def _test_lrn(ishape, size, axis, bias, alpha, beta):
""" testing local response normalization """
lrn_depth_radius = size / 2
inp_array = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array.shape,
dtype=inp_array.dtype,
name="lrn0_data")
nn_ops.local_response_normalization(in1,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
with tf.Session() as sess:
graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(add_shapes=True),
['lrn'],
)
tf_output = run_tf_graph(sess, inp_array, 'lrn0_data:0', 'lrn:0')
tvm_output = run_tvm_graph(graph_def, inp_array, "lrn0_data",
tf_output.shape, tf_output.dtype)
np.testing.assert_allclose(tf_output,
tvm_output,
atol=1e-3,
rtol=1e-3)
sess.close()
def _test_lrn(ishape, size, axis, bias, alpha, beta):
""" testing local response normalization """
lrn_depth_radius = size / 2
inp_array = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype, name="lrn0_data")
nn_ops.local_response_normalization(in1,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
compare_tf_with_tvm(inp_array, 'lrn0_data:0', 'lrn:0')
def _test_lrn(ishape, size, axis, bias, alpha, beta):
""" testing local response normalization """
lrn_depth_radius = size / 2
inp_array = np.random.uniform(size=ishape).astype(np.float32)
with tf.Graph().as_default():
in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype, name="lrn0_data")
nn_ops.local_response_normalization(in1,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
compare_tf_with_tvm(inp_array, 'lrn0_data:0', 'lrn:0')
def call(self, inputs, **kwargs):
return local_response_normalization(input=inputs,
depth_radius=self.n,
bias=self.k,
alpha=self.alpha,
beta=self.beta)
