Python ops.UPDATE_OPS_COLLECTION属性代码示例

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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') 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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):
        output = tf.identity(output)
      # Initialize all variables
      sess.run(tf.global_variables_initializer())
      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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
def testEvalMovingVars(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):
        output = tf.identity(output)
      # Initialize all variables
      sess.run(tf.global_variables_initializer())
      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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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):
        output = tf.identity(output)
      # Initialize all variables
      sess.run(tf.global_variables_initializer())
      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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
def testEvalMovingVars(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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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.global_variables_initializer())
      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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
def testEvalMovingVars(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.global_variables_initializer())
      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) 

# 需要导入模块: from inception.slim import ops [as 别名]
# 或者: from inception.slim.ops import UPDATE_OPS_COLLECTION [as 别名]
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.global_variables_initializer())
      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)