mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
20488d01b4
* [Refactor]: Refactor data flow * [Fix]: Change data sample to data samples * [Fix]: Change batch_inputs to inputs * [Fix]: Fix lint and UT * [Fix]: Fix UT * [Fix]: Fix lint * [Fix]: Fix docstring * [Fix]: Fix UT * [Refactor]: Add assert in data preprocessor * [Fix]: Fix lint
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import tempfile
|
|
from unittest import TestCase
|
|
|
|
import torch
|
|
from mmengine.structures import LabelData
|
|
from torch.utils.data import Dataset
|
|
|
|
from mmselfsup.engine.hooks import DeepClusterHook
|
|
from mmselfsup.structures import SelfSupDataSample
|
|
|
|
num_classes = 5
|
|
with_sobel = True,
|
|
backbone = dict(
|
|
type='ResNet',
|
|
depth=18,
|
|
in_channels=2,
|
|
out_indices=[4], # 0: conv-1, x: stage-x
|
|
norm_cfg=dict(type='BN'))
|
|
neck = dict(type='AvgPool2dNeck')
|
|
head = dict(
|
|
type='ClsHead',
|
|
with_avg_pool=False, # already has avgpool in the neck
|
|
in_channels=512,
|
|
num_classes=num_classes)
|
|
loss = dict(type='mmcls.CrossEntropyLoss')
|
|
|
|
|
|
class DummyDataset(Dataset):
|
|
METAINFO = dict() # type: ignore
|
|
data = torch.randn(12, 2)
|
|
label = torch.ones(12)
|
|
|
|
@property
|
|
def metainfo(self):
|
|
return self.METAINFO
|
|
|
|
def __len__(self):
|
|
return self.data.size(0)
|
|
|
|
def __getitem__(self, index):
|
|
data_sample = SelfSupDataSample()
|
|
gt_label = LabelData(value=self.label[index])
|
|
setattr(data_sample, 'gt_label', gt_label)
|
|
return dict(inputs=self.data[index], data_sample=data_sample)
|
|
|
|
|
|
class TestDeepClusterHook(TestCase):
|
|
|
|
def setUp(self):
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def test_deepcluster_hook(self):
|
|
dummy_dataset = DummyDataset()
|
|
|
|
extract_dataloader = dict(
|
|
dataset=dummy_dataset,
|
|
sampler=dict(type='DefaultSampler', shuffle=False),
|
|
batch_size=1,
|
|
num_workers=0,
|
|
persistent_workers=False)
|
|
deepcluster_hook = DeepClusterHook(
|
|
extract_dataloader=extract_dataloader,
|
|
clustering=dict(type='Kmeans', k=num_classes, pca_dim=16),
|
|
unif_sampling=True,
|
|
reweight=False,
|
|
reweight_pow=0.5,
|
|
initial=True,
|
|
interval=1)
|
|
|
|
# test DeepClusterHook
|
|
assert deepcluster_hook.clustering_type == 'Kmeans'
|