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https://github.com/open-mmlab/mmselfsup.git
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d73c953804
* [Feature]: Add milan ft config * [Feature]: Add milan linear prob * [Feature]: Set diff rank seed in mae ft * [Feature]: V1 * [Feature]: Add target generator * [Feature]: Add MILAN head and loss * [Feature]: Refine milan * [Feature]: Delete redundant mask and ids_shuffle * [Feature]: Delete redundant return value of attention masking * [Feature]: Detele return attention param * [Feature]: Add typehint and docstring for PromptDecoder and PromptAttention * [Feature]: Add type hint and docstring * [Feature]: Fix lint * [Fix]: Remove petrel backend * [Feature]: Delete redundant code in clip * [Feature]: Add ut for milan algorithm * [Feature]: Delete redundant code * [Feature]: Use mock for target generator * [Feature]: Add docstring * [Feature]: Create classification folder in milan * [Feature]: Add README * [Feature]: Add metafile * [Feature]: Add main paper readme * [Feature]: Update model zoom * [Feature]: Fix review * [Feature]: Fix config path bug * [Feature]: Fix review#2 * [Feature]: Delete MILAN loss * [Fix]: Add metafile * [Fix]: Fix lint * [Feature]: Change the test milan * [Feature]: Update the config file name
55 lines
1.5 KiB
Python
55 lines
1.5 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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from mmselfsup.models.algorithms import MILAN
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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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backbone = dict(type='MILANViT', arch='b', patch_size=16, mask_ratio=0.75)
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neck = dict(
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type='MILANPretrainDecoder',
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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decoder_embed_dim=512,
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decoder_depth=8,
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decoder_num_heads=16,
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mlp_ratio=4.,
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)
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loss = dict(type='CosineSimilarityLoss', shift_factor=2.0, scale_factor=2.0)
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head = dict(type='MILANPretrainHead', loss=loss)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_milan():
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data_preprocessor = {
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'mean': [0.5, 0.5, 0.5],
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'std': [0.5, 0.5, 0.5],
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'bgr_to_rgb': True
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}
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alg = MILAN(
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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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target_generator = MagicMock(
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return_value=(torch.ones(2, 197, 512), torch.ones(2, 197, 197)))
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alg.target_generator = target_generator
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fake_data = {
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'inputs': [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_batch_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
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fake_outputs = alg(fake_batch_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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