Add the example of super_resolution

examples/super_resolution/README.md

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+# Super-Resolution using an efficient sub-pixel convolutional neural network
+
+ported from [pytorch-examples](https://github.com/pytorch/examples/tree/main/super_resolution)
+
+This example illustrates how to use the efficient sub-pixel convolution layer described in ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al.](https://arxiv.org/abs/1609.05158) for increasing spatial resolution within your network for tasks such as superresolution.
+
+```
+usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--batchSize BATCHSIZE]
+               [--testBatchSize TESTBATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
+               [--cuda] [--threads THREADS] [--seed SEED]
+
+PyTorch Super Res Example
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --upscale_factor      super resolution upscale factor
+  --batch_size           training batch size
+  --test_batch_size       testing batch size
+  --n_epochs             number of epochs to train for
+  --lr                  Learning Rate. Default=0.01
+  --cuda                use cuda
+  --mps                 enable GPU on macOS
+  --threads             number of threads for data loader to use Default=4
+  --seed                random seed to use. Default=123
+```
+
+This example trains a super-resolution network on the [Cifar10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). A snapshot of the model after every epoch with filename `model_epoch_<epoch_number>.pth`
+
+## Example Usage:
+
+### Train
+
+`python main.py --upscale_factor 3 --batch_size 4 --test_batch_size 100 --n_epochs 30 --lr 0.001`
+
+### Super Resolve
+
+`python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png`

examples/super_resolution/main.py

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+import argparse
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+from model import Net
+from torch.utils.data import DataLoader
+from torchvision.transforms import CenterCrop, Compose, Resize, ToTensor
+
+from ignite.engine import Engine, Events
+from ignite.metrics import PSNR
+
+# Training settings
+parser = argparse.ArgumentParser(description="PyTorch Super Res Example")
+parser.add_argument("--upscale_factor", type=int, required=True, help="super resolution upscale factor")
+parser.add_argument("--batch_size", type=int, default=64, help="training batch size")
+parser.add_argument("--test_batch_size", type=int, default=10, help="testing batch size")
+parser.add_argument("--n_epochs", type=int, default=2, help="number of epochs to train for")
+parser.add_argument("--lr", type=float, default=0.01, help="Learning Rate. Default=0.01")
+parser.add_argument("--cuda", action="store_true", help="use cuda?")
+parser.add_argument("--mps", action="store_true", default=False, help="enables macOS GPU training")
+parser.add_argument("--threads", type=int, default=4, help="number of threads for data loader to use")
+parser.add_argument("--seed", type=int, default=123, help="random seed to use. Default=123")
+opt = parser.parse_args()
+
+print(opt)
+
+if opt.cuda and not torch.cuda.is_available():
+    raise Exception("No GPU found, please run without --cuda")
+if not opt.mps and torch.backends.mps.is_available():
+    raise Exception("Found mps device, please run with --mps to enable macOS GPU")
+
+torch.manual_seed(opt.seed)
+use_mps = opt.mps and torch.backends.mps.is_available()
+
+if opt.cuda:
+    device = torch.device("cuda")
+elif use_mps:
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+
+print("===> Loading datasets")
+
+
+class SRDataset(torch.utils.data.Dataset):
+    def __init__(self, dataset, scale_factor, input_transform=None, target_transform=None):
+        self.dataset = dataset
+        self.input_transform = input_transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        image, _ = self.dataset[index]
+        img = image.convert("YCbCr")
+        lr_image, _, _ = img.split()
+
+        hr_image = lr_image.copy()
+        if self.input_transform:
+            lr_image = self.input_transform(lr_image)
+        if self.target_transform:
+            hr_image = self.target_transform(hr_image)
+        return lr_image, hr_image
+
+    def __len__(self):
+        return len(self.dataset)
+
+
+def calculate_valid_crop_size(crop_size, upscale_factor):
+    return crop_size - (crop_size % upscale_factor)
+
+
+def input_transform(crop_size, upscale_factor):
+    return Compose(
+        [
+            CenterCrop(crop_size),
+            Resize(crop_size // upscale_factor),
+            ToTensor(),
+        ]
+    )
+
+
+def target_transform(crop_size):
+    return Compose(
+        [
+            CenterCrop(crop_size),
+            ToTensor(),
+        ]
+    )
+
+
+crop_size = calculate_valid_crop_size(256, opt.upscale_factor)
+
+trainset = torchvision.datasets.Caltech101(root="./data", download=True)
+testset = torchvision.datasets.Caltech101(root="./data", download=False)
+
+trainset_sr = SRDataset(
+    trainset,
+    scale_factor=opt.upscale_factor,
+    input_transform=input_transform(crop_size, opt.upscale_factor),
+    target_transform=target_transform(crop_size),
+)
+testset_sr = SRDataset(
+    testset,
+    scale_factor=opt.upscale_factor,
+    input_transform=input_transform(crop_size, opt.upscale_factor),
+    target_transform=target_transform(crop_size),
+)
+
+training_data_loader = DataLoader(dataset=trainset_sr, num_workers=opt.threads, batch_size=opt.batch_size, shuffle=True)
+testing_data_loader = DataLoader(
+    dataset=testset_sr, num_workers=opt.threads, batch_size=opt.test_batch_size, shuffle=False
+)
+
+print("===> Building model")
+model = Net(upscale_factor=opt.upscale_factor).to(device)
+criterion = nn.MSELoss()
+
+optimizer = optim.Adam(model.parameters(), lr=opt.lr)
+
+
+def train_step(engine, batch):
+    input, target = batch[0].to(device), batch[1].to(device)
+
+    optimizer.zero_grad()
+    loss = criterion(model(input), target)
+    loss.backward()
+    optimizer.step()
+
+    return loss.item()
+
+
+def validation_step(engine, batch):
+    model.eval()
+    with torch.no_grad():
+        x, y = batch[0].to(device), batch[1].to(device)
+        y_pred = model(x)
+
+    return y_pred, y
+
+
+trainer = Engine(train_step)
+evaluator = Engine(validation_step)
+psnr = PSNR(data_range=1)
+psnr.attach(evaluator, "psnr")
+validate_every = 1
+log_interval = 100
+
+
+@trainer.on(Events.ITERATION_COMPLETED(every=log_interval))
+def log_training_loss(engine):
+    print(
+        "===> Epoch[{}]({}/{}): Loss: {:.4f}".format(
+            engine.state.epoch, engine.state.iteration, len(training_data_loader), engine.state.output
+        )
+    )
+
+
+@trainer.on(Events.EPOCH_COMPLETED(every=validate_every))
+def log_validation():
+    evaluator.run(testing_data_loader)
+    metrics = evaluator.state.metrics
+    print(f"Epoch: {trainer.state.epoch}, Avg. PSNR: {metrics['psnr']} dB")
+
+
+@trainer.on(Events.EPOCH_COMPLETED)
+def log_epoch_time():
+    print(f"Epoch {trainer.state.epoch}, Time Taken : {trainer.state.times['EPOCH_COMPLETED']}")
+
+
+@trainer.on(Events.COMPLETED)
+def log_total_time():
+    print(f"Total Time: {trainer.state.times['COMPLETED']}")
+
+
+@trainer.on(Events.EPOCH_COMPLETED)
+def checkpoint():
+    model_out_path = "model_epoch_{}.pth".format(trainer.state.epoch)
+    torch.save(model, model_out_path)
+    print("Checkpoint saved to {}".format(model_out_path))
+
+
+trainer.run(training_data_loader, opt.n_epochs)

examples/super_resolution/model.py

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+import torch.nn as nn
+import torch.nn.init as init
+
+
+class Net(nn.Module):
+    def __init__(self, upscale_factor):
+        super(Net, self).__init__()
+
+        self.relu = nn.ReLU()
+        self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
+        self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
+        self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
+        self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))
+        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        x = self.relu(self.conv1(x))
+        x = self.relu(self.conv2(x))
+        x = self.relu(self.conv3(x))
+        x = self.pixel_shuffle(self.conv4(x))
+        return x
+
+    def _initialize_weights(self):
+        init.orthogonal_(self.conv1.weight, init.calculate_gain("relu"))
+        init.orthogonal_(self.conv2.weight, init.calculate_gain("relu"))
+        init.orthogonal_(self.conv3.weight, init.calculate_gain("relu"))
+        init.orthogonal_(self.conv4.weight)

examples/super_resolution/super_resolve.py

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+import argparse
+
+import numpy as np
+import torch
+from PIL import Image
+from torchvision.transforms import ToTensor
+
+# Training settings
+parser = argparse.ArgumentParser(description="PyTorch Super Res Example")
+parser.add_argument("--input_image", type=str, required=True, help="input image to use")
+parser.add_argument("--model", type=str, required=True, help="model file to use")
+parser.add_argument("--output_filename", type=str, help="where to save the output image")
+parser.add_argument("--cuda", action="store_true", help="use cuda")
+opt = parser.parse_args()
+
+print(opt)
+img = Image.open(opt.input_image).convert("YCbCr")
+y, cb, cr = img.split()
+
+model = torch.load(opt.model)
+img_to_tensor = ToTensor()
+input = img_to_tensor(y).view(1, -1, y.size[1], y.size[0])
+
+if opt.cuda:
+    model = model.cuda()
+    input = input.cuda()
+
+out = model(input)
+out = out.cpu()
+out_img_y = out[0].detach().numpy()
+out_img_y *= 255.0
+out_img_y = out_img_y.clip(0, 255)
+out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode="L")
+
+out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
+out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
+out_img = Image.merge("YCbCr", [out_img_y, out_img_cb, out_img_cr]).convert("RGB")
+
+out_img.save(opt.output_filename)
+print("output image saved to ", opt.output_filename)