cfg.AVD_ROOT_DIR,
cfg.TEST_LIST,
test_ids,
max_difficulty=cfg.MAX_OBJ_DIFFICULTY,
fraction_of_no_box=cfg.TEST_FRACTION_OF_NO_BOX_IMAGES)
#create train/test loaders, with CUSTOM COLLATE function
testloader = torch.utils.data.DataLoader(testset,
batch_size=1,
shuffle=True,
num_workers=cfg.NUM_WORKERS,
collate_fn=AVD.collate)
# load net
print('Loading ' + cfg.FULL_MODEL_LOAD_NAME + ' ...')
net = TDID(cfg)
load_net(cfg.FULL_MODEL_LOAD_DIR + cfg.FULL_MODEL_LOAD_NAME, net)
net.features.eval() #freeze batchnorms layers?
print('load model successfully!')
net.cuda()
net.eval()
# evaluation
test_net(cfg.MODEL_BASE_SAVE_NAME,
net,
testloader,
target_images,
test_ids,
cfg,
max_dets_per_target=cfg.MAX_DETS_PER_TARGET,
max_difficulty=cfg.MAX_OBJ_DIFFICULTY,
fraction_of_no_box=cfg.FRACTION_OF_NO_BOX_IMAGES)
valset = get_AVD_dataset(cfg.AVD_ROOT_DIR,
cfg.VAL_LIST,
val_ids,
max_difficulty=cfg.MAX_OBJ_DIFFICULTY,
fraction_of_no_box=cfg.VAL_FRACTION_OF_NO_BOX_IMAGES)
trainloader = torch.utils.data.DataLoader(train_set,
batch_size=cfg.BATCH_SIZE,
shuffle=True,
num_workers=cfg.NUM_WORKERS,
collate_fn=AVD.collate)
print('Loading network...')
net = TDID(cfg)
if cfg.LOAD_FULL_MODEL:
load_net(cfg.FULL_MODEL_LOAD_DIR + cfg.FULL_MODEL_LOAD_NAME, net)
else:
weights_normal_init(net, dev=0.01)
if cfg.USE_PRETRAINED_WEIGHTS:
net.features = load_pretrained_weights(cfg.FEATURE_NET_NAME)
net.features.eval() #freeze batchnorms layers?
if not os.path.exists(cfg.SNAPSHOT_SAVE_DIR):
os.makedirs(cfg.SNAPSHOT_SAVE_DIR)
if not os.path.exists(cfg.META_SAVE_DIR):
os.makedirs(cfg.META_SAVE_DIR)
#put net on gpu
net.cuda()
bbox = [int(data[1]), int(data[2]), int(data[3]), int(data[4]), 1]
target1 = cv2.resize(pre_load_target_1, (80, 80),
interpolation=cv2.INTER_AREA)
target2 = cv2.resize(pre_load_target_2, (80, 80),
interpolation=cv2.INTER_AREA)
return image, bbox, target1, target2
# load config
cfg_file = "configAVD1"
cfg = importlib.import_module('configs.' + cfg_file)
cfg = cfg.get_config()
print('Loading network...')
net = TDID(cfg)
if cfg.LOAD_FULL_MODEL:
load_net(cfg.FULL_MODEL_LOAD_DIR + cfg.FULL_MODEL_LOAD_NAME, net)
else:
weights_normal_init(net, dev=0.01)
if cfg.USE_PRETRAINED_WEIGHTS:
net.features = load_pretrained_weights(cfg.FEATURE_NET_NAME)
net.features.eval() #freeze batchnorms layers?
if not os.path.exists(cfg.SNAPSHOT_SAVE_DIR):
os.makedirs(cfg.SNAPSHOT_SAVE_DIR)
if not os.path.exists(cfg.META_SAVE_DIR):
os.makedirs(cfg.META_SAVE_DIR)
#put net on gpu
net.cuda()