how can i turn to GPU - pytorch

Question

how can i turn this classifier to run n the GPU
i tryed to add resent18.cuda() but it gave me an error in when I run the train(epochs=20) and when i run show_preds() before train(epochs=20) and the error that desplays it :
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same
can you help me to do it
import os
import shutil
import random
import torch
import torchvision
import numpy as np
from google.colab import files
import time
import copy
import urllib
import torch
import PIL
import json
import numpy as np
import torch.nn.functional as F
from PIL import Image
from matplotlib import pyplot as plt
torch.manual_seed(0)
class_names = ['normal', 'viral', 'covid']
root_dir = 'COVID-19 Radiography Database'
source_dirs = ['NORMAL', 'Viral Pneumonia', 'COVID-19']
if os.path.isdir(os.path.join(root_dir, source_dirs[1])):
os.mkdir(os.path.join(root_dir, 'test'))
for i, d in enumerate(source_dirs):
os.rename(os.path.join(root_dir, d), os.path.join(root_dir, class_names[i]))
for c in class_names:
os.mkdir(os.path.join(root_dir, 'test', c))
for c in class_names:
images = [x for x in os.listdir(os.path.join(root_dir, c)) if x.lower().endswith('png')]
selected_images = random.sample(images, 30)
for image in selected_images:
source_path = os.path.join(root_dir, c, image)
target_path = os.path.join(root_dir, 'test', c, image)
shutil.move(source_path, target_path)
class ChestXRayDataset(torch.utils.data.Dataset):
def __init__(self, image_dirs, transform):
def get_images(class_name):
images = [x for x in os.listdir(image_dirs[class_name]) if x[-3:].lower().endswith('png')]
print(f'Found {len(images)} {class_name} examples')
return images
self.images = {}
self.class_names = ['normal', 'viral', 'covid']
for class_name in self.class_names:
self.images[class_name] = get_images(class_name)
self.image_dirs = image_dirs
self.transform = transform
def __len__(self):
return sum([len(self.images[class_name]) for class_name in self.class_names])
def __getitem__(self, index):
class_name = random.choice(self.class_names)
index = index % len(self.images[class_name])
image_name = self.images[class_name][index]
image_path = os.path.join(self.image_dirs[class_name], image_name)
image = Image.open(image_path).convert('RGB')
return self.transform(image), self.class_names.index(class_name)
train_transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=(224, 224)),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=(224, 224)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dirs = {
'normal': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/normal',
'viral': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/viral',
'covid': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/covid'
}
train_dataset = ChestXRayDataset(train_dirs, train_transform)
test_dirs = {
'normal': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/test/normal',
'viral': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/test/normal',
'covid': '/content/gdrive/MyDrive/covidcalssifierdataset/COVID-19 Radiography Database/test/covid'
}
test_dataset = ChestXRayDataset(test_dirs, test_transform)
batch_size = 6
dl_train = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
dl_test = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
print('Number of training batches', len(dl_train))
print('Number of test batches', len(dl_test))
class_names = train_dataset.class_names
def show_images(images, labels, preds):
plt.figure(figsize=(8, 4))
for i, image in enumerate(images):
plt.subplot(1, 6, i + 1, xticks=[], yticks=[])
image = image.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = image * std + mean
image = np.clip(image, 0., 1.)
plt.imshow(image)
col = 'green'
if preds[i] != labels[i]:
col = 'red'
plt.xlabel(f'{class_names[int(labels[i].numpy())]}')
plt.ylabel(f'{class_names[int(preds[i].numpy())]}', color=col)
plt.tight_layout()
plt.show()
images, labels = next(iter(dl_train))
show_images(images, labels, labels)
images, labels = next(iter(dl_test))
show_images(images, labels, labels)
resnet18 = torchvision.models.resnet18(pretrained=True)
resnet18.fc = torch.nn.Linear(in_features=512, out_features=3)
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet18.parameters(), lr=3e-5)
def show_preds():
resnet18.eval()
images, labels = next(iter(dl_test))
outputs = resnet18(images)
_, preds = torch.max(outputs, 1)
show_images(images, labels, press)
def train(epochs):
print('Starting training..')
for e in range(0, epochs):
print('='*20)
print(f'Starting epoch {e + 1}/{epochs}')
print('='*20)
train_loss = 0.
val_loss = 0.
resnet18.train() # set model to training phase
for train_step, (images, labels) in enumerate(dl_train):
optimizer.zero_grad()
outputs = resnet18(images)
loss = loss_fn(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
if train_step % 20 == 0:
print('Evaluating at step', train_step)
accuracy = 0
resnet18.eval() # set model to eval phase
for val_step, (images, labels) in enumerate(dl_test):
outputs = resnet18(images)
loss = loss_fn(outputs, labels)
val_loss += loss.item()
_, preds = torch.max(outputs, 1)
accuracy += sum((preds == labels).numpy())
val_loss /= (val_step + 1)
accuracy = accuracy/len(test_dataset)
print(f'Validation Loss: {val_loss:.4f}, Accuracy: {accuracy:.4f}')
show_preds()
resnet18.train()
if accuracy >= 0.95:
print('Performance condition satisfied, stopping..')
return
train_loss /= (train_step + 1)
print(f'Training Loss: {train_loss:.4f}')
print('Training complete..')
train(epochs=20)