mirror of https://github.com/open-mmlab/mmpose
257 lines
8.4 KiB
Python
257 lines
8.4 KiB
Python
checkpoint_config = dict(interval=1)
|
|
# yapf:disable
|
|
log_config = dict(
|
|
interval=50,
|
|
hooks=[
|
|
dict(type='TextLoggerHook'),
|
|
# dict(type='TensorboardLoggerHook')
|
|
])
|
|
# yapf:enable
|
|
dist_params = dict(backend='nccl')
|
|
log_level = 'INFO'
|
|
load_from = None
|
|
resume_from = None
|
|
workflow = [('train', 1)]
|
|
|
|
# optimizer
|
|
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
|
|
optimizer_config = dict(grad_clip=None)
|
|
# learning policy
|
|
lr_config = dict(
|
|
policy='step',
|
|
warmup='linear',
|
|
warmup_iters=500,
|
|
warmup_ratio=0.001,
|
|
step=[16, 19])
|
|
total_epochs = 20
|
|
|
|
# model settings
|
|
model = dict(
|
|
type='CascadeRCNN',
|
|
pretrained='open-mmlab://resnext101_64x4d',
|
|
backbone=dict(
|
|
type='ResNeXt',
|
|
depth=101,
|
|
groups=64,
|
|
base_width=4,
|
|
num_stages=4,
|
|
out_indices=(0, 1, 2, 3),
|
|
frozen_stages=1,
|
|
norm_cfg=dict(type='BN', requires_grad=True),
|
|
style='pytorch'),
|
|
neck=dict(
|
|
type='FPN',
|
|
in_channels=[256, 512, 1024, 2048],
|
|
out_channels=256,
|
|
num_outs=5),
|
|
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],
|
|
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=1.0 / 9.0, 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='Shared2FCBBoxHead',
|
|
in_channels=256,
|
|
fc_out_channels=1024,
|
|
roi_feat_size=7,
|
|
num_classes=80,
|
|
bbox_coder=dict(
|
|
type='DeltaXYWHBBoxCoder',
|
|
target_means=[0., 0., 0., 0.],
|
|
target_stds=[0.1, 0.1, 0.2, 0.2]),
|
|
reg_class_agnostic=True,
|
|
loss_cls=dict(
|
|
type='CrossEntropyLoss',
|
|
use_sigmoid=False,
|
|
loss_weight=1.0),
|
|
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
|
|
loss_weight=1.0)),
|
|
dict(
|
|
type='Shared2FCBBoxHead',
|
|
in_channels=256,
|
|
fc_out_channels=1024,
|
|
roi_feat_size=7,
|
|
num_classes=80,
|
|
bbox_coder=dict(
|
|
type='DeltaXYWHBBoxCoder',
|
|
target_means=[0., 0., 0., 0.],
|
|
target_stds=[0.05, 0.05, 0.1, 0.1]),
|
|
reg_class_agnostic=True,
|
|
loss_cls=dict(
|
|
type='CrossEntropyLoss',
|
|
use_sigmoid=False,
|
|
loss_weight=1.0),
|
|
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
|
|
loss_weight=1.0)),
|
|
dict(
|
|
type='Shared2FCBBoxHead',
|
|
in_channels=256,
|
|
fc_out_channels=1024,
|
|
roi_feat_size=7,
|
|
num_classes=80,
|
|
bbox_coder=dict(
|
|
type='DeltaXYWHBBoxCoder',
|
|
target_means=[0., 0., 0., 0.],
|
|
target_stds=[0.033, 0.033, 0.067, 0.067]),
|
|
reg_class_agnostic=True,
|
|
loss_cls=dict(
|
|
type='CrossEntropyLoss',
|
|
use_sigmoid=False,
|
|
loss_weight=1.0),
|
|
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
|
|
]),
|
|
# 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,
|
|
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),
|
|
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
|
|
dict(type='RandomFlip', flip_ratio=0.5),
|
|
dict(type='Normalize', **img_norm_cfg),
|
|
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', **img_norm_cfg),
|
|
dict(type='Pad', size_divisor=32),
|
|
dict(type='DefaultFormatBundle'),
|
|
dict(type='Collect', keys=['img']),
|
|
])
|
|
]
|
|
data = dict(
|
|
samples_per_gpu=2,
|
|
workers_per_gpu=2,
|
|
train=dict(
|
|
type=dataset_type,
|
|
ann_file=f'{data_root}/annotations/instances_train2017.json',
|
|
img_prefix=f'{data_root}/train2017/',
|
|
pipeline=train_pipeline),
|
|
val=dict(
|
|
type=dataset_type,
|
|
ann_file=f'{data_root}/annotations/instances_val2017.json',
|
|
img_prefix=f'{data_root}/val2017/',
|
|
pipeline=test_pipeline),
|
|
test=dict(
|
|
type=dataset_type,
|
|
ann_file=f'{data_root}/annotations/instances_val2017.json',
|
|
img_prefix=f'{data_root}/val2017/',
|
|
pipeline=test_pipeline))
|
|
evaluation = dict(interval=1, metric='bbox')
|