Sample change making a kernel unittest work for both TF 1.x and 2.x.
This change makes all the tests that don't check gradients in relu_op_test.py work for both TensorFlow 1.x and 2.x (where eager execution, resource variables etc. are enabled by default). PiperOrigin-RevId: 221474407
This commit is contained in:
Asim Shankar
TensorFlower Gardener
parent
2244653d37
commit
00774b2d5e
@ -25,6 +25,7 @@ from tensorflow.python.compat import compat
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import errors
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import gradient_checker
|
||||
from tensorflow.python.ops import gradients_impl
|
||||
@ -55,52 +56,52 @@ class ReluTest(test.TestCase):
|
||||
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
|
||||
0.9]])))
|
||||
|
||||
def _testRelu(self, np_features, use_gpu=False):
|
||||
def _testRelu(self, np_features):
|
||||
np_relu = self._npRelu(np_features)
|
||||
with self.cached_session(use_gpu=use_gpu):
|
||||
relu = nn_ops.relu(np_features)
|
||||
tf_relu = relu.eval()
|
||||
tf_relu = nn_ops.relu(np_features)
|
||||
self.assertAllClose(np_relu, tf_relu)
|
||||
self.assertShapeEqual(np_relu, relu)
|
||||
self.assertShapeEqual(np_relu, tf_relu)
|
||||
|
||||
def testNumbers(self):
|
||||
def testNumbersCPU(self):
|
||||
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
|
||||
self._testRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=False)
|
||||
if t in [np.float16, np.float32, np.float64]:
|
||||
# Force execution on CPU even if a GPU kernel is available for the type.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=True)
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def _testReluInt8x4(self, np_inputs):
|
||||
if not test.is_gpu_available(cuda_only=True):
|
||||
return
|
||||
np_relu = self._npRelu(np_inputs)
|
||||
with self.cached_session(use_gpu=True):
|
||||
relu = nn_ops.relu(constant_op.constant(np_inputs, dtypes.qint8))
|
||||
if np_inputs.size % 4 == 0:
|
||||
tf_relu = relu.eval()
|
||||
self.assertAllClose(np_relu, tf_relu)
|
||||
self.assertShapeEqual(np_relu, relu)
|
||||
else:
|
||||
with self.assertRaisesRegexp(
|
||||
errors.InvalidArgumentError,
|
||||
"Tensor size must be a multiple of 4 for Relu<qint8>. Got %d" %
|
||||
np_inputs.size):
|
||||
tf_relu = relu.eval()
|
||||
def testNumbersGPU(self):
|
||||
if not test.is_gpu_available():
|
||||
self.skipTest("No GPU available")
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testReluInt8x4GoodShape(self):
|
||||
self._testReluInt8x4(np.array([[-50, 7, 23, 0], [-1, -5, 6, 11]]))
|
||||
if not test.is_gpu_available(cuda_only=True):
|
||||
self.skipTest("No GPU available")
|
||||
inputs = np.array([[-50, 7, 23, 0], [-1, -5, 6, 11]])
|
||||
np_relu = self._npRelu(inputs)
|
||||
tf_relu = nn_ops.relu(constant_op.constant(inputs, dtypes.qint8))
|
||||
self.assertAllClose(np_relu, tf_relu)
|
||||
self.assertShapeEqual(np_relu, tf_relu)
|
||||
|
||||
def testReluInt8x4BadShape(self):
|
||||
np_inputs = np.array([[-50, 7, 23], [0, 1, -5], [6, -2, 11]])
|
||||
self.assertEqual(np_inputs.size, 9)
|
||||
self._testReluInt8x4(np_inputs)
|
||||
np_inputs = np.array(
|
||||
[1, -2, 3, -4, 5, -6, 7, -8, 9, -8, 7, -6, 5, -4, 3, -2, 1])
|
||||
self.assertEqual(np_inputs.size, 17)
|
||||
self._testReluInt8x4(np_inputs)
|
||||
if not test.is_gpu_available(cuda_only=True):
|
||||
self.skipTest("No GPU available")
|
||||
inputs = constant_op.constant(
|
||||
np.array([[-50, 7, 23], [0, 1, -5], [6, -2, 11]]), dtypes.qint8)
|
||||
with self.assertRaisesRegexp(
|
||||
errors.InvalidArgumentError,
|
||||
"Tensor size must be a multiple of 4 for Relu<qint8>. Got 9"):
|
||||
self.evaluate(nn_ops.relu(inputs))
|
||||
|
||||
inputs = constant_op.constant(
|
||||
np.array([1, -2, 3, -4, 5, -6, 7, -8, 9, -8, 7, -6, 5, -4, 3, -2, 1]),
|
||||
dtypes.qint8)
|
||||
with self.assertRaisesRegexp(
|
||||
errors.InvalidArgumentError,
|
||||
"Tensor size must be a multiple of 4 for Relu<qint8>. Got 17"):
|
||||
self.evaluate(nn_ops.relu(inputs))
|
||||
|
||||
# The gradient test for ReLU is a bit tricky as the derivative is not well
|
||||
# defined at around zero and we want to avoid that in terms of input values.
|
||||
@ -202,15 +203,15 @@ class ReluTest(test.TestCase):
|
||||
self.assertLess(err, 1e-10)
|
||||
|
||||
def testGradientScalar(self):
|
||||
with self.cached_session() as sess:
|
||||
x = variables.Variable(100.)
|
||||
y = nn_ops.relu(x)
|
||||
loss = y**2
|
||||
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.25)
|
||||
train_op = optimizer.minimize(loss)
|
||||
sess.run(variables.global_variables_initializer())
|
||||
sess.run(train_op)
|
||||
self.assertAllClose(x.eval(), 50.0)
|
||||
x = variables.Variable(100.)
|
||||
|
||||
def loss():
|
||||
return nn_ops.relu(x)**2
|
||||
|
||||
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.25)
|
||||
self.evaluate(variables.global_variables_initializer())
|
||||
self.evaluate(optimizer.minimize(loss))
|
||||
self.assertAllClose(x.read_value(), 50.0)
|
||||
|
||||
|
||||
class Relu6Test(test.TestCase):
|
||||
@ -228,23 +229,25 @@ class Relu6Test(test.TestCase):
|
||||
np.array([[-0.9, 0.7, -0.5, 0.3, 6.0], [0.1, -0.3, 6.5, -0.7,
|
||||
0.9]])))
|
||||
|
||||
def _testRelu6(self, np_features, use_gpu=False):
|
||||
def _testRelu6(self, np_features):
|
||||
np_relu6 = self._npRelu6(np_features)
|
||||
with self.cached_session(use_gpu=use_gpu):
|
||||
relu6 = nn_ops.relu6(np_features)
|
||||
tf_relu6 = relu6.eval()
|
||||
tf_relu6 = nn_ops.relu6(np_features)
|
||||
self.assertAllClose(np_relu6, tf_relu6)
|
||||
self.assertShapeEqual(np_relu6, relu6)
|
||||
self.assertShapeEqual(np_relu6, tf_relu6)
|
||||
|
||||
def testNumbers(self):
|
||||
def testNumbersCPU(self):
|
||||
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
|
||||
self._testRelu6(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=False)
|
||||
if t in [np.float16, np.float, np.double]:
|
||||
# Force execution on CPU even if a GPU kernel is available for the type.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testRelu6(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=True)
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testNumbersGPU(self):
|
||||
if not test.is_gpu_available():
|
||||
self.skipTest("No GPU available")
|
||||
for t in [np.float16, np.float, np.double]:
|
||||
self._testRelu6(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
# The gradient test for ReLU6 is a bit tricky as the derivative is
|
||||
# not well defined at around zero and six and we want to avoid that
|
||||
@ -297,25 +300,27 @@ class LeakyReluTest(test.TestCase):
|
||||
0.9]]),
|
||||
alpha=0.1))
|
||||
|
||||
def _testLeakyRelu(self, np_features, alpha, use_gpu=False):
|
||||
def _testLeakyRelu(self, np_features, alpha):
|
||||
np_leaky_relu = self._npLeakyRelu(np_features, alpha)
|
||||
with self.test_session(use_gpu=use_gpu):
|
||||
leaky_relu = nn_ops.leaky_relu(np_features, alpha)
|
||||
tf_leaky_relu = leaky_relu.eval()
|
||||
tf_leaky_relu = nn_ops.leaky_relu(np_features, alpha)
|
||||
self.assertAllClose(np_leaky_relu, tf_leaky_relu)
|
||||
self.assertShapeEqual(np_leaky_relu, leaky_relu)
|
||||
self.assertShapeEqual(np_leaky_relu, tf_leaky_relu)
|
||||
|
||||
def testNumbers(self):
|
||||
def testNumbersCPU(self):
|
||||
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
|
||||
self._testLeakyRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
alpha=0.2,
|
||||
use_gpu=False)
|
||||
if t in [np.float16, np.float32, np.float64]:
|
||||
# Force execution on CPU even if a GPU kernel is available for the type.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testLeakyRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
alpha=0.1,
|
||||
use_gpu=True)
|
||||
alpha=0.2)
|
||||
|
||||
def testNumbersGPU(self):
|
||||
if not test.is_gpu_available():
|
||||
self.skipTest("No GPU available")
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testLeakyRelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
alpha=0.1)
|
||||
|
||||
# The gradient test for Leaky ReLU is a bit tricky as the derivative is not
|
||||
# well defined at around zero and we want to avoid that in terms of input
|
||||
@ -391,15 +396,15 @@ class LeakyReluTest(test.TestCase):
|
||||
self.assertLess(err, 1e-10)
|
||||
|
||||
def testGradientScalar(self):
|
||||
with self.test_session() as sess:
|
||||
x = variables.Variable(-100.)
|
||||
y = nn_ops.leaky_relu(x, 0.05)
|
||||
loss = y**2
|
||||
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.2)
|
||||
train_op = optimizer.minimize(loss)
|
||||
sess.run(variables.global_variables_initializer())
|
||||
sess.run(train_op)
|
||||
self.assertAllClose(x.eval(), -99.9)
|
||||
x = variables.Variable(-100.)
|
||||
|
||||
def loss():
|
||||
return nn_ops.leaky_relu(x, 0.05)**2
|
||||
|
||||
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.2)
|
||||
self.evaluate(variables.global_variables_initializer())
|
||||
self.evaluate(optimizer.minimize(loss))
|
||||
self.assertAllClose(x.read_value(), -99.9)
|
||||
|
||||
|
||||
class EluTest(test.TestCase):
|
||||
@ -415,22 +420,24 @@ class EluTest(test.TestCase):
|
||||
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
|
||||
0.9]])))
|
||||
|
||||
def _testElu(self, np_features, use_gpu=False):
|
||||
def _testElu(self, np_features):
|
||||
np_elu = self._npElu(np_features)
|
||||
with self.cached_session(use_gpu=use_gpu):
|
||||
elu = nn_ops.elu(np_features)
|
||||
tf_elu = elu.eval()
|
||||
tf_elu = nn_ops.elu(np_features)
|
||||
self.assertAllClose(np_elu, tf_elu)
|
||||
self.assertShapeEqual(np_elu, elu)
|
||||
self.assertShapeEqual(np_elu, tf_elu)
|
||||
|
||||
def testNumbers(self):
|
||||
def testNumbersCPU(self):
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testElu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=False)
|
||||
self._testElu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=True)
|
||||
# Force execution on CPU even if a GPU kernel is available for the type.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testElu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testNumbersGPU(self):
|
||||
if not test.is_gpu_available():
|
||||
self.skipTest("No GPU available")
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testElu(np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testGradientFloat32(self):
|
||||
with self.cached_session():
|
||||
@ -517,22 +524,20 @@ class SeluTest(test.TestCase):
|
||||
np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7,
|
||||
0.9]])))
|
||||
|
||||
def _testSelu(self, np_features, use_gpu=False):
|
||||
def _testSelu(self, np_features):
|
||||
np_selu = self._npSelu(np_features)
|
||||
with self.cached_session(use_gpu=use_gpu):
|
||||
selu = nn_ops.selu(np_features)
|
||||
tf_selu = selu.eval()
|
||||
tf_selu = nn_ops.selu(np_features)
|
||||
self.assertAllClose(np_selu, tf_selu)
|
||||
self.assertShapeEqual(np_selu, selu)
|
||||
self.assertShapeEqual(np_selu, tf_selu)
|
||||
|
||||
def testNumbers(self):
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testSelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=False)
|
||||
self._testSelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=True)
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
# Force executed on CPU in case GPU kernels are avaiable.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testSelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testGradientFloat32(self):
|
||||
with self.cached_session():
|
||||
@ -599,46 +604,44 @@ class CreluTest(test.TestCase):
|
||||
t = nn_ops.crelu(f)
|
||||
self.assertEqual([50, 5, 7, 20], t.get_shape())
|
||||
|
||||
def _testCrelu(self, np_features, use_gpu=False):
|
||||
def _testCrelu(self, np_features):
|
||||
np_relu = np.maximum(np_features, np.zeros_like(np_features))
|
||||
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
|
||||
np_crelu = np.concatenate((np_relu, np_neg_relu),
|
||||
len(np_features.shape) - 1)
|
||||
|
||||
with self.cached_session(use_gpu=use_gpu):
|
||||
crelu = nn_ops.crelu(np_features)
|
||||
tf_relu = crelu.eval()
|
||||
tf_crelu = nn_ops.crelu(np_features)
|
||||
|
||||
self.assertAllClose(np_crelu, tf_relu)
|
||||
self.assertShapeEqual(np_crelu, crelu)
|
||||
self.assertAllClose(np_crelu, tf_crelu)
|
||||
self.assertShapeEqual(np_crelu, tf_crelu)
|
||||
|
||||
def testNumbers(self):
|
||||
def testNumbersCPU(self):
|
||||
for t in [np.int32, np.int64, np.float16, np.float32, np.float64]:
|
||||
self._testCrelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=False)
|
||||
if t in [np.float16, np.float32, np.float64]:
|
||||
# Force execution on CPU even if a GPU kernel is available for the type.
|
||||
with ops.device("/device:CPU:0"):
|
||||
self._testCrelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
|
||||
use_gpu=True)
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testNumbersGPU(self):
|
||||
if not test.is_gpu_available():
|
||||
self.skipTest("No GPU available")
|
||||
for t in [np.float16, np.float32, np.float64]:
|
||||
self._testCrelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t))
|
||||
|
||||
def testNumbersWithAxis0(self):
|
||||
with self.cached_session():
|
||||
crelu = nn_ops.crelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=0)
|
||||
tf_relu = crelu.eval()
|
||||
np_crelu = np.array([[0, 7, 0, 3, 0], [1, 0, 5, 0, 9], [9, 0, 5, 0, 1],
|
||||
[0, 3, 0, 7, 0]])
|
||||
self.assertAllEqual(np_crelu, tf_relu)
|
||||
tf_crelu = nn_ops.crelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=0)
|
||||
np_crelu = np.array([[0, 7, 0, 3, 0], [1, 0, 5, 0, 9], [9, 0, 5, 0, 1],
|
||||
[0, 3, 0, 7, 0]])
|
||||
self.assertAllEqual(np_crelu, tf_crelu)
|
||||
|
||||
def testNumbersWithAxis1(self):
|
||||
with self.cached_session():
|
||||
crelu = nn_ops.crelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
|
||||
tf_relu = crelu.eval()
|
||||
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
|
||||
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
|
||||
self.assertAllEqual(np_crelu, tf_relu)
|
||||
tf_crelu = nn_ops.crelu(
|
||||
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
|
||||
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
|
||||
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
|
||||
self.assertAllEqual(np_crelu, tf_crelu)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user