Fix up tf.pad tests to be eager friendly
PiperOrigin-RevId: 324113822 Change-Id: I2b849ff51274995a8fc5206ccdeb95ca13c19cfc
This commit is contained in:
Gaurav Jain
TensorFlower Gardener
parent
a8213a7b94
commit
9465808ae3
@ -22,10 +22,11 @@ import numpy as np
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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 errors
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import gradient_checker_v2
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from tensorflow.python.platform import test
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@ -86,7 +87,8 @@ class PadOpTest(test.TestCase):
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def _testPad(self, np_inputs, paddings, mode, constant_values):
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np_val = self._npPad(np_inputs, paddings, mode=mode,
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constant_values=constant_values)
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with self.cached_session(use_gpu=True):
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with test_util.use_gpu():
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tf_val = array_ops.pad(np_inputs, paddings, mode=mode,
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constant_values=constant_values)
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out = self.evaluate(tf_val)
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@ -99,16 +101,17 @@ class PadOpTest(test.TestCase):
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mode,
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constant_values,
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paddings_dtype=dtypes.int32):
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with self.cached_session(use_gpu=True):
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inx = ops.convert_to_tensor(x)
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xs = list(x.shape)
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ina = ops.convert_to_tensor(a, paddings_dtype)
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y = array_ops.pad(inx, ina, mode=mode, constant_values=constant_values)
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# Expected y's shape to be:
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ys = list(np.array(x.shape) + np.sum(np.array(a), axis=1))
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jacob_t, jacob_n = gradient_checker.compute_gradient(
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inx, xs, y, ys, x_init_value=x)
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self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)
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def pad(x):
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return array_ops.pad(
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x,
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ops.convert_to_tensor(a, paddings_dtype),
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mode=mode,
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constant_values=constant_values)
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with self.cached_session():
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jacob_t, jacob_n = gradient_checker_v2.compute_gradient(pad, [x])
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self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)
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def _testAll(self, np_inputs, paddings, constant_values):
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for mode in ("CONSTANT", "REFLECT", "SYMMETRIC", "reflect", "symmetric",
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@ -122,71 +125,76 @@ class PadOpTest(test.TestCase):
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self._testGradient(np_inputs, paddings, mode=mode,
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constant_values=constant_values)
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@test_util.run_deprecated_v1
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def testInputDims(self):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"Shape must be rank 1 but is rank 6|"
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"paddings must be the rank of inputs"):
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array_ops.pad(array_ops.reshape(
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[1, 2], shape=[1, 2, 1, 1, 1, 1]),
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array_ops.reshape(
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[1, 2], shape=[1, 2]))
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@test_util.run_deprecated_v1
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def testPaddingsDim(self):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"Shape must be rank 2 but is rank 1|"
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"paddings must be a matrix with 2 columns"):
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array_ops.pad(array_ops.reshape(
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[1, 2], shape=[1, 2]),
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array_ops.reshape(
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[1, 2], shape=[2]))
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@test_util.run_deprecated_v1
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def testPaddingsDim2(self):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"Dimension must be 2 but is 1|"
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"paddings must be a matrix with 2 columns"):
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array_ops.pad(array_ops.reshape(
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[1, 2], shape=[1, 2]),
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array_ops.reshape(
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[1, 2], shape=[2, 1]))
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@test_util.run_deprecated_v1
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def testPaddingsDim3(self):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"Shape must be rank 1 but is rank 2|"
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"paddings must be the rank of inputs"):
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array_ops.pad(array_ops.reshape(
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[1, 2], shape=[1, 2]),
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array_ops.reshape(
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[1, 2], shape=[1, 2]))
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@test_util.run_deprecated_v1
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def testPaddingsDim4(self):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"Shape must be rank 3 but is rank 2|"
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"paddings must be the rank of inputs"):
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array_ops.pad(array_ops.reshape(
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[1, 2], shape=[1, 2]),
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array_ops.reshape(
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[1, 2, 3, 4, 5, 6], shape=[3, 2]))
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@test_util.run_deprecated_v1
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def testPaddingsNonNegative(self):
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with self.session(use_gpu=True):
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with self.assertRaisesRegex(ValueError, "must be non-negative"):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"must be non-negative"):
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array_ops.pad(constant_op.constant(
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[1], shape=[1]),
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constant_op.constant(
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[-1, 0], shape=[1, 2]))
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@test_util.run_deprecated_v1
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def testPaddingsNonNegative2(self):
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with self.session(use_gpu=True):
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with self.assertRaisesRegex(ValueError, "must be non-negative"):
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with test_util.use_gpu():
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"must be non-negative"):
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array_ops.pad(constant_op.constant(
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[1], shape=[1]),
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constant_op.constant(
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[-1, 0], shape=[1, 2]))
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def testPaddingsMaximum(self):
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with self.session(use_gpu=True):
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with test_util.use_gpu():
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with self.assertRaises(Exception):
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array_ops.pad(constant_op.constant(
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[1], shape=[2]),
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@ -204,7 +212,7 @@ class PadOpTest(test.TestCase):
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with self.cached_session():
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x = [[1, 2, 3], [4, 5, 6]]
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with self.assertRaisesRegex(ValueError, "Unknown padding mode"):
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array_ops.pad(x, [[1, 0], [2, 1]], mode="weird").eval()
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self.evaluate(array_ops.pad(x, [[1, 0], [2, 1]], mode="weird"))
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def testPaddingTypes(self):
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paddings = [[1, 0], [2, 0]]
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@ -216,6 +224,7 @@ class PadOpTest(test.TestCase):
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paddings,
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mode=mode,
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constant_values=0)
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with test_util.use_gpu():
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tf_val = array_ops.pad(
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inputs,
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@ -223,6 +232,7 @@ class PadOpTest(test.TestCase):
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mode=mode,
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constant_values=0)
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out = self.evaluate(tf_val)
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self.assertAllEqual(np_val, out)
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self.assertShapeEqual(np_val, tf_val)
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@ -246,7 +256,6 @@ class PadOpTest(test.TestCase):
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np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t),
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[[0, 0], [0, 0], [0, 0], [0, 0]], -123)
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@test_util.run_deprecated_v1
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def testFloatTypes(self):
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for t in [np.float32, np.float64]:
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self._testAll(np.random.rand(2, 5).astype(t), [[1, 0], [2, 0]], 0.0)
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@ -273,7 +282,7 @@ class PadOpTest(test.TestCase):
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constant_values="PAD")
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symmetric = array_ops.pad(x, [[1, 0], [0, 1]], mode="SYMMETRIC",
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constant_values="PAD")
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with self.session(use_gpu=True):
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with test_util.use_gpu():
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self.assertAllEqual(
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[[b"PAD", b"PAD", b"PAD"], [b"Hello", b"World", b"PAD"],
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[b"Goodnight", b"Moon", b"PAD"]], self.evaluate(constant))
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@ -285,73 +294,75 @@ class PadOpTest(test.TestCase):
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[[b"Hello", b"World", b"World"], [b"Hello", b"World", b"World"],
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[b"Goodnight", b"Moon", b"Moon"]], self.evaluate(symmetric))
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@test_util.run_deprecated_v1
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def testShapeFunctionEdgeCases(self):
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# Unknown paddings shape.
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inp = constant_op.constant(0.0, shape=[4, 4, 4, 4])
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padded = array_ops.pad(inp, array_ops.placeholder(dtypes.int32))
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self.assertEqual([None, None, None, None], padded.get_shape().as_list())
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# Shape function requires placeholders and a graph
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with ops.Graph().as_default():
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# Unknown paddings shape.
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inp = constant_op.constant(0.0, shape=[4, 4, 4, 4])
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padded = array_ops.pad(inp, array_ops.placeholder(dtypes.int32))
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self.assertEqual([None, None, None, None], padded.get_shape().as_list())
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# Unknown input shape.
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inp = array_ops.placeholder(dtypes.float32)
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padded = array_ops.pad(inp, [[2, 2], [2, 2]])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Unknown input shape.
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inp = array_ops.placeholder(dtypes.float32)
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padded = array_ops.pad(inp, [[2, 2], [2, 2]])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Unknown input and paddings shape.
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inp = array_ops.placeholder(dtypes.float32)
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padded = array_ops.pad(inp, array_ops.placeholder(dtypes.int32))
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self.assertAllEqual(None, padded.get_shape().ndims)
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# Unknown input and paddings shape.
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inp = array_ops.placeholder(dtypes.float32)
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padded = array_ops.pad(inp, array_ops.placeholder(dtypes.int32))
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self.assertAllEqual(None, padded.get_shape().ndims)
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@test_util.run_deprecated_v1
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def testPartialShapeInformation(self):
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unknown = array_ops.placeholder(dtypes.int32)
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# Partial shapes requires placeholders and a graph
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with ops.Graph().as_default():
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unknown = array_ops.placeholder(dtypes.int32)
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# Known input shape, partial unknown padding (one dimension).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[1, 2], unknown])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (one dimension).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[1, 2], unknown])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[unknown, 0], [1, 2]])
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self.assertEqual([None, 7], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[unknown, 0], [1, 2]])
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self.assertEqual([None, 7], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (end).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[1, 2], [0, unknown]])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (end).
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[1, 2], [0, unknown]])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Unknown input shape, partial unknown padding (one dimension).
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padded = array_ops.pad(unknown, [[1, 2], unknown])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Unknown input shape, partial unknown padding (one dimension).
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padded = array_ops.pad(unknown, [[1, 2], unknown])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Unknown input shape (rank known), partial unknown padding (one dimension).
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rank_known = array_ops.placeholder(dtypes.int32)
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rank_known.set_shape([None, None])
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padded = array_ops.pad(rank_known, [[1, 2], unknown])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Unknown input shape (rank known), partial unknown padding (one dim).
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rank_known = array_ops.placeholder(dtypes.int32)
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rank_known.set_shape([None, None])
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padded = array_ops.pad(rank_known, [[1, 2], unknown])
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self.assertEqual([None, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin), with constant begin.
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp, [[constant_op.constant(1, shape=[]), 2],
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[0, unknown]])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin), with constant begin.
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(
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inp, [[constant_op.constant(1, shape=[]), 2], [0, unknown]])
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self.assertEqual([7, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin), with constant dim.
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp,
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[constant_op.constant(1, shape=[2]), [0, unknown]])
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self.assertEqual([6, None], padded.get_shape().as_list())
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# Known input shape, partial unknown padding (begin), with constant dim.
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inp = constant_op.constant(0.0, shape=[4, 4])
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padded = array_ops.pad(inp,
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[constant_op.constant(1, shape=[2]), [0, unknown]])
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self.assertEqual([6, None], padded.get_shape().as_list())
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# Zero padding on a known dimension.
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inp = array_ops.placeholder(dtypes.int32, [None, None, 20])
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padded = array_ops.pad(inp, [[0, 0], [0, unknown], [0, 0]])
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self.assertEqual([None, None, 20], padded.get_shape().as_list())
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# Zero padding on a known dimension.
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inp = array_ops.placeholder(dtypes.int32, [None, None, 20])
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padded = array_ops.pad(inp, [[0, 0], [0, unknown], [0, 0]])
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self.assertEqual([None, None, 20], padded.get_shape().as_list())
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def testScalars(self):
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paddings = np.zeros((0, 2), dtype=np.int32)
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inp = np.asarray(7)
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with self.session(use_gpu=True):
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with test_util.use_gpu():
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tf_val = array_ops.pad(inp, paddings)
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out = self.evaluate(tf_val)
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self.assertAllEqual(inp, out)
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@ -367,7 +378,6 @@ class PadOpTest(test.TestCase):
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self.assertAllEqual(inp, out)
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self.assertShapeEqual(inp, tf_val)
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@test_util.run_deprecated_v1
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def testCollapseAdjacentNonPaddedDimensions(self):
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# pyformat: disable
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paddings_values = [[[0, 0], [0, 0], [0, 0], [0, 1]],
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