# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmcv.cnn.bricks import ContextBlock def test_context_block(): with pytest.raises(AssertionError): # pooling_type should be in ['att', 'avg'] ContextBlock(16, 1. / 4, pooling_type='unsupport_type') with pytest.raises(AssertionError): # fusion_types should be of type list or tuple ContextBlock(16, 1. / 4, fusion_types='unsupport_type') with pytest.raises(AssertionError): # fusion_types should be in ['channel_add', 'channel_mul'] ContextBlock(16, 1. / 4, fusion_types=('unsupport_type', )) # test pooling_type='att' imgs = torch.randn(2, 16, 20, 20) context_block = ContextBlock(16, 1. / 4, pooling_type='att') out = context_block(imgs) assert context_block.conv_mask.in_channels == 16 assert context_block.conv_mask.out_channels == 1 assert out.shape == imgs.shape # test pooling_type='avg' imgs = torch.randn(2, 16, 20, 20) context_block = ContextBlock(16, 1. / 4, pooling_type='avg') out = context_block(imgs) assert hasattr(context_block, 'avg_pool') assert out.shape == imgs.shape # test fusion_types=('channel_add',) imgs = torch.randn(2, 16, 20, 20) context_block = ContextBlock(16, 1. / 4, fusion_types=('channel_add', )) out = context_block(imgs) assert context_block.channel_add_conv is not None assert context_block.channel_mul_conv is None assert out.shape == imgs.shape # test fusion_types=('channel_mul',) imgs = torch.randn(2, 16, 20, 20) context_block = ContextBlock(16, 1. / 4, fusion_types=('channel_mul', )) out = context_block(imgs) assert context_block.channel_add_conv is None assert context_block.channel_mul_conv is not None assert out.shape == imgs.shape # test fusion_types=('channel_add', 'channel_mul') imgs = torch.randn(2, 16, 20, 20) context_block = ContextBlock( 16, 1. / 4, fusion_types=('channel_add', 'channel_mul')) out = context_block(imgs) assert context_block.channel_add_conv is not None assert context_block.channel_mul_conv is not None assert out.shape == imgs.shape