first commit

.gitattributes

@@ -33,3 +33,5 @@ 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
+*.file_extension filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eff07ee6a9faf1b1cbaf25837bd5990025f46ac083ea629919de57c82a86c157
+size 31314554

app.py

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+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_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 EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu")
+     )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+  # Start the timer
+  start_time = timer()
+
+  # Transform the target image and add a batch dimension
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  # Put model into evaluation mode and turn on inference mode
+  effnetb2.eval()
+  with torch.inference_mode():
+    # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+    pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+  # Create prediction label and prediction probability dictionary for each prediction class
+  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 EfficientNetB2 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,
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),
+                             gr.Number(label="Prediction time (s)")],
+                    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_effnetb2_model(num_classes:int=3,
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+      Args:
+          num_classes (int, optional): number of classes in the classifier head. 
+              Defaults to 3.
+          seed (int, optional): random seed value. Defaults to 42.
+
+      Returns:
+          model (torch.nn.Module): EffNetB2 feature extractor model. 
+          transforms (torchvision.transforms): EffNetB2 image transforms.
+      """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+      param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes)
+    )
+    
+    return model, transforms

requirements.txt

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+torch==2.5.0
+torchvision==0.20.0
+gradio==5.4.0