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# Copyright 2017 The TensorFlow Authors. 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 Local Response Normalization ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
from tensorflow.compiler.tests.xla_test import XLATestCase
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import nn
from tensorflow.python.platform import googletest
CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
# Local response normalization tests. The forward tests are copied from
# tensorflow/python/kernel_tests/lrn_op_test.py
class LRNTest(XLATestCase):
def _LRN(self, input_image, lrn_depth_radius=5, bias=1.0, alpha=1.0,
beta=0.5):
"""Compute expected result."""
output = copy.deepcopy(input_image)
batch_size = input_image.shape[0]
rows = input_image.shape[1]
cols = input_image.shape[2]
depth = input_image.shape[3]
for b in range(batch_size):
for r in range(rows):
for c in range(cols):
for d in range(depth):
begin = max(0, d - lrn_depth_radius)
end = min(depth, d + lrn_depth_radius + 1)
patch = input_image[b, r, c, begin:end]
output[b, r, c, d] /= (
np.power(bias + alpha * np.sum(patch * patch), beta))
return output
def _RunAndVerify(self, dtype):
with self.test_session():
# random shape
shape = np.random.randint(1, 16, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
p = array_ops.placeholder(dtype, shape=shape)
# random depth_radius, bias, alpha, beta
lrn_depth_radius = np.random.randint(1, shape[3])
bias = 1.0 + np.random.rand()
alpha = 2.0 * np.random.rand()
beta = 2.0 * np.random.rand()
with self.test_scope():
lrn_t = nn.local_response_normalization(
p,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
params = {p: np.random.rand(*shape).astype("f")}
result = lrn_t.eval(feed_dict=params)
expected = self._LRN(
params[p],
lrn_depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = np.amax(np.abs(result - expected))
print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta, " is ",
err)
if dtype == dtypes.float32:
self.assertTrue(err < 1e-4)
else:
self.assertTrue(err < 1e-2)
self.assertShapeEqual(expected, lrn_t)
def testCompute(self):
for _ in range(2):
self._RunAndVerify(dtypes.float32)
def testLrnGrad(self):
# Test for LRNGrad that compares against the CPU implementation.
shape = [1, 2, 3, 4]
total_size = np.prod(shape)
in_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
out_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
out_grads_vals = np.arange(1, total_size + 1, dtype=np.float32)
depth_radius = np.random.randint(1, shape[3])
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
beta = 1.0 * np.random.rand()
with self.test_session():
in_image = constant_op.constant(in_image_vals, shape=shape)
out_image = constant_op.constant(out_image_vals, shape=shape)
out_grads = constant_op.constant(out_grads_vals, shape=shape)
with ops.device(CPU_DEVICE):
expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
depth_radius, bias, alpha, beta)
with self.test_scope():
actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
depth_radius, bias, alpha, beta)
expected_val = expected.eval()
actual_val = actual.eval()
self.assertAllClose(actual_val, expected_val, rtol=1e-3)
if __name__ == "__main__":
googletest.main()
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