transforms.Resize((360, 480)), transforms.ToTensor(), transforms.Normalize(mean=[0.4372, 0.4372, 0.4373], std=[0.2479, 0.2475, 0.2485]) ]) datasets_test = KeyPointDatasets(root_dir="./data", transforms=transforms_test) dataloader_test = DataLoader(datasets_test, batch_size=4, shuffle=True, collate_fn=datasets_test.collect_fn) model = KeyPointModel() model.load_state_dict(torch.load("weights/epoch_290_0.232.pt")) img_list = glob.glob(os.path.join("./data/images", "*.jpg")) save_path = "./output" img_tensor_list = [] img_name_list = [] for i in range(len(img_list)): img_path = img_list[i] img_name = os.path.basename(img_path) img_name_list.append(img_name) img = cv2.imread(img_path) img_tensor = transforms_test(img)
transforms.ToPILImage(), transforms.Resize((360, 480)), transforms.ToTensor(), transforms.Normalize(mean=[0.4372, 0.4372, 0.4373], std=[0.2479, 0.2475, 0.2485]) ]) dataset = KeyPointDatasets(root_dir="./data", transforms=transforms_all) dataloader = DataLoader(dataset, shuffle=True, batch_size=1, collate_fn=dataset.collect_fn) model = KeyPointModel() model.load_state_dict(torch.load(args.model)) for iter, (image, label) in enumerate(dataloader): # print(image.shape) bs = image.shape[0] hm = model(image) hm = _nms(hm) scores, inds, clses, ys, xs = _topk(hm, K=1) print(scores, '\n', inds, '\n', clses, '\n', ys, '\n', xs) hm = hm.detach().numpy() for i in range(bs):