netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py

# -*- coding: utf-8 -*-
# File   : test_prroi_pooling2d.py
# Author : Jiayuan Mao
# Email  : maojiayuan@gmail.com
# Date   : 18/02/2018
#
# This file is part of Jacinle.

import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F

from jactorch.utils.unittest import TorchTestCase

from prroi_pool import PrRoIPool2D


class TestPrRoIPool2D(TorchTestCase):
    def test_forward(self):
        pool = PrRoIPool2D(7, 7, spatial_scale=0.5)
        features = torch.rand((4, 16, 24, 32)).cuda()
        rois = torch.tensor([
            [0, 0, 0, 14, 14],
            [1, 14, 14, 28, 28],
        ]).float().cuda()

        out = pool(features, rois)
        out_gold = F.avg_pool2d(features, kernel_size=2, stride=1)

        self.assertTensorClose(out, torch.stack((
            out_gold[0, :, :7, :7],
            out_gold[1, :, 7:14, 7:14],
        ), dim=0))

    def test_backward_shapeonly(self):
        pool = PrRoIPool2D(2, 2, spatial_scale=0.5)

        features = torch.rand((4, 2, 24, 32)).cuda()
        rois = torch.tensor([
            [0, 0, 0, 4, 4],
            [1, 14, 14, 18, 18],
        ]).float().cuda()
        features.requires_grad = rois.requires_grad = True
        out = pool(features, rois)

        loss = out.sum()
        loss.backward()

        self.assertTupleEqual(features.size(), features.grad.size())
        self.assertTupleEqual(rois.size(), rois.grad.size())


if __name__ == '__main__':
    unittest.main()