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