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Diffstat (limited to 'externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py')
-rw-r--r-- | externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py | 161 |
1 files changed, 0 insertions, 161 deletions
diff --git a/externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py b/externals/nnapi_test_generator/tests/P_lstm/lstm.mod.py deleted file mode 100644 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)) |