| # |
| # Copyright (C) 2017 The Android Open Source Project |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| batches = 2 |
| units = 4 |
| input_size = 3 |
| memory_size = 10 |
| |
| model = Model() |
| |
| input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) |
| weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) |
| weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size)) |
| bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) |
| state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) |
| rank_param = Input("rank_param", "TENSOR_INT32", "{1}") |
| activation_param = Input("activation_param", "TENSOR_INT32", "{1}") |
| state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) |
| output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) |
| |
| model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, |
| rank_param, activation_param).To([state_out, output]) |
| |
| input0 = { |
| weights_feature: [ |
| -0.31930989, -0.36118156, 0.0079667, 0.37613347, |
| 0.22197971, 0.12416199, 0.27901134, 0.27557442, |
| 0.3905206, -0.36137494, -0.06634006, -0.10640851 |
| ], |
| weights_time: [ |
| -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, |
| 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, |
| |
| 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, |
| -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, |
| |
| -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, |
| 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, |
| |
| -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, |
| -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657 |
| ], |
| bias: [], |
| rank_param: [1], |
| activation_param: [0], |
| } |
| |
| # TODO: State is an intermediate buffer, don't check this against test result. |
| test_inputs = [ |
| 0.12609188, -0.46347019, -0.89598465, |
| 0.12609188, -0.46347019, -0.89598465, |
| |
| 0.14278367, -1.64410412, -0.75222826, |
| 0.14278367, -1.64410412, -0.75222826, |
| |
| 0.49837467, 0.19278903, 0.26584083, |
| 0.49837467, 0.19278903, 0.26584083, |
| |
| -0.11186574, 0.13164264, -0.05349274, |
| -0.11186574, 0.13164264, -0.05349274, |
| |
| -0.68892461, 0.37783599, 0.18263303, |
| -0.68892461, 0.37783599, 0.18263303, |
| |
| -0.81299269, -0.86831826, 1.43940818, |
| -0.81299269, -0.86831826, 1.43940818, |
| |
| -1.45006323, -0.82251364, -1.69082689, |
| -1.45006323, -0.82251364, -1.69082689, |
| |
| 0.03966608, -0.24936394, -0.77526885, |
| 0.03966608, -0.24936394, -0.77526885, |
| |
| 0.11771342, -0.23761693, -0.65898693, |
| 0.11771342, -0.23761693, -0.65898693, |
| |
| -0.89477462, 1.67204106, -0.53235275, |
| -0.89477462, 1.67204106, -0.53235275 |
| ] |
| |
| golden_outputs = [ |
| 0.014899, -0.0517661, -0.143725, -0.00271883, |
| 0.014899, -0.0517661, -0.143725, -0.00271883, |
| |
| 0.068281, -0.162217, -0.152268, 0.00323521, |
| 0.068281, -0.162217, -0.152268, 0.00323521, |
| |
| -0.0317821, -0.0333089, 0.0609602, 0.0333759, |
| -0.0317821, -0.0333089, 0.0609602, 0.0333759, |
| |
| -0.00623099, -0.077701, -0.391193, -0.0136691, |
| -0.00623099, -0.077701, -0.391193, -0.0136691, |
| |
| 0.201551, -0.164607, -0.179462, -0.0592739, |
| 0.201551, -0.164607, -0.179462, -0.0592739, |
| |
| 0.0886511, -0.0875401, -0.269283, 0.0281379, |
| 0.0886511, -0.0875401, -0.269283, 0.0281379, |
| |
| -0.201174, -0.586145, -0.628624, -0.0330412, |
| -0.201174, -0.586145, -0.628624, -0.0330412, |
| |
| -0.0839096, -0.299329, 0.108746, 0.109808, |
| -0.0839096, -0.299329, 0.108746, 0.109808, |
| |
| 0.419114, -0.237824, -0.422627, 0.175115, |
| 0.419114, -0.237824, -0.422627, 0.175115, |
| |
| 0.36726, -0.522303, -0.456502, -0.175475, |
| 0.36726, -0.522303, -0.456502, -0.175475 |
| ] |
| |
| input_sequence_size = int(len(test_inputs) / input_size / batches) |
| |
| # TODO: enable more data points after fixing the reference issue |
| #for i in range(input_sequence_size): |
| for i in range(1): |
| batch_start = i * input_size * batches |
| batch_end = batch_start + input_size * batches |
| input0[input] = test_inputs[batch_start:batch_end] |
| input0[state_in] = [0 for _ in range(batches * (memory_size - 1) * units)] |
| output0 = {state_out:[0 for x in range(batches * (memory_size - 1) * units)], |
| output: []} |
| golden_start = i * units * batches |
| golden_end = golden_start + units * batches |
| output0[output] = golden_outputs[golden_start:golden_end] |
| Example((input0, output0)) |