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* [CI] Add test mim CI. (#879) * [CI] Add test mim CI. (#879) * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * update * update ci * rebase * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * update * update readme and configs * update readme and configs * refactore eva02 * [CI] Add test mim CI. (#879) * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * update * update ci * rebase * feat: add eva02 backbone * feat: add eva02 backbone * feat: add eva02 backbone * update * update readme and configs * refactore eva02 * update readme and metafile * update readme and metafile * update readme and metafile * update * rename eva02 * rename eva02 * fix uts * rename configs --------- Co-authored-by: Ma Zerun <mzr1996@163.com> Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
22 lines
831 B
Python
22 lines
831 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmpretrain.models.utils import (ConditionalPositionEncoding,
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RotaryEmbeddingFast)
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def test_conditional_position_encoding_module():
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CPE = ConditionalPositionEncoding(in_channels=32, embed_dims=32, stride=2)
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outs = CPE(torch.randn(1, 3136, 32), (56, 56))
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assert outs.shape == torch.Size([1, 784, 32])
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def test_rotary_embedding_fast_module():
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RoPE = RotaryEmbeddingFast(embed_dims=64, patch_resolution=24)
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outs = RoPE(torch.randn(1, 2, 24 * 24, 64), (24, 24))
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assert outs.shape == torch.Size([1, 2, 24 * 24, 64])
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RoPE = RotaryEmbeddingFast(embed_dims=64, patch_resolution=(14, 20))
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outs = RoPE(torch.randn(1, 2, 14 * 20, 64), (14, 20))
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assert outs.shape == torch.Size([1, 2, 14 * 20, 64])
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