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478 changes: 473 additions & 5 deletions
478
tests/data/models/mmdet/cascade_mask_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py
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379 changes: 369 additions & 10 deletions
379
tests/data/models/mmdet/retinanet/retinanet_r50_fpn_1x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,369 @@ | ||
| _base_ = [ | ||
| "../_base_/models/retinanet_r50_fpn.py", | ||
| "../_base_/datasets/coco_detection.py", | ||
| "../_base_/schedules/schedule_1x.py", | ||
| "../_base_/default_runtime.py", | ||
| "./retinanet_tta.py", | ||
| ] | ||
|
|
||
| # optimizer | ||
| optim_wrapper = dict(optimizer=dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)) | ||
| auto_scale_lr = dict(base_batch_size=16, enable=False) | ||
| backend_args = None | ||
| data_root = "data/coco/" | ||
| dataset_type = "CocoDataset" | ||
| default_hooks = dict( | ||
| checkpoint=dict(interval=1, type="CheckpointHook"), | ||
| logger=dict(interval=50, type="LoggerHook"), | ||
| param_scheduler=dict(type="ParamSchedulerHook"), | ||
| sampler_seed=dict(type="DistSamplerSeedHook"), | ||
| timer=dict(type="IterTimerHook"), | ||
| visualization=dict(type="DetVisualizationHook"), | ||
| ) | ||
| default_scope = "mmdet" | ||
| env_cfg = dict( | ||
| cudnn_benchmark=False, dist_cfg=dict(backend="nccl"), mp_cfg=dict(mp_start_method="fork", opencv_num_threads=0) | ||
| ) | ||
| img_scales = [ | ||
| ( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| ( | ||
| 666, | ||
| 400, | ||
| ), | ||
| ( | ||
| 2000, | ||
| 1200, | ||
| ), | ||
| ] | ||
| load_from = None | ||
| log_level = "INFO" | ||
| log_processor = dict(by_epoch=True, type="LogProcessor", window_size=50) | ||
| model = dict( | ||
| backbone=dict( | ||
| depth=50, | ||
| frozen_stages=1, | ||
| init_cfg=dict(checkpoint="torchvision://resnet50", type="Pretrained"), | ||
| norm_cfg=dict(requires_grad=True, type="BN"), | ||
| norm_eval=True, | ||
| num_stages=4, | ||
| out_indices=( | ||
| 0, | ||
| 1, | ||
| 2, | ||
| 3, | ||
| ), | ||
| style="pytorch", | ||
| type="ResNet", | ||
| ), | ||
| bbox_head=dict( | ||
| anchor_generator=dict( | ||
| octave_base_scale=4, | ||
| ratios=[ | ||
| 0.5, | ||
| 1.0, | ||
| 2.0, | ||
| ], | ||
| scales_per_octave=3, | ||
| strides=[ | ||
| 8, | ||
| 16, | ||
| 32, | ||
| 64, | ||
| 128, | ||
| ], | ||
| type="AnchorGenerator", | ||
| ), | ||
| bbox_coder=dict( | ||
| target_means=[ | ||
| 0.0, | ||
| 0.0, | ||
| 0.0, | ||
| 0.0, | ||
| ], | ||
| target_stds=[ | ||
| 1.0, | ||
| 1.0, | ||
| 1.0, | ||
| 1.0, | ||
| ], | ||
| type="DeltaXYWHBBoxCoder", | ||
| ), | ||
| feat_channels=256, | ||
| in_channels=256, | ||
| loss_bbox=dict(loss_weight=1.0, type="L1Loss"), | ||
| loss_cls=dict(alpha=0.25, gamma=2.0, loss_weight=1.0, type="FocalLoss", use_sigmoid=True), | ||
| num_classes=80, | ||
| stacked_convs=4, | ||
| type="RetinaHead", | ||
| ), | ||
| data_preprocessor=dict( | ||
| bgr_to_rgb=True, | ||
| mean=[ | ||
| 123.675, | ||
| 116.28, | ||
| 103.53, | ||
| ], | ||
| pad_size_divisor=32, | ||
| std=[ | ||
| 58.395, | ||
| 57.12, | ||
| 57.375, | ||
| ], | ||
| type="DetDataPreprocessor", | ||
| ), | ||
| neck=dict( | ||
| add_extra_convs="on_input", | ||
| in_channels=[ | ||
| 256, | ||
| 512, | ||
| 1024, | ||
| 2048, | ||
| ], | ||
| num_outs=5, | ||
| out_channels=256, | ||
| start_level=1, | ||
| type="FPN", | ||
| ), | ||
| test_cfg=dict( | ||
| max_per_img=100, min_bbox_size=0, nms=dict(iou_threshold=0.5, type="nms"), nms_pre=1000, score_thr=0.05 | ||
| ), | ||
| train_cfg=dict( | ||
| allowed_border=-1, | ||
| assigner=dict(ignore_iof_thr=-1, min_pos_iou=0, neg_iou_thr=0.4, pos_iou_thr=0.5, type="MaxIoUAssigner"), | ||
| debug=False, | ||
| pos_weight=-1, | ||
| sampler=dict(type="PseudoSampler"), | ||
| ), | ||
| type="RetinaNet", | ||
| ) | ||
| optim_wrapper = dict(optimizer=dict(lr=0.01, momentum=0.9, type="SGD", weight_decay=0.0001), type="OptimWrapper") | ||
| param_scheduler = [ | ||
| dict(begin=0, by_epoch=False, end=500, start_factor=0.001, type="LinearLR"), | ||
| dict( | ||
| begin=0, | ||
| by_epoch=True, | ||
| end=12, | ||
| gamma=0.1, | ||
| milestones=[ | ||
| 8, | ||
| 11, | ||
| ], | ||
| type="MultiStepLR", | ||
| ), | ||
| ] | ||
| resume = False | ||
| test_cfg = dict(type="TestLoop") | ||
| test_dataloader = dict( | ||
| batch_size=1, | ||
| dataset=dict( | ||
| ann_file="annotations/instances_val2017.json", | ||
| backend_args=None, | ||
| data_prefix=dict(img="val2017/"), | ||
| data_root="data/coco/", | ||
| pipeline=[ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| dict( | ||
| meta_keys=( | ||
| "img_id", | ||
| "img_path", | ||
| "ori_shape", | ||
| "img_shape", | ||
| "scale_factor", | ||
| ), | ||
| type="PackDetInputs", | ||
| ), | ||
| ], | ||
| test_mode=True, | ||
| type="CocoDataset", | ||
| ), | ||
| drop_last=False, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(shuffle=False, type="DefaultSampler"), | ||
| ) | ||
| test_evaluator = dict( | ||
| ann_file="data/coco/annotations/instances_val2017.json", | ||
| backend_args=None, | ||
| format_only=False, | ||
| metric="bbox", | ||
| type="CocoMetric", | ||
| ) | ||
| test_pipeline = [ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| dict( | ||
| meta_keys=( | ||
| "img_id", | ||
| "img_path", | ||
| "ori_shape", | ||
| "img_shape", | ||
| "scale_factor", | ||
| ), | ||
| type="PackDetInputs", | ||
| ), | ||
| ] | ||
| train_cfg = dict(max_epochs=12, type="EpochBasedTrainLoop", val_interval=1) | ||
| train_dataloader = dict( | ||
| batch_sampler=dict(type="AspectRatioBatchSampler"), | ||
| batch_size=2, | ||
| dataset=dict( | ||
| ann_file="annotations/instances_train2017.json", | ||
| backend_args=None, | ||
| data_prefix=dict(img="train2017/"), | ||
| data_root="data/coco/", | ||
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | ||
| pipeline=[ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict(prob=0.5, type="RandomFlip"), | ||
| dict(type="PackDetInputs"), | ||
| ], | ||
| type="CocoDataset", | ||
| ), | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(shuffle=True, type="DefaultSampler"), | ||
| ) | ||
| train_pipeline = [ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict(prob=0.5, type="RandomFlip"), | ||
| dict(type="PackDetInputs"), | ||
| ] | ||
| tta_model = dict(tta_cfg=dict(max_per_img=100, nms=dict(iou_threshold=0.5, type="nms")), type="DetTTAModel") | ||
| tta_pipeline = [ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict( | ||
| transforms=[ | ||
| [ | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 666, | ||
| 400, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 2000, | ||
| 1200, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| ], | ||
| [ | ||
| dict(prob=1.0, type="RandomFlip"), | ||
| dict(prob=0.0, type="RandomFlip"), | ||
| ], | ||
| [ | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| ], | ||
| [ | ||
| dict( | ||
| meta_keys=( | ||
| "img_id", | ||
| "img_path", | ||
| "ori_shape", | ||
| "img_shape", | ||
| "scale_factor", | ||
| "flip", | ||
| "flip_direction", | ||
| ), | ||
| type="PackDetInputs", | ||
| ), | ||
| ], | ||
| ], | ||
| type="TestTimeAug", | ||
| ), | ||
| ] | ||
| val_cfg = dict(type="ValLoop") | ||
| val_dataloader = dict( | ||
| batch_size=1, | ||
| dataset=dict( | ||
| ann_file="annotations/instances_val2017.json", | ||
| backend_args=None, | ||
| data_prefix=dict(img="val2017/"), | ||
| data_root="data/coco/", | ||
| pipeline=[ | ||
| dict(backend_args=None, type="LoadImageFromFile"), | ||
| dict( | ||
| keep_ratio=True, | ||
| scale=( | ||
| 1333, | ||
| 800, | ||
| ), | ||
| type="Resize", | ||
| ), | ||
| dict(type="LoadAnnotations", with_bbox=True), | ||
| dict( | ||
| meta_keys=( | ||
| "img_id", | ||
| "img_path", | ||
| "ori_shape", | ||
| "img_shape", | ||
| "scale_factor", | ||
| ), | ||
| type="PackDetInputs", | ||
| ), | ||
| ], | ||
| test_mode=True, | ||
| type="CocoDataset", | ||
| ), | ||
| drop_last=False, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(shuffle=False, type="DefaultSampler"), | ||
| ) | ||
| val_evaluator = dict( | ||
| ann_file="data/coco/annotations/instances_val2017.json", | ||
| backend_args=None, | ||
| format_only=False, | ||
| metric="bbox", | ||
| type="CocoMetric", | ||
| ) | ||
| vis_backends = [ | ||
| dict(type="LocalVisBackend"), | ||
| ] | ||
| visualizer = dict( | ||
| name="visualizer", | ||
| type="DetLocalVisualizer", | ||
| vis_backends=[ | ||
| dict(type="LocalVisBackend"), | ||
| ], | ||
| ) |
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