unet.py

"""
Original U-Net model implementation.
"""
from collections import OrderedDict

import torch
import torch.nn as nn

from model.base_model import BaseModel


class Model(BaseModel):
    """
    This class implements U-Net module and its training functionality. It is equivalent
        to PyTorch's nn.Module in all aspects.

    :param LightningModule: The Pytorch-Lightning module derived from nn.module with
        useful hooks
    :type LightningModule: nn.Module
    """

    def __init__(self, hparams):
        """
        Constructor for Model.

        :param hparams: Holds configuration values
        :type hparams: Namespace
        """

        # init superclass
        super().__init__(hparams)
        self.hparams = hparams
        out_channels = self.hparams.out_days
        features = self.hparams.init_features
        in_channels = self.hparams.in_days * (5 if self.hparams.smos_input else 4)

        self.encoder1 = Model._block(in_channels, features, name="enc1")
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder2 = Model._block(features, features * 2, name="enc2")
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder3 = Model._block(features * 2, features * 4, name="enc3")
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder4 = Model._block(features * 4, features * 8, name="enc4")
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottleneck = Model._block(features * 8, features * 16, name="bottleneck")

        self.upconv4 = nn.ConvTranspose2d(
            features * 16, features * 8, kernel_size=2, stride=2
        )
        self.decoder4 = Model._block((features * 8) * 2, features * 8, name="dec4")
        self.upconv3 = nn.ConvTranspose2d(
            features * 8, features * 4, kernel_size=2, stride=2
        )
        self.decoder3 = Model._block((features * 4) * 2, features * 4, name="dec3")
        self.upconv2 = nn.ConvTranspose2d(
            features * 4, features * 2, kernel_size=2, stride=2
        )
        self.decoder2 = Model._block((features * 2) * 2, features * 2, name="dec2")
        self.upconv1 = nn.ConvTranspose2d(
            features * 2, features, kernel_size=2, stride=2
        )
        self.decoder1 = Model._block(features * 2, features, name="dec1")

        self.conv = nn.Conv2d(
            in_channels=features, out_channels=out_channels, kernel_size=1
        )

    def forward(self, x):
        """
        Does the forward pass on the model.

        :param x: Input tensor batch.
        :type x: torch.Tensor
        :return: Output activations.
        :rtype: torch.Tensor
        """
        enc1 = self.encoder1(x)
        enc2 = self.encoder2(self.pool1(enc1))
        enc3 = self.encoder3(self.pool2(enc2))
        enc4 = self.encoder4(self.pool3(enc3))

        bottleneck = self.bottleneck(self.pool4(enc4))

        dec4 = self.upconv4(bottleneck)
        dec4 = torch.cat((dec4, enc4), dim=1)
        dec4 = self.decoder4(dec4)
        dec3 = self.upconv3(dec4)
        dec3 = torch.cat((dec3, enc3), dim=1)
        dec3 = self.decoder3(dec3)
        dec2 = self.upconv2(dec3)
        dec2 = torch.cat((dec2, enc2), dim=1)
        dec2 = self.decoder2(dec2)
        dec1 = self.upconv1(dec2)
        dec1 = torch.cat((dec1, enc1), dim=1)
        dec1 = self.decoder1(dec1)
        return self.conv(dec1)

    @staticmethod
    def _block(in_channels, features, name):
        """
        Generates a U-Net block.

        :param in_channels: Number of input channels.
        :type in_channels: int
        :param features: Feature number of the layers.
        :type features: int
        :param name: Layer name.
        :type name: str
        :return: Sequention module for the U-Net.
        :rtype: nn.Sequential
        """
        return nn.Sequential(
            OrderedDict(
                [
                    (
                        name + "conv1",
                        nn.Conv2d(
                            in_channels=in_channels,
                            out_channels=features,
                            kernel_size=3,
                            padding=1,
                            bias=True,
                            padding_mode="circular",
                        ),
                    ),
                    (name + "norm1", nn.BatchNorm2d(num_features=features)),
                    (name + "relu1", nn.ReLU(inplace=True)),
                    (
                        name + "conv2",
                        nn.Conv2d(
                            in_channels=features,
                            out_channels=features,
                            kernel_size=3,
                            padding=1,
                            bias=True,
                            padding_mode="circular",
                        ),
                    ),
                    (name + "norm2", nn.BatchNorm2d(num_features=features)),
                    (name + "relu2", nn.ReLU(inplace=True)),
                ]
            )
        )