def feature_extract(args):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # close the warning
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
cudnn.benchmark = True
# == dataset config==
num_class, data_length, image_tmpl = data_config(args)
train_transforms, test_transforms, _ = augmentation_config(args)
train_data_loader, val_data_loader, _, _, _, _ = data_loader_init(
args, data_length, image_tmpl, train_transforms, test_transforms, _)
# == model config==
model = model_config(args, num_class)
tc = TC(args)
features = single_extract(tc, val_data_loader, model)
return features
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
# == dataset config==
num_class, data_length, image_tmpl = data_config(args)
train_transforms, test_transforms, eval_transforms = augmentation_config(args)
_, _, eval_data_loader, _, _, _ = data_loader_init(args, data_length, image_tmpl, train_transforms,
test_transforms, eval_transforms)
model = model_config(args, num_class)
output = []
total_num = len(eval_data_loader)
for i, (data, label, index) in enumerate(eval_data_loader):
proc_start_time = time.time()
rst = eval_video(model, (i, data, label))
output.append(rst)
cnt_time = time.time() - proc_start_time
if i % 10 == 0:
print('video {} done, total {}/{}, average {} sec/video'.format(i, i + 1,
total_num,
float(cnt_time) / (i + 1)))
date = datetime.datetime.today().strftime('%m-%d-%H%M')
# =====output: every video's num and every video's label
# =====x[0]:softmax value x[1]:label
if not os.path.isdir("../experiments/evaluation/{}/{}".format(args.dataset,date)):
os.mkdir("../experiments/evaluation/{}/{}".format(args.dataset,date))
video_pred = [np.argmax(x[0]) for x in output]
np.save("../experiments/evaluation/{}/{}/video_pred.npy".format(args.dataset,date), video_pred)
video_labels = [x[1] for x in output]
np.save("../experiments/evaluation/{}/{}/video_labels.npy".format(args.dataset, date), video_labels)
cf = confusion_matrix(video_labels, video_pred).astype(float)
np.save("../experiments/evaluation/{}/{}/confusion.npy".format(args.dataset, date), cf)
cf_name = "../experiments/evaluation/{}/{}/confusion.npy".format(args.dataset, date)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
print(cls_acc)
print('Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
name_list = [x.strip().split()[0] for x in open(args.val_list)]
order_dict = {e: i for i, e in enumerate(sorted(name_list))}
reorder_output = [None] * len(output)
reorder_label = [None] * len(output)
for i in range(len(output)):
idx = order_dict[name_list[i]]
reorder_output[idx] = output[i]
reorder_label[idx] = video_labels[i]
np.savez('../experiments/evaluation/' + args.dataset + '/' + date + "/" + args.mode + 'res',
scores=reorder_output, labels=reorder_label)
return cf_name
def main():
# =
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # close the warning
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
torch.manual_seed(1)
cudnn.benchmark = True
# == dataset config==
num_class, data_length, image_tmpl = data_config(args)
train_transforms, test_transforms, eval_transforms = augmentation_config(
args)
train_data_loader, val_data_loader, _, _, _, _ = data_loader_init(
args, data_length, image_tmpl, train_transforms, test_transforms,
eval_transforms)
# == model config==
model = model_config(args, num_class)
pos_aug = GenPositive()
# == train and eval==
test(train_data_loader, model, pos_aug)
return 1
def train_and_eval(args):
# =
global lowest_val_loss, best_prec1
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # close the warning
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
torch.manual_seed(1)
cudnn.benchmark = True
timer = Timer()
recorder = Record(args)
# == dataset config==
num_class, data_length, image_tmpl = data_config(args)
train_transforms, test_transforms = augmentation_config(args)
train_data_loader, val_data_loader = data_loader_init(
args, data_length, image_tmpl, train_transforms, test_transforms)
# == model config==
models = []
optimizers = []
for i in range(args.mutual_num):
model = model_config(args, num_class)
models.append(model)
recorder.record_message('a', '=' * 100)
recorder.record_message('a', str(model.module))
recorder.record_message('a', '=' * 100)
# == optim config==
for i in range(args.mutual_num):
train_criterion, val_criterion, optimizer = optim_init(args, model)
optimizers.append(optimizer)
# == data augmentation(self-supervised) config==
tc = TC(args)
# == train and eval==
for epoch in range(args.start_epoch, args.epochs):
timer.tic()
for i in range(args.mutual_num):
adjust_learning_rate(optimizers[i], args.lr, epoch, args.lr_steps)
if args.eval_indict == 'acc':
train_prec1, train_loss = train(args, tc, train_data_loader,
models, train_criterion,
optimizers, epoch, recorder)
# train_prec1, train_loss = random.random() * 100, random.random()
recorder.record_train(train_loss / 5.0, train_prec1 / 100.0)
else:
train_loss = train(args, tc, train_data_loader, models,
train_criterion, optimizers, epoch, recorder)
# train_prec1, train_loss = random.random() * 100, random.random()
recorder.record_train(train_loss)
if (epoch + 1) % args.eval_freq == 0:
if args.eval_indict == 'acc':
val_prec1, val_loss = validate(args, tc, val_data_loader,
models, val_criterion, recorder)
# val_prec1, val_loss = random.random() * 100, random.random()
recorder.record_val(val_loss / 5.0, val_prec1 / 100.0)
is_best = val_prec1 > best_prec1
best_prec1 = max(val_prec1, best_prec1)
checkpoint = {
'epoch': epoch + 1,
'arch': "i3d",
'state_dict': model.state_dict(),
'best_prec1': best_prec1
}
else:
val_loss = validate(args, tc, val_data_loader, models,
val_criterion, recorder)
# val_loss = random.random()
# val_prec1, val_loss = random.random() * 100, random.random()
recorder.record_val(val_loss)
is_best = val_loss < lowest_val_loss
lowest_val_loss = min(val_loss, lowest_val_loss)
checkpoint = {
'epoch': epoch + 1,
'arch': "i3d",
'state_dict': model.state_dict(),
'lowest_val': lowest_val_loss
}
recorder.save_model(checkpoint, is_best)
timer.toc()
left_time = timer.average_time * (args.epochs - epoch)
if args.eval_indict == 'acc':
message = "best_prec1 is: {} left time is : {}".format(
best_prec1, timer.format(left_time))
else:
message = "lowest_val_loss is: {} left time is : {}".format(
lowest_val_loss, timer.format(left_time))
print(message)
recorder.record_message('a', message)
# return recorder.best_name
return recorder.filename