+
+
+
+
+
+
+
+
+
+
+
+
+Code using omegaconf to handle IO.
+
+IO
+source
+
+class_to_str
+
+ class_to_str (cls)
+source
+
+
+load_config
+
+ load_config (file_path)
+source
+
+
+config_to_dict
+
+ config_to_dict (config)
+source
+
+
+save_dataclass_yaml
+
+ save_dataclass_yaml (data_obj, file_path)
+source
+
+
+save_dict_yaml
+
+ save_dict_yaml (dict_obj, file_path)
+Test
+
+
@dataclass class MyConfig: str = class_to_str(OmegaConf)int = 80 list [int ]= None = MyConfig()= [1 ,2 ,3 ]
+
+
{'target': 'omegaconf.omegaconf.OmegaConf', 'clr_dim': 80, 'features': [1, 2, 3]}
+
+
+
+
+Object config load
+Mostly taken from: https://github.com/Stability-AI/stablediffusion
+source
+
+get_obj_from_str
+
+ get_obj_from_str (string, reload=False)
+source
+
+
+instantiate_from_config
+
+ instantiate_from_config (config)
+source
+
+
+load_model_from_config
+
+ load_model_from_config (config, ckpt, device)
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+Quantum circuit dataset that caches the y prompts using the CLIP encoder. This speeds up training significantly!
+source
+
+Cached_OpenClip_Dataset
+
+ Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),
+ **parameters)
+Adds .caching to the Quantum circuit dataset class.
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Some comonly used functions for datasets.
+
+Checking
+source
+
+check_duplicate_in_dataset
+
+ check_duplicate_in_dataset (x, dataset)
+Check if ‘x’ is in ‘dataset’
+source
+
+
+check_duplicates_in_dataset
+
+ check_duplicates_in_dataset (xs, dataset, return_ind=False, invert=False)
+Checks if xs is are dataset. Boolean invert changes if we count duplicates (False) or ones that are not in dataset (True). Uses torch.vmap which copies dataset for every element in xs.
+Check if this works:
+
+
= torch.tensor(0.7 , 1 , 0.5 ], 0.3 , 1 , 0.5 ],0 , 1 , 0.5 ]])= torch.tensor([0.11 , 1 , 0.5 ],0.70 , 1 , 0.5 ], #here a dup 0.71 , 1 , 0.5 ],0.3 , 1 , 0.5 ]])= True )
+
+
+
+
+Manipulating
+source
+
+shuffle_tensor_dataset
+
+ shuffle_tensor_dataset (x, y=None, *z)
+Assumes numpy or tensor objects with same length.
+source
+
+
+get_unique_elements_indices
+
+ get_unique_elements_indices (tensor)
+Returns indices of unique_elements in tensor.
+source
+
+
+uniquify_tensor_dataset
+
+ uniquify_tensor_dataset (x, y=None, *z)
+x has to be tensor, assumes numpy or tensor obj for y and z
+source
+
+
+balance_tensor_dataset
+
+ balance_tensor_dataset (x, y, *z, samples:int=None,
+ make_unique:bool=True, y_uniques=None,
+ shuffle_lables:bool=True, add_balance_fn:<built-
+ infunctioncallable>=None)
+Assumes x is tensor and y is tensor or numpy.
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+Dataset that combines and handles multiple cached datasets, e.g. for multiple qubits. Here we also handle paddings.
+source
+
+Mixed_Cached_OpenClip_Dataset_config
+
+ Mixed_Cached_OpenClip_Dataset_config (store_dict:dict, optimized:bool,
+ dataset_to_gpu:bool,
+ random_samples:int,
+ num_of_qubits:int, min_gates:int,
+ max_gates:int, gate_pool:list[str],
+ pad_constant:int, collate_fn:str,
+ bucket_batch_size:int,
+ num_down_scales:int)
+source
+
+
+Mixed_Cached_OpenClip_Dataset
+
+ Mixed_Cached_OpenClip_Dataset (device:torch.device=device(type='cpu'),
+ **parameters)
+Dataset that uses multiple cached dataset and combines them with padding, either i) Bucket or ii) Max. Also provides a corresponding collate_fn for training.
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+Dataset for quantum circuits.
+source
+
+Qc_Config_Dataset_config
+
+ Qc_Config_Dataset_config (store_dict:dict, optimized:bool,
+ dataset_to_gpu:bool, random_samples:int,
+ num_of_qubits:int, min_gates:int,
+ max_gates:int, gate_pool:list[str])
+source
+
+
+Qc_Config_Dataset
+
+ Qc_Config_Dataset (device:torch.device=device(type='cpu'), **parameters)
+Dataset for quantum circuits, access gate_pool directly and all other paras with .params_config
+
+
= {k:None for k in Qc_Config_Dataset.req_params}"gate_pool" ] = ["qiskit.circuit.library.standard_gates.h.HGate" ,"qiskit.circuit.library.standard_gates.x.CXGate" ]"store_dict" ] = {"x" :"tensor" , "y" :"tensor_list" }= Qc_Config_Dataset(** init)
+
+
{'target': '__main__.Qc_Config_Dataset',
+ 'device': 'cpu',
+ 'comment': '',
+ 'save_path': None,
+ 'save_datetime': '12/06/2023 19:06:49',
+ 'params': Qc_Config_Dataset_config(store_dict={'x': 'tensor', 'y': 'tensor_list'}, optimized=None, dataset_to_gpu=None, random_samples=None, num_of_qubits=None, min_gates=None, max_gates=None, gate_pool=['qiskit.circuit.library.standard_gates.h.HGate', 'qiskit.circuit.library.standard_gates.x.CXGate'])}
+
+
+
+
+
+
+
+
+
+
+
+
+
+