Searched refs:rnn_type (Results 1 – 5 of 5) sorted by relevance
| /external/pytorch/test/onnx/model_defs/ |
| D | word_language_model.py | 16 rnn_type, argument 28 if rnn_type in ["LSTM", "GRU"]: 29 self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout) 32 nonlinearity = {"RNN_TANH": "tanh", "RNN_RELU": "relu"}[rnn_type] 58 self.rnn_type = rnn_type 89 if self.rnn_type == "LSTM":
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| /external/pytorch/benchmarks/functional_autograd_benchmark/ |
| D | torchaudio_models.py | 185 rnn_type=nn.LSTM, argument 196 self.rnn = rnn_type( 264 rnn_type, argument 276 self.rnn_type = rnn_type 305 rnn_type=rnn_type, 314 rnn_type=rnn_type,
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| D | audio_text_models.py | 57 rnn_type=nn.LSTM,
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| /external/pytorch/test/quantization/core/ |
| D | test_quantized_module.py | 1843 for rnn_type in cell_dict.keys(): 1847 if rnn_type == 'RNNReLU': 1849 elif rnn_type == 'RNNTanh': 1852 cell_dq = cell_dict[rnn_type](**kwargs) 1853 result = qfn_dict[rnn_type](x, state[rnn_type], 1856 result_module = cell_dq(x, state[rnn_type]) 1861 self.check_eager_serialization(cell_dq, cell_dict[rnn_type](**kwargs), [x]) 1906 for rnn_type in cell_dict.keys(): 1908 if rnn_type == 'RNNReLU': 1910 elif rnn_type == 'RNNTanh': [all …]
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| D | test_quantized_op.py | 3455 …t_rnn_weights_and_bias(self, input_size, hidden_size, num_directions, per_channel_quant, rnn_type): argument 3457 hidden_mult = hidden_mult_map[rnn_type] 3488 for rnn_type in ['LSTM', 'GRU']: 3504 rnn_type) 3520 if rnn_type == 'LSTM': 3566 if rnn_type == 'GRU': 3622 for rnn_type in ['LSTMCell', 'GRUCell', 'RNNTanh', 'RNNReLU']: 3635 input_size, hidden_size, 1, per_channel_quant, rnn_type) 3663 … result_ref = fn_dict[rnn_type](Xq.dequantize()[0], state[rnn_type], W_ref1, W_ref2, b1, b2) 3664 …result_dynamic = qfn_dict[rnn_type](Xq.dequantize()[0], state[rnn_type], packed_ih, packed_hh, b1,…
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