_base_ = '../_base_/default_runtime.py' # dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' image_size = (1024, 1024) # 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 # Standard Scale Jittering (SSJ) resizes and crops an image # with a resize range of 0.8 to 1.25 of the original image size. 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=(0.8, 1.25), 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')) ] train_dataloader = dict( batch_size=2, num_workers=2, persistent_workers=True, sampler=dict(type='InfiniteSampler'), 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 # The model is trained by 270k iterations with batch_size 64, # which is roughly equivalent to 144 epochs. max_iters = 270000 train_cfg = dict( type='IterBasedTrainLoop', max_iters=max_iters, val_interval=10000) 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 policy # lr steps at [0.9, 0.95, 0.975] of the maximum iterations param_scheduler = [ dict( type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=1000), dict( type='MultiStepLR', begin=0, end=270000, by_epoch=False, milestones=[243000, 256500, 263250], gamma=0.1) ] default_hooks = dict(checkpoint=dict(by_epoch=False, interval=10000)) log_processor = dict(by_epoch=False) # 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)