272 lines
9.0 KiB
Python
272 lines
9.0 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test configs for strided_slice operators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow.compat.v1 as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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from tensorflow.lite.testing.zip_test_utils import TF_TYPE_INFO
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def _make_strided_slice_tests(options, test_parameters, expected_tf_failures=0):
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"""Utility function to make strided_slice_tests based on parameters."""
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def build_graph(parameters):
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"""Build graph for stride_slice test."""
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input_tensor = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input",
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shape=parameters["input_shape"])
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if parameters["constant_indices"]:
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begin = parameters["begin"]
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end = parameters["end"]
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strides = parameters["strides"]
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tensors = [input_tensor]
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else:
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begin = tf.compat.v1.placeholder(
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dtype=parameters["index_type"],
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name="begin",
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shape=[len(parameters["begin"])])
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end = tf.compat.v1.placeholder(
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dtype=parameters["index_type"],
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name="end",
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shape=[len(parameters["end"])])
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strides = None
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if parameters["strides"] is not None:
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strides = tf.compat.v1.placeholder(
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dtype=parameters["index_type"],
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name="strides",
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shape=[len(parameters["strides"])])
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tensors = [input_tensor, begin, end]
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if strides is not None:
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tensors.append(strides)
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out = tf.strided_slice(
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input_tensor,
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begin,
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end,
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strides,
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begin_mask=parameters["begin_mask"],
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end_mask=parameters["end_mask"],
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shrink_axis_mask=parameters["shrink_axis_mask"])
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return tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Build inputs for stride_slice test."""
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input_values = create_tensor_data(
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parameters["dtype"],
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parameters["input_shape"],
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min_value=-1,
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max_value=1)
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index_type = TF_TYPE_INFO[parameters["index_type"]][0]
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values = [input_values]
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if not parameters["constant_indices"]:
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begin_values = np.array(parameters["begin"]).astype(index_type)
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end_values = np.array(parameters["end"]).astype(index_type)
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stride_values = (
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np.array(parameters["strides"]).astype(index_type)
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if parameters["strides"] is not None else None)
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values.append(begin_values)
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values.append(end_values)
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if stride_values is not None:
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values.append(stride_values)
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(
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options,
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test_parameters,
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build_graph,
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build_inputs,
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expected_tf_failures=expected_tf_failures)
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@register_make_test_function()
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def make_strided_slice_tests(options):
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"""Make a set of tests to do strided_slice."""
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# TODO(soroosh): add test/support for uint8.
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test_parameters = [
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# 4-D (basic cases with const/non-const indices).
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{
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"dtype": [tf.float32, tf.int32, tf.int64, tf.bool],
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"index_type": [tf.int32],
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"input_shape": [[12, 2, 2, 5]],
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"strides": [None, [2, 1, 3, 1]],
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"begin": [[0, 0, 0, 0]],
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"end": [[12, 2, 2, 5]],
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"begin_mask": [None],
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"end_mask": [None],
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"shrink_axis_mask": [None],
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"constant_indices": [False, True],
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"fully_quantize": [False],
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},
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# 4-D with non-trivial begin & end.
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[12, 2, 2, 5]],
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"begin": [[0, 0, 0, 0], [1, 0, 1, 0]],
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"end": [[8, 2, 2, 3], [12, 2, 2, 5]],
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"strides": [None, [2, 1, 3, 1]],
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"begin_mask": [None, 8],
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"end_mask": [None, 3],
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"shrink_axis_mask": [None, 15, -1],
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"constant_indices": [True],
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"fully_quantize": [False],
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},
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# Begin, end, strides dim are different from input shape
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[12, 2, 2, 5]],
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"begin": [[0]],
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"end": [[1]],
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"strides": [None, [1]],
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"begin_mask": [0],
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"end_mask": [0],
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"shrink_axis_mask": [1],
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"constant_indices": [True, False],
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"fully_quantize": [False],
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},
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# 2-D
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[2, 3]],
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"begin": [[0, 0]],
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"end": [[2, 2]],
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"strides": [None, [2, 2]],
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"begin_mask": [None, 1, 2],
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"end_mask": [None, 1, 2],
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"shrink_axis_mask": [None, 1, 2, 3, -1],
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"constant_indices": [False, True],
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"fully_quantize": [False],
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},
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# Negative strides
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[2, 3]],
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"begin": [[0, -1]],
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"end": [[2, -3]],
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"strides": [[1, -1]],
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"begin_mask": [None, 1, 2],
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"end_mask": [None, 1, 2],
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"shrink_axis_mask": [None, 1, 2, 3, -1],
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"constant_indices": [False],
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"fully_quantize": [False],
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},
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# 4-D (cases with const indices and batchsize of 1).
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[1, 2, 2, 5]],
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"strides": [None, [1, 1, 1, 1]],
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"begin": [[0, 0, 0, 0], [0, 1, 1, 3]],
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"end": [[1, 2, 2, 5], [1, 2, 2, 4]],
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"begin_mask": [None],
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"end_mask": [None],
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"shrink_axis_mask": [None],
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"constant_indices": [True],
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"fully_quantize": [True],
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},
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# Begin, end, strides dim are different from input shape
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[12, 2, 2, 5]],
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"begin": [[0]],
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"end": [[1]],
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"strides": [None, [1]],
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"begin_mask": [0],
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"end_mask": [0],
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"shrink_axis_mask": [1],
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"constant_indices": [True],
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"fully_quantize": [True],
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},
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]
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if options.use_experimental_converter:
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test_parameters = test_parameters + [
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# Begin equal to input dim.
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[1, 1, 2]],
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"begin": [[1]],
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"end": [[0]],
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"strides": [[1]],
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"begin_mask": [0],
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"end_mask": [1],
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"shrink_axis_mask": [0],
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"constant_indices": [True, False],
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"fully_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[1, 1, 2]],
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"begin": [[1, 0, 0]],
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"end": [[0, -1, -1]],
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"strides": [[1, 1, 1]],
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"begin_mask": [6],
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"end_mask": [7],
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"shrink_axis_mask": [0],
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"constant_indices": [True, False],
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"fully_quantize": [False],
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},
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# String input.
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{
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"dtype": [tf.string],
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"index_type": [tf.int32],
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"input_shape": [[12, 2, 2, 5]],
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"begin": [[0, 0, 0, 0]],
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"end": [[8, 2, 2, 3]],
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"strides": [[2, 1, 3, 1]],
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"begin_mask": [8],
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"end_mask": [3],
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"shrink_axis_mask": [None],
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"constant_indices": [True, False],
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"fully_quantize": [False],
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}
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]
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_make_strided_slice_tests(options, test_parameters, expected_tf_failures=29)
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@register_make_test_function()
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def make_strided_slice_1d_exhaustive_tests(options):
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"""Make a set of exhaustive tests for 1D strided_slice."""
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test_parameters = [
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# 1-D Exhaustive
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{
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"dtype": [tf.float32],
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"index_type": [tf.int32],
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"input_shape": [[3]],
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"begin": [[-2], [-1], [0], [1], [2]],
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"end": [[-2], [-1], [0], [1], [2]],
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"strides": [[-2], [-1], [1], [2]],
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"begin_mask": [0, 1],
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"end_mask": [0, 1],
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"shrink_axis_mask": [0],
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"constant_indices": [False],
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},
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]
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_make_strided_slice_tests(options, test_parameters)
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