application commit

.gitattributes

@@ -32,3 +32,4 @@ 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
+09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ec72b9cc2c48cc80c97617fdba9b28fef3e9e6e39bb84b46de29d258e50dbaf
+size 31856609

app.py

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+# 1. Imports and class names 
+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 
+
+# Set up class names
+with open("class_names.txt", "r") as f: 
+  class_names = [food_name.strip() for food_name in f.readlines()]
+
+# 2. Model and transforms preparations 
+# Create model 
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101) 
+
+# Load saved weights 
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+        map_location=torch.device("cpu") # load to CPU
+    )
+)
+
+# 3. Predict function 
+
+# Create predict function
+def prdict(img) -> Tuple[Dict, float]: 
+  """
+  Transforms and performs a prediction on img and returns predictions and time per prediction
+  """
+
+  # Start the timer 
+  start_time = timer() 
+
+  # Transform the target image and add a batch dimension
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  # Put the model into evaluation mode and turn on inference mode
+  effnetb2.eval() 
+  with torch.inference_mode():
+    # Pass the transformed iamge through the model and turn the prediction logits into prediction probablities 
+    pred_probs = torch.softmax(effnetb2(img), dim=1) 
+
+  # Create a prediction label and prediction probability dictionary for each prediction class (required format for Gradio)
+  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+  # Calculate prediction time
+  pred_time = round(timer() - start_time, 5)
+
+# 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/) course."
+
+# 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=5, label="Predictions"),
+        gr.Number(label="Prediction time (s)")
+    ],
+    examples=example_list, 
+    title=title,
+    description=description, 
+    article=article
+)
+
+# Launch the app 
+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 torch
+import torchvision 
+
+from torch import nn 
+
+def create_effnetb2_model(num_classes:int=101, 
+                          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 the classifier head with random seed for reproducibility 
+  torch.manaul_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==1.12.0
+torchvision==0.13.0
+gradio==3.14.0