You would have to load the model, test image, process it according to your validation processing, and classify it.
Here is some (pseudo-)code:
model = MyModel()  # Initialize model
model.load_state_dict(torch.load(PATH_TO_MODEL))  # Load pretrained parameters
model.eval()  # Set to eval mode to change behavior of Dropout, BatchNorm

transform = transforms.Compose(...)  # Same as for your validation data, e.g. Resize, ToTensor, Normalize, ...

img = Image.open(PATH_TO_IMAGE)  # Load image as PIL.Image
x = transform(img)  # Preprocess image
x = x.unsqueeze(0)  # Add batch dimension

output = model(x)  # Forward pass
pred = torch.argmax(output, 1)  # Get predicted class if multi-class classification
print('Image predicted as ', pred)

Let me know, if you get stuck somewhere.