def test_float_tensor():
PyTorch.manualSeed(123)
print('dir(G)', dir())
print('test_float_tensor')
a = PyTorch.FloatTensor(3, 2)
print('got float a')
myeval('a.dims()')
a.uniform()
myeval('a')
myexec('a[1][1] = 9')
myeval('a')
myeval('a.size()')
def test_long_tensor():
PyTorch.manualSeed(123)
print('test_long_tensor')
a = PyTorch.LongTensor(3, 2).geometric()
print('a', a)
myeval('a')
myexec('a[1][1] = 9')
myeval('a')
myeval('a.size()')
myeval('a + 2')
myexec('a.resize2d(3,3).fill(1)')
myeval('a')
def test_double_tensor():
PyTorch.manualSeed(123)
LongTensor = PyTorch.LongTensor
DoubleTensor = PyTorch.DoubleTensor
LongStorage = PyTorch.LongStorage
print('LongStorage', LongStorage)
print('LongTensor', LongTensor)
print('DoubleTensor', DoubleTensor)
print('dir(G)', dir())
print('test_double_tensor')
a = PyTorch.DoubleTensor(3, 2)
print('got double a')
myeval('a.dims()')
a.uniform()
myeval('a')
myexec('a[1][1] = 9')
myeval('a')
myeval('a.size()')
myeval('a + 2')
myexec('a.resize2d(3,3).fill(1)')
myeval('a')
myexec('size = LongStorage(2)')
myexec('size[0] = 4')
myexec('size[1] = 2')
myexec('a.resize(size)')
myeval('a')
myeval('DoubleTensor(3,4).uniform()')
myeval('DoubleTensor(3,4).bernoulli()')
myeval('DoubleTensor(3,4).normal()')
myeval('DoubleTensor(3,4).cauchy()')
myeval('DoubleTensor(3,4).exponential()')
myeval('DoubleTensor(3,4).logNormal()')
myeval('DoubleTensor(3,4).geometric()')
def test_pytorchByte():
PyTorch.manualSeed(123)
numpy.random.seed(123)
ByteTensor = PyTorch.ByteTensor
D = PyTorch.ByteTensor(5, 3).fill(1)
print('D', D)
D[2][2] = 4
print('D', D)
D[3].fill(9)
print('D', D)
D.narrow(1, 2, 1).fill(0)
print('D', D)
print(PyTorch.ByteTensor(3, 4).bernoulli())
print(PyTorch.ByteTensor(3, 4).geometric())
print(PyTorch.ByteTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.ByteTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.ByteTensor(3, 4).geometric())
print(type(PyTorch.ByteTensor(2, 3)))
size = PyTorch.LongStorage(2)
size[0] = 4
size[1] = 3
D.resize(size)
print('D after resize:\n', D)
print('resize1d', PyTorch.ByteTensor().resize1d(3).fill(1))
print('resize2d', PyTorch.ByteTensor().resize2d(2, 3).fill(1))
print('resize', PyTorch.ByteTensor().resize(size).fill(1))
D = PyTorch.ByteTensor(size).geometric()
# def myeval(expr):
# print(expr, ':', eval(expr))
# def myexec(expr):
# print(expr)
# exec(expr)
myeval('ByteTensor(3,2).nElement()')
myeval('ByteTensor().nElement()')
myeval('ByteTensor(1).nElement()')
A = ByteTensor(3, 4).geometric(0.9)
myeval('A')
myexec('A += 3')
myeval('A')
myexec('A *= 3')
myeval('A')
myeval('A')
print('A //= 3')
A //= 3
myeval('A')
myeval('A + 5')
myeval('A * 5')
print('A // 2')
A // 2
B = ByteTensor().resizeAs(A).geometric(0.9)
myeval('B')
myeval('A + B')
myexec('A += B')
myeval('A')
def test_pytorchFloat():
PyTorch.manualSeed(123)
numpy.random.seed(123)
FloatTensor = PyTorch.FloatTensor
A = numpy.random.rand(6).reshape(3, 2).astype(numpy.float32)
B = numpy.random.rand(8).reshape(2, 4).astype(numpy.float32)
C = A.dot(B)
print('C', C)
print('calling .asTensor...')
tensorA = PyTorch.asFloatTensor(A)
tensorB = PyTorch.asFloatTensor(B)
print(' ... asTensor called')
print('tensorA', tensorA)
tensorA.set2d(1, 1, 56.4)
tensorA.set2d(2, 0, 76.5)
print('tensorA', tensorA)
print('A', A)
print('add 5 to tensorA')
tensorA += 5
print('tensorA', tensorA)
print('A', A)
print('add 7 to tensorA')
tensorA2 = tensorA + 7
print('tensorA2', tensorA2)
print('tensorA', tensorA)
tensorAB = tensorA * tensorB
print('tensorAB', tensorAB)
print('A.dot(B)', A.dot(B))
print('tensorA[2]', tensorA[2])
D = PyTorch.FloatTensor(5, 3).fill(1)
print('D', D)
D[2][2] = 4
print('D', D)
D[3].fill(9)
print('D', D)
D.narrow(1, 2, 1).fill(0)
print('D', D)
print(PyTorch.FloatTensor(3, 4).uniform())
print(PyTorch.FloatTensor(3, 4).normal())
print(PyTorch.FloatTensor(3, 4).cauchy())
print(PyTorch.FloatTensor(3, 4).exponential())
print(PyTorch.FloatTensor(3, 4).logNormal())
print(PyTorch.FloatTensor(3, 4).bernoulli())
print(PyTorch.FloatTensor(3, 4).geometric())
print(PyTorch.FloatTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.FloatTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.FloatTensor(3, 4).geometric())
print(type(PyTorch.FloatTensor(2, 3)))
size = PyTorch.LongStorage(2)
size[0] = 4
size[1] = 3
D.resize(size)
print('D after resize:\n', D)
print('resize1d', PyTorch.FloatTensor().resize1d(3).fill(1))
print('resize2d', PyTorch.FloatTensor().resize2d(2, 3).fill(1))
print('resize', PyTorch.FloatTensor().resize(size).fill(1))
D = PyTorch.FloatTensor(size).geometric()
# def myeval(expr):
# print(expr, ':', eval(expr))
# def myexec(expr):
# print(expr)
# exec(expr)
myeval('FloatTensor(3,2).nElement()')
myeval('FloatTensor().nElement()')
myeval('FloatTensor(1).nElement()')
A = FloatTensor(3, 4).geometric(0.9)
myeval('A')
myexec('A += 3')
myeval('A')
myexec('A *= 3')
myeval('A')
myexec('A -= 3')
myeval('A')
print('A /= 3')
A /= 3
myeval('A')
myeval('A + 5')
myeval('A - 5')
myeval('A * 5')
print('A / 2')
A / 2
B = FloatTensor().resizeAs(A).geometric(0.9)
myeval('B')
myeval('A + B')
myeval('A - B')
myexec('A += B')
myeval('A')
myexec('A -= B')
myeval('A')
D = PyTorch.{{Real}}Tensor(size).geometric()
# def myeval(expr):
# print(expr, ':', eval(expr))
# def myexec(expr):
# print(expr)
# exec(expr)
myeval('{{Real}}Tensor(3,2).nElement()')
myeval('{{Real}}Tensor().nElement()')
myeval('{{Real}}Tensor(1).nElement()')
A = {{Real}}Tensor(3, 4).geometric(0.9)
myeval('A')
myexec('A += 3')
myeval('A')
myexec('A *= 3')
myeval('A')
{% if Real != 'Byte' -%}
myexec('A -= 3')
{% endif -%}
myeval('A')
{% if Real in ['Float', 'Double'] -%}
print('A /= 3')
A /= 3
{% else -%}
print('A //= 3')
A //= 3
{% endif -%}
myeval('A')
def test_pytorchByte():
PyTorch.manualSeed(123)
numpy.random.seed(123)
ByteTensor = PyTorch.ByteTensor
D = PyTorch.ByteTensor(5, 3).fill(1)
print('D', D)
D[2][2] = 4
print('D', D)
D[3].fill(9)
print('D', D)
D.narrow(1, 2, 1).fill(0)
print('D', D)
print(PyTorch.ByteTensor(3, 4).bernoulli())
print(PyTorch.ByteTensor(3, 4).geometric())
print(PyTorch.ByteTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.ByteTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.ByteTensor(3, 4).geometric())
print(type(PyTorch.ByteTensor(2, 3)))
size = PyTorch.LongStorage(2)
size[0] = 4
size[1] = 3
D.resize(size)
print('D after resize:\n', D)
print('resize1d', PyTorch.ByteTensor().resize1d(3).fill(1))
print('resize2d', PyTorch.ByteTensor().resize2d(2, 3).fill(1))
print('resize', PyTorch.ByteTensor().resize(size).fill(1))
D = PyTorch.ByteTensor(size).geometric()
# def myeval(expr):
# print(expr, ':', eval(expr))
# def myexec(expr):
# print(expr)
# exec(expr)
myeval('ByteTensor(3,2).nElement()')
myeval('ByteTensor().nElement()')
myeval('ByteTensor(1).nElement()')
A = ByteTensor(3, 4).geometric(0.9)
myeval('A')
myexec('A += 3')
myeval('A')
myexec('A *= 3')
myeval('A')
myeval('A')
print('A //= 3')
A //= 3
myeval('A')
myeval('A + 5')
myeval('A * 5')
print('A // 2')
A // 2
B = ByteTensor().resizeAs(A).geometric(0.9)
myeval('B')
myeval('A + B')
myexec('A += B')
myeval('A')
def test_pytorchFloat():
PyTorch.manualSeed(123)
numpy.random.seed(123)
FloatTensor = PyTorch.FloatTensor
A = numpy.random.rand(6).reshape(3, 2).astype(numpy.float32)
B = numpy.random.rand(8).reshape(2, 4).astype(numpy.float32)
C = A.dot(B)
print('C', C)
print('calling .asTensor...')
tensorA = PyTorch.asFloatTensor(A)
tensorB = PyTorch.asFloatTensor(B)
print(' ... asTensor called')
print('tensorA', tensorA)
tensorA.set2d(1, 1, 56.4)
tensorA.set2d(2, 0, 76.5)
print('tensorA', tensorA)
print('A', A)
print('add 5 to tensorA')
tensorA += 5
print('tensorA', tensorA)
print('A', A)
print('add 7 to tensorA')
tensorA2 = tensorA + 7
print('tensorA2', tensorA2)
print('tensorA', tensorA)
tensorAB = tensorA * tensorB
print('tensorAB', tensorAB)
print('A.dot(B)', A.dot(B))
print('tensorA[2]', tensorA[2])
D = PyTorch.FloatTensor(5, 3).fill(1)
print('D', D)
D[2][2] = 4
print('D', D)
D[3].fill(9)
print('D', D)
D.narrow(1, 2, 1).fill(0)
print('D', D)
print(PyTorch.FloatTensor(3, 4).uniform())
print(PyTorch.FloatTensor(3, 4).normal())
print(PyTorch.FloatTensor(3, 4).cauchy())
print(PyTorch.FloatTensor(3, 4).exponential())
print(PyTorch.FloatTensor(3, 4).logNormal())
print(PyTorch.FloatTensor(3, 4).bernoulli())
print(PyTorch.FloatTensor(3, 4).geometric())
print(PyTorch.FloatTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.FloatTensor(3, 4).geometric())
PyTorch.manualSeed(3)
print(PyTorch.FloatTensor(3, 4).geometric())
print(type(PyTorch.FloatTensor(2, 3)))
size = PyTorch.LongStorage(2)
size[0] = 4
size[1] = 3
D.resize(size)
print('D after resize:\n', D)
print('resize1d', PyTorch.FloatTensor().resize1d(3).fill(1))
print('resize2d', PyTorch.FloatTensor().resize2d(2, 3).fill(1))
print('resize', PyTorch.FloatTensor().resize(size).fill(1))
D = PyTorch.FloatTensor(size).geometric()
# def myeval(expr):
# print(expr, ':', eval(expr))
# def myexec(expr):
# print(expr)
# exec(expr)
myeval('FloatTensor(3,2).nElement()')
myeval('FloatTensor().nElement()')
myeval('FloatTensor(1).nElement()')
A = FloatTensor(3, 4).geometric(0.9)
myeval('A')
myexec('A += 3')
myeval('A')
myexec('A *= 3')
myeval('A')
myexec('A -= 3')
myeval('A')
print('A /= 3')
A /= 3
myeval('A')
myeval('A + 5')
myeval('A - 5')
myeval('A * 5')
print('A / 2')
A / 2
B = FloatTensor().resizeAs(A).geometric(0.9)
myeval('B')
myeval('A + B')
myeval('A - B')
myexec('A += B')
myeval('A')
myexec('A -= B')
myeval('A')
