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densenet_align.py
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densenet_align.py
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from deeplesion.ENVIRON import data_root
anchor_scales = [4, 6, 8, 12, 24, 48]#, 64
# fp16 = dict(loss_scale=96.)
dataset_transform = dict(
IMG_DO_CLIP = False,
WINDOWING = [-1024, 2050],
DATA_AUG_POSITION = False,
NORM_SPACING = 0.,
SLICE_INTV = 2.0,
NUM_SLICES = 3,
GROUNP_ZSAPACING = False,
)
input_channel = dataset_transform['NUM_SLICES']
feature_channel = 512
# model settings
model = dict(
type='AlignShiftMaskRCNN',
pretrained= False,
backbone=dict(
type='DenseNetCustomTrunc3dAlign',
n_cts=input_channel,
out_dim=feature_channel,
fpn_finest_layer=2,
ref_thickness=2.0,
n_fold=8,
memory_efficient=True),
rpn_head=dict(
type='RPNHead',
in_channels=feature_channel,
feat_channels=feature_channel,
anchor_scales=anchor_scales,
anchor_ratios=[0.5, 1., 2.0],
anchor_base_sizes=[4],
anchor_strides=[4],
target_means=[.0, .0, .0, .0],
target_stds=[1., 1., 1., 1.],
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=feature_channel,
featmap_strides=[4]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=feature_channel,
fc_out_channels=feature_channel * 4,
roi_feat_size=7,
num_classes=2,
num_shared_convs=0,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
# target_stds=[1., 1., 1., 1.],
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
finest_scale=196, #按照面积是这个的倍数， 到该层去取feature
out_channels=feature_channel,
featmap_strides=[4]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=feature_channel,
conv_out_channels=feature_channel // 2,
num_classes=2,
loss_mask=dict(
type='DiceLoss', use_mask=True, loss_weight=1.0)))##DiceLoss， CrossEntropyLoss
# model training and testing settings
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=12000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,# 越大 抑制越少
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=64,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False))
test_cfg = dict(
avgFP=[0.5, 1, 2, 4, 8, 16],
iou_th_astrue=0.5,
rpn=dict(
nms_across_levels=False,
nms_pre=6000,
nms_post=300,
max_num=300,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.001,
nms=dict(type='nms', iou_thr=0.5),
max_per_img=50,
mask_thr_binary=0.5))
# dataset settings
dataset_type = 'DeepLesionDatasetAlign'
img_norm_cfg = dict(
mean=[0.] * input_channel, std=[1.] * input_channel, to_rgb=False)
pre_pipeline=[
dict(
type='Albu',
transforms=[dict(
type='ShiftScaleRotate',
shift_limit=0.04,
scale_limit=0.2,
rotate_limit=15,
interpolation=2,## cv::BORDER_REFLECT
p=0.8),],#dict(type='Rotate', limit=15, p=0.5)
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0),
keymap={
'img': 'image',
'gt_masks': 'masks',
'gt_bboxes': 'bboxes'
},
update_pad_shape=False,
skip_img_without_anno=False),
dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
]
pre_pipeline_test=[
dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
]
train_pipeline = [
dict(type='DefaultFormatBundle_3d'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'thickness']),#, 'flage'#, 'img_info'#, 'thickness'
]
test_pipeline = [
dict(type='ImageToTensor_3d', keys=['img']),
dict(type='Collect', keys=['img', 'gt_bboxes']),
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=0,
train=dict(
type=dataset_type,
ann_file=data_root + 'train_ann.pkl',
image_path=data_root + 'Images_png/',
pipeline=train_pipeline,
pre_pipeline = pre_pipeline,
dicm2png_cfg=dataset_transform),
with_mask=True,
with_label=True,
val=dict(
type=dataset_type,
ann_file=data_root + 'val_ann.pkl',
image_path=data_root + 'Images_png/',
pipeline=train_pipeline,
pre_pipeline = pre_pipeline_test,
dicm2png_cfg=dataset_transform),
test=dict(
type=dataset_type,
ann_file=data_root + 'test_ann.pkl',
image_path=data_root + 'Images_png/',
pipeline=train_pipeline,
pre_pipeline = pre_pipeline_test,
dicm2png_cfg=dataset_transform))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)#momentum=0.9,
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))#, step_inteval=2
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1/100,
step=[9, 16])#10,13
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
#evaluation = dict(interval=1)
# runtime settings
total_epochs = 20#16
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = f'./work_dirs/densenet_3d_alignshift_r3'
load_from = None
resume_from = None
workflow = [('train', 1)]#, ('val',1)
GPU = '0,1,2,3'
description='align no padding zero'