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https://github.com/open-mmlab/mmselfsup.git
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20488d01b4
* [Refactor]: Refactor data flow * [Fix]: Change data sample to data samples * [Fix]: Change batch_inputs to inputs * [Fix]: Fix lint and UT * [Fix]: Fix UT * [Fix]: Fix lint * [Fix]: Fix docstring * [Fix]: Fix UT * [Refactor]: Add assert in data preprocessor * [Fix]: Fix lint
101 lines
2.8 KiB
Python
101 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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from unittest.mock import MagicMock
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import pytest
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import torch
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import mmselfsup
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from mmselfsup.models.algorithms.densecl import DenseCL
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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queue_len = 32
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feat_dim = 2
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momentum = 0.999
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loss_lambda = 0.5
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backbone = dict(
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type='ResNet',
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depth=18,
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in_channels=3,
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out_indices=[4], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN'))
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neck = dict(
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type='DenseCLNeck',
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in_channels=512,
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hid_channels=2,
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out_channels=2,
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num_grid=None)
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head = dict(
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type='ContrastiveHead',
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loss=dict(type='mmcls.CrossEntropyLoss'),
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temperature=0.2)
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def mock_batch_shuffle_ddp(img):
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return img, 0
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def mock_batch_unshuffle_ddp(img, mock_input):
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return img
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def mock_concat_all_gather(img):
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return img
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_densecl():
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data_preprocessor = {
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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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}
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alg = DenseCL(
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backbone=backbone,
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neck=neck,
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head=head,
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queue_len=queue_len,
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feat_dim=feat_dim,
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momentum=momentum,
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loss_lambda=loss_lambda,
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data_preprocessor=copy.deepcopy(data_preprocessor))
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assert alg.queue.size() == torch.Size([feat_dim, queue_len])
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assert alg.queue2.size() == torch.Size([feat_dim, queue_len])
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mmselfsup.models.algorithms.densecl.batch_shuffle_ddp = MagicMock(
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side_effect=mock_batch_shuffle_ddp)
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mmselfsup.models.algorithms.densecl.batch_unshuffle_ddp = MagicMock(
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side_effect=mock_batch_unshuffle_ddp)
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mmselfsup.models.algorithms.densecl.concat_all_gather = MagicMock(
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side_effect=mock_concat_all_gather)
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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 isinstance(fake_loss['loss_single'].item(), float)
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assert isinstance(fake_loss['loss_dense'].item(), float)
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assert fake_loss['loss_single'].item() > 0
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assert fake_loss['loss_dense'].item() > 0
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assert alg.queue_ptr.item() == 2
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assert alg.queue2_ptr.item() == 2
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fake_feat = alg(fake_inputs, fake_data_samples, mode='tensor')
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assert list(fake_feat[0].shape) == [2, 512, 7, 7]
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fake_outputs = alg(fake_inputs, fake_data_samples, mode='predict')
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assert 'q_grid' in fake_outputs
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assert 'value' in fake_outputs.q_grid
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assert list(fake_outputs.q_grid.value.shape) == [2, 512, 49]
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