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https://github.com/open-mmlab/mmcv.git
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869dbf1bf2
* add bias_act * support bias_act * support filtered_lrelu * support filtered_lrelu and upfirdn2d * support conv2d_gradfix and fix filtered_lrelu * fix lint * fix lint * fix c++ lint * fix part comments * fix lint * rm redundant header * fix upgrade pip * fix as comment * fix c++ lint * fix ci * fix-ut * fix as comments * add grad check * remove redundant template * Update mmcv/ops/bias_act.py Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * add typehint * fix as comment: * complete type hints * fix lint * add test for conv_gradfix * add test for conv_gradfix * fix lint * modify licenses and ops.md * add zh op md * add torch version policy for conv2d_gradfix * fix lint * fix as comments * rename impl * rm redudant function and add ut * fix as comment * fix lint * fix lint * fix as comments * fix lint * fix ut * fix as comment * fix as comment * fix as comment --------- Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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_USING_PARROTS = True
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try:
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from parrots.autograd import gradcheck
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except ImportError:
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from torch.autograd import gradcheck, gradgradcheck
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_USING_PARROTS = False
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class TestUpFirDn2d:
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"""Unit test for UpFirDn2d.
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Here, we just test the basic case of upsample version. More gerneal tests
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will be included in other unit test for UpFirDnUpsample and
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UpFirDnDownSample modules.
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"""
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@classmethod
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def setup_class(cls):
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kernel_1d = torch.tensor([1., 3., 3., 1.])
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cls.kernel = kernel_1d[:, None] * kernel_1d[None, :]
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cls.kernel = cls.kernel / cls.kernel.sum()
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cls.factor = 2
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pad = cls.kernel.shape[0] - cls.factor
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cls.pad = ((pad + 1) // 2 + cls.factor - 1, pad // 2)
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cls.input_tensor = torch.randn((2, 3, 4, 4), requires_grad=True)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
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def test_upfirdn2d(self):
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from mmcv.ops import upfirdn2d
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if _USING_PARROTS:
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gradcheck(
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upfirdn2d,
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(self.input_tensor.cuda(),
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self.kernel.type_as(
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self.input_tensor).cuda(), self.factor, 1, self.pad),
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delta=1e-4,
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pt_atol=1e-3)
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else:
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gradcheck(
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upfirdn2d,
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(self.input_tensor.cuda(),
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self.kernel.type_as(
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self.input_tensor).cuda(), self.factor, 1, self.pad),
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eps=1e-4,
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atol=1e-3)
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gradgradcheck(
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upfirdn2d,
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(self.input_tensor.cuda(),
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self.kernel.type_as(
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self.input_tensor).cuda(), self.factor, 1, self.pad),
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eps=1e-4,
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atol=1e-3)
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# test with different up
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kernel = torch.randn(3, 3)
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out = upfirdn2d(
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self.input_tensor.cuda(), filter=kernel.cuda(), up=2, padding=1)
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assert out.shape == (2, 3, 8, 8)
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# test with different down
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input_tensor = torch.randn(2, 3, 8, 8)
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out = upfirdn2d(
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input_tensor.cuda(), filter=self.kernel.cuda(), down=2, padding=1)
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assert out.shape == (2, 3, 4, 4)
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# test with different flip_filter
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out = upfirdn2d(
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self.input_tensor.cuda(),
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filter=self.kernel.cuda(),
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flip_filter=True)
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assert out.shape == (2, 3, 1, 1)
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# test with different gain
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out1 = upfirdn2d(
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self.input_tensor.cuda(), filter=self.kernel.cuda(), gain=0.2)
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out2 = upfirdn2d(
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self.input_tensor.cuda(), filter=self.kernel.cuda(), gain=0.1)
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assert torch.allclose(out1, out2 * 2)
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