谢昕辰 c11da07e18
[Enhancement] Delete convert function and add instruction to ViT/Swin README.md (#791)
* delete convert function and add instruction to README.md

* unified model convert and README

* remove url

* fix import error

* fix unittest

* rename pretrain

* rename vit and deit pretrain

* Update upernet_deit-b16_512x512_160k_ade20k.py

* Update upernet_deit-b16_512x512_80k_ade20k.py

* Update upernet_deit-b16_ln_mln_512x512_160k_ade20k.py

* Update upernet_deit-b16_mln_512x512_160k_ade20k.py

* Update upernet_deit-s16_512x512_160k_ade20k.py

* Update upernet_deit-s16_512x512_80k_ade20k.py

* Update upernet_deit-s16_ln_mln_512x512_160k_ade20k.py

* Update upernet_deit-s16_mln_512x512_160k_ade20k.py

Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
2021-08-25 15:00:41 -07:00

62 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones import SwinTransformer
def test_swin_transformer():
"""Test Swin Transformer backbone."""
with pytest.raises(TypeError):
# Pretrained arg must be str or None.
model = SwinTransformer(pretrained=123)
with pytest.raises(AssertionError):
# Because swin use non-overlapping patch embed, so the stride of patch
# embed must be equal to patch size.
model = SwinTransformer(strides=(2, 2, 2, 2), patch_size=4)
# Test absolute position embedding
temp = torch.randn((1, 3, 224, 224))
model = SwinTransformer(pretrain_img_size=224, use_abs_pos_embed=True)
model.init_weights()
model(temp)
# Test patch norm
model = SwinTransformer(patch_norm=False)
model(temp)
# Test pretrain img size
model = SwinTransformer(pretrain_img_size=(224, ))
with pytest.raises(AssertionError):
model = SwinTransformer(pretrain_img_size=(224, 224, 224))
# Test normal inference
temp = torch.randn((1, 3, 512, 512))
model = SwinTransformer()
outs = model(temp)
assert outs[0].shape == (1, 96, 128, 128)
assert outs[1].shape == (1, 192, 64, 64)
assert outs[2].shape == (1, 384, 32, 32)
assert outs[3].shape == (1, 768, 16, 16)
# Test abnormal inference
temp = torch.randn((1, 3, 511, 511))
model = SwinTransformer()
outs = model(temp)
assert outs[0].shape == (1, 96, 128, 128)
assert outs[1].shape == (1, 192, 64, 64)
assert outs[2].shape == (1, 384, 32, 32)
assert outs[3].shape == (1, 768, 16, 16)
# Test abnormal inference
temp = torch.randn((1, 3, 112, 137))
model = SwinTransformer()
outs = model(temp)
assert outs[0].shape == (1, 96, 28, 35)
assert outs[1].shape == (1, 192, 14, 18)
assert outs[2].shape == (1, 384, 7, 9)
assert outs[3].shape == (1, 768, 4, 5)