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fcos_r50_torch_1x_pai.py
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fcos_r50_torch_1x_pai.py
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_base_ = ['./fcos.py', 'configs/base.py']
log_config = dict(
interval=50, hooks=[
dict(type='TextLoggerHook'),
])
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])
total_epochs = 12
find_unused_parameters = False
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'
]
img_scale = (1333, 800)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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'))
])
]
# dataset settings
data_type = 'DetSourcePAI'
train_path = 'data/coco/train2017.manifest'
val_path = 'data/coco/val2017.manifest'
test_batch_size = 1
train_dataset = dict(
type='DetDataset',
data_source=dict(type=data_type, path=train_path, classes=CLASSES),
pipeline=train_pipeline)
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)
data = dict(
imgs_per_gpu=2, workers_per_gpu=2, train=train_dataset, val=val_dataset)
# evaluation
eval_config = dict(interval=1, gpu_collect=False)
eval_pipelines = [
dict(
mode='test',
evaluators=[
dict(type='CocoDetectionEvaluator', classes=CLASSES),
],
)
]