initial commit

09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b17e6a96bd4b62bf806ca8d41c3d47063079079201c7607b2d71ce672b3da321
+size 31825353

app.py

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+import gradio as gr
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+
+# TK - load in FoodVision Big class names
+# Setup class names
+with open(foodvision_big_class_names_path, "r") as f:
+    class_names = [food_name.strip() for food_name in  f.readlines()]
+
+# Create model
+model, transforms = create_effnetb2_model(
+    num_classes=101,
+)
+
+# Load saved weights
+model.load_state_dict(
+    torch.load(
+        f= foodvision_big_demo_path / "09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+# Create prediction code
+def predict(img):
+    start_time = timer()
+    img = transforms(img).unsqueeze(0)
+    model.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(model(img), dim=1)
+    pred_labels_and_probs = {
+        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
+    }
+    pred_time = round(timer() - start_time, 5)
+    return pred_labels_and_probs, pred_time
+
+
+# Create Gradio app
+title = "FoodVision Big 🍔👁"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into 101 different classes."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+example_dir = "demos/foodvision_big/examples"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Predictions"),
+        gr.Number(label="Prediction time (s)"),
+    ],
+    # examples="demo/foodvision_mini/examples",
+    interpretation="default",
+    title=title,
+    description=description,
+    article=article,
+)
+
+demo.launch()

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 torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes: int):
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Freeze base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head
+    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==1.12.0
+torchvision==0.13.0
+gradio==3.1.4