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-rw-r--r--externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py161
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diff --git a/externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py b/externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py
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index cb1bf6010..000000000
--- a/externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py
+++ /dev/null
@@ -1,161 +0,0 @@
-#
-# 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.
-#
-
-# LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping.
-
-model = Model()
-
-n_batch = 1
-n_input = 2
-# n_cell and n_output have the same size when there is no projection.
-n_cell = 4
-n_output = 4
-
-input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, 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", "{0}")
-cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}")
-cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}")
-
-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", "{0,0}")
-projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
-
-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 = Input("activation_param", "TENSOR_INT32", "{1}")
-cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
-proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
-
-scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
-output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
-output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-
-model = model.Operation("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
-).To([scratch_buffer, output_state_out, cell_state_out, output])
-
-# Example 1. Input in operand 0,
-input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524],
- input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212],
- input_to_cell_weights: [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778],
- input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578],
-
- input_gate_bias: [0.,0.,0.,0.],
- forget_gate_bias: [1.,1.,1.,1.],
- cell_gate_bias: [0.,0.,0.,0.],
- output_gate_bias: [0.,0.,0.,0.],
-
- recurrent_to_input_weights: [
- -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
- -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322,
- -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296],
-
- recurrent_to_cell_weights: [
- -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
- -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
- -0.46367589, 0.26016325, -0.03894562, -0.16368064],
-
- recurrent_to_forget_weights: [
- -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
- -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
- 0.28053468, 0.01560611, -0.20127171, -0.01140004],
-
- recurrent_to_output_weights: [
- 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
- 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
- -0.51818722, -0.15390486, 0.0468148, 0.39922136],
-
- cell_to_input_weights: [],
- cell_to_forget_weights: [],
- cell_to_output_weights: [],
-
- projection_weights: [],
- projection_bias: [],
-
- activation_param: [4], # Tanh
- cell_clip_param: [0.],
- proj_clip_param: [0.],
-}
-
-# Instantiate examples
-# TODO: Add more examples after fixing the reference issue
-test_inputs = [
- [2., 3.],
-# [3., 4.],[1., 1.]
-]
-golden_outputs = [
- [-0.02973187, 0.1229473, 0.20885126, -0.15358765,],
-# [-0.03716109, 0.12507336, 0.41193449, -0.20860538],
-# [-0.15053082, 0.09120187, 0.24278517, -0.12222792]
-]
-
-for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs):
- output0 = {
- scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
- cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
- output_state_out: [ 0 for x in range(n_batch * n_output) ],
- output: output_tensor
- }
- input0[input] = input_tensor
- 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))