def MemoryCleaner(
args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
):
CLass with static methods to clean (gpu) memory.
def virtual(
f:callable
)->callable:
Decorator to enfore subclass method implementations and raises error at method calls.
class A():
def p1(self, x): print("A p1", x)
@virtual
def p2(self, x): pass
class B(A):
def p3(self, x): print("B p2", x)
b = B()
b.p1(1)
try:
b.p2(1)
except BaseException as e:
print("Exception that would be raised: ", e)A p1 1
Exception that would be raised: Virtual method p2 needs to be implemented by subclass B.
def cache_data(
file_name, force_recompute
):
A decorator that memorizes the result of a function and stores it. Note, if the function or its arguments change we ignore it, we only check if the file exists!
Parameters: - file_name (str): The name of the file to store the memoized results. - force_recompute (bool): If True, existing cache is ignored.
def DataLoaders(
dls:list
):
Combines train and valid DataLoader objects.
def infer_torch_device(
):
infer_torch_device()[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.device(type='cuda')
def number_of_paramters(
model:Module
):
def scale_tensor(
t:Tensor
):
[-1,1] to [0,1]
def normalize_tensor(
t:Tensor
):
[0,1] to [-1,1]
def saveSvg(
filename
):
def savePng(
filename
):
def savePdf(
filename
):
def plot_image_grid(
imgs:Union, labels:list=None, labels_fs:str='medium', figsize:tuple=(16, 4), cols:int=8, cmap:str='Greys',
show_colorbar:bool=False, imshow_kwargs:VAR_KEYWORD
):
n = 6
plot_image_grid(torch.randn((n,28,28,1)), [f"label {i}" for i in range(n)])
def latents_to_pil(
latents:Tensor, channels:NoneType=None
):
def set_seed(
seed:int
):
Sets a seed to pytorch, numpy and python. Additionally sets cuda flags.
def get_element_matching_indices(
a:Tensor, b:Tensor
)->Tensor:
Compares (2d) a with b. Returns the indices of b, where a element of a matches with b.
def get_entanglement_bins(
num_of_qubits:int
)->Tuple:
Returns all SRV sorted in entangle bins, corresponding to a number of entangled qubits.
Print the Schmidt-rank-vector bins for 5 qubits:
for srvs,label in zip(*get_entanglement_bins(5)):
print(label, ":", srvs)0 qubit entangled : [[1, 1, 1, 1, 1]]
2 qubit entangled : [[1, 1, 1, 2, 2], [1, 1, 2, 1, 2], [1, 1, 2, 2, 1], [1, 2, 1, 1, 2], [1, 2, 1, 2, 1], [1, 2, 2, 1, 1], [2, 1, 1, 1, 2], [2, 1, 1, 2, 1], [2, 1, 2, 1, 1], [2, 2, 1, 1, 1]]
3 qubit entangled : [[1, 1, 2, 2, 2], [1, 2, 1, 2, 2], [1, 2, 2, 1, 2], [1, 2, 2, 2, 1], [2, 1, 1, 2, 2], [2, 1, 2, 1, 2], [2, 1, 2, 2, 1], [2, 2, 1, 1, 2], [2, 2, 1, 2, 1], [2, 2, 2, 1, 1]]
4 qubit entangled : [[1, 2, 2, 2, 2], [2, 1, 2, 2, 2], [2, 2, 1, 2, 2], [2, 2, 2, 1, 2], [2, 2, 2, 2, 1]]
5 qubit entangled : [[2, 2, 2, 2, 2]]