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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image. 
There are 3 components to this demo
Step1:  Model
Step2:  Input
Step3:  Output
Step4:  Human
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Python Code:
import torch 
import torchvision
import torchvision.transforms as transforms
import timm 
from einops import rearrange
from PIL import Image
Explanation: Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image. 
There are 3 components to this demo:
1. Input
2. Model
3. Output
We will cover these components in detail below.
Let us first import the required packages.
End of explanation
use_timm = False
# Download and load the pretrained ResNet-18.
if use_timm:
    resnet = timm.create_model('resnet18', pretrained=True)
else:
    resnet = torchvision.models.resnet18(pretrained=True)
resnet.eval()
Explanation: Model: Loading a pre-trained ResNet18 model
We use a pre-trained ResNet18 model for inference. The model is available from torchvision or from timm.
When we use a model for inference, we need to specify the eval mode. This is because the model in train mode by default, and we need to disable all the dropout layers.
End of explanation
filename = input()
# Load a PIL Image given a file name from the current directory.
img = Image.open(filename)
# Display the loaded image on notebook.
display(img)
# Resize the image to 256x256. 
# Then crop the center square of the image. 
# Next, convert the image to a PyTorch Tensor.
# Lastly, normalize the image so that it has mean and standard deviation as shown below.
# Reference for image transforms: https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
                    transforms.Resize(256),
                    transforms.CenterCrop(224),
                    transforms.ToTensor(),
                    normalize,
                    ])
# PIL image undergoes transforms.
img = transform(img)
# A simplified version is to simply transform the image to a tensor
#img = transforms.ToTensor()(img)
# Check img type and shape
print("Type:", img.dtype)
print("Shape:", img.shape)
Explanation: Input: Loading an input image
We can use matplotlib image to load an image into a numpy array. 
However, PyTorch transforms expects a PIL image. While we can convert numpy array to PIL, we can load an image directly into a PIL image.
End of explanation
# We need the tensor to have a batch dimension of 1.
img = rearrange(img, 'c h w  -> 1 c h w')
print("New shape:", img.shape)
with torch.no_grad():
    pred = resnet(img)
print("Prediction shape:", pred.shape)
pred = torch.argmax(pred, dim=1)
print("Predicted index", pred)
Explanation: Output: Making a prediction
We will now use img tensor as input to the pre-trained resnet18 model. 
Before running the model for prediction, there are 2 things that we should do:
Include a batch dimension. In this case, we are using a single image, so we need to add a batch size of 1. We use rearrange for this.
Execute inference within torch.no_grad() context manager. This is because we do not want to track the gradients.
The expected output is a torch.Tensor of shape (1, 1000). resnet18 was pre-trained on ImageNet1k. We can use torch.argmax to get the index of the maximum value.
End of explanation
import urllib
filename = "imagenet1000_labels.txt"
url = "https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt"
# Download the file if it does not exist
if not os.path.isfile(filename):
    urllib.request.urlretrieve(url, filename)
with open(filename) as f:
    idx2label = eval(f.read())
print("Predicted label:", idx2label[pred.cpu().numpy()[0]])
Explanation: Human: Convert class index to label
To make sense of the predicted index, we need to convert it to a label. We can use https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a to get the mapping from index to label.
End of explanation
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