59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import copy
|
|
import platform
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from mmselfsup.models.algorithms.simclr import SimCLR
|
|
from mmselfsup.structures import SelfSupDataSample
|
|
|
|
backbone = dict(
|
|
type='ResNet',
|
|
depth=18,
|
|
in_channels=3,
|
|
out_indices=[4], # 0: conv-1, x: stage-x
|
|
norm_cfg=dict(type='BN'))
|
|
neck = dict(
|
|
type='NonLinearNeck', # SimCLR non-linear neck
|
|
in_channels=512,
|
|
hid_channels=2,
|
|
out_channels=2,
|
|
num_layers=2,
|
|
with_avg_pool=True,
|
|
norm_cfg=dict(type='BN1d'))
|
|
head = dict(
|
|
type='ContrastiveHead',
|
|
loss=dict(type='mmcls.CrossEntropyLoss'),
|
|
temperature=0.1)
|
|
|
|
|
|
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
|
def test_simclr():
|
|
data_preprocessor = {
|
|
'mean': (123.675, 116.28, 103.53),
|
|
'std': (58.395, 57.12, 57.375),
|
|
'bgr_to_rgb': True,
|
|
}
|
|
|
|
alg = SimCLR(
|
|
backbone=backbone,
|
|
neck=neck,
|
|
head=head,
|
|
data_preprocessor=copy.deepcopy(data_preprocessor))
|
|
|
|
fake_data = {
|
|
'inputs':
|
|
[torch.randn((2, 3, 224, 224)),
|
|
torch.randn((2, 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 isinstance(fake_loss['loss'].item(), float)
|
|
|
|
# test extract
|
|
fake_feat = alg(fake_inputs, fake_data_samples, mode='tensor')
|
|
assert fake_feat[0].size() == torch.Size([2, 512, 7, 7])
|