33 lines
1.0 KiB
Python
33 lines
1.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import torch
|
|
|
|
from mmseg.models import MultiLevelNeck
|
|
|
|
|
|
def test_multilevel_neck():
|
|
|
|
# Test init_weights
|
|
MultiLevelNeck([266], 32).init_weights()
|
|
|
|
# Test multi feature maps
|
|
in_channels = [32, 64, 128, 256]
|
|
inputs = [torch.randn(1, c, 14, 14) for i, c in enumerate(in_channels)]
|
|
|
|
neck = MultiLevelNeck(in_channels, 32)
|
|
outputs = neck(inputs)
|
|
assert outputs[0].shape == torch.Size([1, 32, 7, 7])
|
|
assert outputs[1].shape == torch.Size([1, 32, 14, 14])
|
|
assert outputs[2].shape == torch.Size([1, 32, 28, 28])
|
|
assert outputs[3].shape == torch.Size([1, 32, 56, 56])
|
|
|
|
# Test one feature map
|
|
in_channels = [768]
|
|
inputs = [torch.randn(1, 768, 14, 14)]
|
|
|
|
neck = MultiLevelNeck(in_channels, 32)
|
|
outputs = neck(inputs)
|
|
assert outputs[0].shape == torch.Size([1, 32, 7, 7])
|
|
assert outputs[1].shape == torch.Size([1, 32, 14, 14])
|
|
assert outputs[2].shape == torch.Size([1, 32, 28, 28])
|
|
assert outputs[3].shape == torch.Size([1, 32, 56, 56])
|