d21682da79
* upload original backbone and configs * ConvNext Refactor * ConvNext Refactor * convnext customization refactor with mmseg style * convnext customization refactor with mmseg style * add ade20k_640x640.py * upload files for training * delete dist_optimizer_hook and remove layer_decay_optimizer_constructor * check max(out_indices) < num_stages * add unittest * fix lint error * use MMClassification backbone * fix bugs in base_1k * add mmcls in requirements/mminstall.txt * add mmcls in requirements/mminstall.txt * fix drop_path_rate and layer_scale_init_value * use logger.info instead of print * add mmcls in runtime.txt * fix f string && delete * add doctring in LearningRateDecayOptimizerConstructor and fix mmcls version in requirements * fix typo in LearningRateDecayOptimizerConstructor * use ConvNext models in unit test for LearningRateDecayOptimizerConstructor * add unit test * fix typo * fix typo * add layer_wise and fix redundant backbone.downsample_norm in it * fix unit test * give a ground truth lr_scale and weight_decay * upload models and readme * delete 'backbone.stem_norm' and 'backbone.downsample_norm' in get_num_layer() * fix unit test and use mmcls url * update md2yml.py and metafile * fix typo
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
_base_ = [
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'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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crop_size = (512, 512)
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model = dict(
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decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150),
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auxiliary_head=dict(in_channels=512, num_classes=150),
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test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
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)
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optimizer = dict(
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constructor='LearningRateDecayOptimizerConstructor',
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_delete_=True,
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type='AdamW',
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lr=0.0001,
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betas=(0.9, 0.999),
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weight_decay=0.05,
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paramwise_cfg={
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'decay_rate': 0.9,
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'decay_type': 'stage_wise',
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'num_layers': 12
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})
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lr_config = dict(
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_delete_=True,
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policy='poly',
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warmup='linear',
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warmup_iters=1500,
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warmup_ratio=1e-6,
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power=1.0,
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min_lr=0.0,
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by_epoch=False)
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# By default, models are trained on 8 GPUs with 2 images per GPU
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data = dict(samples_per_gpu=2)
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# fp16 settings
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optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
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# fp16 placeholder
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fp16 = dict()
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