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#
# Copyright (C) 2018 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
units = 4
input_size = 3
memory_size = 10
model = Model()
input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size))
bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
rank_param = Int32Scalar("rank_param", 1)
activation_param = Int32Scalar("activation_param", 0)
state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
rank_param, activation_param).To([state_out, output])
model = model.RelaxedExecution(True)
input0 = {
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: [],
}
input0[input] = [
0.14278367, -1.64410412, -0.75222826,
0.14278367, -1.64410412, -0.75222826,
]
input0[state_in] = [
0, 0, 0, 0,
0, 0, 0, 0,
0.119996, 0, 0, 0,
0, 0, 0, 0,
0, 0, -0.166701, 0,
0, 0, 0, 0,
0, 0, 0, 0,
-0.44244, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0.0805206, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0.119996, 0, 0, 0,
0, 0, 0, 0,
0, 0, -0.166701, 0,
0, 0, 0, 0,
0, 0, 0, 0,
-0.44244, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0.0805206, 0,
]
output0 = {
state_out : [
0, 0, 0, 0,
0, 0, 0, 0.119996,
0.542235, 0, 0, 0,
0, 0, 0, 0,
0, -0.166701, -0.40465, 0,
0, 0, 0, 0,
0, 0, 0, -0.44244,
-0.706995, 0, 0, 0,
0, 0, 0, 0,
0, 0.0805206, 0.137515, 0,
0, 0, 0, 0,
0, 0, 0, 0.119996,
0.542235, 0, 0, 0,
0, 0, 0, 0,
0, -0.166701, -0.40465, 0,
0, 0, 0, 0,
0, 0, 0, -0.44244,
-0.706995, 0, 0, 0,
0, 0, 0, 0,
0, 0.0805206, 0.137515, 0,
],
output : [
0.068281, -0.162217, -0.152268, 0.00323521,
0.068281, -0.162217, -0.152268, 0.00323521,
]
}
Example((input0, output0))
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