def test_alternative_dtypes(self):
shape = [3, 4, 5, 6]
array = numpy.zeros(shape)
# Setting dtype to numpy.int64 should produce a torch.LongTensor when field is converted to
# a tensor
array_field1 = TensorField(array, dtype=numpy.int64)
returned_tensor1 = array_field1.as_tensor(
array_field1.get_padding_lengths())
assert returned_tensor1.dtype == torch.int64
# Setting dtype to numpy.uint8 should produce a torch.ByteTensor when field is converted to
# a tensor
array_field2 = TensorField(array, dtype=numpy.uint8)
returned_tensor2 = array_field2.as_tensor(
array_field2.get_padding_lengths())
assert returned_tensor2.dtype == torch.uint8
# Padding should not affect dtype
padding_lengths = {
"dimension_" + str(i): 10
for i, _ in enumerate(shape)
}
padded_tensor = array_field2.as_tensor(padding_lengths)
assert padded_tensor.dtype == torch.uint8
# Empty fields should have the same dtype
empty_field = array_field2.empty_field()
assert empty_field.tensor.dtype == array_field2.tensor.dtype
def test_as_tensor_handles_larger_padding_dimensions(self):
shape = [3, 4]
array = numpy.ones(shape)
array_field = TensorField(array)
padded_tensor = (array_field.as_tensor({
"dimension_0": 5,
"dimension_1": 6
}).detach().cpu().numpy())
numpy.testing.assert_array_equal(padded_tensor[:3, :4], array)
numpy.testing.assert_array_equal(padded_tensor[3:, 4:], 0.0)
def test_as_tensor_with_scalar_keeps_dtype(self):
array = TensorField(numpy.asarray(42, dtype=numpy.float32))
returned_tensor = array.as_tensor(array.get_padding_lengths())
assert returned_tensor.dtype == torch.float32
def test_as_tensor_works_with_scalar(self):
array = TensorField(numpy.asarray(42))
returned_tensor = array.as_tensor(array.get_padding_lengths())
current_tensor = numpy.asarray(42)
numpy.testing.assert_array_equal(returned_tensor, current_tensor)