144 lines
5.1 KiB
Python
144 lines
5.1 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 depthwiseconv."""
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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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@register_make_test_function()
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def make_depthwiseconv_tests(options):
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"""Make a set of tests to do convolution."""
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# Tensorflow only supports equal strides
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test_parameters = [
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{
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"input_shape": [[1, 3, 4, 3], [1, 10, 10, 3]],
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"filter_size": [[1, 1], [1, 2], [3, 3]],
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"strides": [[1, 1, 1, 1], [1, 3, 3, 1]],
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"dilations": [[1, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]],
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"channel_multiplier": [1, 2],
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"rate": [[1, 1]],
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"padding": ["SAME", "VALID"],
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"data_format": ["NHWC"],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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},
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{
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"input_shape": [[1, 3, 4, 3]],
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"filter_size": [[1, 1]],
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"strides": [[1, 1, 2, 1]], # TF needs [1, x, x, 1]
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"dilations": [[1, 1, 1, 1], [1, 2, 2, 1]],
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"channel_multiplier": [2],
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"rate": [[2, 2]], # Only [1, 1] is supported
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"padding": ["SAME"],
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"data_format": ["NHWC"],
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"constant_filter": [True, False],
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"fully_quantize": [False],
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"quant_16x8": [False]
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},
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{
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"input_shape": [[1, 3, 4, 3], [1, 10, 10, 3]],
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"filter_size": [[1, 1], [1, 2], [3, 3]],
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"strides": [[1, 1, 1, 1], [1, 3, 3, 1]],
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"dilations": [[1, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]],
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"channel_multiplier": [1, 2],
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"rate": [[1, 1]],
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"padding": ["SAME", "VALID"],
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"data_format": ["NHWC"],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [False]
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},
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{
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"input_shape": [[1, 3, 4, 3]],
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"filter_size": [[1, 2]],
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"strides": [[1, 3, 3, 1]],
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"dilations": [[1, 3, 2, 1]],
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"channel_multiplier": [1],
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"rate": [[1, 1]],
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"padding": ["SAME", "VALID"],
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"data_format": ["NHWC"],
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"constant_filter": [True],
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"fully_quantize": [True],
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"quant_16x8": [True]
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},
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]
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def get_tensor_shapes(parameters):
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input_shape = parameters["input_shape"]
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filter_size = parameters["filter_size"]
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filter_shape = filter_size + [
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input_shape[3], parameters["channel_multiplier"]
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]
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return [input_shape, filter_shape]
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def build_graph(parameters):
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"""Build a depthwise conv graph given `parameters`."""
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input_shape, filter_shape = get_tensor_shapes(parameters)
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input_tensor = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input", shape=input_shape)
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# Get filter input either as a placeholder or constants. Also get a list of
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# the input tensors that are represented as placeholders.
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if parameters["constant_filter"]:
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filter_input = create_tensor_data(np.float32, filter_shape)
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input_tensors = [input_tensor]
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else:
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filter_input = tf.compat.v1.placeholder(
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dtype=tf.float32, name="filter", shape=filter_shape)
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input_tensors = [input_tensor, filter_input]
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out = tf.nn.depthwise_conv2d(
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input_tensor,
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filter_input,
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strides=parameters["strides"],
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rate=parameters["rate"],
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padding=parameters["padding"],
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data_format=parameters["data_format"])
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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# pylint: disable=g-doc-return-or-yield, g-doc-args
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"""Build list of input values.
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It either contains 1 tensor (input) or 2 tensors (input, filter) based on
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whether filter is constant or variable input.
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"""
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input_shape, filter_shape = get_tensor_shapes(parameters)
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values = [
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create_tensor_data(np.float32, input_shape, min_value=-1, max_value=1)
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]
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if not parameters["constant_filter"]:
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values.append(
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create_tensor_data(
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np.float32, filter_shape, min_value=-1, max_value=1))
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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=4)
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