train.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import transforms, datasets, models
from torchvision.utils import make_grid
import os
import time
from PIL import ImageFile
import math
from model import ConvolutionalNet

ImageFile.LOAD_TRUNCATED_IMAGES = True

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

train_transforms = transforms.Compose([
    transforms.RandomRotation(10),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

test_transform = transforms.Compose([
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

train_dataset = datasets.ImageFolder(root='./data/train', transform=train_transforms)
test_dataset = datasets.ImageFolder(root='./data/test', transform=test_transform)

torch.manual_seed(42)
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=10, shuffle=False)

class_names = train_dataset.classes

for images, labels in train_loader:
  break

torch.manual_seed(101)
model = ConvolutionalNet()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

start_time = time.time()
epochs = 5

# BATCH LIMITS
max_trn_batch = 800
max_tst_batch = 300

train_losses = []
test_losses = []
train_correct = []
test_correct = []

for epoch in range(epochs):
  trn_corr = 0
  tst_corr = 0

  for b, (X_train, y_train) in enumerate(train_loader):
    # if b == max_trn_batch:
    #   break
    
    y_pred = model(X_train)
    loss = criterion(y_pred, y_train)

    if b % 200 == 0:
      print(f"Epoch: {epoch+1}/{epochs}\tBatch: {b+1}\tLoss: {loss.item()}")

    predicted = torch.max(y_pred, 1)[1]
    batch_corr = (predicted == y_train).sum()
    trn_corr += batch_corr

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    train_losses.append(loss)
    train_correct.append(trn_corr)
  
  # TEST
  with torch.no_grad():
    for b, (X_test, y_test) in enumerate(test_loader):
      # if b == max_tst_batch:
      #   break
      
      try:
        y_pred = model(X_test)
      except:
        print("Error testing images")
        continue
      loss = criterion(y_pred, y_test)

      predicted = torch.max(y_pred, 1)[1]
      batch_corr = (predicted == y_test).sum()
      tst_corr += batch_corr

      test_losses.append(loss)
      test_correct.append(tst_corr)

end_time = time.time()
total_time = end_time - start_time
print(f"Time taken: minutes: {math.floor(total_time / 60)} seconds: {math.floor(total_time % 60)}")

torch.save(model.state_dict(), 'model.pt')

plt.plot([x.detach().numpy() for x in train_losses], label='train loss')
plt.plot(test_losses, label='test loss')
plt.legend()
plt.plot()

plt.plot([t/80 for t in train_correct], label='train accuracy')
plt.plot([t/30 for t in test_correct], label='test accuracy')
plt.legend()
plt.plot()

print(f'Accuracy: {100*test_correct[-1].item()/1000}')