def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
vgg.vgg_a(inputs, num_classes)
expected_names = ['vgg_a/conv1/conv1_1/weights',
'vgg_a/conv1/conv1_1/biases',
'vgg_a/conv2/conv2_1/weights',
'vgg_a/conv2/conv2_1/biases',
'vgg_a/conv3/conv3_1/weights',
'vgg_a/conv3/conv3_1/biases',
'vgg_a/conv3/conv3_2/weights',
'vgg_a/conv3/conv3_2/biases',
'vgg_a/conv4/conv4_1/weights',
'vgg_a/conv4/conv4_1/biases',
'vgg_a/conv4/conv4_2/weights',
'vgg_a/conv4/conv4_2/biases',
'vgg_a/conv5/conv5_1/weights',
'vgg_a/conv5/conv5_1/biases',
'vgg_a/conv5/conv5_2/weights',
'vgg_a/conv5/conv5_2/biases',
'vgg_a/fc6/weights',
'vgg_a/fc6/biases',
'vgg_a/fc7/weights',
'vgg_a/fc7/biases',
'vgg_a/fc8/weights',
'vgg_a/fc8/biases',
]
model_variables = [v.op.name for v in slim.get_model_variables()]
self.assertSetEqual(set(model_variables), set(expected_names))
def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
vgg.vgg_a(inputs, num_classes)
expected_names = [
'vgg_a/conv1/conv1_1/weights',
'vgg_a/conv1/conv1_1/biases',
'vgg_a/conv2/conv2_1/weights',
'vgg_a/conv2/conv2_1/biases',
'vgg_a/conv3/conv3_1/weights',
'vgg_a/conv3/conv3_1/biases',
'vgg_a/conv3/conv3_2/weights',
'vgg_a/conv3/conv3_2/biases',
'vgg_a/conv4/conv4_1/weights',
'vgg_a/conv4/conv4_1/biases',
'vgg_a/conv4/conv4_2/weights',
'vgg_a/conv4/conv4_2/biases',
'vgg_a/conv5/conv5_1/weights',
'vgg_a/conv5/conv5_1/biases',
'vgg_a/conv5/conv5_2/weights',
'vgg_a/conv5/conv5_2/biases',
'vgg_a/fc6/weights',
'vgg_a/fc6/biases',
'vgg_a/fc7/weights',
'vgg_a/fc7/biases',
'vgg_a/fc8/weights',
'vgg_a/fc8/biases',
]
model_variables = [v.op.name for v in slim.get_model_variables()]
self.assertSetEqual(set(model_variables), set(expected_names))
def testNoClasses(self):
batch_size = 5
height, width = 224, 224
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = vgg.vgg_a(inputs, num_classes)
expected_names = [
'vgg_a/conv1/conv1_1',
'vgg_a/pool1',
'vgg_a/conv2/conv2_1',
'vgg_a/pool2',
'vgg_a/conv3/conv3_1',
'vgg_a/conv3/conv3_2',
'vgg_a/pool3',
'vgg_a/conv4/conv4_1',
'vgg_a/conv4/conv4_2',
'vgg_a/pool4',
'vgg_a/conv5/conv5_1',
'vgg_a/conv5/conv5_2',
'vgg_a/pool5',
'vgg_a/fc6',
'vgg_a/fc7',
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('vgg_a/fc7'))
def testNoClasses(self):
batch_size = 5
height, width = 224, 224
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = vgg.vgg_a(inputs, num_classes)
expected_names = ['vgg_a/conv1/conv1_1',
'vgg_a/pool1',
'vgg_a/conv2/conv2_1',
'vgg_a/pool2',
'vgg_a/conv3/conv3_1',
'vgg_a/conv3/conv3_2',
'vgg_a/pool3',
'vgg_a/conv4/conv4_1',
'vgg_a/conv4/conv4_2',
'vgg_a/pool4',
'vgg_a/conv5/conv5_1',
'vgg_a/conv5/conv5_2',
'vgg_a/pool5',
'vgg_a/fc6',
'vgg_a/fc7',
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('vgg_a/fc7'))
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = vgg.vgg_a(inputs, num_classes)
expected_names = ['vgg_a/conv1/conv1_1',
'vgg_a/pool1',
'vgg_a/conv2/conv2_1',
'vgg_a/pool2',
'vgg_a/conv3/conv3_1',
'vgg_a/conv3/conv3_2',
'vgg_a/pool3',
'vgg_a/conv4/conv4_1',
'vgg_a/conv4/conv4_2',
'vgg_a/pool4',
'vgg_a/conv5/conv5_1',
'vgg_a/conv5/conv5_2',
'vgg_a/pool5',
'vgg_a/fc6',
'vgg_a/fc7',
'vgg_a/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = vgg.vgg_a(inputs, num_classes)
expected_names = ['vgg_a/conv1/conv1_1',
'vgg_a/pool1',
'vgg_a/conv2/conv2_1',
'vgg_a/pool2',
'vgg_a/conv3/conv3_1',
'vgg_a/conv3/conv3_2',
'vgg_a/pool3',
'vgg_a/conv4/conv4_1',
'vgg_a/conv4/conv4_2',
'vgg_a/pool4',
'vgg_a/conv5/conv5_1',
'vgg_a/conv5/conv5_2',
'vgg_a/pool5',
'vgg_a/fc6',
'vgg_a/fc7',
'vgg_a/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testFullyConvolutional(self):
batch_size = 1
height, width = 256, 256
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testFullyConvolutional(self):
batch_size = 1
height, width = 256, 256
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 224, 224
eval_height, eval_width = 256, 256
num_classes = 1000
with self.test_session():
train_inputs = tf.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_a(train_inputs)
self.assertListEqual(logits.get_shape().as_list(),
[train_batch_size, num_classes])
tf.get_variable_scope().reuse_variables()
eval_inputs = tf.random_uniform(
(eval_batch_size, eval_height, eval_width, 3))
logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
spatial_squeeze=False)
self.assertListEqual(logits.get_shape().as_list(),
[eval_batch_size, 2, 2, num_classes])
logits = tf.reduce_mean(logits, [1, 2])
predictions = tf.argmax(logits, 1)
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 224, 224
eval_height, eval_width = 256, 256
num_classes = 1000
with self.test_session():
train_inputs = tf.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_a(train_inputs)
self.assertListEqual(logits.get_shape().as_list(),
[train_batch_size, num_classes])
tf.get_variable_scope().reuse_variables()
eval_inputs = tf.random_uniform(
(eval_batch_size, eval_height, eval_width, 3))
logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
spatial_squeeze=False)
self.assertListEqual(logits.get_shape().as_list(),
[eval_batch_size, 2, 2, num_classes])
logits = tf.reduce_mean(logits, [1, 2])
predictions = tf.argmax(logits, 1)
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
def main():
"""
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python alexnet.py
"""
height, width = 224, 224
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
net, end_points = vgg.vgg_a(inputs, is_training = False)
print("nodes in the graph")
for n in end_points:
print(n + " => " + str(end_points[n]))
net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split())
run_model(net_outputs, argv[1])
def main():
"""
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python alexnet.py
"""
height, width = 224, 224
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
inputs = tf.identity(inputs, "input_node")
net, end_points = vgg.vgg_a(inputs, is_training = False)
print("nodes in the graph")
for n in end_points:
print(n + " => " + str(end_points[n]))
net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
run_model(net_outputs, argv[1], 'vgg_a', argv[3] == 'True')
def _construct_model(model_type='resnet_v1_50'):
"""Constructs model for the desired type of CNN.
Args:
model_type: Type of model to be used.
Returns:
end_points: A dictionary from components of the network to the corresponding
activations.
Raises:
ValueError: If the model_type is not supported.
"""
# Placeholder input.
images = array_ops.placeholder(
dtypes.float32, shape=(1, None, None, 3), name=_INPUT_NODE)
# Construct model.
if model_type == 'inception_resnet_v2':
_, end_points = inception.inception_resnet_v2_base(images)
elif model_type == 'inception_resnet_v2-same':
_, end_points = inception.inception_resnet_v2_base(
images, align_feature_maps=True)
elif model_type == 'inception_v2':
_, end_points = inception.inception_v2_base(images)
elif model_type == 'inception_v2-no-separable-conv':
_, end_points = inception.inception_v2_base(
images, use_separable_conv=False)
elif model_type == 'inception_v3':
_, end_points = inception.inception_v3_base(images)
elif model_type == 'inception_v4':
_, end_points = inception.inception_v4_base(images)
elif model_type == 'alexnet_v2':
_, end_points = alexnet.alexnet_v2(images)
elif model_type == 'vgg_a':
_, end_points = vgg.vgg_a(images)
elif model_type == 'vgg_16':
_, end_points = vgg.vgg_16(images)
elif model_type == 'mobilenet_v1':
_, end_points = mobilenet_v1.mobilenet_v1_base(images)
elif model_type == 'mobilenet_v1_075':
_, end_points = mobilenet_v1.mobilenet_v1_base(
images, depth_multiplier=0.75)
elif model_type == 'resnet_v1_50':
_, end_points = resnet_v1.resnet_v1_50(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_101':
_, end_points = resnet_v1.resnet_v1_101(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_152':
_, end_points = resnet_v1.resnet_v1_152(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v1_200':
_, end_points = resnet_v1.resnet_v1_200(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_50':
_, end_points = resnet_v2.resnet_v2_50(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_101':
_, end_points = resnet_v2.resnet_v2_101(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_152':
_, end_points = resnet_v2.resnet_v2_152(
images, num_classes=None, is_training=False, global_pool=False)
elif model_type == 'resnet_v2_200':
_, end_points = resnet_v2.resnet_v2_200(
images, num_classes=None, is_training=False, global_pool=False)
else:
raise ValueError('Unsupported model_type %s.' % model_type)
return end_points
def _construct_model(model_type='resnet_v1_50'):
"""Constructs model for the desired type of CNN.
Args:
model_type: Type of model to be used.
Returns:
end_points: A dictionary from components of the network to the corresponding
activations.
Raises:
ValueError: If the model_type is not supported.
"""
# Placeholder input.
images = array_ops.placeholder(dtypes.float32,
shape=(1, None, None, 3),
name=_INPUT_NODE)
# Construct model.
if model_type == 'inception_resnet_v2':
_, end_points = inception.inception_resnet_v2_base(images)
elif model_type == 'inception_resnet_v2-same':
_, end_points = inception.inception_resnet_v2_base(
images, align_feature_maps=True)
elif model_type == 'inception_v2':
_, end_points = inception.inception_v2_base(images)
elif model_type == 'inception_v2-no-separable-conv':
_, end_points = inception.inception_v2_base(images,
use_separable_conv=False)
elif model_type == 'inception_v3':
_, end_points = inception.inception_v3_base(images)
elif model_type == 'inception_v4':
_, end_points = inception.inception_v4_base(images)
elif model_type == 'alexnet_v2':
_, end_points = alexnet.alexnet_v2(images)
elif model_type == 'vgg_a':
_, end_points = vgg.vgg_a(images)
elif model_type == 'vgg_16':
_, end_points = vgg.vgg_16(images)
elif model_type == 'mobilenet_v1':
_, end_points = mobilenet_v1.mobilenet_v1_base(images)
elif model_type == 'mobilenet_v1_075':
_, end_points = mobilenet_v1.mobilenet_v1_base(images,
depth_multiplier=0.75)
elif model_type == 'resnet_v1_50':
_, end_points = resnet_v1.resnet_v1_50(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_101':
_, end_points = resnet_v1.resnet_v1_101(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_152':
_, end_points = resnet_v1.resnet_v1_152(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v1_200':
_, end_points = resnet_v1.resnet_v1_200(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_50':
_, end_points = resnet_v2.resnet_v2_50(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_101':
_, end_points = resnet_v2.resnet_v2_101(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_152':
_, end_points = resnet_v2.resnet_v2_152(images,
num_classes=None,
is_training=False,
global_pool=False)
elif model_type == 'resnet_v2_200':
_, end_points = resnet_v2.resnet_v2_200(images,
num_classes=None,
is_training=False,
global_pool=False)
else:
raise ValueError('Unsupported model_type %s.' % model_type)
return end_points