def test_base(self):
a = sail.random.uniform(10, 11, (4, 1))
a_np = a.numpy()
b = sail.broadcast_to(a, (4, 4))
for i in range(4):
for j in range(4):
self.assert_eq_np_sail(a_np[i][0], b[i][j])
a = sail.random.uniform(10, 11, (4))
a_np = a.numpy()
b = sail.broadcast_to(a, (4, 4))
for i in range(4):
self.assert_eq_np_sail(a_np, b[i])
a = sail.random.uniform(10, 11, (10, 12, 4, 1, 45))
b = sail.broadcast_to(a, (10, 12, 4, 10, 45))
a_np = a.numpy()
b_np = np.broadcast_to(a_np, (10, 12, 4, 10, 45))
self.assert_eq_np_sail(a_np, a)
self.assert_eq_np_sail(b_np, b)
def test_broadcast(self):
choices = elementwise_options
times = []
for c in choices:
for i in range(0, len(c)):
b = list(c)
c_ = list(c)
c_[i - 1] = 1
x1 = sail.random.uniform(10, 20, c_)
x1 = sail.broadcast_to(x1, c)
x2 = sail.random.uniform(10, 20, c)
x3 = sail.stack([x1, x2], axis=i)
arr3 = np.stack([x1.numpy(), x2.numpy()], axis=i)
self.assert_eq_np_sail(arr3, x3)
def test_broadcast(self):
choices = elementwise_options
times = []
for c in choices:
for i in range(0, len(c)):
b = list(c)
b[i] = random.randint(min(b), max(c) + 10)
c_ = list(c)
c_[i - 1] = 1
x1 = sail.random.uniform(10, 20, c_)
x1 = sail.broadcast_to(x1, c)
x2 = sail.random.uniform(10, 20, b)
x3 = sail.cat([x1, x2], axis=i)
arr3 = np.concatenate([x1.numpy(), x2.numpy()], axis=i)
self.assert_eq_np_sail(arr3, x3)
def test_broadcast_float(self, rq):
choices = broadcasted_options
times = []
for c in choices:
for i in range(len(c)):
b = list(c)
b[i] = 1
arr1 = np.random.uniform(0, 1, (b)) #.astype(np.float32)
x1 = sail.Tensor(arr1, requires_grad=rq)
x1 = sail.broadcast_to(x1, c)
arr1 = np.broadcast_to(arr1, c)
x3 = 1.1 - x1 - 2.2
arr3 = 1.1 - arr1 - 2.2
self.assert_eq_np_sail(arr3, x3)
self.assert_eq(x3.requires_grad, rq)
return
def test_broadcast(self):
choices = unary_broadcasted_options
times = []
for c in choices:
for i in range(len(c)):
b = list(c)
b[i] = 1
arr1 = np.random.uniform(1, 2, (b))
x1 = sail.Tensor(arr1, requires_grad=False)
x2 = sail.broadcast_to(x1, c)
arr2 = np.broadcast_to(arr1, c)
t = time.time()
x3 = -x2
times.append(time.time() - t)
arr3 = -arr2
self.assert_eq_np_sail(arr3, x3, eps=1e-6)
return
Please reference :ref:`view-or-copy` to understand the view and copy semantics of mutation ops. Args: tensor (Tensor): Input tensor axis ((tuple of ints)): New order of axes Examples: >>> x = sail.random.uniform(2, 4, (2, 3, 4)) >>> y = sail.transpose(x, (2, 0, 1)) >>> y.shape (4, 2, 3) """ add_docstring(sail.transpose, descr) descr = r""" sail.broadcast_to(tensor, shape) -> Tensor Returns a broadcasted view of `tensor` .. note:: In order for broadcasting to work, `tensor`'s shape must be compatible with `shape`. Compatability means that, starting from the last shape to the first, the size of the shapes between ``tensor.shape`` and `shape` are either equal, or one of the shapes is 1 or doesn't exist. Please reference :ref:`view-or-copy` to understand the view and copy semantics of mutation ops. Args: tensor (Tensor): Input tensor shape (int or tuple of ints): Shape to broadcast `tensor` to Examples: >>> x = sail.random.uniform(0, 1, (1, 5)) >>> y = sail.broadcast_to(x, (5, 5))
def test_general(self):
x = np.ones((32, 32)).astype(np.int32)
x *= 10
x[16:] *= 20
comparison = '''
tensor([[ 10 10 10 ... 10
10 10]
[ 10 10 10 ... 10
10 10]
[ 10 10 10 ... 10
10 10]
...
[200 200 200 ... 200
200 200]
[200 200 200 ... 200
200 200]
[200 200 200 ... 200
200 200]], shape=(32, 32))'''
comparison = comparison[1:]
comparison = comparison
x = sail.Tensor(x)
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.ones((32)).astype(np.float64)
x *= -25.6
x[16:] *= 13.8
comparison = '''
tensor([ -25.6 -25.6 -25.6 -25.6 -25.6 -25.6 -25.6 -25.6 -25.6 -25.6
-25.6 -25.6 -25.6 -25.6 -25.6 -25.6 -353.28 -353.28 -353.28 -353.28
-353.28 -353.28 -353.28 -353.28 -353.28 -353.28 -353.28 -353.28 -353.28 -353.28
-353.28 -353.28], shape=(32))'''
comparison = comparison[1:]
x = sail.Tensor(x)
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.ones((32)).astype(np.int32)
x *= -1000000000
comparison = '''
tensor([-1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000
-1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000
-1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000 -1000000000
-1000000000 -1000000000], shape=(32))'''
comparison = comparison[1:]
x = sail.Tensor(x)
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.ones(1)
comparison = '''
tensor([[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(10, 10))'''
comparison = comparison[1:]
x = sail.broadcast_to(sail.Tensor(x), (10, 10))
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.ones(10)
comparison = '''
tensor([[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(10, 10))'''
comparison = comparison[1:]
x = sail.broadcast_to(sail.Tensor(x), (10, 10))
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.array([1.25234312345])
comparison = '''
tensor([[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]
[1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312 1.25234312]], shape=(10, 10))'''
comparison = comparison[1:]
x = sail.broadcast_to(sail.Tensor(x), (10, 10))
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
x = np.array([1.25234312345])
comparison = '''tensor([1.25234312], shape=(1))'''
x = sail.Tensor(x)
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
comparison = '''
tensor([ True False True True True False False False True True
False False True True False False True True False True
True False False False False False True], shape=(27))'''
comparison = comparison[1:]
x = sail.Tensor(np.array([1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0,0,0,0,1])).astype(sail.bool_)
self.assert_eq(x.__repr__().replace(" ", ""), comparison.replace(" ", ""))
