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1047daa28e
* add hornet * add hornet * add hornet * add hornet * add hornet * add hornet * add hornet * fix test for torch before 1.7.0 * del timm * fix readme * fix readme * Update mmcls/models/backbones/hornet.py Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> * fix docs * fix docs * s -> scale * fix dims and dpr impl * fix layer scale * refactor gnconv * add dw_cfg * add convert tools * update code * update docs * update readme * update URLs Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import torch
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from mmcls.models.utils import LayerScale
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class TestLayerScale(TestCase):
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def test_init(self):
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with self.assertRaisesRegex(AssertionError, "'data_format' could"):
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cfg = dict(
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dim=10,
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inplace=False,
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data_format='BNC',
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)
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LayerScale(**cfg)
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cfg = dict(dim=10)
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ls = LayerScale(**cfg)
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assert torch.equal(ls.weight,
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torch.ones(10, requires_grad=True) * 1e-5)
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def forward(self):
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# Test channels_last
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cfg = dict(dim=256, inplace=False, data_format='channels_last')
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ls_channels_last = LayerScale(**cfg)
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x = torch.randn((4, 49, 256))
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out = ls_channels_last(x)
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self.assertEqual(tuple(out.size()), (4, 49, 256))
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assert torch.equal(x * 1e-5, out)
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# Test channels_first
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cfg = dict(dim=256, inplace=False, data_format='channels_first')
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ls_channels_first = LayerScale(**cfg)
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x = torch.randn((4, 256, 7, 7))
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out = ls_channels_first(x)
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self.assertEqual(tuple(out.size()), (4, 256, 7, 7))
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assert torch.equal(x * 1e-5, out)
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# Test inplace True
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cfg = dict(dim=256, inplace=True, data_format='channels_first')
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ls_channels_first = LayerScale(**cfg)
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x = torch.randn((4, 256, 7, 7))
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out = ls_channels_first(x)
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self.assertEqual(tuple(out.size()), (4, 256, 7, 7))
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self.assertIs(x, out)
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