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_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_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_cltorch():
if 'ALLOW_NON_GPUS' in os.environ:
PyClTorch.setAllowNonGpus(True)
# a = PyTorch.foo(3,2)
# print('a', a)
# print(PyTorch.FloatTensor(3,2))
a = PyClTorch.ClTensor([3, 4, 9])
assert a[0] == 3
assert a[1] == 4
assert a[2] == 9
print('a', a)
a = PyClTorch.ClTensor([[3, 5, 7], [9, 2, 4]])
print('a', a)
print('a[0]', a[0])
print('a[0][0]', a[0][0])
assert a[0][0] == 3
assert a[1][0] == 9
assert a[1][2] == 4
PyTorch.manualSeed(123)
a = PyTorch.FloatTensor(4, 3).uniform()
print('a', a)
a_cl = a.cl()
print(type(a_cl))
assert str(type(a_cl)) == '<class \'PyClTorch.ClTensor\'>'
print('a_cl[0]', a_cl[0])
print('a_cl[0][0]', a_cl[0][0])
assert a[0][0] == a_cl[0][0]
assert a[0][1] == a_cl[0][1]
assert a[1][1] == a_cl[1][1]
print('a.dims()', a.dims())
print('a.size()', a.size())
print('a', a)
assert a.dims() == 2
assert a.size()[0] == 4
assert a.size()[1] == 3
a_sum = a.sum()
a_cl_sum = a_cl.sum()
assert abs(a_sum - a_cl_sum) < 1e-4
a_cl2 = a_cl + 3.2
assert abs(a_cl2[1][0] - a[1][0] - 3.2) < 1e-4
b = PyClTorch.ClTensor()
print('got b')
myeval('b')
assert b.dims() == -1
b.resizeAs(a)
myeval('b')
assert b.dims() == 2
assert b.size()[0] == 4
assert b.size()[1] == 3
print('run uniform')
b.uniform()
myeval('b')
print('create new b')
b = PyClTorch.ClTensor()
print('b.dims()', b.dims())
print('b.size()', b.size())
print('b', b)
c = PyTorch.FloatTensor().cl()
print('c.dims()', c.dims())
print('c.size()', c.size())
print('c', c)
assert b.dims() == -1
assert b.size() is None
print('creating Linear...')
linear = nn.Linear(3, 5).float()
print('created linear')
print('linear:', linear)
myeval('linear.output')
myeval('linear.output.dims()')
myeval('linear.output.size()')
myeval('linear.output.nElement()')
linear_cl = linear.clone().cl()
print('type(linear.output)', type(linear.output))
print('type(linear_cl.output)', type(linear_cl.output))
assert str(type(linear.output)) == '<class \'PyTorch._FloatTensor\'>'
assert str(type(linear_cl.output)) == '<class \'PyClTorch.ClTensor\'>'
# myeval('type(linear)')
# myeval('type(linear.output)')
myeval('linear_cl.output.dims()')
myeval('linear_cl.output.size()')
# myeval('linear.output')
assert str(type(linear)) == '<class \'PyTorchAug.Linear\'>'
assert str(type(linear_cl)) == '<class \'PyTorchAug.Linear\'>'
# assert str(type(linear.output)) == '<class \'PyClTorch.ClTensor\'>'
# assert linear.output.dims() == -1 # why is this 0? should be -1???
# assert linear.output.size() is None # again, should be None?
a_cl = PyClTorch.ClTensor(4, 3).uniform()
# print('a_cl', a_cl)
output_cl = linear_cl.forward(a_cl)
# print('output', output)
assert str(type(output_cl)) == '<class \'PyClTorch.ClTensor\'>'
assert output_cl.dims() == 2
assert output_cl.size()[0] == 4
assert output_cl.size()[1] == 5
a = a_cl.float()
output = linear.forward(a)
assert str(type(output)) == '<class \'PyTorch._FloatTensor\'>'
assert output.dims() == 2
assert output.size()[0] == 4
assert output.size()[1] == 5
print('a.size()', a.size())
print('a_cl.size()', a_cl.size())
assert a[1][0] == a_cl[1][0]
assert a[2][1] == a_cl[2][1]
mlp = nn.Sequential()
mlp.add(nn.SpatialConvolutionMM(1, 16, 5, 5, 1, 1, 2, 2))
mlp.add(nn.ReLU())
mlp.add(nn.SpatialMaxPooling(3, 3, 3, 3))
mlp.add(nn.SpatialConvolutionMM(16, 32, 5, 5, 1, 1, 2, 2))
mlp.add(nn.ReLU())
mlp.add(nn.SpatialMaxPooling(2, 2, 2, 2))
mlp.add(nn.Reshape(32 * 4 * 4))
mlp.add(nn.Linear(32 * 4 * 4, 150))
mlp.add(nn.Tanh())
mlp.add(nn.Linear(150, 10))
mlp.add(nn.LogSoftMax())
mlp.float()
mlp_cl = mlp.clone().cl()
print('mlp_cl', mlp_cl)
# myeval('mlp.output')
input = PyTorch.FloatTensor(128, 1, 28, 28).uniform()
input_cl = PyClTorch.FloatTensorToClTensor(
input.clone()) # This is a bit hacky...
output = mlp.forward(input)
# myeval('input[0]')
output_cl = mlp_cl.forward(input_cl)
# myeval('output[0]')
assert (output_cl.float() - output).abs().max() < 1e-4
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')
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_cltorch():
if "ALLOW_NON_GPUS" in os.environ:
PyClTorch.setAllowNonGpus(True)
# a = PyTorch.foo(3,2)
# print('a', a)
# print(PyTorch.FloatTensor(3,2))
a = PyClTorch.ClTensor([3, 4, 9])
assert a[0] == 3
assert a[1] == 4
assert a[2] == 9
print("a", a)
a = PyClTorch.ClTensor([[3, 5, 7], [9, 2, 4]])
print("a", a)
print("a[0]", a[0])
print("a[0][0]", a[0][0])
assert a[0][0] == 3
assert a[1][0] == 9
assert a[1][2] == 4
PyTorch.manualSeed(123)
a = PyTorch.FloatTensor(4, 3).uniform()
print("a", a)
a_cl = a.cl()
print(type(a_cl))
assert str(type(a_cl)) == "<class 'PyClTorch.ClTensor'>"
print("a_cl[0]", a_cl[0])
print("a_cl[0][0]", a_cl[0][0])
assert a[0][0] == a_cl[0][0]
assert a[0][1] == a_cl[0][1]
assert a[1][1] == a_cl[1][1]
print("a.dims()", a.dims())
print("a.size()", a.size())
print("a", a)
assert a.dims() == 2
assert a.size()[0] == 4
assert a.size()[1] == 3
a_sum = a.sum()
a_cl_sum = a_cl.sum()
assert abs(a_sum - a_cl_sum) < 1e-4
a_cl2 = a_cl + 3.2
assert abs(a_cl2[1][0] - a[1][0] - 3.2) < 1e-4
b = PyClTorch.ClTensor()
print("got b")
myeval("b")
assert b.dims() == -1
b.resizeAs(a)
myeval("b")
assert b.dims() == 2
assert b.size()[0] == 4
assert b.size()[1] == 3
print("run uniform")
b.uniform()
myeval("b")
print("create new b")
b = PyClTorch.ClTensor()
print("b.dims()", b.dims())
print("b.size()", b.size())
print("b", b)
c = PyTorch.FloatTensor().cl()
print("c.dims()", c.dims())
print("c.size()", c.size())
print("c", c)
assert b.dims() == -1
assert b.size() is None
print("creating Linear...")
linear = nn.Linear(3, 5).float()
print("created linear")
print("linear:", linear)
myeval("linear.output")
myeval("linear.output.dims()")
myeval("linear.output.size()")
myeval("linear.output.nElement()")
linear_cl = linear.clone().cl()
print("type(linear.output)", type(linear.output))
print("type(linear_cl.output)", type(linear_cl.output))
assert str(type(linear.output)) == "<class 'PyTorch._FloatTensor'>"
assert str(type(linear_cl.output)) == "<class 'PyClTorch.ClTensor'>"
# myeval('type(linear)')
# myeval('type(linear.output)')
myeval("linear_cl.output.dims()")
myeval("linear_cl.output.size()")
# myeval('linear.output')
assert str(type(linear)) == "<class 'PyTorchAug.Linear'>"
assert str(type(linear_cl)) == "<class 'PyTorchAug.Linear'>"
# assert str(type(linear.output)) == '<class \'PyClTorch.ClTensor\'>'
# assert linear.output.dims() == -1 # why is this 0? should be -1???
# assert linear.output.size() is None # again, should be None?
a_cl = PyClTorch.ClTensor(4, 3).uniform()
# print('a_cl', a_cl)
output_cl = linear_cl.forward(a_cl)
# print('output', output)
assert str(type(output_cl)) == "<class 'PyClTorch.ClTensor'>"
assert output_cl.dims() == 2
assert output_cl.size()[0] == 4
assert output_cl.size()[1] == 5
a = a_cl.float()
output = linear.forward(a)
assert str(type(output)) == "<class 'PyTorch._FloatTensor'>"
assert output.dims() == 2
assert output.size()[0] == 4
assert output.size()[1] == 5
print("a.size()", a.size())
print("a_cl.size()", a_cl.size())
assert a[1][0] == a_cl[1][0]
assert a[2][1] == a_cl[2][1]
mlp = nn.Sequential()
mlp.add(nn.SpatialConvolutionMM(1, 16, 5, 5, 1, 1, 2, 2))
mlp.add(nn.ReLU())
mlp.add(nn.SpatialMaxPooling(3, 3, 3, 3))
mlp.add(nn.SpatialConvolutionMM(16, 32, 5, 5, 1, 1, 2, 2))
mlp.add(nn.ReLU())
mlp.add(nn.SpatialMaxPooling(2, 2, 2, 2))
mlp.add(nn.Reshape(32 * 4 * 4))
mlp.add(nn.Linear(32 * 4 * 4, 150))
mlp.add(nn.Tanh())
mlp.add(nn.Linear(150, 10))
mlp.add(nn.LogSoftMax())
mlp.float()
mlp_cl = mlp.clone().cl()
print("mlp_cl", mlp_cl)
# myeval('mlp.output')
input = PyTorch.FloatTensor(128, 1, 28, 28).uniform()
input_cl = PyClTorch.FloatTensorToClTensor(input.clone()) # This is a bit hacky...
output = mlp.forward(input)
# myeval('input[0]')
output_cl = mlp_cl.forward(input_cl)
# myeval('output[0]')
assert (output_cl.float() - output).abs().max() < 1e-4
print('D after resize:\n', D)
print('resize1d', PyTorch.{{Real}}Tensor().resize1d(3).fill(1))
print('resize2d', PyTorch.{{Real}}Tensor().resize2d(2, 3).fill(1))
print('resize', PyTorch.{{Real}}Tensor().resize(size).fill(1))
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')
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')
def test_refcount():
D = PyTorch.FloatTensor(1000, 1000).fill(1)
myeval('D.isContiguous()')
myeval('D.refCount')
assert D.refCount == 1
print('\nget storage into Ds')
Ds = D.storage()
myeval('D.refCount')
myeval('Ds.refCount')
assert D.refCount == 1
assert Ds.refCount == 2
print('\nget E')
E = D.narrow(1, 100, 800)
myeval('Ds.refCount')
myeval('E.isContiguous()')
myeval('D.refCount')
myeval('E.refCount')
assert Ds.refCount == 3
assert E.refCount == 1
assert D.refCount == 1
print('\nget Es')
Es = E.storage()
myeval('Ds.refCount')
myeval('Es.refCount')
myeval('E.isContiguous()')
myeval('D.refCount')
myeval('E.refCount')
assert Es.refCount == 4
assert Ds.refCount == 4
assert E.refCount == 1
assert D.refCount == 1
print('\nget Ec')
Ec = E.contiguous()
myeval('Ds.refCount')
myeval('Es.refCount')
myeval('D.refCount')
myeval('E.refCount')
myeval('Ec.refCount')
assert Es.refCount == 4
assert Ds.refCount == 4
assert E.refCount == 1
assert D.refCount == 1
assert Ec.refCount == 1
print('\nget Ecs')
Ecs = Ec.storage()
myeval('Ds.refCount')
myeval('Es.refCount')
myeval('D.refCount')
myeval('E.refCount')
myeval('Ec.refCount')
myeval('Ecs.refCount')
assert Es.refCount == 4
assert Ds.refCount == 4
assert E.refCount == 1
assert D.refCount == 1
assert Ec.refCount == 1
assert Ecs.refCount == 2
Dc = D.contiguous()
print('\nafter creating Dc')
myeval('D.refCount')
myeval('Dc.refCount')
myeval('Ds.refCount')
assert D.refCount == 2
assert Dc.refCount == 2
assert Ds.refCount == 4
Dcs = Dc.storage()
print('\n after get Dcs')
assert D.refCount == 2
assert Dc.refCount == 2
myeval('Ds.refCount')
myeval('Dcs.refCount')
assert Ds.refCount == 5
assert Dcs.refCount == 5
D = None
E = None
Ec = None
Dc = None
gc.collect()
print('\n after setting tensors to None')
myeval('Ds.refCount')
myeval('Es.refCount')
myeval('Ecs.refCount')
assert Dcs.refCount == 3
assert Ds.refCount == 3
assert Es.refCount == 3
assert Ecs.refCount == 1