RoIAlign for PyTorch

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.

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

conda activate <CONDA_ENVIRONMENT>
./install.sh
./test.sh

Use RoIAlign or crop_and_resize

from roi_align.roi_align import RoIAlign      # RoIAlign module
from roi_align.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)

Issue1: gradcheck and difference of RoIAlign and crop_and_resize.

Changing -arch in make.sh for your GPU

# Which CUDA capabilities do we want to pre-build for?
# https://developer.nvidia.com/cuda-gpus
# Compute/shader model   Cards
# 6.1                    P4, P40, Titan Xp, GTX 1080 Ti, GTX 1080
# 6.0                    P100
# 5.2                    M40, Titan X, GTX 980
# 3.7                    K80
# 3.5                    K40, K20
# 3.0                    K10, Grid K520 (AWS G2)

Usage

import numpy as np
import torch
from torch.autograd import Variable

from roi_align.roi_align import RoIAlign


def to_varabile(arr, requires_grad=False, is_cuda=True):
    tensor = torch.from_numpy(arr)
    if is_cuda:
        tensor = tensor.cuda()
    var = Variable(tensor, requires_grad=requires_grad)
    return var


# inputs
is_cuda = False
image_data = np.tile(np.arange(7, dtype=np.float32), 7).reshape(7, 7) # attention the type
image_data = image_data[np.newaxis, np.newaxis]
boxes_data = np.asarray([[0, 0, 2, 2]], dtype=np.float32) # attention the type
box_index_data = np.asarray([0], dtype=np.int32) # attention the type

image_torch = 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)

# set transform_fpcoor to False is the crop_and_resize
roi_align = RoIAlign(3, 3, transform_fpcoor=False)
print(roi_align(image_torch, boxes, box_index))

(0 ,0 ,.,.) = 
  0  1  2
  0  1  2
  0  1  2
[torch.cuda.FloatTensor of size 1x1x3x3 (GPU 0)]

Fangyh09/RoIAlign.pytorch

RoIAlign for PyTorch

Introduction

Usage

Usage