data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict['train'], data_dict['val'] 1. 2. 3. 4. 5. 6. 7. check_dataset函数中,将训练集、验证集、测试集的路径拼凑出来,最后封装成一个Dictionary返回给data_dict变量,因此train_path, val_path就是...
初始化函数首先读取了list_path(data/coco/trainvalno5k.txt)这个文件,这个文件存储的是该数据集每个图片的位置与名字,所以self.img_files存储了所有图片文件的路径,self.label_files则存储了所有label文件的路径,self.max_object表示一个图片中包含的object最大数量。 class COCODataset(Dataset): def __init__(se...
img_file = os.path.join(self.data_path, 'JPEGImages', filename + '.jpg') im = cv2.imread(img_file) width = im.shape[1] height = im.shape[0] seg_data = json.load(open(json_file, 'r')) assert type(seg_data) == type(dict()), 'annotation file format {} not supported'.fo...
KeyError: '/home/xzm/github_repository/PersonDetectionTFLite/VOCOutput/VOC/train/VOC2020/JPEGImages/000000470960.jpg does not exist ... ' 您好,我根据这个博客教程:https://blog.csdn.net/weixin_43978056/article/details/109542579?spm=1001.2101.3001.6650.1&utm_medium=distribute.pc_relevant.none-task-...
data初始化 class ListDataset(Dataset): def __init__(self, list_path, img_size=416, augment=True, multiscale=True, normalized_labels=True): with open(list_path, "r") as file: self.img_files = file.readlines() self.label_files = [ ...
Data pre-processing in the INT8 calibration step is the same as in the training process. The data batches are sampled randomly across the entire training dataset, thereby improving the accuracy of the INT8 model. Option 2: Pointing the tool to a directory of images that you want to use to...
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self.image_shape = self.input_var.get_shape().as_list()[1:] def __call__(self, data_x, batch_size=32): out = np.zeros((len(data_x), self.feature_dim), np.float32) _run_in_batches( lambda x: self.session.run(self.output_var, feed_dict=x), {self.input_var: data_x},...
Label consistency.All instances of all classes in all images must be labelled. Partial labelling will not work. Label accuracy.Labels must closely enclose each object. No space should exist between an object and it's bounding box. No objects should be missing a label. ...
[ptr : ptr + num_b]).view_as(bn_layer.running_mean) bn_layer.running_mean.data.copy_(bn_rm) ptr += num_b # Running Var bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var) bn_layer.running_var.data.copy_(bn_rv) ptr += num_b else: #...