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- # Copyright (c) OpenMMLab. All rights reserved.
- import pytest
- import torch
- from mmdet.models.layers import ConvUpsample
- @pytest.mark.parametrize('num_layers', [0, 1, 2])
- def test_conv_upsample(num_layers):
- num_upsample = num_layers if num_layers > 0 else 0
- num_layers = num_layers if num_layers > 0 else 1
- layer = ConvUpsample(
- 10,
- 5,
- num_layers=num_layers,
- num_upsample=num_upsample,
- conv_cfg=None,
- norm_cfg=None)
- size = 5
- x = torch.randn((1, 10, size, size))
- size = size * pow(2, num_upsample)
- x = layer(x)
- assert x.shape[-2:] == (size, size)
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