135 lines
5.2 KiB
Python
135 lines
5.2 KiB
Python
![]() |
import os
|
||
|
from pathlib import Path
|
||
|
from typing import List
|
||
|
|
||
|
from loguru import logger
|
||
|
import pandas as pd
|
||
|
from PIL import Image
|
||
|
import torch
|
||
|
import typer
|
||
|
|
||
|
try:
|
||
|
from cloud_dataset import CloudDataset
|
||
|
from cloud_model import CloudModel
|
||
|
except ImportError:
|
||
|
from benchmark.cloud_dataset import CloudDataset
|
||
|
from benchmark.cloud_model import CloudModel
|
||
|
|
||
|
|
||
|
ROOT_DIRECTORY = Path("/codeexecution")
|
||
|
PREDICTIONS_DIRECTORY = ROOT_DIRECTORY / "predictions"
|
||
|
ASSETS_DIRECTORY = Path("./submission/assets")
|
||
|
DATA_DIRECTORY = ROOT_DIRECTORY / "data"
|
||
|
INPUT_IMAGES_DIRECTORY = DATA_DIRECTORY / "test_features"
|
||
|
|
||
|
# Set the pytorch cache directory and include cached models in your submission.zip
|
||
|
os.environ["TORCH_HOME"] = str(ASSETS_DIRECTORY / "assets/torch")
|
||
|
|
||
|
|
||
|
def get_metadata(features_dir: os.PathLike, bands: List[str]):
|
||
|
"""
|
||
|
Given a folder of feature data, return a dataframe where the index is the chip id
|
||
|
and there is a column for the path to each band's TIF image.
|
||
|
Args:
|
||
|
features_dir (os.PathLike): path to the directory of feature data, which should have
|
||
|
a folder for each chip
|
||
|
bands (list[str]): list of bands provided for each chip
|
||
|
"""
|
||
|
chip_metadata = pd.DataFrame(index=[f"{band}_path" for band in bands])
|
||
|
chip_ids = (
|
||
|
pth.name for pth in features_dir.iterdir() if not pth.name.startswith(".")
|
||
|
)
|
||
|
|
||
|
for chip_id in chip_ids:
|
||
|
chip_bands = [features_dir / chip_id / f"{band}.tif" for band in bands]
|
||
|
chip_metadata[chip_id] = chip_bands
|
||
|
|
||
|
return chip_metadata.transpose().reset_index().rename(columns={"index": "chip_id"})
|
||
|
|
||
|
|
||
|
def make_predictions(
|
||
|
model: CloudModel,
|
||
|
x_paths: pd.DataFrame,
|
||
|
bands: List[str],
|
||
|
predictions_dir: os.PathLike,
|
||
|
):
|
||
|
"""Predicts cloud cover and saves results to the predictions directory.
|
||
|
Args:
|
||
|
model (CloudModel): an instantiated CloudModel based on pl.LightningModule
|
||
|
x_paths (pd.DataFrame): a dataframe with a row for each chip. There must be a column for chip_id,
|
||
|
and a column with the path to the TIF for each of bands provided
|
||
|
bands (list[str]): list of bands provided for each chip
|
||
|
predictions_dir (os.PathLike): Destination directory to save the predicted TIF masks
|
||
|
"""
|
||
|
test_dataset = CloudDataset(x_paths=x_paths, bands=bands)
|
||
|
test_dataloader = torch.utils.data.DataLoader(
|
||
|
test_dataset,
|
||
|
batch_size=model.batch_size,
|
||
|
num_workers=model.num_workers,
|
||
|
shuffle=False,
|
||
|
pin_memory=True,
|
||
|
)
|
||
|
|
||
|
for batch_index, batch in enumerate(test_dataloader):
|
||
|
logger.debug(f"Predicting batch {batch_index} of {len(test_dataloader)}")
|
||
|
x = batch["chip"]
|
||
|
preds = model.forward(x)
|
||
|
preds = torch.softmax(preds, dim=1)[:, 1]
|
||
|
preds = (preds > 0.5).detach().numpy().astype("uint8")
|
||
|
for chip_id, pred in zip(batch["chip_id"], preds):
|
||
|
chip_pred_path = predictions_dir / f"{chip_id}.tif"
|
||
|
chip_pred_im = Image.fromarray(pred)
|
||
|
chip_pred_im.save(chip_pred_path)
|
||
|
|
||
|
|
||
|
def main(
|
||
|
model_weights_path: Path = ASSETS_DIRECTORY / "cloud_model.pt",
|
||
|
test_features_dir: Path = DATA_DIRECTORY / "test_features",
|
||
|
predictions_dir: Path = PREDICTIONS_DIRECTORY,
|
||
|
bands: List[str] = ["B02", "B03", "B04", "B08"],
|
||
|
fast_dev_run: bool = False,
|
||
|
):
|
||
|
"""
|
||
|
Generate predictions for the chips in test_features_dir using the model saved at
|
||
|
model_weights_path.
|
||
|
Predictions are saved in predictions_dir. The default paths to all three files are based on
|
||
|
the structure of the code execution runtime.
|
||
|
Args:
|
||
|
model_weights_path (os.PathLike): Path to the weights of a trained CloudModel.
|
||
|
test_features_dir (os.PathLike, optional): Path to the features for the test data. Defaults
|
||
|
to 'data/test_features' in the same directory as main.py
|
||
|
predictions_dir (os.PathLike, optional): Destination directory to save the predicted TIF masks
|
||
|
Defaults to 'predictions' in the same directory as main.py
|
||
|
bands (List[str], optional): List of bands provided for each chip
|
||
|
"""
|
||
|
if not test_features_dir.exists():
|
||
|
raise ValueError(
|
||
|
f"The directory for test feature images must exist and {test_features_dir} does not exist"
|
||
|
)
|
||
|
predictions_dir.mkdir(exist_ok=True, parents=True)
|
||
|
|
||
|
logger.info("Loading model")
|
||
|
model = CloudModel(bands=bands, hparams={"weights": None})
|
||
|
try:
|
||
|
model.load_state_dict(torch.load(model_weights_path))
|
||
|
except RuntimeError:
|
||
|
model.load_state_dict(torch.load(model_weights_path, map_location=torch.device('cpu')))
|
||
|
|
||
|
logger.info("Loading test metadata")
|
||
|
test_metadata = get_metadata(test_features_dir, bands=bands)
|
||
|
train_metadata = get_metadata(Path('data/train_features'), bands=bands)
|
||
|
|
||
|
if fast_dev_run:
|
||
|
test_metadata = test_metadata.head()
|
||
|
logger.info(f"Found {len(test_metadata)} chips")
|
||
|
|
||
|
logger.info("Generating predictions in batches")
|
||
|
make_predictions(model, test_metadata, bands, predictions_dir)
|
||
|
|
||
|
make_predictions(model, train_metadata, bands, Path('data/predictions'))
|
||
|
|
||
|
logger.info(f"""Saved {len(list(predictions_dir.glob("*.tif")))} predictions""")
|
||
|
|
||
|
|
||
|
if __name__ == "__main__":
|
||
|
typer.run(main)
|