Example #1
0
 def testReuseUpdateOps(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     ops.batch_norm(images, scope='bn')
     self.assertEquals(len(tf.get_collection(ops.UPDATE_OPS_COLLECTION)), 2)
     ops.batch_norm(images, scope='bn', reuse=True)
     self.assertEquals(len(tf.get_collection(ops.UPDATE_OPS_COLLECTION)), 4)
Example #2
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 def testMovingAverageVariables(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     ops.batch_norm(images, scale=True)
     moving_mean = tf.moving_average_variables()[0]
     moving_variance = tf.moving_average_variables()[1]
     self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
     self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
Example #3
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 def testReuseVariables(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     ops.batch_norm(images, scale=True, scope='bn')
     ops.batch_norm(images, scale=True, scope='bn', reuse=True)
     beta = variables.get_variables_by_name('beta')
     gamma = variables.get_variables_by_name('gamma')
     self.assertEquals(len(beta), 1)
     self.assertEquals(len(gamma), 1)
     moving_vars = tf.get_collection('moving_vars')
     self.assertEquals(len(moving_vars), 2)
Example #4
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 def testUpdateOps(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     ops.batch_norm(images)
     update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
     update_moving_mean = update_ops[0]
     update_moving_variance = update_ops[1]
     self.assertEquals(update_moving_mean.op.name,
                       'BatchNorm/AssignMovingAvg')
     self.assertEquals(update_moving_variance.op.name,
                       'BatchNorm/AssignMovingAvg_1')
Example #5
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 def testCreateVariablesWithoutCenterWithoutScale(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     ops.batch_norm(images, center=False, scale=False)
     beta = variables.get_variables_by_name('beta')
     self.assertEquals(beta, [])
     gamma = variables.get_variables_by_name('gamma')
     self.assertEquals(gamma, [])
     moving_mean = tf.moving_average_variables()[0]
     moving_variance = tf.moving_average_variables()[1]
     self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
     self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
Example #6
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 def testReuseVars(self):
   height, width = 3, 3
   with self.test_session() as sess:
     image_shape = (10, height, width, 3)
     image_values = np.random.rand(*image_shape)
     expected_mean = np.mean(image_values, axis=(0, 1, 2))
     expected_var = np.var(image_values, axis=(0, 1, 2))
     images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
     output = ops.batch_norm(images, decay=0.1, is_training=False)
     update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
     with tf.control_dependencies(update_ops):
       barrier = tf.no_op(name='gradient_barrier')
       output = control_flow_ops.with_dependencies([barrier], output)
     # Initialize all variables
     sess.run(tf.initialize_all_variables())
     moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
     moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
     mean, variance = sess.run([moving_mean, moving_variance])
     # After initialization moving_mean == 0 and moving_variance == 1.
     self.assertAllClose(mean, [0] * 3)
     self.assertAllClose(variance, [1] * 3)
     # Simulate assigment from saver restore.
     init_assigns = [tf.assign(moving_mean, expected_mean),
                     tf.assign(moving_variance, expected_var)]
     sess.run(init_assigns)
     for _ in range(10):
       sess.run([output], {images: np.random.rand(*image_shape)})
     mean = moving_mean.eval()
     variance = moving_variance.eval()
     # Although we feed different images, the moving_mean and moving_variance
     # shouldn't change.
     self.assertAllClose(mean, expected_mean)
     self.assertAllClose(variance, expected_var)
Example #7
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 def testComputeMovingVars(self):
   height, width = 3, 3
   with self.test_session() as sess:
     image_shape = (10, height, width, 3)
     image_values = np.random.rand(*image_shape)
     expected_mean = np.mean(image_values, axis=(0, 1, 2))
     expected_var = np.var(image_values, axis=(0, 1, 2))
     images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
     output = ops.batch_norm(images, decay=0.1)
     update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)
     with tf.control_dependencies(update_ops):
       barrier = tf.no_op(name='gradient_barrier')
       output = control_flow_ops.with_dependencies([barrier], output)
     # Initialize all variables
     sess.run(tf.initialize_all_variables())
     moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]
     moving_variance = variables.get_variables('BatchNorm/moving_variance')[0]
     mean, variance = sess.run([moving_mean, moving_variance])
     # After initialization moving_mean == 0 and moving_variance == 1.
     self.assertAllClose(mean, [0] * 3)
     self.assertAllClose(variance, [1] * 3)
     for _ in range(10):
       sess.run([output])
     mean = moving_mean.eval()
     variance = moving_variance.eval()
     # After 10 updates with decay 0.1 moving_mean == expected_mean and
     # moving_variance == expected_var.
     self.assertAllClose(mean, expected_mean)
     self.assertAllClose(variance, expected_var)
Example #8
0
 def testCreateOp(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     output = ops.batch_norm(images)
     self.assertTrue(output.op.name.startswith('BatchNorm/batchnorm'))
     self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
Example #9
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 def testCreateMovingVars(self):
   height, width = 3, 3
   with self.test_session():
     images = tf.random_uniform((5, height, width, 3), seed=1)
     _ = ops.batch_norm(images, moving_vars='moving_vars')
     moving_mean = tf.get_collection('moving_vars',
                                     'BatchNorm/moving_mean')
     self.assertEquals(len(moving_mean), 1)
     self.assertEquals(moving_mean[0].op.name, 'BatchNorm/moving_mean')
     moving_variance = tf.get_collection('moving_vars',
                                         'BatchNorm/moving_variance')
     self.assertEquals(len(moving_variance), 1)
     self.assertEquals(moving_variance[0].op.name, 'BatchNorm/moving_variance')