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author | Lev Proleev <levp@google.com> | 2020-01-14 15:40:39 +0000 |
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committer | Lev Proleev <levp@google.com> | 2020-02-05 12:41:48 +0000 |
commit | fab4fb12ca85d226a9b41391df7334aab892bb3f (patch) | |
tree | 389fae5a4a25635011ac37c5fe1f9dd666260ae7 /nn/runtime/test/specs/V1_3 | |
parent | 88195b1c6e3f6653615b0ae2795263de5f56f86f (diff) | |
download | ml-fab4fb12ca85d226a9b41391df7334aab892bb3f.tar.gz |
Add a state output for UNIDIRECTIONAL_SEQUENCE_RNN
Bug: 138443991
Test: NNTest_static and VtsHalNeuralnetworksV1_3TargetTest
Change-Id: I87c4735b9ced983142cac502635c1a6a21deed0c
Diffstat (limited to 'nn/runtime/test/specs/V1_3')
-rw-r--r-- | nn/runtime/test/specs/V1_3/unidirectional_sequence_rnn.mod.py | 229 |
1 files changed, 229 insertions, 0 deletions
diff --git a/nn/runtime/test/specs/V1_3/unidirectional_sequence_rnn.mod.py b/nn/runtime/test/specs/V1_3/unidirectional_sequence_rnn.mod.py new file mode 100644 index 000000000..75563e12c --- /dev/null +++ b/nn/runtime/test/specs/V1_3/unidirectional_sequence_rnn.mod.py @@ -0,0 +1,229 @@ +# +# Copyright (C) 2019 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. +# +import numpy as np + + +def test(name, input, weights, recurrent_weights, bias, hidden_state, + activation, time_major, output, output_state, input_data, weights_data, + recurrent_weights_data, bias_data, hidden_state_data, output_data, + output_state_data): + activation = Int32Scalar("activation", activation) + time_major = Int32Scalar("time_major", time_major) + model = Model().Operation("UNIDIRECTIONAL_SEQUENCE_RNN", input, weights, + recurrent_weights, bias, hidden_state, activation, + time_major).To(output, output_state) + example = Example( + { + input: input_data, + weights: weights_data, + recurrent_weights: recurrent_weights_data, + bias: bias_data, + hidden_state: hidden_state_data, + output: output_data, + output_state: output_state_data, + }, + model=model, + name=name).AddVariations("relaxed", "float16") + + +def convert_to_time_major(tensor, num_batches, max_time, input_size): + return np.array(tensor).reshape([num_batches, max_time, input_size + ]).transpose([1, 0, 2]).flatten().tolist() + + +num_batches = 2 +max_time = 16 +input_size = 8 +num_units = 16 + +input_data = [ + 0.23689353, 0.285385, 0.037029743, -0.19858193, -0.27569133, 0.43773448, + 0.60379338, 0.35562468, -0.69424844, -0.93421471, -0.87287879, 0.37144363, + -0.62476718, 0.23791671, 0.40060222, 0.1356622, -0.99774903, -0.98858172, + -0.38952237, -0.47685933, 0.31073618, 0.71511042, -0.63767755, -0.31729108, + 0.33468103, 0.75801885, 0.30660987, -0.37354088, 0.77002847, -0.62747043, + -0.68572164, 0.0069220066, 0.65791464, 0.35130811, 0.80834007, -0.61777675, + -0.21095741, 0.41213346, 0.73784804, 0.094794154, 0.47791874, 0.86496925, + -0.53376222, 0.85315156, 0.10288584, 0.86684, -0.011186242, 0.10513687, + 0.87825835, 0.59929144, 0.62827742, 0.18899453, 0.31440187, 0.99059987, + 0.87170351, -0.35091716, 0.74861872, 0.17831337, 0.2755419, 0.51864719, + 0.55084288, 0.58982027, -0.47443086, 0.20875752, -0.058871567, -0.66609079, + 0.59098077, 0.73017097, 0.74604273, 0.32882881, -0.17503482, 0.22396147, + 0.19379807, 0.29120302, 0.077113032, -0.70331609, 0.15804303, -0.93407321, + 0.40182066, 0.036301374, 0.66521823, 0.0300982, -0.7747041, -0.02038002, + 0.020698071, -0.90300065, 0.62870288, -0.23068321, 0.27531278, -0.095755219, + -0.712036, -0.17384434, -0.50593495, -0.18646687, -0.96508682, 0.43519354, + 0.14744234, 0.62589407, 0.1653645, -0.10651493, -0.045277178, 0.99032974, + -0.88255352, -0.85147917, 0.28153265, 0.19455957, -0.55479527, -0.56042433, + 0.26048636, 0.84702539, 0.47587705, -0.074295521, -0.12287641, 0.70117295, + 0.90532446, 0.89782166, 0.79817224, 0.53402734, -0.33286154, 0.073485017, + -0.56172788, -0.044897556, 0.89964068, -0.067662835, 0.76863563, 0.93455386, + -0.6324693, -0.083922029 +] * 2 +weights_data = [ + 0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, 0.317493, + 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, 0.448504, 0.317662, + 0.523556, -0.323514, 0.480877, 0.333113, -0.757714, -0.674487, -0.643585, + 0.217766, -0.0251462, 0.79512, -0.595574, -0.422444, 0.371572, -0.452178, + -0.556069, -0.482188, -0.685456, -0.727851, 0.841829, 0.551535, -0.232336, + 0.729158, -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241, + 0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, 0.306261, + -0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, 0.0354295, 0.566564, + -0.485469, -0.620498, 0.832546, 0.697884, -0.279115, 0.294415, -0.584313, + 0.548772, 0.0648819, 0.968726, 0.723834, -0.0080452, -0.350386, -0.272803, + 0.115121, -0.412644, -0.824713, -0.992843, -0.592904, -0.417893, 0.863791, + -0.423461, -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158, + 0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, 0.0960841, + 0.368357, 0.244191, -0.817703, -0.211223, 0.442012, 0.37225, -0.623598, + -0.405423, 0.455101, 0.673656, -0.145345, -0.511346, -0.901675, -0.81252, + -0.127006, 0.809865, -0.721884, 0.636255, 0.868989, -0.347973, -0.10179, + -0.777449, 0.917274, 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872, + 0.972934, -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077, + 0.277308, 0.415818 +] +recurrent_weights_data = [ + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1 +] +bias_data = [ + 0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568, + -0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, + 0.37197268, 0.61957061, 0.3956964, -0.37609905 +] + +output_data = [ + 0.496726, 0, 0.965996, 0, 0.0584254, 0, 0, 0.12315, 0, 0, 0.612266, + 0.456601, 0, 0.52286, 1.16099, 0.0291232, 0, 0, 0.524901, 0, 0, 0, 0, + 1.02116, 0, 1.35762, 0, 0.356909, 0.436415, 0.0355727, 0, 0, 0, 0, 0, + 0.262335, 0, 0, 0, 1.33992, 0, 2.9739, 0, 0, 1.31914, 2.66147, 0, 0, + 0.942568, 0, 0, 0, 0.025507, 0, 0, 0, 0.321429, 0.569141, 1.25274, 1.57719, + 0.8158, 1.21805, 0.586239, 0.25427, 1.04436, 0, 0.630725, 0, 0.133801, + 0.210693, 0.363026, 0, 0.533426, 0, 1.25926, 0.722707, 0, 1.22031, 1.30117, + 0.495867, 0.222187, 0, 0.72725, 0, 0.767003, 0, 0, 0.147835, 0, 0, 0, + 0.608758, 0.469394, 0.00720298, 0.927537, 0, 0.856974, 0.424257, 0, 0, + 0.937329, 0, 0, 0, 0.476425, 0, 0.566017, 0.418462, 0.141911, 0.996214, + 1.13063, 0, 0.967899, 0, 0, 0, 0.0831304, 0, 0, 1.00378, 0, 0, 0, 1.44818, + 1.01768, 0.943891, 0.502745, 0, 0.940135, 0, 0, 0, 0, 0, 0, 2.13243, 0, + 0.71208, 0.123918, 1.53907, 1.30225, 1.59644, 0.70222, 0, 0.804329, 0, + 0.430576, 0, 0.505872, 0.509603, 0.343448, 0, 0.107756, 0.614544, 1.44549, + 1.52311, 0.0454298, 0.300267, 0.562784, 0.395095, 0.228154, 0, 0.675323, 0, + 1.70536, 0.766217, 0, 0, 0, 0.735363, 0.0759267, 1.91017, 0.941888, 0, 0, 0, + 0, 0, 1.5909, 0, 0, 0, 0, 0.5755, 0, 0.184687, 0, 1.56296, 0.625285, 0, 0, + 0, 0, 0, 0.0857888, 0, 0, 0, 0, 0.488383, 0.252786, 0, 0, 0, 1.02817, + 1.85665, 0, 0, 0.00981836, 0, 1.06371, 0, 0, 0, 0, 0, 0, 0.290445, 0.316406, + 0, 0.304161, 1.25079, 0.0707152, 0, 0.986264, 0.309201, 0, 0, 0, 0, 0, + 1.64896, 0.346248, 0, 0.918175, 0.78884, 0.524981, 1.92076, 2.07013, + 0.333244, 0.415153, 0.210318, 0, 0, 0, 0, 0, 2.02616, 0, 0.728256, 0.84183, + 0.0907453, 0.628881, 3.58099, 1.49974, 0 +] * 2 + +output_state_data = [ + 0.415153, + 0.210318, + 0, + 0, + 0, + 0, + 0, + 2.02616, + 0, + 0.728256, + 0.84183, + 0.090745, + 0.628881, + 3.58099, + 1.49974, + 0, + 0.415153, + 0.210318, + 0, + 0, + 0, + 0, + 0, + 2.02616, + 0, + 0.728256, + 0.84183, + 0.090745, + 0.628881, + 3.58099, + 1.49974, + 0, +] + +test( + name="blackbox_state_output", + input=Input("input", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(num_batches, max_time, input_size)), + weights=Input("weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_units, input_size)), + recurrent_weights=Input("recurrent_weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_units, num_units)), + bias=Input("bias", "TENSOR_FLOAT32", "{{{}}}".format(num_units)), + hidden_state=Input("hidden_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_batches, num_units)), + output=Output("output", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(num_batches, max_time, num_units)), + output_state=Output("output_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_batches, num_units)), + activation=1, + time_major=0, + input_data=input_data, + weights_data=weights_data, + recurrent_weights_data=recurrent_weights_data, + bias_data=bias_data, + hidden_state_data=[0] * num_batches * num_units, + output_data=output_data, + output_state_data=output_state_data, +) + +test( + name="blackbox_time_major_state_output", + input=Input("input", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, num_batches, input_size)), + weights=Input("weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_units, input_size)), + recurrent_weights=Input("recurrent_weights", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_units, num_units)), + bias=Input("bias", "TENSOR_FLOAT32", "{{{}}}".format(num_units)), + hidden_state=Input("hidden_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_batches, num_units)), + output=Output("output", "TENSOR_FLOAT32", + "{{{}, {}, {}}}".format(max_time, num_batches, num_units)), + output_state=Output("output_state", "TENSOR_FLOAT32", + "{{{}, {}}}".format(num_batches, num_units)), + activation=1, + time_major=1, + input_data=convert_to_time_major(input_data, num_batches, max_time, + input_size), + weights_data=weights_data, + recurrent_weights_data=recurrent_weights_data, + bias_data=bias_data, + hidden_state_data=[0] * num_batches * num_units, + output_data=convert_to_time_major(output_data, num_batches, max_time, + num_units), + output_state_data=output_state_data, +) |