Create 19_ResNet.py

pages/19_ResNet.py

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+# Install necessary packages
+# Ensure you have PyTorch, torchvision, and Streamlit installed
+# You can install them using pip if you haven't already:
+# pip install torch torchvision streamlit
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, models, transforms
+from torch.utils.data import DataLoader
+import numpy as np
+import time
+import os
+import copy
+import streamlit as st
+from PIL import Image
+import matplotlib.pyplot as plt
+import torchvision.transforms as T
+
+# Data transformations
+data_transforms = {
+    'train': transforms.Compose([
+        transforms.RandomResizedCrop(224),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ]),
+    'val': transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ]),
+}
+
+# Load datasets
+data_dir = 'path/to/data'
+image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
+                  for x in ['train', 'val']}
+dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4)
+               for x in ['train', 'val']}
+dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
+class_names = image_datasets['train'].classes
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load the pre-trained model
+model_ft = models.resnet18(pretrained=True)
+num_ftrs = model_ft.fc.in_features
+model_ft.fc = nn.Linear(num_ftrs, len(class_names))
+model_ft = model_ft.to(device)
+
+# Define loss function and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
+scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
+
+# Training and evaluation functions
+def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
+    since = time.time()
+
+    best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+
+    for epoch in range(num_epochs):
+        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
+        print('-' * 10)
+
+        for phase in ['train', 'val']:
+            if phase == 'train':
+                model.train()  
+            else:
+                model.eval()   
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            for inputs, labels in dataloaders[phase]:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                optimizer.zero_grad()
+
+                with torch.set_grad_enabled(phase == 'train'):
+                    outputs = model(inputs)
+                    _, preds = torch.max(outputs, 1)
+                    loss = criterion(outputs, labels)
+
+                    if phase == 'train':
+                        loss.backward()
+                        optimizer.step()
+
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            if phase == 'train':
+                scheduler.step()
+
+            epoch_loss = running_loss / dataset_sizes[phase]
+            epoch_acc = running_corrects.double() / dataset_sizes[phase]
+
+            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
+                phase, epoch_loss, epoch_acc))
+
+            if phase == 'val' and epoch_acc > best_acc:
+                best_acc = epoch_acc
+                best_model_wts = copy.deepcopy(model.state_dict())
+
+        print()
+
+    time_elapsed = time.time() - since
+    print('Training complete in {:.0f}m {:.0f}s'.format(
+        time_elapsed // 60, time_elapsed % 60))
+    print('Best val Acc: {:4f}'.format(best_acc))
+
+    model.load_state_dict(best_model_wts)
+    return model
+
+# Train the model
+model_ft = train_model(model_ft, criterion, optimizer_ft, scheduler, num_epochs=25)
+
+# Save the trained model
+torch.save(model_ft.state_dict(), 'model_ft.pth')
+
+# Streamlit Interface
+st.title("Image Classification with Fine-tuned ResNet")
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    model_ft = models.resnet18(pretrained=True)
+    num_ftrs = model_ft.fc.in_features
+    model_ft.fc = nn.Linear(num_ftrs, len(class_names))
+    model_ft.load_state_dict(torch.load('model_ft.pth'))
+    model_ft = model_ft.to(device)
+    model_ft.eval()
+
+    preprocess = T.Compose([
+        T.Resize(256),
+        T.CenterCrop(224),
+        T.ToTensor(),
+        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+
+    img = preprocess(image).unsqueeze(0)
+    img = img.to(device)
+
+    with torch.no_grad():
+        outputs = model_ft(img)
+        _, preds = torch.max(outputs, 1)
+        predicted_class = class_names[preds[0]]
+
+    st.write(f"Predicted Class: {predicted_class}")
+
+    # Plotting the image with matplotlib
+    fig, ax = plt.subplots()
+    ax.imshow(image)
+    ax.set_title(f"Predicted: {predicted_class}")
+    st.pyplot(fig)