wehub-resource-sync
108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
# 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 batchmatmul."""
|
|
import tensorflow 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("make_batchmatmul_tests")
|
|
def make_batchmatmul_tests(options):
|
|
"""Make a set of tests to do basic batch matrix multiply."""
|
|
|
|
test_parameters = [
|
|
{
|
|
"dtype": [tf.float32],
|
|
"shapes": [((3, 4, 7), (7, 9), (3, 4, 7), (7, 9)),
|
|
((None, 4, 5), (None, 5, 6), (3, 4, 5), (3, 5, 6)),
|
|
((None, 1, 3, 4), (None, 4, 2), (2, 1, 3, 4), (5, 4, 2)),
|
|
((None, None, None, 3, 4), (None, None, None, 4, 3),
|
|
(2, 2, 2, 3, 4), (2, 2, 2, 4, 3))],
|
|
"adjoint_b": [False, True],
|
|
"adjoint_a": [False, True],
|
|
"rhs_constant": [False],
|
|
"fully_quantize": [False, True],
|
|
},
|
|
]
|
|
|
|
def swap_last_two_dims(*args):
|
|
"""Return a tuple with the last two dimensions swapped."""
|
|
return args[:-2] + (args[-1],) + (args[-2],)
|
|
|
|
def build_graph(parameters):
|
|
"""Build a simple graph with BatchMatMul."""
|
|
placeholder0_shape = parameters["shapes"][0]
|
|
adj_a = parameters["adjoint_a"]
|
|
adj_b = parameters["adjoint_b"]
|
|
rhs_constant = parameters["rhs_constant"]
|
|
if adj_a:
|
|
placeholder0_shape = swap_last_two_dims(*placeholder0_shape)
|
|
input0_tensor = tf.compat.v1.placeholder(
|
|
dtype=parameters["dtype"], shape=placeholder0_shape)
|
|
if rhs_constant:
|
|
if adj_b:
|
|
constant1_shape = swap_last_two_dims(*parameters["shapes"][3])
|
|
else:
|
|
constant1_shape = parameters["shapes"][3]
|
|
data = create_tensor_data(
|
|
parameters["dtype"], constant1_shape, min_value=-1.0, max_value=1.0)
|
|
input1_constant = tf.constant(
|
|
data, shape=constant1_shape, dtype=parameters["dtype"])
|
|
out = tf.matmul(
|
|
input0_tensor, input1_constant, adjoint_a=adj_a, adjoint_b=adj_b)
|
|
return [input0_tensor], [out]
|
|
else:
|
|
if adj_b:
|
|
placeholder1_shape = swap_last_two_dims(*parameters["shapes"][1])
|
|
else:
|
|
placeholder1_shape = parameters["shapes"][1]
|
|
input1_tensor = tf.compat.v1.placeholder(
|
|
dtype=parameters["dtype"], shape=placeholder1_shape)
|
|
out = tf.matmul(
|
|
input0_tensor, input1_tensor, adjoint_a=adj_a, adjoint_b=adj_b)
|
|
return [input0_tensor, input1_tensor], [out]
|
|
|
|
def build_inputs(parameters, sess, inputs, outputs):
|
|
"""Feed inputs, assign variables, and freeze graph."""
|
|
input0_shape = parameters["shapes"][2]
|
|
adj_a = parameters["adjoint_a"]
|
|
adj_b = parameters["adjoint_b"]
|
|
rhs_constant = parameters["rhs_constant"]
|
|
if adj_a:
|
|
input0_shape = swap_last_two_dims(*input0_shape)
|
|
input0_value = create_tensor_data(
|
|
parameters["dtype"], input0_shape, min_value=-1.0, max_value=1.0)
|
|
if rhs_constant:
|
|
output_values = sess.run(
|
|
outputs, feed_dict=dict(zip(inputs, [input0_value])))
|
|
return [input0_value], output_values
|
|
else:
|
|
input1_shape = parameters["shapes"][
|
|
3] if not adj_b else swap_last_two_dims(*parameters["shapes"][3])
|
|
input1_value = create_tensor_data(
|
|
parameters["dtype"], input1_shape, min_value=-1.0, max_value=1.0)
|
|
output_values = sess.run(
|
|
outputs, feed_dict=dict(zip(inputs, [input0_value, input1_value])))
|
|
return [input0_value, input1_value], output_values
|
|
|
|
options.disable_batchmatmul_unfold = True
|
|
make_zip_of_tests(
|
|
options,
|
|
test_parameters,
|
|
build_graph,
|
|
build_inputs,
|
|
expected_tf_failures=0)
|