Initial Commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pretrained_vit_foodvision.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,87 @@
+### Imports and class names setup ---------------------------------------------------- ###
+import os
+import torch
+import torchvision
+import gradio as gr
+
+from model import create_vit
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+# Device agnostic code
+if torch.backends.mps.is_available():
+    device = "mps"
+elif torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+### Model and transforms preparation ---------------------------------------------------- ###
+vit_model, vit_transforms = create_vit(
+    pretrained_weights=torchvision.models.ViT_B_16_Weights.DEFAULT,
+    model=torchvision.models.vit_b_16,
+    in_features=768,
+    out_features=3,
+    device="cpu",
+)
+
+# Load save weights
+vit_model.load_state_dict(
+    torch.load(f="pretrained_vit_foodvision.pth", map_location=torch.device("cpu"))
+)  # load the model to the CPU
+
+
+### Predict function ---------------------------------------------------- ###
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+    # Transform the input image for use with ViT Model
+    img = vit_transforms(img).unsqueeze(
+        0
+    )  # unsqueeze = add batch dimension on 0th index (3, 224, 224) into (1, 3, 224, 224)
+    # Put model into eval mode, make prediction
+    vit_model.eval()
+    with torch.inference_mode():
+        # Pass transformed image through the model and turn the prediction logits into probabilities
+        pred_logits = vit_model(img)
+        pred_probs = torch.softmax(pred_logits, dim=1)
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {
+        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
+    }
+
+    # Calculate pred time
+    end_timer = timer()
+    pred_time = round(end_timer - start_time, 4)
+
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+
+### Gradio interface and launch ------------------------------------------------------------------ ###
+
+# Create title and description
+title = "FoodVision Mini: ViT Model"
+description = "A ViT model trained on 20% of the Food101 dataset to classify images of pizza, steak or sushi."
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predictions"),
+        gr.Number(label="Prediction time(s)"),
+    ],
+    title=title,
+    description=description,
+    examples=example_list,
+)
+demo.launch(
+    debug=False, share=True  # print errors locally
+)  # generate a publically shareable URL

model.py

@@ -0,0 +1,28 @@
+
+import torch
+import torchvision
+
+from torch import nn
+
+def create_vit(pretrained_weights: torchvision.models.Weights,
+               model: torchvision.models,
+               in_features: int,
+               out_features: int,
+               device: torch.device):
+    """Creates a Vision Transformer (ViT) instance from torchvision
+    and returns it.
+    """
+    # Create a pretrained ViT model
+    model = torchvision.models.vit_b_16(weights=pretrained_weights).to(device)
+    transforms = pretrained_weights.transforms()
+
+    # Freeze the feature extractor
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change the head of the ViT
+    model.heads = nn.Sequential(
+        nn.Linear(in_features=in_features, out_features=out_features)
+    ).to(device)
+
+    return model, transforms

pretrained_vit_foodvision.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c9067188086ff537cdb76de31c205acb865cca5ee0a25ed2d7ffe6b05376fea
+size 343264485

requirements.txt

@@ -0,0 +1,4 @@
+
+torch==2.0.1
+torchvision==0.15.2
+gradio==3.23.0