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experiments.py
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# ---------------------------------------------------------------
# Copyright (c) 2022 ETH Zurich, Lukas Hoyer. All rights reserved.
# Licensed under the Apache License, Version 2.0
# ---------------------------------------------------------------
import itertools
import os
from mmcv import Config
# flake8: noqa
def get_model_base(architecture, backbone):
architecture = architecture.replace('sfa_', '')
for j in range(1, 100):
hrda_name = [e for e in architecture.split('_') if f'hrda{j}' in e]
for n in hrda_name:
architecture = architecture.replace(f'{n}_', '')
architecture = architecture.replace('_nodbn', '')
if 'segformer' in architecture:
return {
'mitb5': f'_base_/models/{architecture}_b5.py',
# It's intended that <=b4 refers to b5 config
'mitb4': f'_base_/models/{architecture}_b5.py',
'mitb3': f'_base_/models/{architecture}_b5.py',
'r101v1c': f'_base_/models/{architecture}_r101.py',
}[backbone]
if 'daformer_' in architecture and 'mitb5' in backbone:
return f'_base_/models/{architecture}_mitb5.py'
if 'upernet' in architecture and 'mit' in backbone:
return f'_base_/models/{architecture}_mit.py'
assert 'mit' not in backbone or '-del' in backbone
return {
'dlv2': '_base_/models/deeplabv2_r50-d8.py',
'dlv2red': '_base_/models/deeplabv2red_r50-d8.py',
'dlv3p': '_base_/models/deeplabv3plus_r50-d8.py',
'da': '_base_/models/danet_r50-d8.py',
'isa': '_base_/models/isanet_r50-d8.py',
'uper': '_base_/models/upernet_r50.py',
}[architecture]
def get_pretraining_file(backbone):
if 'mitb5' in backbone:
return 'pretrained/mit_b5.pth'
if 'mitb4' in backbone:
return 'pretrained/mit_b4.pth'
if 'mitb3' in backbone:
return 'pretrained/mit_b3.pth'
if 'r101v1c' in backbone:
return 'open-mmlab://resnet101_v1c'
return {
'r50v1c': 'open-mmlab://resnet50_v1c',
'x50-32': 'open-mmlab://resnext50_32x4d',
'x101-32': 'open-mmlab://resnext101_32x4d',
's50': 'open-mmlab://resnest50',
's101': 'open-mmlab://resnest101',
's200': 'open-mmlab://resnest200',
}[backbone]
def get_backbone_cfg(backbone):
for i in [1, 2, 3, 4, 5]:
if backbone == f'mitb{i}':
return dict(type=f'mit_b{i}')
if backbone == f'mitb{i}-del':
return dict(_delete_=True, type=f'mit_b{i}')
return {
'r50v1c': {
'depth': 50
},
'r101v1c': {
'depth': 101
},
'x50-32': {
'type': 'ResNeXt',
'depth': 50,
'groups': 32,
'base_width': 4,
},
'x101-32': {
'type': 'ResNeXt',
'depth': 101,
'groups': 32,
'base_width': 4,
},
's50': {
'type': 'ResNeSt',
'depth': 50,
'stem_channels': 64,
'radix': 2,
'reduction_factor': 4,
'avg_down_stride': True
},
's101': {
'type': 'ResNeSt',
'depth': 101,
'stem_channels': 128,
'radix': 2,
'reduction_factor': 4,
'avg_down_stride': True
},
's200': {
'type': 'ResNeSt',
'depth': 200,
'stem_channels': 128,
'radix': 2,
'reduction_factor': 4,
'avg_down_stride': True,
},
}[backbone]
def update_decoder_in_channels(cfg, architecture, backbone):
cfg.setdefault('model', {}).setdefault('decode_head', {})
if 'dlv3p' in architecture and 'mit' in backbone:
cfg['model']['decode_head']['c1_in_channels'] = 64
if 'sfa' in architecture:
cfg['model']['decode_head']['in_channels'] = 512
return cfg
def setup_rcs(cfg, temperature, min_crop_ratio):
cfg.setdefault('data', {}).setdefault('train', {})
cfg['data']['train']['rare_class_sampling'] = dict(
min_pixels=3000, class_temp=temperature, min_crop_ratio=min_crop_ratio)
return cfg
def generate_experiment_cfgs(id):
def config_from_vars():
cfg = {
'_base_': ['_base_/default_runtime.py'],
'gpu_model': gpu_model,
'n_gpus': n_gpus
}
if seed is not None:
cfg['seed'] = seed
# Setup model config
architecture_mod = architecture
sync_crop_size_mod = sync_crop_size
inference_mod = inference
model_base = get_model_base(architecture_mod, backbone)
model_base_cfg = Config.fromfile(os.path.join('configs', model_base))
cfg['_base_'].append(model_base)
cfg['model'] = {
'pretrained': get_pretraining_file(backbone),
'backbone': get_backbone_cfg(backbone),
}
if 'sfa_' in architecture_mod:
cfg['model']['neck'] = dict(type='SegFormerAdapter')
if '_nodbn' in architecture_mod:
cfg['model'].setdefault('decode_head', {})
cfg['model']['decode_head']['norm_cfg'] = None
cfg = update_decoder_in_channels(cfg, architecture_mod, backbone)
hrda_ablation_opts = None
outer_crop_size = sync_crop_size_mod \
if sync_crop_size_mod is not None \
else (int(crop.split('x')[0]), int(crop.split('x')[1]))
if 'hrda1' in architecture_mod:
o = [e for e in architecture_mod.split('_') if 'hrda' in e][0]
hr_crop_size = (int((o.split('-')[1])), int((o.split('-')[1])))
hr_loss_w = float(o.split('-')[2])
hrda_ablation_opts = o.split('-')[3:]
cfg['model']['type'] = 'HRDAEncoderDecoder'
cfg['model']['scales'] = [1, 0.5]
cfg['model'].setdefault('decode_head', {})
cfg['model']['decode_head']['single_scale_head'] = model_base_cfg[
'model']['decode_head']['type']
cfg['model']['decode_head']['type'] = 'HRDAHead'
cfg['model']['hr_crop_size'] = hr_crop_size
cfg['model']['feature_scale'] = 0.5
cfg['model']['crop_coord_divisible'] = 8
cfg['model']['hr_slide_inference'] = True
cfg['model']['decode_head']['attention_classwise'] = True
cfg['model']['decode_head']['hr_loss_weight'] = hr_loss_w
if outer_crop_size == hr_crop_size:
# If the hr crop is smaller than the lr crop (hr_crop_size <
# outer_crop_size), there is direct supervision for the lr
# prediction as it is not fused in the region without hr
# prediction. Therefore, there is no need for a separate
# lr_loss.
cfg['model']['decode_head']['lr_loss_weight'] = hr_loss_w
# If the hr crop covers the full lr crop region, calculating
# the FD loss on both scales stabilizes the training for
# difficult classes.
cfg['model']['feature_scale'] = 'all' if '_fd' in uda else 0.5
# HRDA Ablations
if hrda_ablation_opts is not None:
for o in hrda_ablation_opts:
if o == 'fixedatt':
# Average the predictions from both scales instead of
# learning a scale attention.
cfg['model']['decode_head']['fixed_attention'] = 0.5
elif o == 'nooverlap':
# Don't use overlapping slide inference for the hr
# prediction.
cfg['model']['hr_slide_overlapping'] = False
elif o == 'singleatt':
# Use the same scale attention for all class channels.
cfg['model']['decode_head']['attention_classwise'] = False
elif o == 'blurhr':
# Use an upsampled lr crop (blurred) for the hr crop
cfg['model']['blur_hr_crop'] = True
elif o == 'samescale':
# Use the same scale/resolution for both crops.
cfg['model']['scales'] = [1, 1]
cfg['model']['feature_scale'] = 1
elif o[:2] == 'sc':
cfg['model']['scales'] = [1, float(o[2:])]
if not isinstance(cfg['model']['feature_scale'], str):
cfg['model']['feature_scale'] = float(o[2:])
else:
raise NotImplementedError(o)
# Setup inference mode
if inference_mod == 'whole' or crop == '2048x1024':
assert model_base_cfg['model']['test_cfg']['mode'] == 'whole'
elif inference_mod == 'slide':
cfg['model'].setdefault('test_cfg', {})
cfg['model']['test_cfg']['mode'] = 'slide'
cfg['model']['test_cfg']['batched_slide'] = True
crsize = sync_crop_size_mod if sync_crop_size_mod is not None \
else [int(e) for e in crop.split('x')]
cfg['model']['test_cfg']['stride'] = [e // 2 for e in crsize]
cfg['model']['test_cfg']['crop_size'] = crsize
architecture_mod += '_sl'
else:
raise NotImplementedError(inference_mod)
# Setup UDA config
if uda == 'target-only':
cfg['_base_'].append(f'_base_/datasets/{target}_{crop}.py')
elif uda == 'source-only':
cfg['_base_'].append(
f'_base_/datasets/{source}_to_{target}_{crop}.py')
else:
cfg['_base_'].append(
f'_base_/datasets/uda_{source}_to_{target}_{crop}.py')
cfg['_base_'].append(f'_base_/uda/{uda}.py')
cfg['data'] = dict(
samples_per_gpu=batch_size,
workers_per_gpu=workers_per_gpu,
train={})
# DAFormer legacy cropping that only works properly if the training
# crop has the height of the (resized) target image.
if ('dacs' in uda or mask_mode is not None) and plcrop in [True, 'v1']:
cfg.setdefault('uda', {})
cfg['uda']['pseudo_weight_ignore_top'] = 15
cfg['uda']['pseudo_weight_ignore_bottom'] = 120
# Generate mask of the pseudo-label margins in the data loader before
# the image itself is cropped to ensure that the pseudo-label margins
# are only masked out if the training crop is at the periphery of the
# image.
if ('dacs' in uda or mask_mode is not None) and plcrop == 'v2':
cfg['data']['train'].setdefault('target', {})
cfg['data']['train']['target']['crop_pseudo_margins'] = \
[30, 240, 30, 30]
if 'dacs' in uda and rcs_T is not None:
cfg = setup_rcs(cfg, rcs_T, rcs_min_crop)
if 'dacs' in uda and sync_crop_size_mod is not None:
cfg.setdefault('data', {}).setdefault('train', {})
cfg['data']['train']['sync_crop_size'] = sync_crop_size_mod
if mask_mode is not None:
cfg.setdefault('uda', {})
cfg['uda']['mask_mode'] = mask_mode
cfg['uda']['mask_alpha'] = mask_alpha
cfg['uda']['mask_pseudo_threshold'] = mask_pseudo_threshold
cfg['uda']['mask_lambda'] = mask_lambda
cfg['uda']['mask_generator'] = dict(
type='block',
mask_ratio=mask_ratio,
mask_block_size=mask_block_size,
_delete_=True)
# Setup optimizer and schedule
if 'dacs' in uda or 'minent' in uda or 'advseg' in uda:
cfg['optimizer_config'] = None # Don't use outer optimizer
cfg['_base_'].extend(
[f'_base_/schedules/{opt}.py', f'_base_/schedules/{schedule}.py'])
cfg['optimizer'] = {'lr': lr}
cfg['optimizer'].setdefault('paramwise_cfg', {})
cfg['optimizer']['paramwise_cfg'].setdefault('custom_keys', {})
opt_param_cfg = cfg['optimizer']['paramwise_cfg']['custom_keys']
if pmult:
opt_param_cfg['head'] = dict(lr_mult=10.)
if 'mit' in backbone:
opt_param_cfg['pos_block'] = dict(decay_mult=0.)
opt_param_cfg['norm'] = dict(decay_mult=0.)
# Setup runner
cfg['runner'] = dict(type='IterBasedRunner', max_iters=iters)
cfg['checkpoint_config'] = dict(
by_epoch=False, interval=iters, max_keep_ckpts=1)
cfg['evaluation'] = dict(interval=iters // 10, metric='mIoU')
# Construct config name
uda_mod = uda
if 'dacs' in uda and rcs_T is not None:
uda_mod += f'_rcs{rcs_T}'
if rcs_min_crop != 0.5:
uda_mod += f'-{rcs_min_crop}'
if 'dacs' in uda and sync_crop_size_mod is not None:
uda_mod += f'_sf{sync_crop_size_mod[0]}x{sync_crop_size_mod[1]}'
if 'dacs' in uda or mask_mode is not None:
if not plcrop:
pass
elif plcrop in [True, 'v1']:
uda_mod += '_cpl'
elif plcrop[0] == 'v':
uda_mod += f'_cpl{plcrop[1:]}'
else:
raise NotImplementedError(plcrop)
if mask_mode is not None:
uda_mod += f'_m{mask_block_size}-' \
f'{mask_ratio}-'
if mask_alpha != 'same':
uda_mod += f'a{mask_alpha}-'
if mask_pseudo_threshold != 'same':
uda_mod += f'p{mask_pseudo_threshold}-'
uda_mod += {
'separate': 'sep',
'separateaug': 'spa',
'separatesrc': 'sps',
'separatesrcaug': 'spsa',
'separatetrg': 'spt',
'separatetrgaug': 'spta',
}[mask_mode]
if mask_lambda != 1:
uda_mod += f'-w{mask_lambda}'
crop_name = f'_{crop}' if crop != '512x512' else ''
cfg['name'] = f'{source}2{target}{crop_name}_{uda_mod}_' \
f'{architecture_mod}_{backbone}_{schedule}'
if opt != 'adamw':
cfg['name'] += f'_{opt}'
if lr != 0.00006:
cfg['name'] += f'_{lr}'
if not pmult:
cfg['name'] += f'_pm{pmult}'
cfg['exp'] = id
cfg['name_dataset'] = f'{source}2{target}{crop_name}'
cfg['name_architecture'] = f'{architecture_mod}_{backbone}'
cfg['name_encoder'] = backbone
cfg['name_decoder'] = architecture_mod
cfg['name_uda'] = uda_mod
cfg['name_opt'] = f'{opt}_{lr}_pm{pmult}_{schedule}' \
f'_{n_gpus}x{batch_size}_{iters // 1000}k'
if seed is not None:
cfg['name'] += f'_s{seed}'
cfg['name'] = cfg['name'].replace('.', '').replace('True', 'T') \
.replace('False', 'F').replace('None', 'N').replace('[', '')\
.replace(']', '').replace(',', 'j').replace(' ', '') \
.replace('cityscapes', 'cs') \
.replace('synthia', 'syn') \
.replace('darkzurich', 'dzur')
return cfg
# -------------------------------------------------------------------------
# Set some defaults
# -------------------------------------------------------------------------
cfgs = []
n_gpus = 1
batch_size = 2
iters = 40000
opt, lr, schedule, pmult = 'adamw', 0.00006, 'poly10warm', True
crop = '512x512'
gpu_model = 'NVIDIAGeForceRTX2080Ti'
datasets = [
('gta', 'cityscapes'),
]
architecture = None
workers_per_gpu = 1
rcs_T = None
rcs_min_crop = 0.5
plcrop = False
inference = 'whole'
sync_crop_size = None
mask_mode = None
mask_alpha = 'same'
mask_pseudo_threshold = 'same'
mask_lambda = 1
mask_block_size = None
mask_ratio = 0
# -------------------------------------------------------------------------
# MIC with HRDA for Different UDA Benchmarks (Table 2)
# -------------------------------------------------------------------------
# yapf: disable
if id == 80:
seeds = [0, 1, 2]
architecture, backbone = 'hrda1-512-0.1_daformer_sepaspp', 'mitb5'
uda, rcs_T = 'dacs_a999_fdthings', 0.01
crop, rcs_min_crop = '1024x1024', 0.5 * (2 ** 2)
inference = 'slide'
mask_block_size, mask_ratio = 64, 0.7
for source, target, mask_mode in [
('gtaHR', 'cityscapesHR', 'separatetrgaug'),
('synthiaHR', 'cityscapesHR', 'separatetrgaug'),
('cityscapesHR', 'acdcHR', 'separate'),
('cityscapesHR', 'darkzurichHR', 'separate'),
]:
for seed in seeds:
gpu_model = 'NVIDIATITANRTX'
# plcrop is only necessary for Cityscapes as target domains
# ACDC and DarkZurich have no rectification artifacts.
plcrop = 'v2' if 'cityscapes' in target else False
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# MIC with Further UDA Methods (Table 1)
# -------------------------------------------------------------------------
elif id == 81:
seeds = [0, 1, 2]
# opt, lr, schedule, pmult
sgd = ('sgd', 0.0025, 'poly10warm', False)
adamw = ('adamw', 0.00006, 'poly10warm', True)
# uda, rcs_T, plcrop, opt_hp
uda_advseg = ('advseg', None, False, *sgd)
uda_minent = ('minent', None, False, *sgd)
uda_dacs = ('dacs', None, False, *adamw)
uda_daformer = ('dacs_a999_fdthings', 0.01, True, *adamw)
uda_hrda = ('dacs_a999_fdthings', 0.01, 'v2', *adamw)
mask_mode, mask_ratio = 'separatetrgaug', 0.7
for architecture, backbone, uda_hp in [
('dlv2red', 'r101v1c', uda_advseg),
('dlv2red', 'r101v1c', uda_minent),
('dlv2red', 'r101v1c', uda_dacs),
('dlv2red', 'r101v1c', uda_daformer),
('hrda1-512-0.1_dlv2red', 'r101v1c', uda_hrda),
('daformer_sepaspp', 'mitb5', uda_daformer),
# ('hrda1-512-0.1_daformer_sepaspp', 'mibt5', uda_hrda), # already run in exp 80
]:
if 'hrda' in architecture:
source, target, crop = 'gtaHR', 'cityscapesHR', '1024x1024'
rcs_min_crop = 0.5 * (2 ** 2)
gpu_model = 'NVIDIATITANRTX'
inference = 'slide'
mask_block_size = 64
else:
source, target, crop = 'gta', 'cityscapes', '512x512'
rcs_min_crop = 0.5
gpu_model = 'NVIDIAGeForceRTX2080Ti'
inference = 'whole'
# Use half the patch size when training with half resolution
mask_block_size = 32
for seed in seeds:
uda, rcs_T, plcrop, opt, lr, schedule, pmult = uda_hp
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# MIC Domain Study (Table 6)
# -------------------------------------------------------------------------
elif id == 82:
seeds = [0, 1, 2]
architecture, backbone = 'hrda1-512-0.1_daformer_sepaspp', 'mitb5'
uda, rcs_T = 'dacs_a999_fdthings', 0.01
crop, rcs_min_crop = '1024x1024', 0.5 * (2 ** 2)
inference = 'slide'
mask_block_size, mask_ratio = 64, 0.7
for source, target, mask_mode in [
('gtaHR', 'cityscapesHR', 'separatesrcaug'),
# ('gtaHR', 'cityscapesHR', 'separatetrgaug'), # already run in exp 80
('gtaHR', 'cityscapesHR', 'separateaug'),
('cityscapesHR', 'acdcHR', 'separatesrc'),
('cityscapesHR', 'acdcHR', 'separatetrg'),
# ('cityscapesHR', 'acdcHR', 'separate'), # already run in exp 80
]:
for seed in seeds:
gpu_model = 'NVIDIATITANRTX'
# plcrop is only necessary for Cityscapes as target domains
# ACDC and DarkZurich have no rectification artifacts.
plcrop = 'v2' if 'cityscapes' in target else False
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# MIC Ablation Study (Table 7)
# -------------------------------------------------------------------------
elif id == 83:
seeds = [0, 1, 2]
source, target = 'gta', 'cityscapes'
architecture, backbone = 'daformer_sepaspp', 'mitb5'
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, True
masking = [
# mode, b, r, a, tau
# ('separatetrgaug', 32, 0.7, 'same'), # complete; already run in exp 81
('separatetrg', 32, 0.7, 'same', 'same'), # w/o color augmentation
('separatetrgaug', 32, 0, 'same', 'same'), # w/o masking
('separatetrgaug', 32, 0.7, 0, 'same'), # w/o EMA teacher
('separatetrgaug', 32, 0.7, 'same', None), # w/o pseudo label confidence weight
]
for (mask_mode, mask_block_size, mask_ratio, mask_alpha, mask_pseudo_threshold), seed in \
itertools.product(masking, seeds):
if mask_alpha != 'same' or mask_pseudo_threshold != 'same':
# Needs more gpu memory due to additional teacher
gpu_model = 'NVIDIATITANRTX'
else:
gpu_model = 'NVIDIAGeForceRTX2080Ti'
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# MIC Block Size/Ratio (Table 8)
# -------------------------------------------------------------------------
elif id == 84:
seeds = [0, 1, 2]
source, target = 'gta', 'cityscapes'
architecture, backbone = 'daformer_sepaspp', 'mitb5'
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, True
# The patch sizes are divided by 2 here, as DAFormer uses half resolution
block_sizes = [16, 32, 64, 128]
ratios = [0.3, 0.5, 0.7, 0.9]
mask_mode = 'separatetrgaug'
for mask_block_size, mask_ratio, seed in \
itertools.product(block_sizes, ratios, seeds):
if mask_block_size == 32 and mask_ratio == 0.7:
continue # already run in exp 81
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# MIC for Supervised Training (Table 9 column mIoU_Superv.)
# -------------------------------------------------------------------------
elif id == 85:
seeds = [0, 1, 2]
architecture, backbone = 'daformer_sepaspp', 'mitb5'
# Hack for supervised target training with MIC
source, target, uda = 'cityscapes', 'cityscapes', 'dacs_srconly'
mask_mode, mask_block_size, mask_ratio = 'separatesrc', 32, 0.7
for seed in seeds:
cfg = config_from_vars()
cfgs.append(cfg)
else:
raise NotImplementedError('Unknown id {}'.format(id))
return cfgs