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base.py
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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: 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.
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Tuple
import numpy as np
import torch
@dataclass
class ChannelMetadata:
"""Metadata describing a data channel."""
name: str
level: str = ""
auxiliary: bool = False
class DownscalingDataset(torch.utils.data.Dataset, ABC):
"""Abstract class for dataset with downscaling paired data
A DownscalingDataset has high-resolution output data (target) paired with
low-resolution input data.
"""
@abstractmethod
def longitude(self) -> np.ndarray:
"""Get longitude values from the dataset."""
pass
@abstractmethod
def latitude(self) -> np.ndarray:
"""Get latitude values from the dataset."""
pass
@abstractmethod
def input_channels(self) -> List[ChannelMetadata]:
"""Metadata for the input channels. A list of ChannelMetadata, one for each channel"""
pass
@abstractmethod
def output_channels(self) -> List[ChannelMetadata]:
"""Metadata for the output channels. A list of ChannelMetadata, one for each channel"""
pass
@abstractmethod
def time(self) -> List:
"""Get time values from the dataset."""
pass
@abstractmethod
def image_shape(self) -> Tuple[int, int]:
"""Get the (height, width) of the data (same for input and output)."""
pass
def normalize_input(self, x: np.ndarray) -> np.ndarray:
"""Convert input from physical units to normalized data."""
return x
def denormalize_input(self, x: np.ndarray) -> np.ndarray:
"""Convert input from normalized data to physical units."""
return x
def normalize_output(self, x: np.ndarray) -> np.ndarray:
"""Convert output from physical units to normalized data."""
return x
def denormalize_output(self, x: np.ndarray) -> np.ndarray:
"""Convert output from normalized data to physical units."""
return x
def info(self) -> dict:
"""Get information about the dataset."""
return {}