[CodeCamp2023-488] Add new configuration files for SoftTeacher algorithm in mmdetection.

mmdet/configs/_base_/datasets/semi_coco_detection.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from mmdet.datasets.transforms import *
+
+from mmcv import RandomResize
+from mmcv.transforms import LoadImageFromFile
+from mmengine.dataset import ConcatDataset
+from mmengine.dataset.sampler import DefaultSampler
+
+from mmdet.datasets import (AspectRatioBatchSampler, CocoDataset,
+                            GroupMultiSourceSampler)
+from mmdet.datasets.transforms.augment_wrappers import RandAugment
+from mmdet.evaluation import CocoMetric
+
+# dataset settings
+dataset_type = CocoDataset
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+color_space = [
+    [dict(type=ColorTransform)],
+    [dict(type=AutoContrast)],
+    [dict(type=Equalize)],
+    [dict(type=Sharpness)],
+    [dict(type=Posterize)],
+    [dict(type=Solarize)],
+    [dict(type=Color)],
+    [dict(type=Contrast)],
+    [dict(type=Brightness)],
+]
+
+geometric = [
+    [dict(type=Rotate)],
+    [dict(type=ShearX)],
+    [dict(type=ShearY)],
+    [dict(type=TranslateX)],
+    [dict(type=TranslateY)],
+]
+
+scale = [(1333, 400), (1333, 1200)]
+
+branch_field = ['sup', 'unsup_teacher', 'unsup_student']
+# pipeline used to augment labeled data,
+# which will be sent to student model for supervised training.
+sup_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(type=LoadAnnotations, with_bbox=True),
+    dict(
+        type=RandomResize,
+        scale=scale,
+        ratio_range=(0.1, 2.0),
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=RandAugment, aug_space=color_space, aug_num=1),
+    dict(type=FilterAnnotations, min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(
+        type=MultiBranch,
+        branch_field=branch_field,
+        sup=dict(type=PackDetInputs))
+]
+
+# pipeline used to augment unlabeled data weakly,
+# which will be sent to teacher model for predicting pseudo instances.
+weak_pipeline = [
+    dict(
+        type=RandomResize,
+        scale=scale,
+        ratio_range=(0.1, 2.0),
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(
+        type=PackDetInputs,
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix')),
+]
+
+# pipeline used to augment unlabeled data strongly,
+# which will be sent to student model for unsupervised training.
+strong_pipeline = [
+    dict(
+        type=RandomResize,
+        scale=scale,
+        ratio_range=(0.1, 2.0),
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomFlip, prob=0.5),
+    dict(
+        type=RandomOrder,
+        transforms=[
+            dict(type=RandAugment, aug_space=color_space, aug_num=1),
+            dict(type=RandAugment, aug_space=geometric, aug_num=1),
+        ]),
+    dict(type=RandomErasing, n_patches=(1, 5), ratio=(0, 0.2)),
+    dict(type=FilterAnnotations, min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(
+        type=PackDetInputs,
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'flip', 'flip_direction',
+                   'homography_matrix')),
+]
+
+# pipeline used to augment unlabeled data into different views
+unsup_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(type=LoadEmptyAnnotations),
+    dict(
+        type=MultiBranch,
+        branch_field=branch_field,
+        unsup_teacher=weak_pipeline,
+        unsup_student=strong_pipeline,
+    )
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=backend_args),
+    dict(type=Resize, scale=(1333, 800), keep_ratio=True),
+    dict(
+        type=PackDetInputs,
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+batch_size = 4
+num_workers = 5
+# There are two common semi-supervised learning settings on the coco dataset：
+# (1) Divide the train2017 into labeled and unlabeled datasets
+# by a fixed percentage, such as 1%, 2%, 5% and 10%.
+# The format of labeled_ann_file and unlabeled_ann_file are
+# instances_train2017.{fold}@{percent}.json, and
+# instances_train2017.{fold}@{percent}-unlabeled.json
+# `fold` is used for cross-validation, and `percent` represents
+# the proportion of labeled data in the train2017.
+# (2) Choose the train2017 as the labeled dataset
+# and unlabeled2017 as the unlabeled dataset.
+# The labeled_ann_file and unlabeled_ann_file are
+# instances_train2017.json and image_info_unlabeled2017.json
+# We use this configuration by default.
+labeled_dataset = dict(
+    type=dataset_type,
+    data_root=data_root,
+    ann_file='annotations/instances_train2017.json',
+    data_prefix=dict(img='train2017/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
+    pipeline=sup_pipeline,
+    backend_args=backend_args)
+
+unlabeled_dataset = dict(
+    type=dataset_type,
+    data_root=data_root,
+    ann_file='annotations/instances_unlabeled2017.json',
+    data_prefix=dict(img='unlabeled2017/'),
+    filter_cfg=dict(filter_empty_gt=False),
+    pipeline=unsup_pipeline,
+    backend_args=backend_args)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=num_workers,
+    persistent_workers=True,
+    sampler=dict(
+        type=GroupMultiSourceSampler,
+        batch_size=batch_size,
+        source_ratio=[1, 4]),
+    dataset=dict(
+        type=ConcatDataset, datasets=[labeled_dataset, unlabeled_dataset]))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type=CocoMetric,
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator

mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.models.faster_rcnn_r50_fpn import *
+    from .._base_.datasets.semi_coco_detection import *
+    from .._base_.default_runtime import *
+
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop
+from torch.optim.sgd import SGD
+
+from mmdet.engine.hooks.mean_teacher_hook import MeanTeacherHook
+from mmdet.engine.runner import TeacherStudentValLoop
+from mmdet.models.backbones.resnet import ResNet
+from mmdet.models.data_preprocessors.data_preprocessor import (
+    DetDataPreprocessor, MultiBranchDataPreprocessor)
+from mmdet.models.detectors.soft_teacher import SoftTeacher
+
+detector = model
+detector.data_preprocessor.update(
+    dict(
+        type=DetDataPreprocessor,
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32))
+
+model = dict(
+    type=SoftTeacher,
+    detector=detector,
+    data_preprocessor=dict(
+        type=MultiBranchDataPreprocessor,
+        data_preprocessor=detector.data_preprocessor),
+    semi_train_cfg=dict(
+        freeze_teacher=True,
+        sup_weight=1.0,
+        unsup_weight=4.0,
+        pseudo_label_initial_score_thr=0.5,
+        rpn_pseudo_thr=0.9,
+        cls_pseudo_thr=0.9,
+        reg_pseudo_thr=0.02,
+        jitter_times=10,
+        jitter_scale=0.06,
+        min_pseudo_bbox_wh=(1e-2, 1e-2)),
+    semi_test_cfg=dict(predict_on='teacher'))
+
+# 10% coco train2017 is set as labeled dataset
+labeled_dataset = labeled_dataset
+unlabeled_dataset = unlabeled_dataset
+labeled_dataset.ann_file = 'semi_anns/[email protected]'
+unlabeled_dataset.ann_file = 'semi_anns/' \
+                             '[email protected]'
+unlabeled_dataset.data_prefix = dict(img='train2017/')
+train_dataloader.update(
+    dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])))
+
+# training schedule for 180k
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=180000, val_interval=5000)
+val_cfg = dict(type=TeacherStudentValLoop)
+test_cfg = dict(type=TestLoop)
+
+# learning rate policy
+param_scheduler = [
+    dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=180000,
+        by_epoch=False,
+        milestones=[120000, 160000],
+        gamma=0.1)
+]
+
+# optimizer
+# The learning rate in the old configuration was 0.01,
+# but there was a loss cls error during runtime.
+# Because the process of calculating losses is too close to zero,
+# the learning rate is adjusted to 0.001.
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(type=SGD, lr=0.001, momentum=0.9, weight_decay=0.0001))
+
+default_hooks.update(
+    dict(checkpoint=dict(by_epoch=False, interval=10000, max_keep_ckpts=2)))
+log_processor.update(dict(by_epoch=False))
+
+custom_hooks = [dict(type=MeanTeacherHook)]

mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_1_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco import *
+
+# 1% coco train2017 is set as labeled dataset
+labeled_dataset = labeled_dataset
+unlabeled_dataset = unlabeled_dataset
+labeled_dataset.ann_file = 'semi_anns/[email protected]'
+unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
+train_dataloader.update(
+    dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])))

mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_2_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco import *
+
+# 1% coco train2017 is set as labeled dataset
+labeled_dataset = labeled_dataset
+unlabeled_dataset = unlabeled_dataset
+labeled_dataset.ann_file = 'semi_anns/[email protected]'
+unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
+train_dataloader.update(
+    dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])))

mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_5_coco.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco import *
+
+# 1% coco train2017 is set as labeled dataset
+labeled_dataset = labeled_dataset
+unlabeled_dataset = unlabeled_dataset
+labeled_dataset.ann_file = 'semi_anns/[email protected]'
+unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
+train_dataloader.update(
+    dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])))