mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 21:53:55 +08:00
08dc8c75d3
* remove basehead * add moco series * add byol simclr simsiam * add ut * update configs * add simsiam hook * add and refactor beit * update ut * add cae * update extract_feat * refactor cae * add mae * refactor data preprocessor * update heads * add maskfeat * add milan * add simmim * add mixmim * fix lint * fix ut * fix lint * add eva * add densecl * add barlowtwins * add swav * fix lint * update readtherdocs rst * update docs * update * Decrease UT memory usage * Fix docstring * update DALLEEncoder * Update model docs * refactor dalle encoder * update docstring * fix ut * fix config error * add val_cfg and test_cfg * refactor clip generator * fix lint * pass check * fix ut * add lars * update type of BEiT in configs * Use MMEngine style momentum in EMA. * apply mmpretrain solarize --------- Co-authored-by: mzr1996 <mzr1996@163.com>
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import copy
|
|
import platform
|
|
from unittest.mock import MagicMock
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from mmpretrain.models import MILAN, MILANViT
|
|
from mmpretrain.structures import DataSample
|
|
|
|
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
|
def test_milan_vit():
|
|
backbone = dict(arch='b', patch_size=16, mask_ratio=0.75)
|
|
milan_backbone = MILANViT(**backbone)
|
|
milan_backbone.init_weights()
|
|
fake_inputs = torch.randn((2, 3, 224, 224))
|
|
|
|
# test with mask
|
|
fake_outputs = milan_backbone(fake_inputs,
|
|
torch.ones(2, 197, 197)[:, 0, 1:])[0]
|
|
assert list(fake_outputs.shape) == [2, 50, 768]
|
|
|
|
# test without mask
|
|
fake_outputs = milan_backbone(fake_inputs, None)
|
|
assert len(fake_outputs[0]) == 2
|
|
assert fake_outputs[0][0].shape == torch.Size([2, 768, 14, 14])
|
|
assert fake_outputs[0][1].shape == torch.Size([2, 768])
|
|
|
|
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
|
def test_milan():
|
|
data_preprocessor = {
|
|
'mean': [0.5, 0.5, 0.5],
|
|
'std': [0.5, 0.5, 0.5],
|
|
'to_rgb': True
|
|
}
|
|
|
|
backbone = dict(type='MILANViT', arch='b', patch_size=16, mask_ratio=0.75)
|
|
neck = dict(
|
|
type='MILANPretrainDecoder',
|
|
patch_size=16,
|
|
in_chans=3,
|
|
embed_dim=768,
|
|
decoder_embed_dim=512,
|
|
decoder_depth=8,
|
|
decoder_num_heads=16,
|
|
mlp_ratio=4.)
|
|
head = dict(
|
|
type='MIMHead',
|
|
loss=dict(
|
|
type='CosineSimilarityLoss', shift_factor=2.0, scale_factor=2.0))
|
|
|
|
alg = MILAN(
|
|
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_samples': [DataSample() for _ in range(2)]
|
|
}
|
|
fake_inputs = alg.data_preprocessor(fake_data)
|
|
fake_outputs = alg(**fake_inputs, mode='loss')
|
|
assert isinstance(fake_outputs['loss'].item(), float)
|