mirror of
https://github.com/open-mmlab/mmselfsup.git
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104 lines
2.8 KiB
Python
104 lines
2.8 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.core import SelfSupDataSample
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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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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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temperature=0.2)
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loss = dict(type='mmcls.CrossEntropyLoss', loss_weight=2 * 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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preprocess_cfg = {
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'to_rgb': True
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}
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with pytest.raises(AssertionError):
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alg = MoCoV3(
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backbone=None,
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neck=neck,
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head=head,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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alg = MoCoV3(
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backbone=backbone,
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neck=None,
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head=head,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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alg = MoCoV3(
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backbone=backbone,
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neck=neck,
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head=None,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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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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loss=None,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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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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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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fake_data = [{
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'inputs': [torch.randn((3, 224, 224)),
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torch.randn((3, 224, 224))],
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'data_sample':
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SelfSupDataSample()
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} for _ in range(2)]
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# test extract
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fake_inputs, fake_data_samples = alg.preprocss_data(fake_data)
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fake_backbone_out = alg.extract_feat(
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inputs=fake_inputs, data_samples=fake_data_samples)
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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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