From 12d88feea8573f8490629cf62fc342b152e57d65 Mon Sep 17 00:00:00 2001 From: Chunseok Lee Date: Mon, 14 Dec 2020 14:43:04 +0900 Subject: Imported Upstream version 1.11.0 --- tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py | 138 ++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py (limited to 'tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py') diff --git a/tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py b/tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py new file mode 100644 index 000000000..2b0f368d3 --- /dev/null +++ b/tests/nnapi/specs/skip/V1_2/svdf_float16.mod.py @@ -0,0 +1,138 @@ +# +# 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 +features = 4 +rank = 1 +units = int(features / rank) +input_size = 3 +memory_size = 10 + +model = Model() + +input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size)) +weights_feature = Input("weights_feature", "TENSOR_FLOAT16", "{%d, %d}" % (features, input_size)) +weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size)) +bias = Input("bias", "TENSOR_FLOAT16", "{%d}" % (units)) +state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features)) +rank_param = Int32Scalar("rank_param", rank) +activation_param = Int32Scalar("activation_param", 0) +state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features)) +output = Output("output", "TENSOR_FLOAT16", "{%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 = { + input: [], + 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: [], + state_in: [0 for _ in range(batches * memory_size * features)], +} + +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 +] + +output0 = {state_out: [0 for _ in range(batches * memory_size * features)], + output: []} + +# TODO: enable more data points after fixing the reference issue +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] + golden_start = i * units * batches + golden_end = golden_start + units * batches + output0[output] = golden_outputs[golden_start:golden_end] + Example((input0, output0)) -- cgit v1.2.3