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_base_ = [ |
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'../_base_/datasets/sunrgbd-3d.py', '../_base_/default_runtime.py', |
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'../_base_/models/imvotenet.py' |
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] |
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|
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backend_args = None |
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|
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train_pipeline = [ |
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dict(type='LoadImageFromFile', backend_args=backend_args), |
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dict( |
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type='LoadAnnotations3D', |
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with_bbox=True, |
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with_label=True, |
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with_bbox_3d=False, |
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with_label_3d=False), |
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dict( |
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type='RandomChoiceResize', |
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scales=[(1333, 480), (1333, 504), (1333, 528), (1333, 552), |
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(1333, 576), (1333, 600)], |
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keep_ratio=True), |
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dict(type='RandomFlip', prob=0.5), |
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dict( |
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type='Pack3DDetInputs', keys=['img', 'gt_bboxes', 'gt_bboxes_labels']), |
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] |
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|
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test_pipeline = [ |
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dict(type='LoadImageFromFile', backend_args=backend_args), |
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dict(type='Resize', scale=(1333, 600), keep_ratio=True), |
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dict( |
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type='Pack3DDetInputs', |
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keys=(['img']), |
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', |
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'scale_factor')) |
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] |
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train_dataloader = dict( |
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batch_size=2, |
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num_workers=2, |
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sampler=dict(type='DefaultSampler', shuffle=True), |
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dataset=dict( |
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type='RepeatDataset', times=1, dataset=dict(pipeline=train_pipeline))) |
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|
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline)) |
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test_dataloader = dict(dataset=dict(pipeline=test_pipeline)) |
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|
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=8, val_interval=1) |
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val_cfg = dict(type='ValLoop') |
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test_cfg = dict(type='TestLoop') |
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|
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param_scheduler = [ |
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dict( |
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type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=8, |
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by_epoch=True, |
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milestones=[6], |
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gamma=0.1) |
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] |
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val_evaluator = dict(type='Indoor2DMetric') |
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test_evaluator = val_evaluator |
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|
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|
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) |
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|
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load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' |
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