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coco_multilabel_unet.py
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coco_multilabel_unet.py
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import cv2
# 1. configuration for inference
nclasses = 80
ignore_label = 255
multi_label = True
crop_size_h, crop_size_w = 513, 513
test_size_h, test_size_w = 641, 641
image_pad_value = (123.675, 116.280, 103.530)
img_norm_cfg = dict(
max_pixel_value=255.0,
std=(0.229, 0.224, 0.225),
mean=(0.485, 0.456, 0.406),
)
norm_cfg = dict(type='SyncBN')
gfpn_post = dict(
type='ConvModules',
kernel_size=3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=dict(type='Relu', inplace=True),
num_convs=2,
)
gfpn_upsample_2x = dict(
type='Upsample',
scale_factor=2,
scale_bias=-1,
mode='bilinear',
align_corners=True,
)
inference = dict(
multi_label=multi_label,
transforms=[
dict(type='PadIfNeeded', min_height=test_size_h, min_width=test_size_w,
value=image_pad_value, mask_value=ignore_label),
dict(type='Normalize', **img_norm_cfg),
dict(type='ToTensor'),
],
model=dict(
# model/encoder
encoder=dict(
backbone=dict(
type='ResNet',
arch='resnet101',
pretrain=True,
norm_cfg=norm_cfg,
),
),
# model/decoder
decoder=dict(
type='GFPN',
# model/decoder/blocks
neck=[
# model/decoder/blocks/block1
dict(
type='JunctionBlock',
fusion_method='concat',
verticals=[{'from_layer': 'c5', **gfpn_upsample_2x}, ],
laterals=[dict(from_layer='c4', type='Identity'), ],
to_layer='m4',
), # 16
dict(
type='JunctionBlock',
laterals=[
{
'from_layer': 'm4',
'in_channels': 3072, # 2048 + 1024 resnet101
# in_channels: 768, # 512 + 256 resnet18
'out_channels': 256,
**gfpn_post,
},
],
to_layer='p4',
), # 16
# model/decoder/blocks/block2
dict(
type='JunctionBlock',
fusion_method='concat',
verticals=[{'from_layer': 'p4', **gfpn_upsample_2x}, ],
laterals=[dict(from_layer='c3', type='Identity'), ],
to_layer='m3',
), # 8
dict(
type='JunctionBlock',
laterals=[
{
'from_layer': 'm3',
'in_channels': 768, # 256 + 512
# 'in_channels': 384, # 256 + 128
'out_channels': 128,
**gfpn_post,
},
],
to_layer='p3',
), # 8
# model/decoder/blocks/block3
dict(
type='JunctionBlock',
fusion_method='concat',
verticals=[{'from_layer': 'p3', **gfpn_upsample_2x}, ],
laterals=[dict(from_layer='c2', type='Identity'), ],
to_layer='m2',
), # 4
dict(
type='JunctionBlock',
laterals=[
{
'from_layer': 'm2',
'in_channels': 384, # 128 + 256
# 'in_channels': 192, # 128 + 64
'out_channels': 64,
**gfpn_post,
},
],
to_layer='p2',
), # 4
# model/decoder/blocks/block4
dict(
type='JunctionBlock',
fusion_method='concat',
verticals=[{'from_layer': 'p2', **gfpn_upsample_2x}, ],
laterals=[dict(from_layer='c1', type='Identity'), ],
to_layer='m1',
), # 2
dict(
type='JunctionBlock',
laterals=[
{
'from_layer': 'm1',
'in_channels': 128,
'out_channels': 32,
**gfpn_post,
},
],
to_layer='p1',
), # 2
# model/decoder/blocks/block5
dict(
type='JunctionBlock',
verticals=[{'from_layer': 'p1', **gfpn_upsample_2x}, ],
to_layer='m0',
), # 1
dict(
type='JunctionBlock',
laterals=[
{
'from_layer': 'm0',
'in_channels': 32,
'out_channels': 16,
**gfpn_post,
},
],
to_layer='p0',
), # 1
]),
# model/decoer/head
head=dict(
type='Head',
in_channels=16,
out_channels=nclasses,
num_convs=0,
),
),
)
# 2. configuration for train/test
root_workdir = 'workdir'
dataset_type = 'CocoDataset'
dataset_root = 'data/COCO2017'
common = dict(
seed=0,
logger=dict(
handlers=(
dict(type='StreamHandler', level='INFO'),
dict(type='FileHandler', level='INFO'),
),
),
cudnn_deterministic=False,
cudnn_benchmark=True,
metrics=[
dict(type='MultiLabelIoU', num_classes=nclasses),
dict(type='MultiLabelMIoU', num_classes=nclasses),
],
dist_params=dict(backend='nccl'),
)
## 2.1 configuration for test
test = dict(
data=dict(
dataset=dict(
type=dataset_type,
root=dataset_root,
ann_file='instances_val2017.json',
img_prefix='val2017',
multi_label=multi_label,
),
transforms=inference['transforms'],
sampler=dict(
type='DefaultSampler',
),
dataloader=dict(
type='DataLoader',
samples_per_gpu=4,
workers_per_gpu=4,
shuffle=False,
drop_last=False,
pin_memory=True,
),
),
# tta=dict(
# scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
# biases=[0.5, 0.25, 0.0, -0.25, -0.5, -0.75],
# flip=True,
# ),
)
## 2.2 configuration for train
max_epochs = 50
train = dict(
data=dict(
train=dict(
dataset=dict(
type=dataset_type,
root=dataset_root,
ann_file='instances_train2017.json',
img_prefix='train2017',
multi_label=multi_label,
),
transforms=[
dict(type='RandomScale', scale_limit=(0.5, 2),
interpolation=cv2.INTER_LINEAR),
dict(type='PadIfNeeded', min_height=crop_size_h,
min_width=crop_size_w, value=image_pad_value,
mask_value=ignore_label),
dict(type='RandomCrop', height=crop_size_h, width=crop_size_w),
dict(type='Rotate', limit=10, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT, value=image_pad_value,
mask_value=ignore_label, p=0.5),
dict(type='GaussianBlur', blur_limit=7, p=0.5),
dict(type='HorizontalFlip', p=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='ToTensor'),
],
sampler=dict(
type='DefaultSampler',
),
dataloader=dict(
type='DataLoader',
samples_per_gpu=8,
workers_per_gpu=4,
shuffle=True,
drop_last=True,
pin_memory=True,
),
),
val=dict(
dataset=dict(
type=dataset_type,
root=dataset_root,
ann_file='instances_val2017.json',
img_prefix='val2017',
multi_label=multi_label,
),
transforms=inference['transforms'],
sampler=dict(
type='DefaultSampler',
),
dataloader=dict(
type='DataLoader',
samples_per_gpu=8,
workers_per_gpu=4,
shuffle=False,
drop_last=False,
pin_memory=True,
),
),
),
resume=None,
criterion=dict(type='BCEWithLogitsLoss', ignore_index=ignore_label),
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
lr_scheduler=dict(type='PolyLR', max_epochs=max_epochs),
max_epochs=max_epochs,
trainval_ratio=1,
log_interval=10,
snapshot_interval=5,
save_best=True,
)