def main():
args = parse_args()
cfg = Config.fromfile(args.config)
img_tensor = get_img_tensor(args.input, args.use_cuda)
# global attribute predictor will not use landmarks
# just set a default value
landmark_tensor = torch.zeros(8)
print(cfg.model)
model = build_predictor(cfg.model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
print('model loaded from {}'.format(args.checkpoint))
if args.use_cuda:
model.cuda()
landmark_tensor = landmark_tensor.cuda()
model.eval()
# predict probabilities for each attribute
attr_prob, cate_prob = model(
img_tensor, attr=None, landmark=landmark_tensor, return_loss=False)
attr_predictor = AttrPredictor(cfg.data.test)
cate_predictor = CatePredictor(cfg.data.test)
attr_predictor.show_prediction(attr_prob)
cate_predictor.show_prediction(cate_prob)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
img_tensor = get_img_tensor(args.input, args.use_cuda)
cfg.model.pretrained = None
model = build_predictor(cfg.model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.use_cuda:
model.cuda()
model.eval()
# predict probabilities for each attribute
attr_prob = model(img_tensor, attr=None, landmark=None, return_loss=False)
attr_predictor = AttrPredictor(cfg.data.test)
attr_predictor.show_prediction(attr_prob)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
img = cv2.imread(args.input, -1)
img_tensor = img_to_tensor(img, squeeze=True, cuda=args.use_cuda)
model = build_predictor(cfg.model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.use_cuda:
model.cuda()
model.eval()
# predict probabilities for each attribute
attr_prob = model(img_tensor, attr=None, landmark=None, return_loss=False)
attr_predictor = AttrPredictor(cfg.data.test)
attr_predictor.show_prediction(attr_prob)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
# init distributed env first
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
# build model
model = build_predictor(cfg.model)
print('model built')
if cfg.init_weights_from:
model = init_weights_from(cfg.init_weights_from, model)
# data loader
dataset = get_dataset(cfg.data.train)
print('dataset loaded')
# train
train_predictor(
model,
dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.model.pretrained = None
model = build_predictor(cfg.model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.use_cuda:
model.cuda()
model.eval()
attr_predictor = AttrPredictor(cfg.data.test)
files=os.listdir(args.input)
for i in tqdm(range(len(files))) :
filename=files[i]
#print(filename)
filename=args.input+'/'+filename
img_tensor = get_img_tensor(filename, args.use_cuda)
attr_prob = model(img_tensor, attr=None, landmark=None, return_loss=False)
attr_predictor.show_prediction(attr_prob,filename)
def process_recordings(q, webapi):
# Initialize clothe category classifier
cfg = Config.fromfile(CONFIG_FILE)
landmark_tensor = torch.zeros(8)
model = build_predictor(cfg.model)
load_checkpoint(model, CHECKPOINT_FILE, map_location='cpu')
print('model loaded from {}'.format(CHECKPOINT_FILE))
if USE_CUDA:
model.cuda()
landmark_tensor = landmark_tensor.cuda()
model.eval()
cate_predictor = CatePredictor(cfg.data.test, tops_type=[1])
# Initialize tracker model
yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE)
load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
while True:
if q.empty():
time.sleep(1)
continue
recording_id, recording_filename = q.get()
if recording_id == -1:
break
recording_filepath = os.path.join(RECORDINGS_DIRECTORY,
recording_filename)
Object_tracking(yolo,
webapi,
recording_id,
recording_filepath,
model,
cate_predictor,
landmark_tensor,
iou_threshold=0.1,
rectangle_colors=(255, 0, 0),
Track_only=["person"])
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.checkpoint is not None:
cfg.load_from = args.checkpoint
# init distributed env first
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# init logger
logger = get_root_logger(cfg.log_level)
logger.info('Distributed test: {}'.format(distributed))
# data loader
dataset = get_dataset(cfg.data.test)
print('dataset loaded')
# build model and load checkpoint
model = build_predictor(cfg.model)
print('model built')
load_checkpoint(model, cfg.load_from, map_location='cpu')
print('load checkpoint from: {}'.format(cfg.load_from))
# test
test_predictor(
model,
dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
def basis_model(config, checkpoint):
cfg = Config.fromfile(config)
model = build_predictor(cfg.model)
load_checkpoint(model, checkpoint, map_location='cpu')
return model
return jsonify(resultDict)
if __name__ == '__main__':
cfg_fine = Config.fromfile(
'./configs/category_attribute_predict/global_predictor_vgg.py')
cfg_coarse = Config.fromfile(
'./configs/attribute_predict_coarse/global_predictor_resnet_attr.py')
cfg_ret = Config.fromfile('configs/retriever_in_shop/global_retriever_vgg_loss_id.py')
# global attribute predictor will not use landmarks
# just set a default value
landmark_tensor = torch.zeros(8)
model_fine = build_predictor(cfg_fine.model)
load_checkpoint(model_fine, './checkpoint/vgg16_fine_global.pth',
map_location='cpu')
model_coarse = build_predictor(cfg_coarse.model)
load_checkpoint(model_coarse, './checkpoint/resnet_coarse_global.pth',
map_location='cpu')
model_ret = build_retriever(cfg_ret.model).cuda()
load_checkpoint(model_ret, 'checkpoint/Retrieve/vgg/global/epoch_100.pth', map_location=torch.device('cuda:0'))
print('Models loaded.')
model_fine.eval()
model_coarse.eval()
