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
PyTorch implementation of saliency map-aided GAN for Auto-demosaic+denosing

Saiency Map-aided GAN for RAW2RGB Mapping The PyTorch implementations and guideline for Saiency Map-aided GAN for RAW2RGB Mapping. 1 Implementations B

Yuzhi ZHAO 20 Oct 24, 2022
Type4Py: Deep Similarity Learning-Based Type Inference for Python

Type4Py: Deep Similarity Learning-Based Type Inference for Python This repository contains the implementation of Type4Py and instructions for re-produ

Software Analytics Lab 45 Dec 15, 2022
Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.

This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement

Tuomas Haarnoja 752 Jan 07, 2023
Official repository for "Intriguing Properties of Vision Transformers" (2021)

Intriguing Properties of Vision Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, & Ming-Hsuan Yang P

Muzammal Naseer 155 Dec 27, 2022
A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

Convolutional Neural Network (CNN). This repository contains a source code of a deep learning network built with TensorFlow and Keras to classify gend

Pawel Dziemiach 1 Dec 18, 2021
Python package for Bayesian Machine Learning with scikit-learn API

Python package for Bayesian Machine Learning with scikit-learn API Installing & Upgrading package pip install https://github.com/AmazaspShumik/sklearn

Amazasp Shaumyan 482 Jan 04, 2023
On the adaptation of recurrent neural networks for system identification

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Marco Forgione 3 Jan 13, 2022
Pose estimation for iOS and android using TensorFlow 2.0

💃 Mobile 2D Single Person (Or Your Own Object) Pose Estimation for TensorFlow 2.0 This repository is forked from edvardHua/PoseEstimationForMobile wh

tucan9389 165 Nov 16, 2022
Learning to Stylize Novel Views

Learning to Stylize Novel Views [Project] [Paper] Contact: Hsin-Ping Huang ([ema

34 Nov 27, 2022
Official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

Datascience IIT-ISM 13 Dec 14, 2022
Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

video_lie_detector_using_xgboost a video lie detector using OpenFace and xgboost

2 Jan 11, 2022
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
EXplainable Artificial Intelligence (XAI)

EXplainable Artificial Intelligence (XAI) This repository includes the codes for different projects on eXplainable Artificial Intelligence (XAI) by th

4 Nov 28, 2022
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments [Project website] [Paper] This project is a PyTorch

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 49 Nov 28, 2022
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 09, 2022
Discover hidden deepweb pages

DeepWeb Scapper Att: Demo version An simple script to scrappe deepweb to find pages. Will return if any of those exists and will save on a file. You s

Héber Júlio 77 Oct 02, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code

Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.

Yasunori Shimura 7 Jul 27, 2022
Official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels".

WarPI The official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels". Run python main.py --corruption_type

Haoliang Sun 3 Sep 03, 2022
Simple and ready-to-use tutorials for TensorFlow

TensorFlow World To support maintaining and upgrading this project, please kindly consider Sponsoring the project developer. Any level of support is a

Amirsina Torfi 4.5k Dec 23, 2022