def test_reflection_pad_1d(self):
m = ReflectionPad1d(2)
inputs = mnp.arange(8).reshape(1, 2, 4)
outputs = m(inputs)
expected = Tensor([[[2, 1, 0, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 7, 6,
5]]])
assert mnp.array_equal(outputs, expected)
def test_reflection_pad_2d(self):
m = ReflectionPad2d(2)
inputs = mnp.arange(9).reshape(1, 1, 3, 3)
outputs = m(inputs)
expected = Tensor([[[[8, 7, 6, 7, 8, 7, 6], [5, 4, 3, 4, 5, 4, 3],
[2, 1, 0, 1, 2, 1, 0], [5, 4, 3, 4, 5, 4, 3],
[8, 7, 6, 7, 8, 7, 6], [5, 4, 3, 4, 5, 4, 3],
[2, 1, 0, 1, 2, 1, 0]]]])
assert mnp.array_equal(outputs, expected)
def test_constant_pad_1d(self):
m = ConstantPad1d(2, 3.5)
inputs = mnp.arange(8, dtype=mindspore.float32).reshape(1, 2, 4)
outputs = m(inputs)
print(outputs)
expected = Tensor([[[3.5, 3.5, 0., 1., 2., 3., 3.5, 3.5],
[3.5, 3.5, 4., 5., 6., 7., 3.5, 3.5]]],
mindspore.float32)
assert mnp.array_equal(outputs, expected)
m = ConstantPad1d((3, 1), 3.5)
outputs = m(inputs)
expected = Tensor([[[3.5, 3.5, 3.5, 0., 1., 2., 3., 3.5],
[3.5, 3.5, 3.5, 4., 5., 6., 7., 3.5]]],
mindspore.float32)
assert mnp.array_equal(outputs, expected)
def test_reflection_pad_3d(self):
m = ReflectionPad3d(1)
inputs = mnp.arange(8, dtype=mindspore.float32).reshape(1, 1, 2, 2, 2)
print(inputs)
outputs = m(inputs)
print(outputs)
expected = Tensor([[[[[7., 6., 7., 6.], [5., 4., 5., 4.],
[7., 6., 7., 6.], [5., 4., 5., 4.]],
[[3., 2., 3., 2.], [1., 0., 1., 0.],
[3., 2., 3., 2.], [1., 0., 1., 0.]],
[[7., 6., 7., 6.], [5., 4., 5., 4.],
[7., 6., 7., 6.], [5., 4., 5., 4.]],
[[3., 2., 3., 2.], [1., 0., 1., 0.],
[3., 2., 3., 2.], [1., 0., 1., 0.]]]]],
mindspore.float32)
assert mnp.array_equal(outputs, expected)
def test_zero_pad_2d(self):
m = ZeroPad2d(2)
inputs = mnp.arange(9).reshape(1, 1, 3, 3)
outputs = m(inputs)
expected = Tensor([[[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 2, 0, 0], [0, 0, 3, 4, 5, 0, 0],
[0, 0, 6, 7, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]]])
assert mnp.array_equal(outputs, expected)
# using different paddings for different sides
m = ZeroPad2d((1, 1, 2, 0))
outputs = m(inputs)
print(outputs)
expected = Tensor([[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 2, 0],
[0, 3, 4, 5, 0], [0, 6, 7, 8, 0]]]])
assert mnp.array_equal(outputs, expected)
def test_arange():
actual = onp.arange(10)
expected = mnp.arange(10).asnumpy()
match_array(actual, expected)
actual = onp.arange(0, 10)
expected = mnp.arange(0, 10).asnumpy()
match_array(actual, expected)
actual = onp.arange(start=10)
expected = mnp.arange(start=10).asnumpy()
match_array(actual, expected)
actual = onp.arange(start=10, step=0.1)
expected = mnp.arange(start=10, step=0.1).asnumpy()
match_array(actual, expected, error=6)
actual = onp.arange(10, step=0.1)
expected = mnp.arange(10, step=0.1).asnumpy()
match_array(actual, expected, error=6)
actual = onp.arange(0.1, 9.9)
expected = mnp.arange(0.1, 9.9).asnumpy()
match_array(actual, expected, error=6)
def construct(self, input_x):
shape = input_x.shape
dim_size = shape[self.dim]
reversed_indexes = mnp.arange(dim_size - 1, -1, -1)
output = ops.Gather()(input_x, reversed_indexes, self.dim)
return output
def arange(start, stop=None, step=None, dtype=None):
return mnp.arange(start, stop, step, dtype)