diff --git a/mmdet/configs/_base_/datasets/semi_coco_detection.py b/mmdet/configs/_base_/datasets/semi_coco_detection.py new file mode 100644 index 00000000000..5aad14f8364 --- /dev/null +++ b/mmdet/configs/_base_/datasets/semi_coco_detection.py @@ -0,0 +1,209 @@ +# 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 diff --git a/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco.py b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco.py new file mode 100644 index 00000000000..7884a16fcf6 --- /dev/null +++ b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_10_coco.py @@ -0,0 +1,89 @@ +# 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/instances_train2017.1@10.json' +unlabeled_dataset.ann_file = 'semi_anns/' \ + 'instances_train2017.1@10-unlabeled.json' +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)] diff --git a/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_1_coco.py b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_1_coco.py new file mode 100644 index 00000000000..185d2a94a05 --- /dev/null +++ b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_1_coco.py @@ -0,0 +1,13 @@ +# 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/instances_train2017.1@1.json' +unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@1-unlabeled.json' +train_dataloader.update( + dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))) diff --git a/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_2_coco.py b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_2_coco.py new file mode 100644 index 00000000000..ca37a9867e6 --- /dev/null +++ b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_2_coco.py @@ -0,0 +1,13 @@ +# 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/instances_train2017.1@2.json' +unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@2-unlabeled.json' +train_dataloader.update( + dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))) diff --git a/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_5_coco.py b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_5_coco.py new file mode 100644 index 00000000000..fad48a7508a --- /dev/null +++ b/mmdet/configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_180k_semi_5_coco.py @@ -0,0 +1,13 @@ +# 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/instances_train2017.1@5.json' +unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@5-unlabeled.json' +train_dataloader.update( + dict(dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])))