def testModelVariables(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = random_ops.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 variables_lib.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.cached_session():
inputs = random_ops.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 variables_lib.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 = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs)
sess.run(variables.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def testForward(self):
batch_size = 1
height, width = 231, 231
with self.cached_session() as sess:
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = overfeat.overfeat(inputs)
sess.run(variables.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.initialize_all_variables())
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 = random_ops.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 = random_ops.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 testBuild(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.cached_session():
inputs = random_ops.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 testEvaluation(self):
batch_size = 2
height, width = 231, 231
num_classes = 1000
with self.test_session():
eval_inputs = random_ops.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 = math_ops.argmax(logits, 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 = random_ops.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])
variable_scope.get_variable_scope().reuse_variables()
eval_inputs = random_ops.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 = math_ops.reduce_mean(logits, [1, 2])
predictions = math_ops.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 = 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 testEndPoints(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.test_session():
inputs = random_ops.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 testEndPoints(self):
batch_size = 5
height, width = 231, 231
num_classes = 1000
with self.cached_session():
inputs = random_ops.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 network_overfeat():
input_shape = [1, 231, 231, 3]
input_ = tf.placeholder(dtype=tf.float32, name='input', shape=input_shape)
net, _end_points = overfeat(input_, num_classes=1000, is_training=False)
return net
