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tood_r101_dconv_10epoch.py
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dataset_type = 'CocoDataset'
data_root = '/workspace/data/Chula-ParasiteEgg-11/'
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),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
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='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
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=8,
workers_per_gpu=8,
train=dict(
type='CocoDataset',
ann_file=[
'/workspace/data/Chula-ParasiteEgg-11/labels_train.json',
'/workspace/data/Chula-ParasiteEgg-11/labels_val.json',
'/workspace/data/Chula-ParasiteEgg-11/labels_test_fusion_929.json'
],
img_prefix=[
'/workspace/data/Chula-ParasiteEgg-11/train/',
'/workspace/data/Chula-ParasiteEgg-11/val/',
'/workspace/data/Chula-ParasiteEgg-11/test/data'
],
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
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='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
],
classes=('Ascaris lumbricoides', 'Capillaria philippinensis',
'Enterobius vermicularis', 'Fasciolopsis buski',
'Hookworm egg', 'Hymenolepis diminuta', 'Hymenolepis nana',
'Opisthorchis viverrine', 'Paragonimus spp',
'Taenia spp. egg', 'Trichuris trichiura')),
val=dict(
type='CocoDataset',
ann_file='/workspace/data/Chula-ParasiteEgg-11/labels_val.json',
img_prefix='/workspace/data/Chula-ParasiteEgg-11/val/',
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=('Ascaris lumbricoides', 'Capillaria philippinensis',
'Enterobius vermicularis', 'Fasciolopsis buski',
'Hookworm egg', 'Hymenolepis diminuta', 'Hymenolepis nana',
'Opisthorchis viverrine', 'Paragonimus spp',
'Taenia spp. egg', 'Trichuris trichiura')),
test=dict(
type='CocoDataset',
ann_file='/workspace/data/Chula-ParasiteEgg-11/labels_test.json',
img_prefix='/workspace/data/Chula-ParasiteEgg-11/test/data',
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=('Ascaris lumbricoides', 'Capillaria philippinensis',
'Enterobius vermicularis', 'Fasciolopsis buski',
'Hookworm egg', 'Hymenolepis diminuta', 'Hymenolepis nana',
'Opisthorchis viverrine', 'Paragonimus spp',
'Taenia spp. egg', 'Trichuris trichiura')))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, 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=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='SetEpochInfoHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'pretrained_models/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728-4a824142.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
model = dict(
type='TOOD',
backbone=dict(
type='ResNet',
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='torchvision://resnet101'),
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='TOODHead',
num_classes=11,
in_channels=256,
stacked_convs=6,
feat_channels=256,
anchor_type='anchor_free',
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
initial_loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
activated=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
activated=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
num_dcn=2),
train_cfg=dict(
initial_epoch=4,
initial_assigner=dict(type='ATSSAssigner', topk=9),
assigner=dict(type='TaskAlignedAssigner', topk=13),
alpha=1,
beta=6,
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
classes = ('Ascaris lumbricoides', 'Capillaria philippinensis',
'Enterobius vermicularis', 'Fasciolopsis buski', 'Hookworm egg',
'Hymenolepis diminuta', 'Hymenolepis nana',
'Opisthorchis viverrine', 'Paragonimus spp', 'Taenia spp. egg',
'Trichuris trichiura')
work_dir = './work_dirs/tood_r101_fpn_dconv_c3-c5_mstrain_1x_chulacell'
auto_resume = False
gpu_ids = range(0, 2)