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## Motivation Support SegNeXt in MMSeg 1.x branch. 0.x PR: https://github.com/open-mmlab/mmsegmentation/pull/2600 --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmseg.models.backbones import MSCAN
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from mmseg.models.backbones.mscan import (MSCAAttention, MSCASpatialAttention,
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OverlapPatchEmbed, StemConv)
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def test_mscan_backbone():
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# Test MSCAN Standard Forward
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model = MSCAN(
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embed_dims=[8, 16, 32, 64],
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norm_cfg=dict(type='BN', requires_grad=True))
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model.init_weights()
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model.train()
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batch_size = 2
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imgs = torch.randn(batch_size, 3, 64, 128)
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feat = model(imgs)
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assert len(feat) == 4
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# output for segment Head
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assert feat[0].shape == torch.Size([batch_size, 8, 16, 32])
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assert feat[1].shape == torch.Size([batch_size, 16, 8, 16])
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assert feat[2].shape == torch.Size([batch_size, 32, 4, 8])
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assert feat[3].shape == torch.Size([batch_size, 64, 2, 4])
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# Test input with rare shape
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batch_size = 2
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imgs = torch.randn(batch_size, 3, 95, 27)
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feat = model(imgs)
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assert len(feat) == 4
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def test_mscan_overlap_patch_embed_module():
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x_overlap_patch_embed = OverlapPatchEmbed(
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norm_cfg=dict(type='BN', requires_grad=True))
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assert x_overlap_patch_embed.proj.in_channels == 3
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assert x_overlap_patch_embed.norm.weight.shape == torch.Size([768])
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x = torch.randn(2, 3, 16, 32)
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x_out, H, W = x_overlap_patch_embed(x)
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assert x_out.shape == torch.Size([2, 32, 768])
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def test_mscan_spatial_attention_module():
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x_spatial_attention = MSCASpatialAttention(8)
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assert x_spatial_attention.proj_1.kernel_size == (1, 1)
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assert x_spatial_attention.proj_2.stride == (1, 1)
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x = torch.randn(2, 8, 16, 32)
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x_out = x_spatial_attention(x)
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assert x_out.shape == torch.Size([2, 8, 16, 32])
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def test_mscan_attention_module():
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x_attention = MSCAAttention(8)
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assert x_attention.conv0.weight.shape[0] == 8
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assert x_attention.conv3.kernel_size == (1, 1)
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x = torch.randn(2, 8, 16, 32)
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x_out = x_attention(x)
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assert x_out.shape == torch.Size([2, 8, 16, 32])
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def test_mscan_stem_module():
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x_stem = StemConv(8, 8, norm_cfg=dict(type='BN', requires_grad=True))
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assert x_stem.proj[0].weight.shape[0] == 4
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assert x_stem.proj[-1].weight.shape[0] == 8
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x = torch.randn(2, 8, 16, 32)
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x_out, H, W = x_stem(x)
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assert x_out.shape == torch.Size([2, 32, 8])
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assert (H, W) == (4, 8)
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