# coding=utf-8
# coding=utf-8
# Copyright 2016 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.
# ==============================================================================
"""Tests for nets.inception_v1."""
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 tf_slim import model_analyzer
from tf_slim.nets import inception_v1
from tf_slim.ops import variables as variables_lib
from tf_slim.ops.arg_scope import arg_scope
# pylint:disable=g-direct-tensorflow-import
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
# pylint:enable=g-direct-tensorflow-import
def setUpModule():
tf.disable_eager_execution()
class InceptionV1Test(test.TestCase):
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, end_points = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_6c, end_points = inception_v1.inception_v1_base(inputs)
self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
self.assertListEqual(mixed_6c.get_shape().as_list(),
[batch_size, 7, 7, 1024])
expected_endpoints = [
'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b',
'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
'Mixed_5b', 'Mixed_5c'
]
self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildOnlyUptoFinalEndpoint(self):
batch_size = 5
height, width = 224, 224
endpoints = [
'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3',
'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b',
'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
'Mixed_5b', 'Mixed_5c'
]
for index, endpoint in enumerate(endpoints):
with ops.Graph().as_default():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = inception_v1.inception_v1_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV1/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points)
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v1.inception_v1_base(
inputs, final_endpoint='Mixed_5c')
endpoints_shapes = {
'Conv2d_1a_7x7': [5, 112, 112, 64],
'MaxPool_2a_3x3': [5, 56, 56, 64],
'Conv2d_2b_1x1': [5, 56, 56, 64],
'Conv2d_2c_3x3': [5, 56, 56, 192],
'MaxPool_3a_3x3': [5, 28, 28, 192],
'Mixed_3b': [5, 28, 28, 256],
'Mixed_3c': [5, 28, 28, 480],
'MaxPool_4a_3x3': [5, 14, 14, 480],
'Mixed_4b': [5, 14, 14, 512],
'Mixed_4c': [5, 14, 14, 512],
'Mixed_4d': [5, 14, 14, 512],
'Mixed_4e': [5, 14, 14, 528],
'Mixed_4f': [5, 14, 14, 832],
'MaxPool_5a_2x2': [5, 7, 7, 832],
'Mixed_5b': [5, 7, 7, 832],
'Mixed_5c': [5, 7, 7, 1024]
}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
expected_shape = endpoints_shapes[endpoint_name]
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v1.inception_v1_arg_scope()):
inception_v1.inception_v1_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(5607184, total_params)
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_5c, _ = inception_v1.inception_v1_base(inputs)
self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
self.assertListEqual(mixed_5c.get_shape().as_list(),
[batch_size, 4, 4, 1024])
def testUnknownImageShape(self):
ops.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.cached_session() as sess:
inputs = array_ops.placeholder(
dtypes.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_5c']
feed_dict = {inputs: input_np}
variables.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testUnknownBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = array_ops.placeholder(dtypes.float32, (None, height, width, 3))
logits, _ = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
images = random_ops.random_uniform((batch_size, height, width, 3))
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch_size, num_classes))
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = inception_v1.inception_v1(
eval_inputs, num_classes, is_training=False)
predictions = math_ops.argmax(logits, 1)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 224, 224
num_classes = 1000
train_inputs = random_ops.random_uniform(
(train_batch_size, height, width, 3))
inception_v1.inception_v1(train_inputs, num_classes)
eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception_v1.inception_v1(eval_inputs, num_classes, reuse=True)
predictions = math_ops.argmax(logits, 1)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (eval_batch_size,))
def testLogitsNotSqueezed(self):
num_classes = 25
images = random_ops.random_uniform([1, 224, 224, 3])
logits, _ = inception_v1.inception_v1(
images, num_classes=num_classes, spatial_squeeze=False)
with self.cached_session() as sess:
variables.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
if __name__ == '__main__':
test.main()
