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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
import numpy as np
import torch.nn.functional as F
from torchvision.models import resnet50, ResNet50_Weights
# In[4]:
class MalariaResNet50(nn.Module):
def __init__(self, num_classes=2):
super(MalariaResNet50, self).__init__()
# Load pretrained ResNet50
self.backbone = models.resnet50(weights=ResNet50_Weights.DEFAULT)
# Replace final fully connected layer for binary classification
num_ftrs = self.backbone.fc.in_features
self.backbone.fc = nn.Linear(num_ftrs, num_classes)
def forward(self, x):
return self.backbone(x)
def predict(self, image_path, device='cpu', show_image=False):
"""
Predict class of a single image.
Args:
image_path (str): Path to input image
device (torch.device): 'cuda' or 'cpu'
show_image (bool): Whether to display the image
Returns:
pred_label (str): "Infected" or "Uninfected"
confidence (float): Confidence score (softmax output)
"""
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Load and preprocess image
img = Image.open(image_path).convert('RGB')
img_tensor = transform(img).unsqueeze(0).to(device)
# Inference
self.eval()
with torch.no_grad():
output = self(img_tensor)
probs = F.softmax(output, dim=1)
_, preds = torch.max(output, 1)
pred_idx = preds.item()
confidence = probs[0][pred_idx].item()
classes = ['Uninfected', 'Infected']
pred_label = classes[pred_idx]
if show_image:
plt.imshow(img)
plt.title(f"Predicted: {pred_label} ({confidence:.2%})")
plt.axis("off")
plt.show()
return pred_label, confidence
def save(self, path):
"""Save model state dict"""
torch.save(self.state_dict(), path)
print(f"Model saved to {path}")
def load(self, path):
"""Load model state dict from file"""
state_dict = torch.load(path, map_location=torch.device('cpu'))
self.load_state_dict(state_dict)
print(f"Model loaded from {path}")
# In[ ]:
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