本文整理匯總了Python中roi_pooling_op.roi_pool_grad方法的典型用法代碼示例。如果您正苦於以下問題：Python roi_pooling_op.roi_pool_grad方法的具體用法？Python roi_pooling_op.roi_pool_grad怎麽用？Python roi_pooling_op.roi_pool_grad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roi_pooling_op的用法示例。
在下文中一共展示了roi_pooling_op.roi_pool_grad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: _roi_pool_grad
# 需要導入模塊: import roi_pooling_op [as 別名]
# 或者: from roi_pooling_op import roi_pool_grad [as 別名]
def _roi_pool_grad(op, grad, _):
"""The gradients for `roi_pool`.
Args:
op: The `roi_pool` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `roi_pool` op.
Returns:
Gradients with respect to the input of `zero_out`.
"""
data = op.inputs[0]
rois = op.inputs[1]
argmax = op.outputs[1]
pooled_height = op.get_attr('pooled_height')
pooled_width = op.get_attr('pooled_width')
spatial_scale = op.get_attr('spatial_scale')
# compute gradient
data_grad = roi_pooling_op.roi_pool_grad(data, rois, argmax, grad, pooled_height, pooled_width, spatial_scale)
return [data_grad, None] # List of one Tensor, since we have one input 示例2: _roi_pool_grad
# 需要導入模塊: import roi_pooling_op [as 別名]
# 或者: from roi_pooling_op import roi_pool_grad [as 別名]
def _roi_pool_grad(op, grad, _):
#The gradients for `roi_pool`.
#Args:
# op: The `roi_pool` `Operation` that we are differentiating, which we can use
# to find the inputs and outputs of the original op.
# grad: Gradient with respect to the output of the `roi_pool` op.
#Returns:
# Gradients with respect to the input of `zero_out`.
data = op.inputs[0]
rois = op.inputs[1]
orientations = op.inputs[2]
argmax = op.outputs[1]
pooled_height = op.get_attr('pooled_height')
pooled_width = op.get_attr('pooled_width')
spatial_scale = op.get_attr('spatial_scale')
# compute gradient
data_grad = roi_pooling_op.roi_pool_grad(data, rois, argmax, grad, orientations, pooled_height, pooled_width, spatial_scale)
# data_grad contains the gradients with respect to the VGG output and the two 'none's indicate that there is no
# gradient with respect to the bounding box positions or rotations
return [data_grad, None, None] # List of one Tensor, since we have one input 示例3: _roi_pool_grad
# 需要導入模塊: import roi_pooling_op [as 別名]
# 或者: from roi_pooling_op import roi_pool_grad [as 別名]
def _roi_pool_grad(op, grad, _):
#The gradients for `roi_pool`.
#Args:
# op: The `roi_pool` `Operation` that we are differentiating, which we can use
# to find the inputs and outputs of the original op.
# grad: Gradient with respect to the output of the `roi_pool` op.
#Returns:
# Gradients with respect to the input of `zero_out`.
data = op.inputs[0]
rois = op.inputs[1]
argmax = op.outputs[1]
pooled_height = op.get_attr('pooled_height')
pooled_width = op.get_attr('pooled_width')
spatial_scale = op.get_attr('spatial_scale')
# compute gradient
data_grad = roi_pooling_op.roi_pool_grad(data, rois, argmax, grad, pooled_height, pooled_width, spatial_scale)
return [data_grad, None] # List of one Tensor, since we have one input