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num_batch_size = 4
num_epochs = 15
num_frozen_stages = 2
auto_scale_lr = dict(base_batch_size=2, enable=False)
backend_args = None
data_root = 'C:/vs_code_workspaces/mmdetection/mmdetection/data/ins/v9'
dataset_type = 'CocoDataset'
default_hooks = dict(
checkpoint=dict(interval=1, type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = 'C:/vs_code_workspaces/mmdetection/mmdetection/ins_development/resources/add300_frozen2/epoch_9.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
metainfo = dict(classes=('waste', ), palette=[ (220, 20, 60, ),])
model = dict(
backbone=dict(
depth=101,
frozen_stages=num_frozen_stages,
init_cfg=dict(checkpoint='C:/Users/INS/.cache/torch/hub/checkpoints/resnet101-63fe2227.pth', type='Pretrained'),
norm_cfg=dict(requires_grad=True, type='BN'),
norm_eval=True,
num_stages=4,
out_indices=(0, 1, 2, 3, ),
style='pytorch',
type='ResNet'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[123.675, 116.28, 103.53, ],
pad_size_divisor=32,
std=[58.395, 57.12, 57.375, ],
type='DetDataPreprocessor'),
neck=dict(in_channels=[256, 512, 1024, 2048, ],
num_outs=5,
out_channels=256,
type='FPN'),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(
target_means=[0.0, 0.0, 0.0, 0.0,],
target_stds=[0.1, 0.1, 0.2, 0.2,],
type='DeltaXYWHBBoxCoder'),
fc_out_channels=1024,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
num_classes=1,
reg_class_agnostic=False,
roi_feat_size=7,
type='Shared2FCBBoxHead'),
bbox_roi_extractor=dict(
featmap_strides=[4, 8, 16, 32, ],
out_channels=256,
roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
type='StandardRoIHead'),
rpn_head=dict(
anchor_generator=dict(
ratios=[0.5, 1.0, 2.0, ],
scales=[8,],
strides=[4, 8, 16, 32, 64, ],
type='AnchorGenerator'),
bbox_coder=dict(
target_means=[0.0, 0.0, 0.0, 0.0, ],
target_stds=[1.0, 1.0, 1.0, 1.0, ],
type='DeltaXYWHBBoxCoder'),
feat_channels=256,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
type='RPNHead'),
test_cfg=dict(
rcnn=dict(
max_per_img=100,
nms=dict(iou_threshold=0.5, type='nms'),
score_thr=0.05),
rpn=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=1000)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=False,
min_pos_iou=0.5,
neg_iou_thr=0.5,
pos_iou_thr=0.5,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=True,
neg_pos_ub=-1,
num=512,
pos_fraction=0.25,
type='RandomSampler')),
rpn=dict(
allowed_border=-1,
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.3,
neg_iou_thr=0.3,
pos_iou_thr=0.7,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=False,
neg_pos_ub=-1,
num=256,
pos_fraction=0.5,
type='RandomSampler')),
rpn_proposal=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=2000)),
type='FasterRCNN')
optim_wrapper = dict(
optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
type='OptimWrapper')
param_scheduler = [
dict(begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
dict(begin=0, by_epoch=True, end=12, gamma=0.1, milestones=[8, 11, ], type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=num_batch_size,
dataset=dict(
ann_file='test/annotations_coco.json',
backend_args=None,
data_prefix=dict(img='test/'),
data_root=data_root,
metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ), ]),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
ann_file='data/ins_annotated_v9/test/annotations_coco.json',
backend_args=None,
format_only=False,
metric='bbox',
type='CocoMetric')
test_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',),type='PackDetInputs'),
]
train_cfg = dict(max_epochs=num_epochs, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
batch_sampler=dict(type='AspectRatioBatchSampler'),
batch_size=num_batch_size,
dataset=dict(
ann_file='train/annotations_coco.json',
backend_args=None,
data_prefix=dict(img='train/'),
data_root=data_root,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
],
type='CocoDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=num_batch_size,
dataset=dict(
ann_file='valid/annotations_coco.json',
backend_args=None,
data_prefix=dict(img='valid/'),
data_root=data_root,
metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ),type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
ann_file='data/ins_annotated_v9/valid/annotations_coco.json',
backend_args=None,
format_only=False,
metric='bbox',
type='CocoMetric')
val_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True),
dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'),
]
vis_backends = [dict(type='LocalVisBackend'), ]
visualizer = dict(name='visualizer', type='DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend'), ])
work_dir = './ins_development/training/ins_annotated_v9/pretrained/add300/faster/2frozen/e9\\'
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