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import torch
import torch.nn as nn
import torchmetrics
import pytorch_lightning as pl
import wandb
from torch.optim import Adam
from torch.optim.lr_scheduler import StepLR
from transformers import SegformerForSemanticSegmentation
class LandslideModel(pl.LightningModule):
def __init__(self, config, alpha=0.5):
super(LandslideModel, self).__init__()
self.model_type = config['model_config']['model_type']
self.in_channels = config['model_config']['in_channels']
self.num_classes = config['model_config']['num_classes']
self.alpha = alpha
self.lr = config['train_config']['lr']
self.model = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b2-finetuned-ade-512-512",
ignore_mismatched_sizes=True,
num_labels=self.num_classes
)
# Modify the input layer for 14 channels
self.model.segformer.encoder.patch_embeddings[0].proj = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.model.segformer.encoder.patch_embeddings[0].proj.out_channels,
kernel_size=self.model.segformer.encoder.patch_embeddings[0].proj.kernel_size,
stride=self.model.segformer.encoder.patch_embeddings[0].proj.stride,
padding=self.model.segformer.encoder.patch_embeddings[0].proj.padding
)
self.weights = torch.tensor([5], dtype=torch.float32).to(self.device)
self.wce = nn.BCELoss(weight=self.weights)
self.train_f1 = torchmetrics.F1Score(task='binary')
self.val_f1 = torchmetrics.F1Score(task='binary')
self.train_precision = torchmetrics.Precision(task='binary')
self.val_precision = torchmetrics.Precision(task='binary')
self.train_recall = torchmetrics.Recall(task='binary')
self.val_recall = torchmetrics.Recall(task='binary')
self.train_iou = torchmetrics.JaccardIndex(task='binary')
self.val_iou = torchmetrics.JaccardIndex(task='binary')
def forward(self, x):
return self.model(x).logits
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = torch.sigmoid(self(x))
# Resize y_hat to match the size of y
y_hat = nn.functional.interpolate(y_hat, size=y.shape[2:], mode='bilinear', align_corners=False)
wce_loss = self.wce(y_hat, y)
dice = dice_loss(y_hat, y)
combined_loss = (1 - self.alpha) * wce_loss + self.alpha * dice
precision = self.train_precision(y_hat, y)
recall = self.train_recall(y_hat, y)
iou = self.train_iou(y_hat, y)
loss_f1 = self.train_f1(y_hat, y)
self.log('train_precision', precision)
self.log('train_recall', recall)
self.log('train_wce', wce_loss)
self.log('train_dice', dice)
self.log('train_iou', iou)
self.log('train_f1', loss_f1)
self.log('train_loss', combined_loss)
return {'loss': combined_loss}
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = torch.sigmoid(self(x))
# Resize y_hat to match the size of y
y_hat = nn.functional.interpolate(y_hat, size=y.shape[2:], mode='bilinear', align_corners=False)
wce_loss = self.wce(y_hat, y)
dice = dice_loss(y_hat, y)
combined_loss = (1 - self.alpha) * wce_loss + self.alpha * dice
precision = self.val_precision(y_hat, y)
recall = self.val_recall(y_hat, y)
iou = self.val_iou(y_hat, y)
loss_f1 = self.val_f1(y_hat, y)
self.log('val_precision', precision)
self.log('val_recall', recall)
self.log('val_wce', wce_loss)
self.log('val_dice', dice)
self.log('val_iou', iou)
self.log('val_f1', loss_f1)
self.log('val_loss', combined_loss)
if self.current_epoch % 10 == 0:
x = (x - x.min()) / (x.max() - x.min())
x = x[:, 0:3]
x = x.permute(0, 2, 3, 1)
y_hat = (y_hat > 0.5).float()
class_labels = {0: "no landslide", 1: "landslide"}
self.logger.experiment.log({
"image": wandb.Image(x[0].cpu().detach().numpy(), masks={
"predictions": {
"mask_data": y_hat[0][0].cpu().detach().numpy(),
"class_labels": class_labels
},
"ground_truth": {
"mask_data": y[0][0].cpu().detach().numpy(),
"class_labels": class_labels
}
})
})
return {'val_loss': combined_loss}
def configure_optimizers(self):
optimizer = Adam(self.parameters(), lr=self.lr)
scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
return [optimizer], [scheduler]
def dice_loss(y_hat, y):
smooth = 1e-6
y_hat = y_hat.view(-1)
y = y.view(-1)
intersection = (y_hat * y).sum()
union = y_hat.sum() + y.sum()
dice = (2 * intersection + smooth) / (union + smooth)
return 1 - dice |