# 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])