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upernet_deit_adapter_base_768_160k_bdd100k_bge_base_gpt3.5_cluster_32_cosine_simi_with_sigmoid_cosine_loss_temp_0.04_unnormalized.py
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upernet_deit_adapter_base_768_160k_bdd100k_bge_base_gpt3.5_cluster_32_cosine_simi_with_sigmoid_cosine_loss_temp_0.04_unnormalized.py
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norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder_cluster_embed',
pretrained='pretrained/deit_base_patch16_224-b5f2ef4d.pth',
backbone=dict(
type='ViTAdapter',
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
drop_path_rate=0.3,
conv_inplane=64,
n_points=4,
deform_num_heads=12,
cffn_ratio=0.25,
deform_ratio=0.5,
interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]],
window_attn=[
False, False, False, False, False, False, False, False, False,
False, False, False
],
window_size=[
None, None, None, None, None, None, None, None, None, None, None,
None
]),
decode_head=dict(
type='UPerHead_cluster_embed',
in_channels=[768, 768, 768, 768],
in_index=[0, 1, 2, 3],
pool_scales=(1, 2, 3, 6),
channels=512,
dropout_ratio=0.1,
num_classes=768,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CosineSimilarityLoss', use_sigmoid=False, loss_weight=1.0),
ignore_index=None,
desc_model_name=
'bge-base-en-v1.5_gpt3.5_cluster_32_embeddings_and_labels',
desc_weights_dict_path=
'embeddings/cityscapes_bdd_desc_bge-base-en-v1.5_gpt3.5_cluster_32_embedding_bank.pth',
get_logit_mode='cosine_similarity_with_sigmoid',
sigmoid_temperature=0.04,
image_embedding_normalize=False),
auxiliary_head=dict(
type='FCNHead_cluster_embed',
in_channels=768,
in_index=2,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
num_classes=768,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CosineSimilarityLoss', use_sigmoid=False, loss_weight=0.4),
ignore_index=None,
desc_model_name=
'bge-base-en-v1.5_gpt3.5_cluster_32_embeddings_and_labels',
desc_weights_dict_path=
'embeddings/cityscapes_bdd_desc_bge-base-en-v1.5_gpt3.5_cluster_32_embedding_bank.pth',
get_logit_mode='cosine_similarity_with_sigmoid',
sigmoid_temperature=0.04,
image_embedding_normalize=False),
train_cfg=dict(),
test_cfg=dict(
mode='slide',
crop_size=(768, 768),
stride=(512, 512),
test_dataset_name='cityscapes'),
desc_model_name='bge-base-en-v1.5_gpt3.5_cluster_32_embeddings_and_labels',
desc_weights_dict_path=
'embeddings/cityscapes_bdd_desc_bge-base-en-v1.5_gpt3.5_cluster_32_embedding_bank.pth',
get_logit_mode='cosine_similarity_with_sigmoid',
sigmoid_temperature=0.04,
image_embedding_normalize=False)
dataset_type = 'BDD100K_Dataset'
data_root = 'data/bdd100k/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (768, 768)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(768, 768), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(768, 768), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type='BDD100K_Dataset',
data_root='data/bdd100k/',
img_dir='images/10k/train',
ann_dir='labels/sem_seg/masks/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(768, 768), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(768, 768), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]),
val=dict(
type='BDD100K_Dataset',
data_root='data/bdd100k/',
img_dir='images/10k/val',
ann_dir='labels/sem_seg/masks/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='BDD100K_Dataset',
data_root='data/bdd100k/',
img_dir='images/10k/val',
ann_dir='labels/sem_seg/masks/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='ResizeToMultiple', size_divisor=32),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook')
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
nchmark = True
optimizer = dict(
type='AdamW',
lr=6e-05,
betas=(0.9, 0.999),
weight_decay=0.01,
constructor='LayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.95))
optimizer_config = dict()
lr_config = dict(
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-06,
power=1.0,
min_lr=0.0,
by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1)
evaluation = dict(
interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU')
pretrained = 'pretrained/deit_base_patch16_224-b5f2ef4d.pth'
work_dir = './work_dirs/bdd100k/upernet_deit_adapter_base_768_160k_bdd100k_bge_base_gpt3.5_cluster_32_cosine_simi_with_sigmoid_cosine_loss_temp_0.04_unnormalized/'
gpu_ids = range(0, 8)
auto_resume = False