def test_torch_tensor(self):
log("Testing: torch_tensor")
self.s.x = pymm.torch_tensor((10, ))
n = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
# shelf type S
self.assertTrue(
str(type(self.s.x)) ==
"<class 'pymm.torch_tensor.shelved_torch_tensor'>")
self.s.x.fill(1)
self.assertTrue(self.s.x[0] == 1)
# shelf type S after in-place operation
self.assertTrue(
str(type(self.s.x)) ==
"<class 'pymm.torch_tensor.shelved_torch_tensor'>")
# shelf type S * NS (non-shelf type)
self.assertTrue(str(type(self.s.x * n)) == "<class 'torch.Tensor'>")
# shelf type NS * S
self.assertTrue(str(type(n * self.s.x)) == "<class 'torch.Tensor'>")
# shelf type S * shelf type S
self.assertTrue(
str(type(self.s.x * self.s.x)) == "<class 'torch.Tensor'>")
self.s.x += 1
self.s.x *= 2
self.s.x -= 0.4
self.s.x /= 2
self.s.erase('x')
log("Testing: torch_tensor OK")
def test_torch_tensor(self):
log("Testing: torch.tensor RHS ...")
self.s.w = torch.tensor((100, 100, 100), dtype=torch.uint8)
self.s.w = torch.tensor([2, 2, 2], dtype=torch.uint8)
w = torch.tensor([2, 2, 2], dtype=torch.uint8)
self.s.w.fill_(9)
w.fill_(9)
print("self.s.w = {}".format(self.s.w))
print("w = {}".format(w))
self.assertTrue(torch.equal(self.s.w, w))
log("Testing: pymm.ndarray RHS ...")
#self.s.x = pymm.torch_tensor((100,100,100),dtype=torch.uint8)
self.s.x = pymm.torch_tensor([[1, 1, 1, 1], [2, 2, 2, 2]],
dtype=torch.uint8)
x = torch.tensor([[1, 1, 1, 1], [2, 2, 2, 2]], dtype=torch.uint8)
print("self.s.x =\n {}".format(self.s.x))
print("x =\n {}".format(x))
self.assertTrue(torch.equal(self.s.x, x))
log("Testing: ndarray OK!")
def test_torch_tensor_shadow_ndarray_B(self):
log("Testing: tensor ctor")
print(shelf.y)
shelf.y = pymm.torch_tensor(np.arange(0,10))
shelf.y.fill(-1.3)
print(shelf.y)
def test_torch_zerodim_shadow(self):
log("Testing: zero dim shadow")
shelf.x = pymm.torch_tensor(1.0)
self.assertTrue(shelf.x.dim() == 0)
print(type(shelf.x))
self.assertTrue(str(type(shelf.x)) == "<class 'pymm.torch_tensor.shelved_torch_tensor'>")
def test_torch_tensor_shadow_list(self):
log("Testing: tensor ctor")
shelf.x = pymm.torch_tensor([1,1,1,1,1])
print(shelf.x)