mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
163277bfe0
* init commits * fix crop size * add seg_data2instance_data method * add ut and update requirement * update configs and readme * add model-indel * update optional requirements * fix results * fix lint error * update results * update results * remove mmdet from requirements/optional.txt * use try import and update README * add docstring to overwrtied method * minor change Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
207 lines
7.1 KiB
Python
207 lines
7.1 KiB
Python
_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/cityscapes.py']
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custom_imports = dict(imports='mmdet.models', allow_failed_imports=False)
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crop_size = (512, 1024)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_val=0,
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seg_pad_val=255,
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size=crop_size,
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test_cfg=dict(size_divisor=32))
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num_classes = 19
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model = dict(
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type='EncoderDecoder',
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data_preprocessor=data_preprocessor,
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backbone=dict(
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type='ResNet',
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depth=50,
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deep_stem=False,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=-1,
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norm_cfg=dict(type='SyncBN', requires_grad=False),
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style='pytorch',
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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decode_head=dict(
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type='Mask2FormerHead',
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in_channels=[256, 512, 1024, 2048],
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strides=[4, 8, 16, 32],
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feat_channels=256,
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out_channels=256,
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num_classes=num_classes,
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num_queries=100,
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num_transformer_feat_level=3,
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align_corners=False,
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pixel_decoder=dict(
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type='mmdet.MSDeformAttnPixelDecoder',
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num_outs=3,
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norm_cfg=dict(type='GN', num_groups=32),
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act_cfg=dict(type='ReLU'),
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encoder=dict(
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type='mmdet.DetrTransformerEncoder',
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num_layers=6,
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transformerlayers=dict(
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type='mmdet.BaseTransformerLayer',
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attn_cfgs=dict(
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type='mmdet.MultiScaleDeformableAttention',
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embed_dims=256,
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num_heads=8,
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num_levels=3,
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num_points=4,
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im2col_step=64,
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dropout=0.0,
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batch_first=False,
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norm_cfg=None,
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init_cfg=None),
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ffn_cfgs=dict(
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type='FFN',
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embed_dims=256,
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feedforward_channels=1024,
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num_fcs=2,
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ffn_drop=0.0,
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act_cfg=dict(type='ReLU', inplace=True)),
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operation_order=('self_attn', 'norm', 'ffn', 'norm')),
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init_cfg=None),
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positional_encoding=dict(
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type='mmdet.SinePositionalEncoding',
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num_feats=128,
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normalize=True),
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init_cfg=None),
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enforce_decoder_input_project=False,
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positional_encoding=dict(
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type='mmdet.SinePositionalEncoding', num_feats=128,
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normalize=True),
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transformer_decoder=dict(
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type='mmdet.DetrTransformerDecoder',
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return_intermediate=True,
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num_layers=9,
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transformerlayers=dict(
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type='mmdet.DetrTransformerDecoderLayer',
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attn_cfgs=dict(
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type='mmdet.MultiheadAttention',
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embed_dims=256,
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num_heads=8,
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attn_drop=0.0,
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proj_drop=0.0,
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dropout_layer=None,
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batch_first=False),
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ffn_cfgs=dict(
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embed_dims=256,
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feedforward_channels=2048,
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num_fcs=2,
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act_cfg=dict(type='ReLU', inplace=True),
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ffn_drop=0.0,
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dropout_layer=None,
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add_identity=True),
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feedforward_channels=2048,
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operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
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'ffn', 'norm')),
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init_cfg=None),
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loss_cls=dict(
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type='mmdet.CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=2.0,
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reduction='mean',
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class_weight=[1.0] * num_classes + [0.1]),
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loss_mask=dict(
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type='mmdet.CrossEntropyLoss',
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use_sigmoid=True,
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reduction='mean',
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loss_weight=5.0),
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loss_dice=dict(
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type='mmdet.DiceLoss',
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use_sigmoid=True,
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activate=True,
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reduction='mean',
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naive_dice=True,
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eps=1.0,
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loss_weight=5.0),
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train_cfg=dict(
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num_points=12544,
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oversample_ratio=3.0,
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importance_sample_ratio=0.75,
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assigner=dict(
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type='mmdet.HungarianAssigner',
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match_costs=[
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dict(type='mmdet.ClassificationCost', weight=2.0),
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dict(
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type='mmdet.CrossEntropyLossCost',
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weight=5.0,
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use_sigmoid=True),
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dict(
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type='mmdet.DiceCost',
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weight=5.0,
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pred_act=True,
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eps=1.0)
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]),
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sampler=dict(type='mmdet.MaskPseudoSampler'))),
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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# dataset config
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(
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type='RandomChoiceResize',
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scales=[int(1024 * x * 0.1) for x in range(5, 21)],
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resize_type='ResizeShortestEdge',
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max_size=4096),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='PackSegInputs')
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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# optimizer
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embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
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optimizer = dict(
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type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=optimizer,
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clip_grad=dict(max_norm=0.01, norm_type=2),
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paramwise_cfg=dict(
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custom_keys={
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'backbone': dict(lr_mult=0.1, decay_mult=1.0),
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'query_embed': embed_multi,
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'query_feat': embed_multi,
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'level_embed': embed_multi,
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},
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norm_decay_mult=0.0))
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# learning policy
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param_scheduler = [
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dict(
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type='PolyLR',
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eta_min=0,
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power=0.9,
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begin=0,
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end=90000,
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by_epoch=False)
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]
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# training schedule for 90k
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train_cfg = dict(type='IterBasedTrainLoop', max_iters=90000, val_interval=5000)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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default_hooks = dict(
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(
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type='CheckpointHook', by_epoch=False, interval=5000,
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save_best='mIoU'),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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visualization=dict(type='SegVisualizationHook'))
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# Default setting for scaling LR automatically
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# - `enable` means enable scaling LR automatically
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# or not by default.
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# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
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auto_scale_lr = dict(enable=False, base_batch_size=16)
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