I'm simply trying to train a ResNet18 model using PyTorch library. The training dataset consists of 25,000 images. Therefore, it is taking a lot of time for even the first epoch to complete. Therefore, I want to save the progress after a certain no. of batch iteration is completed. But I can't figure out how to modify my code and how to use the torch.save() and torch.load() functions in my code to save the periodic progress.
My code is given below:
# BUILD THE NETWORK
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import torchvision
import torchvision.models as models
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
# DOWNLOAD PRETRAINED MODELS ON ImageNet
model_resnet18 = torch.hub.load('pytorch/vision', 'resnet18', pretrained = True)
model_resnet34 = torch.hub.load('pytorch/vision', 'resnet34', pretrained = True)
for name, param in model_resnet18.named_parameters():
if('bn' not in name):
param.requires_grad = False
for name, param in model_resnet34.named_parameters():
if('bn' not in name):
param.requires_grad = False
num_classes = 2
model_resnet18.fc = nn.Sequential(nn.Linear(model_resnet18.fc.in_features, 512),
nn.ReLU(),
nn.Dropout(),
nn.Linear(512, num_classes))
model_resnet34.fc = nn.Sequential(nn.Linear(model_resnet34.fc.in_features, 512),
nn.ReLU(),
nn.Dropout(),
nn.Linear(512, num_classes))
# FUNCTIONS FOR TRAINING AND LOADING DATA
def train(model, optimizer, loss_fn, train_loader, val_loader, epochs = 5, device = "cuda"):
print("Inside Train Function\n")
for epoch in range(epochs):
print("Epoch : {} running".format(epoch))
training_loss = 0.0
valid_loss = 0.0
model.train()
k = 0
for batch in train_loader:
optimizer.zero_grad()
inputs, targets = batch
inputs = inputs.to(device)
output = model(inputs)
loss = loss_fn(output, targets)
loss.backward()
optimizer.step()
training_loss += loss.data.item() * inputs.size(0)
print("End of batch loop iteration {} \n".format(k))
k = k + 1
training_loss /= len(train_loader.dataset)
model.eval()
num_correct = 0
num_examples = 0
for batch in val_loader:
inputs, targets = batch
inputs.to(device)
output = model(inputs)
targets = targets.to(device)
loss = loss_fn(output, targets)
valid_loss += loss.data.item() * inputs.size(0)
correct = torch.eq(torch.max(F.softmax(output, dim = 1), dim = 1)[1], targets).view(-1)
num_correct += torch.sum(correct).item()
num_examples += correct.shape[0]
valid_loss /= len(val_loader.dataset)
print('Epoch: {}, Training Loss: {:.4f}, Validation Loss: {:.4f}, accuracy = {:.4f}'.format(epoch, training_loss, valid_loss, num_correct / num_examples))
batch_size = 32
img_dimensions = 224
img_transforms = transforms.Compose([ transforms.Resize((img_dimensions, img_dimensions)),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])
img_test_transforms = transforms.Compose([ transforms.Resize((img_dimensions, img_dimensions)),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])
def check_image(path):
try:
im = Image.open(path)
return True
except:
return False
train_data_path = "E:\Image Recognition\dogsandcats\\train\\"
train_data = torchvision.datasets.ImageFolder(root=train_data_path,transform=img_transforms, is_valid_file=check_image)
validation_data_path = "E:\\Image Recognition\\dogsandcats\\validation\\"
validation_data = torchvision.datasets.ImageFolder(root=validation_data_path,transform=img_test_transforms, is_valid_file=check_image)
test_data_path = "E:\\Image Recognition\\dogsandcats\\test\\"
test_data = torchvision.datasets.ImageFolder(root=test_data_path,transform=img_test_transforms, is_valid_file=check_image)
num_workers = 6
train_data_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)
validation_data_loader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)
test_data_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)
print(torch.cuda.is_available(), "\n")
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
print(f'Num training images: {len(train_data_loader.dataset)}')
print(f'Num validation images: {len(validation_data_loader.dataset)}')
print(f'Num test images: {len(test_data_loader.dataset)}')
def test_model(model):
print("Inside Test Model Function\n")
correct = 0
total = 0
with torch.no_grad():
for data in test_data_loader:
images, labels = data[0].to(device), data[1].to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('correct: {:d} total: {:d}'.format(correct, total))
print('accuracy = {:f}'.format(correct / total))
model_resnet18.to(device)
optimizer = optim.Adam(model_resnet18.parameters(), lr=0.001)
if __name__ == "__main__":
train(model_resnet18, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, validation_data_loader, epochs=2, device=device)
test_model(model_resnet18)
model_resnet34.to(device)
optimizer = optim.Adam(model_resnet34.parameters(), lr=0.001)
if __name__ == "__main__":
train(model_resnet34, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, validation_data_loader, epochs=2, device=device)
test_model(model_resnet34)
import os
def find_classes(dir):
classes = os.listdir(dir)
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
def make_prediction(model, filename):
labels, _ = find_classes('E:\\Image Recognition\\dogsandcats\\test\\test')
img = Image.open(filename)
img = img_test_transforms(img)
img = img.unsqueeze(0)
prediction = model(img.to(device))
prediction = prediction.argmax()
print(labels[prediction])
make_prediction(model_resnet34, 'E:\\Image Recognition\\dogsandcats\\test\\test\\3.jpg') #dog
make_prediction(model_resnet34, 'E:\\Image Recognition\\dogsandcats\\test\\test\\5.jpg') #cat
torch.save(model_resnet18.state_dict(), "./model_resnet18.pth")
torch.save(model_resnet34.state_dict(), "./model_resnet34.pth")
# Remember that you must call model.eval() to set dropout and batch normalization layers to
# evaluation mode before running inference. Failing to do this will yield inconsistent inference results.
resnet18 = torch.hub.load('pytorch/vision', 'resnet18')
resnet18.fc = nn.Sequential(nn.Linear(resnet18.fc.in_features,512),nn.ReLU(), nn.Dropout(), nn.Linear(512, num_classes))
resnet18.load_state_dict(torch.load('./model_resnet18.pth'))
resnet18.eval()
resnet34 = torch.hub.load('pytorch/vision', 'resnet34')
resnet34.fc = nn.Sequential(nn.Linear(resnet34.fc.in_features,512),nn.ReLU(), nn.Dropout(), nn.Linear(512, num_classes))
resnet34.load_state_dict(torch.load('./model_resnet34.pth'))
resnet34.eval()
# Test against the average of each prediction from the two models
models_ensemble = [resnet18.to(device), resnet34.to(device)]
correct = 0
total = 0
if __name__ == '__main__':
with torch.no_grad():
for data in test_data_loader:
images, labels = data[0].to(device), data[1].to(device)
predictions = [i(images).data for i in models_ensemble]
avg_predictions = torch.mean(torch.stack(predictions), dim=0)
_, predicted = torch.max(avg_predictions, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
if total != 0:
print('accuracy = {:f}'.format(correct / total))
print('correct: {:d} total: {:d}'.format(correct, total))
To be very precise, I want to save my progress at the end of for batch in train_loader: loop, for say k = 1500.
If anyone can guide me about modifying my code so that I can save my progress and resume it later, then it will be a great and highly appreciated.
Whenever you want to save your training progress, you need to save two things:
This can be done in the following way:
def save_checkpoint(model, optimizer, save_path, epoch):
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch
}, save_path)
To resume training, you can restore your model and optimizer's state dict.
def load_checkpoint(model, optimizer, load_path):
checkpoint = torch.load(load_path)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
return model, optimizer, epoch
You can save your model at any point in training, wherever you need to. However, it should be ideal to save after finishing an epoch.