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09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6eea20fd0394f3bbe67404ca7fcb74c1e0e46f8b04b5a432d58edda1233034ba
+size 343273342

app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_vit_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+### 2. Model and transforms preparation ###
+
+# Create VIT model
+vit, vit_transforms = create_vit_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# Load saved weights
+vit.load_state_dict(
+    torch.load(
+        f="09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+
+    # Transform the target image and add a batch dimension
+    img = vit_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    vit.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(vit(img), dim=1)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An vit feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+def create_vit_model(num_classes:int=3,
+                     seed:int=42):
+    """Creates a ViT-B/16 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of target classes. Defaults to 3.
+        seed (int, optional): random seed value for output layer. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): ViT-B/16 feature extractor model.
+        transforms (torchvision.transforms): ViT-B/16 image transforms.
+    """
+    # Create ViT_B_16 pretrained weights, transforms and model
+    weights = torchvision.models.ViT_B_16_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.vit_b_16(weights=weights)
+
+    # Freeze all layers in model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head to suit our needs (this will be trainable)
+    torch.manual_seed(seed)
+    model.heads = nn.Sequential(nn.Linear(in_features=768, # keep this the same as original model
+                                          out_features=num_classes)) # update to reflect target number of classes
+
+    return model, transforms