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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| _base_ = ['./fcos.py', 'configs/base.py'] | ||
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| log_config = dict( | ||
| interval=50, hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| ]) | ||
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| checkpoint_config = dict(interval=10) | ||
| # optimizer | ||
| optimizer = dict( | ||
| type='SGD', | ||
| lr=0.01, | ||
| momentum=0.9, | ||
| weight_decay=0.0001, | ||
| paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.)) | ||
| optimizer_config = dict(grad_clip=None) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=1.0 / 3, | ||
| warmup_by_epoch=False, | ||
| step=[8, 11]) | ||
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| total_epochs = 12 | ||
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| find_unused_parameters = False | ||
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| CLASSES = [ | ||
| 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', | ||
| 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', | ||
| 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', | ||
| 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', | ||
| 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', | ||
| 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', | ||
| 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', | ||
| 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', | ||
| 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', | ||
| 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', | ||
| 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', | ||
| 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', | ||
| 'hair drier', 'toothbrush' | ||
| ] | ||
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| img_scale = (1333, 800) | ||
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| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
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| train_pipeline = [ | ||
| dict(type='MMResize', img_scale=img_scale, keep_ratio=True), | ||
| dict(type='MMRandomFlip', flip_ratio=0.5), | ||
| dict(type='MMNormalize', **img_norm_cfg), | ||
| dict(type='MMPad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img', 'gt_bboxes', 'gt_labels'], | ||
| meta_keys=('filename', 'ori_filename', 'ori_shape', 'ori_img_shape', | ||
| 'img_shape', 'pad_shape', 'scale_factor', 'flip', | ||
| 'flip_direction', 'img_norm_cfg')) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='MMMultiScaleFlipAug', | ||
| img_scale=img_scale, | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='MMResize', keep_ratio=True), | ||
| dict(type='MMRandomFlip'), | ||
| dict(type='MMNormalize', **img_norm_cfg), | ||
| dict(type='MMPad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img'], | ||
| meta_keys=('filename', 'ori_filename', 'ori_shape', | ||
| 'ori_img_shape', 'img_shape', 'pad_shape', | ||
| 'scale_factor', 'flip', 'flip_direction', | ||
| 'img_norm_cfg')) | ||
| ]) | ||
| ] | ||
|
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||
| # dataset settings | ||
| data_type = 'DetSourcePAI' | ||
| train_path = 'data/coco/train2017.manifest' | ||
| val_path = 'data/coco/val2017.manifest' | ||
| test_batch_size = 1 | ||
|
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| train_dataset = dict( | ||
| type='DetDataset', | ||
| data_source=dict(type=data_type, path=train_path, classes=CLASSES), | ||
| pipeline=train_pipeline) | ||
|
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| val_dataset = dict( | ||
| type='DetDataset', | ||
| imgs_per_gpu=test_batch_size, | ||
| data_source=dict(type=data_type, path=val_path, classes=CLASSES), | ||
| pipeline=test_pipeline) | ||
|
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| data = dict( | ||
| imgs_per_gpu=2, workers_per_gpu=2, train=train_dataset, val=val_dataset) | ||
|
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| # evaluation | ||
| eval_config = dict(interval=1, gpu_collect=False) | ||
| eval_pipelines = [ | ||
| dict( | ||
| mode='test', | ||
| evaluators=[ | ||
| dict(type='CocoDetectionEvaluator', classes=CLASSES), | ||
| ], | ||
| ) | ||
| ] |
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