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#
# Copyright (C) 2018 The Android Open Source Project
# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
#
# 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.
#
# refer to tanh_v1_dynamic.mod.py about the structore
# This adds reshape as the first op in a model and
# returns output of reshape, which is dynamic tensor
'''
Testing Slice op when the input is dynamic.
input [3, 2, 3, 1] shape [4] (value of shape will be [3, 2, 3, 1])
| |
+-------------+
|
Reshape (added by DynamicInputGenerator since it generates its output to be dynamic)
|
| dynamic tensor at compilation time but the shape will be [3, 2, 3, 1] at execution time
|
Slice
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output (dynamic tensor, [2, 1, 3, 1] at execution time)
'''
import dynamic_tensor
model = Model()
model_input_shape = [3, 2, 3, 1]
dynamic_layer = dynamic_tensor.DynamicInputGenerator(model, model_input_shape, "TENSOR_FLOAT32")
test_node_input = dynamic_layer.getTestNodeInput()
begins = Parameter("begins", "TENSOR_INT32", "{4}", [1, 0, 0, 0])
sizes = Parameter("sizes", "TENSOR_INT32", "{4}", [2, 1, -1, 1])
# note output shape is used by expected output's shape
model_output = Output("output", "TENSOR_FLOAT32", "{2, 1, 3, 1}")
model.Operation("SLICE", test_node_input, begins, sizes,).To(model_output)
model_input_data = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
model_output_data = [3, 3, 3, 5, 5, 5]
Example({
# use these two as input
dynamic_layer.getModelInput() : model_input_data,
dynamic_layer.getShapeInput() : model_input_shape,
model_output: model_output_data,
})
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