Commit a8562a56 authored by luopl's avatar luopl
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Initial commit

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Pipeline #1564 canceled with stages
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_8xb8_amp_lsj_200e_coco import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_instance import *
from .._base_.default_runtime import *
from .._base_.models.mask_rcnn_r50_caffe_c4 import *
from .._base_.schedules.schedule_1x import *
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_1x_coco import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_1x_coco import *
from mmcv.transforms import RandomChoiceResize
from mmengine.model.weight_init import PretrainedInit
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args={{_base_.backend_args}}),
dict(type=LoadAnnotations, with_bbox=True, with_mask=True),
dict(
type=RandomChoiceResize,
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type=RandomFlip, prob=0.5),
dict(type=PackDetInputs),
]
train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_1x_coco import *
from mmcv.transforms import RandomChoiceResize
from mmengine.model.weight_init import PretrainedInit
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
train_pipeline = [
dict(type=LoadImageFromFile, backend_args={{_base_.backend_args}}),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(
type=RandomChoiceResize,
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type=RandomFlip, prob=0.5),
dict(type=PackDetInputs)
]
train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_caffe_fpn_ms_poly_1x_coco import *
train_cfg = dict(max_epochs=24)
# learning rate
param_scheduler = [
dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type=MultiStepLR,
begin=0,
end=24,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_caffe_fpn_ms_poly_1x_coco import *
train_cfg = dict(max_epochs=36)
# learning rate
param_scheduler = [
dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type=MultiStepLR,
begin=0,
end=24,
by_epoch=True,
milestones=[28, 34],
gamma=0.1)
]
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_1x_coco import *
from mmengine.model.weight_init import PretrainedInit
from mmdet.models.losses import SmoothL1Loss
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
rpn_head=dict(
loss_bbox=dict(type=SmoothL1Loss, beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
bbox_roi_extractor=dict(
roi_layer=dict(
type=RoIAlign, output_size=7, sampling_ratio=2,
aligned=False)),
bbox_head=dict(
loss_bbox=dict(type=SmoothL1Loss, beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
roi_layer=dict(
type=RoIAlign, output_size=14, sampling_ratio=2,
aligned=False))))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_instance import *
from .._base_.default_runtime import *
from .._base_.models.mask_rcnn_r50_fpn import *
from .._base_.schedules.schedule_1x import *
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_instance import *
from .._base_.default_runtime import *
from .._base_.models.mask_rcnn_r50_fpn import *
from .._base_.schedules.schedule_1x import *
from mmengine.visualization import LocalVisBackend, WandbVisBackend
vis_backends.update(dict(type=WandbVisBackend))
vis_backends.update(dict(type=LocalVisBackend))
visualizer.update(dict(vis_backends=vis_backends))
# MMEngine support the following two ways, users can choose
# according to convenience
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
default_hooks.update(dict(checkpoint=dict(interval=4)))
train_cfg.update(dict(val_interval=2))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_instance import *
from .._base_.default_runtime import *
from .._base_.models.mask_rcnn_r50_fpn import *
from .._base_.schedules.schedule_2x import *
# Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_1x_coco import *
from mmengine.optim.optimizer.amp_optimizer_wrapper import AmpOptimWrapper
optim_wrapper.update(dict(type=AmpOptimWrapper))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.models.mask_rcnn_r50_fpn import *
from ..common.ms_poly_3x_coco_instance import *
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_instance import *
from .._base_.default_runtime import *
from .._base_.models.mask_rcnn_r50_fpn import *
from .._base_.schedules.schedule_1x import *
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(type=Resize, scale=(1333, 800), keep_ratio=True),
dict(type=RandomFlip, prob=0.5),
dict(type=PackDetInputs),
]
train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r101_fpn_1x_coco import *
from mmengine.model.weight_init import PretrainedInit
from mmdet.models.backbones.resnext import ResNeXt
model = dict(
backbone=dict(
type=ResNeXt,
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
style='pytorch',
init_cfg=dict(
type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_2x_coco import *
from mmengine.model.weight_init import PretrainedInit
from mmdet.models import ResNeXt
model = dict(
backbone=dict(
type=ResNeXt,
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
style='pytorch',
init_cfg=dict(
type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from ..common.ms_poly_3x_coco_instance import *
from .._base_.models.mask_rcnn_r50_fpn import *
from mmengine.model.weight_init import PretrainedInit
from mmdet.models.backbones import ResNeXt
model = dict(
backbone=dict(
type=ResNeXt,
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=True),
style='pytorch',
init_cfg=dict(
type=PretrainedInit, checkpoint='open-mmlab://resnext101_32x4d')))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_x101_32x4d_fpn_1x_coco import *
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
type=ResNeXt,
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
style='pytorch',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r101_fpn_1x_coco import *
from mmcv.transforms import RandomChoiceResize, RandomFlip
from mmcv.transforms.loading import LoadImageFromFile
from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import LoadAnnotations
from mmdet.models.backbones import ResNeXt
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
type=ResNeXt,
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
style='pytorch',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
backend_args = None
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(
type=LoadAnnotations, with_bbox=True, with_mask=True, poly2mask=False),
dict(
type=RandomChoiceResize,
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type=RandomFlip, prob=0.5),
dict(type=PackDetInputs),
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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