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cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_nucleus.py
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cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_nucleus.py
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# The new config inherits a base config to highlight the necessary modification
_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py'
# We also need to change the num_classes in head to match the dataset's annotation
model = dict(
roi_head=dict(
bbox_head=[
dict(type='Shared2FCBBoxHead', num_classes=1),
dict(type='Shared2FCBBoxHead', num_classes=1),
dict(type='Shared2FCBBoxHead', num_classes=1)
],
mask_head=dict(num_classes=1)
),
backbone=dict(
with_cp=True
),
# train_cfg=dict(
# rpn=dict(
# assigner=dict(
# type='MaxIoUAssigner',
# gpu_assign_thr=1000)),
# rcnn=dict(
# assigner=dict(
# type='MaxIoUAssigner',
# gpu_assign_thr=1000))
# )
)
# Modify dataset related settings
dataset_type = 'CocoDataset'
classes = ('nucleus',)
runner = dict(type='EpochBasedRunner', max_epochs=200)
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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']),
])
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
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', 'gt_masks']),
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type='CocoDataset',
ann_file='/work/zchin31415/nucleus_data/annotations/instance_all_train.json',
img_prefix='/work/zchin31415/nucleus_data/all_train',
# classes=('tennis', )
pipeline=train_pipeline
),
classes=classes,
ann_file='/work/zchin31415/nucleus_data/annotations/instance_all_train.json',
img_prefix='/work/zchin31415/nucleus_data/all_train'),
val=dict(
type=dataset_type,
ann_file='/work/zchin31415/nucleus_data/annotations/instance_all_train.json',
img_prefix='/work/zchin31415/nucleus_data/all_train',
classes=classes),
test=dict(
type=dataset_type,
ann_file='/work/zchin31415/nucleus_data/annotations/instance_test.json',
img_prefix='/work/zchin31415/nucleus_data/test',
pipeline=test_pipeline,
classes=classes)
)
load_from = '/home/zchin31415/mmdet-nucleus-instance-segmentation/mmdetection/checkpoints/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth'