)
inference_dirname = opt.inference_dir
vlist = dataset._test_videos[opt.split - 1]
# load detections
# alldets = for each label in 1..nlabels, list of tuple (v,score,tube as Kx5 array)
alldets = {ilabel: [] for ilabel in range(len(dataset.labels))}
for v in vlist:
tubename = os.path.join(inference_dirname, v + '_tubes.pkl')
if not os.path.isfile(tubename):
print("ERROR: Missing extracted tubes " + tubename)
sys.exit()
with open(tubename, 'rb') as fid:
tubes = pickle.load(fid)
for ilabel in range(len(dataset.labels)):
ltubes = tubes[ilabel]
idx = nms3dt(ltubes, 0.3)
alldets[ilabel] += [(v, ltubes[i][1], ltubes[i][0]) for i in idx]
# compute AP for each class
res = {}
for ilabel in range(len(dataset.labels)):
detections = alldets[ilabel]
# load ground-truth
gt = {}
for v in vlist:
tubes = dataset._gttubes[v]
if ilabel not in tubes:
continue
gt[v] = tubes[ilabel]
if len(gt[v]) == 0:
del gt[v]
# precision,recall
pr = np.empty((len(detections) + 1, 2), dtype=np.float32)
pr[0, 0] = 1.0
pr[0, 1] = 0.0
fn = sum([len(g) for g in gt.values()]) # false negatives
fp = 0 # false positives
tp = 0 # true positives
for i, j in enumerate(
np.argsort(-np.array([dd[1] for dd in detections]))):
v, score, tube = detections[j]
ispositive = False
if v in gt:
ious = [iou3dt(g, tube) for g in gt[v]]
amax = np.argmax(ious)
if ious[amax] >= th:
ispositive = True
del gt[v][amax]
if len(gt[v]) == 0:
del gt[v]
if ispositive:
tp += 1
fn -= 1
else:
fp += 1
pr[i + 1, 0] = float(tp) / float(tp + fp)
pr[i + 1, 1] = float(tp) / float(tp + fn)
res[dataset.labels[ilabel]] = pr
# display results
ap = 100 * np.array([pr_to_ap(res[label]) for label in dataset.labels])
videoap_result = np.mean(ap)
if print_info:
log_file = open(os.path.join(opt.root_dir, 'result', opt.exp_id), 'a+')
log_file.write('\nTask_{} VideoAP_{}\n'.format(model_name, th))
print('Task_{} VideoAP_{}\n'.format(opt.model_name, th))
# for il, _ in enumerate(dataset.labels):
# print("{:20s} {:8.2f}".format('', ap[il]))
# log_file.write("{:20s} {:8.2f}\n".format('', ap[il]))
log_file.write("\n{:20s} {:8.2f}\n\n".format("mAP", videoap_result))
log_file.close()
print("{:20s} {:8.2f}".format("mAP", videoap_result))
return videoap_result
def main(opt):
set_seed(opt.seed)
torch.backends.cudnn.benchmark = True
print()
print('dataset: ' + opt.dataset + ' task: ' + opt.task)
Dataset = get_dataset(opt.dataset)
opt = opts().update_dataset(opt, Dataset)
train_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'train'))
epoch_train_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'train_epoch'))
val_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'val'))
epoch_val_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'val_epoch'))
logger = Logger(opt, epoch_train_writer, epoch_val_writer)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
model = create_model(opt.arch, opt.branch_info, opt.head_conv, opt.K)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = opt.start_epoch
if opt.pretrain_model == 'coco':
model = load_coco_pretrained_model(opt, model)
else:
model = load_imagenet_pretrained_model(opt, model)
if opt.load_model != '':
model, optimizer, _, _ = load_model(model, opt.load_model, optimizer,
opt.lr, opt.ucf_pretrain)
trainer = MOCTrainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
train_loader = torch.utils.data.DataLoader(Dataset(opt, 'train'),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory,
drop_last=True,
worker_init_fn=worker_init_fn)
val_loader = torch.utils.data.DataLoader(Dataset(opt, 'val'),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory,
drop_last=True,
worker_init_fn=worker_init_fn)
print('training...')
print('GPU allocate:', opt.chunk_sizes)
best_ap = 0
best_epoch = 0
stop_step = 0
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
print('eopch is ', epoch)
log_dict_train = trainer.train(epoch, train_loader, train_writer)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('epcho/{}'.format(k), v, epoch, 'train')
logger.write('train: {} {:8f} | '.format(k, v))
logger.write('\n')
if opt.save_all and not opt.auto_stop:
time_str = time.strftime('%Y-%m-%d-%H-%M')
model_name = 'model_[{}]_{}.pth'.format(epoch, time_str)
save_model(os.path.join(opt.save_dir, model_name), model,
optimizer, epoch, log_dict_train['loss'])
else:
model_name = 'model_last.pth'
save_model(os.path.join(opt.save_dir, model_name), model,
optimizer, epoch, log_dict_train['loss'])
# this step evaluate the model
if opt.val_epoch:
with torch.no_grad():
log_dict_val = trainer.val(epoch, val_loader, val_writer)
for k, v in log_dict_val.items():
logger.scalar_summary('epcho/{}'.format(k), v, epoch, 'val')
logger.write('val: {} {:8f} | '.format(k, v))
logger.write('\n')
if opt.auto_stop:
tmp_rgb_model = opt.rgb_model
tmp_flow_model = opt.flow_model
if opt.rgb_model != '':
opt.rgb_model = os.path.join(opt.rgb_model, model_name)
if opt.flow_model != '':
opt.flow_model = os.path.join(opt.flow_model, model_name)
stream_inference(opt)
ap = frameAP(opt, print_info=opt.print_log)
os.system("rm -rf tmp")
if ap > best_ap:
best_ap = ap
best_epoch = epoch
saved1 = os.path.join(opt.save_dir, model_name)
saved2 = os.path.join(opt.save_dir, 'model_best.pth')
os.system("cp " + str(saved1) + " " + str(saved2))
if stop_step < len(
opt.lr_step) and epoch >= opt.lr_step[stop_step]:
model, optimizer, _, _ = load_model(
model, os.path.join(opt.save_dir, 'model_best.pth'),
optimizer, opt.lr)
opt.lr = opt.lr * 0.1
logger.write('Drop LR to ' + str(opt.lr) + '\n')
print('Drop LR to ' + str(opt.lr))
print('load epoch is ', best_epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = opt.lr
torch.cuda.empty_cache()
trainer = MOCTrainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
stop_step = stop_step + 1
opt.rgb_model = tmp_rgb_model
opt.flow_model = tmp_flow_model
else:
# this step drop lr
if epoch in opt.lr_step:
lr = opt.lr * (0.1**(opt.lr_step.index(epoch) + 1))
logger.write('Drop LR to ' + str(lr) + '\n')
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if opt.auto_stop:
print('best epoch is ', best_epoch)
logger.close()
def frameAP_error(opt, redo=False):
th = opt.th
split = 'val'
Dataset = get_dataset(opt.dataset)
dataset = Dataset(opt, split)
inference_dirname = opt.inference_dir
print('inference_dirname is ', inference_dirname)
print('threshold is ', th)
eval_file = os.path.join(inference_dirname,
"frameAP{:g}ErrorAnalysis.pkl".format(th))
if os.path.isfile(eval_file) and not redo:
with open(eval_file, 'rb') as fid:
res = pickle.load(fid)
else:
vlist = dataset._test_videos[opt.split - 1]
# load per- frame detections
frame_detections_file = os.path.join(inference_dirname,
'frame_detections.pkl')
if os.path.isfile(frame_detections_file) and not redo:
print('load frameAP pre-result')
with open(frame_detections_file, 'rb') as fid:
alldets = pickle.load(fid)
else:
alldets = load_frame_detections(opt, dataset, opt.K, vlist,
inference_dirname)
with open(frame_detections_file, 'wb') as fid:
pickle.dump(alldets, fid)
res = {}
# alldets: list of numpy array with <video_index> <frame_index> <ilabel> <score> <x1> <y1> <x2> <y2>
# compute AP for each class
print(len(dataset.labels))
for ilabel, label in enumerate(dataset.labels):
# detections of this class
detections = alldets[alldets[:, 2] == ilabel, :]
gt = {}
othergt = {}
labellist = {}
# iv,v : 0 Basketball/v_Basketball_g01_c01
for iv, v in enumerate(vlist):
# tubes: dict {ilabel: (list of)<frame number> <x1> <y1> <x2> <y2>}
tubes = dataset._gttubes[v]
# labellist[iv]: label list for v
labellist[iv] = tubes.keys()
for il in tubes:
# tube: list of <frame number> <x1> <y1> <x2> <y2>
for tube in tubes[il]:
for i in range(tube.shape[0]):
# k: (video_index, frame_index)
k = (iv, int(tube[i, 0]))
if il == ilabel:
if k not in gt:
gt[k] = []
gt[k].append(tube[i, 1:5].tolist())
else:
if k not in othergt:
othergt[k] = []
othergt[k].append(tube[i, 1:5].tolist())
for k in gt:
gt[k] = np.array(gt[k])
for k in othergt:
othergt[k] = np.array(othergt[k])
dupgt = deepcopy(gt)
# pr will be an array containing precision-recall values and 4 types of errors:
# localization, classification, timing, others
pr = np.empty((detections.shape[0] + 1, 6),
dtype=np.float32) # precision, recall
pr[0, 0] = 1.0
pr[0, 1:] = 0.0
fn = sum([g.shape[0] for g in gt.values()]) # false negatives
fp = 0 # false positives
tp = 0 # true positives
EL = 0 # localization errors
EC = 0 # classification error: overlap >=0.5 with an another object
EO = 0 # other errors
ET = 0 # timing error: the video contains the action but not at this frame
for i, j in enumerate(np.argsort(-detections[:, 3])):
k = (int(detections[j, 0]), int(detections[j, 1]))
box = detections[j, 4:8]
ispositive = False
if k in dupgt:
if k in gt:
ious = iou2d(gt[k], box)
amax = np.argmax(ious)
if k in gt and ious[amax] >= th:
ispositive = True
gt[k] = np.delete(gt[k], amax, 0)
if gt[k].size == 0:
del gt[k]
else:
EL += 1
elif k in othergt:
ious = iou2d(othergt[k], box)
if np.max(ious) >= th:
EC += 1
else:
EO += 1
elif ilabel in labellist[k[0]]:
ET += 1
else:
EO += 1
if ispositive:
tp += 1
fn -= 1
else:
fp += 1
pr[i + 1, 0] = float(tp) / float(tp + fp) # precision
pr[i + 1, 1] = float(tp) / float(tp + fn) # recall
pr[i + 1, 2] = float(EL) / float(tp + fp)
pr[i + 1, 3] = float(EC) / float(tp + fp)
pr[i + 1, 4] = float(ET) / float(tp + fp)
pr[i + 1, 5] = float(EO) / float(tp + fp)
res[label] = pr
# save results
with open(eval_file, 'wb') as fid:
pickle.dump(res, fid)
# display results
AP = 100 * np.array(
[pr_to_ap(res[label][:, [0, 1]]) for label in dataset.labels])
othersap = [
100 *
np.array([pr_to_ap(res[label][:, [j, 1]]) for label in dataset.labels])
for j in range(2, 6)
]
EL = othersap[0]
EC = othersap[1]
ET = othersap[2]
EO = othersap[3]
# missed detections = 1 - recall
EM = 100 - 100 * np.array([res[label][-1, 1] for label in dataset.labels])
LIST = [AP, EL, EC, ET, EO, EM]
print('Error Analysis')
print("")
print("{:20s} {:8s} {:8s} {:8s} {:8s} {:8s} {:8s}".format(
'label', ' AP ', ' Loc. ', ' Cls. ', ' Time ', ' Other ',
' missed '))
print("")
for il, label in enumerate(dataset.labels):
print("{:20s} ".format(label) +
" ".join(["{:8.2f}".format(L[il]) for L in LIST]))
print("")
print("{:20s} ".format("mean") +
" ".join(["{:8.2f}".format(np.mean(L)) for L in LIST]))
print("")
def main(opt):
# added to specify gpu id; the gpus arg in the provided code does not work
torch.cuda.set_device(opt.gpus[0])
set_seed(opt.seed)
print('dataset: ' + opt.dataset + ' task: ' + opt.task)
Dataset = get_dataset(opt.dataset)
opt = opts().update_dataset(opt, Dataset)
train_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'train'))
epoch_train_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'train_epoch'))
val_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'val'))
epoch_val_writer = tensorboardX.SummaryWriter(
log_dir=os.path.join(opt.log_dir, 'val_epoch'))
logger = Logger(opt, epoch_train_writer, epoch_val_writer)
opt.device = torch.device('cuda')
is_pa = False
if opt.pa_model != '':
is_pa = True
model = create_model(opt.arch,
opt.branch_info,
opt.head_conv,
opt.K,
is_pa=is_pa,
pa_fuse_mode=opt.pa_fuse_mode,
rgb_w3=opt.rgb_w3)
# TODO: Compute grad magnitude (maybe check youssef's snippet)
# TODO: Log grad to TB
# default (single set of hyperparam)
# Complexity analysis
'''
with torch.cuda.device(1):
macs, params = get_model_complexity_info(model, (15, 288, 288), input_constructor=prepare_input, as_strings=True,
print_per_layer_stat=True, verbose=True)
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
'''
# orig
#optimizer = torch.optim.Adam(model.parameters(), opt.lr)
# custom
lr_factor = 1.0
if opt.pa_model != '':
optimizer = torch.optim.Adam([{
"params": model.pa.parameters(),
"lr": opt.lr * lr_factor
}, {
"params": model.backbone.parameters(),
"lr": opt.lr
}, {
"params": model.deconv_layer.parameters(),
"lr": opt.lr
}, {
"params": model.branch.parameters(),
"lr": opt.lr
}], opt.lr)
else: # rgb model
optimizer = torch.optim.Adam([{
"params": model.backbone.parameters(),
"lr": opt.lr
}, {
"params": model.deconv_layer.parameters(),
"lr": opt.lr
}, {
"params": model.branch.parameters(),
"lr": opt.lr
}], opt.lr)
start_epoch = opt.start_epoch
# ADDED: allowing automatica lr dropping upon resuming a training
step_count = 0
for step in range(len(opt.lr_step)):
if start_epoch >= opt.lr_step[step]:
step_count += 1
opt.lr = opt.lr * (opt.lr_drop**step_count)
if opt.pretrain_model == 'coco':
model = load_coco_pretrained_model(opt, model)
elif opt.pretrain_model == 'imagenet':
model = load_imagenet_pretrained_model(opt, model)
else:
model = load_custom_pretrained_model(opt, model)
if opt.load_model != '':
model, optimizer, _, _ = load_model(model, opt.load_model, optimizer,
opt.lr, opt.ucf_pretrain)
for i, child in enumerate(model.children()):
pass
#if i == 2 or i == 3: # unfreeze branch, deconv: reproducible! but not pa nor backbone
# for l, param in enumerate(child.parameters()):
# param.requires_grad = False
'''
if i == 0: # PA
continue
#for l, param in enumerate(child.parameters()):
#if l < 3: # 3: conv1 15: block2
#param.requires_grad = False
elif i == 1: # backbone
continue
#for l, param in enumerate(child.parameters()):
#print ('layer {} shape: {}'.format(l, param.size()))
#if l == 2 or l == 3 or l == 4: # 5: conv1 and conv1_5, 30: resnext_layer1
#param.requires_grad = False
elif i == 2: # deconv
for l, param in enumerate(child.parameters()):
param.requires_grad = False
'''
#else:
#for name, module in child.named_modules():
#if name in list_of_lay_freeze:
#for param in module.parameters():
#param.requires_grad = False
#if isinstance(module, torch.nn.ReLU):
#break
#print (name)
trainer = MOCTrainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
train_loader = torch.utils.data.DataLoader(Dataset(opt, 'train'),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory,
drop_last=True,
worker_init_fn=worker_init_fn)
val_loader = torch.utils.data.DataLoader(Dataset(opt, 'val'),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory,
drop_last=True,
worker_init_fn=worker_init_fn)
print('training...')
print('GPU allocate:', opt.chunk_sizes)
best_ap = 0
best_epoch = 0
stop_step = 0 # TODO: this needs to be adjusted otherwise lr is dropped incorrectly when resuming training! (can set to 1 now if resuming from drop-once)
# added: to ensure no decrease of lr too early (for jh s1?)
if stop_step == 0:
drop_early_flag = False # should be False if wanting more reproducible results (e.g., jh s1)
else:
drop_early_flag = True
set_seed(opt.seed) #317
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
print('eopch is ', epoch)
log_dict_train = trainer.train(epoch, train_loader, train_writer)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('epcho/{}'.format(k), v, epoch, 'train')
logger.write('train: {} {:8f} | '.format(k, v))
logger.write('\n')
if opt.save_all and not opt.auto_stop:
time_str = time.strftime('%Y-%m-%d-%H-%M')
model_name = 'model_[{}]_{}.pth'.format(epoch, time_str)
save_model(os.path.join(opt.save_dir, model_name), model,
optimizer, epoch, log_dict_train['loss'])
else:
model_name = 'model_last.pth'
save_model(os.path.join(opt.save_dir, model_name), model,
optimizer, epoch, log_dict_train['loss'])
# this step evaluate the model
if opt.val_epoch:
with torch.no_grad():
log_dict_val = trainer.val(epoch, val_loader, val_writer)
for k, v in log_dict_val.items():
logger.scalar_summary('epcho/{}'.format(k), v, epoch, 'val')
logger.write('val: {} {:8f} | '.format(k, v))
logger.write('\n')
if opt.auto_stop:
tmp_rgb_model = opt.rgb_model
tmp_flow_model = opt.flow_model
tmp_pa_model = opt.pa_model
if opt.rgb_model != '':
opt.rgb_model = os.path.join(opt.rgb_model, model_name)
if opt.flow_model != '':
opt.flow_model = os.path.join(opt.flow_model, model_name)
if opt.pa_model != '':
opt.pa_model = os.path.join(opt.pa_model, model_name)
# orig: difficult to handle with long-range mem
#stream_inference(opt)
normal_inference(opt)
ap = frameAP(opt, print_info=opt.print_log)
### added for debug
print('frame mAP: {}'.format(ap))
os.system("rm -rf tmp")
if ap > best_ap:
best_ap = ap
best_epoch = epoch
saved1 = os.path.join(opt.save_dir, model_name)
saved2 = os.path.join(opt.save_dir, 'model_best.pth')
os.system("cp " + str(saved1) + " " + str(saved2))
if stop_step < len(
opt.lr_step) and epoch >= opt.lr_step[stop_step]:
# added: don't want it to decrease lr too early just bc the map was higher there ...
# seemed to create problem for jh s1
if drop_early_flag is False:
model, optimizer, _, _ = load_model(
model, os.path.join(opt.save_dir, 'model_last.pth'),
optimizer, opt.lr) # model_best -> model_last?
drop_early_flag = True
print('load epoch is ', epoch)
else: # after the first drop, the rest could drop based on mAP
model, optimizer, _, _ = load_model(
model, os.path.join(opt.save_dir, 'model_best.pth'),
optimizer, opt.lr) # model_best -> model_last?
print('load epoch is ', best_epoch)
opt.lr = opt.lr * opt.lr_drop
logger.write('Drop LR to ' + str(opt.lr) + '\n')
for ii, param_group in enumerate(optimizer.param_groups):
if ii >= 1: # backbone + deconv
param_group['lr'] = opt.lr
else:
param_group['lr'] = opt.lr * lr_factor
print('Drop PA LR to ' + str(opt.lr * lr_factor))
print('Drop backbone LR to ' + str(opt.lr))
print('Drop branch LR to ' + str(opt.lr))
torch.cuda.empty_cache()
trainer = MOCTrainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
stop_step = stop_step + 1
opt.rgb_model = tmp_rgb_model
opt.flow_model = tmp_flow_model
opt.pa_model = tmp_pa_model
else:
# this step drop lr
if epoch in opt.lr_step:
lr = opt.lr * (opt.lr_drop**(opt.lr_step.index(epoch) + 1))
logger.write('Drop LR to ' + str(lr) + '\n')
# added for debug
print('Drop LR to ' + str(lr) + '\n')
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if opt.auto_stop:
print('best epoch is ', best_epoch)
logger.close()
def videoAP(opt, print_info=True):
th = opt.th
model_name = opt.model_name
split = 'val'
Dataset = get_dataset(opt.dataset)
dataset = Dataset(opt, split)
inference_dirname = opt.inference_dir
vlist = dataset._test_videos[opt.split - 1]
# load detections
# alldets = for each label in 1..nlabels, list of tuple (v,score,tube as Kx5 array)
alldets = {ilabel: [] for ilabel in range(len(dataset.labels))}
for v in vlist:
tubename = os.path.join(inference_dirname, v + '_tubes.pkl')
if not os.path.isfile(tubename):
print("ERROR: Missing extracted tubes " + tubename)
sys.exit()
with open(tubename, 'rb') as fid:
tubes = pickle.load(fid)
for ilabel in range(len(dataset.labels)):
ltubes = tubes[ilabel]
idx = nms3dt(ltubes, 0.3)
alldets[ilabel] += [(v, ltubes[i][1], ltubes[i][0]) for i in idx]
# compute AP for each class
#target_class = [5]
res = {}
for ilabel in range(len(dataset.labels)):
#if ilabel not in target_class:
# continue
detections = alldets[ilabel]
# load ground-truth
gt = {}
for v in vlist:
tubes = dataset._gttubes[v]
if ilabel not in tubes:
continue
gt[v] = tubes[ilabel]
if len(gt[v]) == 0:
del gt[v]
num_gt = len(gt)
# precision,recall
pr = np.empty((len(detections) + 1, 2), dtype=np.float32)
pr[0, 0] = 1.0
pr[0, 1] = 0.0
fn = sum([len(g) for g in gt.values()]) # false negatives
fp = 0 # false positives
tp = 0 # true positives
for i, j in enumerate(
np.argsort(-np.array([dd[1] for dd in detections]))):
v, score, tube = detections[j]
ispositive = False
#if v == 'jump/Gregoire_Airman_showreel_2008_jump_f_cm_np1_le_bad_2' or v == 'jump/Gregoire_Airman_showreel_2008_jump_f_cm_np1_ri_bad_5' or v == 'jump/Sommerland_Syd_sprung_in_den_tod_jump_f_cm_np1_fr_bad_0':
#print()
if v in gt:
ious = [iou3dt(g, tube) for g in gt[v]]
amax = np.argmax(ious)
if ious[amax] >= th:
ispositive = True
del gt[v][amax]
if len(gt[v]) == 0:
del gt[v]
if ispositive:
tp += 1
fn -= 1
else:
fp += 1
pr[i + 1, 0] = float(tp) / float(tp + fp)
pr[i + 1, 1] = float(tp) / float(tp + fn)
#if fn != 0:
print('Failed to detect {}/{} tubes for class {}'.format(
fn, num_gt, ilabel))
res[dataset.labels[ilabel]] = pr
# display results
ap = 100 * np.array([pr_to_ap(res[label]) for label in dataset.labels])
# ADDED: display individual class performance
frameap_percls = {}
for cl, cls_name in enumerate(dataset.labels):
frameap_percls[cls_name] = ap[cl]
for key, value in frameap_percls.items():
print(key, ':', value)
videoap_result = np.mean(ap)
if print_info:
log_file = open(os.path.join(opt.root_dir, 'result', opt.exp_id), 'a+')
log_file.write('\nTask_{} VideoAP_{}\n'.format(model_name, th))
print('Task_{} VideoAP_{}\n'.format(opt.model_name, th))
# for il, _ in enumerate(dataset.labels):
# print("{:20s} {:8.2f}".format('', ap[il]))
# log_file.write("{:20s} {:8.2f}\n".format('', ap[il]))
log_file.write("\n{:20s} {:8.2f}\n\n".format("mAP", videoap_result))
log_file.close()
print("{:20s} {:8.2f}".format("mAP", videoap_result))
return videoap_result
def frameAP(opt, print_info=True):
redo = opt.redo
th = opt.th
split = 'val'
model_name = opt.model_name
Dataset = get_dataset(opt.dataset)
dataset = Dataset(opt, split)
inference_dirname = opt.inference_dir
print('inference_dirname is ', inference_dirname)
print('threshold is ', th)
# ORIG
vlist = dataset._test_videos[opt.split - 1]
#vlist = dataset._train_videos[opt.split - 1]
'''
# ADDED: to analyze a specific class
tar_class = 'wave'
vlist_filt = []
for vv in range(len(vlist)):
cls_name, clip_name = vlist[vv].split('/')
if cls_name == tar_class:
vlist_filt.append(vlist[vv])
vlist = vlist_filt
'''
# load per-frame detections
frame_detections_file = os.path.join(inference_dirname,
'frame_detections.pkl')
if os.path.isfile(frame_detections_file) and not redo:
print('load previous linking results...')
print('if you want to reproduce it, please add --redo')
with open(frame_detections_file, 'rb') as fid:
alldets = pickle.load(fid)
else:
if opt.inference_mode == 'stream':
alldets = load_frame_detections_stream(opt, dataset, opt.K, vlist,
inference_dirname)
else:
alldets = load_frame_detections(opt, dataset, opt.K, vlist,
inference_dirname)
try:
with open(frame_detections_file, 'wb') as fid:
pickle.dump(alldets, fid, protocol=4)
except:
print(
"OverflowError: cannot serialize a bytes object larger than 4 GiB"
)
results = {}
# compute AP for each class
for ilabel, label in enumerate(dataset.labels): # e.g.: 0, 'brush_hair'
# detections of this class
detections = alldets[alldets[:, 2] == ilabel, :]
# load ground-truth of this class
gt = {}
for iv, v in enumerate(vlist):
tubes = dataset._gttubes[v]
if ilabel not in tubes:
continue
for tube in tubes[ilabel]:
for i in range(tube.shape[0]): # for each frame
k = (iv, int(tube[i, 0])) # video id, frame id
if k not in gt: # if not yet added to gt
gt[k] = []
gt[k].append(tube[i, 1:5].tolist())
for k in gt:
gt[k] = np.array(gt[k])
# added: record of the original gt of a class (it won't be deleted or modified during evaluation)
if opt.evaluation_mode == 'trimmed':
gt_past = copy.deepcopy(gt)
gt_keys_list = list(gt.keys())
gt_vid = []
for vv in gt_keys_list:
if vv[0] in gt_vid:
continue
gt_vid.append(vv[0])
# pr will be an array containing precision-recall values
#pr = np.empty((detections.shape[0] + 1, 2), dtype=np.float32) # precision,recall
pr = -1 * np.ones(
(detections.shape[0] + 1, 2), dtype=np.float32) # precision,recall
pr[0, 0] = 1.0
pr[0, 1] = 0.0
fn = sum(
[g.shape[0] for g in gt.values()]
) # false negatives # ALPHA: == number of frames (each frame has exactly 1 action instance)
fp = 0 # false positives
tp = 0 # true positives
'''
# Below may not be needed now if detection is conducted on all frames
# ADDED: remove potential fn (when not evaluating all frames)?
# Confirmed: can still be used when evaluating the whole set (at least for JHMDB)
if opt.dataset == 'hmdb':
num_tp = 0
prev_k = (-1, -1)
for ii, jj in enumerate(detections):
k = (int(detections[ii, 0]), int(detections[ii, 1])) # (video id, frame id)
if k in gt and k != prev_k:
num_tp += 1
prev_k = k
fn = num_tp
'''
for i, j in enumerate(
np.argsort(-detections[:, 3]
)): # j: index of the det (highest to lowest score)
k = (int(detections[j, 0]), int(detections[j, 1])
) # (video id, frame id)
box = detections[j, 4:8]
ispositive = False
if k in gt:
ious = iou2d(gt[k], box)
amax = np.argmax(ious)
if ious[amax] >= th:
ispositive = True
gt[k] = np.delete(gt[k], amax, 0)
if gt[k].size == 0:
del gt[k]
# untrimmed evaluation (for ucf24)
# basically, when a frame is not in the non-modified gt list but its video id appears ...
if opt.evaluation_mode == 'trimmed':
if k[0] in gt_vid and not (k in gt_past):
continue
if ispositive:
tp += 1
fn -= 1
else:
fp += 1
# ADDED: to avoid division by zero error; is it needed?
if tp + fp == 0 or tp + fn == 0:
continue
pr[i + 1, 0] = float(tp) / float(tp + fp)
pr[i + 1, 1] = float(tp) / float(tp + fn)
pr_trimmed = pr[pr[:, 0] != -1]
results[label] = pr_trimmed
# display results
ap = 100 * np.array([pr_to_ap(results[label]) for label in dataset.labels])
# ADDED: display individual class performance
frameap_percls = {}
for cl, cls_name in enumerate(dataset.labels):
frameap_percls[cls_name] = ap[cl]
for key, value in frameap_percls.items():
print(key, ':', value)
frameap_result = np.mean(ap)
if print_info:
log_file = open(os.path.join(opt.root_dir, 'result', opt.exp_id), 'a+')
log_file.write('\nTask_{} frameAP_{}\n'.format(model_name, th))
print('Task_{} frameAP_{}\n'.format(model_name, th))
log_file.write("\n{:20s} {:8.2f}\n\n".format("mAP", frameap_result))
log_file.close()
print("{:20s} {:8.2f}".format("mAP", frameap_result))
return frameap_result
def BuildTubes(opt):
redo = opt.redo
if not redo:
print('load previous linking results...')
print('if you want to reproduce it, please add --redo')
Dataset = get_dataset(opt.dataset)
inference_dirname = opt.inference_dir
K = opt.K
split = 'val'
dataset = Dataset(opt, split)
print('inference_dirname is ', inference_dirname)
vlist = dataset._test_videos[opt.split - 1]
bar = Bar('{}'.format('BuildTubes'), max=len(vlist))
# DEBUG: target cerain video / class to build tube
#'shoot_ball/ImprovingBasketballSkills-BasketballTurnaroundFadeAway_shoot_ball_f_nm_np1_ri_med_0', 'shoot_ball/KELVIN_shoot_ball_u_cm_np1_ba_med_0', 'shoot_ball/KELVIN_shoot_ball_u_cm_np1_ba_med_2',
#target_video = ['jump/Gregoire_Airman_showreel_2008_jump_f_cm_np1_le_bad_2']
for iv, v in enumerate(vlist):
#if v not in target_video:
# continue
outfile = os.path.join(inference_dirname, v + "_tubes.pkl")
if os.path.isfile(outfile) and not redo:
continue
RES = {}
nframes = dataset._nframes[v]
# TODO: hardcoded for jhmdb for now
if dataset._nframes[v] >= K * opt.ninput:
ok_frame_inds = [16, 21, 26, 31, 36, dataset._nframes[v]]
else:
print('video: {}; Number of frames: {}'.format(v, dataset._nframes[v]))
ok_frame_inds = [dataset._nframes[v] - opt.ninput + 1, dataset._nframes[v]]
#ok_frame_inds = []
#for kk in range(opt.K):
# ok_frame_inds.append(max(dataset._nframes[v] - kk*opt.ninput, 1))
#ok_frame_inds.reverse()
if opt.inference_mode == 'stream':
# record the latest four frame index (for the final frame allocate detection)
last_k_ind = []
last_k_ind_init = min(K * opt.ninput, dataset._nframes[v]) - opt.ninput + 1
last_k_ind.append(last_k_ind_init)
for _ in range(opt.K - 1):
last_k_ind_init = max(1, last_k_ind_init - opt.ninput)
last_k_ind.append(last_k_ind_init)
last_k_ind.reverse()
# load detected tubelets
VDets = {}
# orig: for startframe in range(1, nframes + 2 - K):
for startframe in range(min(K * opt.ninput , dataset._nframes[v]) - opt.ninput + 1 , 1 + dataset._nframes[v]):
if opt.inference_mode == 'stream': # otherwise ignore
if startframe not in ok_frame_inds:
continue
if startframe != min(K * opt.ninput, dataset._nframes[v]) - opt.ninput + 1: # not initial frame (ex: 16)
if opt.inference_mode == 'stream': # otherwise ignore
last_k_ind.append(startframe)
if len(last_k_ind) > opt.K: # only keep the last K index
del last_k_ind[0]
resname = os.path.join(inference_dirname, v, "{:0>5}.pkl".format(startframe))
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets " + resname)
sys.exit()
with open(resname, 'rb') as fid:
VDets[startframe] = pickle.load(fid)
# added: may not be correct but proceed with learning tube building
first_endframe = list(VDets.keys())[0]
for ilabel in range(len(dataset.labels)):
FINISHED_TUBES = []
CURRENT_TUBES = [] # tubes is a list of tuple (frame, lstubelets)
# calculate average scores of tubelets in tubes
def tubescore(tt):
return np.mean(np.array([tt[i][1][-1] for i in range(len(tt))])) # a tube could contain multiple linked mini-tubes (linked over time); hence the for loop
#orig: for frame in range(1, dataset._nframes[v] + 2 - K):
for frame in range(min(K * opt.ninput , dataset._nframes[v]) - opt.ninput + 1 , 1 + dataset._nframes[v]):
# load boxes of the new frame and do nms while keeping Nkeep highest scored
if opt.inference_mode == 'stream':
if frame not in ok_frame_inds:
continue
ltubelets = VDets[frame][ilabel + 1] # [:,range(4*K) + [4*K + 1 + ilabel]] Nx(4K+1) with (x1 y1 x2 y2)*K ilabel-score
ltubelets = nms_tubelets(ltubelets, 0.6, top_k=10)
# just start new tubes
if frame == first_endframe: # orig: 1
for i in range(ltubelets.shape[0]):
CURRENT_TUBES.append([(first_endframe, ltubelets[i, :])]) # orig: 1
continue
# sort current tubes according to average score
avgscore = [tubescore(t) for t in CURRENT_TUBES]
argsort = np.argsort(-np.array(avgscore))
CURRENT_TUBES = [CURRENT_TUBES[i] for i in argsort]
# loop over tubes
finished = []
for it, t in enumerate(CURRENT_TUBES):
# compute ious between the last box of t and ltubelets # mine interpretation: for each tube in the memory, associates with any possible current tubelet
last_endframe, last_tubelet = t[-1] # confusing -1? -> # a tube could contain multiple linked mini-tubes (linked over time)
ious = []
offset = round((frame - last_endframe) / opt.ninput) # orig: frame - last_endframe
if offset < K:
nov = K - offset # number of overlaps
ious = sum([iou2d(ltubelets[:, 4 * iov:4 * iov + 4], last_tubelet[4 * (iov + offset):4 * (iov + offset + 1)]) for iov in range(nov)]) / float(nov)
else:
ious = iou2d(ltubelets[:, :4], last_tubelet[4 * K - 4:4 * K])# head and tail matching
valid = np.where(ious >= 0.5)[0] # 0.5
if valid.size > 0: # ONLY match the best QUERY tube to the database, then delete this query
# take the one with maximum score
idx = valid[np.argmax(ltubelets[valid, -1])]
CURRENT_TUBES[it].append((frame, ltubelets[idx, :]))
ltubelets = np.delete(ltubelets, idx, axis=0)
else:
if offset >= opt.K:
finished.append(it)
# finished tubes that are done
for it in finished[::-1]: # process in reverse order to delete them with the right index why --++--
FINISHED_TUBES.append(CURRENT_TUBES[it][:])
del CURRENT_TUBES[it]
# start new tubes
for i in range(ltubelets.shape[0]):
CURRENT_TUBES.append([(frame, ltubelets[i, :])])
# all tubes are not finished
FINISHED_TUBES += CURRENT_TUBES
# build real tubes
output = []
for t_i, t in enumerate(FINISHED_TUBES):
# DEBUG
#print(t_i)
score = tubescore(t)
# just start new tubes
if score < 0.005:
continue
beginframe = max(t[0][0] - opt.ninput*(K-1), 1) #t[0][0] # TODO: needs to be taken care of (forward vs backward)
endframe = t[-1][0] #t[-1][0] + K - 1 # TODO
length = endframe + 1 - beginframe
# delete tubes with short duraton (contibuting to many fp?)
#if length < min(dataset._nframes[v], (K *opt.ninput - opt.ninput + 1) + opt.ninput*3): # 15
# continue
if length < dataset._nframes[v] // 2: # 15
continue
# build final tubes by average the tubelets
out = np.zeros((length, 6), dtype=np.float32)
out[:, 0] = np.arange(beginframe, endframe + 1)
n_per_frame = np.zeros((length, 1), dtype=np.int32) # orig: zeros
for i in range(len(t)):
frame, box = t[i] # frame: end frame of a tube
n_mem = K - 1
if opt.inference_mode == 'stream':
# for stream detection
if frame != nframes:
for k in range(K):
out[max(frame - k*opt.ninput, 1) - beginframe, 1:5] += box[4 * n_mem:4 * n_mem + 4]
out[max(frame - k*opt.ninput, 1) - beginframe, -1] += box[-1]
n_per_frame[max(frame - k*opt.ninput, 1) - beginframe, 0] += 1
n_mem -= 1
# for the last frame
else:
for k in reversed(range(0, K)):
out[last_k_ind[k] - beginframe, 1:5] += box[4 * n_mem:4 * n_mem + 4]
out[last_k_ind[k] - beginframe, -1] += box[-1]
n_per_frame[last_k_ind[k] - beginframe, 0] += 1
n_mem -= 1
else:
for k in range(K):
out[max(frame - k*opt.ninput, 1) - beginframe, 1:5] += box[4 * n_mem:4 * n_mem + 4]
out[max(frame - k*opt.ninput, 1) - beginframe, -1] += box[-1]
n_per_frame[max(frame - k*opt.ninput, 1) - beginframe, 0] += 1
n_mem -= 1
''' orig
for i in range(len(t)):
frame, box = t[i]
for k in range(K):
out[frame - beginframe + k, 1:5] += box[4 * k:4 * k + 4] # avg effect on the box coord; more stable?
out[frame - beginframe + k, -1] += box[-1] # single frame confidence
n_per_frame[frame - beginframe + k, 0] += 1
'''
nonzero_ind =n_per_frame!=0 # sparse! would be dividing a lot of zeros
out[nonzero_ind[:,0], 1:] /= n_per_frame[nonzero_ind[:,0]]
# orig
#out[:, 1:] /= n_per_frame
if 0 in out[:,-1]: # if any frame index contains 0 (meaning not filled! This line was creating issues!)
#print ('Frame detection interpolation takes place!')
# ADDED: extrapolation?
nonzero_ind = np.where(nonzero_ind==True)[0]
nz_v_prev = -5
nonzero_nonlink = []
for nz_i, nz_v in enumerate(nonzero_ind):
nz_offset = nz_v - nz_v_prev
if nz_offset == 1:
nz_v_prev = nz_v
continue
if nz_i > 0:
nonzero_nonlink.append((nz_v_prev, nz_v))
nz_v_prev = nz_v
for idx, lo_hi in enumerate(nonzero_nonlink):
lo_hi_dist = lo_hi[1] - lo_hi[0]
lo_box = out[lo_hi[0], 1:]
hi_box = out[lo_hi[1], 1:]
score = (out[lo_hi[0], -1] + out[lo_hi[1], -1]) / 2.
diff_box = (hi_box - lo_box) / lo_hi_dist
for offset in range(1, lo_hi_dist):
if out[lo_hi[0] + offset, -1] == 0: # if the cell was not filled before
out[lo_hi[0] + offset, 1:] = lo_box + offset*diff_box
out[lo_hi[0] + offset, -1] = score
output.append([out, score])
# out: [num_frames, (frame idx, x1, y1, x2, y2, score)]
RES[ilabel] = output
# RES{ilabel:[(out[length,6],score)]}ilabel[0,...]
with open(outfile, 'wb') as fid:
pickle.dump(RES, fid)
Bar.suffix = '[{0}/{1}]:{2}|Tot: {total:} |ETA: {eta:} '.format(
iv + 1, len(vlist), v, total=bar.elapsed_td, eta=bar.eta_td)
bar.next()
bar.finish()
def BuildTubes(opt):
redo = opt.redo
if not redo:
print('load previous linking results...')
print('if you want to reproduce it, please add --redo')
Dataset = get_dataset(opt.dataset)
inference_dirname = opt.inference_dir
K = opt.K
split = 'val'
dataset = Dataset(opt, split)
print('inference_dirname is ', inference_dirname)
vlist = dataset._test_videos[opt.split - 1]
bar = Bar('{}'.format('BuildTubes'), max=len(vlist))
for iv, v in enumerate(vlist):
outfile = os.path.join(inference_dirname, v + "_tubes.pkl")
if os.path.isfile(outfile) and not redo:
continue
RES = {}
nframes = dataset._nframes[v]
# load detected tubelets
VDets = {}
for startframe in range(1, nframes + 2 - K):
resname = os.path.join(inference_dirname, v,
"{:0>5}.pkl".format(startframe))
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets " + resname)
sys.exit()
with open(resname, 'rb') as fid:
VDets[startframe] = pickle.load(fid)
for ilabel in range(len(dataset.labels)):
FINISHED_TUBES = []
CURRENT_TUBES = [] # tubes is a list of tuple (frame, lstubelets)
# calculate average scores of tubelets in tubes
def tubescore(tt):
return np.mean(np.array([tt[i][1][-1]
for i in range(len(tt))]))
for frame in range(1, dataset._nframes[v] + 2 - K):
# load boxes of the new frame and do nms while keeping Nkeep highest scored
ltubelets = VDets[frame][
ilabel +
1] # [:,range(4*K) + [4*K + 1 + ilabel]] Nx(4K+1) with (x1 y1 x2 y2)*K ilabel-score
ltubelets = nms_tubelets(ltubelets, 0.6, top_k=10)
# just start new tubes
if frame == 1:
for i in range(ltubelets.shape[0]):
CURRENT_TUBES.append([(1, ltubelets[i, :])])
continue
# sort current tubes according to average score
avgscore = [tubescore(t) for t in CURRENT_TUBES]
argsort = np.argsort(-np.array(avgscore))
CURRENT_TUBES = [CURRENT_TUBES[i] for i in argsort]
# loop over tubes
finished = []
for it, t in enumerate(CURRENT_TUBES):
# compute ious between the last box of t and ltubelets
last_frame, last_tubelet = t[-1]
ious = []
offset = frame - last_frame
if offset < K:
nov = K - offset
ious = sum([
iou2d(
ltubelets[:, 4 * iov:4 * iov + 4],
last_tubelet[4 * (iov + offset):4 *
(iov + offset + 1)])
for iov in range(nov)
]) / float(nov)
else:
ious = iou2d(ltubelets[:, :4],
last_tubelet[4 * K - 4:4 * K])
valid = np.where(ious >= 0.5)[0]
if valid.size > 0:
# take the one with maximum score
idx = valid[np.argmax(ltubelets[valid, -1])]
CURRENT_TUBES[it].append((frame, ltubelets[idx, :]))
ltubelets = np.delete(ltubelets, idx, axis=0)
else:
if offset >= opt.K:
finished.append(it)
# finished tubes that are done
for it in finished[::
-1]: # process in reverse order to delete them with the right index why --++--
FINISHED_TUBES.append(CURRENT_TUBES[it][:])
del CURRENT_TUBES[it]
# start new tubes
for i in range(ltubelets.shape[0]):
CURRENT_TUBES.append([(frame, ltubelets[i, :])])
# all tubes are not finished
FINISHED_TUBES += CURRENT_TUBES
# build real tubes
output = []
for t in FINISHED_TUBES:
score = tubescore(t)
# just start new tubes
if score < 0.005:
continue
beginframe = t[0][0]
endframe = t[-1][0] + K - 1
length = endframe + 1 - beginframe
# delete tubes with short duraton
if length < 15:
continue
# build final tubes by average the tubelets
out = np.zeros((length, 6), dtype=np.float32)
out[:, 0] = np.arange(beginframe, endframe + 1)
n_per_frame = np.zeros((length, 1), dtype=np.int32)
for i in range(len(t)):
frame, box = t[i]
for k in range(K):
out[frame - beginframe + k,
1:5] += box[4 * k:4 * k + 4]
out[frame - beginframe + k,
-1] += box[-1] # single frame confidence
n_per_frame[frame - beginframe + k, 0] += 1
out[:, 1:] /= n_per_frame
output.append([out, score])
# out: [num_frames, (frame idx, x1, y1, x2, y2, score)]
RES[ilabel] = output
# RES{ilabel:[(out[length,6],score)]}ilabel[0,...]
with open(outfile, 'wb') as fid:
pickle.dump(RES, fid)
Bar.suffix = '[{0}/{1}]:{2}|Tot: {total:} |ETA: {eta:} '.format(
iv + 1, len(vlist), v, total=bar.elapsed_td, eta=bar.eta_td)
bar.next()
bar.finish()
