33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import pytest
|
|
import torch
|
|
|
|
from mmselfsup.models.algorithms import NPID
|
|
|
|
backbone = dict(
|
|
type='ResNet',
|
|
depth=50,
|
|
in_channels=3,
|
|
out_indices=[4], # 0: conv-1, x: stage-x
|
|
norm_cfg=dict(type='BN'))
|
|
neck = dict(
|
|
type='LinearNeck', in_channels=2048, out_channels=4, with_avg_pool=True)
|
|
head = dict(type='ContrastiveHead', temperature=0.07)
|
|
memory_bank = dict(type='SimpleMemory', length=8, feat_dim=4, momentum=0.5)
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
not torch.cuda.is_available(), reason='CUDA is not available.')
|
|
def test_npid():
|
|
with pytest.raises(AssertionError):
|
|
alg = NPID(backbone=backbone, neck=neck, head=head, memory_bank=None)
|
|
with pytest.raises(AssertionError):
|
|
alg = NPID(
|
|
backbone=backbone, neck=neck, head=None, memory_bank=memory_bank)
|
|
|
|
alg = NPID(
|
|
backbone=backbone, neck=neck, head=head, memory_bank=memory_bank)
|
|
fake_input = torch.randn((16, 3, 224, 224))
|
|
fake_backbone_out = alg.extract_feat(fake_input)
|
|
assert fake_backbone_out[0].size() == torch.Size([16, 2048, 7, 7])
|