mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import pytest
|
|
import torch
|
|
|
|
from mmselfsup.models.algorithms import DeepCluster
|
|
|
|
num_classes = 5
|
|
with_sobel = True,
|
|
backbone = dict(
|
|
type='ResNet',
|
|
depth=50,
|
|
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=2048,
|
|
num_classes=num_classes)
|
|
|
|
|
|
def test_deepcluster():
|
|
with pytest.raises(AssertionError):
|
|
alg = DeepCluster(
|
|
backbone=backbone, with_sobel=with_sobel, neck=neck, head=None)
|
|
alg = DeepCluster(
|
|
backbone=backbone, with_sobel=with_sobel, neck=neck, head=head)
|
|
assert alg.num_classes == num_classes
|
|
assert hasattr(alg, 'sobel_layer')
|
|
assert hasattr(alg, 'neck')
|
|
assert hasattr(alg, 'head')
|
|
|
|
fake_input = torch.randn((16, 3, 224, 224))
|
|
fake_labels = torch.ones(16, dtype=torch.long)
|
|
fake_backbone_out = alg.extract_feat(fake_input)
|
|
assert fake_backbone_out[0].size() == torch.Size([16, 2048, 7, 7])
|
|
fake_out = alg.forward_train(fake_input, fake_labels)
|
|
assert fake_out['loss'].item() > 0
|