1 # 2 # Copyright (C) 2017 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 17 batches = 2 18 features = 8 19 rank = 2 20 units = int(features / rank) 21 input_size = 3 22 memory_size = 10 23 24 model = Model() 25 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)) 35 36 model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in, 37 rank_param, activation_param).To([state_out, output]) 38 39 input0 = { 40 input: [], 41 weights_feature: [ 42 -0.31930989, 0.0079667, 0.39296314, 0.37613347, 43 0.12416199, 0.15785322, 0.27901134, 0.3905206, 44 0.21931258, -0.36137494, -0.10640851, 0.31053296, 45 -0.36118156, -0.0976817, -0.36916667, 0.22197971, 46 0.15294972, 0.38031587, 0.27557442, 0.39635518, 47 -0.21580373, -0.06634006, -0.02702999, 0.27072677 48 ], 49 weights_time: [ 50 -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 51 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 52 53 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, 54 -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, 55 56 -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 57 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, 58 59 -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, 60 -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, 61 62 -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, 63 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, 64 65 -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, 66 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, 67 68 -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, 69 -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, 70 71 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, 72 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763 73 ], 74 bias: [], 75 state_in: [0 for _ in range(batches * memory_size * features)], 76 } 77 78 test_inputs = [ 79 0.12609188, -0.46347019, -0.89598465, 80 0.35867718, 0.36897406, 0.73463392, 81 82 0.14278367, -1.64410412, -0.75222826, 83 -0.57290924, 0.12729003, 0.7567004, 84 85 0.49837467, 0.19278903, 0.26584083, 86 0.17660543, 0.52949083, -0.77931279, 87 88 -0.11186574, 0.13164264, -0.05349274, 89 -0.72674477, -0.5683046, 0.55900657, 90 91 -0.68892461, 0.37783599, 0.18263303, 92 -0.63690937, 0.44483393, -0.71817774, 93 94 -0.81299269, -0.86831826, 1.43940818, 95 -0.95760226, 1.82078898, 0.71135032, 96 97 -1.45006323, -0.82251364, -1.69082689, 98 -1.65087092, -1.89238167, 1.54172635, 99 100 0.03966608, -0.24936394, -0.77526885, 101 2.06740379, -1.51439476, 1.43768692, 102 103 0.11771342, -0.23761693, -0.65898693, 104 0.31088525, -1.55601168, -0.87661445, 105 106 -0.89477462, 1.67204106, -0.53235275, 107 -0.6230064, 0.29819036, 1.06939757, 108 ] 109 110 golden_outputs = [ 111 -0.09623547, -0.10193135, 0.11083051, -0.0347917, 112 0.1141196, 0.12965347, -0.12652366, 0.01007236, 113 114 -0.16396809, -0.21247184, 0.11259045, -0.04156673, 115 0.10132131, -0.06143532, -0.00924693, 0.10084561, 116 117 0.01257364, 0.0506071, -0.19287863, -0.07162561, 118 -0.02033747, 0.22673416, 0.15487903, 0.02525555, 119 120 -0.1411963, -0.37054959, 0.01774767, 0.05867489, 121 0.09607603, -0.0141301, -0.08995658, 0.12867066, 122 123 -0.27142537, -0.16955489, 0.18521598, -0.12528358, 124 0.00331409, 0.11167502, 0.02218599, -0.07309391, 125 126 0.09593632, -0.28361851, -0.0773851, 0.17199151, 127 -0.00075242, 0.33691186, -0.1536046, 0.16572715, 128 129 -0.27916506, -0.27626723, 0.42615682, 0.3225764, 130 -0.37472126, -0.55655634, -0.05013514, 0.289112, 131 132 -0.24418658, 0.07540751, -0.1940318, -0.08911639, 133 0.00732617, 0.46737891, 0.26449674, 0.24888524, 134 135 -0.17225097, -0.54660404, -0.38795233, 0.08389944, 136 0.07736043, -0.28260678, 0.15666828, 1.14949894, 137 138 -0.57454878, -0.64704704, 0.73235172, -0.34616736, 139 0.21120001, -0.22927976, 0.02455296, -0.35906726, 140 ] 141 142 output0 = {state_out: [0 for _ in range(batches * memory_size * features)], 143 output: []} 144 145 # TODO: enable more data points after fixing the reference issue 146 for i in range(1): 147 batch_start = i * input_size * batches 148 batch_end = batch_start + input_size * batches 149 input0[input] = test_inputs[batch_start:batch_end] 150 golden_start = i * units * batches 151 golden_end = golden_start + units * batches 152 output0[output] = golden_outputs[golden_start:golden_end] 153 Example((input0, output0)) 154
