# coding=utf-8
# Copyright 2019 Google LLC.
#
# 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.
"""Tests for parse_layer_parameters module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
from receptive_field.python.util import graph_compute_order
from receptive_field.python.util import parse_layer_parameters
import tensorflow.compat.v1 as tf
import tf_slim as slim
def create_test_network(image_resolution, convert_variables_to_constants):
"""Convolutional neural network for test.
Args:
image_resolution: Resolution to use for input placeholder. Used for height
and width dimensions.
convert_variables_to_constants: Whether to convert variables to constants.
Returns:
graph_def: GraphDef proto of the model.
"""
g = tf.Graph()
sess = tf.Session(graph=g)
with g.as_default():
# An input test image with unknown spatial resolution.
x = tf.placeholder(
tf.float32, (1, image_resolution, image_resolution, 1),
name='input_image')
# Left branch before first addition.
l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
# Right branch before first addition.
l2_pad = tf.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]], name='L2_pad')
l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
l3 = slim.max_pool2d(l2, [3, 3], stride=2, scope='L3', padding='SAME')
# First addition.
l4 = tf.nn.relu(l1 + l3, name='L4_relu')
# Left branch after first addition.
l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
# Right branch after first addition.
l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
# Final addition.
tf.add(l5, l6, name='L7_add')
if convert_variables_to_constants:
sess.run(tf.global_variables_initializer())
graph_def = tf.graph_util.convert_variables_to_constants(
sess, g.as_graph_def(), ['L7_add'])
else:
graph_def = g.as_graph_def()
return graph_def
def create_test_network_keras(image_resolution, convert_variables_to_constants):
"""Convolutional neural network for test, using Keras.
It is exactly the same network as for the "create_test_network" function.
Args:
image_resolution: Resolution to use for input image. Used for height and
width dimensions.
convert_variables_to_constants: Whether to convert variables to constants.
Returns:
graph_def: GraphDef proto of the model.
"""
g = tf.Graph()
sess = tf.Session(graph=g)
with g.as_default():
x = tf.keras.Input([image_resolution, image_resolution, 1],
name='input_image')
l1 = tf.keras.layers.Conv2D(
filters=1, kernel_size=1, strides=4, padding='valid', name='L1')(
x)
l2_pad = tf.keras.layers.ZeroPadding2D(
padding=[[1, 0], [1, 0]], name='L2_pad')(
x)
l2 = tf.keras.layers.Conv2D(
filters=1, kernel_size=3, strides=2, padding='valid', name='L2')(
l2_pad)
l3 = tf.keras.layers.MaxPool2D(
pool_size=3, strides=2, name='L3', padding='same')(
l2)
l4 = tf.keras.layers.ReLU(name='L4_relu')(l1 + l3)
l5 = tf.keras.layers.Conv2D(
filters=1, kernel_size=1, strides=2, padding='same', name='L5')(
l4)
l6 = tf.keras.layers.Conv2D(
filters=1, kernel_size=3, strides=2, padding='same', name='L6')(
l4)
l7 = tf.keras.layers.Add(name='L7_add')([l5, l6])
tf.keras.models.Model(x, l7)
if convert_variables_to_constants:
sess.run(tf.global_variables_initializer())
graph_def = tf.graph_util.convert_variables_to_constants(
sess, g.as_graph_def(), ['L7_add/add'])
else:
graph_def = g.as_graph_def()
return graph_def
class ParseLayerParametersTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
('NoneResolution', None, False, False),
('224Resolution', 224, False, False),
('NoneResolutionVarAsConst', None, True, False),
('224ResolutionVarAsConst', 224, True, False),
('KerasNoneResolution', None, False, True),
('Keras224Resolution', 224, False, True),
('KerasNoneResolutionVarAsConst', None, True, True),
('Keras224ResolutionVarAsConst', 224, True, True))
def testParametersAreParsedCorrectly(self, image_resolution,
convert_variables_to_constants,
use_keras_network):
"""Checks parameters from create_test_network() are parsed correctly."""
if use_keras_network:
graph_def = create_test_network_keras(image_resolution,
convert_variables_to_constants)
else:
graph_def = create_test_network(image_resolution,
convert_variables_to_constants)
name_to_node = graph_compute_order.parse_graph_nodes(graph_def)
# L1.
l1_node_name = 'L1/Conv2D'
l1_params = parse_layer_parameters.get_layer_params(
name_to_node[l1_node_name], name_to_node)
expected_l1_params = (1, 1, 4, 4, 0, 0, 0, 0)
self.assertEqual(l1_params, expected_l1_params)
# L2 padding.
l2_pad_name = 'L2_pad'
if use_keras_network:
l2_pad_name += '/Pad'
l2_pad_params = parse_layer_parameters.get_layer_params(
name_to_node[l2_pad_name], name_to_node)
expected_l2_pad_params = (1, 1, 1, 1, 1, 1, 1, 1)
self.assertEqual(l2_pad_params, expected_l2_pad_params)
# L2.
l2_node_name = 'L2/Conv2D'
l2_params = parse_layer_parameters.get_layer_params(
name_to_node[l2_node_name], name_to_node)
expected_l2_params = (3, 3, 2, 2, 0, 0, 0, 0)
self.assertEqual(l2_params, expected_l2_params)
# L3.
l3_node_name = 'L3/MaxPool'
# - Without knowing input size.
l3_params = parse_layer_parameters.get_layer_params(
name_to_node[l3_node_name], name_to_node)
expected_l3_params = (3, 3, 2, 2, None, None, None, None)
self.assertEqual(l3_params, expected_l3_params)
# - Input size is even.
l3_even_params = parse_layer_parameters.get_layer_params(
name_to_node[l3_node_name], name_to_node, input_resolution=[4, 4])
expected_l3_even_params = (3, 3, 2, 2, 0, 0, 1, 1)
self.assertEqual(l3_even_params, expected_l3_even_params)
# - Input size is odd.
l3_odd_params = parse_layer_parameters.get_layer_params(
name_to_node[l3_node_name], name_to_node, input_resolution=[5, 5])
expected_l3_odd_params = (3, 3, 2, 2, 1, 1, 2, 2)
self.assertEqual(l3_odd_params, expected_l3_odd_params)
# L4.
l4_node_name = 'L4_relu'
if use_keras_network:
l4_node_name += '/Relu'
l4_params = parse_layer_parameters.get_layer_params(
name_to_node[l4_node_name], name_to_node)
expected_l4_params = (1, 1, 1, 1, 0, 0, 0, 0)
self.assertEqual(l4_params, expected_l4_params)
# L5.
l5_node_name = 'L5/Conv2D'
l5_params = parse_layer_parameters.get_layer_params(
name_to_node[l5_node_name], name_to_node)
expected_l5_params = (1, 1, 2, 2, 0, 0, 0, 0)
self.assertEqual(l5_params, expected_l5_params)
# L6.
l6_node_name = 'L6/Conv2D'
# - Without knowing input size.
l6_params = parse_layer_parameters.get_layer_params(
name_to_node[l6_node_name], name_to_node)
expected_l6_params = (3, 3, 2, 2, None, None, None, None)
self.assertEqual(l6_params, expected_l6_params)
# - Input size is even.
l6_even_params = parse_layer_parameters.get_layer_params(
name_to_node[l6_node_name], name_to_node, input_resolution=[4, 4])
expected_l6_even_params = (3, 3, 2, 2, 0, 0, 1, 1)
self.assertEqual(l6_even_params, expected_l6_even_params)
# - Input size is odd.
l6_odd_params = parse_layer_parameters.get_layer_params(
name_to_node[l6_node_name], name_to_node, input_resolution=[5, 5])
expected_l6_odd_params = (3, 3, 2, 2, 1, 1, 2, 2)
self.assertEqual(l6_odd_params, expected_l6_odd_params)
# L7.
l7_node_name = 'L7_add'
if use_keras_network:
l7_node_name += '/add'
l7_params = parse_layer_parameters.get_layer_params(
name_to_node[l7_node_name], name_to_node)
expected_l7_params = (1, 1, 1, 1, 0, 0, 0, 0)
self.assertEqual(l7_params, expected_l7_params)
if __name__ == '__main__':
tf.test.main()
