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import os
import datetime
import numpy as np
import torch
from torch.utils.data import random_split,DataLoader
from lightning import LightningDataModule,seed_everything
from .sevir_const import (
SEVIR_ROOT_DIR,SEVIR_ROOT_LR_DIR,
SEVIR_CATALOG,SEVIR_LR_CATALOG,SEVIR_LR_FILTERED_CATALOG,
SEVIR_DATA_DIR,SEVIR_LR_DATA_DIR,
SEVIR_RAW_SEQ_LEN,SEVIR_LR_RAW_SEQ_LEN,
SEVIR_INTERVAL_REAL_TIME,SEVIR_LR_INTERVAL_REAL_TIME,
SEVIR_H_W_SIZE,SEVIR_LR_H_W_SIZE
)
from .sevir_torch_dataset import SEVIRTorchDataset
class SEVIRLightningDataModule(LightningDataModule):
def __init__(
self,
seq_len: int = 25,stride: int = 12,
sample_mode: str = "sequent",
layout: str = "NTHWC",
output_type = np.float32,
preprocess: bool = True,
rescale_method: str = "01",
verbose: bool = False,
aug_mode: str = "0",
ret_contiguous: bool = True,
# region Datamodule Config
dataset_name:Literal['sevir','sevirlr'] = "sevir",
sevir_dir: str = None,
start_date: Tuple[int] = None,
train_test_split_date: Tuple[int] = (2019, 6, 1),
end_date: Tuple[int] = None,
val_ratio: float = 0.1,
batch_size: int = 1,
num_workers: int = 1,
seed: int = 0,
# endregion
):
super(SEVIRLightningDataModule, self).__init__()
# region Data Formatting
self.seq_len = seq_len
self.stride = stride
self.sample_mode = sample_mode
assert layout[0] == "N"
self.layout = layout.replace("N", "")
# endregion
# region Operation Config
self.output_type = output_type
self.preprocess = preprocess
self.rescale_method = rescale_method
self.verbose = verbose
self.aug_mode = aug_mode
self.ret_contiguous = ret_contiguous
self.batch_size = batch_size
self.num_workers = num_workers
self.seed = seed
# endregion
self._config_sevir(sevir_dir,dataset_name)
# train val test split
self.start_date = datetime.datetime(*start_date) \
if start_date is not None else None
self.train_test_split_date = datetime.datetime(*train_test_split_date) \
if train_test_split_date is not None else None
self.end_date = datetime.datetime(*end_date) \
if end_date is not None else None
self.val_ratio = val_ratio
def _config_sevir(self,sevir_dir:Optional[str], dataset_name:Literal['sevir','sevirlr']):
assert dataset_name in ['sevir','sevirlr','filtered_sevirlr'], f"Unknown dataset configuration {dataset_name}"
sevir_dir = os.path.abspath(sevir_dir) if sevir_dir is not None else None
if dataset_name == "sevir":
if sevir_dir is None:
sevir_dir = SEVIR_ROOT_DIR
catalog_path = SEVIR_CATALOG
raw_data_dir = SEVIR_DATA_DIR
raw_seq_len = SEVIR_RAW_SEQ_LEN
interval_real_time = SEVIR_INTERVAL_REAL_TIME
img_height,img_width = SEVIR_H_W_SIZE
elif dataset_name == "sevirlr":
if sevir_dir is None:
sevir_dir = SEVIR_ROOT_LR_DIR
catalog_path = SEVIR_LR_CATALOG
raw_data_dir = SEVIR_LR_DATA_DIR
raw_seq_len = SEVIR_LR_RAW_SEQ_LEN
interval_real_time = SEVIR_LR_INTERVAL_REAL_TIME
img_height,img_width = SEVIR_LR_H_W_SIZE
elif dataset_name == "filtered_sevirlr":
if sevir_dir is None:
sevir_dir = SEVIR_ROOT_LR_DIR
catalog_path = SEVIR_LR_FILTERED_CATALOG
raw_data_dir = SEVIR_LR_DATA_DIR
raw_seq_len = SEVIR_LR_RAW_SEQ_LEN
interval_real_time = SEVIR_LR_INTERVAL_REAL_TIME
img_height,img_width = SEVIR_LR_H_W_SIZE
else:
raise ValueError(f"Wrong dataset name {dataset_name}. Must be 'sevir' or 'sevirlr'.")
self.dataset_name = dataset_name
self.sevir_dir = sevir_dir
self.catalog_path = catalog_path
self.raw_data_dir = raw_data_dir
self.raw_seq_len = raw_seq_len
self.interval_real_time = interval_real_time
self.img_height = img_height
self.img_width = img_width
def prepare_data(self) -> None:
if os.path.exists(self.sevir_dir):
# Further check
assert os.path.exists(self.catalog_path), f"CATALOG.csv not found! Should be located at {self.catalog_path}"
assert os.path.exists(self.raw_data_dir), f"SEVIR data not found! Should be located at {self.raw_data_dir}"
else:
raise NotImplementedError(f'Data not available in specified directory {self.sevir_dir}')
def setup(self, stage = None) -> None:
seed_everything(seed=self.seed)
if stage in (None, "fit"):
sevir_train_val = SEVIRTorchDataset(
sevir_catalog=self.catalog_path,
sevir_data_dir=self.raw_data_dir,
raw_seq_len=self.raw_seq_len,
split_mode="uneven",
shuffle=True,
seq_len=self.seq_len,
stride=self.stride,
sample_mode=self.sample_mode,
layout=self.layout,
start_date=self.start_date,
end_date=self.train_test_split_date,
output_type=self.output_type,
preprocess=self.preprocess,
rescale_method=self.rescale_method,
verbose=self.verbose,
aug_mode=self.aug_mode,
ret_contiguous=self.ret_contiguous,
)
self.sevir_train, self.sevir_val = random_split(
dataset=sevir_train_val,
lengths=[1 - self.val_ratio, self.val_ratio],
generator=torch.Generator().manual_seed(self.seed)
)
if stage in (None, "test"):
self.sevir_test = SEVIRTorchDataset(
sevir_catalog=self.catalog_path,
sevir_data_dir=self.raw_data_dir,
raw_seq_len=self.raw_seq_len,
split_mode="uneven",
shuffle=False,
seq_len=self.seq_len,
stride=self.stride,
sample_mode=self.sample_mode,
layout=self.layout,
start_date=self.train_test_split_date,
end_date=self.end_date,
output_type=self.output_type,
preprocess=self.preprocess,
rescale_method=self.rescale_method,
verbose=self.verbose,
aug_mode="0",
ret_contiguous=self.ret_contiguous,
)
def train_dataloader(self):
return DataLoader(
self.sevir_train,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers
)
def val_dataloader(self):
return DataLoader(
self.sevir_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers
)
def test_dataloader(self):
return DataLoader(
self.sevir_test,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers
)
@property
def num_train_samples(self):
return len(self.sevir_train)
@property
def num_val_samples(self):
return len(self.sevir_val)
@property
def num_test_samples(self):
return len(self.sevir_test)
if __name__ == "__main__":
dm = SEVIRLightningDataModule(
seq_len=22,
sample_mode='sequent',
stride=3,
layout='NTHWC',
aug_mode='2',
ret_contiguous=False,
dataset_name='sevirlr',
start_date=None,
train_test_split_date=[2019, 6, 1],
end_date=None,
val_ratio=0.1,
num_workers=8
)
dm.prepare_data()
dm.setup()
for batch in dm.train_dataloader():
print(batch[0].shape)
break
# print(dm.num_train_samples)
# print(dm.num_val_samples)
# print(dm.num_test_samples) |