def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
for is_training in [True, False]:
with ops.Graph().as_default():
inputs = random_ops.random_uniform(
(batch_size, height, width, 3))
_, end_points = vgg.vgg_16(inputs,
num_classes,
is_training=is_training)
expected_names = [
'vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2',
'vgg_16/pool1', 'vgg_16/conv2/conv2_1',
'vgg_16/conv2/conv2_2', 'vgg_16/pool2',
'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2',
'vgg_16/conv3/conv3_3', 'vgg_16/pool3',
'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2',
'vgg_16/conv4/conv4_3', 'vgg_16/pool4',
'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2',
'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6',
'vgg_16/fc7', 'vgg_16/fc8'
]
self.assertSetEqual(set(end_points.keys()),
set(expected_names))
def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.cached_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
vgg.vgg_16(inputs, num_classes)
expected_names = [
'vgg_16/conv1/conv1_1/weights',
'vgg_16/conv1/conv1_1/biases',
'vgg_16/conv1/conv1_2/weights',
'vgg_16/conv1/conv1_2/biases',
'vgg_16/conv2/conv2_1/weights',
'vgg_16/conv2/conv2_1/biases',
'vgg_16/conv2/conv2_2/weights',
'vgg_16/conv2/conv2_2/biases',
'vgg_16/conv3/conv3_1/weights',
'vgg_16/conv3/conv3_1/biases',
'vgg_16/conv3/conv3_2/weights',
'vgg_16/conv3/conv3_2/biases',
'vgg_16/conv3/conv3_3/weights',
'vgg_16/conv3/conv3_3/biases',
'vgg_16/conv4/conv4_1/weights',
'vgg_16/conv4/conv4_1/biases',
'vgg_16/conv4/conv4_2/weights',
'vgg_16/conv4/conv4_2/biases',
'vgg_16/conv4/conv4_3/weights',
'vgg_16/conv4/conv4_3/biases',
'vgg_16/conv5/conv5_1/weights',
'vgg_16/conv5/conv5_1/biases',
'vgg_16/conv5/conv5_2/weights',
'vgg_16/conv5/conv5_2/biases',
'vgg_16/conv5/conv5_3/weights',
'vgg_16/conv5/conv5_3/biases',
'vgg_16/fc6/weights',
'vgg_16/fc6/biases',
'vgg_16/fc7/weights',
'vgg_16/fc7/biases',
'vgg_16/fc8/weights',
'vgg_16/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 = 224, 224
with self.cached_session() as sess:
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_16(inputs)
sess.run(variables.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.cached_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_16/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.cached_session():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_16(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_16/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.cached_session():
eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_16(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 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.cached_session():
train_inputs = random_ops.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_16(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, _ = vgg.vgg_16(
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.assertEqual(predictions.get_shape().as_list(), [eval_batch_size])
