File size: 1,765 Bytes
39a6d9b 9dbdf7e 39a6d9b 54a23ba 97655a1 54a23ba 39a6d9b 54a23ba 39a6d9b 9dbdf7e 39a6d9b 97655a1 39a6d9b 97655a1 39a6d9b 9dbdf7e | 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 | from ben_txt_datamodule import BENTxTDataset, BENTxTDataModule
def create_dataset_example():
# Datasets example using the Red (B04), Green (B03), and Blue (B02) band from the Sentinel-2 images.
ds_rgb = BENTxTDataset(
lmdb_file = "Encoded-BigEarthNet/",
metadata_file = "BigEarthNet.txt.parquet",
bands = ("B04", "B03", "B02"),
img_size = 120
)
sample = ds_rgb[0]
print(f"RGB input image: {sample['image_input'].shape}")
print(f"Text input: {sample['text_input']}")
print(f"Reference output: {sample['reference_output']}")
def create_datamodule_example():
# Lightning DataModule example using the 10m and 20m spatial resolution bands from Sentinel-1 and Sentinel-2 and multiple metadata filters.
# The datamodule will create 4 dataloaders: train, val, test, and bench.
dm = BENTxTDataModule(
image_lmdb_file = "Encoded-BigEarthNet/",
metadata_file = "BigEarthNet.txt.parquet",
bands = 'S1S2-10m20m',
img_size = 120,
batch_size = 1,
num_workers_dataloader = 0,
types = ['mcq'],
categories = ['climate zone'],
countries = ['Portugal', 'Finland'],
seasons = ['Summer'],
climate_zones = None,
point_token = ['<point>', '</point>'],
ref_token = ['<ref>', '</ref>']
)
dm.setup()
train_dl = dm.train_dataloader()
for batch in train_dl:
print(f"Batch image input shape: {batch['image_input'].shape}")
print(f"First batch sample text input: {batch['text_input'][0]}")
print(f"First batch sample text reference output: {batch['reference_output']}")
break
if __name__ == "__main__":
create_dataset_example()
create_datamodule_example() |