[Feature] Support RepLKnet backbone. #1129
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mzr1996
requested changes
Nov 14, 2022
tools/test.py
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| @@ -138,6 +138,7 @@ def main(): | |||
| # load config | |||
| cfg = Config.fromfile(args.config) | |||
| cfg = merge_args(cfg, args) | |||
| print(cfg) | |||
There was a problem hiding this comment.
Please remove the unrelated modification.
mmcls/models/backbones/replknet.py
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| # def create_RepLKNet31B(drop_path_rate=0.3, num_classes=1000, with_cp=True, small_kernel_merged=False): | ||
| # return RepLKNet(arch='31B') | ||
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| # def create_RepLKNet31L(drop_path_rate=0.3, num_classes=1000, with_cp=True, small_kernel_merged=False): | ||
| # return RepLKNet(large_kernel_sizes=[31,29,27,13], layers=[2,2,18,2], channels=[192,384,768,1536], | ||
| # drop_path_rate=drop_path_rate, small_kernel=5, num_classes=num_classes, with_cp=with_cp, | ||
| # small_kernel_merged=small_kernel_merged) | ||
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| # def create_RepLKNetXL(drop_path_rate=0.3, num_classes=1000, with_cp=True, small_kernel_merged=False): | ||
| # return RepLKNet(large_kernel_sizes=[27,27,27,13], layers=[2,2,18,2], channels=[256,512,1024,2048], | ||
| # drop_path_rate=drop_path_rate, small_kernel=None, dw_ratio=1.5, | ||
| # num_classes=num_classes, with_cp=with_cp, | ||
| # small_kernel_merged=small_kernel_merged) | ||
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| # if __name__ == '__main__': | ||
| # model = create_RepLKNet31B(small_kernel_merged=False) | ||
| # model.eval() | ||
| # print('------------------- training-time model -------------') | ||
| # for i in model.state_dict().keys(): | ||
| # print(i) | ||
| # print(model) | ||
| # x = torch.randn(2, 3, 224, 224) | ||
| # origin_y = model(x) | ||
| # model.switch_to_deploy() | ||
| # print('------------------- after re-param -------------') | ||
| # print(model) | ||
| # reparam_y = model(x) | ||
| # print('------------------- the difference is ------------------------') | ||
| # print((origin_y - reparam_y).abs().sum()) |
mmcls/models/backbones/replknet.py
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| def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias): | ||
| if type(kernel_size) is int: | ||
| use_large_impl = kernel_size > 5 | ||
| else: | ||
| assert len(kernel_size) == 2 and kernel_size[0] == kernel_size[1] | ||
| use_large_impl = kernel_size[0] > 5 | ||
| has_large_impl = 'LARGE_KERNEL_CONV_IMPL' in os.environ | ||
| if has_large_impl and in_channels == out_channels and out_channels == groups and use_large_impl and stride == 1 and padding == kernel_size // 2 and dilation == 1: | ||
| sys.path.append(os.environ['LARGE_KERNEL_CONV_IMPL']) | ||
| # Please follow the instructions https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/README.md | ||
| # export LARGE_KERNEL_CONV_IMPL=absolute_path_to_where_you_cloned_the_example (i.e., depthwise_conv2d_implicit_gemm.py) | ||
| # TODO more efficient PyTorch implementations of large-kernel convolutions. Pull requests are welcomed. | ||
| # Or you may try MegEngine. We have integrated an efficient implementation into MegEngine and it will automatically use it. | ||
| from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM | ||
| return DepthWiseConv2dImplicitGEMM(in_channels, kernel_size, bias=bias) | ||
| else: | ||
| return nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, | ||
| padding=padding, dilation=dilation, groups=groups, bias=bias) |
There was a problem hiding this comment.
Since we don't have the DepthWiseConv2dImplicitGEMM in MMCV, please remove this function.
You can also add this operator to mmcv package.
mmcls/models/backbones/replknet.py
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| use_sync_bn = False | ||
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| def enable_sync_bn(): | ||
| global use_sync_bn | ||
| use_sync_bn = True | ||
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| def get_bn(channels): | ||
| if use_sync_bn: | ||
| return nn.SyncBatchNorm(channels) | ||
| else: | ||
| return nn.BatchNorm2d(channels) |
There was a problem hiding this comment.
We have our sync_bn configuration, please use norm_cfg instead of this kind of code.
| self.small_kernel_merged = True | ||
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| class ConvFFN(BaseModule): |
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| @MODELS.register_module() | ||
| class RepLKNet(BaseBackbone): | ||
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Codecov ReportBase: 0.02% // Head: 89.16% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## dev-1.x #1129 +/- ##
============================================
+ Coverage 0.02% 89.16% +89.14%
============================================
Files 121 144 +23
Lines 8217 11086 +2869
Branches 1368 1764 +396
============================================
+ Hits 2 9885 +9883
+ Misses 8215 952 -7263
- Partials 0 249 +249
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mzr1996
approved these changes
Nov 21, 2022
mzr1996
added a commit
to mzr1996/mmpretrain
that referenced
this pull request
Nov 24, 2022
* update replknet configs * update replknet test * update replknet model * update replknet model * update replknet model * update replknet model * Fix docs and config names Co-authored-by: mzr1996 <mzr1996@163.com>
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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
Add RepLKNet inference and test files.
Modification
Add RepLKNet inference and test files.
Checklist
After PR: