我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用torch.backends.cudnn.CUDNN_LINEAR_INPUT。
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]
