Add MedNext implementation #8004
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Signed-off-by: Suraj Pai <[email protected]>
| return self.conv_out(x) | ||
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| class LayerNorm(nn.Module): |
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Do we need this LayerNorm implementation? Or can we just use the torch.nn.LayerNorm? I don't see the channels_last used anywhere.
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This is a copy from the original codebase and can probably be replaced with torch.nn.LayerNorm as 'channels_first' is always assumed.
| self.grn_beta = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | ||
| self.grn_gamma = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | ||
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| def forward(self, x, dummy_tensor=None): |
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Do we know what the dummy_tensor is used for? I don't see it being used anywhere. This applies to the other forward functions as well
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This also come from a copy from the original codebase and should be removed as it's never supposed to be used.
| self, | ||
| spatial_dims: int = 3, | ||
| init_filters: int = 32, | ||
| in_channels: int = 1, |
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How about setting this to None per default and using LazyConv if the in_channels are set to None?
| blocks_down: list = [2, 2, 2, 2], | ||
| blocks_bottleneck: int = 2, | ||
| blocks_up: list = [2, 2, 2, 2], | ||
| norm_type: str = "group", |
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Should we make this a StrEnum? Makes it easy to see the options. Ofc also applies to the Block
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@johnzielke Can you give me an exemple of its usage in one of network implementation. Because it's not straighforward for me how to use it.
monai/networks/nets/mednext.py
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| enc_exp_r: int = 2, | ||
| dec_exp_r: int = 2, | ||
| bottlenec_exp_r: int = 2, |
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IMO encoder_expansion_ratio (the same for decoder and bottleneck) is better here. It's a public API, makes it easier to see what it refers to without having to look at the docs and the few letters more don't matter that much
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I've kept this convention similar to the original implementation which uses a joint exp_r argument. Happy to rename it. Agree with the comment
monai/networks/nets/mednext.py
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| bottlenec_exp_r: int = 2, | ||
| kernel_size: int = 7, | ||
| deep_supervision: bool = False, | ||
| do_res: bool = False, |
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maybe use_residual_connections?
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I think use_res would probably suffice? Similar to args in other APIs like SegResNet such as use_conv_final
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Yeah, my only concern is that res can be sth. like resolution or result.
monai/networks/nets/mednext.py
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| kernel_size: int = 7, | ||
| deep_supervision: bool = False, | ||
| do_res: bool = False, | ||
| do_res_up_down: bool = False, |
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maybe use_residual_connections_up_down_blocks?
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This sounds very verbose to me and may not be necessary. I think the contraction res is easily understood. If playing with these params, users are encouraged to read the code nonetheless to figure out where it goes.
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How about use_residual_up_down ?
| if dim == "2d": | ||
| self.grn_beta = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1), requires_grad=True) | ||
| self.grn_gamma = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1), requires_grad=True) | ||
| else: | ||
| self.grn_beta = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | ||
| self.grn_gamma = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | ||
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I think this is easier to understand, but the current one is fine as well.
| if dim == "2d": | |
| self.grn_beta = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1), requires_grad=True) | |
| self.grn_gamma = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1), requires_grad=True) | |
| else: | |
| self.grn_beta = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | |
| self.grn_gamma = nn.Parameter(torch.zeros(1, exp_r * in_channels, 1, 1, 1), requires_grad=True) | |
| if dim == "2d": | |
| grn_parameter_shape = (1,1) | |
| elif dim == "3d": | |
| grn_parameter_shape = (1,1,1) | |
| else: | |
| raise ValueError() | |
| grn_parameter_shape = (1, exp_r * in_channels,) + grn_parameter_shape | |
| self.grn_beta = nn.Parameter(torch.zeros(grn_parameter_shape), requires_grad=True) | |
| self.grn_gamma = nn.Parameter(torch.zeros(grn_parameter_shape), requires_grad=True) | |
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Can't it be usefull to adapt the factory functions of create_mednext_v1 script inside your |
…edNext variants (S, B, M, L) + integration of remarks from @johnzilke (Project-MONAI#8004 (review)) for renaming class arguments - removal of self defined LayerNorm - linked residual connection for encoder and decoder Signed-off-by: Robin CREMESE <[email protected]>
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Thanks for the comments @johnzielke and thank you for the updates @rcremese. I'll update my branch to dev and look through the changes in your PR asap. |
…edNext variants (S, B, M, L) + integration of remarks from @johnzilke (Project-MONAI#8004 (review)) for renaming class arguments - removal of self defined LayerNorm - linked residual connection for encoder and decoder Signed-off-by: Robin CREMESE <[email protected]>
…edNext variants (S, B, M, L) + integration of remarks from @johnzilke (Project-MONAI#8004 (review)) for renaming class arguments - removal of self defined LayerNorm - linked residual connection for encoder and decoder Signed-off-by: Robin CREMESE <[email protected]>
Signed-off-by: Suraj Pai <[email protected]>
for more information, see https://pre-commit.ci
Signed-off-by: Suraj Pai <[email protected]>
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@KumoLiu This implementation should be ready now. Please let me know if you have any comments |
Signed-off-by: Suraj Pai <[email protected]>
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@KumoLiu Added. Do you think there would be interest to add this as a candidate for Auto3DSeg? I refer to this paper for its performance benchmarking: https://arxiv.org/abs/2404.09556 |
Thank you for bringing this up, it's an interesting suggestion. I believe it could be worthwhile to consider this as a potential candidate for Auto3DSeg. However, before moving forward, I would appreciate hearing others' thoughts and insights on whether this aligns with the current goals and roadmap for Auto3DSeg. cc @mingxin-zheng @dongyang0122 @Nic-Ma @myron |
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Great work @surajpaib ! any plans on when we're gonna be able to get this into main? |
Fixes #7786
Description
Added MedNext architectures implementation for MONAI.
Since a lot of the code is heavily sourced from the original MedNext repo, https://github.com/MIC-DKFZ/MedNeXt, I wanted to check if there is an attribution policy with regarded to borrowed source code. I've added a derivative notice bellow the monai copyright comment. Let me know if this needs to be changed.
The blocks have been taken almost as is but the network implementation has been changed largely to allow flexible blocks and follow MONAI segresnet styling.
Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.