text: """
  =========================
  Tensor transforms and JIT
  =========================
  
  This example illustrates various features that are now supported by the
  :ref:`image transformations <transforms>` on Tensor images. In particular, we
  show how image transforms can be performed on GPU, and how one can also script
  them using JIT compilation.
  
  Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric
  and presented multiple limitations due to that. Now, since v0.8.0, transforms
  implementations are Tensor and PIL compatible and we can achieve the following
  new features:
  
  - transform multi-band torch tensor images (with more than 3-4 channels)
  - torchscript transforms together with your model for deployment
  - support for GPU acceleration
  - batched transformation such as for videos
  - read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)
  
  .. note::
      These features are only possible with **Tensor** images.
  """
  
  from pathlib import Path
  
  import matplotlib.pyplot as plt
  import numpy as np
  
  import torch
  import torchvision.transforms as T
  from torchvision.io import read_image
  
  
  plt.rcParams["savefig.bbox"] = 'tight'
  torch.manual_seed(1)
  
  
  def show(imgs):
      fix, axs = plt.subplots(ncols=len(imgs), squeeze=False)
      for i, img in enumerate(imgs):
          img = T.ToPILImage()(img.to('cpu'))
          axs[0, i].imshow(np.asarray(img))
          axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
  
  
  ####################################
  # The :func:`~torchvision.io.read_image` function allows to read an image and
  # directly load it as a tensor
  
  dog1 = read_image(str(Path('assets') / 'dog1.jpg'))
  dog2 = read_image(str(Path('assets') / 'dog2.jpg'))
  show([dog1, dog2])
  
  ####################################
  # Transforming images on GPU
  # --------------------------
  # Most transforms natively support tensors on top of PIL images (to visualize
  # the effect of the transforms, you may refer to see
  # :ref:`sphx_glr_auto_examples_plot_transforms.py`).
  # Using tensor images, we can run the transforms on GPUs if cuda is available!
  
  import torch.nn as nn
  
  transforms = torch.nn.Sequential(
      T.RandomCrop(224),
      T.RandomHorizontalFlip(p=0.3),
  )
  
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
  dog1 = dog1.to(device)
  dog2 = dog2.to(device)
  
  transformed_dog1 = transforms(dog1)
  transformed_dog2 = transforms(dog2)
  show([transformed_dog1, transformed_dog2])
  
  ####################################
  # Scriptable transforms for easier deployment via torchscript
  # -----------------------------------------------------------
  # We now show how to combine image transformations and a model forward pass,
  # while using ``torch.jit.script`` to obtain a single scripted module.
  #
  # Let's define a ``Predictor`` module that transforms the input tensor and then
  # applies an ImageNet model on it.
  
  from torchvision.models import resnet18, ResNet18_Weights
  
  
  class Predictor(nn.Module):
  
      def __init__(self):
          super().__init__()
          weights = ResNet18_Weights.DEFAULT
          self.resnet18 = resnet18(weights=weights, progress=False).eval()
          self.transforms = weights.transforms()
  
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          with torch.no_grad():
              x = self.transforms(x)
              y_pred = self.resnet18(x)
              return y_pred.argmax(dim=1)
  
  
  ####################################
  # Now, let's define scripted and non-scripted instances of ``Predictor`` and
  # apply it on multiple tensor images of the same size
  
  predictor = Predictor().to(device)
  scripted_predictor = torch.jit.script(predictor).to(device)
  
  batch = torch.stack([dog1, dog2]).to(device)
  
  res = predictor(batch)
  res_scripted = scripted_predictor(batch)
  
  ####################################
  # We can verify that the prediction of the scripted and non-scripted models are
  # the same:
  
  import json
  
  with open(Path('assets') / 'imagenet_class_index.json') as labels_file:
      labels = json.load(labels_file)
  
  for i, (pred, pred_scripted) in enumerate(zip(res, res_scripted)):
      assert pred == pred_scripted
      print(f"Prediction for Dog {i + 1}: {labels[str(pred.item())]}")
  
  ####################################
  # Since the model is scripted, it can be easily dumped on disk and re-used
  
  import tempfile
  
  with tempfile.NamedTemporaryFile() as f:
      scripted_predictor.save(f.name)
  
      dumped_scripted_predictor = torch.jit.load(f.name)
      res_scripted_dumped = dumped_scripted_predictor(batch)
  assert (res_scripted_dumped == res_scripted).all()
  