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| """ | |
| Source url: https://github.com/OPHoperHPO/image-background-remove-tool | |
| Author: Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO]. | |
| License: Apache License 2.0 | |
| """ | |
| import random | |
| import warnings | |
| from typing import Union, Tuple, Any | |
| import torch | |
| from torch import autocast | |
| class EmptyAutocast(object): | |
| """ | |
| Empty class for disable any autocasting. | |
| """ | |
| def __enter__(self): | |
| return None | |
| def __exit__(self, exc_type, exc_val, exc_tb): | |
| return | |
| def __call__(self, func): | |
| return | |
| def get_precision_autocast( | |
| device="cpu", fp16=True, override_dtype=None | |
| ) -> Union[ | |
| Tuple[EmptyAutocast, Union[torch.dtype, Any]], | |
| Tuple[autocast, Union[torch.dtype, Any]], | |
| ]: | |
| """ | |
| Returns precision and autocast settings for given device and fp16 settings. | |
| Args: | |
| device: Device to get precision and autocast settings for. | |
| fp16: Whether to use fp16 precision. | |
| override_dtype: Override dtype for autocast. | |
| Returns: | |
| Autocast object, dtype | |
| """ | |
| dtype = torch.float32 | |
| cache_enabled = None | |
| if device == "cpu" and fp16: | |
| warnings.warn('FP16 is not supported on CPU. Using FP32 instead.') | |
| dtype = torch.float32 | |
| # TODO: Implement BFP16 on CPU. There are unexpected slowdowns on cpu on a clean environment. | |
| # warnings.warn( | |
| # "Accuracy BFP16 has experimental support on the CPU. " | |
| # "This may result in an unexpected reduction in quality." | |
| # ) | |
| # dtype = ( | |
| # torch.bfloat16 | |
| # ) # Using bfloat16 for CPU, since autocast is not supported for float16 | |
| if "cuda" in device and fp16: | |
| dtype = torch.float16 | |
| cache_enabled = True | |
| if override_dtype is not None: | |
| dtype = override_dtype | |
| if dtype == torch.float32 and device == "cpu": | |
| return EmptyAutocast(), dtype | |
| return ( | |
| torch.autocast( | |
| device_type=device, dtype=dtype, enabled=True, cache_enabled=cache_enabled | |
| ), | |
| dtype, | |
| ) | |
| def cast_network(network: torch.nn.Module, dtype: torch.dtype): | |
| """Cast network to given dtype | |
| Args: | |
| network: Network to be casted | |
| dtype: Dtype to cast network to | |
| """ | |
| if dtype == torch.float16: | |
| network.half() | |
| elif dtype == torch.bfloat16: | |
| network.bfloat16() | |
| elif dtype == torch.float32: | |
| network.float() | |
| else: | |
| raise ValueError(f"Unknown dtype {dtype}") | |
| def fix_seed(seed=42): | |
| """Sets fixed random seed | |
| Args: | |
| seed: Random seed to be set | |
| """ | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| # noinspection PyUnresolvedReferences | |
| torch.backends.cudnn.deterministic = True | |
| # noinspection PyUnresolvedReferences | |
| torch.backends.cudnn.benchmark = False | |
| return True | |
| def suppress_warnings(): | |
| # Suppress PyTorch 1.11.0 warning associated with changing order of args in nn.MaxPool2d layer, | |
| # since source code is not affected by this issue and there aren't any other correct way to hide this message. | |
| warnings.filterwarnings( | |
| "ignore", | |
| category=UserWarning, | |
| message="Note that order of the arguments: ceil_mode and " | |
| "return_indices will changeto match the args list " | |
| "in nn.MaxPool2d in a future release.", | |
| module="torch", | |
| ) | |