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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # 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_.default_runtime import * | |
| from mmengine.dataset.sampler import DefaultSampler | |
| from mmengine.optim import OptimWrapper | |
| from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR | |
| from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop | |
| from torch.optim import SGD | |
| from mmdet.datasets import CocoDataset, RepeatDataset | |
| from mmdet.datasets.transforms.formatting import PackDetInputs | |
| from mmdet.datasets.transforms.loading import (FilterAnnotations, | |
| LoadAnnotations, | |
| LoadImageFromFile) | |
| from mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic, | |
| Pad, RandomCrop, RandomFlip, | |
| RandomResize, Resize) | |
| from mmdet.evaluation import CocoMetric | |
| # dataset settings | |
| dataset_type = CocoDataset | |
| data_root = 'data/coco/' | |
| image_size = (1024, 1024) | |
| backend_args = None | |
| train_pipeline = [ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=LoadAnnotations, with_bbox=True, with_mask=True), | |
| dict( | |
| type=RandomResize, | |
| scale=image_size, | |
| ratio_range=(0.1, 2.0), | |
| keep_ratio=True), | |
| dict( | |
| type=RandomCrop, | |
| crop_type='absolute_range', | |
| crop_size=image_size, | |
| recompute_bbox=True, | |
| allow_negative_crop=True), | |
| dict(type=FilterAnnotations, min_gt_bbox_wh=(1e-2, 1e-2)), | |
| dict(type=RandomFlip, prob=0.5), | |
| dict(type=PackDetInputs) | |
| ] | |
| test_pipeline = [ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=Resize, scale=(1333, 800), keep_ratio=True), | |
| dict(type=LoadAnnotations, with_bbox=True, with_mask=True), | |
| dict( | |
| type=PackDetInputs, | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| # Use RepeatDataset to speed up training | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type=DefaultSampler, shuffle=True), | |
| dataset=dict( | |
| type=RepeatDataset, | |
| times=4, # simply change this from 2 to 16 for 50e - 400e training. | |
| 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=train_pipeline, | |
| backend_args=backend_args))) | |
| 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', 'segm'], | |
| format_only=False, | |
| backend_args=backend_args) | |
| test_evaluator = val_evaluator | |
| max_epochs = 25 | |
| train_cfg = dict( | |
| type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=5) | |
| val_cfg = dict(type=ValLoop) | |
| test_cfg = dict(type=TestLoop) | |
| # optimizer assumes bs=64 | |
| optim_wrapper = dict( | |
| type=OptimWrapper, | |
| optimizer=dict(type=SGD, lr=0.1, momentum=0.9, weight_decay=0.00004)) | |
| # learning rate | |
| param_scheduler = [ | |
| dict(type=LinearLR, start_factor=0.067, by_epoch=False, begin=0, end=500), | |
| dict( | |
| type=MultiStepLR, | |
| begin=0, | |
| end=max_epochs, | |
| by_epoch=True, | |
| milestones=[22, 24], | |
| gamma=0.1) | |
| ] | |
| # only keep latest 2 checkpoints | |
| default_hooks.update(dict(checkpoint=dict(max_keep_ckpts=2))) | |
| # NOTE: `auto_scale_lr` is for automatically scaling LR, | |
| # USER SHOULD NOT CHANGE ITS VALUES. | |
| # base_batch_size = (32 GPUs) x (2 samples per GPU) | |
| auto_scale_lr = dict(base_batch_size=64) | |