| # 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.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| 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))], |
| "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.use_experimental_converter = True |
| make_zip_of_tests( |
| options, |
| test_parameters, |
| build_graph, |
| build_inputs, |
| expected_tf_failures=0) |
