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09_pretrained_effnetb2_fine_tuned_food101_full_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4abcd9124ec1b95399c2809669d00b0b9e9d25e0a11847b656fdab2b531fa8ee
+size 31825353

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
+with open("class_names.txt", "r") as f:  # reading them in from class_names.txt
+    class_names = [food_name.strip() for food_name in f.readlines()]
+
+### 2. Model and transforms preparation ###
+
+# Create model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=101,  # could also use len(class_names)
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_fine_tuned_food101_full_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 = 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 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 Big 🍔👁"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
+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 Gradio interface
+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 app!
+demo.launch(share=True)

class_names.txt

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+Apple pie
+Baby back ribs
+Baklava
+Beef carpaccio
+Beef tartare
+Beet salad
+Beignets
+Bibimbap
+Bread pudding
+Breakfast burrito
+Bruschetta
+Caesar salad
+Cannoli
+Caprese salad
+Carrot cake
+Ceviche
+Cheese plate
+Cheesecake
+Chicken curry
+Chicken quesadilla
+Chicken wings
+Chocolate cake
+Chocolate mousse
+Churros
+Clam chowder
+Club sandwich
+Crab cakes
+Creme brulee
+Croque madame
+Cup cakes
+Deviled eggs
+Donuts
+Dumplings
+Edamame
+Eggs benedict
+Escargots
+Falafel
+Filet mignon
+Fish and chips
+Foie gras
+French fries
+French onion soup
+French toast
+Fried calamari
+Fried rice
+Frozen yogurt
+Garlic bread
+Gnocchi
+Greek salad
+Grilled cheese sandwich
+Grilled salmon
+Guacamole
+Gyoza
+Hamburger
+Hot and sour soup
+Hot dog
+Huevos rancheros
+Hummus
+Ice cream
+Lasagna
+Lobster bisque
+Lobster roll sandwich
+Macaroni and cheese
+Macarons
+Miso soup
+Mussels
+Nachos
+Omelette
+Onion rings
+Oysters
+Pad thai
+Paella
+Pancakes
+Panna cotta
+Peking duck
+Pho
+Pizza
+Pork chop
+Poutine
+Prime rib
+Pulled pork sandwich
+Ramen
+Ravioli
+Red velvet cake
+Risotto
+Samosa
+Sashimi
+Scallops
+Seaweed salad
+Shrimp and grits
+Spaghetti bolognese
+Spaghetti carbonara
+Spring rolls
+Steak
+Strawberry shortcake
+Sushi
+Tacos
+Takoyaki
+Tiramisu
+Tuna tartare
+Waffles

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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+pandas==1.4.3
+protobuf==3.19.4
+tensorflow==2.9.1
+numpy==1.23.1
+gradio==3.4.0