def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception.inception_v3_arg_scope()):
inception.inception_v3_base(inputs)
total_params, _ = slim.model_analyzer.analyze_vars(
slim.get_model_variables())
self.assertAlmostEqual(21802784, total_params)
def testBuildAndCheckAllEndPointsUptoMixed7c(self):
batch_size = 5
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_v3_base(inputs,
final_endpoint='Mixed_7c')
endpoints_shapes = {
'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
'Mixed_5b': [batch_size, 35, 35, 256],
'Mixed_5c': [batch_size, 35, 35, 288],
'Mixed_5d': [batch_size, 35, 35, 288],
'Mixed_6a': [batch_size, 17, 17, 768],
'Mixed_6b': [batch_size, 17, 17, 768],
'Mixed_6c': [batch_size, 17, 17, 768],
'Mixed_6d': [batch_size, 17, 17, 768],
'Mixed_6e': [batch_size, 17, 17, 768],
'Mixed_7a': [batch_size, 8, 8, 1280],
'Mixed_7b': [batch_size, 8, 8, 2048],
'Mixed_7c': [batch_size, 8, 8, 2048]
}
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 testBuildBaseNetwork(self):
batch_size = 5
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
final_endpoint, end_points = inception.inception_v3_base(inputs)
self.assertTrue(
final_endpoint.op.name.startswith('InceptionV3/Mixed_7c'))
self.assertListEqual(final_endpoint.get_shape().as_list(),
[batch_size, 8, 8, 2048])
expected_endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a',
'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a',
'Mixed_7b', 'Mixed_7c'
]
self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildOnlyUptoFinalEndpoint(self):
batch_size = 5
height, width = 299, 299
endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a',
'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a',
'Mixed_7b', 'Mixed_7c'
]
for index, endpoint in enumerate(endpoints):
with tf.Graph().as_default():
inputs = tf.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = inception.inception_v3_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV3/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points.keys())
