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|
#
# 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.
#
# Unidirectional Sequence LSTM Test:
# 4 Time Step, No Layer Normalization, No Cifg, Peephole, Projection w/ Bias, and No Clipping.
# Adapted from TFLite's NoCifgPeepholeProjectionAndBiasClippingLstmTest.LstmBlackBoxTest.
import copy
model = Model()
max_time = 4
n_batch = 2
n_input = 5
n_cell = 20
n_output = 16
input = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, max_time, n_input))
input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_input))
input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_input))
input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_input))
input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_input))
recurrent_to_input_weights = Input("recurrent_to_intput_weights",
"TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_output))
recurrent_to_forget_weights = Input("recurrent_to_forget_weights",
"TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_output))
recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_output))
recurrent_to_output_weights = Input("recurrent_to_output_weights",
"TENSOR_FLOAT32",
"{%d, %d}" % (n_cell, n_output))
cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32",
"{%d}" % (n_cell))
cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32",
"{%d}" % (n_cell))
cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32",
"{%d}" % (n_cell))
input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32",
"{%d}" % (n_cell))
cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32",
"{%d}" % (n_cell))
projection_weights = Input("projection_weights", "TENSOR_FLOAT32",
"{%d,%d}" % (n_output, n_cell))
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{%d}" % n_output)
output_state_in = Input("output_state_in", "TENSOR_FLOAT32",
"{%d, %d}" % (n_batch, n_output))
cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32",
"{%d, %d}" % (n_batch, n_cell))
activation_param = Int32Scalar("activation_param", 4) # Tanh
cell_clip_param = Float32Scalar("cell_clip_param", 0.)
proj_clip_param = Float32Scalar("proj_clip_param", 0.)
time_major_param = BoolScalar("time_major_param", False)
input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, max_time, n_output))
model = model.Operation(
"UNIDIRECTIONAL_SEQUENCE_LSTM", input, input_to_input_weights, input_to_forget_weights,
input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
recurrent_to_forget_weights, recurrent_to_cell_weights,
recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights,
cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias,
output_gate_bias, projection_weights, projection_bias, output_state_in,
cell_state_in, activation_param, cell_clip_param, proj_clip_param, time_major_param,
input_layer_norm_weights, forget_layer_norm_weights,
cell_layer_norm_weights, output_layer_norm_weights).To([output])
# Example 1. Input in operand 0,
input0 = {
input_to_input_weights: [
0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
-0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
-0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
-0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
-0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
-0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
-0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
-0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
-0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
-0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
-0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
-0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
-0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
-0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677,
],
input_to_forget_weights: [
-0.0018401089, -0.004852237, 0.03698424, 0.014181704,
0.028273236, -0.016726194, -0.05249759, -0.10204261,
0.00861066, -0.040979505, -0.009899187, 0.01923892,
-0.028177269, -0.08535103, -0.14585495, 0.10662567,
-0.01909731, -0.017883534, -0.0047269356, -0.045103323,
0.0030784295, 0.076784775, 0.07463696, 0.094531395,
0.0814421, -0.12257899, -0.033945758, -0.031303465,
0.045630626, 0.06843887, -0.13492945, -0.012480007,
-0.0811829, -0.07224499, -0.09628791, 0.045100946,
0.0012300825, 0.013964662, 0.099372394, 0.02543059,
0.06958324, 0.034257296, 0.0482646, 0.06267997,
0.052625068, 0.12784666, 0.07077897, 0.025725935,
0.04165009, 0.07241905, 0.018668644, -0.037377294,
-0.06277783, -0.08833636, -0.040120605, -0.011405586,
-0.007808335, -0.010301386, -0.005102167, 0.027717464,
0.05483423, 0.11449111, 0.11289652, 0.10939839,
0.13396506, -0.08402166, -0.01901462, -0.044678304,
-0.07720565, 0.014350063, -0.11757958, -0.0652038,
-0.08185733, -0.076754324, -0.092614375, 0.10405491,
0.052960336, 0.035755895, 0.035839386, -0.012540553,
0.036881298, 0.02913376, 0.03420159, 0.05448447,
-0.054523353, 0.02582715, 0.02327355, -0.011857179,
-0.0011980024, -0.034641717, -0.026125094, -0.17582615,
-0.15923657, -0.27486774, -0.0006143371, 0.0001771948,
-8.470171e-05, 0.02651807, 0.045790765, 0.06956496,
],
input_to_cell_weights: [
-0.04580283, -0.09549462, -0.032418985, -0.06454633,
-0.043528453, 0.043018587, -0.049152344, -0.12418144,
-0.078985475, -0.07596889, 0.019484362, -0.11434962,
-0.0074034138, -0.06314844, -0.092981495, 0.0062155537,
-0.025034338, -0.0028890965, 0.048929527, 0.06235075,
0.10665918, -0.032036792, -0.08505916, -0.10843358,
-0.13002433, -0.036816437, -0.02130134, -0.016518239,
0.0047691227, -0.0025825808, 0.066017866, 0.029991534,
-0.10652836, -0.1037554, -0.13056071, -0.03266643,
-0.033702414, -0.006473424, -0.04611692, 0.014419339,
-0.025174323, 0.0396852, 0.081777506, 0.06157468,
0.10210095, -0.009658194, 0.046511717, 0.03603906,
0.0069369148, 0.015960095, -0.06507666, 0.09551598,
0.053568836, 0.06408714, 0.12835667, -0.008714329,
-0.20211966, -0.12093674, 0.029450472, 0.2849013,
-0.029227901, 0.1164364, -0.08560263, 0.09941786,
-0.036999565, -0.028842626, -0.0033637602, -0.017012902,
-0.09720865, -0.11193351, -0.029155117, -0.017936034,
-0.009768936, -0.04223324, -0.036159635, 0.06505112,
-0.021742892, -0.023377212, -0.07221364, -0.06430552,
0.05453865, 0.091149814, 0.06387331, 0.007518393,
0.055960953, 0.069779344, 0.046411168, 0.10509911,
0.07463894, 0.0075130584, 0.012850982, 0.04555431,
0.056955688, 0.06555285, 0.050801456, -0.009862683,
0.00826772, -0.026555609, -0.0073611983, -0.0014897042,
],
input_to_output_weights: [
-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
-0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
-0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
-0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
-0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
-0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
-0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
-0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
-0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
-0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
-0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
-0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956,
],
input_gate_bias: [0.02234832, 0.14757581, 0.18176508, 0.10380666,
0.053110216, -0.06928846, -0.13942584, -0.11816189,
0.19483899, 0.03652339, -0.10250295, 0.036714908,
-0.18426876, 0.036065217, 0.21810818, 0.02383196,
-0.043370757, 0.08690144, -0.04444982, 0.00030581196],
forget_gate_bias: [0.035185695, -0.042891346, -0.03032477, 0.23027696,
0.11098921, 0.15378423, 0.09263801, 0.09790885,
0.09508917, 0.061199076, 0.07665568, -0.015443159,
-0.03499149, 0.046190713, 0.08895977, 0.10899629,
0.40694186, 0.06030037, 0.012413437, -0.06108739],
cell_gate_bias: [-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
-0.1483596, -0.10639995, -0.091433935, 0.058573797,
-0.06809782, -0.07889636, -0.043246906, -0.09829136,
-0.4279842, 0.034901652, 0.18797937, 0.0075234566,
0.016178843, 0.1749513, 0.13975595, 0.92058027],
output_gate_bias: [0.046159424, -0.0012809046, 0.03563469, 0.12648113,
0.027195795, 0.35373217, -0.018957434, 0.008907322,
-0.0762701, 0.12018895, 0.04216877, 0.0022856654,
0.040952638, 0.3147856, 0.08225149, -0.057416286,
-0.14995944, -0.008040261, 0.13208859, 0.029760877],
recurrent_to_input_weights: [
-0.001374326, -0.078856036, 0.10672688, 0.029162422,
-0.11585556, 0.02557986, -0.13446963, -0.035785314,
-0.01244275, 0.025961924, -0.02337298, -0.044228926,
-0.055839065, -0.046598054, -0.010546039, -0.06900766,
0.027239809, 0.022582639, -0.013296484, -0.05459212,
0.08981, -0.045407712, 0.08682226, -0.06867011,
-0.14390695, -0.02916037, 0.000996957, 0.091420636,
0.14283475, -0.07390571, -0.06402044, 0.062524505,
-0.093129106, 0.04860203, -0.08364217, -0.08119002,
0.009352075, 0.22920375, 0.0016303885, 0.11583097,
-0.13732095, 0.012405723, -0.07551853, 0.06343048,
0.12162708, -0.031923793, -0.014335606, 0.01790974,
-0.10650317, -0.0724401, 0.08554849, -0.05727212,
0.06556731, -0.042729504, -0.043227166, 0.011683251,
-0.013082158, -0.029302018, -0.010899579, -0.062036745,
-0.022509435, -0.00964907, -0.01567329, 0.04260106,
-0.07787477, -0.11576462, 0.017356863, 0.048673786,
-0.017577527, -0.05527947, -0.082487635, -0.040137455,
-0.10820036, -0.04666372, 0.022746278, -0.07851417,
0.01068115, 0.032956902, 0.022433773, 0.0026891115,
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cell_to_input_weights: [
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cell_to_forget_weights: [
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cell_to_output_weights: [
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projection_weights: [
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projection_bias: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
input_layer_norm_weights: [],
forget_layer_norm_weights: [],
cell_layer_norm_weights: [],
output_layer_norm_weights: []
}
test_input = [
# batch 0
0.787926, 0.151646, 0.071352, 0.118426, 0.458058, # step 0
0.596268, 0.998386, 0.568695, 0.864524, 0.571277, # step 1
0.073204, 0.296072, 0.743333, 0.069199, 0.045348, # step 2
0.867394, 0.291279, 0.013714, 0.482521, 0.626339, # step 3
# batch 1
0.295743, 0.544053, 0.690064, 0.858138, 0.497181, # step 0
0.642421, 0.524260, 0.134799, 0.003639, 0.162482, # step 1
0.640394, 0.930399, 0.050782, 0.432485, 0.988078, # step 2
0.082922, 0.563329, 0.865614, 0.333232, 0.259916, # step 3
]
golden_output = [
# batch 0
0.0960319489, 0.229351997, 0.297207743, 0.415997744, 0.491644233,
0.578822136, 0.728351235, 0.788540304, 0.909073055, 0.975599587,
1.08478093, 1.17409372, 1.30914319, 1.4041512, 1.51714694,
1.61342025, 0.0634541437, 0.190279216, 0.317923307, 0.415168911,
0.458113253, 0.609743774, 0.731511116, 0.795806408, 0.876155913,
0.960330188, 1.12396312, 1.22149014, 1.33917773, 1.43213499,
1.54139447, 1.65451813, 0.0485293195, 0.160991609, 0.337073475,
0.428976893, 0.459505379, 0.617044866, 0.743735075, 0.790821671,
0.85271728, 0.946818829, 1.12779701, 1.23345077, 1.35309088,
1.44595909, 1.56173062, 1.67839324, 0.0445971154, 0.156434938,
0.341761589, 0.425259203, 0.449760497, 0.633765697, 0.745093822,
0.791106999, 0.84820503, 0.952787101, 1.13438797, 1.24063754,
1.34668994, 1.44879568, 1.57038593, 1.67956686,
# batch 1
0.0861309841, 0.228726774, 0.296653062, 0.40733397, 0.47120741,
0.581307411, 0.719366193, 0.788456261, 0.904226124, 0.965476751,
1.10223258, 1.19042683, 1.32106233, 1.41333091, 1.51509535,
1.62168002, 0.0652779415, 0.18218407, 0.324066937, 0.42611438,
0.47292757, 0.602282405, 0.739310443, 0.791508496, 0.870626807,
0.955534995, 1.10976851, 1.21598971, 1.34197009, 1.43256509,
1.54804492, 1.65581059, 0.0492607877, 0.169714347, 0.332315415,
0.419173867, 0.44699502, 0.630063772, 0.737177074, 0.792844594,
0.858417571, 0.956391335, 1.13453305, 1.23976779, 1.34693861,
1.4410423, 1.55988359, 1.67204297, 0.0390465111, 0.15099439,
0.3439475, 0.424439192, 0.444207728, 0.632501483, 0.742233515,
0.791400731, 0.845713973, 0.944575012, 1.14116096, 1.24791968,
1.35954499, 1.45086145, 1.56633317, 1.68943977,
]
output0 = {
output: golden_output,
}
input0[input] = test_input
input0[output_state_in] = [0 for _ in range(n_batch * n_output)]
input0[cell_state_in] = [0 for _ in range(n_batch * n_cell)]
Example((input0, output0))
|
