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
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
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