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Using a dedicated datamodule to perform inference: the crop classification example.#

To run this example, the following packages are necessary.#

!pip install terratorch gdown tensorboard >& install.log

import os
import sys
import torch
import gdown
import terratorch
import albumentations
import lightning.pytorch as pl
import matplotlib.pyplot as plt
from terratorch.datamodules import MultiTemporalCropClassificationDataModule
import warnings

Downloading a random subset of the required dataset (~1 GB).#

if not os.path.isfile('multi-temporal-crop-classification-subset.tar.gz'):
    !gdown 1LL6thkuKA0kVyMI39PxgsrJ1FJJDV7-u

if not os.path.isdir('multi-temporal-crop-classification-subset/'):
    !tar -xzvf multi-temporal-crop-classification-subset.tar.gz

dataset_path = "multi-temporal-crop-classification-subset"

Instantiating the corresponding datamodule.#

datamodule = MultiTemporalCropClassificationDataModule(
    batch_size=8,
    num_workers=2,
    data_root=dataset_path,
    train_transform=[
        terratorch.datasets.transforms.FlattenTemporalIntoChannels(),  # Required for temporal data
        albumentations.D4(), # Random flips and rotation
        albumentations.pytorch.transforms.ToTensorV2(),
        terratorch.datasets.transforms.UnflattenTemporalFromChannels(n_timesteps=3),
    ],
    val_transform=None,  # Using ToTensor() by default
    test_transform=None,
    expand_temporal_dimension=True,
    use_metadata=False, # The crop dataset has metadata for location and time
    reduce_zero_label=True,
)

# Setup train and val datasets
datamodule.setup("predict")

Visualizing a few samples.#

for i in range(5):
    datamodule.predict_dataset.plot(datamodule.predict_dataset[i])

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Downloading the best pretrained checkpoint.#

best_ckpt_100_epoch_path = "multicrop_best-epoch=76.ckpt"

if not os.path.isfile(best_ckpt_100_epoch_path):
    gdown.download("https://drive.google.com/uc?id=1o1Hzd4yyiKyYdzfotQlEOeGTjsM8cHSw")

Instantiating the Lightning Trainer.#

checkpoint_callback = pl.callbacks.ModelCheckpoint(
    dirpath="output/multicrop/checkpoints/",
    mode="max",
    monitor="val/Multiclass_Jaccard_Index", # Variable to monitor
    filename="best-{epoch:02d}",
)
trainer = pl.Trainer(
    accelerator="auto",
    strategy="auto",
    devices=1, # Lightning multi-gpu often fails in notebooks
    precision='bf16-mixed',  # Speed up training
    num_nodes=1,
    logger=True, # Uses TensorBoard by default
    max_epochs=1, # For demos
    log_every_n_steps=5,
    enable_checkpointing=True,
    callbacks=[checkpoint_callback, pl.callbacks.RichProgressBar()],
    default_root_dir="output/multicrop",
)

Instantiating the task to handle the model.#

model = terratorch.tasks.SemanticSegmentationTask(
    model_factory="EncoderDecoderFactory",
    model_args={
        # Backbone
        "backbone": "prithvi_eo_v2_300", 
        "backbone_pretrained": True,
        "backbone_num_frames": 3,
        "backbone_bands": ["BLUE", "GREEN", "RED", "NIR_NARROW", "SWIR_1", "SWIR_2"],
        "backbone_coords_encoding": [], # use ["time", "location"] for time and location metadata

        # Necks 
        "necks": [
            {
                "name": "SelectIndices",
                "indices": [5, 11, 17, 23] 
            },
            {
                "name": "ReshapeTokensToImage",
                "effective_time_dim": 3
            },
            {"name": "LearnedInterpolateToPyramidal"},            
        ],

        # Decoder
        "decoder": "UNetDecoder",
        "decoder_channels": [512, 256, 128, 64],

        # Head
        "head_dropout": 0.1,
        "num_classes": 13,
    },

    loss="ce",
    lr=1e-4,
    optimizer="AdamW",
    ignore_index=-1,
    freeze_backbone=True,  
    freeze_decoder=False,
    plot_on_val=True,

)

Predicting for some samples in the prediction dataset.#

preds = trainer.predict(model, datamodule=datamodule, ckpt_path=best_ckpt_100_epoch_path)
# get data 
data_loader = trainer.predict_dataloaders
batch = next(iter(data_loader))

BATCH_SIZE = 8
for i in range(BATCH_SIZE):

    sample = {key: batch[key][i] for key in batch}
    sample["prediction"] = preds[0][0][i].cpu().numpy()

    datamodule.predict_dataset.plot(sample)

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Last update: March 31, 2025