File size: 1,966 Bytes
717af72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import cv2
import os
# Define LeNet-5 CNN architecture
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
self.relu = nn.ReLU()
self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(self.relu(self.conv1(x)))
x = self.pool(self.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
# ImageClassifier wrapper
class ImageClassifier:
def __init__(self, model_path):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = LeNet5().to(self.device)
self.model.load_state_dict(torch.load(model_path, map_location=self.device))
self.model.eval()
self.transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
def preprocess_image(self, image_path):
image = Image.open(image_path).convert("RGB")
image = self.transform(image)
image = image.unsqueeze(0) # Add batch dimension
return image.to(self.device)
def predict(self, image_path):
image_tensor = self.preprocess_image(image_path)
with torch.no_grad():
output = self.model(image_tensor)
_, predicted = output.max(1)
return predicted.item(), image_path
|