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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext',
['roi_pool_forward', 'roi_pool_backward'])
class RoIPoolFunction(Function):
@staticmethod
def symbolic(g, input, rois, output_size, spatial_scale):
return g.op(
'MaxRoiPool',
input,
rois,
pooled_shape_i=output_size,
spatial_scale_f=spatial_scale)
@staticmethod
def forward(ctx, input, rois, output_size, spatial_scale=1.0):
ctx.output_size = _pair(output_size)
ctx.spatial_scale = spatial_scale
ctx.input_shape = input.size()
assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
ctx.output_size[1])
output = input.new_zeros(output_shape)
argmax = input.new_zeros(output_shape, dtype=torch.int)
ext_module.roi_pool_forward(
input,
rois,
output,
argmax,
pooled_height=ctx.output_size[0],
pooled_width=ctx.output_size[1],
spatial_scale=ctx.spatial_scale)
ctx.save_for_backward(rois, argmax)
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
rois, argmax = ctx.saved_tensors
grad_input = grad_output.new_zeros(ctx.input_shape)
ext_module.roi_pool_backward(
grad_output,
rois,
argmax,
grad_input,
pooled_height=ctx.output_size[0],
pooled_width=ctx.output_size[1],
spatial_scale=ctx.spatial_scale)
return grad_input, None, None, None
roi_pool = RoIPoolFunction.apply
class RoIPool(nn.Module):
def __init__(self, output_size, spatial_scale=1.0):
super(RoIPool, self).__init__()
self.output_size = _pair(output_size)
self.spatial_scale = float(spatial_scale)
def forward(self, input, rois):
return roi_pool(input, rois, self.output_size, self.spatial_scale)
def __repr__(self):
s = self.__class__.__name__
s += f'(output_size={self.output_size}, '
s += f'spatial_scale={self.spatial_scale})'
return s
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