def testVariablesByLayer(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs)
self.assertEqual(len(get_variables()), 388)
self.assertEqual(len(get_variables('conv0')), 4)
self.assertEqual(len(get_variables('conv1')), 4)
self.assertEqual(len(get_variables('conv2')), 4)
self.assertEqual(len(get_variables('conv3')), 4)
self.assertEqual(len(get_variables('conv4')), 4)
self.assertEqual(len(get_variables('mixed_35x35x256a')), 28)
self.assertEqual(len(get_variables('mixed_35x35x288a')), 28)
self.assertEqual(len(get_variables('mixed_35x35x288b')), 28)
self.assertEqual(len(get_variables('mixed_17x17x768a')), 16)
self.assertEqual(len(get_variables('mixed_17x17x768b')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768c')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768d')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768e')), 40)
self.assertEqual(len(get_variables('mixed_8x8x2048a')), 36)
self.assertEqual(len(get_variables('mixed_8x8x2048b')), 36)
self.assertEqual(len(get_variables('logits')), 2)
self.assertEqual(len(get_variables('aux_logits')), 10)
def testTotalLossWithRegularization(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
dense_labels = tf.random_uniform((batch_size, num_classes))
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc],
weight_decay=0.00004):
logits, end_points = slim.inception.inception_v3(
inputs, num_classes)
# Cross entropy loss for the main softmax prediction.
slim.losses.cross_entropy_loss(logits,
dense_labels,
label_smoothing=0.1,
weight=1.0)
# Cross entropy loss for the auxiliary softmax head.
slim.losses.cross_entropy_loss(end_points['aux_logits'],
dense_labels,
label_smoothing=0.1,
weight=0.4,
scope='aux_loss')
losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
self.assertEqual(len(losses), 2)
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
self.assertEqual(len(reg_losses), 98)
def testRegularizationLosses(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc],
weight_decay=0.00004):
slim.inception.inception_v3(inputs)
losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
self.assertEqual(len(losses),
len(get_variables_by_name('weights')))
def testVariablesToRestoreWithoutLogits(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs, restore_logits=False)
variables_to_restore = tf.get_collection(
slim.variables.VARIABLES_TO_RESTORE)
self.assertEqual(len(variables_to_restore), 384)
def testVariablesWithoutBatchNorm(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d], batch_norm_params=None):
slim.inception.inception_v3(inputs)
self.assertEqual(len(get_variables()), 196)
self.assertEqual(len(get_variables_by_name('weights')), 98)
self.assertEqual(len(get_variables_by_name('biases')), 98)
self.assertEqual(len(get_variables_by_name('beta')), 0)
self.assertEqual(len(get_variables_by_name('gamma')), 0)
self.assertEqual(len(get_variables_by_name('moving_mean')), 0)
self.assertEqual(len(get_variables_by_name('moving_variance')), 0)