1b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# 2b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# Copyright (C) 2018 The Android Open Source Project 3b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# 4b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# Licensed under the Apache License, Version 2.0 (the "License"); 5b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# you may not use this file except in compliance with the License. 6b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# You may obtain a copy of the License at 7b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# 8b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# http://www.apache.org/licenses/LICENSE-2.0 9b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# 10b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# Unless required by applicable law or agreed to in writing, software 11b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# distributed under the License is distributed on an "AS IS" BASIS, 12b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# See the License for the specific language governing permissions and 14b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# limitations under the License. 15b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# 16b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 17b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangbatches = 2 18b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangfeatures = 4 19b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangrank = 1 20b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangunits = int(features / rank) 21b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wanginput_size = 3 22b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangmemory_size = 10 23b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 24b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangmodel = Model() 25b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 26b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wanginput = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) 27b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangweights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size)) 28b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangweights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size)) 29b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangbias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) 30b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangstate_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 31e538bb0f9db55531dbc018c6b81ff1d6e4fbd8acMichael Butlerrank_param = Int32Scalar("rank_param", rank) 32e538bb0f9db55531dbc018c6b81ff1d6e4fbd8acMichael Butleractivation_param = Int32Scalar("activation_param", 0) 33b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangstate_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features)) 34b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangoutput = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) 35b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 36b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangmodel = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, 37b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang rank_param, activation_param).To([state_out, output]) 38b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangmodel = model.RelaxedExecution(True) 39b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 40b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wanginput0 = { 41b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang input: [], 42b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang weights_feature: [ 43b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.31930989, -0.36118156, 0.0079667, 0.37613347, 44b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.22197971, 0.12416199, 0.27901134, 0.27557442, 45b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.3905206, -0.36137494, -0.06634006, -0.10640851 46b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang ], 47b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang weights_time: [ 48b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 49b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 50b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 51b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, 52b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, 53b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 54b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 55b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, 56b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 57b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, 58b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657 59b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang ], 60b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang bias: [], 61b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang state_in: [0 for _ in range(batches * memory_size * features)], 62b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang} 63b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 64b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangtest_inputs = [ 65b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.12609188, -0.46347019, -0.89598465, 66b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.12609188, -0.46347019, -0.89598465, 67b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 68b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.14278367, -1.64410412, -0.75222826, 69b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.14278367, -1.64410412, -0.75222826, 70b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 71b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.49837467, 0.19278903, 0.26584083, 72b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.49837467, 0.19278903, 0.26584083, 73b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 74b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.11186574, 0.13164264, -0.05349274, 75b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.11186574, 0.13164264, -0.05349274, 76b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 77b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.68892461, 0.37783599, 0.18263303, 78b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.68892461, 0.37783599, 0.18263303, 79b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 80b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.81299269, -0.86831826, 1.43940818, 81b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.81299269, -0.86831826, 1.43940818, 82b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 83b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -1.45006323, -0.82251364, -1.69082689, 84b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -1.45006323, -0.82251364, -1.69082689, 85b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 86b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.03966608, -0.24936394, -0.77526885, 87b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.03966608, -0.24936394, -0.77526885, 88b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 89b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.11771342, -0.23761693, -0.65898693, 90b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.11771342, -0.23761693, -0.65898693, 91b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 92b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.89477462, 1.67204106, -0.53235275, 93b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.89477462, 1.67204106, -0.53235275 94b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang] 95b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 96b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wanggolden_outputs = [ 97b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.014899, -0.0517661, -0.143725, -0.00271883, 98b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.014899, -0.0517661, -0.143725, -0.00271883, 99b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 100b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.068281, -0.162217, -0.152268, 0.00323521, 101b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.068281, -0.162217, -0.152268, 0.00323521, 102b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 103b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.0317821, -0.0333089, 0.0609602, 0.0333759, 104b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.0317821, -0.0333089, 0.0609602, 0.0333759, 105b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 106b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.00623099, -0.077701, -0.391193, -0.0136691, 107b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.00623099, -0.077701, -0.391193, -0.0136691, 108b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 109b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.201551, -0.164607, -0.179462, -0.0592739, 110b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.201551, -0.164607, -0.179462, -0.0592739, 111b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 112b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.0886511, -0.0875401, -0.269283, 0.0281379, 113b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.0886511, -0.0875401, -0.269283, 0.0281379, 114b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 115b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.201174, -0.586145, -0.628624, -0.0330412, 116b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.201174, -0.586145, -0.628624, -0.0330412, 117b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 118b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.0839096, -0.299329, 0.108746, 0.109808, 119b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang -0.0839096, -0.299329, 0.108746, 0.109808, 120b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 121b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.419114, -0.237824, -0.422627, 0.175115, 122b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.419114, -0.237824, -0.422627, 0.175115, 123b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 124b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.36726, -0.522303, -0.456502, -0.175475, 125b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 0.36726, -0.522303, -0.456502, -0.175475 126b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang] 127b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 128b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangoutput0 = {state_out: [0 for _ in range(batches * memory_size * features)], 129b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang output: []} 130b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang 131b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang# TODO: enable more data points after fixing the reference issue 132b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wangfor i in range(1): 133b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang batch_start = i * input_size * batches 134b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang batch_end = batch_start + input_size * batches 135b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang input0[input] = test_inputs[batch_start:batch_end] 136b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang golden_start = i * units * batches 137b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang golden_end = golden_start + units * batches 138b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang output0[output] = golden_outputs[golden_start:golden_end] 139b74d2837ab1687c1a4f913aa5f90a9838efe0addMiao Wang Example((input0, output0)) 140