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mask2former_r50_8xb2_e50_instance.py
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_base_ = ['configs/base.py']
CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'
]
PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70), (0, 0, 192),
(250, 170, 30), (100, 170, 30), (220, 220, 0), (175, 116, 175),
(250, 0, 30), (165, 42, 42), (255, 77, 255), (0, 226, 252),
(182, 182, 255), (0, 82, 0), (120, 166, 157), (110, 76, 0),
(174, 57, 255), (199, 100, 0), (72, 0, 118), (255, 179, 240),
(0, 125, 92), (209, 0, 151), (188, 208, 182), (0, 220, 176),
(255, 99, 164), (92, 0, 73), (133, 129, 255), (78, 180, 255),
(0, 228, 0), (174, 255, 243), (45, 89, 255), (134, 134, 103),
(145, 148, 174), (255, 208, 186), (197, 226, 255), (171, 134, 1),
(109, 63, 54), (207, 138, 255), (151, 0, 95), (9, 80, 61),
(84, 105, 51), (74, 65, 105), (166, 196, 102), (208, 195, 210),
(255, 109, 65), (0, 143, 149), (179, 0, 194), (209, 99, 106),
(5, 121, 0), (227, 255, 205), (147, 186, 208), (153, 69, 1),
(3, 95, 161), (163, 255, 0), (119, 0, 170), (0, 182, 199),
(0, 165, 120), (183, 130, 88), (95, 32, 0), (130, 114, 135),
(110, 129, 133), (166, 74, 118), (219, 142, 185), (79, 210, 114),
(178, 90, 62), (65, 70, 15), (127, 167, 115), (59, 105, 106),
(142, 108, 45), (196, 172, 0), (95, 54, 80), (128, 76, 255),
(201, 57, 1), (246, 0, 122), (191, 162, 208)]
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=80,
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=80,
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(
instance_on=True,
max_per_image=100,
),
pretrained=True,
)
# dataset settings
data_root = 'database/coco/'
image_size = (1024, 1024)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
pad_cfg = dict(img=(128, 128, 128), masks=0, seg=255)
train_pipeline = [
dict(type='MMRandomFlip', flip_ratio=0.5),
dict(
type='MMResize',
img_scale=image_size,
ratio_range=(0.1, 2.0),
multiscale_mode='range',
keep_ratio=True),
dict(
type='MMRandomCrop',
crop_size=image_size,
crop_type='absolute',
recompute_bbox=True,
allow_negative_crop=True),
dict(
type='MMFilterAnnotations', min_gt_bbox_wh=(1e-5, 1e-5), by_mask=True),
dict(type='MMPad', size=image_size, pad_val=pad_cfg),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'],
meta_keys=('filename', 'ori_filename', 'ori_shape', 'ori_img_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg')),
]
test_pipeline = [
# dict(type='LoadImageFromFile'),
dict(
type='MMMultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='MMResize', keep_ratio=True),
dict(type='MMRandomFlip'),
dict(type='MMPad', size_divisor=32, pad_val=pad_cfg),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'ori_img_shape', 'img_shape', 'pad_shape',
'scale_factor', 'flip', 'flip_direction',
'img_norm_cfg')),
])
]
train_dataset = dict(
type='DetDataset',
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
# seg_prefix=data_root + 'panoptic_train2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True)
],
classes=CLASSES,
filter_empty_gt=True,
iscrowd=False,
),
pipeline=train_pipeline)
val_dataset = dict(
type='DetDataset',
imgs_per_gpu=1,
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True)
],
classes=CLASSES,
test_mode=True,
iscrowd=True,
),
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.),
})
optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
total_epochs = 50
# learning policy
lr_config = dict(
policy='step',
gamma=0.1,
by_epoch=False,
step=[327778, 355092],
warmup='linear',
warmup_by_epoch=False,
warmup_ratio=1.0, # no warmup
warmup_iters=10)
checkpoint_config = dict(interval=1)
eval_config = dict(initial=False, interval=1, gpu_collect=False)
eval_pipelines = [
dict(
mode='test',
dist_eval=True,
evaluators=[
dict(type='CocoDetectionEvaluator', classes=CLASSES),
dict(type='CocoMaskEvaluator', classes=CLASSES)
],
)
]