def testCreateConvNCHW(self):
height, width = 7, 9
with self.cached_session():
images = np.random.uniform(size=(5, 4, height, width)).astype(np.float32)
output = conv2d_ws.conv2d(images, 32, [3, 3], data_format='NCHW')
self.assertEqual(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, 32, height, width])
weights = contrib_framework.get_variables_by_name('weights')[0]
self.assertListEqual(weights.get_shape().as_list(), [3, 3, 4, 32])
biases = contrib_framework.get_variables_by_name('biases')[0]
self.assertListEqual(biases.get_shape().as_list(), [32])
def testCreateVerticalConv(self):
height, width = 7, 9
with self.cached_session():
images = tf.random_uniform((5, height, width, 4), seed=1)
output = conv2d_ws.conv2d(images, 32, [3, 1])
self.assertEqual(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, height, width, 32])
weights = contrib_framework.get_variables_by_name('weights')[0]
self.assertListEqual(weights.get_shape().as_list(), [3, 1, 4, 32])
biases = contrib_framework.get_variables_by_name('biases')[0]
self.assertListEqual(biases.get_shape().as_list(), [32])
def testLocalTrainOp(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = BatchNormClassifier
model_args = (tf_inputs, tf_labels)
deploy_config = model_deploy.DeploymentConfig(num_clones=2,
clone_on_cpu=True)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
self.assertEqual(slim.get_variables(), [])
model = model_deploy.deploy(deploy_config,
model_fn,
model_args,
optimizer=optimizer)
update_ops = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.UPDATE_OPS)
self.assertEqual(len(update_ops), 4)
self.assertEqual(len(model.clones), 2)
self.assertEqual(model.total_loss.op.name, 'total_loss')
self.assertEqual(model.summary_op.op.name, 'summary_op/summary_op')
self.assertEqual(model.train_op.op.name, 'train_op')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
moving_mean = contrib_framework.get_variables_by_name(
'moving_mean')[0]
moving_variance = contrib_framework.get_variables_by_name(
'moving_variance')[0]
initial_loss = sess.run(model.total_loss)
initial_mean, initial_variance = sess.run(
[moving_mean, moving_variance])
self.assertAllClose(initial_mean, [0.0, 0.0, 0.0, 0.0])
self.assertAllClose(initial_variance, [1.0, 1.0, 1.0, 1.0])
for _ in range(10):
sess.run(model.train_op)
final_loss = sess.run(model.total_loss)
self.assertLess(final_loss, initial_loss / 5.0)
final_mean, final_variance = sess.run(
[moving_mean, moving_variance])
expected_mean = np.array([0.125, 0.25, 0.375, 0.25])
expected_var = np.array([0.109375, 0.1875, 0.234375, 0.1875])
expected_var = self._addBesselsCorrection(16, expected_var)
self.assertAllClose(final_mean, expected_mean)
self.assertAllClose(final_variance, expected_var)
def testCreateFullyConv(self):
height, width = 7, 9
with self.cached_session():
images = tf.random_uniform((5, height, width, 32), seed=1)
output = conv2d_ws.conv2d(
images, 64, images.get_shape()[1:3], padding='VALID')
self.assertEqual(output.op.name, 'Conv/Relu')
self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 64])
biases = contrib_framework.get_variables_by_name('biases')[0]
self.assertListEqual(biases.get_shape().as_list(), [64])
def testCreateConvWithWD(self):
height, width = 7, 9
weight_decay = 0.01
with self.cached_session() as sess:
images = tf.random_uniform((5, height, width, 3), seed=1)
regularizer = contrib_layers.l2_regularizer(weight_decay)
conv2d_ws.conv2d(images, 32, [3, 3], weights_regularizer=regularizer)
l2_loss = tf.nn.l2_loss(
contrib_framework.get_variables_by_name('weights')[0])
wd = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)[0]
self.assertEqual(wd.op.name, 'Conv/kernel/Regularizer/l2_regularizer')
sess.run(tf.global_variables_initializer())
self.assertAlmostEqual(sess.run(wd), weight_decay * l2_loss.eval())
def _get_l2_loss_by_names(self, loss, names):
for name in names:
weights_list = get_variables_by_name(name)
for weights in weights_list:
loss += tf.nn.l2_loss(weights) * self.__penal_coef
return loss
def _get_l2_loss_by_names(self, loss, names):
for name in names:
weights_list = get_variables_by_name(name)
for weights in weights_list:
loss += tf.nn.l2_loss(weights) * self.__penal_coef
return loss
