def testIntegerSamplesIncludeUpperBound(self, dtype):
if dtype.is_floating_point: # Only test on integer dtypes.
return
spec = BoundedTensorSpec(self._shape, dtype, 3, 3)
sample = spec.sample()
self.assertEqual(sample.shape, self._shape)
self.assertTrue(torch.all(sample == 3))
def testIntegerSamplesExcludeMaxOfDtype(self, dtype):
# Exclude non integer types and uint8 (has special sampling logic).
if dtype.is_floating_point or dtype == torch.uint8:
return
info = np.iinfo(torch_dtype_to_str(dtype))
spec = BoundedTensorSpec(self._shape, dtype, info.max - 1,
info.max - 1)
sample = spec.sample(outer_dims=(1, ))
self.assertEqual(sample.shape, (1, ) + self._shape)
self.assertTrue(torch.all(sample == info.max - 1))
def testBoundedTensorSpecSample(self, dtype):
if not dtype.is_floating_point:
return
# minimum and maximum shape broadcasting
spec = BoundedTensorSpec(self._shape, dtype, (0, ) * 30, 3)
sample = spec.sample()
self.assertEqual(self._shape, sample.shape)
self.assertTrue(torch.all(sample <= 3))
self.assertTrue(torch.all(0 <= sample))
# last minimum is greater than last maximum
self.assertRaises(AssertionError, BoundedTensorSpec, self._shape,
dtype, (0, ) * 29 + (2, ), (1, ) * 30)