1# 2# Copyright (C) 2018 The Android Open Source Project 3# 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 7# 8# http://www.apache.org/licenses/LICENSE-2.0 9# 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. 15# 16 17batches = 2 18units = 4 19input_size = 3 20memory_size = 10 21 22model = Model() 23 24input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size)) 25weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) 26weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size)) 27bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units)) 28state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) 29rank_param = Int32Scalar("rank_param", 1) 30activation_param = Int32Scalar("activation_param", 0) 31state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units)) 32output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) 33 34model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, 35 rank_param, activation_param).To([state_out, output]) 36model = model.RelaxedExecution(True) 37 38input0 = { 39 weights_feature: [ 40 -0.31930989, -0.36118156, 0.0079667, 0.37613347, 41 0.22197971, 0.12416199, 0.27901134, 0.27557442, 42 0.3905206, -0.36137494, -0.06634006, -0.10640851 43 ], 44 weights_time: [ 45 -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 46 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 47 48 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, 49 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, 50 51 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 52 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, 53 54 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, 55 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657 56 ], 57 bias: [], 58} 59 60input0[input] = [ 61 0.14278367, -1.64410412, -0.75222826, 62 0.14278367, -1.64410412, -0.75222826, 63] 64input0[state_in] = [ 65 0, 0, 0, 0, 66 0, 0, 0, 0, 67 0.119996, 0, 0, 0, 68 0, 0, 0, 0, 69 0, 0, -0.166701, 0, 70 0, 0, 0, 0, 71 0, 0, 0, 0, 72 -0.44244, 0, 0, 0, 73 0, 0, 0, 0, 74 0, 0, 0.0805206, 0, 75 0, 0, 0, 0, 76 0, 0, 0, 0, 77 0.119996, 0, 0, 0, 78 0, 0, 0, 0, 79 0, 0, -0.166701, 0, 80 0, 0, 0, 0, 81 0, 0, 0, 0, 82 -0.44244, 0, 0, 0, 83 0, 0, 0, 0, 84 0, 0, 0.0805206, 0, 85] 86output0 = { 87 state_out : [ 88 0, 0, 0, 0, 89 0, 0, 0, 0.119996, 90 0.542235, 0, 0, 0, 91 0, 0, 0, 0, 92 0, -0.166701, -0.40465, 0, 93 0, 0, 0, 0, 94 0, 0, 0, -0.44244, 95 -0.706995, 0, 0, 0, 96 0, 0, 0, 0, 97 0, 0.0805206, 0.137515, 0, 98 0, 0, 0, 0, 99 0, 0, 0, 0.119996, 100 0.542235, 0, 0, 0, 101 0, 0, 0, 0, 102 0, -0.166701, -0.40465, 0, 103 0, 0, 0, 0, 104 0, 0, 0, -0.44244, 105 -0.706995, 0, 0, 0, 106 0, 0, 0, 0, 107 0, 0.0805206, 0.137515, 0, 108 ], 109 output : [ 110 0.068281, -0.162217, -0.152268, 0.00323521, 111 0.068281, -0.162217, -0.152268, 0.00323521, 112 ] 113} 114 115Example((input0, output0)) 116