def convert_to_onnx_with_hydra(cfg: DictConfig):
# create folder for onnx
createFolderOnnx(cfg)
# set cfg
set_cfg(cfg.onnx.yolact_cfg)
model = Yolact()
model.load_weights(cfg.onnx.model_ckpt_path)
model.eval()
model = model.cpu()
dummy_input = torch.rand(
(cfg.onnx.model_batch_size, cfg.onnx.model_channel_input,
cfg.onnx.model_height_input, cfg.onnx.model_width_input))
torch.onnx.export(model,
dummy_input,
cfg.onnx.model_onnx_path,
verbose=cfg.onnx.verbose,
opset_version=cfg.onnx.opset_version)
if __name__ == '__main__':
# 数据集与标签
valid_dataset = COCODetection(image_path='./data/coco/images/val2017/',
info_file='./data/coco/annotations/instances_val2017.json',
transform=BaseTransform(),
has_gt=True
)
prep_coco_cats()
# 模型
print('Loading model...', end='')
model = Yolact()
model.load_weights(args.trained_model)
model.eval()
model = model.cuda() if args.cuda else model.cpu()
print(' Done.')
# 核心入口
with torch.no_grad():
if not os.path.exists('results'):
os.makedirs('results')
if args.cuda:
torch.backends.cudnn.fastest = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
# if args.resume and not args.display:
# with open(args.ap_data_file, 'rb') as f:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
if args.resume and not args.display:
with open(args.ap_data_file, 'rb') as f:
ap_data = pickle.load(f)
calc_map(ap_data)
exit()
if args.image is None and args.video is None and args.images is None:
dataset = COCODetection(cfg.dataset.valid_images,
cfg.dataset.valid_info,
transform=BaseTransform(),
has_gt=cfg.dataset.has_gt)
prep_coco_cats()
else:
dataset = None
dataset = None
print('Loading model...', end='')
print(args.trained_model)
input("test")
net = Yolact()
net.load_weights(args.trained_model)
net.eval()
net.cpu()
print(' Done.')
evaluate(net, dataset)
