本文整理汇总了Python中utils.batch_slice方法的典型用法代码示例。如果您正苦于以下问题：Python utils.batch_slice方法的具体用法？Python utils.batch_slice怎么用？Python utils.batch_slice使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils的用法示例。
在下文中一共展示了utils.batch_slice方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: call
# 需要导入模块: import utils [as 别名]
# 或者: from utils import batch_slice [as 别名]
def call(self, inputs):
rois = inputs[0]
mrcnn_class = inputs[1]
mrcnn_bbox = inputs[2]
image_meta = inputs[3]
# Run detection refinement graph on each item in the batch
_, _, window, _ = parse_image_meta_graph(image_meta)
window = norm_boxes_graph(window, self.config.IMAGE_SHAPE[:2])
detections_batch = utils.batch_slice(
[rois, mrcnn_class, mrcnn_bbox, window],
lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
self.config.IMAGES_PER_GPU)
# Reshape output
# [batch, num_detections, (y1, x1, y2, x2, class_score)] in
# normalized coordinates
return tf.reshape(
detections_batch,
[self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6]) 示例2: call
# 需要导入模块: import utils [as 别名]
# 或者: from utils import batch_slice [as 别名]
def call(self, inputs):
rois = inputs[0]
mrcnn_class = inputs[1]
mrcnn_bbox = inputs[2]
image_meta = inputs[3]
# Run detection refinement graph on each item in the batch
_, _, window, _ = parse_image_meta_graph(image_meta)
detections_batch = utils.batch_slice(
[rois, mrcnn_class, mrcnn_bbox, window],
lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
self.config.IMAGES_PER_GPU)
# Reshape output
# [batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
return tf.reshape(
detections_batch,
[self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6]) 示例3: call
# 需要导入模块: import utils [as 别名]
# 或者: from utils import batch_slice [as 别名]
def call(self, inputs):
proposals = inputs[0]
gt_class_ids = inputs[1]
gt_boxes = inputs[2]
gt_masks = inputs[3]
# Slice the batch and run a graph for each slice
# TODO: Rename target_bbox to target_deltas for clarity
names = ["rois", "target_class_ids", "target_bbox", "target_mask"]
outputs = utils.batch_slice(
[proposals, gt_class_ids, gt_boxes, gt_masks],
lambda w, x, y, z: detection_targets_graph(
w, x, y, z, self.config),
self.config.IMAGES_PER_GPU, names=names)
return outputs