model.py

from torch import nn
import segmentation_models_pytorch as smp
from segmentation_models_pytorch.losses import DiceLoss

ENCODER = 'timm-efficientnet-b0'
WEIGHTS = 'imagenet'

class SegmentationModel(nn.Module):

  def __init__(self):
    super(SegmentationModel, self).__init__()

    self.arc = smp.Unet(
        encoder_name = ENCODER,
        encoder_weights = WEIGHTS,
        in_channels = 3,
        classes = 1,
        activation = None
    )

  def forward(self, images, masks = None):

    logits = self.arc(images)

    if masks != None:
      loss1 = DiceLoss(mode='binary')(logits, masks)
      loss2 = nn.BCEWithLogitsLoss()(logits, masks)
      return logits, loss1 + loss2
    
    return logits