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from torch.utils.data import DataLoader, Dataset, random_split
import numpy as np
from datetime import datetime, timedelta
from torchvision import transforms
from lightning import LightningDataModule
from pytorch_lightning.cli import LightningCLI
from torch.utils.data import DataLoader
import pytorch_lightning as L
import torch
import torch.nn as nn
from typing import Tuple, Dict, List
from einops import rearrange
# import optim
class DataReader(Dataset):
def __init__(
self, dir_data : str,
type_data : str,
radar_attribute :List[str] ,
sat_attribute : List[str],
hours_predicted : int,
rad_predicted : str,
sat_predicted : str,
time_points_radar : int,
time_points_sat : int,
short_timestep: bool,
augmentation: bool,
servir_format : bool = False
):
"""
Input:
dir_data: directory to data folder (contain train, test, val)
type: type of dataset (train, test, val)
radar_attribute: list of radar field used
sat_attribute: list of satellite field used
hours_predicted: predicted image's hour
rad_predicted: the attribute of predicted radar image
sat_predicted: the attribute of predicted satellite image
Output:
input_radar: tensor: (number attributes, 400,400)
input_satellite: tensor (number attributes, 25,25)
output_radar: tensor (1, 400,400)
output_satellite: tensor (1, 25,25)
Note:
Each radar image has 2 channels (precipitation and velocity)
Each satellite image has 5 channels (10m_u_component_of_wind, 10m_v_component_of_wind, 2m_dewpoint_temperature, 2m_temperature, total_precipitation)
"""
super().__init__()
self.base_dir=dir_data
self.type_data = type_data
if self.type_data == "train":
self.dir_data=os.path.join(dir_data, "train")
elif self.type_data =="test":
self.dir_data=os.path.join(dir_data, 'test')
elif self.type_data =="val":
self.dir_data=os.path.join(dir_data, 'val')
else:
raise ValueError("Type must be train, test or val")
self.augmentation=augmentation
self.short_timestep=short_timestep
self.hours_predicted = hours_predicted
self.radar_attribute = radar_attribute
self.sat_attribute = sat_attribute
self.rad_predicted = rad_predicted
self.sat_predicted = sat_predicted
self.time_points_radar = time_points_radar
self.time_points_sat = time_points_sat
self.list_transform_radar = None
self.list_transform_satellite = None
self.servir_format = servir_format
# Create path for img
self.dir_img_radar = os.path.join(self.dir_data, "rad")
self.dir_img_satellite = os.path.join(self.dir_data, "sat")
self.rad_mean = np.load(os.path.join(self.base_dir, "rad_mean.npz"))
self.rad_std = np.load(os.path.join(self.base_dir, "rad_std.npz"))
self.sat_mean = np.load(os.path.join(self.base_dir, "sat_mean.npz"))
self.sat_std = np.load(os.path.join(self.base_dir, "sat_std.npz"))
#Create transform
self.create_transform()
#Get list img
self.list_img_radar, self.list_img_satellite= self.gen_list_img(self.dir_img_radar,self.dir_img_satellite)
# self.__len__()
def __len__(self):
# print('lendataset', len(self.list_img_radar))
return len(self.list_img_radar)
def __getitem__(self, idx:int)->Tuple[torch.tensor,torch.tensor,torch.tensor,torch.tensor]:
"""
:param idx: index of data
:return: input_radar (dictionary 2 fileds: 4x400x400 tensor), input_satellite (1 tensor 8x25x25), output_radar (1 tensor x25x25), output_satellite (1 tensor 1x25x25)
"""
#Get input and output
if self.servir_format:
input_radar={}
input_satellite={}
inp_radar=[np.load(self.list_img_radar[idx][i]) for i in range(0,self.time_points_radar)]
#Transform data
input_radar_temp={key: [] for key in self.radar_attribute}
for x in inp_radar:
for key in self.radar_attribute:
input_radar_temp[key].append(self.list_transform_radar[key](x[key]))
for key in self.radar_attribute:
input_radar_temp[key]=torch.cat(input_radar_temp[key],dim=0).float()
input_radar=input_radar_temp
if len(self.radar_attribute)==1:
output = input_radar[self.radar_attribute[0]]
# N,C,T,H,W => NTHWC
target_bchw = rearrange(output, "t h w -> t h w 1").contiguous()
return target_bchw
else:
return [input_radar[key] for key in self.radar_attribute ]
else:
input_radar={}
output_radar={}
input_satellite={}
output_satellite={}
inp_radar=[np.load(self.list_img_radar[idx][i]) for i in range(0,self.time_points_radar)]
inp_satellite=np.load(self.list_img_satellite[idx][0])
out_radar=np.load(self.list_img_radar[idx][-1])
out_satellite=np.load(self.list_img_satellite[idx][-1])
#Transform data
input_radar_temp={key: [] for key in self.radar_attribute}
for x in inp_radar:
for key in self.radar_attribute:
input_radar_temp[key].append(self.list_transform_radar[key](x[key]))
for key in self.radar_attribute:
input_radar_temp[key]=torch.cat(input_radar_temp[key],dim=0).float()
temp_list=[]
for x in self.radar_attribute:
temp_list.append(input_radar_temp[x])
input_radar=torch.stack(temp_list,dim=0)
input_satellite= torch.cat([self.list_transform_satellite[key](inp_satellite[key]).permute(1,2,0) for key in self.sat_attribute],dim=0).float()
output_radar=self.list_transform_radar[self.rad_predicted](out_radar[self.rad_predicted]).float()
output_satellite=self.list_transform_satellite[self.sat_predicted](out_satellite[self.sat_predicted]).permute(1,2,0).float()
return input_radar,input_satellite,output_radar,output_satellite
def create_transform(self):
self.list_transform_radar = {
key : transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(self.rad_mean[key], self.rad_std[key]),
transforms.Resize((200,200))
]
)
for key in self.rad_mean.keys()
}
self.list_transform_satellite ={
key : transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(self.sat_mean[key], self.sat_std[key])
]
)
for key in self.sat_mean.keys()
}
def gen_list_img(self,dir_img_radar: str, dir_img_satellite: str):
if self.servir_format:
radar_train_list_dir = []
satellite_train_list_dir = []
list_radar_img = os.listdir(self.dir_img_radar)
# print(self.dir_img_radar,len(list_radar_img))
for i,name in enumerate(list_radar_img):
name_datetime=self.get_date_time(name)
#Get sattelite image
temp_sattelite = []
timeadd_radar=timedelta(minutes=10*self.time_points_radar)
out_datetime_radar=name_datetime+timeadd_radar
#Get radar image
temp_radar=self.get_input_radar(name_datetime,out_datetime_radar)
# print(len(temp_radar))
if len(temp_radar) == self.time_points_radar:
radar_train_list_dir.append(temp_radar)
return radar_train_list_dir,[]
else:
radar_train_list_dir = []
satellite_train_list_dir = []
list_radar_img = os.listdir(dir_img_radar)
list_satellite_img = os.listdir(dir_img_satellite)
for i,name in enumerate(list_satellite_img):
name_datetime=self.get_date_time(name)
#Get sattelite image
temp_sattelite = []
timeadd=timedelta(hours=self.hours_predicted)
out_datetime=name_datetime+timeadd
out_img_sattelite=os.path.join(self.dir_img_satellite,str(out_datetime.year)+f"{out_datetime.month:02}"+f"{out_datetime.day:02}"+f"{out_datetime.hour:02}"+".npz")
if os.path.exists(out_img_sattelite):
temp_sattelite+=[os.path.join(self.dir_img_satellite,name),out_img_sattelite]
#Get radar image
temp_radar=self.get_input_radar(name_datetime,out_datetime)
if len(temp_radar) == self.time_points_radar+1 and len(temp_sattelite) == self.time_points_sat+1:
radar_train_list_dir.append(temp_radar)
satellite_train_list_dir.append(temp_sattelite)
if self.augmentation:
temp_radar_add_3=self.get_input_radar(name_datetime,out_datetime,time_add=3)
if len(temp_radar_add_3) == self.time_points_radar+1 and len(temp_sattelite) == self.time_points_sat+1:
radar_train_list_dir.append(temp_radar_add_3)
satellite_train_list_dir.append(temp_sattelite)
return radar_train_list_dir,satellite_train_list_dir
def get_date_time(self,name):
year=int(name[0:4])
month=int(name[4:6])
day=int(name[6:8])
hour=int(name[8:10])
return datetime(year,month,day,hour)
def get_input_radar(self,name_datetime,out_datetime, time_add=0):
if self.servir_format:
temp_radar=[]
for i in range(0,self.time_points_radar):
if self.short_timestep:
timeadd=timedelta(minutes=10*(int(i/2))+int(7*(i%2)))
addditional_time=timedelta(minutes= (3 if i%2==0 else 7))
else:
timeadd=timedelta(minutes=10*i)
addditional_time=timedelta(minutes=time_add)
input_radar_datetime=name_datetime-timeadd+addditional_time
input_img_radar=os.path.join(
self.dir_img_radar,
str(input_radar_datetime.year)+f"{input_radar_datetime.month:02}"+f"{input_radar_datetime.day:02}"+f"{input_radar_datetime.hour:02}"+f"{input_radar_datetime.minute:02}"+".npz"
)
if os.path.exists(input_img_radar):
temp_radar+=[input_img_radar]
else:
break
return temp_radar
else:
temp_radar=[]
for i in range(0,self.time_points_radar):
if self.short_timestep:
timeadd=timedelta(minutes=10*(int(i/2))+int(7*(i%2)))
addditional_time=timedelta(minutes= (3 if i%2==0 else 7))
else:
timeadd=timedelta(minutes=10*i)
addditional_time=timedelta(minutes=time_add)
input_radar_datetime=name_datetime-timeadd+addditional_time
input_img_radar=os.path.join(
self.dir_img_radar,
str(input_radar_datetime.year)+f"{input_radar_datetime.month:02}"+f"{input_radar_datetime.day:02}"+f"{input_radar_datetime.hour:02}"+f"{input_radar_datetime.minute:02}"+".npz"
)
if os.path.exists(input_img_radar):
temp_radar+=[input_img_radar]
else:
break
addditional_time=timedelta(minutes=time_add)
out_datetime=out_datetime+addditional_time
out_img_radar=os.path.join(
self.dir_img_radar,
str(out_datetime.year)+f"{out_datetime.month:02}"+f"{out_datetime.day:02}"+f"{out_datetime.hour:02}"+"00"+".npz"
)
if(os.path.exists(out_img_radar)):
temp_radar+=[out_img_radar]
return temp_radar
class WeatherForecastDataModuleOld(LightningDataModule):
def __init__(
self,
dir_data: str,
batch_size:int ,
hours_predicted :int,
num_workers:int ,
pin_memory: bool ,
time_points_radar : int,
time_points_sat : int,
sat_inp_vars: List[str],
sat_out_vars : str,
rad_inp_vars : List[str],
rad_out_vars : str ,
short_timestep : bool,
augmentation: bool,
rebuild_val: bool,
servir_format: bool,
):
"""
dir_img_radar: directory to radar image folder
dir_img_satellite: directory to satellite image folder
batch_size: batch size
"""
super(WeatherForecastDataModuleOld, self).__init__()
self.save_hyperparameters(logger=False)
# this line allows to access init params with 'self.hparams' attribute
self.data_train = None
self.data_test = None
self.data_val = None
self.dir_data = dir_data
self.batch_size = batch_size
self.hours_predicted = hours_predicted
self.num_workers = num_workers
self.pin_memory = pin_memory
self.time_points_radar = time_points_radar
self.time_points_sat = time_points_sat
self.sat_inp_vars = sat_inp_vars
self.sat_out_vars = sat_out_vars
self.rad_inp_vars = rad_inp_vars
self.rad_out_vars = rad_out_vars
self.short_timestep = short_timestep
self.augmentation = augmentation
self.rebuild_val = rebuild_val
self.servir_format = servir_format
self.rad_mean = np.load(os.path.join(self.dir_data, "rad_mean.npz"))
self.rad_std = np.load(os.path.join(self.dir_data, "rad_std.npz"))
self.sat_mean = np.load(os.path.join(self.dir_data, "sat_mean.npz"))
self.sat_std = np.load(os.path.join(self.dir_data, "sat_std.npz"))
# def prepare_data(self):
# pass
def setup(self, stage):
# print(self.dir_data)
self.data_train = DataReader(
dir_data=self.dir_data,
type_data= "train",
radar_attribute = self.rad_inp_vars,
sat_attribute = self.sat_inp_vars,
hours_predicted = self.hours_predicted,
rad_predicted = self.rad_out_vars,
sat_predicted = self.sat_out_vars,
time_points_radar = self.time_points_radar,
time_points_sat = self.time_points_sat,
short_timestep = self.short_timestep,
augmentation = self.augmentation,
servir_format=self.servir_format
)
if self.rebuild_val:
self.old_data_test = DataReader(
dir_data=self.dir_data,
type_data ="test",
radar_attribute = self.rad_inp_vars,
sat_attribute = self.sat_inp_vars,
hours_predicted = self.hours_predicted,
rad_predicted = self.rad_out_vars,
sat_predicted = self.sat_out_vars,
time_points_radar = self.time_points_radar,
time_points_sat = self.time_points_sat,
short_timestep = self.short_timestep,
augmentation = self.augmentation,
servir_format=self.servir_format
)
total_length= len(self.old_data_test)
length_val = int(total_length/6)
length_test = total_length-length_val
self.data_test, self.data_val = random_split(self.old_data_test,[length_test,length_val])
else:
self.data_test = DataReader(
dir_data=self.dir_data,
type_data ="test",
radar_attribute = self.rad_inp_vars,
sat_attribute = self.sat_inp_vars,
hours_predicted = self.hours_predicted,
rad_predicted = self.rad_out_vars,
sat_predicted = self.sat_out_vars,
time_points_radar = self.time_points_radar,
time_points_sat = self.time_points_sat,
short_timestep = self.short_timestep,
augmentation = self.augmentation,
servir_format=self.servir_format
)
self.data_val = DataReader(
dir_data=self.dir_data,
type_data = "val",
radar_attribute = self.rad_inp_vars,
sat_attribute = self.sat_inp_vars,
hours_predicted = self.hours_predicted,
rad_predicted = self.rad_out_vars,
sat_predicted = self.sat_out_vars,
time_points_radar = self.time_points_radar,
time_points_sat = self.time_points_sat,
short_timestep = self.short_timestep,
augmentation = self.augmentation,
servir_format=self.servir_format
)
def train_dataloader(self):
return DataLoader(
self.data_train,
batch_size=self.batch_size,
num_workers=self.num_workers,
drop_last=False,
pin_memory=self.pin_memory,
shuffle=True,
)
def val_dataloader(self):
return DataLoader(
self.data_val,
batch_size=self.batch_size,
num_workers=self.num_workers,
drop_last=False,
pin_memory=self.pin_memory,
shuffle=False,
)
def test_dataloader(self):
return DataLoader(
self.data_test,
batch_size=self.batch_size,
num_workers=self.num_workers,
drop_last=False,
pin_memory=self.pin_memory,
shuffle=False,
)
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