2 # Copyright (C) 2017 The Android Open Source Project
4 # Licensed under the Apache License, Version 2.0 (the "License");
5 # you may not use this file except in compliance with the License.
6 # You may obtain a copy of the License at
8 # http://www.apache.org/licenses/LICENSE-2.0
10 # Unless required by applicable law or agreed to in writing, software
11 # distributed under the License is distributed on an "AS IS" BASIS,
12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
20 units = int(features / rank)
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
28 weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
29 bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
31 rank_param = Int32Scalar("rank_param", rank)
32 activation_param = Int32Scalar("activation_param", 0)
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
34 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
37 rank_param, activation_param).To([state_out, output])
42 -0.31930989, -0.36118156, 0.0079667, 0.37613347,
43 0.22197971, 0.12416199, 0.27901134, 0.27557442,
44 0.3905206, -0.36137494, -0.06634006, -0.10640851
47 -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
48 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
50 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
51 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
53 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
54 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
56 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
57 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657
60 state_in: [0 for _ in range(batches * memory_size * features)],
64 0.12609188, -0.46347019, -0.89598465,
65 0.12609188, -0.46347019, -0.89598465,
67 0.14278367, -1.64410412, -0.75222826,
68 0.14278367, -1.64410412, -0.75222826,
70 0.49837467, 0.19278903, 0.26584083,
71 0.49837467, 0.19278903, 0.26584083,
73 -0.11186574, 0.13164264, -0.05349274,
74 -0.11186574, 0.13164264, -0.05349274,
76 -0.68892461, 0.37783599, 0.18263303,
77 -0.68892461, 0.37783599, 0.18263303,
79 -0.81299269, -0.86831826, 1.43940818,
80 -0.81299269, -0.86831826, 1.43940818,
82 -1.45006323, -0.82251364, -1.69082689,
83 -1.45006323, -0.82251364, -1.69082689,
85 0.03966608, -0.24936394, -0.77526885,
86 0.03966608, -0.24936394, -0.77526885,
88 0.11771342, -0.23761693, -0.65898693,
89 0.11771342, -0.23761693, -0.65898693,
91 -0.89477462, 1.67204106, -0.53235275,
92 -0.89477462, 1.67204106, -0.53235275
96 0.014899, -0.0517661, -0.143725, -0.00271883,
97 0.014899, -0.0517661, -0.143725, -0.00271883,
99 0.068281, -0.162217, -0.152268, 0.00323521,
100 0.068281, -0.162217, -0.152268, 0.00323521,
102 -0.0317821, -0.0333089, 0.0609602, 0.0333759,
103 -0.0317821, -0.0333089, 0.0609602, 0.0333759,
105 -0.00623099, -0.077701, -0.391193, -0.0136691,
106 -0.00623099, -0.077701, -0.391193, -0.0136691,
108 0.201551, -0.164607, -0.179462, -0.0592739,
109 0.201551, -0.164607, -0.179462, -0.0592739,
111 0.0886511, -0.0875401, -0.269283, 0.0281379,
112 0.0886511, -0.0875401, -0.269283, 0.0281379,
114 -0.201174, -0.586145, -0.628624, -0.0330412,
115 -0.201174, -0.586145, -0.628624, -0.0330412,
117 -0.0839096, -0.299329, 0.108746, 0.109808,
118 -0.0839096, -0.299329, 0.108746, 0.109808,
120 0.419114, -0.237824, -0.422627, 0.175115,
121 0.419114, -0.237824, -0.422627, 0.175115,
123 0.36726, -0.522303, -0.456502, -0.175475,
124 0.36726, -0.522303, -0.456502, -0.175475
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
130 # TODO: enable more data points after fixing the reference issue
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
134 input0[input] = test_inputs[batch_start:batch_end]
135 golden_start = i * units * batches
136 golden_end = golden_start + units * batches
137 output0[output] = golden_outputs[golden_start:golden_end]
138 Example((input0, output0))