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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.
# ==============================================================================
"""Functional tests for BiasAdd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def GetTestConfigs():
"""Get all the valid tests configs to run.
Returns:
all the valid test configs as tuples of data_format and use_gpu.
"""
test_configs = [("NHWC", False), ("NHWC", True)]
if tf.test.is_gpu_available(cuda_only=True):
# "NCHW" format is currently only supported on CUDA.
test_configs += [("NCHW", True)]
return test_configs
class BiasAddTest(tf.test.TestCase):
def _npBias(self, inputs, bias):
assert len(bias.shape) == 1
print(inputs.shape)
print(bias.shape)
assert inputs.shape[-1] == bias.shape[0]
return inputs + bias.reshape(([1] * (len(inputs.shape) - 1))
+ [bias.shape[0]])
def testNpBias(self):
self.assertAllClose(np.array([[11, 22, 33], [41, 52, 63]]),
self._npBias(np.array([[10, 20, 30], [40, 50, 60]]),
np.array([1, 2, 3])))
def _testBias(self, np_inputs, np_bias, use_gpu=False):
np_val = self._npBias(np_inputs, np_bias)
with self.test_session(use_gpu=use_gpu):
tf_val = tf.nn.bias_add(np_inputs, np_bias).eval()
self.assertAllCloseAccordingToType(np_val, tf_val)
def _AtLeast3d(self, np_value):
# fill the input value to at least 3-dimension
if np_value.ndim < 3:
return np.reshape(np_value, (1,) * (3 - np_value.ndim) + np_value.shape)
return np_value
def _NHWCToNCHW(self, np_value):
# fill the input value to at least 3-dimension
np_value = self._AtLeast3d(np_value)
# move the last dimension to third-to-last
np_dim = list(range(np_value.ndim))
np_dim_new = list(np_dim[0:-3]) + list(np_dim[-1:]) + list(np_dim[-3:-1])
return np.transpose(np_value, np_dim_new)
def _NCHWToNHWC(self, np_value):
assert len(np_value.shape) >= 3
np_dim = list(range(np_value.ndim))
# move the third-to-last dimension to the last
np_dim_new = list(np_dim[0:-3]) + list(np_dim[-2:]) + list(np_dim[-3:-2])
return np.transpose(np_value, np_dim_new)
def _testBiasNCHW(self, np_inputs, np_bias, use_gpu):
np_val = self._npBias(np_inputs, np_bias)
np_inputs = self._NHWCToNCHW(np_inputs)
with self.test_session(use_gpu=use_gpu):
tf_val = tf.nn.bias_add(np_inputs, np_bias, data_format="NCHW").eval()
tf_val = self._NCHWToNHWC(tf_val)
self.assertAllCloseAccordingToType(self._AtLeast3d(np_val), tf_val)
def _testAll(self, np_inputs, np_bias):
self._testBias(np_inputs, np_bias, use_gpu=False)
if np_inputs.dtype in [np.float16, np.float32, np.float64]:
self._testBias(np_inputs, np_bias, use_gpu=True)
if tf.test.is_gpu_available(cuda_only=True):
self._testBiasNCHW(np_inputs, np_bias, use_gpu=True)
def testInputDims(self):
with self.assertRaises(ValueError):
tf.nn.bias_add([1, 2], [1])
def testBiasVec(self):
with self.assertRaises(ValueError):
tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2], shape=[1, 2]))
def testBiasInputsMatch(self):
with self.assertRaises(ValueError):
tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1], shape=[1]))
def testIntTypes(self):
for t in [np.int8, np.int16, np.int32, np.int64]:
self._testAll(np.array([[10, 20, 30], [40, 50, 60]]).astype(t),
np.array([1, 2, 3]).astype(t))
def testFloatTypes(self):
for t in [np.float16, np.float32, np.float64]:
self._testAll(np.random.rand(4, 3, 3).astype(t),
np.random.rand(3).astype(t))
def _testGradient(self, np_input, bias, dtype, data_format, use_gpu):
with self.test_session(use_gpu=use_gpu):
if data_format == "NCHW":
np_input = self._NHWCToNCHW(np_input)
input_tensor = tf.constant(np_input, shape=np_input.shape, dtype=dtype)
bias_tensor = tf.constant(bias, shape=bias.shape, dtype=dtype)
output_tensor = tf.nn.bias_add(input_tensor, bias_tensor,
data_format=data_format)
tensor_jacob_t, tensor_jacob_n = tf.test.compute_gradient(
input_tensor, np_input.shape, output_tensor, np_input.shape)
bias_jacob_t, bias_jacob_n = tf.test.compute_gradient(
bias_tensor, bias.shape, output_tensor, np_input.shape)
# Test gradient of BiasAddGrad
bias_add_grad = tf.gradients(tf.nn.l2_loss(output_tensor),
bias_tensor)[0]
grad_jacob_t, grad_jacob_n = tf.test.compute_gradient(
output_tensor, np_input.shape, bias_add_grad, bias.shape)
if dtype == np.float16:
# Compare fp16 theoretical gradients to fp32 numerical gradients,
# since fp16 numerical gradients are too imprecise unless great
# care is taken with choosing the inputs and the delta. This is
# a weaker check (in particular, it does not test the op itself,
# only its gradient), but it's much better than nothing.
input_tensor = tf.constant(np_input, shape=np_input.shape,
dtype=np.float32)
bias_tensor = tf.constant(bias, shape=bias.shape, dtype=np.float32)
output_tensor = tf.nn.bias_add(input_tensor, bias_tensor,
data_format=data_format)
_, tensor_jacob_n = tf.test.compute_gradient(
input_tensor, np_input.shape, output_tensor, np_input.shape)
_, bias_jacob_n = tf.test.compute_gradient(
bias_tensor, bias.shape, output_tensor, np_input.shape)
bias_add_grad = tf.gradients(tf.nn.l2_loss(output_tensor),
bias_tensor)[0]
_, grad_jacob_n = tf.test.compute_gradient(
output_tensor, np_input.shape, bias_add_grad, bias.shape)
threshold = 2e-3
if dtype == tf.float64:
threshold = 1e-10
self.assertAllClose(tensor_jacob_t, tensor_jacob_n, threshold, threshold)
self.assertAllClose(bias_jacob_t, bias_jacob_n, threshold, threshold)
self.assertAllClose(grad_jacob_t, grad_jacob_n, threshold, threshold)
def testGradientTensor(self):
for (data_format, use_gpu) in GetTestConfigs():
for dtype in (tf.float16, tf.float32, tf.float64):
np_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
dtype=dtype.as_numpy_dtype).reshape(3, 2)
bias = np.array([1.3, 2.4], dtype=dtype.as_numpy_dtype)
self._testGradient(np_input, bias, dtype, data_format, use_gpu)
def testGradientTensor4D(self):
for (data_format, use_gpu) in GetTestConfigs():
for dtype in (tf.float16, tf.float32, tf.float64):
np_input = np.arange(1.0, 49.0, dtype=dtype.as_numpy_dtype).reshape(
[2, 3, 4, 2]).astype(np.float32)
bias = np.array([1.3, 2.4], dtype=dtype.as_numpy_dtype)
self._testGradient(np_input, bias, dtype, data_format, use_gpu)
def testEmpty(self):
np.random.seed(7)
for shape in (0, 0), (2, 0), (0, 2), (4, 3, 0), (4, 0, 3), (0, 4, 3):
self._testAll(np.random.randn(*shape), np.random.randn(shape[-1]))
def testEmptyGradient(self):
for data_format, use_gpu in GetTestConfigs():
for shape in (0, 0), (2, 0), (0, 2), (4, 3, 0), (4, 0, 3), (0, 4, 3):
self._testGradient(np.random.randn(*shape), np.random.randn(shape[-1]),
tf.float64, data_format, use_gpu)
if __name__ == "__main__":
tf.test.main()
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