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# Copyright 2015 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."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import nn
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
class LRNOpTest(test.TestCase):
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(use_gpu=True):
# 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. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 2.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 2.0 * np.random.rand()
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)
# Enable when LRN supports tf.float16 on GPU.
if not test.is_gpu_available():
self._RunAndVerify(dtypes.float16)
def testGradientsZeroInput(self):
with self.test_session(use_gpu=True):
shape = [4, 4, 4, 4]
p = array_ops.placeholder(dtypes.float32, shape=shape)
inp_array = np.zeros(shape).astype("f")
lrn_op = nn.local_response_normalization(p, 2, 1.0, 0.0, 1.0, name="lrn")
grad = gradients_impl.gradients([lrn_op], [p])[0]
params = {p: inp_array}
r = grad.eval(feed_dict=params)
expected = np.ones(shape).astype("f")
self.assertAllClose(r, expected)
self.assertShapeEqual(expected, grad)
def _RunAndVerifyGradients(self, dtype):
with self.test_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 5, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 1.0 * np.random.rand()
if dtype == dtypes.float32:
inp_array = np.random.rand(*shape).astype(np.float32)
else:
inp_array = np.random.rand(*shape).astype(np.float16)
inp = constant_op.constant(
list(inp_array.ravel(order="C")), shape=shape, dtype=dtype)
lrn_op = nn.local_response_normalization(
inp,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = gradient_checker.compute_gradient_error(inp, shape, lrn_op, shape)
print("LRN Gradient error for bias ", bias, "alpha ", alpha, " beta ", beta,
" is ", err)
if dtype == dtypes.float32:
self.assertLess(err, 1e-4)
else:
self.assertLess(err, 1.0)
def testGradients(self):
for _ in range(2):
self._RunAndVerifyGradients(dtypes.float32)
# Enable when LRN supports tf.float16 on GPU.
if not test.is_gpu_available():
self._RunAndVerifyGradients(dtypes.float16)
if __name__ == "__main__":
test.main()
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