Yixiao Fang ff5f5ae2a9 [CI] update CI package (#476)
* update circleci

* update github action

* fix windows error

* update

* update pr and merge state

* fix lint

* update

* update

* update circle ci

* fix

* fix ut
2022-10-12 19:35:44 +08:00

75 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import pytest
import torch
from mmengine.structures import InstanceData
from mmselfsup.models.algorithms import ODC
from mmselfsup.structures import SelfSupDataSample
from mmselfsup.utils import register_all_modules
register_all_modules()
num_classes = 5
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='ODCNeck',
in_channels=512,
hid_channels=2,
out_channels=2,
norm_cfg=dict(type='BN1d'),
with_avg_pool=True)
head = dict(
type='ClsHead',
loss=dict(type='mmcls.CrossEntropyLoss'),
with_avg_pool=False,
in_channels=2,
num_classes=num_classes)
memory_bank = dict(
type='ODCMemory',
length=8,
feat_dim=2,
momentum=0.5,
num_classes=num_classes,
min_cluster=2,
debug=False)
@pytest.mark.skipif(
not torch.cuda.is_available() or platform.system() == 'Windows',
reason='CUDA is not available or Windows mem limit')
def test_odc():
data_preprocessor = {
'mean': (123.675, 116.28, 103.53),
'std': (58.395, 57.12, 57.375),
'bgr_to_rgb': True
}
alg = ODC(
backbone=backbone,
neck=neck,
head=head,
memory_bank=memory_bank,
data_preprocessor=copy.deepcopy(data_preprocessor))
fake_data_sample = SelfSupDataSample()
fake_sample_idx = InstanceData(value=torch.tensor([0]))
fake_data_sample.sample_idx = fake_sample_idx
fake_data = {
'inputs': [torch.randn((2, 3, 224, 224))],
'data_samples': [fake_data_sample for _ in range(2)]
}
fake_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
# test extract
fake_feats = alg(fake_inputs, fake_data_samples, mode='tensor')
assert fake_feats[0].size() == torch.Size([2, 512, 7, 7])