在线时间:8:00-16:00
迪恩网络APP
随时随地掌握行业动态
扫描二维码
关注迪恩网络微信公众号
开源软件名称(OpenSource Name):achaiah/GulpIO开源软件地址(OpenSource Url):https://github.com/achaiah/GulpIO开源编程语言(OpenSource Language):Python 100.0%开源软件介绍(OpenSource Introduction):GulpIOAboutBinary storage format for deep learning on videos. StatusInstallThere are many ways to setup a Python environment for installation. Here we outline an approach using virtualenvironment. Note: this package does not support legacy Python and will only work with Python 3.x. The following will setup a virtualenvironment and activate it: $ python -m venv gulpio $ source gulpio/bin/activate Then, install the package using: $ pip install gulpio Usage'Gulp' a DatasetThe You should be able to get going quickly by looking at the following examples, that we use internally to gulp our video datasets.
And an example invocation would be: $ gulp_20bn_json_videos videos.json input_dir output_dir ... Additionally, if you would like to ingest your dataset from the command line,
the $ gulp_register_adapter gulpio.adapters <NewAdapterClassName> The script that provides the command line interface will be in the main directory of the repository. To use it, execute Sanity check the 'Gulped' FilesA very basic test to check the correctness of the gulped files is provided by the $ gulp_sanity_check <folder containing the gulped files> It tests:
The file names of the files where any test fails will be printed. Currently no script to fix possible errors is provided, 'regulping' is the only solution. Read a 'Gulped' DatasetIn order to read from the gulps, you can let yourself be inspired by the following snippet: # import the main interface for reading
from gulpio import GulpDirectory
# instantiate the GulpDirectory
gulp_directory = GulpDirectory('/tmp/something_something_gulps')
# iterate over all chunks
for chunk in gulp_directory:
# for each 'video' get the metadata and all frames
for frames, meta in chunk:
# do something with the metadata
for i, f in enumerate(frames):
# do something with the frames
pass Alternatively, a video with a specific # import the main interface for reading
from gulpio import GulpDirectory
#instantiate the GulpDirectory
gulp_directory = GulpDirectory('/tmp/something_something_gulps')
frames, meta = gulp_directory[<id>] For down-sampling or loading only a part of a video, a python slice can be passed as well: frames, meta = gulp_directory[<id>, slice(1,10,2)] or: frames, meta = gulp_directory[<id>, 1:10:2] Loading DataYou can use GulpIO data iterator and augmentation functions to load GulpIO dataset into memory.
For a working example given on different deep learning libraries please refer to We provide archetypical Below is an example loading an image dataset with GulpIO loader and defining augmentation pipeline. Transformations are applied to each instance on the fly. Some transformations have separate video and image versions since some of the augmentations need to be aligned video-wise. from gulpio.dataset import GulpImageDataset
from gulpio.loader import DataLoader
from gulpio.transforms import Scale, CenterCrop, Compose, UnitNorm
# define data augmentations. Notice that there are different functions for videos and images
transforms = Compose([
Scale(28), # resize image by the shortest edge
CenterCrop(28),
UnitNorm(), # instance wise mean and std norm
])
# define dataset wrapper and pick this up by the data loader interface.
dataset = GulpImageDataset('/path/to/train_data', transform=transforms)
loader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True)
dataset_val = GulpImageDataset('/path/to/validation_data/', transform=transforms)
loader_val = DataLoader(dataset_val, batch_size=256, shuffle=True, num_workers=0, drop_last=True) Here we iterate through the dataset we loaded. Iterator returns data and label as numpy arrays. You might need to cast these into the format of you deep learning library. for data, label in loader:
# train your model here
# ... GulpIO data loader is branched from great PyTorch implementation. Format DescriptionWhen gulping a dataset, two different files are created for every chunk: a
The layout of the |-jpeg-|-pad-|-jpeg-|-pad-|... Essentially, the data file is simply a series of concatenated JPEG images, i.e. the frames of the video. Each frame is padded to be divisible by four bytes, since this makes it easier to read JPEGs from disk. Here is a more visual example: As you can see there are 6 records in the example. They have the following paddings and lengths:
The layout of the meta file is a mapping, where each 'id' | |-> meta_data: [{}] | |-> frame_info: [[], [], ...] . . . By default, the meta file is serialized in JSON format. For example, here is a meta file snippet: {"702766": {"frame_info": [[0, 3, 7260], [7260, 3, 7252], [14512, 2, 7256], [21768, 2, 7260], [29028, 1, 7308], [36336, 1, 7344], [43680, 0, 7352], [51032, 1, 7364], [58396, 0, 7348], [65744, 1, 7352], [73096, 1, 7352], [80448, 1, 7408], [87856, 1, 7400], [95256, 0, 7376], [102632, 1, 7384], [110016, 2, 7404], [117420, 0, 7396], [124816, 1, 7400], [132216, 2, 7428], [139644, 1, 7420], [147064, 0, 7428], [154492, 2, 7472], [161964, 3, 7456], [169420, 2, 7444], [176864, 2, 7436]], "meta_data": [{"label": "something something", "id": 702766}]}, "803959": {"frame_info": [[184300, 1, 9256], [193556, 3, 9232], [202788, 2, 9340], [212128, 2, 9184], [221312, 1, 9112], [230424, 3, 9100], [239524, 0, 9144], [248668, 1, 9120], [257788, 0, 9104], [266892, 0, 9220], [276112, 1, 9140], [285252, 1, 9076], [294328, 2, 9100], [303428, 0, 9224], [312652, 3, 9200], [321852, 3, 9136], [330988, 2, 9136], [340124, 1, 9152], [349276, 0, 8984], [358260, 1, 9048], [367308, 0, 9116], [376424, 1, 9136], [385560, 1, 9108], [394668, 2, 9084], [403752, 1, 9112], [412864, 2, 9108]], "meta_data": [{"label": "something something", "id": 803959}]}, "803957": {"frame_info": [[421972, 2, 8592], [430564, 1, 8608], [439172, 2, 8872], [448044, 3, 8852], [456896, 2, 8860], [465756, 0, 8908], [474664, 2, 8912], [483576, 1, 8884], [492460, 1, 8752], [501212, 3, 8692], [509904, 0, 8612], [518516, 0, 8816], [527332, 2, 8784], [536116, 1, 8840], [544956, 1, 8844], [553800, 1, 8988], [562788, 0, 8992], [571780, 0, 8972], [580752, 3, 9044], [589796, 2, 9012], [598808, 3, 9060], [607868, 2, 9032], [616900, 1, 9052], [625952, 2, 9056], [635008, 0, 9084], [644092, 2, 9100]], "meta_data": [{"label": "something something", "id": 803957}]}, "773430": {"frame_info": [[653192, 1, 7964], [661156, 2, 7996], [669152, 1, 7960], [677112, 0, 8024], [685136, 0, 8008], [693144, 1, 7972], [701116, 0, 7980], [709096, 0, 8036], [717132, 0, 8016], [725148, 0, 8016], [733164, 1, 8004], [741168, 1, 8008], [749176, 1, 7996], [757172, 1, 8016], [765188, 1, 8032], [773220, 0, 8040], [781260, 2, 8044], [789304, 2, 8004], [797308, 1, 8008], [805316, 0, 8056], [813372, 3, 8088], [821460, 0, 8044]], "meta_data": [{"label": "something something", "id": 773430}]}, "803963": {"frame_info": [[829504, 2, 8952], [838456, 1, 8928], [847384, 0, 8972], [856356, 1, 8992], [865348, 1, 8936], [874284, 1, 8992], [883276, 3, 8988], [892264, 1, 9008], [901272, 2, 8996], [910268, 2, 8976], [919244, 0, 9180], [928424, 0, 9128], [937552, 2, 9100], [946652, 2, 9096], [955748, 3, 9044], [964792, 0, 9096], [973888, 2, 9068], [982956, 1, 8996], [991952, 3, 8928], [1000880, 1, 9040], [1009920, 0, 9084], [1019004, 0, 9076], [1028080, 2, 9056], [1037136, 2, 9040], [1046176, 2, 9052], [1055228, 3, 9096]], "meta_data": [{"label": "something something", "id": 803963}]} } Benchmarks
Other Formats
LicenseCopyright (c) 2017 Twenty Billion Neurons GmbH, Berlin, Germany MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
2023-10-27
2022-08-15
2022-08-17
2022-09-23
2022-08-13
请发表评论