def testModelVariables(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
overfeat.overfeat(inputs, num_classes)
expected_names = ['overfeat/conv1/weights',
'overfeat/conv1/biases',
'overfeat/conv2/weights',
'overfeat/conv2/biases',
'overfeat/conv3/weights',
'overfeat/conv3/biases',
'overfeat/conv4/weights',
'overfeat/conv4/biases',
'overfeat/conv5/weights',
'overfeat/conv5/biases',
'overfeat/fc6/weights',
'overfeat/fc6/biases',
'overfeat/fc7/weights',
'overfeat/fc7/biases',
'overfeat/fc8/weights',
'overfeat/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 = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random.uniform((batch_size, height, width, 3))
overfeat.overfeat(inputs, num_classes)
expected_names = ['overfeat/conv1/weights',
'overfeat/conv1/biases',
'overfeat/conv2/weights',
'overfeat/conv2/biases',
'overfeat/conv3/weights',
'overfeat/conv3/biases',
'overfeat/conv4/weights',
'overfeat/conv4/biases',
'overfeat/conv5/weights',
'overfeat/conv5/biases',
'overfeat/fc6/weights',
'overfeat/fc6/biases',
'overfeat/fc7/weights',
'overfeat/fc7/biases',
'overfeat/fc8/weights',
'overfeat/fc8/biases',
]
model_variables = [v.op.name for v in slim.get_model_variables()]
self.assertSetEqual(set(model_variables), set(expected_names))
def testForward(self):
batch_size = 1
height, width = 231, 231
with self.test_session() as sess:
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testForward(self):
batch_size = 1
height, width = 231, 231
with self.test_session() as sess:
inputs = tf.random.uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testFullyConvolutional(self):
batch_size = 1
height, width = 281, 281
num_classes = 1000
with self.test_session():
inputs = tf.random.uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs, num_classes)
self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testFullyConvolutional(self):
batch_size = 1
height, width = 281, 281
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random.uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs, num_classes)
self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testEvaluation(self):
batch_size = 2
height, width = 231, 231
num_classes = 1000
with self.test_session():
eval_inputs = tf.random.uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(input=logits, axis=1)
self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
def testEvaluation(self):
batch_size = 2
height, width = 231, 231
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(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 = 231, 231
eval_height, eval_width = 281, 281
num_classes = 1000
with self.test_session():
train_inputs = tf.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = overfeat.overfeat(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, _ = overfeat.overfeat(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 testGlobalPool(self):
batch_size = 1
height, width = 281, 281
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs,
num_classes,
spatial_squeeze=False,
global_pool=True)
self.assertEqual(logits.op.name, 'overfeat/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 1, 1, num_classes])
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 231, 231
eval_height, eval_width = 281, 281
num_classes = 1000
with self.test_session():
train_inputs = tf.random.uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = overfeat.overfeat(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, _ = overfeat.overfeat(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(input_tensor=logits, axis=[1, 2])
predictions = tf.argmax(input=logits, axis=1)
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
def testEndPoints(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = overfeat.overfeat(inputs, num_classes)
expected_names = [
'overfeat/conv1', 'overfeat/pool1', 'overfeat/conv2',
'overfeat/pool2', 'overfeat/conv3', 'overfeat/conv4',
'overfeat/conv5', 'overfeat/pool5', 'overfeat/fc6',
'overfeat/fc7', 'overfeat/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def testNoClasses(self):
batch_size = 5
height, width = 231, 231
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = overfeat.overfeat(inputs, num_classes)
expected_names = [
'overfeat/conv1', 'overfeat/pool1', 'overfeat/conv2',
'overfeat/pool2', 'overfeat/conv3', 'overfeat/conv4',
'overfeat/conv5', 'overfeat/pool5', 'overfeat/fc6',
'overfeat/fc7'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('overfeat/fc7'))
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 = 231, 231
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
with slim.arg_scope(overfeat.overfeat_arg_scope()):
net, end_points = overfeat.overfeat(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], 'overfeat', argv[3] == 'True')
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 = 231, 231
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
inputs = tf.identity(inputs, "input_node")
with slim.arg_scope(overfeat.overfeat_arg_scope()):
net, end_points = overfeat.overfeat(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], 'overfeat', argv[3] == 'True')
def testEndPoints(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = overfeat.overfeat(inputs, num_classes)
expected_names = ['overfeat/conv1',
'overfeat/pool1',
'overfeat/conv2',
'overfeat/pool2',
'overfeat/conv3',
'overfeat/conv4',
'overfeat/conv5',
'overfeat/pool5',
'overfeat/fc6',
'overfeat/fc7',
'overfeat/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def testNoClasses(self):
batch_size = 5
height, width = 231, 231
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = overfeat.overfeat(inputs, num_classes)
expected_names = ['overfeat/conv1',
'overfeat/pool1',
'overfeat/conv2',
'overfeat/pool2',
'overfeat/conv3',
'overfeat/conv4',
'overfeat/conv5',
'overfeat/pool5',
'overfeat/fc6',
'overfeat/fc7'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('overfeat/fc7'))
