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[Feature] Add DMCP and fix the deploy pipeline of NAS algorithms #406

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merged 69 commits into from
Mar 2, 2023

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Lxtccc
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@Lxtccc Lxtccc commented Dec 22, 2022

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.

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Modification

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# To static ops for loaded pruned network.
self._deploy()

def _deploy(self):
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_deploy is not needed here.

ForwardResults = Union[LossResults, TensorResults, PredictResults]

@MODELS.register_module()
class DMCP(ItePruneAlgorithm):
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inherit from ItePruneAlgorithm for ?

def __init__(self,
channel_unit_cfg: Union[dict, Type[ChannelUnitType]] = dict(
type='DMCPChannelUnit', units={}),
parse_cfg=dict(
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update parse_cfg to ChannelAnalyzer, according to:

else:
raise NotImplementedError

def calc_current_flops(self, model):
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move to Algorithm.

@@ -172,7 +171,8 @@ def params_units_convert(num_params, units='M', precision=3):
>>> params_units_convert(3e-9)
'3e-09'
"""

if getattr(num_params, 'requires_grad', None):
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If you want to get num_params without units, just pass the argument units with the None.

@sunnyxiaohu sunnyxiaohu self-requested a review December 23, 2022 06:42
class DynamicSyncBatchNorm(nn.SyncBatchNorm, DynamicBatchNormMixin):
"""DynamicOp for sync bn."""
@MODELS.register_module()
class DMCPBatchNorm2d(DynamicBatchNorm2d):
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fix dynamic_norm.py


self.modify_supernet_forward(arch_train)

def _prune_by_arch(self, mode, group_id):
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docstr

group_arch_param = self._build_arch_param(arch_message[1])
self.arch_params[str(group_id)] = group_arch_param

for m in module[0].output_related:
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modify name:m->unit

min_ratio: float = 0.5) -> None:
super().__init__(num_channels, choice_mode, divisor, min_value,
min_ratio)
self._traceable_choice = tracable_choice()
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traceable

@sunnyxiaohu sunnyxiaohu self-requested a review December 28, 2022 07:24
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codecov bot commented Jan 11, 2023

Codecov Report

Patch coverage: 75.92% and project coverage change: +0.35 🎉

Comparison is base (60469c0) 79.14% compared to head (7d57599) 79.49%.

Additional details and impacted files
@@             Coverage Diff             @@
##           dev-1.x     #406      +/-   ##
===========================================
+ Coverage    79.14%   79.49%   +0.35%     
===========================================
  Files          276      279       +3     
  Lines        13597    14027     +430     
  Branches      2084     2155      +71     
===========================================
+ Hits         10761    11151     +390     
- Misses        2395     2416      +21     
- Partials       441      460      +19     
Flag Coverage Δ
unittests 79.49% <75.92%> (+0.35%) ⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files Coverage Δ
mmrazor/engine/hooks/dump_subnet_hook.py 35.00% <0.00%> (ø)
mmrazor/engine/runner/subnet_val_loop.py 24.00% <0.00%> (ø)
...architectures/backbones/searchable_mobilenet_v3.py 89.11% <ø> (ø)
...dynamic_ops/bricks/dynamic_multi_head_attention.py 69.73% <ø> (ø)
mmrazor/models/mutables/__init__.py 100.00% <ø> (ø)
mmrazor/models/mutators/__init__.py 100.00% <ø> (ø)
mmrazor/models/algorithms/nas/bignas.py 38.28% <10.66%> (+1.05%) ⬆️
.../models/task_modules/predictor/metric_predictor.py 65.62% <16.66%> (-7.32%) ⬇️
...zor/models/architectures/utils/mutable_register.py 75.75% <28.57%> (-14.57%) ⬇️
mmrazor/engine/hooks/dmcp_subnet_hook.py 30.55% <30.55%> (ø)
... and 27 more

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@sunnyxiaohu sunnyxiaohu changed the title [Feature] Add DMCP [Feature] Add DMCP and fix the deploy pipeline of NAS algorithms Mar 2, 2023
@sunnyxiaohu sunnyxiaohu merged commit 0b24276 into open-mmlab:dev-1.x Mar 2, 2023
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vansin commented Mar 8, 2023

@Lxtccc Congratulations on having your first PR merged in OpenMMLab! I am a technical operations staff member of OpenMMLab. My WeChat ID is van-sin ,please add me . I would like to invite you to join the OpenMMLab contributors WeChat group.

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Dear Lxtccc,
First of all, we want to express our gratitude for your significant PR in the MMRazor project. Your contribution is highly appreciated, and we are grateful for your efforts in helping improve this open-source project during your personal time. We believe that many developers will benefit from your PR.
We are looking forward to continuing our collaboration with you. OpenMMLab has established a special contributors' organization called MMSIG, which provides contributors with open-source certificates, a recognition system, and exclusive rewards. You can contact us by adding our WeChat(if you have WeChat): openmmlabwx, or join in our discord:https://discord.gg/raweFPmdzG. We sincerely hope you will join us!
Best regards! @Lxtccc

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6 participants