data =
dict
( videos_per_gpu=8,
workers_per_gpu=4,
train=
dict
(type
=dataset_type,
ann_file=ann_file_train,
data_prefix=data_root,
pipeline=train_pipeline)
, val=
dict
(type
=dataset_type,
ann_file=ann_file_val,
data_prefix=data_root_val,
pipeline=val_pipeline)
, test=
dict
(type
=dataset_type,
ann_file=ann_file_val,
data_prefix=data_root_val,
pipeline=test_pipeline)
)
pipeline的構建過程：將配置檔案中定義的內容轉換為乙個list，並將該list傳入mmaction.datasets.pipelines.compose.compose中。
pipeline 配置檔案概述：
train_pipeline =
[dict
(type
='sampleframes'
, clip_len=
32, frame_interval=
2, num_clips=1)
,dict
(type
='frameselector'
, decoding_backend=
'turbojpeg'),
dict
(type
='resize'
, scale=(-
1,256)
, lazy=
true),
dict
(type
='multiscalecrop'
, input_size=
224,
scales=(1
,0.8),
random_crop=
false
, max_wh_scale_gap=0,
lazy=
true),
dict
(type
='resize'
, scale=
(224
,224
), keep_ratio=
false
, lazy=
true),
dict
(type
='flip'
, flip_ratio=
0.5, lazy=
true),
dict
(type
='fuse'),
dict
(type
='normalize'
,**img_norm_cfg)
,dict
(type
='formatshape'
, input_format=
'ncthw'),
dict
(type
='collect'
, keys=
['imgs'
,'label'
], meta_keys=
),dict
(type
='totensor'
, keys=
['imgs'
,'label'])
]val_pipeline =
[dict
(type
='sampleframes'
, clip_len=32,
frame_interval=2,
num_clips=1,
test_mode=
true),
dict
(type
='frameselector'
, decoding_backend=
'turbojpeg'),
dict
(type
='resize'
, scale=(-
1,256)
, lazy=
true),
dict
(type
='centercrop'
, crop_size=
224, lazy=
true),
dict
(type
='flip'
, flip_ratio=
0, lazy=
true),
dict
(type
='fuse'),
dict
(type
='normalize'
,**img_norm_cfg)
,dict
(type
='formatshape'
, input_format=
'ncthw'),
dict
(type
='collect'
, keys=
['imgs'
,'label'
], meta_keys=
),dict
(type
='totensor'
, keys=
['imgs'])
]test_pipeline =
[dict
(type
='sampleframes'
, clip_len=32,
frame_interval=2,
num_clips=10,
test_mode=
true),
dict
(type
='frameselector'
, decoding_backend=
'turbojpeg'),
dict
(type
='resize'
, scale=(-
1,256)),
dict
(type
='threecrop'
, crop_size=
256)
,dict
(type
='flip'
, flip_ratio=0)
,dict
(type
='normalize'
,**img_norm_cfg)
,dict
(type
='formatshape'
, input_format=
'ncthw'),
dict
(type
='collect'
, keys=
['imgs'
,'label'
], meta_keys=
),dict
(type
='totensor'
, keys=
['imgs'])
]
呼叫compose類實現的功能就是將資料依次通過所有元件，得到結果。
pipeline的分類
資料增強
資料型別/格式轉換
上面介紹過的compose
普通形式：在連續的幀中，間隔x幀提取幀，一共獲取y幀。
densesampleframes
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