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

67 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 DeepCluster
from mmselfsup.structures import SelfSupDataSample
from mmselfsup.utils import register_all_modules
register_all_modules()
num_classes = 5
with_sobel = True,
backbone = dict(
type='ResNetSobel',
depth=18,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(type='AvgPool2dNeck')
head = dict(
type='ClsHead',
loss=dict(type='mmcls.CrossEntropyLoss'),
with_avg_pool=False, # already has avgpool in the neck
in_channels=512,
num_classes=num_classes)
@pytest.mark.skipif(
not torch.cuda.is_available() or platform.system() == 'Windows',
reason='CUDA is not available or Windows mem limit')
def test_deepcluster():
data_preprocessor = {
'mean': (123.675, 116.28, 103.53),
'std': (58.395, 57.12, 57.375),
'bgr_to_rgb': True
}
alg = DeepCluster(
backbone=backbone,
neck=neck,
head=head,
data_preprocessor=copy.deepcopy(data_preprocessor))
assert alg.num_classes == num_classes
assert hasattr(alg, 'neck')
assert hasattr(alg, 'head')
fake_data_sample = SelfSupDataSample()
clustering_label = InstanceData(clustering_label=torch.tensor([1]))
fake_data_sample.pseudo_label = clustering_label
fake_data = {
'inputs':
[torch.randn((2, 3, 224, 224)),
torch.randn((2, 3, 224, 224))],
'data_sample': [fake_data_sample for _ in range(2)]
}
fake_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
fake_loss = alg(fake_inputs, fake_data_samples, mode='loss')
assert fake_loss['loss'] > 0
# test extract
fake_feats = alg(fake_inputs, fake_data_samples, mode='tensor')
assert fake_feats[0].size() == torch.Size([2, 512, 7, 7])