74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||
|
|
import copy
|
||
|
|
import platform
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from mmselfsup.core import SelfSupDataSample
|
||
|
|
from mmselfsup.models.algorithms import NPID
|
||
|
|
|
||
|
|
backbone = dict(
|
||
|
|
type='ResNet',
|
||
|
|
depth=18,
|
||
|
|
in_channels=3,
|
||
|
|
out_indices=[4], # 0: conv-1, x: stage-x
|
||
|
|
norm_cfg=dict(type='BN'))
|
||
|
|
neck = dict(
|
||
|
|
type='LinearNeck', in_channels=512, out_channels=2, with_avg_pool=True)
|
||
|
|
head = dict(type='ContrastiveHead', temperature=0.07)
|
||
|
|
loss = dict(type='mmcls.CrossEntropyLoss'),
|
||
|
|
memory_bank = dict(type='SimpleMemory', length=8, feat_dim=2, momentum=0.5)
|
||
|
|
preprocess_cfg = {
|
||
|
|
'mean': [0.5, 0.5, 0.5],
|
||
|
|
'std': [0.5, 0.5, 0.5],
|
||
|
|
'to_rgb': True
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.skipif(
|
||
|
|
not torch.cuda.is_available() or platform.system() == 'Windows',
|
||
|
|
reason='CUDA is not available or Windows mem limit')
|
||
|
|
def test_npid():
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
alg = NPID(
|
||
|
|
backbone=backbone,
|
||
|
|
neck=neck,
|
||
|
|
head=head,
|
||
|
|
memory_bank=None,
|
||
|
|
loss=loss,
|
||
|
|
preprocess_cfg=copy.deepcopy(preprocess_cfg))
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
alg = NPID(
|
||
|
|
backbone=backbone,
|
||
|
|
neck=neck,
|
||
|
|
head=None,
|
||
|
|
loss=loss,
|
||
|
|
memory_bank=memory_bank,
|
||
|
|
preprocess_cfg=copy.deepcopy(preprocess_cfg))
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
alg = NPID(
|
||
|
|
backbone=backbone,
|
||
|
|
neck=neck,
|
||
|
|
head=head,
|
||
|
|
loss=None,
|
||
|
|
memory_bank=memory_bank,
|
||
|
|
preprocess_cfg=copy.deepcopy(preprocess_cfg))
|
||
|
|
|
||
|
|
alg = NPID(
|
||
|
|
backbone=backbone,
|
||
|
|
neck=neck,
|
||
|
|
head=head,
|
||
|
|
loss=loss,
|
||
|
|
memory_bank=memory_bank,
|
||
|
|
preprocess_cfg=copy.deepcopy(preprocess_cfg))
|
||
|
|
|
||
|
|
fake_data = [{
|
||
|
|
'inputs': torch.randn((3, 224, 224)),
|
||
|
|
'data_sample': SelfSupDataSample()
|
||
|
|
} for _ in range(2)]
|
||
|
|
|
||
|
|
fake_inputs, _ = alg.preprocss_data(fake_data)
|
||
|
|
fake_backbone_out = alg.extract_feat(fake_inputs)
|
||
|
|
assert fake_backbone_out[0].size() == torch.Size([2, 512, 7, 7])
|