Repository accompanying the "Sign Pose-based Transformer for Word-level Sign Language Recognition" paper

Related tags

Deep Learningtransformerslrlsa64sign-language-recognitionwlasl
Overview

Alt Text

by Matyáš Boháček and Marek Hrúz, University of West Bohemia
Should you have any questions or inquiries, feel free to contact us here.

PWC

Repository accompanying the Sign Pose-based Transformer for Word-level Sign Language Recognition paper, where we present a novel architecture for word-level sign language recognition based on the Transformer model. We designed our solution with low computational cost in mind, since we see egreat potential in the usage of such recognition system on hand-held devices. We introduce multiple original augmentation techniques tailored for the task of sign language recognition and propose a unique normalization scheme based on sign language linguistics.

Alt Text

Get Started

First, make sure to install all necessary dependencies using:

pip install -r requirements.txt

To train the model, simply specify the hyperparameters and run the following:

python -m train
  --experiment_name [str; name of the experiment to name the output logs and plots]
  
  --epochs [int; number of epochs]
  --lr [float; learning rate]
  
  --training_set_path [str; path to the csv file with training set's skeletal data]
  --validation_set_path [str; path to the csv file with validation set's skeletal data]
  --testing_set_path [str; path to the csv file with testing set's skeletal data]

If either the validation or testing sets' paths are left empty, these corresponding metrics will not be calculated. We also provide out-of-the box parameter to split the validation set as a desired split of the training set while preserving the label distribution for datasets without author-specified splits. These and many other specific hyperparameters with their descriptions can be found in the train.py file. All of them are provided a default value we found to be working well in our experiments.

Data

As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data for WLASL100 and LSA64 datasets along with the repository. You can find the data here.

Alt Text

License

The code is published under the Apache License 2.0 which allows for both academic and commercial use if relevant License and copyright notice is included, our work is cited and all changes are stated.

The accompanying skeletal data of the WLASL and LSA64 datasets used for experiments are, however, shared under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allowing only for non-commercial usage.

Citation

If you find our work relevant, build upon it or compare your approaches with it, please cite our work as stated below:

@InProceedings{Bohacek_2022_WACV,
    author    = {Boh\'a\v{c}ek, Maty\'a\v{s} and Hr\'uz, Marek},
    title     = {Sign Pose-Based Transformer for Word-Level Sign Language Recognition},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
    month     = {January},
    year      = {2022},
    pages     = {182-191}
}
Comments
  • Pose based GRU model

    Pose based GRU model

    Thank you for providing this dataset. I'm trying to reproduce your results using the Pose based GRU model however I'm unable to do so. Could you please share the model architecture and hyperparameters. It would be quite helpful

    EDIT: Wrong repository, please delete this issue

    opened by farhaan-mukarram 0
  • Testing new data

    Testing new data

    I'm trying to use the model, but I'm having problems with the following step. I have already trained the model, and now I have the checkpoint_v_0.pth file, with which I do the following to load the generated model:

    model = torch.load(PATH/to/pth/file)
print(model)

    And this returns something like:

    SPOTER(
  (transformer): Transformer(
    (encoder): TransformerEncoder(
      (layers): ModuleList(
        (0): TransformerEncoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): _LinearWithBias(in_features=108, out_features=108, bias=True)
          )
          (linear1): Linear(in_features=108, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=108, bias=True)
          (norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
        )
        (1): TransformerEncoderLayer(
...

    which makes me believe that everything is OK to this point.

    Now I would like to see how I can use the model to make a prediction with new data, and there's where the problem is.

    When running model(input) to get the results, some errors appear, and I believe that I'm not giving the correct kind of input. I'm using the second line from WLASL100_train_25fps.csv, changing the "s for [s in order to get a hierarchy like the following:

    [
           [a, b, c],
           [d],
           [e, f]
]

    However, this doesn't seem to work. Am I using a different format to the one the model should be given?

    The exact input I'm giving the model follows, with the np.array conversion:

    parsed_example = [[0.398577,0.398577,0.398577,0,0,0,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.398577,0.393627,0.398276,0.402398,0.400453,0.392099,0.389084,0.390115,0.390122,0.389504,0.389435,0.391493,0,0,0.404229,0,0.375582],[0.492837,0.492837,0.492837,0,0,0,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.492837,0.493243,0.493652,0.493095,0.493131,0.489589,0.487246,0.482847,0.461756,0.449853,0.454416,0.470932,0,0,0,0,0],[0.528982,0.528982,0.528982,0,0,0,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.528982,0.51803,0.497051,0.526573,0.514082,0.518499,0.509619,0.49415,0.472887,0.446121,0.418547,0.298164,0,0,0.0109521,0,0.132508],[0.471492,0.471492,0.471492,0,0,0,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.471492,0.47169,0.471791,0.472173,0.471784,0.471199,0.468871,0.464683,0.457271,0.451461,0.444686,0.442517,0,0,0,0,0],[0.0485368,0.0485368,0.0485368,0,0,0,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0485368,0.0484803,0.0485052,0.0437046,0.0458512,0.0410389,0.0284424,0.0229825,0.0172411,0.0192034,0.0174977,0.0778546,0,0,0,0,0],[0.444718,0.444718,0.444718,0,0,0,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.444718,0.410827,0.403959,0.461306,0.422898,0.468788,0.459742,0.435957,0.406939,0.3709,0.33533,0.238105,0,0,0,0,0.0313517],[0.595102,0.595102,0.595102,0,0,0,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.595102,0.555913,0.551131,0.624477,0.588528,0.650886,0.647908,0.627087,0.602364,0.573864,0.529823,0.44072,0,0,0.0573245,0,0.169048],[0.171264,0.171264,0.171264,0,0,0,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171264,0.171651,0.173042,0.170316,0.171098,0.165272,0.156861,0.150788,0.145757,0.128707,0.110712,0.070122,0,0,0,0,0],[0.473231,0.473231,0.473231,0,0,0,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.473231,0.45867,0.453409,0.493301,0.457943,0.478756,0.466767,0.4517,0.428147,0.40929,0.380941,0.318115,0,0,0.134512,0,0.16755],[0.536177,0.536177,0.536177,0,0,0,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.536177,0.517749,0.51461,0.529266,0.574933,0.532947,0.521931,0.502335,0.482224,0.459312,0.427157,0.32395,0,0,0.0160242,0,0.185826],[0.407958,0.407958,0.407958,0,0,0,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.407958,0.409048,0.409384,0.408326,0.408705,0.407319,0.405705,0.402739,0.395891,0.385984,0.3856,0.432976,0,0,0,0,0],[0.077801,0.077801,0.077801,0.0807571,0.0807571,0.0807571,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.077801,0.0762554,0.0732151,0.0731308,0.0731247,0.0724378,0.0797846,0.0793943,0.0781348,0.0832884,0.072022,0.0677893,0.0737613,0.0725894,0.0822043,0.0762998,0.0776332],[0.422698,0.422698,0.422698,0,0,0,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.422698,0.430718,0.440697,0.419992,0.431422,0.406726,0.40075,0.401792,0.405708,0.405919,0.404651,0.395599,0,0,0,0,0.402912],360,[0.556073,0.556073,0.556073,0,0,0,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.556073,0.536601,0.530958,0.55007,0.569923,0.549784,0.538729,0.52079,0.500619,0.477842,0.446324,0.330212,0,0,0.0192175,0,0.186272],[0.397988,0.397988,0.397988,0,0,0,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.397988,0.388859,0.386205,0.396229,0.380661,0.403792,0.401147,0.377418,0.346069,0.317356,0.289145,0.204902,0,0,0.0614293,0,0.119065],[0.417861,0.417861,0.417861,0.439912,0.439912,0.439912,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417861,0.417895,0.418117,0.418545,0.4183,0.419513,0.418768,0.420636,0.422949,0.424417,0.425064,0.427494,0.428411,0.428882,0.432829,0.438765,0.430258],[0.392557,0.392557,0.392557,0,0,0,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.392557,0.393167,0.393362,0.392457,0.393228,0.391265,0.388041,0.384542,0.381818,0.375773,0.375523,0.419973,0,0,0,0,0],[0.559241,0.559241,0.559241,0,0,0,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.559241,0.558091,0.557011,0.557938,0.557323,0.55621,0.556297,0.553147,0.548757,0.536598,0.533963,0.400428,0,0,0,0,0],[0.868213,0.868213,0.868213,0.866513,0.866513,0.866513,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868213,0.868191,0.868267,0.867593,0.868017,0.867536,0.865815,0.865587,0.866913,0.866851,0.866919,0.866125,0.864815,0.859497,0.851707,0.846945,0.855105],[0.498047,0.498047,0.498047,0.485291,0.485291,0.485291,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.498047,0.50619,0.497473,0.49691,0.496445,0.495318,0.495816,0.497309,0.500691,0.500798,0.495025,0.489368,0.486047,0.483157,0.477592,0.475032,0.480784],[0.0266556,0.0266556,0.0266556,0,0,0,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0266556,0.0267041,0.0261174,0.0241547,0.0247721,0.0226099,0.01849,0.0169849,0.0164648,0.020179,0.0184484,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0.418166,0.418166,0.418166,0,0,0,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.418166,0.426177,0.437865,0.416506,0.430483,0.401749,0.395495,0.397367,0.400526,0.402528,0.402191,0.416395,0,0,0.421347,0,0.364901],[0.510201,0.510201,0.510201,0,0,0,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.510201,0.483359,0.472029,0.47806,0.504377,0.473304,0.466987,0.44647,0.425754,0.398267,0.372791,0.26556,0,0,0,0,0.0290775],[0.393632,0.393632,0.393632,0,0,0,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.393632,0.394368,0.394109,0.393648,0.39417,0.392089,0.388192,0.385397,0.380724,0.376536,0.373512,0.510908,0,0,0,0,0],[0.514143,0.514143,0.514143,0,0,0,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.514143,0.488864,0.478717,0.499149,0.490987,0.487244,0.47975,0.463163,0.442731,0.411967,0.38342,0.262672,0,0,0,0,0.029516],[0.14742,0.14742,0.14742,0,0,0,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.14742,0.147526,0.148093,0.143568,0.145532,0.137696,0.122592,0.113845,0.095867,0.0645544,0.0335655,0.181972,0,0,0,0,0],[0.160598,0.160598,0.160598,0,0,0,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.160598,0.16084,0.161477,0.15815,0.159689,0.153363,0.14376,0.138518,0.120059,0.0925082,0.0678847,0.123008,0,0,0,0,0],[0.354376,0.354376,0.354376,0,0,0,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.354376,0.355446,0.357547,0.356205,0.355636,0.346679,0.342398,0.341331,0.341753,0.339144,0.337713,0.333239,0,0,0.416292,0,0.370757],[0.420521,0.420521,0.420521,0,0,0,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.420521,0.421522,0.421487,0.42075,0.421606,0.417578,0.413965,0.408728,0.403928,0.385775,0.397049,0,0,0,0,0,0],640,[0.541161,0.541161,0.541161,0,0,0,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.541161,0.54111,0.540695,0.541796,0.540932,0.53986,0.539377,0.533142,0.524508,0.516081,0.512644,0.484296,0,0,0,0,0],[0.556609,0.556609,0.556609,0,0,0,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.556609,0.540239,0.524339,0.561196,0.541111,0.561099,0.551605,0.534581,0.511972,0.485199,0.455068,0.340676,0,0,0.0149052,0,0.178714],[0.408994,0.408994,0.408994,0,0,0,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.408994,0.410046,0.409876,0.409208,0.409532,0.408416,0.40626,0.404551,0.396929,0.388693,0.380822,0.492037,0,0,0,0,0],[0.501293,0.501293,0.501293,0,0,0,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.501293,0.500814,0.500966,0.502335,0.501341,0.4978,0.496459,0.484296,0.466156,0.462022,0.462566,0.470642,0,0,0,0,0],[0.568233,0.568233,0.568233,0,0,0,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.568233,0.534232,0.531382,0.633149,0.593982,0.662144,0.663524,0.646581,0.624724,0.596565,0.546316,0.426962,0,0,0.0220607,0,0.160657],[0.466392,0.466392,0.466392,0,0,0,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.466392,0.426378,0.417419,0.498705,0.439079,0.504048,0.493154,0.475294,0.447406,0.417194,0.381258,0.290019,0,0,0,0,0.13123],[0.416737,0.416737,0.416737,0,0,0,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.416737,0.417708,0.41797,0.416587,0.417454,0.413279,0.416709,0.412278,0.402862,0.396735,0.399356,0.435406,0,0,0,0,0],[0.505452,0.505452,0.505452,0,0,0,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505452,0.505407,0.505082,0.505261,0.505529,0.504117,0.501017,0.496164,0.489083,0.479575,0.473823,0.465006,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0.415748,0.415748,0.415748,0,0,0,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.415748,0.413991,0.41782,0.425666,0.42046,0.419226,0.417096,0.416879,0.413618,0.409052,0.407485,0.401017,0,0,0,0,0.381397],[0.473815,0.473815,0.473815,0.473295,0.473295,0.473295,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.473815,0.477564,0.47261,0.473035,0.472369,0.471813,0.472967,0.473136,0.473406,0.473849,0.470469,0.470649,0.470665,0.468623,0.472882,0.471804,0.470694],[0.834083,0.834083,0.834083,0.850918,0.850918,0.850918,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.834083,0.833912,0.833459,0.832564,0.83294,0.832221,0.831697,0.831018,0.831089,0.831369,0.833802,0.842826,0.844824,0.847571,0.8431,0.837603,0.846742],[0.449698,0.449698,0.449698,0.470586,0.470586,0.470586,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449698,0.449782,0.449879,0.450069,0.450108,0.449954,0.449856,0.450556,0.451792,0.452467,0.453156,0.45535,0.457895,0.461187,0.464011,0.469523,0.462232],[0.335874,0.335874,0.335874,0.331081,0.331081,0.331081,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.335874,0.341357,0.338583,0.340809,0.337994,0.338086,0.344122,0.345942,0.338647,0.337904,0.341081,0.336325,0.335902,0.333218,0.330601,0.329757,0.333555],[0.59528,0.59528,0.59528,0,0,0,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.59528,0.567118,0.557465,0.582364,0.582105,0.569597,0.559301,0.545132,0.527121,0.498238,0.473415,0.380939,0,0,0.104355,0,0.181015],[0.496958,0.496958,0.496958,0,0,0,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.496958,0.497091,0.497496,0.498371,0.497851,0.493398,0.491022,0.482091,0.461508,0.455779,0.458801,0.467427,0,0,0,0,0],[0.361768,0.361768,0.361768,0,0,0,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.361768,0.359179,0.359225,0.370511,0.365155,0.36131,0.354589,0.358833,0.354196,0.356534,0.360185,0.41851,0.363539,0.320566,0.366688,0,0.3458],[0.684028,0.684028,0.684028,0.679071,0.679071,0.679071,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.684028,0.685253,0.687811,0.687514,0.685538,0.68235,0.681399,0.682819,0.678458,0.676241,0.67612,0.682778,0.684522,0.685962,0.686465,0.688187,0.685772],[0.0544637,0.0544637,0.0544637,0,0,0,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0544637,0.0550249,0.0553504,0.0499061,0.0534384,0.044793,0.0322963,0.0245405,0.0181975,0.0202103,0.019411,0.170797,0,0,0,0,0],[0.0891951,0.0891951,0.0891951,0,0,0,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0891951,0.0932565,0.0959771,0.0958182,0.0954115,0.0875102,0.0879518,0.0898014,0.0899711,0,0,0,0,0,0,0,0],[0.53426,0.53426,0.53426,0,0,0,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.53426,0.518446,0.504989,0.518253,0.532188,0.513162,0.504528,0.487462,0.468042,0.444239,0.417316,0.296324,0,0,0.00939417,0,0.181328],[0.446253,0.446253,0.446253,0,0,0,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.446253,0.451696,0.455806,0.438116,0.451011,0.425048,0.420168,0.421585,0.425422,0.424959,0.42439,0.417447,0,0,0,0,0.409357],[0.102753,0.102753,0.102753,0,0,0,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102753,0.102729,0.102828,0.0987104,0.100436,0.0935981,0.0806946,0.0702772,0.054906,0.0299774,0.0198809,0.12341,0,0,0,0,0],[0.396484,0.396484,0.396484,0,0,0,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.396484,0.401827,0.409132,0.424724,0.412457,0.41568,0.421796,0.429313,0.436448,0.442276,0.439053,0.460547,0,0,0.434043,0,0.344103],[0.110049,0.110049,0.110049,0,0,0,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.110049,0.109349,0.109123,0.105126,0.106634,0.0995601,0.0802612,0.0683592,0.0451567,0.0277143,0.0186325,0.111674,0,0,0,0,0],[0.539246,0.539246,0.539246,0.546963,0.546963,0.546963,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539246,0.539066,0.539134,0.539199,0.539123,0.539077,0.539273,0.53932,0.539677,0.540008,0.54017,0.540263,0.540521,0.540596,0.541036,0.541248,0.540771],[0.0548425,0.0548425,0.0548425,0,0,0,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0548425,0.0542418,0.0533832,0.0498548,0.051891,0.0463195,0.0273696,0.0221977,0.0176158,0.0217204,0.0170223,0,0,0,0,0,0],[0.542839,0.542839,0.542839,0,0,0,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.542839,0.524727,0.516811,0.553083,0.525625,0.537226,0.526861,0.512477,0.490337,0.47245,0.444906,0.377974,0,0,0.130359,0,0.18641],[0.150671,0.150671,0.150671,0,0,0,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150671,0.150852,0.150788,0.146919,0.14878,0.142768,0.126964,0.119384,0.102077,0.0744487,0.0384642,0.1673,0,0,0,0,0],[0.410095,0.410095,0.410095,0,0,0,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.410095,0.398424,0.393819,0.415969,0.402977,0.407646,0.398718,0.393197,0.368544,0.347764,0.330697,0.404279,0.230818,0.154058,0.0596943,0,0.100313],[0.416503,0.416503,0.416503,0,0,0,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.416503,0.422869,0.431879,0.413538,0.426203,0.400101,0.394413,0.395265,0.397295,0.397438,0.397063,0.396927,0,0,0.441229,0,0.382928],[0.43507,0.43507,0.43507,0,0,0,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.43507,0.438539,0.448614,0.428002,0.434548,0.414545,0.407394,0.411703,0.417309,0.418202,0.417991,0.422711,0,0,0.417827,0,0.370956],[0.821136,0.821136,0.821136,0.811291,0.811291,0.811291,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821136,0.821926,0.822495,0.822522,0.822497,0.823278,0.822569,0.82126,0.821443,0.821284,0.818062,0.813727,0.815449,0.810539,0.807021,0.798309,0.807851],[0.430851,0.430851,0.430851,0,0,0,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.430851,0.42683,0.433715,0.435988,0.434987,0.428295,0.424971,0.423909,0.422747,0.418467,0.415278,0.405242,0,0,0,0,0.405511],[0.101606,0.101606,0.101606,0,0,0,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101606,0.101141,0.101381,0.0968134,0.0981556,0.0916496,0.0752112,0.0632372,0.0494419,0.0262216,0.0183296,0.112508,0,0,0,0,0],[0.449582,0.449582,0.449582,0.461298,0.461298,0.461298,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.449582,0.448939,0.447747,0.44916,0.448292,0.448309,0.450119,0.448963,0.446121,0.4469,0.445914,0.451929,0.455284,0.454089,0.468171,0.468576,0.460603],[0.466496,0.466496,0.466496,0,0,0,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.466496,0.467094,0.467914,0.469095,0.470014,0.460429,0.457925,0.450468,0.43648,0.438498,0.433476,0.455836,0,0,0,0,0],[0.624708,0.624708,0.624708,0.622507,0.622507,0.622507,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.624708,0.623141,0.622646,0.62599,0.622981,0.622384,0.625291,0.624968,0.622767,0.621487,0.620373,0.619648,0.616427,0.614055,0.612757,0.612532,0.615279],[0.526177,0.526177,0.526177,0,0,0,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.526177,0.480287,0.470079,0.553857,0.489107,0.561176,0.552853,0.538499,0.507974,0.485248,0.449111,0.386123,0,0,0.107808,0,0.163175],[0.45481,0.45481,0.45481,0,0,0,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.45481,0.455389,0.455943,0.454923,0.45635,0.44954,0.447753,0.440691,0.423731,0.427265,0.423051,0.490448,0,0,0,0,0],[0.204234,0.204234,0.204234,0,0,0,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204234,0.204113,0.205368,0.202543,0.20299,0.196526,0.185895,0.178505,0.170746,0.148086,0.129567,0.0828415,0,0,0,0,0],[0.367898,0.367898,0.367898,0,0,0,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.367898,0.370057,0.374154,0.372622,0.372275,0.355544,0.350507,0.352565,0.354334,0.355713,0.353757,0.357839,0,0,0.428125,0,0.351858],[0.117691,0.117691,0.117691,0.138631,0.138631,0.138631,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.117691,0.121325,0.11973,0.115287,0.118254,0.111078,0.109886,0.109614,0.109523,0.1113,0.109874,0.108667,0.101558,0.0960157,0.130688,0.140281,0.108177],[0.778065,0.778065,0.778065,0.78798,0.78798,0.78798,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.778065,0.776055,0.774519,0.7694,0.774433,0.774804,0.77705,0.775125,0.774783,0.775015,0.775836,0.778349,0.781946,0.781645,0.777685,0.78165,0.779866],[0.51824,0.51824,0.51824,0,0,0,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.51824,0.501449,0.49275,0.500936,0.528518,0.500707,0.491616,0.472689,0.452038,0.428517,0.400894,0.287902,0,0,0.00840527,0,0.108004],[0.406896,0.406896,0.406896,0,0,0,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.406896,0.414483,0.423803,0.41058,0.423448,0.394538,0.388644,0.389319,0.391539,0.391864,0.391256,0.405444,0,0,0.435431,0,0.36059],[0.439912,0.439912,0.439912,0,0,0,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.439912,0.446466,0.451574,0.429526,0.441795,0.417601,0.412222,0.413791,0.417597,0.418591,0.418759,0.418723,0,0,0.412832,0,0.413656],[0.261884,0.261884,0.261884,0,0,0,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.261884,0.262196,0.263839,0.258682,0.260631,0.253516,0.240025,0.231006,0.215567,0.187305,0.160848,0.101928,0,0,0,0,0],[0.579374,0.579374,0.579374,0,0,0,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579374,0.579022,0.57576,0.573264,0.57638,0.579428,0.569769,0.553384,0.539439,0,0,0,0,0,0,0,0],[0.230018,0.230018,0.230018,0.265555,0.265555,0.265555,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.230018,0.228681,0.227422,0.228515,0.226399,0.23087,0.23231,0.22904,0.228337,0.22853,0.227974,0.225432,0.225165,0.223333,0.237067,0.253136,0.224881],[0.815262,0.815262,0.815262,0.821639,0.821639,0.821639,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815262,0.815063,0.814983,0.813652,0.814503,0.813854,0.811651,0.81111,0.811488,0.81148,0.812017,0.815096,0.815878,0.813429,0.808218,0.797769,0.812485],[0.425065,0.425065,0.425065,0,0,0,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.425065,0.433385,0.442779,0.419189,0.434403,0.406565,0.40141,0.402252,0.404794,0.406132,0.406414,0.407944,0,0,0.432907,0,0.398722],[0.112402,0.112402,0.112402,0,0,0,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112402,0.112145,0.113432,0.110913,0.112488,0.107522,0.103476,0.0970407,0.0860801,0.066747,0.0423101,0.223387,0,0,0,0,0],[0.430419,0.430419,0.430419,0,0,0,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.430419,0.442212,0.448338,0.426143,0.440351,0.413631,0.408111,0.408833,0.412923,0.413494,0.412733,0.40684,0,0,0,0,0.404998],[0.520902,0.520902,0.520902,0,0,0,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.520902,0.496065,0.482911,0.493291,0.507911,0.484245,0.477791,0.459485,0.441084,0.41259,0.385504,0.268533,0,0,0,0,0.0289197],[0.393807,0.393807,0.393807,0.402961,0.402961,0.402961,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.393807,0.395141,0.395114,0.396246,0.395592,0.397371,0.39706,0.398014,0.398622,0.398965,0.399404,0.400009,0.399706,0.39797,0.399346,0.39906,0.398453],[0.0241121,0.0241121,0.0241121,0,0,0,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0241121,0.0243541,0.0245614,0.0226802,0.0236107,0.018773,0.0148085,0.0147978,0.0150427,0.0182812,0.0208316,0.177875,0,0,0,0,0],[0.491392,0.491392,0.491392,0,0,0,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.491392,0.444631,0.434354,0.524604,0.459176,0.538472,0.526777,0.511007,0.477358,0.450819,0.412703,0.335655,0,0,0,0,0.156115],[0.429912,0.429912,0.429912,0,0,0,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.429912,0.427394,0.435999,0.420681,0.421897,0.406905,0.408883,0.411225,0.414191,0.414511,0.414761,0.421739,0,0,0.403948,0,0.352846],[0.0237761,0.0237761,0.0237761,0,0,0,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237761,0.0237831,0.0237747,0.0218054,0.0223853,0.0203356,0.01715,0.0162433,0.0157508,0.0177439,0.0189165,0.17213,0,0,0,0,0],[0.443515,0.443515,0.443515,0,0,0,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.443515,0.4443,0.445452,0.443732,0.444447,0.441558,0.438953,0.43511,0.423936,0.412579,0.408079,0.464436,0,0,0,0,0],[0.45634,0.45634,0.45634,0,0,0,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.45634,0.456923,0.457293,0.457284,0.45688,0.455834,0.452731,0.448578,0.443363,0.434433,0.424495,0.419894,0,0,0,0,0],[0.477422,0.477422,0.477422,0.501547,0.501547,0.501547,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477422,0.477432,0.47748,0.477331,0.477479,0.477313,0.477678,0.47875,0.480577,0.48196,0.482681,0.487939,0.489213,0.490749,0.495524,0.50114,0.492736],[0.0638131,0.0638131,0.0638131,0,0,0,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0638131,0.0636309,0.0632372,0.0616828,0.0624193,0.0568239,0.044646,0.0367491,0.0279695,0.0211512,0.0198449,0.152649,0,0,0,0,0],[0.411805,0.411805,0.411805,0,0,0,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.411805,0.416394,0.424926,0.409986,0.406284,0.390013,0.381802,0.383461,0.391434,0.393855,0.391449,0.400279,0,0,0.434856,0,0.346088],25,[0.368108,0.368108,0.368108,0,0,0,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.368108,0.363292,0.366387,0.364083,0.36808,0.356994,0.356186,0.353955,0.350064,0.344502,0.342,0.329341,0,0,0.355768,0,0.378391],[0.399114,0.399114,0.399114,0,0,0,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399114,0.399894,0.399905,0.399106,0.3997,0.397057,0.395637,0.391855,0.390962,0.380463,0.383624,0,0,0,0,0,0],[0.110802,0.110802,0.110802,0,0,0,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.110802,0.111332,0.111822,0.108947,0.11035,0.103296,0.0906004,0.0859786,0.062927,0.0391179,0.0310239,0.133727,0,0,0,0,0],[0.41466,0.41466,0.41466,0,0,0,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.41466,0.417034,0.423521,0.423729,0.418714,0.413116,0.416772,0.421493,0.426379,0.428085,0.428693,0.448324,0,0,0.429682,0,0.346237],[0.435688,0.435688,0.435688,0,0,0,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.435688,0.43697,0.437748,0.43703,0.438256,0.430639,0.428863,0.423764,0.415232,0.409347,0.412642,0.459792,0,0,0,0,0],[0.442893,0.442893,0.442893,0,0,0,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.442893,0.439508,0.444029,0.446504,0.446885,0.434358,0.430223,0.429864,0.430755,0.426764,0.423663,0.411071,0,0,0,0,0.415579],[0.587632,0.587632,0.587632,0,0,0,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.587632,0.562825,0.55653,0.573737,0.564234,0.566475,0.555575,0.538977,0.519672,0.495886,0.470314,0.389479,0,0,0.11266,0,0.18808],[0.480291,0.480291,0.480291,0.476794,0.476794,0.476794,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.480291,0.482249,0.480615,0.4834,0.480488,0.480235,0.484707,0.485455,0.480707,0.479695,0.480727,0.477987,0.476165,0.473637,0.471679,0.471144,0.474417],[0.487587,0.487587,0.487587,0,0,0,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.487587,0.488348,0.488563,0.487894,0.488274,0.485159,0.483058,0.480062,0.464431,0.450689,0.44785,0.465273,0,0,0,0,0],[0.441586,0.441586,0.441586,0,0,0,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.441586,0.442258,0.442371,0.441065,0.441773,0.439819,0.436891,0.435743,0.424649,0.412579,0.399066,0.476791,0,0,0,0,0],[0.0318525,0.0318525,0.0318525,0,0,0,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0318525,0.0316939,0.0309895,0.0295434,0.0300431,0.0269248,0.0199101,0.0173464,0.0180922,0.01764,0.0191318,0.196985,0,0,0,0,0],[0.388652,0.388652,0.388652,0,0,0,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.388652,0.395811,0.404267,0.393654,0.39265,0.373147,0.367536,0.370049,0.374525,0.375855,0.375904,0.382444,0,0,0.430766,0,0.346908],[0.58603,0.58603,0.58603,0,0,0,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.58603,0.552916,0.54602,0.603386,0.561927,0.626661,0.622056,0.599757,0.571055,0.543242,0.504178,0.433521,0,0,0.109474,0,0.172189],[0.227427,0.227427,0.227427,0,0,0,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.227427,0.226712,0.228167,0.224049,0.225356,0.218891,0.205752,0.195831,0.184292,0.158103,0.132525,0.0926074,0,0,0,0,0],61,[0.145215,0.145215,0.145215,0,0,0,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.145215,0.144662,0.145876,0.140813,0.142778,0.134423,0.11845,0.107384,0.0866141,0.0551191,0.0283827,0.190118,0,0,0,0,0]]

input = np.array([np.array(sublist) for sublist in parsed_example])
    opened by RodGal-2020 0
  • IndexError when training model

    IndexError when training model

    This is the command used to train the model : python -m train --experiment_name "Spoter" --training_set_path "data/WLASL100_train_25fps.csv" --validation_set_path "data/WLASL100_val_25fps.csv" --testing_set_path "data/WLASL100_test_25fps.csv"

    I get the following error after the program runs for awhile:

    Starting Spoter... Traceback (most recent call last): File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/usr/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/content/drive/MyDrive/Spoter/train.py", line 272, in train(args) File "/content/drive/MyDrive/Spoter/train.py", line 174, in train train_loss, _, _, train_acc = train_epoch(slrt_model, train_loader, cel_criterion, sgd_optimizer, device) File "/content/drive/MyDrive/Spoter/spoter/utils.py", line 19, in train_epoch loss = criterion(outputs[0], labels[0]) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py", line 1152, in forward label_smoothing=self.label_smoothing) File "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py", line 2846, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) IndexError: Target 78 is out of bounds.

    Changing parameters like the epochs and learning rate does not fix the issue.

    question 
    opened by adhithiyaa-git 3
  • Problematic normalization

    Problematic normalization

    Screen Shot 2022-02-13 at 5 36 52 PM Got a validation accuracy around 58%, lower than the one proposed in the paper. Is the lower accuracy caused by this problematic normalization error?

    bug 
    opened by Coco-hanqi 6
  • Thank for your work! Please comment,when training ,report another error.

    Thank for your work! Please comment,when training ,report another error.

    RuntimeError: CUDA error: device-side assert triggered. ` for i, data in enumerate(dataloader): inputs, labels = data # inputs, labels = Variable(inputs), Variable(labels)-1 inputs = inputs.squeeze(0).to(device) labels = labels.to(device, dtype=torch.long)

        optimizer.zero_grad()
    outputs = model(inputs).expand(1, -1, -1)

    loss = criterion(outputs[0], labels[0])`
    bug 
    opened by showfaker66 5
Releases(supplementary-data)

  • supplementary-data(Dec 9, 2021)

    As SPOTER works on top of sequences of signers' skeletal data extracted from videos, we wanted to eliminate the computational demands of such annotation for each training run by pre-collecting this. For this reason and reproducibility, we are open-sourcing this data along with the code as well.

    This data is shared under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allowing only for non-commercial usage only.

    We employed the WLASL100 and LSA64 datasets for our experiments. Their corresponding citations can be found below:

    @inproceedings{li2020word,
    title={Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison},
    author={Li, Dongxu and Rodriguez, Cristian and Yu, Xin and Li, Hongdong},
    booktitle={The IEEE Winter Conference on Applications of Computer Vision},
    pages={1459--1469},
    year={2020}
}

    @inproceedings{ronchetti2016lsa64,
    title={LSA64: an Argentinian sign language dataset},
    author={Ronchetti, Franco and Quiroga, Facundo and Estrebou, C{\'e}sar Armando and Lanzarini, Laura Cristina and Rosete, Alejandro},
    booktitle={XXII Congreso Argentino de Ciencias de la Computaci{\'o}n (CACIC 2016).},
    year={2016}
}
    Source code(tar.gz)
    Source code(zip)
    LSA64_60fps.csv(185.14 MB)
    WLASL100_test_25fps.csv(10.37 MB)
    WLASL100_train_25fps.csv(57.16 MB)
    WLASL100_val_25fps.csv(13.57 MB)
Owner
Matyáš Boháček
ML&NLP Researcher at @dataclair • Research Fellow with the University of West Bohemia •  WWDC19 & 21 Scholarship Winner
Matyáš Boháček
GitHub Repository https://spoter.signlanguagerecognition.com
Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

Zehao Wang 2 Dec 14, 2021
Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

NVIDIA Research Projects 130 Jan 06, 2023
MoveNet Single Pose on OpenVINO

MoveNet Single Pose tracking on OpenVINO Running Google MoveNet Single Pose models on OpenVINO. A convolutional neural network model that runs on RGB

35 Nov 11, 2022
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

8 Nov 14, 2022
codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

Self-paced Deep Regression Forests with Consideration on Ranking Fairness This is official codes for paper Self-paced Deep Regression Forests with Con

Learning in Vision 4 Sep 11, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Can Active Learning Preemptively Mitigate Fairness Issues? Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented a

ElementAI 7 Aug 12, 2022
Pyramid Scene Parsing Network, CVPR2017.

Pyramid Scene Parsing Network by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page. Introduction This

Hengshuang Zhao 1.5k Jan 05, 2023
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Adversarially-Robust-Periphery Code + Data from the paper "Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks" by A

Anne Harrington 2 Feb 07, 2022
Facilitates implementing deep neural-network backbones, data augmentations

Introduction Nowadays, the training of Deep Learning models is fragmented and unified. When AI engineers face up with one specific task, the common wa

40 Dec 29, 2022
🌎 The Modern Declarative Data Flow Framework for the AI Empowered Generation.

🌎 JSONClasses JSONClasses is a declarative data flow pipeline and data graph framework. Official Website: https://www.jsonclasses.com Official Docume

Fillmula Inc. 53 Dec 09, 2022
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh

LightOn 69 Dec 22, 2022
Visual Tracking by TridenAlign and Context Embedding

Visual Tracking by TridentAlign and Context Embedding (TACT) Test code for "Visual Tracking by TridentAlign and Context Embedding" Janghoon Choi, Juns

Janghoon Choi 32 Aug 25, 2021
CRNN With PyTorch

CRNN-PyTorch Implementation of https://arxiv.org/abs/1507.05717

Vadim 4 Sep 01, 2022
Net2net - Network-to-Network Translation with Conditional Invertible Neural Networks

Net2Net Code accompanying the NeurIPS 2020 oral paper Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach*, Patri

CompVis Heidelberg 206 Dec 20, 2022
ICCV2021 - A New Journey from SDRTV to HDRTV.

ICCV2021 - A New Journey from SDRTV to HDRTV.

XyChen 82 Dec 27, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
[NeurIPS2021] Code Release of Learning Transferable Perturbations

Learning Transferable Adversarial Perturbations This is an official release of the paper Learning Transferable Adversarial Perturbations. The code is

Krishna Kanth 17 Nov 11, 2022