| # 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 conv followed with bias Add and activations.""" |
| 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 |
| |
| |
| def make_conv_bias_activation_tests(activation_op): |
| """Make a set of tests to do convolution with activation and bias. |
| |
| This test will create multiple consecutive convolutions with NCHW layout to |
| make sure that the tranformations to NHWC works as expected. Note this |
| doesn't check any performance so manual checking of the generated model is |
| advised. |
| |
| Args: |
| activation_op: The activation op to be used in the test. |
| |
| Returns: |
| The function that creates the test. |
| """ |
| |
| def create_test(options): |
| """Actual function that generates examples.""" |
| test_parameters = [ |
| { |
| "input_shape": [[1, 3, 4, 3]], |
| "filter_shape": [[2, 3], [3, 3]], |
| "filter_2_shape": [[2, 1, 1, 3]], |
| "strides": [[1, 1, 1, 1]], |
| "dilations": [[1, 1, 1, 1]], |
| "data_format": ["NCHW"], |
| "channel_multiplier": [1, 2], |
| "fully_quantize": [False], |
| "dynamic_range_quantize": [False], |
| }, |
| ] |
| |
| 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] |
| |
| # TF CPU doesn't support cases with NCHW. Instead |
| # use XLA which doesn't have the same restrictions. |
| @tf.function(jit_compile=True) |
| def add_conv(input_tensor, filter_input, parameters): |
| out = tf.nn.conv2d( |
| input_tensor, |
| filter_input, |
| strides=parameters["strides"], |
| dilations=parameters["dilations"], |
| padding="VALID", |
| data_format=parameters["data_format"]) |
| return out |
| |
| def add_bias_add(data_input, filter_shape): |
| bias_input = create_tensor_data(np.float32, (filter_shape[-1],)) |
| out = tf.nn.bias_add(data_input, bias_input, data_format="NHWC") |
| return out |
| |
| 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) |
| |
| filter_input = create_tensor_data( |
| np.float32, filter_shape, min_value=-10, max_value=10) |
| input_tensors = [input_tensor] |
| |
| if parameters["data_format"] == "NCHW": |
| out = add_conv(input_tensor, filter_input, parameters) |
| else: |
| out = tf.nn.conv2d( |
| input_tensor, |
| filter_input, |
| strides=parameters["strides"], |
| dilations=parameters["dilations"], |
| padding="VALID", |
| data_format=parameters["data_format"]) |
| out = add_bias_add(out, filter_shape) |
| out = activation_op(out) |
| |
| # Add another conv + bias_add + activation. |
| |
| # Create constant filter for the second conv2d. |
| filter_input_2 = create_tensor_data( |
| np.float32, parameters["filter_2_shape"], min_value=-10, max_value=10) |
| if parameters["data_format"] == "NCHW": |
| out = add_conv(out, filter_input_2, parameters) |
| else: |
| out = tf.nn.conv2d( |
| out, |
| filter_input_2, |
| strides=parameters["strides"], |
| dilations=parameters["dilations"], |
| padding="VALID", |
| data_format=parameters["data_format"]) |
| out = add_bias_add(out, filter_shape) |
| out = activation_op(out) |
| return input_tensors, [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| """Build inputs for conv with activation.""" |
| |
| input_shape, _ = get_tensor_shapes(parameters) |
| values = [ |
| create_tensor_data( |
| np.float32, input_shape, min_value=-1, max_value=1) |
| ] |
| 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=2) |
| |
| return create_test |
| |
| |
| @register_make_test_function() |
| def make_conv_bias_relu6_tests(options): |
| """Make a set of tests to do conv_bias_relu6.""" |
| return make_conv_bias_activation_tests(tf.nn.relu6)(options) |