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mask2former_flash_internimage_b_640_160k_ade20k_ss.py
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mask2former_flash_internimage_b_640_160k_ade20k_ss.py
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# --------------------------------------------------------
# DCNv4
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
_base_ = [
'../_base_/models/mask2former_beit.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
num_classes = 150
pretrained = 'https://huggingface.co/OpenGVLab/DCNv4/resolve/main/flash_intern_image_b_1k_224.pth'
model = dict(
backbone=dict(
_delete_=True,
type='FlashInternImage',
core_op='DCNv4',
channels=112,
depths=[4, 4, 21, 4],
groups=[7, 14, 28, 56],
mlp_ratio=4.,
drop_path_rate=0.4,
norm_layer='LN',
layer_scale=1.0,
offset_scale=0.5,
post_norm=True,
with_cp=False,
dw_kernel_size=3,
out_indices=(0, 1, 2, 3),
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
decode_head=dict(
in_channels=[112, 224, 448, 896],
feat_channels=256,
out_channels=256,
num_classes=num_classes,
num_queries=200,
pixel_decoder=dict(
type='MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=256,
num_heads=8,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=False,
norm_cfg=None,
init_cfg=None),
ffn_cfgs=dict(
type='FFN',
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.0,
with_cp=False, # set with_cp=True to save memory
act_cfg=dict(type='ReLU', inplace=True)),
operation_order=('self_attn', 'norm', 'ffn', 'norm')),
init_cfg=None),
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=128, normalize=True),
init_cfg=None),
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=128, normalize=True),
transformer_decoder=dict(
type='DetrTransformerDecoder',
return_intermediate=True,
num_layers=9,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=False),
ffn_cfgs=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
with_cp=False, # set with_cp=True to save memory
add_identity=True),
feedforward_channels=2048,
operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
'ffn', 'norm')),
init_cfg=None),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1])
),
test_cfg=dict(mode='whole'))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (640, 640)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='ToMask'),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2560, 640),
# img_ratios=[768./896., 832./896., 1.0, 960./896., 1024./896.],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
optimizer = dict(
_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
constructor='CustomLayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=33, layer_decay_rate=1.0,
depths=[4, 4, 21, 4]))
lr_config = dict(_delete_=True, policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0, min_lr=0.0, by_epoch=False)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
runner = dict(type='IterBasedRunner')
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
checkpoint_config = dict(by_epoch=False, interval=5000, max_keep_ckpts=1)
evaluation = dict(interval=5000, metric='mIoU', save_best='mIoU')
# fp16 = dict(loss_scale=dict(init_scale=512))