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bacteria_r2_101dcn_dyhead.py
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bacteria_r2_101dcn_dyhead.py
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model = dict(
type='CascadeRCNN',
backbone=dict(
type='Res2Net',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest101'),
scales=4,
base_width=26,
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
stem_channels=128),
neck=[
dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='DyHead',
in_channels=256,
out_channels=256,
num_blocks=6,
zero_init_offset=False)
],
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='SABLHead',
num_classes=8,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=0.1, loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=8,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=1.5),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=0.1, loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=8,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder',
num_buckets=14,
scale_factor=1.3),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
]),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
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_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
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,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='Pad', size_divisor=32),
dict(
type='RandomFlip',
direction=['horizontal', 'vertical'],
flip_ratio=0.5),
dict(
type='Albu',
transforms=[
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',
'pad_shape', 'scale_factor'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_train_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='Pad', size_divisor=32),
dict(
type='RandomFlip',
direction=['horizontal', 'vertical'],
flip_ratio=0.5),
dict(
type='Albu',
transforms=[
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels'],
meta_keys=('filename', 'ori_shape', 'img_shape',
'img_norm_cfg', 'pad_shape', 'scale_factor'))
],
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec')),
val=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_val_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec')),
test=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_test_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec')))
evaluation = dict(interval=1, metric='bbox', save_best='auto', classwise=True)
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[100, 150, 200, 250])
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=10)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
image_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing'
train_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_train_jbing.json'
test_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_test_jbing.json'
val_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_val_jbing.json'
CLASSES = ('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec')
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
]
rpn_weight = 0.7
work_dir = './work_dirs/crcnn_rs101_dcnv2_sabl_dyhead_8class_26-2-23'
auto_resume = False
gpu_ids = [0]