68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import copy
|
|
import platform
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from mmselfsup.core import SelfSupDataSample
|
|
from mmselfsup.models.algorithms import SimSiam
|
|
|
|
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='NonLinearNeck',
|
|
in_channels=512,
|
|
hid_channels=2,
|
|
out_channels=2,
|
|
num_layers=3,
|
|
with_last_bn_affine=False,
|
|
with_avg_pool=True,
|
|
norm_cfg=dict(type='BN1d'))
|
|
head = dict(
|
|
type='LatentPredictHead',
|
|
loss=dict(type='CosineSimilarityLoss'),
|
|
predictor=dict(
|
|
type='NonLinearNeck',
|
|
in_channels=2,
|
|
hid_channels=2,
|
|
out_channels=2,
|
|
with_avg_pool=False,
|
|
with_last_bn=False,
|
|
with_last_bias=True,
|
|
norm_cfg=dict(type='BN1d')))
|
|
|
|
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
|
def test_simsiam():
|
|
data_preprocessor = {
|
|
'mean': (123.675, 116.28, 103.53),
|
|
'std': (58.395, 57.12, 57.375),
|
|
'bgr_to_rgb': True,
|
|
}
|
|
|
|
alg = SimSiam(
|
|
backbone=backbone,
|
|
neck=neck,
|
|
head=head,
|
|
data_preprocessor=copy.deepcopy(data_preprocessor))
|
|
|
|
fake_data = [{
|
|
'inputs': [torch.randn((3, 224, 224)),
|
|
torch.randn((3, 224, 224))],
|
|
'data_sample':
|
|
SelfSupDataSample()
|
|
} 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'] > -1
|
|
|
|
# test extract
|
|
fake_feat = alg(fake_inputs, fake_data_samples, mode='tensor')
|
|
assert fake_feat[0].size() == torch.Size([2, 512, 7, 7])
|