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experiments.py
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# Adapted from: https://github.com/lhoyer/DAFormer
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'pipa{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 'pipa' 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 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 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
# 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:
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)
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('cityscapes', 'cs') \
.replace('synthia', 'syn')
return cfg
# -------------------------------------------------------------------------
# Set some defaults
# -------------------------------------------------------------------------
cfgs = []
n_gpus = 1
batch_size = 2
iters = 60000
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
# -------------------------------------------------------------------------
# Final HRDA (Table 1)
# -------------------------------------------------------------------------
# yapf: disable
if id == 40:
seeds = [73]
# rcs_min_crop=2.0 for HRDA instead of rcs_min_crop=0.5 for
# DAFormer as HRDA is trained with twice the input resolution, which
# means that the inputs have 4 times more pixels.
# source, target, crop, rcs_min_crop
#gta2cs = ('gtaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2))
syn2cs = ('synthiaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2))
dec, backbone = 'daformer_sepaspp', 'mitb5'
# dec, backbone = 'dlv2red', 'r101v1c'
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, 'v2'
inference = 'slide'
for dataset, architecture, sync_crop_size in [
#(gta2cs, f'hrda1-512-0.1_{dec}', None),
(syn2cs, f'hrda1-512-0.1_{dec}', None),
]:
for seed in seeds:
source, target, crop, rcs_min_crop = dataset
gpu_model = 'NVIDIATITANRTX'
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# HRDA with Further UDA Methods (Table 2)
# -------------------------------------------------------------------------
elif id == 41:
seeds = [0, 1, 2]
# opt, lr, schedule, pmult
sgd = ('sgd', 0.0025, 'poly10warm', False)
adamw = ('adamw', 0.00006, 'poly10warm', True)
hrda = 'hrda1-512-0.1'
for source, target, crop, architecture, backbone, uda, opt_hp in [
('gtaCAug', 'cityscapes', '512x512', 'dlv2red', 'r101v1c', 'advseg', sgd),
('gtaCAugHR', 'cityscapesHR', '1024x1024', f'{hrda}_dlv2red', 'r101v1c', 'advseg', sgd),
('gtaCAug', 'cityscapes', '512x512', 'dlv2red', 'r101v1c', 'minent', sgd),
('gtaCAugHR', 'cityscapesHR', '1024x1024', f'{hrda}_dlv2red', 'r101v1c', 'minent', sgd),
('gta', 'cityscapes', '512x512', 'dlv2red', 'r101v1c', 'dacs', adamw),
('gtaHR', 'cityscapesHR', '1024x1024', f'{hrda}_dlv2red', 'r101v1c', 'dacs', adamw),
('gta', 'cityscapes', '512x512', 'dlv2red', 'r101v1c', 'daformer_uda', adamw),
('gtaHR', 'cityscapesHR', '1024x1024', f'{hrda}_dlv2red', 'r101v1c', 'daformer_uda', adamw),
]:
if uda == 'daformer_uda':
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, 'v2'
if crop == '1024x1024':
rcs_min_crop = 0.5 * (2 ** 2)
for seed in seeds:
opt, lr, schedule, pmult = opt_hp
gpu_model = 'NVIDIATITANRTX' if 'HR' in source \
else 'NVIDIAGeForceRTX2080Ti'
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# Crop Size LR/HR Study for UDA (Figure 4 Left)
# -------------------------------------------------------------------------
elif id == 42:
seeds = [0, 1, 2]
architecture, backbone = 'daformer_sepaspp', 'mitb5'
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, 'v2'
inference = 'slide'
datasets = [
# source, target, crop, rcs_min_crop, sync_crop_size
('gta', 'cityscapes', '512x512', 0.5, (128, 128)),
('gta', 'cityscapes', '512x512', 0.5, (256, 256)),
('gta', 'cityscapes', '512x512', 0.5, (384, 384)),
('gta', 'cityscapes', '512x512', 0.5, None),
('gtaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2), (256, 256)),
('gtaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2), (512, 512)),
('gtaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2), (768, 768)),
]
for (source, target, crop, rcs_min_crop, sync_crop_size), seed in \
itertools.product(datasets, seeds):
s = sync_crop_size[0] if sync_crop_size is not None \
else int(crop.split('x')[0])
gpu_model = 'NVIDIATITANRTX' if s >= 768 \
else 'NVIDIAGeForceRTX2080Ti'
cfg = config_from_vars()
if crop == '1024x1024' and sync_crop_size == (256, 256):
# Too memory intensive for small GPU during evaluation
cfg['model']['test_cfg']['batched_slide'] = False
cfgs.append(cfg)
# -------------------------------------------------------------------------
# Crop Size LR/HR Study for Oracle (Figure 4 Right)
# -------------------------------------------------------------------------
elif id == 43:
seeds = [0, 1, 2]
architecture, backbone = 'daformer_sepaspp', 'mitb5'
uda = 'target-only'
inference = 'slide'
datasets = [
# source, target, crop
('', 'cityscapes', '128x128'),
('', 'cityscapes', '256x256'),
('', 'cityscapes', '384x384'),
('', 'cityscapes', '512x512'),
('', 'cityscapesHR', '256x256'),
('', 'cityscapesHR', '512x512'),
('', 'cityscapesHR', '768x768'),
]
for (source, target, crop), seed in \
itertools.product(datasets, seeds):
s = int(crop.split('x')[0])
gpu_model = 'NVIDIATITANRTX' if s >= 768 \
else 'NVIDIAGeForceRTX2080Ti'
cfg = config_from_vars()
cfgs.append(cfg)
# -------------------------------------------------------------------------
# HRDA Crop and Component Ablations (Table 3 - 7)
# -------------------------------------------------------------------------
elif id == 44:
seeds = [0, 1, 2]
gta2cs = ('gtaHR', 'cityscapesHR', '1024x1024', 0.5 * (2 ** 2))
gta2csLR = ('gta', 'cityscapes', '512x512', 0.5)
dec, backbone = 'daformer_sepaspp', 'mitb5'
uda, rcs_T, plcrop = 'dacs_a999_fdthings', 0.01, 'v2'
inference = 'slide'
for dataset, architecture, sync_crop_size in [
# --------------------------
# Table 3: Context Crop Size
# --------------------------
# (gta2csLR, dec, (256, 256)), # LR0.5; already run in exp 42
# (gta2cs, dec, (512, 512)), # HR0.5; already run in exp 42
(gta2cs, f'hrda1-512-0.1_{dec}', (512, 512)), # LR0.5+HR0.5
(gta2cs, f'hrda1-512-0.1_{dec}', (768, 768)), # LR0.75+HR0.5
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # LR1.0+HR0.5; already run in exp 40
# -------------------------
# Table 4: Detail Crop Size
# -------------------------
# (gta2csLR, dec, None), # LR1.0; already run in exp 42
# (gta2cs, dec, (256, 256)), # HR0.25; already run in exp 42
(gta2cs, f'hrda1-256-0.1_{dec}', None), # LR1.0+HR0.25
(gta2cs, f'hrda1-384-0.1_{dec}', None), # LR1.0+HR0.375
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # LR1.0+HR0.5; already run in exp 40
# ---------------------------------
# Table 5: Comparison with Naive HR
# ---------------------------------
# (gta2cs, dec, (768, 768)), # HR0.75; already run in exp 42
(gta2cs, f'hrda1-384-0.1_{dec}', (768, 768)), # LR0.75+HR0.375
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # LR1.0+HR0.5; already run in exp 40
# -----------------------------
# Table 6: Detail Crop Variants
# -----------------------------
# (gta2csLR, dec, None), # LR1.0; already run in exp 42
(gta2csLR, f'hrda1-256-0.1-samescale_{dec}', None), # LR1.0+LR0.5
(gta2cs, f'hrda1-512-0.1-blurhr_{dec}', None), # LR1.0+Up-LR0.5
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # already run in exp 40
# ----------------------------
# Table 7: Component Ablations
# ----------------------------
# (gta2cs, dec, (512, 512)), # Row 1; already run in exp 42
# (gta2csLR, dec, None), # Row 2; already run in exp 42
(gta2cs, f'hrda1-512-0-fixedatt-nooverlap_{dec}', None), # Row 3
(gta2cs, f'hrda1-512-0-nooverlap_{dec}', None), # Row 4
(gta2cs, f'hrda1-512-0_{dec}', None), # Row 5
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # Row 6; already run in exp 40
# -----------------------------
# Figure S1: Detail Loss Weight
# -----------------------------
# (gta2cs, f'hrda1-512-0_{dec}', None), # already run above for Tab. 7
# (gta2cs, f'hrda1-512-0.1_{dec}', None), # already run in exp 40
(gta2cs, f'hrda1-512-0.2_{dec}', None),
(gta2cs, f'hrda1-512-0.3_{dec}', None),
(gta2cs, f'hrda1-512-0.4_{dec}', None),
(gta2cs, f'hrda1-512-0.5_{dec}', None),
(gta2cs, f'hrda1-512-0.6_{dec}', None),
(gta2cs, f'hrda1-512-0.7_{dec}', None),
# ---------------------------------
# Table S1: Context Crop Resolution
# ---------------------------------
# (gta2cs, dec, (512, 512)), # No context crop; already run in exp42
(gta2cs, f'hrda1-512-0.1-sc0.25_{dec}', (512, 512)), # s_c=4
# (gta2cs, f'hrda1-512-0.1_{dec}', (512, 512)), # s_c=2; already run above for Tab. 3
(gta2cs, f'hrda1-512-0.1-sc0.75_{dec}', (512, 512)), # s_c=1.33
]:
for seed in seeds:
source, target, crop, rcs_min_crop = dataset
gpu_model = 'NVIDIATITANRTX'
cfg = config_from_vars()
cfgs.append(cfg)
else:
raise NotImplementedError('Unknown id {}'.format(id))
return cfgs