lawpdas / RoIAlign

RoIAlign - New style torch.autograd.Function for PyTorch > 1.3

RoIAlign - New style autograd.Function for PyTorch > 1.3

pytorch warning:
"Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method.

#### Old style
class F(torch.autograd.Function):
    def __init__(self, gamma=0.1):
        super().__init__()
        self.gamma = gamma

    def forward(self, args):
        pass
    def backward(self, args):
        pass

#### New style
class F_new(torch.autograd.Function):
    @staticmethod
    def forward(ctx, args, gamma):
        ctx.gamma = gamma
        pass

    @staticmethod
    def backward(ctx, args):
        pass

# Using your old style Function from your code sample:
F(gamma)(inp)
# Using the new style Function:
F_new.apply(inp, gamma)

reference

RoIAlign for PyTorch (original version)

This is a PyTorch version of RoIAlign. This implementation is based on crop_and_resize and supports both forward and backward on CPU and GPU.

NOTE: Thanks meikuam for updating this repo for PyTorch 1.0. You can find the original version for torch <= 0.4.1 in pytorch_0.4 branch.

Introduction

The crop_and_resize function is ported from tensorflow, and has the same interface with tensorflow version, except the input feature map should be in NCHW order in PyTorch. They also have the same output value (error < 1e-5) for both forward and backward as we expected, see the comparision in test.py.

Note: Document of crop_and_resize can be found here. And RoIAlign is a wrap of crop_and_resize that uses boxes with unnormalized (x1, y1, x2, y2) as input (while crop_and_resize use normalized (y1, x1, y2, x2) as input). See more details about the difference of RoIAlign and crop_and_resize in tensorpack.

Warning: Currently it only works using the default GPU (index 0)

Usage

• Install and test

  python setup.py install
  ./test.sh
  

• Use RoIAlign or crop_and_resize

  from roi_align import RoIAlign      # RoIAlign module
  from roi_align import CropAndResize # crop_and_resize module
  
  # input data
  image = to_varabile(image_data, requires_grad=True, is_cuda=is_cuda)
  boxes = to_varabile(boxes_data, requires_grad=False, is_cuda=is_cuda)
  box_index = to_varabile(box_index_data, requires_grad=False, is_cuda=is_cuda)
  
  # RoIAlign layer
  roi_align = RoIAlign(crop_height, crop_width)
  crops = roi_align(image, boxes, box_index)