Generator for Xception models. This function generates a family of Xception models. See the xception_*() methods for specific model instantiation

Constructor. Args: matching_iou_threshold: IOU threshold to use for matching groundtruth boxes to detection boxes. metric_pre

Converts the ADE20k dataset into into tfrecord format. Args: dataset_split: Dataset split (e.g., train, val). dataset_dir: Dir in which the

Copies the extra fields of boxlist_to_copy_from to boxlist_to_copy_to. Args: boxlist_to_copy_to: BoxList to which extra fields are copied.

Returns the classification loss for a model.

Gets files for the specified data type and dataset split. Args: data: String, desired data ('image' or 'label'). dataset_split: String, dat

Returns numpy arrays belonging to class with index `class_index`. Args: detected_boxes: A numpy array containing detected boxes. dete

Wrapper for separable conv2d.

Splits separable conv2d into depthwise and pointwise conv2d.

Updates input configuration to reflect a new input path. The input_config object is updated in place, and hence not returned. Args: input_co

Updates input configuration to reflect a new shuffle configuration. The input_config object is updated in place, and hence not returned. Args:

验证方法 :param video_list: 视频列表 :return:

Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detec

Add a word to dictionary. freq and tag can be omitted, freq defaults to be a calculated value that ensures the word can be c

检查一个样本的某个特征是否合法 :param data: 特征值的大小 :param key: 对应的特征名称 :param threshold: 字典,存放每个特征值对应的合理值区间 :return: True or False

Evaluates VRD as being tp, fp from a single image. Args: detected_box_tuples: A numpy array of structures with shape [N,], repres

Evaluates detections as being tp, fp or weighted from a single image. The evaluation is done in two stages: 1. All detections are matched to

Adds a bounding box to an image. Bounding box coordinates can be specified in either absolute (pixel) or normalized coordinates by setting the us

Draws bounding boxes, masks, and keypoints on batch of image tensors. Args: images: A 4D uint8 image tensor of shape [N, H, W, C]. If C > 3, ad

Draws mask on an image. Args: image: uint8 numpy array with shape (img_height, img_height, 3) mask: a uint8 numpy array of shape (img_heigh

Inserts multiple ones into a shape vector. Inserts an all-1 vector of length num_dims at position start_dim into a shape. Can be combined with tf

Filter alls elements in the path given the lambda_fn function :param path: :param lambda_fn: :param return_relative:

Filter to keep only boxes and masks with score exceeding a given threshold. This op keeps the collection of boxes and masks whose corresponding sco

Filter to keep only boxes with score exceeding a given threshold. This op keeps the collection of boxes whose corresponding scores are greater th

Filters out the variables matching the filter_regex. Filter out the variables whose name matches the any of the regular expressions in filter_reg

Returns the optimizer type for training. Args: train_config: A train_pb2.TrainConfig. Returns: The type of the optimizer

算法处理 :param video_dir: 待处理单视频路径 :return: 返回预测分类列表 eg:[1, 3, 4]

Compute pairwise intersection areas between boxes. Args: boxes1: a numpy array with shape [N, 4] holding N boxes boxes2: a numpy array with

Compute pairwise intersection areas between masks. Args: masks1: a numpy array with shape [N, height, width] holding N masks. Masks value

Ensure that the file exists.

Ensure that the file exists.

Loads label map proto. Args: path: path to StringIntLabelMap proto text file. Returns: a StringIntLabelMapProto

Sort boxes and associated fields according to a scalar field. A common use case is reordering the boxes according to descending scores. Args:

Compute CorLoc score for a single class. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinate

Labels as true/false positives detection tuples across all classes. Args: detected_box_tuples: A numpy array of structures with shape [N,],

Labels true/false positives of detections of an image across all classes. Args: detected_boxes: A float numpy array of shape [N, 4], repres

Labels boxes detected with the same class from the same image as tp/fp. Detection boxes are expected to be already sorted by score. Args:

Converts the specified dataset split to TFRecord format. Args: dataset_split: The dataset split (e.g., train, val). Raises: RuntimeError

Converts the specified dataset split to TFRecord format. Args: dataset_split: The dataset split (e.g., train, test). Raises: RuntimeErro

Converts the predicted label for evaluation. There are cases where the training labels are not equal to the evaluation labels. This function is u

Creates and prepares input tensors for DeepLab model. This method creates a 4-D uint8 image tensor 'ImageTensor' with shape [1, None, None, 3]. T

Crops the given image using the provided offsets and sizes. Note that the method doesn't assume we know the input image size but it does assume w

cal score :param fore_file: 超算结果 :param obs_file: 正确答案 :param anen_file: 选手提交结果 :return:

Gets data from data provider. Args: data_provider: An object of slim.data_provider. dataset_split: Dataset split. Returns: image: Im

Gets the logits by atrous/image spatial pyramid pooling. Args: images: A tensor of size [batch, height, width, channels]. model_options: A

Computes overlaps and scores between detected and groudntruth boxes. Args: detected_boxes: A numpy array of shape [N, 4] representing detec

Computes overlaps and scores between detected and groudntruth masks. Args: detected_boxes: A numpy array of shape [N, 4] representing detec

Computes overlaps and scores between detected and groundtruth tuples. Both detections and groundtruth boxes have the same class tuples. Args

Determines whether the key starts with a generic config dictionary key.

Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin]. Args: data: a numpy array of shape [N, 4] representing box coordinat