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import os
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from PIL import Image
from models import ResNet18
from datasets import HandGestureDataset
# Set the path to the dataset directory
data_dir = 'dataset'
# Define the image transforms
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Define the dataset
dataset = HandGestureDataset(data_dir, transform=transform)
# Define the data loader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Load the pre-trained neural network
model = ResNet18(pretrained=True)
# Replace the final fully connected layer with a new one
num_classes = 7
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
# Set the model to evaluation mode
model.eval()
# Load an image from the dataset
for i, (image, label) in enumerate(dataloader):
# Apply the image transforms
image = transform(image)
# Add a batch dimension
image = image.unsqueeze(0)
# Make a prediction on the image
with torch.no_grad():
output = model(image)
prediction = torch.argmax(output)
# Print the prediction
print(f'Image {i+1}, Predicted note: {prediction.item()}') |