MemoryCleaner ()
CLass with static methods to clean (gpu) memory.
virtual (f:<built-infunctioncallable>)
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.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.*
DataLoaders (*dls:list[torch.utils.data.dataloader.DataLoader])
Combines train and valid DataLoader objects.
infer_torch_device ()
infer_torch_device()[INFO]: Cuda device has a capability of 8.6 (>= 8), allowing tf32 matmul.device(type='cuda')number_of_paramters (model:torch.nn.modules.module.Module)
scale_tensor (t:torch.Tensor)
[-1,1] to [0,1]
normalize_tensor (t:torch.Tensor)
[0,1] to [-1,1]
saveSvg (filename)
savePng (filename)
savePdf (filename)
plot_image_grid (imgs:Union[list,<built-infunctionarray>,torch.Tensor], labels:list=None, labels_fs='medium', figsize=(16, 4), cols=8, cmap='Greys', show_colorbar=False, **imshow_kwargs)
n = 6
plot_image_grid(torch.randn((n,28,28,1)), [f"label {i}" for i in range(n)])
latents_to_pil (latents:torch.Tensor, channels=None)
set_seed (seed:int)
Sets a seed to pytorch, numpy and python. Additionally sets cuda flags.
get_element_matching_indices (a:torch.Tensor, b:torch.Tensor)
Compares (2d) a with b. Returns the indices of b, where a element of a matches with b.
get_entanglement_bins (num_of_qubits:int)
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]]