Example #1
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        import tensorflow as tf
        tf.set_random_seed(seed)
    if module_is_in_cache('torch'):
        import torch
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
Example #2
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache("tensorflow"):
        tf = opt.get_tensorflow()
        tf.set_random_seed(seed)
    if module_is_in_cache("torch"):
        import torch

        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
Example #3
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        import tensorflow as tf
        tf.set_random_seed(seed)
Example #4
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        import tensorflow as tf
        tf.set_random_seed(seed)
Example #5
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        import tensorflow as tf
        from packaging import version
        if version.parse(tf.__version__) < version.parse('2.0.0a0'):
            tf.set_random_seed(seed)
        else:
            tf.random.set_seed(seed)
    if module_is_in_cache('torch'):
        import torch
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
Example #6
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        # Ensures backward and forward compatibility with TensorFlow 1 and 2.
        if get_package_version('tensorflow') < parse_version('1.13.1'):
            import warnings
            warnings.warn("Use of TensorFlow 1.12 and older is deprecated. "
                          "Use Tensorflow 1.13 or newer instead.",
                          DeprecationWarning)
            import tensorflow as tf
        else:
            import tensorflow.compat.v1 as tf
        tf.set_random_seed(seed)
    if module_is_in_cache('torch'):
        import torch
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
Example #7
0
def set_global_seed(seed):
    random.seed(seed)
    if opt.has_numpy:
        opt.np.random.seed(seed)
    if module_is_in_cache('tensorflow'):
        # Ensures backward and forward compatibility with TensorFlow 1 and 2.
        if get_package_version('tensorflow') < parse_version('1.13.1'):
            import warnings
            warnings.warn(
                "Use of TensorFlow 1.12 and older is deprecated. "
                "Use Tensorflow 1.13 or newer instead.", DeprecationWarning)
            import tensorflow as tf
        else:
            import tensorflow.compat.v1 as tf
        tf.set_random_seed(seed)
    if module_is_in_cache('torch'):
        import torch
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
Example #8
0
def test_module_is_in_cache():
    assert module_is_in_cache("pytest")
    assert module_is_in_cache("pkgutil")
    assert not module_is_in_cache("does_not_even_exist")
Example #9
0
def test_module_is_in_cache():
    assert module_is_in_cache('pytest')
    assert module_is_in_cache('pkgutil')
    assert not module_is_in_cache('does_not_even_exist')
Example #10
0
def test_module_is_in_cache():
    assert module_is_in_cache('pytest')
    assert module_is_in_cache('pkgutil')
    assert not module_is_in_cache('does_not_even_exist')