| # Copyright 2020 The TensorFlow Authors. 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. |
| # ============================================================================== |
| """Test configs for identifying dilated conv.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| import tensorflow.compat.v1 as tf |
| from tensorflow.lite.testing.zip_test_utils import create_tensor_data |
| from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests |
| from tensorflow.lite.testing.zip_test_utils import register_make_test_function |
| |
| |
| @register_make_test_function() |
| def make_identify_dilated_conv_tests(options): |
| """Make a set of tests to do dilated convolution.""" |
| |
| test_parameters = [ |
| { |
| "input_shape": [[1, 3, 4, 3], [4, 6, 6, 1]], |
| "filter_shape": [[1, 1], [2, 3], [3, 3]], |
| "strides": [[1, 1, 1, 1], [1, 2, 3, 1]], |
| "dilations": [[1, 3, 2, 1], [1, 2, 2, 1], [1, 2, 1, 1]], |
| "padding": ["VALID", "SAME"], |
| "data_format": ["NHWC"], |
| "constant_filter": [True, False], |
| "channel_multiplier": [1, 2], |
| }, |
| ] |
| |
| def get_tensor_shapes(parameters): |
| input_shape = parameters["input_shape"] |
| filter_size = parameters["filter_shape"] |
| filter_shape = filter_size + [ |
| input_shape[3], parameters["channel_multiplier"] |
| ] |
| return [input_shape, filter_shape] |
| |
| def build_graph(parameters): |
| """Build a conv graph given `parameters`.""" |
| input_shape, filter_shape = get_tensor_shapes(parameters) |
| input_tensor = tf.compat.v1.placeholder( |
| dtype=tf.float32, name="input", shape=input_shape) |
| |
| # Get filter input either as a placeholder or constants. Also get a list of |
| # the input tensors that are represented as placeholders. |
| if parameters["constant_filter"]: |
| filter_input = create_tensor_data( |
| np.float32, filter_shape, min_value=-10, max_value=10) |
| input_tensors = [input_tensor] |
| else: |
| filter_input = tf.compat.v1.placeholder( |
| dtype=tf.float32, name="filter", shape=filter_shape) |
| input_tensors = [input_tensor, filter_input] |
| |
| # Use `tf.nn.convolution` here since it will create the `batch_to_space` and |
| # the `space_to_batch` ops respectively. |
| out = tf.nn.convolution( |
| input_tensor, |
| filter_input, |
| strides=parameters["strides"], |
| dilations=parameters["dilations"], |
| padding=parameters["padding"], |
| data_format=parameters["data_format"]) |
| return input_tensors, [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| # Build list of input values either containing 1 tensor (input) or 2 tensors |
| # (input, filter) based on whether filter is constant or variable input. |
| input_shape, filter_shape = get_tensor_shapes(parameters) |
| values = [ |
| create_tensor_data(np.float32, input_shape, min_value=-1, max_value=1) |
| ] |
| if not parameters["constant_filter"]: |
| values.append(create_tensor_data(np.float32, filter_shape)) |
| return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) |
| |
| make_zip_of_tests( |
| options, |
| test_parameters, |
| build_graph, |
| build_inputs, |
| expected_tf_failures=168) |