mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-11-03 08:14:29 +08:00
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Utility functions for PyTorch tensors."""
|
|
|
|
from collections import abc
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
__all__ = ["numpy_to_torch", "torch_detach", "torch_to", "torch_to_numpy"]
|
|
|
|
|
|
def torch_to(data, *args, **kwargs):
|
|
"""Try to recursively move the data to the specified args/kwargs."""
|
|
if isinstance(data, torch.Tensor):
|
|
return data.to(*args, **kwargs)
|
|
elif isinstance(data, (tuple, list)):
|
|
return type(data)([torch_to(val, *args, **kwargs) for val in data])
|
|
elif isinstance(data, abc.Mapping):
|
|
return {k: torch_to(val, *args, **kwargs) for k, val in data.items()}
|
|
return data
|
|
|
|
|
|
def torch_detach(data):
|
|
"""Try to recursively detach the data from the computation graph."""
|
|
if isinstance(data, torch.Tensor):
|
|
return torch.detach(data)
|
|
elif isinstance(data, (tuple, list)):
|
|
return type(data)([torch_detach(val) for val in data])
|
|
elif isinstance(data, abc.Mapping):
|
|
return {k: torch_detach(val) for k, val in data.items()}
|
|
return data
|
|
|
|
|
|
def torch_to_numpy(inputs: list[torch.Tensor]) -> list[np.ndarray]:
|
|
"""Convert torch tensors to numpy arrays."""
|
|
return [t.detach().cpu().numpy() for t in inputs]
|
|
|
|
|
|
def numpy_to_torch(np_outputs: list[np.ndarray]) -> list[torch.Tensor]:
|
|
"""Convert numpy arrays to torch tensors."""
|
|
return [torch.from_numpy(arr) for arr in np_outputs]
|