_base_ = [ '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' ] model = dict( type='DeformableDETR', num_queries=300, num_feature_levels=4, with_box_refine=False, as_two_stage=False, data_preprocessor=dict( type='DetDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_size_divisor=1), backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='ChannelMapper', in_channels=[512, 1024, 2048], kernel_size=1, out_channels=256, act_cfg=None, norm_cfg=dict(type='GN', num_groups=32), num_outs=4), encoder=dict( # DeformableDetrTransformerEncoder num_layers=6, layer_cfg=dict( # DeformableDetrTransformerEncoderLayer self_attn_cfg=dict( # MultiScaleDeformableAttention embed_dims=256, batch_first=True), ffn_cfg=dict( embed_dims=256, feedforward_channels=1024, ffn_drop=0.1))), decoder=dict( # DeformableDetrTransformerDecoder num_layers=6, return_intermediate=True, layer_cfg=dict( # DeformableDetrTransformerDecoderLayer self_attn_cfg=dict( # MultiheadAttention embed_dims=256, num_heads=8, dropout=0.1, batch_first=True), cross_attn_cfg=dict( # MultiScaleDeformableAttention embed_dims=256, batch_first=True), ffn_cfg=dict( embed_dims=256, feedforward_channels=1024, ffn_drop=0.1)), post_norm_cfg=None), positional_encoding=dict(num_feats=128, normalize=True, offset=-0.5), bbox_head=dict( type='DeformableDETRHead', num_classes=80, sync_cls_avg_factor=True, loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0), loss_bbox=dict(type='L1Loss', loss_weight=5.0), loss_iou=dict(type='GIoULoss', loss_weight=2.0)), # training and testing settings train_cfg=dict( assigner=dict( type='HungarianAssigner', match_costs=[ dict(type='FocalLossCost', weight=2.0), dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), dict(type='IoUCost', iou_mode='giou', weight=2.0) ])), test_cfg=dict(max_per_img=100)) # train_pipeline, NOTE the img_scale and the Pad's size_divisor is different # from the default setting in mmdet. train_pipeline = [ dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), dict(type='LoadAnnotations', with_bbox=True), dict(type='RandomFlip', prob=0.5), # dict( # type='RandomChoice', # transforms=[ # [ # dict( # type='RandomChoiceResize', # scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), # (608, 1333), (640, 1333), (672, 1333), (704, 1333), # (736, 1333), (768, 1333), (800, 1333)], # keep_ratio=True) # ], # [ # dict( # type='RandomChoiceResize', # # The radio of all image in train dataset < 7 # # follow the original implement # scales=[(400, 4200), (500, 4200), (600, 4200)], # keep_ratio=True), # dict( # type='RandomCrop', # crop_type='absolute_range', # crop_size=(384, 600), # allow_negative_crop=True), # dict( # type='RandomChoiceResize', # scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), # (608, 1333), (640, 1333), (672, 1333), (704, 1333), # (736, 1333), (768, 1333), (800, 1333)], # keep_ratio=True) # ] # ]), dict( type='RandomResize', scale=(640, 640), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=(640, 640)), dict(type='PackDetInputs') ] train_dataloader = dict( dataset=dict( filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline)) # optimizer optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), clip_grad=dict(max_norm=0.1, norm_type=2), paramwise_cfg=dict( custom_keys={ 'backbone': dict(lr_mult=0.1), 'sampling_offsets': dict(lr_mult=0.1), 'reference_points': dict(lr_mult=0.1) })) # learning policy max_epochs = 50 train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=max_epochs, by_epoch=True, milestones=[40], gamma=0.1) ] # NOTE: `auto_scale_lr` is for automatically scaling LR, # USER SHOULD NOT CHANGE ITS VALUES. # base_batch_size = (16 GPUs) x (2 samples per GPU) auto_scale_lr = dict(base_batch_size=32)