def _get_bn_params(sess: tf.compat.v1.Session,
bn: tf.Operation) -> libpymo.BNParams():
"""
helper to populate BN params from given BN op, required for fold
:param sess: tf.compat.v1.Session type
:param bn: BatchNorm or a FusedBatch Norm op
:return: bn_params
"""
# make sure you define the session and graph scope before loading vars from graph.
with sess.graph.as_default():
# create BNParams type and populate
bn_params = libpymo.BNParams()
bn_params.beta = BNUtils.get_beta_as_numpy_data(sess, bn).reshape(-1)
bn_params.gamma = BNUtils.get_gamma_as_numpy_data(sess, bn).reshape(-1)
bn_params.runningMean = BNUtils.get_moving_mean_as_numpy_data(
sess, bn).reshape(-1)
bn_params.runningVar = BNUtils.get_moving_variance_as_numpy_data(
sess, bn).reshape(-1)
epsilon = BNUtils.get_epsilon(bn)
var = BNUtils.get_moving_variance_as_numpy_data(sess, bn).reshape(-1)
var_with_epsilon = var + epsilon
sigma = np.sqrt(var_with_epsilon)
# sigma = tf.sqrt(BNUtils.get_moving_variance_as_numpy_data(sess, bn).reshape(-1) + epsilon)
bn_params.runningVar = sigma # sess.run(sigma).reshape(-1)
return bn_params
def test_with_slim_bn_op(self):
"""
Test with Tf Slim BN op
:return:
"""
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph())
inp = tf.compat.v1.placeholder(tf.float32, [1, 32, 32, 3])
net = slim.conv2d(inp, 32, [3, 3])
_ = slim.batch_norm(net, decay=.7, epsilon=.65, is_training=True)
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
with sess.graph.as_default():
bn_op = sess.graph.get_operation_by_name('BatchNorm/FusedBatchNormV3')
moving_mean = BNUtils.get_moving_mean_as_numpy_data(sess, bn_op)
moving_var = BNUtils.get_moving_variance_as_numpy_data(sess, bn_op)
beta = BNUtils.get_beta_as_numpy_data(sess, bn_op)
gamma = BNUtils.get_gamma_as_numpy_data(sess, bn_op)
# check the values read are equal to init values
expected_beta = np.zeros_like(beta)
expected_gamma = np.ones_like(gamma)
expected_mean = np.zeros_like(moving_mean)
expected_variance = np.ones_like(moving_var)
self.assertTrue(np.allclose(expected_beta, beta))
self.assertTrue(np.allclose(expected_gamma, gamma))
self.assertTrue(np.allclose(expected_mean, moving_mean))
self.assertTrue(np.allclose(expected_variance, moving_var))
def get_bn_params_aimet_api(sess, bn_op):
"""
Helper to get param values from BN layer using AIMET api(s)
:param bn_op: BN layer
:return: beta, gamma, mean and vairance values extracted from BN layer
"""
beta = BNUtils.get_beta_as_numpy_data(sess, bn_op)
gamma = BNUtils.get_gamma_as_numpy_data(sess, bn_op)
moving_mean = BNUtils.get_moving_mean_as_numpy_data(sess, bn_op)
moving_var = BNUtils.get_moving_variance_as_numpy_data(sess, bn_op)
return [beta, gamma, moving_mean, moving_var]