Evaluates one single batch qualitatively. Args: sess: TensorFlow session. original_images: One batch of original images. semantic_predi

Pre-processes boxes before adding them to the VRDDetectionEvaluation. Phrase detection and Relation detection subclasses re-implement this method

Pre-processes boxes before adding them to the VRDDetectionEvaluation. Phrase detection and Relation detection subclasses re-implement this method

Creates a list of boxes [(ymin,xmin, ...), ...] from features.

Removes the color map from the annotation. Args: filename: Ground truth annotation filename. Returns: Annotation without color map.

Removes entries with invalid boxes. A box is invalid if either its xmax is smaller than its xmin, or its ymax is smaller than its ymin.

Saves the annotation as png file. Args: annotation: Segmentation annotation. filename: Output filename.

Updates `configs` to reflect the new training batch size. The configs dictionary is updated in place, and hence not returned. Args: configs:

Updates the classification/localization weight loss ratio. Detection models usually define a loss weight for both classification and objectness.

Updates `configs` to reflect new number of eval steps per evaluation.

Updates the alpha value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Diction

Updates the gamma value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Diction

Update a pipeline configuration parameter based on a generic key/value. Args: configs: Dictionary of pipeline configuration protos. key: A

Update grouth truth statitistics. 1. Difficult boxes are ignored when counting the number of ground truth instances as done in Pascal VOC dev

Updates grouth truth statistics. Args: groundtruth_class_tuples: A numpy array of structures shape [M, 1], representing the clas

Updates `configs` to reflect the new initial learning rate. This function updates the initial learning rate. For learning rate schedules, all oth

Updates the label map path for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args:

Updates the mask type for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args: confi

Updates `configs` to reflect the new momentum value. Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any other optimiz

Adds a new constant learning rate.

Adds a new cosine decay learning rate.

Adds a new exponential decay learning rate.

Adds a learning rate schedule.

Updates eval config with option to retain original images. The eval_config object is updated in place, and hence not returned. Args: eval_co

Updates `configs` to reflect new number of training steps.

Updates the eval config option to use or not use moving averages. The configs dictionary is updated in place, and hence not returned. Args:

Checks if a label map is valid. Args: label_map: StringIntLabelMap to validate. Raises: ValueError: if label map is invalid.

准确率统计 :return: 准确率

准确率统计 :return: 准确率

Computes area of boxes. Args: boxlist: BoxList holding N boxes Returns: a numpy array with shape [N*1] representing box areas

Gets the bit value. Args: val: Input value, int or numpy int array. idx: Which bit of the input val. Returns: The "idx"-th bit of in

generate individual bounding box from label

generate individual bounding box from label

Implementation refers to https://github.com/rbgirshick/py-faster-rcnn

Implementation refers to https://github.com/rbgirshick/py-faster-rcnn

Implementation refers to https://github.com/rbgirshick/py-faster-rcnn

Clears detections.

Clips the input tensor along the first dimension up to the length. Args: t: the input tensor, assuming the rank is at least 1. length: a te

Concatenate list of BoxLists. This op concatenates a list of input BoxLists into a larger BoxList. It also handles concatenation of BoxList fiel

Loads label map proto and returns categories list compatible with eval. This function loads a label map and returns a list of dicts, each of which

Creates a label colormap used in ADE20K segmentation benchmark. Returns: A colormap for visualizing segmentation results.

Creates dictionary of COCO compatible categories keyed by category id. Args: categories: a list of dicts, each of which has the following keys:

Creates a label colormap used in CITYSCAPES segmentation benchmark. Returns: A colormap for visualizing segmentation results.

Creates a configs dictionary from pipeline_pb2.TrainEvalPipelineConfig. Args: pipeline_config: pipeline_pb2.TrainEvalPipelineConfig proto objec

Creates a label colormap for the specified dataset. Args: dataset: The colormap used in the dataset. Returns: A numpy array of the datas

Creates a label colormap used in Mapillary Vistas segmentation benchmark. Returns: A colormap for visualizing segmentation results.

Creates a label colormap used in PASCAL VOC segmentation benchmark. Returns: A colormap for visualizing segmentation results.

Global `cut` function that supports parallel processing. Note that this only works using dt, custom POSTokenizer instances are not suppo

Finer segmentation for search engines.

Decodes the image data string. Args: image_data: string of image data. Returns: Decoded image data. Raises: ValueErro

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

Draws bounding boxes on image. Args: image: a PIL.Image object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).

Draws keypoints on an image. Args: image: a PIL.Image object. keypoints: a numpy array with shape [num_keypoints, 2]. color: color to d

Draws keypoints on an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. keypoints: a numpy array with shape [n

Evaluates detections and returns a dictionary of metrics.

Constructs the graph, executes it on CPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. T

Constructs the graph, executes it on TPU and returns the result. Args: graph_fn: a callable that constructs the tensorflow graph to test. T

Extracts features by the particular model_variant. Args: images: A tensor of size [batch, height, width, channels]. model_options: A ModelO

Get the feature dict. In the default case it is filled with all the keys and the items set to None. If the type=reading_shape the sha

Filters all elements in the path :param path: :param return_relative: :param regex: :return:

Pads the input along the spatial dimensions independently of input size. Args: inputs: A tensor of size [batch, height_in, width_in, channels].