Python get_test_transform Examples

Example #1

File: predict.py Project: huhanGitHub/jihualab_image_classification

def predict(model):
    # 读入模型
    model = load_checkpoint(model)
    print('..... Finished loading model! ......')
    ##将模型放置在gpu上运行
    if torch.cuda.is_available():
        model.cuda()
    pred_list, _id, pred_pro_list = [], [], []
    for i in tqdm(range(len(imgs))):
        img_path = imgs[i].strip()
        # print(img_path)
        _id.append(os.path.basename(img_path).split('.')[0])
        img = Image.open(img_path).convert('RGB')
        # print(type(img))
        img = get_test_transform(size=cfg.INPUT_SIZE)(img).unsqueeze(0)

        if torch.cuda.is_available():
            img = img.cuda()
        with torch.no_grad():
            out = model(img)
        softmax = F.softmax(out, dim=-1)
        #print(softmax.tolist()[0])
        pred_pro_list.append(softmax.tolist()[0])
        prediction = torch.argmax(out, dim=1).cpu().item()
        pred_list.append(prediction)
    return _id, pred_list, pred_pro_list

Example #2

File: dataset.py Project: lxztju/pytorch_classification

 def __init__(self, label_file, imageset):
     '''
     img_dir: 图片路径：img_dir + img_name.jpg构成图片的完整路径      
     '''
     # 所有图片的绝对路径
     with open(label_file, 'r') as f:
         #label_file的格式， （label_file image_label)
         self.imgs = list(map(lambda line: line.strip().split(' '), f))
   # 相关预处理的初始化
   #   self.transforms=transform
     self.img_aug=True
     if imageset == 'train':
         self.transform= get_train_transform(size=cfg.INPUT_SIZE)
     else:
         self.transform = get_test_transform(size = cfg.INPUT_SIZE)
     self.input_size = cfg.INPUT_SIZE

Example #3

File: Feature_Visualization.py Project: zhufenglong2/pytorch_classification

def predict(model):
    # 读入模型
    model = load_checkpoint(model)
    print('..... Finished loading model! ......')
    ##将模型放置在gpu上运行
    if torch.cuda.is_available():
        model.cuda()

    img = Image.open(img_path).convert('RGB')

    img = get_test_transform(size=cfg.INPUT_SIZE)(img).unsqueeze(0)

    if torch.cuda.is_available():
        img = img.cuda()
    with torch.no_grad():
        x = model.conv1(img)
        draw_features(8, 8, 64,  x.cpu().numpy(), "{}/f1_conv1.png".format(savepath))

Example #4

 def __init__(self, label_file, imageset):
     '''
     img_dir: 图片路径：img_dir + img_name.jpg构成图片的完整路径      
     '''
     # 所有图片的绝对路径
     label_file =  "D:\\05分类图片\\pytorch_classification-master\\data\\train.txt"
     print(label_file)
     with open(label_file, 'r') as f:
         #label_file的格式， （label_file image_label)
         self.imgs = list(map(lambda line: line.strip().split(' '), f))
   # 相关预处理的初始化
   #   self.transforms=transform
     self.img_aug=True
     if imageset == 'train':
         self.transform= get_train_transform(size=cfg.INPUT_SIZE)
     else:
         self.transform = get_test_transform(size = cfg.INPUT_SIZE)
     self.eraser = get_random_eraser( s_h=0.1, pixel_level=True)
     self.input_size = cfg.INPUT_SIZE