WMICraft/algorithms/neural_network/neural_network.py

import torch
import pytorch_lightning as pl
import torch.nn as nn
from torch.optim import SGD, Adam, lr_scheduler
import torch.nn.functional as F
from torch.utils.data import DataLoader
from watersandtreegrass import WaterSandTreeGrass
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS


class NeuralNetwork(pl.LightningModule):
    def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
        super().__init__()
        self.layer = nn.Sequential(
            nn.Linear(36*36*3, 300),
            nn.ReLU(),
            nn.Linear(300, 4),
            nn.LogSoftmax(dim=-1)
        )
        self.batch_size = batch_size
        self.learning_rate = learning_rate

    def forward(self, x):
        x = x.reshape(x.shape[0], -1)
        x = self.layer(x)
        return x

    def configure_optimizers(self):
        optimizer = SGD(self.parameters(), lr=self.learning_rate)
        return optimizer

    def training_step(self, batch, batch_idx):
        x, y = batch
        scores = self(x)
        loss = F.nll_loss(scores, y)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        scores = self(x)
        val_loss = F.nll_loss(scores, y)
        self.log("val_loss", val_loss, on_step=True, on_epoch=True, sync_dist=True)

    def test_step(self, batch, batch_idx):
        x, y = batch
        scores = self(x)
        test_loss = F.nll_loss(scores, y)
        self.log("test_loss", test_loss, on_step=True, on_epoch=True, sync_dist=True)