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1ee2b6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import numpy as np
import netCDF4 as nc
import torch
import torch.utils.data as data
class train_Dataset(data.Dataset):
def __init__(self, args):
super(train_Dataset, self).__init__()
self.args = args
self.years = range(1993, 2018)
self.dates = range(12, 357, 3)
self.indices = []
for m in self.years:
train_data = nc.Dataset(f'{self.args["data_path"]}/{m}_norm.nc')
max_time_index = train_data.variables['atmosphere_variables'].shape[0] - 1
train_data.close()
for n in self.dates:
input_start = n - self.args['atmosphere_lead_time'] + 1
target_end = n + self.args['ocean_lead_time'] + 1
if input_start >= 0 and target_end <= max_time_index:
self.indices.append((m, n))
def __getitem__(self, index):
year, date = self.indices[index]
train_data = nc.Dataset(f'{self.args["data_path"]}/{year}_norm.nc')
# Calculate indices
input_start = date - self.args['atmosphere_lead_time'] + 1
input_end = date + 1
target_start = date + 1
target_end = date + self.args['ocean_lead_time'] + 1
# Load input data
input = train_data.variables['atmosphere_variables'][
input_start:input_end,
self.args['variables_input'],
self.args['lat_start']:self.args['lat_end']:self.args['ds_factor'],
self.args['lon_start']:self.args['lon_end']:self.args['ds_factor']
]
# Load target data
target = train_data.variables['atmosphere_variables'][
target_start:target_end,
self.args['variables_output'],
self.args['lat_start']:self.args['lat_end']:self.args['ds_factor'],
self.args['lon_start']:self.args['lon_end']:self.args['ds_factor']
]
train_data.close() # Close the dataset after use
# Convert to tensors and handle NaNs
input = torch.tensor(input, dtype=torch.float32)
target = torch.tensor(target, dtype=torch.float32)
input = torch.nan_to_num(input, nan=0.0)
target = torch.nan_to_num(target, nan=0.0)
# Ensure matching time dimensions
min_time_steps = min(input.shape[0], target.shape[0])
input = input[:min_time_steps]
target = target[:min_time_steps]
return input, target
def __len__(self):
return len(self.indices)
class test_Dataset(data.Dataset):
def __init__(self, args):
super(test_Dataset, self).__init__()
self.args = args
self.years = range(2018, 2022)
self.dates = range(12, 357, 3)
self.indices = []
# Build valid indices to avoid out-of-bounds errors
for m in self.years:
test_data = nc.Dataset(f'{self.args["data_path"]}/{m}_norm.nc')
max_time_index = test_data.variables['atmosphere_variables'].shape[0] - 1 # Adjust for zero-based indexing
test_data.close() # Close the dataset after use
for n in self.dates:
input_start = n - self.args['atmosphere_lead_time'] + 1
target_end = n + self.args['ocean_lead_time'] + 1
# Ensure indices are within bounds
if input_start >= 0 and target_end <= max_time_index:
self.indices.append((m, n))
def __getitem__(self, index):
year, date = self.indices[index]
test_data = nc.Dataset(f'{self.args["data_path"]}/{year}_norm.nc')
# Calculate indices
input_start = date - self.args['atmosphere_lead_time'] + 1
input_end = date + 1
target_start = date + 1
target_end = date + self.args['ocean_lead_time'] + 1
# Load input data
input = test_data.variables['atmosphere_variables'][
input_start:input_end,
self.args['variables_input'],
self.args['lat_start']:self.args['lat_end']:self.args['ds_factor'],
self.args['lon_start']:self.args['lon_end']:self.args['ds_factor']
]
# Load target data
target = test_data.variables['atmosphere_variables'][
target_start:target_end,
self.args['variables_output'],
self.args['lat_start']:self.args['lat_end']:self.args['ds_factor'],
self.args['lon_start']:self.args['lon_end']:self.args['ds_factor']
]
test_data.close() # Close the dataset after use
# Convert to tensors and handle NaNs
input = torch.tensor(input, dtype=torch.float32)
target = torch.tensor(target, dtype=torch.float32)
input = torch.nan_to_num(input, nan=0.0)
target = torch.nan_to_num(target, nan=0.0)
# Ensure matching time dimensions
min_time_steps = min(input.shape[0], target.shape[0])
input = input[:min_time_steps]
target = target[:min_time_steps]
return input, target
def __len__(self):
return len(self.indices)
if __name__ == '__main__':
args = {
'data_path': '/jizhicfs/easyluwu/scaling_law/ft_local/low_res',
'ocean_lead_time': 1,
'atmosphere_lead_time': 1,
'shuffle': True,
'variables_input': list(range(69)),
'variables_output': list(range(69)),
'lon_start': 0,
'lat_start': 0,
'lon_end': 1440,
'lat_end': 720,
'ds_factor': 1,
}
train_dataset = train_Dataset(args)
test_dataset = test_Dataset(args)
train_loader = data.DataLoader(train_dataset, batch_size=1)
test_loader = data.DataLoader(test_dataset, batch_size=1)
for inputs, targets in iter(train_loader):
print(inputs.shape, targets.shape)
break |