140 lines
5.3 KiB
Python
140 lines
5.3 KiB
Python
# Copyright 2019 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 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_conv_tests(options):
|
|
"""Make a set of tests to do 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, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]],
|
|
"padding": ["SAME", "VALID"],
|
|
"data_format": ["NHWC"], # TODO(aselle): NCHW would be good
|
|
"constant_filter": [True, False],
|
|
"channel_multiplier": [1, 2],
|
|
"fully_quantize": [False],
|
|
"quant_16x8": [False],
|
|
"dynamic_range_quantize": [False]
|
|
},
|
|
{
|
|
"input_shape": [[1, 3, 4, 3]],
|
|
"filter_shape": [[1, 1], [2, 3]],
|
|
"strides": [[1, 1, 1, 1]],
|
|
"dilations": [[1, 1, 1, 1]],
|
|
"padding": ["SAME"],
|
|
"data_format": ["NHWC"],
|
|
"constant_filter": [True],
|
|
"channel_multiplier": [1, 2],
|
|
"fully_quantize": [True],
|
|
"quant_16x8": [True],
|
|
"dynamic_range_quantize": [False],
|
|
},
|
|
# TODO(b/134702301): The fully_quantize param is just ignored by the MLIR
|
|
# testing path now, resulting in duplicate tests. Either ignore these
|
|
# tests or handle it properly in the mlir_convert() function.
|
|
{
|
|
"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, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]],
|
|
"padding": ["SAME", "VALID"],
|
|
"data_format": ["NHWC"], # TODO(aselle): NCHW would be good
|
|
"constant_filter": [True],
|
|
"channel_multiplier": [1, 2],
|
|
"fully_quantize": [True],
|
|
"quant_16x8": [False],
|
|
"dynamic_range_quantize": [False]
|
|
},
|
|
{
|
|
"input_shape": [[1, 3, 4, 3]],
|
|
"filter_shape": [[1, 1]],
|
|
"strides": [[1, 1, 1, 1], [1, 2, 3, 1]],
|
|
"dilations": [[1, 1, 1, 1]],
|
|
"padding": ["SAME", "VALID"],
|
|
"data_format": ["NHWC"],
|
|
"constant_filter": [True],
|
|
"channel_multiplier": [2],
|
|
"fully_quantize": [False],
|
|
"quant_16x8": [False],
|
|
"dynamic_range_quantize": [True]
|
|
},
|
|
]
|
|
|
|
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]
|
|
|
|
out = tf.nn.conv2d(
|
|
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=60)
|