mirror of https://github.com/open-mmlab/mmcv.git
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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import pytest
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import torch
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from torch.autograd import gradcheck
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from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
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class TestCarafe:
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def test_carafe_naive_gradcheck(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import CARAFENaive
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feat = torch.randn(
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2, 64, 3, 3, requires_grad=True, device='cuda').double()
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mask = torch.randn(
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2, 100, 6, 6, requires_grad=True,
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device='cuda').sigmoid().double()
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gradcheck(CARAFENaive(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
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def test_carafe_gradcheck(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import CARAFE
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feat = torch.randn(
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2, 64, 3, 3, requires_grad=True, device='cuda').double()
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mask = torch.randn(
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2, 100, 6, 6, requires_grad=True,
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device='cuda').sigmoid().double()
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gradcheck(CARAFE(5, 4, 2), (feat, mask), atol=1e-4, eps=1e-4)
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@pytest.mark.parametrize('device', [
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'mlu',
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marks=pytest.mark.skipif(
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not IS_MLU_AVAILABLE, reason='requires MLU support'))
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])
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def test_carafe_allclose(self, device):
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try:
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from mmcv.ops import CARAFE
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except ModuleNotFoundError:
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pytest.skip('test requires compilation')
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np_feat = np.fromfile(
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'tests/data/for_carafe/carafe_feat.bin', dtype=np.float32)
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np_mask = np.fromfile(
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'tests/data/for_carafe/carafe_mask.bin', dtype=np.float32)
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np_output = np.fromfile(
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'tests/data/for_carafe/carafe_output.bin', dtype=np.float32)
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np_feat_grad = np.fromfile(
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'tests/data/for_carafe/carafe_feat_grad.bin', dtype=np.float32)
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np_mask_grad = np.fromfile(
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'tests/data/for_carafe/carafe_mask_grad.bin', dtype=np.float32)
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np_feat = np_feat.reshape((2, 64, 3, 3))
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np_mask = np_mask.reshape((2, 100, 6, 6))
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np_output = np_output.reshape((2, 64, 6, 6))
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np_feat_grad = np_feat_grad.reshape((2, 64, 3, 3))
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np_mask_grad = np_mask_grad.reshape((2, 100, 6, 6))
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feat = torch.tensor(
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np_feat, dtype=torch.float, device=device, requires_grad=True)
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mask = torch.tensor(
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np_mask, dtype=torch.float, device=device, requires_grad=True)
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carafe = CARAFE(5, 4, 2)
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output = carafe(feat, mask)
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output.backward(torch.ones_like(output))
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assert np.allclose(
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output.data.type(torch.float).cpu().numpy(), np_output, atol=1e-3)
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assert np.allclose(
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feat.grad.data.type(torch.float).cpu().numpy(),
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np_feat_grad,
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atol=1e-3)
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assert np.allclose(
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mask.grad.data.type(torch.float).cpu().numpy(),
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np_mask_grad,
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atol=1e-3)
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