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DDPS-all / deeplabv3plus_r50_multistep /deeplabv3plus_r50-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune.py

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	norm_cfg = dict(type='SyncBN', requires_grad=True)
	model = dict(
	type='EncoderDecoderDiffusion',
	pretrained=
	'work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_64000.pth',
	backbone=dict(
	type='ResNetV1cCustomInitWeights',
	depth=50,
	num_stages=4,
	out_indices=(0, 1, 2, 3),
	dilations=(1, 1, 2, 4),
	strides=(1, 2, 1, 1),
	norm_cfg=dict(type='SyncBN', requires_grad=True),
	norm_eval=False,
	style='pytorch',
	contract_dilation=True),
	decode_head=dict(
	type='DepthwiseSeparableASPPHeadUnetFCHeadMultiStep',
	pretrained=
	'work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_64000.pth',
	dim=128,
	out_dim=256,
	unet_channels=528,
	dim_mults=[1, 1, 1],
	cat_embedding_dim=16,
	ignore_index=0,
	diffusion_timesteps=100,
	collect_timesteps=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99],
	in_channels=2048,
	in_index=3,
	channels=512,
	dilations=(1, 12, 24, 36),
	c1_in_channels=256,
	c1_channels=48,
	dropout_ratio=0.1,
	num_classes=151,
	norm_cfg=dict(type='SyncBN', requires_grad=True),
	align_corners=False,
	loss_decode=dict(
	type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
	auxiliary_head=None,
	train_cfg=dict(),
	test_cfg=dict(mode='whole'),
	freeze_parameters=['backbone', 'decode_head'])
	dataset_type = 'ADE20K151Dataset'
	data_root = 'data/ade/ADEChallengeData2016'
	img_norm_cfg = dict(
	mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
	crop_size = (512, 512)
	train_pipeline = [
	dict(type='LoadImageFromFile'),
	dict(type='LoadAnnotations', reduce_zero_label=False),
	dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
	dict(type='RandomCrop', crop_size=(512, 512), 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=(512, 512), pad_val=0, seg_pad_val=0),
	dict(type='DefaultFormatBundle'),
	dict(type='Collect', keys=['img', 'gt_semantic_seg'])
	]
	test_pipeline = [
	dict(type='LoadImageFromFile'),
	dict(
	type='MultiScaleFlipAug',
	img_scale=(2048, 512),
	flip=False,
	transforms=[
	dict(type='Resize', keep_ratio=True),
	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='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
	dict(type='ImageToTensor', keys=['img']),
	dict(type='Collect', keys=['img'])
	])
	]
	data = dict(
	samples_per_gpu=4,
	workers_per_gpu=4,
	train=dict(
	type='ADE20K151Dataset',
	data_root='data/ade/ADEChallengeData2016',
	img_dir='images/training',
	ann_dir='annotations/training',
	pipeline=[
	dict(type='LoadImageFromFile'),
	dict(type='LoadAnnotations', reduce_zero_label=False),
	dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
	dict(type='RandomCrop', crop_size=(512, 512), 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=(512, 512), pad_val=0, seg_pad_val=0),
	dict(type='DefaultFormatBundle'),
	dict(type='Collect', keys=['img', 'gt_semantic_seg'])
	]),
	val=dict(
	type='ADE20K151Dataset',
	data_root='data/ade/ADEChallengeData2016',
	img_dir='images/validation',
	ann_dir='annotations/validation',
	pipeline=[
	dict(type='LoadImageFromFile'),
	dict(
	type='MultiScaleFlipAug',
	img_scale=(2048, 512),
	flip=False,
	transforms=[
	dict(type='Resize', keep_ratio=True),
	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='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
	dict(type='ImageToTensor', keys=['img']),
	dict(type='Collect', keys=['img'])
	])
	]),
	test=dict(
	type='ADE20K151Dataset',
	data_root='data/ade/ADEChallengeData2016',
	img_dir='images/validation',
	ann_dir='annotations/validation',
	pipeline=[
	dict(type='LoadImageFromFile'),
	dict(
	type='MultiScaleFlipAug',
	img_scale=(2048, 512),
	flip=False,
	transforms=[
	dict(type='Resize', keep_ratio=True),
	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='Pad', size_divisor=16, pad_val=0, seg_pad_val=0),
	dict(type='ImageToTensor', keys=['img']),
	dict(type='Collect', keys=['img'])
	])
	]))
	log_config = dict(
	interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
	dist_params = dict(backend='nccl')
	log_level = 'INFO'
	load_from = None
	resume_from = None
	workflow = [('train', 1)]
	cudnn_benchmark = True
	optimizer = dict(
	type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045)
	optimizer_config = dict()
	lr_config = dict(
	policy='step',
	warmup='linear',
	warmup_iters=1000,
	warmup_ratio=1e-06,
	step=20000,
	gamma=0.5,
	min_lr=1e-06,
	by_epoch=False)
	runner = dict(type='IterBasedRunner', max_iters=160000)
	checkpoint_config = dict(by_epoch=False, interval=16000, max_keep_ckpts=1)
	evaluation = dict(
	interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU')
	checkpoint = 'work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_single_step_ade_pretrained_freeze_embed_80k_ade20k151/best_mIoU_iter_64000.pth'
	custom_hooks = [
	dict(
	type='ConstantMomentumEMAHook',
	momentum=0.01,
	interval=25,
	eval_interval=16000,
	auto_resume=True,
	priority=49)
	]
	work_dir = './work_dirs2/deeplabv3plus_r50-d8_aspp_head_unet_fc_multi_step_ade_pretrained_freeze_embed_160k_ade20k151_finetune'
	gpu_ids = range(0, 8)
	auto_resume = True