mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||
|
|
import copy
|
||
|
|
import platform
|
||
|
|
from unittest.mock import MagicMock
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from mmselfsup.models.algorithms import EVA
|
||
|
|
from mmselfsup.structures import SelfSupDataSample
|
||
|
|
from mmselfsup.utils import register_all_modules
|
||
|
|
|
||
|
|
register_all_modules()
|
||
|
|
|
||
|
|
backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
|
||
|
|
neck = dict(
|
||
|
|
type='MAEPretrainDecoder',
|
||
|
|
patch_size=16,
|
||
|
|
in_chans=3,
|
||
|
|
embed_dim=768,
|
||
|
|
decoder_embed_dim=512,
|
||
|
|
decoder_depth=8,
|
||
|
|
decoder_num_heads=16,
|
||
|
|
predict_feature_dim=512,
|
||
|
|
mlp_ratio=4.,
|
||
|
|
)
|
||
|
|
loss = dict(type='CosineSimilarityLoss', shift_factor=1.0, scale_factor=1.0)
|
||
|
|
head = dict(type='MILANPretrainHead', loss=loss)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
||
|
|
def test_eva():
|
||
|
|
data_preprocessor = {
|
||
|
|
'mean': [0.5, 0.5, 0.5],
|
||
|
|
'std': [0.5, 0.5, 0.5],
|
||
|
|
'bgr_to_rgb': True
|
||
|
|
}
|
||
|
|
|
||
|
|
alg = EVA(
|
||
|
|
backbone=backbone,
|
||
|
|
neck=neck,
|
||
|
|
head=head,
|
||
|
|
data_preprocessor=copy.deepcopy(data_preprocessor))
|
||
|
|
|
||
|
|
target_generator = MagicMock(
|
||
|
|
return_value=(torch.ones(2, 197, 512), torch.ones(2, 197, 197)))
|
||
|
|
alg.target_generator = target_generator
|
||
|
|
|
||
|
|
fake_data = {
|
||
|
|
'inputs': [torch.randn((2, 3, 224, 224))],
|
||
|
|
'data_sample': [SelfSupDataSample() for _ in range(2)]
|
||
|
|
}
|
||
|
|
fake_batch_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
|
||
|
|
fake_outputs = alg(fake_batch_inputs, fake_data_samples, mode='loss')
|
||
|
|
assert isinstance(fake_outputs['loss'].item(), float)
|