58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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import pytest
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import torch
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from mmselfsup.models import MoCoV3
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backbone = dict(
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type='VisionTransformer',
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arch='mocov3-small', # embed_dim = 384
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img_size=224,
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patch_size=16,
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stop_grad_conv1=True)
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neck = dict(
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type='NonLinearNeck',
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in_channels=384,
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hid_channels=2,
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out_channels=2,
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num_layers=2,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False,
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vit_backbone=True)
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head = dict(
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type='MoCoV3Head',
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predictor=dict(
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type='NonLinearNeck',
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in_channels=2,
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hid_channels=2,
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out_channels=2,
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num_layers=2,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False),
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temperature=0.2)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_mocov3():
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with pytest.raises(AssertionError):
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alg = MoCoV3(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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alg = MoCoV3(backbone=backbone, neck=neck, head=None)
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alg = MoCoV3(backbone, neck, head)
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alg.init_weights()
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alg.momentum_update()
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fake_input = torch.randn((2, 3, 224, 224))
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fake_backbone_out = alg.forward(fake_input, mode='extract')
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assert fake_backbone_out[0][0].size() == torch.Size([2, 384, 14, 14])
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assert fake_backbone_out[0][1].size() == torch.Size([2, 384])
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