def testCreateConvWithoutWD(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.conv2d(images, 32, [3, 3], weight_decay=0)
self.assertEquals(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES), [])
def testNonReuseVars(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.conv2d(images, 32, [3, 3])
self.assertEquals(len(variables.get_variables()), 2)
ops.conv2d(images, 32, [3, 3])
self.assertEquals(len(variables.get_variables()), 4)
def testCreateConvCreatesWeightsAndBiasesVars(self):
height, width = 3, 3
images = tf.random_uniform((5, height, width, 3), seed=1)
with self.test_session():
self.assertFalse(variables.get_variables('conv1/weights'))
self.assertFalse(variables.get_variables('conv1/biases'))
ops.conv2d(images, 32, [3, 3], scope='conv1')
self.assertTrue(variables.get_variables('conv1/weights'))
self.assertTrue(variables.get_variables('conv1/biases'))
def testReuseConvWithBatchNorm(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 32), seed=1)
with scopes.arg_scope([ops.conv2d], batch_norm_params={'decay': 0.9}):
net = ops.conv2d(images, 32, [3, 3], scope='Conv')
net = ops.conv2d(net, 32, [3, 3], scope='Conv', reuse=True)
self.assertEquals(len(variables.get_variables()), 4)
self.assertEquals(len(variables.get_variables('Conv/BatchNorm')), 3)
self.assertEquals(len(variables.get_variables('Conv_1/BatchNorm')), 0)
def testCreateConvWithWD(self):
height, width = 3, 3
with self.test_session() as sess:
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.conv2d(images, 32, [3, 3], weight_decay=0.01)
wd = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)[0]
self.assertEquals(wd.op.name,
'Conv/weights/Regularizer/L2Regularizer/value')
sess.run(tf.global_variables_initializer())
self.assertTrue(sess.run(wd) <= 0.01)
def testReuseConvWithWD(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.conv2d(images, 32, [3, 3], weight_decay=0.01, scope='conv1')
self.assertEquals(len(variables.get_variables()), 2)
self.assertEquals(
len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)
ops.conv2d(images, 32, [3, 3], weight_decay=0.01, scope='conv1',
reuse=True)
self.assertEquals(len(variables.get_variables()), 2)
self.assertEquals(
len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)
def testCreateFullyConv(self):
height, width = 6, 6
with self.test_session():
images = tf.random_uniform((5, height, width, 32), seed=1)
output = ops.conv2d(images, 64, images.get_shape()[1:3], padding='VALID')
self.assertEquals(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 64])
def testCreateConvWithTensorShape(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
output = ops.conv2d(images, 32, images.get_shape()[1:3])
self.assertEquals(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
def testCreateConvWithStride(self):
height, width = 6, 6
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
output = ops.conv2d(images, 32, [3, 3], stride=2)
self.assertEquals(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(),
[5, height/2, width/2, 32])
def testCreateConvWithScope(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
output = ops.conv2d(images, 32, [3, 3], scope='conv1')
self.assertEquals(output.op.name, 'conv1/Relu')
def testCreateConvValid(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
output = ops.conv2d(images, 32, [3, 3], padding='VALID')
self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 32])
def testCreateConvWithoutActivation(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
output = ops.conv2d(images, 32, [3, 3], activation=None)
self.assertEquals(output.op.name, 'Conv/BiasAdd')
