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09_pretrained_effnetb2_feature_extractor_food101.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:4d7532f5f6796c171174c1e0415d7a106c3c64deacb0a93900c17a3b6f6f440d
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+ size 31850487
app.py ADDED
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+ ### 1. Imports and class names setup ###
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+ import gradio as gr
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+ import os
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+ import torch
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+
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+ from model import create_effnetb2_model
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+ from timeit import default_timer as timer
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+ from typing import Tuple, Dict
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+
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+ # Setup class names
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+ with open("class_names.txt", "r") as f: # reading them in from class_names.txt
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+ class_names = [food_name.strip() for food_name in f.readlines()]
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+
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+ ### 2. Model and transforms preparation ###
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+
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+ # Create model
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+ effnetb2, effnetb2_transforms = create_effnetb2_model(
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+ num_classes=101, # could also use len(class_names)
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+ )
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+
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+ # Load saved weights
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+ effnetb2.load_state_dict(
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+ torch.load(
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+ f="09_pretrained_effnetb2_feature_extractor_food101.pth",
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+ map_location=torch.device("cpu"), # load to CPU
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+ )
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+ )
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+
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+ ### 3. Predict function ###
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+
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+ # Create predict function
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+ def predict(img) -> Tuple[Dict, float]:
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+ """Transforms and performs a prediction on img and returns prediction and time taken.
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+ """
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+ # Start the timer
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+ start_time = timer()
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+
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+ # Transform the target image and add a batch dimension
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+ img = effnetb2_transforms(img).unsqueeze(0)
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+
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+ # Put model into evaluation mode and turn on inference mode
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+ effnetb2.eval()
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+ with torch.inference_mode():
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+ # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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+ pred_probs = torch.softmax(effnetb2(img), dim=1)
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+
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+ # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
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+ pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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+
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+ # Calculate the prediction time
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+ pred_time = round(timer() - start_time, 5)
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+
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+ # Return the prediction dictionary and prediction time
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+ return pred_labels_and_probs, pred_time
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+
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+ ### 4. Gradio app ###
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+
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+ # Create title, description and article strings
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+ title = "FoodVision Big 🍔👁"
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+ 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)."
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+ article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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+
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+ # Create examples list from "examples/" directory
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+ example_list = [["examples/" + example] for example in os.listdir("examples")]
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+
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+ # Create Gradio interface
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+ demo = gr.Interface(
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+ fn=predict,
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+ inputs=gr.Image(type="pil"),
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+ outputs=[
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+ gr.Label(num_top_classes=5, label="Predictions"),
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+ gr.Number(label="Prediction time (s)"),
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+ ],
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+ examples=example_list,
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+ title=title,
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+ description=description,
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+ article=article,
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+ )
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+
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+ # Launch the app!
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+ demo.launch()
class_names.txt ADDED
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+ apple_pie
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+ baby_back_ribs
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+ baklava
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+ beef_carpaccio
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+ beef_tartare
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+ beet_salad
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+ beignets
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+ bibimbap
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+ bread_pudding
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+ breakfast_burrito
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+ bruschetta
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+ caesar_salad
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+ cannoli
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+ caprese_salad
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+ carrot_cake
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+ ceviche
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+ cheese_plate
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+ cheesecake
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+ chicken_curry
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+ chicken_quesadilla
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+ chicken_wings
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+ chocolate_cake
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+ chocolate_mousse
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+ churros
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+ clam_chowder
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+ club_sandwich
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+ crab_cakes
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+ creme_brulee
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+ croque_madame
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+ cup_cakes
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+ deviled_eggs
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+ donuts
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+ dumplings
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+ edamame
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+ eggs_benedict
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+ escargots
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+ falafel
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+ filet_mignon
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+ fish_and_chips
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+ foie_gras
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+ french_fries
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+ french_onion_soup
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+ french_toast
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+ fried_calamari
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+ fried_rice
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+ frozen_yogurt
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+ garlic_bread
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+ gnocchi
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+ greek_salad
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+ grilled_cheese_sandwich
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+ grilled_salmon
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+ guacamole
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+ gyoza
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+ hamburger
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+ hot_and_sour_soup
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+ hot_dog
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+ huevos_rancheros
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+ hummus
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+ ice_cream
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+ lasagna
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+ lobster_bisque
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+ lobster_roll_sandwich
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+ macaroni_and_cheese
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+ macarons
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+ miso_soup
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+ mussels
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+ nachos
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+ omelette
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+ onion_rings
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+ oysters
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+ pad_thai
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+ paella
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+ pancakes
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+ panna_cotta
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+ peking_duck
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+ pho
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+ pizza
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+ pork_chop
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+ poutine
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+ prime_rib
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+ pulled_pork_sandwich
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+ ramen
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+ ravioli
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+ red_velvet_cake
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+ risotto
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+ samosa
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+ sashimi
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+ scallops
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+ seaweed_salad
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+ shrimp_and_grits
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+ spaghetti_bolognese
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+ spaghetti_carbonara
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+ spring_rolls
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+ steak
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+ strawberry_shortcake
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+ sushi
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+ tacos
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+ takoyaki
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+ tiramisu
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+ tuna_tartare
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+ waffles
model.py ADDED
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+ import torch
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+ import torchvision
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+
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+ from torch import nn
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+
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+
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+ def create_effnetb2_model(num_classes:int=3,
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+ seed:int=42):
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+ """Creates an EfficientNetB2 feature extractor model and transforms.
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+
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+ Args:
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+ num_classes (int, optional): number of classes in the classifier head.
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+ Defaults to 3.
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+ seed (int, optional): random seed value. Defaults to 42.
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+
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+ Returns:
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+ model (torch.nn.Module): EffNetB2 feature extractor model.
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+ transforms (torchvision.transforms): EffNetB2 image transforms.
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+ """
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+ # Create EffNetB2 pretrained weights, transforms and model
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+ weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
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+ transforms = weights.transforms()
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+ model = torchvision.models.efficientnet_b2(weights=weights)
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+
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+ # Freeze all layers in base model
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+ for param in model.parameters():
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+ param.requires_grad = False
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+
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+ # Change classifier head with random seed for reproducibility
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+ torch.manual_seed(seed)
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+ model.classifier = nn.Sequential(
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+ nn.Dropout(p=0.3, inplace=True),
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+ nn.Linear(in_features=1408, out_features=num_classes),
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+ )
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+
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+ return model, transforms
requirements.txt ADDED
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+ torch
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+ torchvision
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+ gradio