def forward(ctx,
image,
boxes,
box_ind,
crop_height,
crop_width,
extrapolation_value=0):
ctx.crop_height = crop_height
ctx.crop_width = crop_width
ctx.extrapolation_value = extrapolation_value
crops = torch.zeros_like(image)
if image.is_cuda:
crop_and_resize_gpu.forward(image, boxes, box_ind,
ctx.extrapolation_value,
ctx.crop_height, ctx.crop_width, crops)
else:
crop_and_resize_cpu.forward(image, boxes, box_ind,
ctx.extrapolation_value,
ctx.crop_height, ctx.crop_width, crops)
# save for backward
ctx.im_size = image.size()
ctx.save_for_backward(boxes, box_ind)
return crops
def forward(self, image, boxes, box_ind):
crops = torch.zeros_like(image)
if image.is_cuda:
crop_and_resize_gpu.forward(
image, boxes, box_ind,
self.extrapolation_value, self.crop_height, self.crop_width, crops)
else:
crop_and_resize_cpu.forward(
image, boxes, box_ind,
self.extrapolation_value, self.crop_height, self.crop_width, crops)
# save for backward
self.im_size = image.size()
self.save_for_backward(boxes, box_ind)
return crops