python类CUDNN_LINEAR_INPUT的实例源码

def __init__(self, mode, input_size, hidden_size, num_layers=1,
                 batch_first=False, dropout=0, train=True, bidirectional=False,
                 batch_sizes=None, dropout_state=None):
        super(CudnnRNN, self).__init__()
        if dropout_state is None:
            dropout_state = {}
        self.mode = cudnn.rnn.get_cudnn_mode(mode)
        self.input_mode = cudnn.CUDNN_LINEAR_INPUT
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.dropout = dropout
        self.train = train
        self.bidirectional = 1 if bidirectional else 0
        self.num_directions = 2 if bidirectional else 1
        self.batch_sizes = batch_sizes
        self.dropout_seed = torch.IntTensor(1).random_()[0]
        self.dropout_state = dropout_state

def __init__(self, mode, input_size, hidden_size, num_layers=1,
                 batch_first=False, dropout=0, train=True, bidirectional=False,
                 batch_sizes=None, dropout_state=None):
        super(CudnnRNN, self).__init__()
        if dropout_state is None:
            dropout_state = {}
        self.mode = cudnn.rnn.get_cudnn_mode(mode)
        self.input_mode = cudnn.CUDNN_LINEAR_INPUT
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.dropout = dropout
        self.train = train
        self.bidirectional = 1 if bidirectional else 0
        self.num_directions = 2 if bidirectional else 1
        self.batch_sizes = batch_sizes
        self.dropout_seed = torch.IntTensor(1).random_()[0]
        self.dropout_state = dropout_state

def __init__(self, mode, input_size, hidden_size, num_layers=1,
                 batch_first=False, dropout=0, train=True, bidirectional=False,
                 batch_sizes=None, dropout_state=None, flat_weight=None):
        super(CudnnRNN, self).__init__()
        if dropout_state is None:
            dropout_state = {}
        self.mode = cudnn.rnn.get_cudnn_mode(mode)
        self.input_mode = cudnn.CUDNN_LINEAR_INPUT
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.dropout = dropout
        self.train = train
        self.bidirectional = 1 if bidirectional else 0
        self.num_directions = 2 if bidirectional else 1
        self.batch_sizes = batch_sizes
        self.dropout_seed = torch.IntTensor(1).random_()[0]
        self.dropout_state = dropout_state
        self.weight_buf = flat_weight
        if flat_weight is None:
            warnings.warn("RNN module weights are not part of single contiguous "
                          "chunk of memory. This means they need to be compacted "
                          "at every call, possibly greately increasing memory usage. "
                          "To compact weights again call flatten_parameters().", stacklevel=5)

def __init__(self, mode, input_size, hidden_size, num_layers=1,
                 batch_first=False, dropout=0, train=True, bidirectional=False,
                 batch_sizes=None, dropout_state=None, flat_weight=None):
        super(CudnnRNN, self).__init__()
        if dropout_state is None:
            dropout_state = {}
        self.mode = cudnn.rnn.get_cudnn_mode(mode)
        self.input_mode = cudnn.CUDNN_LINEAR_INPUT
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.dropout = dropout
        self.train = train
        self.bidirectional = 1 if bidirectional else 0
        self.num_directions = 2 if bidirectional else 1
        self.batch_sizes = batch_sizes
        self.dropout_seed = torch.IntTensor(1).random_()[0]
        self.dropout_state = dropout_state
        self.weight_buf = flat_weight
        if flat_weight is None:
            warnings.warn("RNN module weights are not part of single contiguous "
                          "chunk of memory. This means they need to be compacted "
                          "at every call, possibly greatly increasing memory usage. "
                          "To compact weights again call flatten_parameters().", stacklevel=5)

def __init__(self, mode, input_size, hidden_size, num_layers=1, batch_first=False, dropout=0, train=True, bidirectional=False):
        super(CudnnRNN, self).__init__()
        self.mode = cudnn.rnn.get_cudnn_mode(mode)
        self.input_mode = cudnn.CUDNN_LINEAR_INPUT
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.dropout = dropout
        self.train = train
        self.bidirectional = 1 if bidirectional else 0
        self.num_directions = 2 if bidirectional else 1
        self.seed = torch.IntTensor(1).random_()[0]