Merge branch 'main' of https://github.com/patrycjalazna/cloud-detection-challenge into main

.gitignore

@@ -2,4 +2,4 @@ venv/
 **/images*
 **/data/train_features*
 **/data/train_labels*
-**/__pychache__*
+**/__pycache__*

benchmark/cloud_dataset.py

@@ -0,0 +1,68 @@
+import numpy as np
+import pandas as pd
+import rasterio
+import torch
+from typing import Optional, List
+
+
+class CloudDataset(torch.utils.data.Dataset):
+    """Reads in images, transforms pixel values, and serves a
+    dictionary containing chip ids, image tensors, and
+    label masks (where available).
+    """
+
+    def __init__(
+        self,
+        x_paths: pd.DataFrame,
+        bands: List[str],
+        y_paths: Optional[pd.DataFrame] = None,
+        transforms: Optional[list] = None,
+    ):
+        """
+        Instantiate the CloudDataset class.
+
+        Args:
+            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
+            bands (list[str]): list of the bands included in the data
+            y_paths (pd.DataFrame, optional): a dataframe with a for each chip and columns for chip_id
+                and the path to the label TIF with ground truth cloud cover
+            transforms (list, optional): list of transforms to apply to the feature data (eg augmentations)
+        """
+        self.data = x_paths
+        self.label = y_paths
+        self.transforms = transforms
+        self.bands = bands
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx: int):
+        # Loads an n-channel image from a chip-level dataframe
+        img = self.data.loc[idx]
+        band_arrs = []
+        for band in self.bands:
+            with rasterio.open(img[f"{band}_path"]) as b:
+                band_arr = b.read(1).astype("float32")
+            band_arrs.append(band_arr)
+        x_arr = np.stack(band_arrs, axis=-1)
+
+        # Apply data augmentations, if provided
+        if self.transforms:
+            x_arr = self.transforms(image=x_arr)["image"]
+        x_arr = np.transpose(x_arr, [2, 0, 1])
+
+        # Prepare dictionary for item
+        item = {"chip_id": img.chip_id, "chip": x_arr}
+
+        # Load label if available
+        if self.label is not None:
+            label_path = self.label.loc[idx].label_path
+            with rasterio.open(label_path) as lp:
+                y_arr = lp.read(1).astype("float32")
+            # Apply same data augmentations to the label
+            if self.transforms:
+                y_arr = self.transforms(image=y_arr)["image"]
+            item["label"] = y_arr
+
+        return item

benchmark/cloud_model.py

@@ -0,0 +1,197 @@
+from typing import Optional, List
+
+import pandas as pd
+import pytorch_lightning as pl
+import segmentation_models_pytorch as smp
+import torch
+
+try:
+    from cloud_dataset import CloudDataset
+    from losses import intersection_over_union
+except ImportError:
+    from benchmark_src.cloud_dataset import CloudDataset
+    from benchmark_src.losses import intersection_over_union
+
+
+class CloudModel(pl.LightningModule):
+    def __init__(
+        self,
+        bands: List[str],
+        x_train: Optional[pd.DataFrame] = None,
+        y_train: Optional[pd.DataFrame] = None,
+        x_val: Optional[pd.DataFrame] = None,
+        y_val: Optional[pd.DataFrame] = None,
+        hparams: dict = {},
+    ):
+        """
+        Instantiate the CloudModel class based on the pl.LightningModule
+        (https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html).
+
+        Args:
+            bands (list[str]): Names of the bands provided for each chip
+            x_train (pd.DataFrame, optional): a dataframe of the training features 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.
+                Required for model training
+            y_train (pd.DataFrame, optional): a dataframe of the training labels with a for each chip
+                and columns for chip_id and the path to the label TIF with ground truth cloud cover.
+                Required for model training
+            x_val (pd.DataFrame, optional): a dataframe of the validation features 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.
+                Required for model training
+            y_val (pd.DataFrame, optional): a dataframe of the validation labels with a for each chip
+                and columns for chip_id and the path to the label TIF with ground truth cloud cover.
+                Required for model training
+            hparams (dict, optional): Dictionary of additional modeling parameters.
+        """
+        super().__init__()
+        self.hparams.update(hparams)
+        self.save_hyperparameters()
+
+        # required
+        self.bands = bands
+
+        # optional modeling params
+        self.backbone = self.hparams.get("backbone", "resnet34")
+        self.weights = self.hparams.get("weights", "imagenet")
+        self.learning_rate = self.hparams.get("lr", 1e-3)
+        self.patience = self.hparams.get("patience", 4)
+        self.num_workers = self.hparams.get("num_workers", 2)
+        self.batch_size = self.hparams.get("batch_size", 32)
+        self.gpu = self.hparams.get("gpu", False)
+        self.transform = None
+
+        # Instantiate datasets, model, and trainer params if provided
+        self.train_dataset = CloudDataset(
+            x_paths=x_train,
+            bands=self.bands,
+            y_paths=y_train,
+            transforms=self.transform,
+        )
+        self.val_dataset = CloudDataset(
+            x_paths=x_val,
+            bands=self.bands,
+            y_paths=y_val,
+            transforms=None,
+        )
+        self.model = self._prepare_model()
+
+    ## Required LightningModule methods ##
+
+    def forward(self, image: torch.Tensor):
+        # Forward pass
+        return self.model(image)
+
+    def training_step(self, batch: dict, batch_idx: int):
+        """
+        Training step.
+
+        Args:
+            batch (dict): dictionary of items from CloudDataset of the form
+                {'chip_id': list[str], 'chip': list[torch.Tensor], 'label': list[torch.Tensor]}
+            batch_idx (int): batch number
+        """
+        if self.train_dataset.data is None:
+            raise ValueError(
+                "x_train and y_train must be specified when CloudModel is instantiated to run training"
+            )
+
+        # Switch on training mode
+        self.model.train()
+        torch.set_grad_enabled(True)
+
+        # Load images and labels
+        x = batch["chip"]
+        y = batch["label"].long()
+        if self.gpu:
+            x, y = x.cuda(non_blocking=True), y.cuda(non_blocking=True)
+
+        # Forward pass
+        preds = self.forward(x)
+
+        # Log batch loss
+        loss = torch.nn.CrossEntropyLoss(reduction="none")(preds, y).mean()
+        self.log(
+            "loss",
+            loss,
+            on_step=True,
+            on_epoch=True,
+            prog_bar=True,
+            logger=True,
+        )
+        return loss
+
+    def validation_step(self, batch: dict, batch_idx: int):
+        """
+        Validation step.
+
+        Args:
+            batch (dict): dictionary of items from CloudDataset of the form
+                {'chip_id': list[str], 'chip': list[torch.Tensor], 'label': list[torch.Tensor]}
+            batch_idx (int): batch number
+        """
+        if self.val_dataset.data is None:
+            raise ValueError(
+                "x_val and y_val must be specified when CloudModel is instantiated to run validation"
+            )
+
+        # Switch on validation mode
+        self.model.eval()
+        torch.set_grad_enabled(False)
+
+        # Load images and labels
+        x = batch["chip"]
+        y = batch["label"].long()
+        if self.gpu:
+            x, y = x.cuda(non_blocking=True), y.cuda(non_blocking=True)
+
+        # Forward pass & softmax
+        preds = self.forward(x)
+        preds = torch.softmax(preds, dim=1)[:, 1]
+        preds = (preds > 0.5) * 1  # convert to int
+
+        # Log batch IOU
+        batch_iou = intersection_over_union(preds, y)
+        self.log(
+            "iou", batch_iou, on_step=True, on_epoch=True, prog_bar=True, logger=True
+        )
+        return batch_iou
+
+    def train_dataloader(self):
+        # DataLoader class for training
+        return torch.utils.data.DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=True,
+            pin_memory=True,
+        )
+
+    def val_dataloader(self):
+        # DataLoader class for validation
+        return torch.utils.data.DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            num_workers=0,
+            shuffle=False,
+            pin_memory=True,
+        )
+
+    def configure_optimizers(self):
+        opt = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate)
+        sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=10)
+        return [opt], [sch]
+
+    ## Convenience Methods ##
+
+    def _prepare_model(self):
+        # Instantiate U-Net model
+        unet_model = smp.Unet(
+            encoder_name=self.backbone,
+            encoder_weights=self.weights,
+            in_channels=4,
+            classes=2,
+        )
+        if self.gpu:
+            unet_model.cuda()
+
+        return unet_model

benchmark/losses.py

@@ -0,0 +1,23 @@
+import numpy as np
+
+
+def intersection_over_union(pred, true):
+    """
+    Calculates intersection and union for a batch of images.
+
+    Args:
+        pred (torch.Tensor): a tensor of predictions
+        true (torc.Tensor): a tensor of labels
+
+    Returns:
+        intersection (int): total intersection of pixels
+        union (int): total union of pixels
+    """
+    valid_pixel_mask = true.ne(255)  # valid pixel mask
+    true = true.masked_select(valid_pixel_mask).to("cpu")
+    pred = pred.masked_select(valid_pixel_mask).to("cpu")
+
+    # Intersection and union totals
+    intersection = np.logical_and(true, pred)
+    union = np.logical_or(true, pred)
+    return intersection.sum() / union.sum()

benchmark/main.py

@@ -0,0 +1,135 @@
+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)