mirror of https://github.com/alibaba/EasyCV.git
127 lines
4.0 KiB
Python
127 lines
4.0 KiB
Python
_base_ = './yolox_s_8xb16_300e_coco.py'
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# model settings
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model = dict(model_type='tiny')
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CLASSES = [
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'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
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'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
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'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
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'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
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'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
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'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
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'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
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'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush'
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]
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img_scale = (416, 416)
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random_size = (10, 20)
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scale_ratio = (0.5, 1.5)
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='MMMosaic', img_scale=img_scale, pad_val=114.0),
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dict(
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type='MMRandomAffine',
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scaling_ratio_range=scale_ratio,
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border=(-img_scale[0] // 2, -img_scale[1] // 2)),
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dict(
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type='MMMixUp', # s m x l; tiny nano will detele
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img_scale=img_scale,
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ratio_range=(0.8, 1.6),
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pad_val=114.0),
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dict(
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type='MMPhotoMetricDistortion',
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brightness_delta=32,
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contrast_range=(0.5, 1.5),
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saturation_range=(0.5, 1.5),
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hue_delta=18),
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dict(type='MMRandomFlip', flip_ratio=0.5),
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dict(type='MMResize', keep_ratio=True),
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dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
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dict(type='MMNormalize', **img_norm_cfg),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
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]
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test_pipeline = [
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dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
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dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
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dict(type='MMNormalize', **img_norm_cfg),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img'])
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]
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data_root = 'data/coco/'
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train_dataset = dict(
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type='DetImagesMixDataset',
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data_source=dict(
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type='DetSourceCoco',
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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pipeline=[
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations', with_bbox=True)
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],
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classes=CLASSES,
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filter_empty_gt=False,
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iscrowd=False),
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pipeline=train_pipeline,
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dynamic_scale=img_scale)
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val_dataset = dict(
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type='DetImagesMixDataset',
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imgs_per_gpu=2,
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data_source=dict(
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type='DetSourceCoco',
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=[
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations', with_bbox=True)
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],
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classes=CLASSES,
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filter_empty_gt=False,
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iscrowd=True),
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pipeline=test_pipeline,
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dynamic_scale=None,
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label_padding=False)
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data = dict(
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imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)
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# additional hooks
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interval = 10
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custom_hooks = [
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dict(
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type='YOLOXModeSwitchHook',
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no_aug_epochs=15,
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skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
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priority=48),
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dict(
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type='SyncRandomSizeHook',
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ratio_range=random_size,
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img_scale=img_scale,
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interval=interval,
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priority=48),
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dict(
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type='SyncNormHook',
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num_last_epochs=15,
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interval=interval,
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priority=48)
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]
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eval_pipelines = [
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dict(
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mode='test',
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data=data['val'],
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evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
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)
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]
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