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_base_ = '../common/lsj-200e_coco-detection.py' |
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image_size = (1024, 1024) |
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batch_augments = [dict(type='BatchFixedSizePad', size=image_size)] |
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model = dict( |
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type='CenterNet', |
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data_preprocessor=dict( |
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type='DetDataPreprocessor', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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bgr_to_rgb=True, |
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pad_size_divisor=32, |
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batch_augments=batch_augments), |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_output', |
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num_outs=5, |
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init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'), |
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relu_before_extra_convs=True), |
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bbox_head=dict( |
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type='CenterNetUpdateHead', |
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num_classes=80, |
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in_channels=256, |
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stacked_convs=4, |
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feat_channels=256, |
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strides=[8, 16, 32, 64, 128], |
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loss_cls=dict( |
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type='GaussianFocalLoss', |
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pos_weight=0.25, |
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neg_weight=0.75, |
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loss_weight=1.0), |
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loss_bbox=dict(type='GIoULoss', loss_weight=2.0), |
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), |
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train_cfg=None, |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.6), |
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max_per_img=100)) |
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train_dataloader = dict(batch_size=8, num_workers=4) |
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optim_wrapper = dict( |
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type='AmpOptimWrapper', |
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optimizer=dict( |
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type='SGD', lr=0.01 * 4, momentum=0.9, weight_decay=0.00004), |
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paramwise_cfg=dict(norm_decay_mult=0.)) |
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param_scheduler = [ |
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dict( |
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type='LinearLR', |
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start_factor=0.00025, |
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by_epoch=False, |
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begin=0, |
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end=4000), |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=25, |
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by_epoch=True, |
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milestones=[22, 24], |
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gamma=0.1) |
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] |
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auto_scale_lr = dict(base_batch_size=64) |
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