75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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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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from mmselfsup.structures import SelfSupDataSample
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backbone = dict(
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type='MoCoV3ViT',
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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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norm_cfg=dict(type='BN1d'))
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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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norm_cfg=dict(type='BN1d')),
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loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=2 * 0.2),
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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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data_preprocessor = dict(
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mean=(123.675, 116.28, 103.53),
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std=(58.395, 57.12, 57.375),
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bgr_to_rgb=True)
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alg = MoCoV3(
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backbone=backbone,
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neck=neck,
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head=head,
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data_preprocessor=copy.deepcopy(data_preprocessor))
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fake_data = {
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'inputs':
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[torch.randn((2, 3, 224, 224)),
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torch.randn((2, 3, 224, 224))],
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'data_sample': [SelfSupDataSample() for _ in range(2)]
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}
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fake_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
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fake_loss = alg(fake_inputs, fake_data_samples, mode='loss')
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assert fake_loss['loss'] > 0
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# test extract
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fake_feats = alg(fake_inputs, fake_data_samples, mode='tensor')
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assert fake_feats[0][0].size() == torch.Size([2, 384, 14, 14])
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assert fake_feats[0][1].size() == torch.Size([2, 384])
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