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update config
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@dyabel
dyabel committed Mar 22, 2022
1 parent 84ac9f3 commit dc674f0
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2 changes: 1 addition & 1 deletion configs/_base_/schedules/schedule_20e.py
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warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 19])
step=[16])
total_epochs = 20
181 changes: 181 additions & 0 deletions configs/lvis/cascade_mask_rcnn_r50_fpn_sample1e-3_mstrain_20e_lvis_v1_pretrain_ens.py
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_base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.000025)
# evaluation = dict(type="SubModulesDistEvalHook", interval=4000)
evaluation = dict(interval=2,metric=['bbox', 'segm'])
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
norm_cfg=dict(type='SyncBN', requires_grad=True),
num_outs=5),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
ensemble=True,
roi_feat_size=7,
with_cls=False,
num_classes=1203,
norm_cfg=dict(type='SyncBN', requires_grad=True),
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='Shared4Conv1FCBBoxHead',
ensemble=True,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
with_cls=False,
num_classes=1203,
norm_cfg=dict(type='SyncBN', requires_grad=True),
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='Shared4Conv1FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
ensemble=True,
roi_feat_size=7,
with_cls=False,
norm_cfg=dict(type='SyncBN', requires_grad=True),
num_classes=1203,
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))
],
# bbox_head=dict(
# type='Shared4Conv1FCBBoxHead',
# in_channels=256,
# ensemble=False,
# fc_out_channels=1024,
# roi_feat_size=7,
# with_cls=False,
# num_classes=1203,
# norm_cfg=dict(type='SyncBN', requires_grad=True),
# 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='L1Loss', loss_weight=1.0)),
mask_head=dict(num_classes=1203)))
# train_cfg = dict(
# rpn_proposal=dict(
# nms_across_levels=False,
# nms_pre=2000,
# nms_post=1000,
# max_num=1000,
# nms_thr=0.7,
# min_bbox_size=0.001),
# )
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
score_thr=0,
min_bbox_size=0.001),
rcnn=dict(
score_thr=0.001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=300,
mask_thr_binary=0.5))
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='LoadProposals', num_max_proposals=None),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
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','img_no_normalize','proposals', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadProposals', num_max_proposals=None),
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='ImageToTensor', keys=['img','img_no_normalize']),
dict(type='ToTensor', keys=['proposals']),
dict(
type='ToDataContainer',
fields=[dict(key='proposals', stack=False)]),
dict(type='Collect', keys=['img', 'img_no_normalize', 'proposals']),
# dict(type='Collect', keys=['img']),
])
]

checkpoint_config = dict(interval=1,create_symlink=False)
load_from = 'current_mmdetection_Head.pth'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
# train=dict(pipeline=train_pipeline))
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# fp16 = dict(loss_scale=512.)
# checkpoint_config = dict(by_epoch=False, interval=100, max_keep_ckpts=40)
0 configs/lvis/detpro_ens_2x.py → configs/lvis/detpro_ens_20e.py
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