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* update candidates * update subnet_sampler_loop * update candidate * add readme * rename variable * rename variable * clean * update * add doc string * Revert "[Improvement] Support for candidate multiple dimensional search constraints." * [Improvement] Update Candidate with multi-dim search constraints. (#322) * update doc * add support type * clean code * update candidates * clean * xx * set_resource -> set_score * fix ci bug * py36 lint * fix bug * fix check constrain * py36 ci * redesign candidate * fix pre-commit * update cfg * add build_resource_estimator * fix ci bug * remove runner.epoch in testcase * [Feature] Autoformer architecture and dynamicOPs (#327) * add DynamicSequential * dynamiclayernorm * add dynamic_pathchembed * add DynamicMultiheadAttention and DynamicRelativePosition2D * add channel-level dynamicOP * add autoformer algo * clean notes * adapt channel_mutator * vit fly * fix import * mutable init * remove annotation * add DynamicInputResizer * add unittest for mutables * add OneShotMutableChannelUnit_VIT * clean code * reset unit for vit * remove attr * add autoformer backbone UT * add valuemutator UT * clean code * add autoformer algo UT * update classifier UT * fix test error * ignore * make lint * update * fix lint * mutable_attrs * fix test * fix error * remove DynamicInputResizer * fix test ci * remove InputResizer * rename variables * modify type * Continued improvements of ChannelUnit * fix lint * fix lint * remove OneShotMutableChannelUnit * adjust derived type * combination mixins * clean code * fix sample subnet * search loop fly * more annotations * avoid counter warning and modify batch_augment cfg by gy * restore * source_value_mutables restriction * simply arch_setting api * update * clean * fix ut
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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from mmrazor.models import SearchableImageClassifier
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class TestSearchableImageClassifier(TestCase):
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def test_init(self):
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arch_setting = dict(
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mlp_ratios=[3.0, 3.5, 4.0],
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num_heads=[8, 9, 10],
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depth=[14, 15, 16],
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embed_dims=[528, 576, 624])
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supernet_kwargs = dict(
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backbone=dict(
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_scope_='mmrazor',
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type='AutoformerBackbone',
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arch_setting=arch_setting),
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neck=None,
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head=dict(
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_scope_='mmrazor',
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type='DynamicLinearClsHead',
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num_classes=1000,
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in_channels=624,
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loss=dict(
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type='mmcls.LabelSmoothLoss',
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mode='original',
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num_classes=1000,
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label_smooth_val=0.1,
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loss_weight=1.0),
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topk=(1, 5)),
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connect_head=dict(connect_with_backbone='backbone.last_mutable'),
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)
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supernet = SearchableImageClassifier(**supernet_kwargs)
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# test connect_with_backbone
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self.assertEqual(
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supernet.backbone.last_mutable.activated_channels,
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len(
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supernet.head.fc.get_mutable_attr(
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'in_channels').current_choice))
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