If you have a folder containing the test data, the easiest approach would be to create a new Dataset and transformation using the validation implementations as a template.
However, if you just would like to classify a single sample, you could just load the image, preprocess it, and pass it to your model. Here is some pseudo-code:
img = Image.open(PATH)
x_test = data_transforms['val'](img)
x_test.unsqueeze_(0)  # Add batch dimension
output = model(x_test)
pred = torch.argmax(output, 1)