本文整理匯總了Python中utils.batch_slice方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.batch_slice方法的具體用法？Python utils.batch_slice怎麽用？Python utils.batch_slice使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils的用法示例。
在下文中一共展示了utils.batch_slice方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: call
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import batch_slice [as 別名]
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
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import batch_slice [as 別名]
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
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import batch_slice [as 別名]
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