| /dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/utils/scripts/ |
| H A D | gen_onnx_lstm_model.py | 83 hidden_size, :], [hidden_size, hidden_size])
85 hidden_size, :], [hidden_size, hidden_size])
87 hidden_size, :], [hidden_size, hidden_size])
89 hidden_size, :], [hidden_size, hidden_size])
91 hidden_size], [hidden_size])
93 hidden_size], [hidden_size])
95 hidden_size], [hidden_size])
97 hidden_size], [hidden_size])
99 hidden_size], [hidden_size])
128 hidden_size=hidden_size,
[all …]
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| H A D | gen_onnx_gru_model.py | 76 hidden_size, :], [hidden_size, hidden_size])
78 hidden_size, :], [hidden_size, hidden_size])
80 hidden_size, :], [hidden_size, hidden_size])
82 hidden_size], [hidden_size])
84 hidden_size], [hidden_size])
86 hidden_size], [hidden_size])
88 hidden_size], [hidden_size])
90 hidden_size], [hidden_size])
92 hidden_size], [hidden_size])
109 hidden_size=hidden_size,
[all …]
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| H A D | gen_onnx_rnn_model.py | 50 R_shape = [num_directions, 1 * hidden_size, hidden_size]
70 hidden_size, :], [hidden_size, input_size])
72 hidden_size, :], [hidden_size, hidden_size])
74 hidden_size], [hidden_size])
76 hidden_size], [hidden_size])
92 hidden_size=hidden_size,
239 hidden_size=hidden_size,
320 hidden_size=4,
331 hidden_size=4,
342 hidden_size=4,
[all …]
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| /dports/misc/py-onnx/onnx-1.10.2/onnx/backend/test/case/node/ |
| H A D | rnn.py | 94 hidden_size = 4
101 hidden_size=hidden_size
105 R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
116 hidden_size = 5
124 hidden_size=hidden_size
128 R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
146 hidden_size = 5
152 hidden_size=hidden_size
156 R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
172 hidden_size = 4
[all …]
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| H A D | lstm.py | 118 hidden_size = 3
126 hidden_size=hidden_size
130 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
141 hidden_size = 4
150 hidden_size=hidden_size
154 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
170 hidden_size = 3
179 hidden_size=hidden_size
184 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
201 hidden_size = 7
[all …]
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| H A D | gru.py | 114 hidden_size = 5
122 hidden_size=hidden_size
126 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
137 hidden_size = 3
146 hidden_size=hidden_size
150 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
167 hidden_size = 5
174 hidden_size=hidden_size
178 R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(np.float32)
194 hidden_size = 6
[all …]
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| /dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/natural_language_inference/ |
| H A D | decomposable_attention.py | 34 def __init__(self, vocab_size, word_embed_size, hidden_size, argument
38 self.hidden_size = hidden_size
46 self.intra_attention = IntraSentenceAttention(hidden_size, hidden_size, dropout)
47 input_size = hidden_size * 2
50 input_size = hidden_size
84 self.hidden_size = hidden_size
93 self.intra_attn_emb.add(nn.Dense(hidden_size, in_units=hidden_size,
126 self.g = self._ff_layer(in_units=hidden_size * 2, out_units=hidden_size, flatten=False)
128 self.h = self._ff_layer(in_units=hidden_size * 2, out_units=hidden_size, flatten=True)
129 self.h.add(nn.Dense(num_class, in_units=hidden_size))
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| H A D | esim.py | 49 def __init__(self, vocab_size, num_classes, word_embed_size, hidden_size, dense_size, argument
55 …self.lstm_encoder1 = rnn.LSTM(hidden_size, input_size=word_embed_size, bidirectional=True, layout=…
56 …self.ff_proj = nn.Dense(hidden_size, in_units=hidden_size * 2 * 4, flatten=False, activation='relu…
57 …self.lstm_encoder2 = rnn.LSTM(hidden_size, input_size=hidden_size, bidirectional=True, layout='NTC…
62 self.classifier.add(nn.Dense(units=hidden_size, activation='relu'))
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| /dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/thirdparty/onnx/onnx/backend/test/case/node/ |
| H A D | rnn.py | 34 hidden_size = params[R].shape[-1]
72 hidden_size = 4
79 hidden_size=hidden_size
83 R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
94 hidden_size = 5
102 hidden_size=hidden_size
106 R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
110 R_B = np.zeros((1, hidden_size)).astype(np.float32)
124 hidden_size = 5
130 hidden_size=hidden_size
[all …]
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| H A D | lstm.py | 38 hidden_size = params[R].shape[-1]
96 hidden_size = 3
104 hidden_size=hidden_size
108 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
119 hidden_size = 4
128 hidden_size=hidden_size
132 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
136 R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
148 hidden_size = 3
157 hidden_size=hidden_size
[all …]
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| H A D | gru.py | 36 hidden_size = params[R].shape[-1]
93 hidden_size = 5
101 hidden_size=hidden_size
105 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
116 hidden_size = 3
125 hidden_size=hidden_size
129 … R = weight_scale * np.ones((1, number_of_gates * hidden_size, hidden_size)).astype(np.float32)
133 R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
146 hidden_size = 5
153 hidden_size=hidden_size
[all …]
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/frontend/pytorch/ |
| H A D | test_lstm.py | 37 def __init__(self, input_size, hidden_size): argument
40 self.hidden_size = hidden_size
42 self.weight_hh = Parameter(torch.randn(4 * hidden_size, hidden_size))
46 self.layernorm_i = ln(4 * hidden_size)
47 self.layernorm_h = ln(4 * hidden_size)
48 self.layernorm_c = ln(hidden_size)
176 def lstm(input_size, hidden_size): argument
185 other_layer_args=[LayerNormLSTMCell, hidden_size, hidden_size],
189 def bidir_lstm(input_size, hidden_size): argument
198 other_layer_args=[LayerNormLSTMCell, hidden_size, hidden_size],
[all …]
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| /dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/ncnn/ |
| H A D | test_nn_GRU.py | 23 self.gru_0_0 = nn.GRU(input_size=32, hidden_size=16)
24 self.gru_0_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False)
25 … self.gru_0_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
26 … self.gru_0_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
27 … self.gru_0_4 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
29 self.gru_1_0 = nn.GRU(input_size=25, hidden_size=16, batch_first=True)
30 … self.gru_1_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
31 …self.gru_1_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bi…
32 …self.gru_1_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bi…
33 …self.gru_1_4 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bi…
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| H A D | test_nn_RNN.py | 23 self.rnn_0_0 = nn.RNN(input_size=32, hidden_size=16)
24 …self.rnn_0_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False)
25 …self.rnn_0_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
26 …self.rnn_0_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
27 …self.rnn_0_4 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
29 self.rnn_1_0 = nn.RNN(input_size=25, hidden_size=16, batch_first=True)
30 …self.rnn_1_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False…
31 …self.rnn_1_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
32 …self.rnn_1_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
33 …self.rnn_1_4 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
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| H A D | test_nn_LSTM.py | 23 self.lstm_0_0 = nn.LSTM(input_size=32, hidden_size=16)
24 self.lstm_0_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False)
25 …self.lstm_0_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
26 …self.lstm_0_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
27 …self.lstm_0_4 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
29 self.lstm_1_0 = nn.LSTM(input_size=25, hidden_size=16, batch_first=True)
30 … self.lstm_1_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
31 …self.lstm_1_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, …
32 …self.lstm_1_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, …
33 …self.lstm_1_4 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, …
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| /dports/misc/py-gluonnlp/gluon-nlp-0.10.0/tests/unittest/ |
| H A D | test_bilm_encoder.py | 28 hidden_size = 100
35 hidden_size=hidden_size,
40 assert output.shape == (num_layers, seq_len, batch_size, 2 * hidden_size), output.shape
47 hidden_size = 100
55 hidden_size=hidden_size,
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| H A D | test_sequence_sampler.py | 175 def __init__(self, vocab_size, hidden_size, prefix=None, params=None): argument
179 self._embed = nn.Embedding(input_dim=vocab_size, output_dim=hidden_size)
180 self._rnn = rnn.RNNCell(input_size=hidden_size, hidden_size=hidden_size)
181 self._map_to_vocab = nn.Dense(vocab_size, in_units=hidden_size)
198 self._embed = nn.Embedding(input_dim=vocab_size, output_dim=hidden_size)
199 self._rnn1 = rnn.RNNCell(input_size=hidden_size, hidden_size=hidden_size)
200 self._rnn2 = rnn.RNNCell(input_size=hidden_size, hidden_size=hidden_size)
201 self._map_to_vocab = nn.Dense(vocab_size, in_units=hidden_size)
228 def __init__(self, vocab_size, hidden_size, prefix=None, params=None): argument
233 …self._rnn = rnn.RNN(input_size=hidden_size, hidden_size=hidden_size, num_layers=1, activation='tan…
[all …]
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| /dports/misc/ncnn/ncnn-20211208/tools/pnnx/tests/ |
| H A D | test_nn_RNN.py | 23 self.rnn_0_0 = nn.RNN(input_size=32, hidden_size=16)
24 …self.rnn_0_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False)
25 …self.rnn_0_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='relu', bias=True,…
26 …self.rnn_0_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
28 self.rnn_1_0 = nn.RNN(input_size=25, hidden_size=16, batch_first=True)
29 …self.rnn_1_1 = nn.RNN(input_size=16, hidden_size=16, num_layers=3, nonlinearity='tanh', bias=False…
30 …self.rnn_1_2 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='relu', bias=True,…
31 …self.rnn_1_3 = nn.RNN(input_size=16, hidden_size=16, num_layers=4, nonlinearity='tanh', bias=True,…
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| H A D | test_nn_GRU.py | 23 self.gru_0_0 = nn.GRU(input_size=32, hidden_size=16)
24 self.gru_0_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False)
25 … self.gru_0_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
26 … self.gru_0_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
28 self.gru_1_0 = nn.GRU(input_size=25, hidden_size=16, batch_first=True)
29 … self.gru_1_1 = nn.GRU(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
30 …self.gru_1_2 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bi…
31 …self.gru_1_3 = nn.GRU(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bi…
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| H A D | test_nn_LSTM.py | 23 self.lstm_0_0 = nn.LSTM(input_size=32, hidden_size=16)
24 self.lstm_0_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False)
25 …self.lstm_0_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
26 …self.lstm_0_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
28 self.lstm_1_0 = nn.LSTM(input_size=25, hidden_size=16, batch_first=True)
29 … self.lstm_1_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
30 …self.lstm_1_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, …
31 …self.lstm_1_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, …
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| /dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/machine_translation/ |
| H A D | gnmt.py | 71 def __init__(self, cell_type='lstm', num_layers=2, num_bi_layers=1, hidden_size=128, argument
83 self._hidden_size = hidden_size
92 l_cell=self._cell_type(hidden_size=self._hidden_size,
98 r_cell=self._cell_type(hidden_size=self._hidden_size,
106 self._cell_type(hidden_size=self._hidden_size,
165 num_layers=2, hidden_size=128, argument
173 self._hidden_size = hidden_size
183 self._cell_type(hidden_size=self._hidden_size,
492 hidden_size=hidden_size, dropout=dropout, use_residual=use_residual,
499 hidden_size=hidden_size, dropout=dropout, use_residual=use_residual,
[all …]
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| /dports/misc/py-onnx/onnx-1.10.2/onnx/defs/rnn/ |
| H A D | defs.cc | 11 hidden_size; in RNNShapeInference() local
22 hidden_size.set_dim_value(hidden_size_value); in RNNShapeInference()
42 auto dims = {seq_length, num_directions, batch_size, hidden_size}; in RNNShapeInference()
45 auto dims = {batch_size, seq_length, num_directions, hidden_size}; in RNNShapeInference()
55 auto dims = {num_directions, batch_size, hidden_size}; in RNNShapeInference()
58 auto dims = {batch_size, num_directions, hidden_size}; in RNNShapeInference()
68 auto dims = {num_directions, batch_size, hidden_size}; in RNNShapeInference()
71 auto dims = {batch_size, num_directions, hidden_size}; in RNNShapeInference()
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| H A D | old.cc | 208 hidden_size; in RNNShapeInference1() local
219 hidden_size.set_dim_value(hidden_size_value); in RNNShapeInference1()
244 ctx, 0, {seq_length, num_directions, batch_size, hidden_size}); // Y in RNNShapeInference1()
247 ctx, 1, {num_directions, batch_size, hidden_size}); // Y_h in RNNShapeInference1()
250 ctx, 2, {num_directions, batch_size, hidden_size}); // Y_c in RNNShapeInference1()
723 hidden_size; in RNNShapeInference2() local
734 hidden_size.set_dim_value(hidden_size_value); in RNNShapeInference2()
751 ctx, 0, {seq_length, num_directions, batch_size, hidden_size}); in RNNShapeInference2()
757 updateOutputShape(ctx, 1, {num_directions, batch_size, hidden_size}); in RNNShapeInference2()
763 updateOutputShape(ctx, 2, {num_directions, batch_size, hidden_size}); in RNNShapeInference2()
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| /dports/misc/mxnet/incubator-mxnet-1.9.0/example/gluon/tree_lstm/ |
| H A D | tree_lstm.py | 23 def __init__(self, hidden_size, argument
33 self._hidden_size = hidden_size
35 self.i2h_weight = self.params.get('i2h_weight', shape=(4*hidden_size, input_size),
37 self.hs2h_weight = self.params.get('hs2h_weight', shape=(3*hidden_size, hidden_size),
39 self.hc2h_weight = self.params.get('hc2h_weight', shape=(hidden_size, hidden_size),
41 self.i2h_bias = self.params.get('i2h_bias', shape=(4*hidden_size,),
43 self.hs2h_bias = self.params.get('hs2h_bias', shape=(3*hidden_size,),
45 self.hc2h_bias = self.params.get('hc2h_bias', shape=(hidden_size,),
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| /dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/gluon/tree_lstm/ |
| H A D | tree_lstm.py | 23 def __init__(self, hidden_size, argument
33 self._hidden_size = hidden_size
35 self.i2h_weight = self.params.get('i2h_weight', shape=(4*hidden_size, input_size),
37 self.hs2h_weight = self.params.get('hs2h_weight', shape=(3*hidden_size, hidden_size),
39 self.hc2h_weight = self.params.get('hc2h_weight', shape=(hidden_size, hidden_size),
41 self.i2h_bias = self.params.get('i2h_bias', shape=(4*hidden_size,),
43 self.hs2h_bias = self.params.get('hs2h_bias', shape=(3*hidden_size,),
45 self.hc2h_bias = self.params.get('hc2h_bias', shape=(hidden_size,),
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