Homec4science
Diffusion ML_students_works (master) Browse

ML_students/proj_transferLearn_PV_classification/inception/slim/collections_test.py
ab71824feb99master

Last Change
Raw File

collections_test.py
View Options

# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for inception."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception.slim import slim
def get_variables(scope=None):
return slim.variables.get_variables(scope)
def get_variables_by_name(name):
return slim.variables.get_variables_by_name(name)
class CollectionsTest(tf.test.TestCase):
def testVariables(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_by_name('weights')), 98)
self.assertEqual(len(get_variables_by_name('biases')), 2)
self.assertEqual(len(get_variables_by_name('beta')), 96)
self.assertEqual(len(get_variables_by_name('gamma')), 0)
self.assertEqual(len(get_variables_by_name('moving_mean')), 96)
self.assertEqual(len(get_variables_by_name('moving_variance')), 96)
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)
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 testVariablesToRestore(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)
variables_to_restore = tf.get_collection(
slim.variables.VARIABLES_TO_RESTORE)
self.assertEqual(len(variables_to_restore), 388)
self.assertListEqual(variables_to_restore, get_variables())
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 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 testTotalLossWithoutRegularization(self):
batch_size = 5
height, width = 299, 299
num_classes = 1001
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):
logits, end_points = slim.inception.inception_v3(
inputs,
num_classes=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)
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)
if __name__ == '__main__':
tf.test.main()
c4science · Help