def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self):
batch_size = 5
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_resnet_v2_base(
inputs, final_endpoint='PreAuxLogits', align_feature_maps=True)
endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32],
'Conv2d_2a_3x3': [5, 150, 150, 32],
'Conv2d_2b_3x3': [5, 150, 150, 64],
'MaxPool_3a_3x3': [5, 75, 75, 64],
'Conv2d_3b_1x1': [5, 75, 75, 80],
'Conv2d_4a_3x3': [5, 75, 75, 192],
'MaxPool_5a_3x3': [5, 38, 38, 192],
'Mixed_5b': [5, 38, 38, 320],
'Mixed_6a': [5, 19, 19, 1088],
'PreAuxLogits': [5, 19, 19, 1088]
}
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 testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self):
batch_size = 5
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_resnet_v2_base(
inputs, final_endpoint='PreAuxLogits', output_stride=8)
endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32],
'Conv2d_2a_3x3': [5, 147, 147, 32],
'Conv2d_2b_3x3': [5, 147, 147, 64],
'MaxPool_3a_3x3': [5, 73, 73, 64],
'Conv2d_3b_1x1': [5, 73, 73, 80],
'Conv2d_4a_3x3': [5, 71, 71, 192],
'MaxPool_5a_3x3': [5, 35, 35, 192],
'Mixed_5b': [5, 35, 35, 320],
'Mixed_6a': [5, 33, 33, 1088],
'PreAuxLogits': [5, 33, 33, 1088]
}
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))
net, end_points = inception.inception_resnet_v2_base(inputs)
self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 8, 8, 1536])
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_6a',
'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
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_6a',
'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
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_resnet_v2_base(
inputs, final_endpoint=endpoint)
if endpoint != 'PreAuxLogits':
self.assertTrue(out_tensor.op.name.startswith(
'InceptionResnetV2/' + endpoint))
self.assertItemsEqual(endpoints[:index+1], end_points.keys())