from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests

url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)

model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
inputs = ImageLoader.load_image(image)
preds = model(inputs)

ImageLoader.save_image(preds, './scaled_2x.png')
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')

from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop

augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
    .map(augment_five_crop, batched=True, desc="Augmenting Dataset")                                # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset)                                                     # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation'))      # prepare the eval dataset for the PyTorch DataLoader

from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig

training_args = TrainingArguments(
    output_dir='./results',                 # output directory
    num_train_epochs=1000,                  # total number of training epochs
)

config = EdsrConfig(
    scale=4,                                # train a model to upscale 4x
)
model = EdsrModel(config)

trainer = Trainer(
    model=model,                         # the instantiated model to be trained
    args=training_args,                  # training arguments, defined above
    train_dataset=train_dataset,         # training dataset
    eval_dataset=eval_dataset            # evaluation dataset
)

trainer.train()