56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import platform
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from mmselfsup.models.algorithms import SwAV
|
|
|
|
nmb_crops = [2, 6]
|
|
backbone = dict(
|
|
type='ResNet',
|
|
depth=18,
|
|
in_channels=3,
|
|
out_indices=[4], # 0: conv-1, x: stage-x
|
|
norm_cfg=dict(type='BN'),
|
|
zero_init_residual=True)
|
|
neck = dict(
|
|
type='SwAVNeck',
|
|
in_channels=512,
|
|
hid_channels=2,
|
|
out_channels=2,
|
|
norm_cfg=dict(type='BN1d'),
|
|
with_avg_pool=True)
|
|
head = dict(
|
|
type='SwAVHead',
|
|
feat_dim=2, # equal to neck['out_channels']
|
|
epsilon=0.05,
|
|
temperature=0.1,
|
|
num_crops=nmb_crops)
|
|
|
|
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
|
def test_swav():
|
|
with pytest.raises(AssertionError):
|
|
alg = SwAV(backbone=backbone, neck=neck, head=None)
|
|
with pytest.raises(AssertionError):
|
|
alg = SwAV(backbone=backbone, neck=None, head=head)
|
|
|
|
alg = SwAV(backbone=backbone, neck=neck, head=head)
|
|
fake_input = torch.randn((2, 3, 224, 224))
|
|
fake_backbone_out = alg.extract_feat(fake_input)
|
|
assert fake_backbone_out[0].size() == torch.Size([2, 512, 7, 7])
|
|
|
|
fake_input = [
|
|
torch.randn((2, 3, 224, 224)),
|
|
torch.randn((2, 3, 224, 224)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
torch.randn((2, 3, 96, 96)),
|
|
]
|
|
fake_out = alg.forward_train(fake_input)
|
|
assert fake_out['loss'].item() > 0
|