Once you have the model and load its state_dict, you should set it to evaluation mode (to use the running stats in batchnorm layers and disable dropout). Afterwards, you would have to use the same preprocessing pipeline, which was used during training to get reasonable results (e.g. normalizing with the same mean and stddev). Once you’ve created the input tensors, you could pass them to your model to get the output. Here is a small dummy code snippet:
# Create the model
model = MyModel()

# Load state_dict
model.load_state_dict(torch.load(...))

# Create the preprocessing transformation here
transform = transforms.ToTensor()

# load your image(s)
img = Image.open(...)

# Transform
input = transform(img)

# unsqueeze batch dimension, in case you are dealing with a single image
input = input.unsquueeze(0)

# Set model to eval
model.eval()

# Get prediction
output = model(input)