# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Local Response Normalization ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.platform import googletest
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CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
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# Local response normalization tests. The forward tests are copied from
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# tensorflow/python/kernel_tests/lrn_op_test.py
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class LRNTest(xla_test.XLATestCase):
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def _LRN(self, input_image, lrn_depth_radius=5, bias=1.0, alpha=1.0,
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beta=0.5):
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"""Compute expected result."""
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output = copy.deepcopy(input_image)
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batch_size = input_image.shape[0]
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rows = input_image.shape[1]
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cols = input_image.shape[2]
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depth = input_image.shape[3]
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for b in range(batch_size):
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for r in range(rows):
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for c in range(cols):
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for d in range(depth):
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begin = max(0, d - lrn_depth_radius)
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end = min(depth, d + lrn_depth_radius + 1)
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patch = input_image[b, r, c, begin:end]
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output[b, r, c, d] /= (
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np.power(bias + alpha * np.sum(patch * patch), beta))
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return output
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def _RunAndVerify(self, dtype):
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with self.cached_session():
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# random shape
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shape = np.random.randint(1, 16, size=4)
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# Make depth at least 2 to make it meaningful
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shape[3] += 1
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p = array_ops.placeholder(dtype, shape=shape)
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# random depth_radius, bias, alpha, beta
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lrn_depth_radius = np.random.randint(1, shape[3])
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bias = 1.0 + np.random.rand()
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alpha = 2.0 * np.random.rand()
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beta = 2.0 * np.random.rand()
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with self.test_scope():
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lrn_t = nn.local_response_normalization(
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p,
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name="lrn",
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depth_radius=lrn_depth_radius,
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bias=bias,
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alpha=alpha,
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beta=beta)
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params = {p: np.random.rand(*shape).astype("f")}
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result = lrn_t.eval(feed_dict=params)
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expected = self._LRN(
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params[p],
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lrn_depth_radius=lrn_depth_radius,
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bias=bias,
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alpha=alpha,
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beta=beta)
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err = np.amax(np.abs(result - expected))
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print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta, " is ",
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err)
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if dtype == dtypes.float32:
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self.assertTrue(err < 1e-4)
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else:
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self.assertTrue(err < 1e-2)
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self.assertShapeEqual(expected, lrn_t)
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def testCompute(self):
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for _ in range(2):
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self._RunAndVerify(dtypes.float32)
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def testLrnGrad(self):
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# Test for LRNGrad that compares against the CPU implementation.
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shape = [1, 2, 3, 4]
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total_size = np.prod(shape)
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in_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
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out_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
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out_grads_vals = np.arange(1, total_size + 1, dtype=np.float32)
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depth_radius = np.random.randint(1, shape[3])
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bias = 1.0 + np.random.rand()
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alpha = 1.0 * np.random.rand()
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beta = 1.0 * np.random.rand()
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with self.cached_session():
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in_image = constant_op.constant(in_image_vals, shape=shape)
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out_image = constant_op.constant(out_image_vals, shape=shape)
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out_grads = constant_op.constant(out_grads_vals, shape=shape)
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with ops.device(CPU_DEVICE):
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expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
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depth_radius, bias, alpha, beta)
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with self.test_scope():
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actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
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depth_radius, bias, alpha, beta)
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expected_val = self.evaluate(expected)
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actual_val = self.evaluate(actual)
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self.assertAllClose(actual_val, expected_val, rtol=1e-3)
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if __name__ == "__main__":
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googletest.main()
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