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mask2former_r50_8xb2_e127_semantic.py
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mask2former_r50_8xb2_e127_semantic.py
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_base_ = ['configs/base.py']
CLASSES = [
'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road, route',
'bed', 'window ', 'grass', 'cabinet', 'sidewalk, pavement', 'person',
'earth, ground', 'door', 'table', 'mountain, mount', 'plant', 'curtain',
'chair', 'car', 'water', 'painting, picture', 'sofa', 'shelf', 'house',
'sea', 'mirror', 'rug', 'field', 'armchair', 'seat', 'fence', 'desk',
'rock, stone', 'wardrobe, closet, press', 'lamp', 'tub', 'rail', 'cushion',
'base, pedestal, stand', 'box', 'column, pillar', 'signboard, sign',
'chest of drawers, chest, bureau, dresser', 'counter', 'sand', 'sink',
'skyscraper', 'fireplace', 'refrigerator, icebox',
'grandstand, covered stand', 'path', 'stairs', 'runway',
'case, display case, showcase, vitrine',
'pool table, billiard table, snooker table', 'pillow',
'screen door, screen', 'stairway, staircase', 'river', 'bridge, span',
'bookcase', 'blind, screen', 'coffee table',
'toilet, can, commode, crapper, pot, potty, stool, throne', 'flower',
'book', 'hill', 'bench', 'countertop', 'stove', 'palm, palm tree',
'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
'arcade machine', 'hovel, hut, hutch, shack, shanty', 'bus', 'towel',
'light', 'truck', 'tower', 'chandelier', 'awning, sunshade, sunblind',
'street lamp', 'booth', 'tv', 'plane', 'dirt track', 'clothes', 'pole',
'land, ground, soil',
'bannister, banister, balustrade, balusters, handrail',
'escalator, moving staircase, moving stairway',
'ottoman, pouf, pouffe, puff, hassock', 'bottle',
'buffet, counter, sideboard',
'poster, posting, placard, notice, bill, card', 'stage', 'van', 'ship',
'fountain',
'conveyer belt, conveyor belt, conveyer, conveyor, transporter', 'canopy',
'washer, automatic washer, washing machine', 'plaything, toy', 'pool',
'stool', 'barrel, cask', 'basket, handbasket', 'falls', 'tent', 'bag',
'minibike, motorbike', 'cradle', 'oven', 'ball', 'food, solid food',
'step, stair', 'tank, storage tank', 'trade name', 'microwave', 'pot',
'animal', 'bicycle', 'lake', 'dishwasher', 'screen', 'blanket, cover',
'sculpture', 'hood, exhaust hood', 'sconce', 'vase', 'traffic light',
'tray', 'trash can', 'fan', 'pier', 'crt screen', 'plate', 'monitor',
'bulletin board', 'shower', 'radiator', 'glass, drinking glass', 'clock',
'flag'
]
PALETTE = [(120, 120, 120), (180, 120, 120), (6, 230, 230), (80, 50, 50),
(4, 200, 3), (120, 120, 80), (140, 140, 140), (204, 5, 255),
(230, 230, 230), (4, 250, 7), (224, 5, 255), (235, 255, 7),
(150, 5, 61), (120, 120, 70), (8, 255, 51), (255, 6, 82),
(143, 255, 140), (204, 255, 4), (255, 51, 7), (204, 70, 3),
(0, 102, 200), (61, 230, 250), (255, 6, 51), (11, 102, 255),
(255, 7, 71), (255, 9, 224), (9, 7, 230), (220, 220, 220),
(255, 9, 92), (112, 9, 255), (8, 255, 214), (7, 255, 224),
(255, 184, 6), (10, 255, 71), (255, 41, 10), (7, 255, 255),
(224, 255, 8), (102, 8, 255), (255, 61, 6), (255, 194, 7),
(255, 122, 8), (0, 255, 20), (255, 8, 41), (255, 5, 153),
(6, 51, 255), (235, 12, 255), (160, 150, 20), (0, 163, 255),
(140, 140, 140), (250, 10, 15), (20, 255, 0), (31, 255, 0),
(255, 31, 0), (255, 224, 0), (153, 255, 0), (0, 0, 255),
(255, 71, 0), (0, 235, 255), (0, 173, 255), (31, 0, 255),
(11, 200, 200), (255, 82, 0), (0, 255, 245), (0, 61, 255),
(0, 255, 112), (0, 255, 133), (255, 0, 0), (255, 163, 0),
(255, 102, 0), (194, 255, 0), (0, 143, 255), (51, 255, 0),
(0, 82, 255), (0, 255, 41), (0, 255, 173), (10, 0, 255),
(173, 255, 0), (0, 255, 153), (255, 92, 0), (255, 0, 255),
(255, 0, 245), (255, 0, 102), (255, 173, 0), (255, 0, 20),
(255, 184, 184), (0, 31, 255), (0, 255, 61), (0, 71, 255),
(255, 0, 204), (0, 255, 194), (0, 255, 82), (0, 10, 255),
(0, 112, 255), (51, 0, 255), (0, 194, 255), (0, 122, 255),
(0, 255, 163), (255, 153, 0), (0, 255, 10), (255, 112, 0),
(143, 255, 0), (82, 0, 255), (163, 255, 0), (255, 235, 0),
(8, 184, 170), (133, 0, 255), (0, 255, 92), (184, 0, 255),
(255, 0, 31), (0, 184, 255), (0, 214, 255), (255, 0, 112),
(92, 255, 0), (0, 224, 255), (112, 224, 255), (70, 184, 160),
(163, 0, 255), (153, 0, 255), (71, 255, 0), (255, 0, 163),
(255, 204, 0), (255, 0, 143), (0, 255, 235), (133, 255, 0),
(255, 0, 235), (245, 0, 255), (255, 0, 122), (255, 245, 0),
(10, 190, 212), (214, 255, 0), (0, 204, 255), (20, 0, 255),
(255, 255, 0), (0, 153, 255), (0, 41, 255), (0, 255, 204),
(41, 0, 255), (41, 255, 0), (173, 0, 255), (0, 245, 255),
(71, 0, 255), (122, 0, 255), (0, 255, 184), (0, 92, 255),
(184, 255, 0), (0, 133, 255), (255, 214, 0), (25, 194, 194),
(102, 255, 0), (92, 0, 255)]
model = dict(
type='Mask2Former',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3, 4),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True),
head=dict(
type='Mask2FormerHead',
pixel_decoder=dict(
input_stride=[4, 8, 16, 32],
input_channel=[256, 512, 1024, 2048],
transformer_dropout=0.0,
transformer_nheads=8,
transformer_dim_feedforward=1024,
transformer_enc_layers=6,
conv_dim=256,
mask_dim=256,
norm='GN',
transformer_in_features=[1, 2, 3],
common_stride=4,
),
transformer_decoder=dict(
in_channels=256,
num_classes=150,
hidden_dim=256,
num_queries=100,
nheads=8,
dim_feedforward=2048,
dec_layers=9,
pre_norm=False,
mask_dim=256,
enforce_input_project=False,
),
num_things_classes=150,
num_stuff_classes=0,
),
train_cfg=dict(
class_weight=2.0,
mask_weight=5.0,
dice_weight=5.0,
deep_supervision=True,
dec_layers=10,
num_points=12554,
no_object_weight=0.1,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
),
test_cfg=dict(
semantic_on=True,
max_per_image=100,
),
pretrained=True,
)
data_root = '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='MMResize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
dict(type='SegRandomCrop', crop_size=crop_size),
dict(type='MMRandomFlip', flip_ratio=0.5),
dict(type='MMPhotoMetricDistortion'),
dict(type='MMPad', size=crop_size),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_semantic_seg'],
meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'flip', 'flip_direction',
'img_norm_cfg')),
]
test_pipeline = [
dict(
type='MMMultiScaleFlipAug',
img_scale=(2048, 512),
flip=False,
transforms=[
dict(type='MMResize', keep_ratio=True),
dict(type='MMRandomFlip'),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg')),
])
]
train_dataset = dict(
type='SegDataset',
data_source=dict(
type='SegSourceRaw',
cache_on_the_fly=True,
img_root=data_root + 'images/training',
label_root=data_root + 'annotations/training',
reduce_zero_label=True,
classes=CLASSES,
),
pipeline=train_pipeline)
val_dataset = dict(
type='SegDataset',
imgs_per_gpu=1,
data_source=dict(
type='SegSourceRaw',
cache_on_the_fly=True,
img_root=data_root + 'images/validation',
label_root=data_root + 'annotations/validation',
reduce_zero_label=True,
classes=CLASSES,
),
pipeline=test_pipeline)
data = dict(
imgs_per_gpu=2, workers_per_gpu=2, train=train_dataset, val=val_dataset)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
# optimizer
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.05,
eps=1e-8,
betas=(0.9, 0.999),
paramwise_options={
'backbone': dict(lr_mult=0.1),
'query_embed': dict(weight_decay=0.),
'query_feat': dict(weight_decay=0.),
'level_embed': dict(weight_decay=0.),
'norm': dict(weight_decay=0.),
})
# it seems grad clip not influence result
# optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
total_epochs = 127
lr_config = dict(
policy='Poly',
min_lr=0,
warmup='linear',
warmup_iters=1,
warmup_ratio=1e-4,
warmup_by_epoch=False,
by_epoch=False,
power=0.9)
checkpoint_config = dict(interval=1)
eval_config = dict(initial=False, interval=1, gpu_collect=False)
eval_pipelines = [
dict(
mode='test',
evaluators=[
dict(
type='SegmentationEvaluator',
classes=CLASSES,
ignore_index=255,
metric_names=['mIoU'])
],
)
]