initial commit

app.py

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+import os
+from timeit import default_timer as timer
+from typing import Tuple
+from pathlib import Path
+from PIL import Image
+
+import gradio as gr
+import torch
+from torch import nn
+from torchvision import transforms
+
+from model import create_effnetb3_model
+
+class_names = ['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']
+
+device = "cpu"
+
+# Create model
+effnetb3, effnetb3_transforms = create_effnetb3_model(num_classes=len(class_names))
+
+# Load saved weights
+effnetb3_state_dict = torch.load("effnetb3_full_food101.pth",
+                                  map_location=torch.device(device))
+effnetb3_state_dict['classifier.1.weight'] = effnetb3_state_dict.pop('classifier.weight')
+effnetb3_state_dict['classifier.1.bias'] = effnetb3_state_dict.pop('classifier.bias')
+effnetb3.load_state_dict(effnetb3_state_dict)
+effnetb3.to(device);
+
+# Define predict function
+def predict(img: Image) -> Tuple[dict, float]:
+    """Uses EffnetB3 model to transform and predict on img. Returns prediction
+    probabilities and time taken.
+    
+    Args:
+      img (PIL.Image): Image to predict on.
+    
+    Returns:
+      A tuple (pred_labels_and_probs, pred_time), where pred_labels_and_probs
+      is a dict mapping each class name to the probability the model assigns to
+      it, and pred_time is the time taken to predict (in seconds).
+    """
+    start_time = timer()
+    img = effnetb3_transforms(img).unsqueeze(0)
+    effnetb3.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb3(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, 4)
+    return pred_labels_and_probs, pred_time
+
+# Initialize Gradio app
+title = "FoodVision"
+description = "EfficientNetB3 feature extractor to classify images of food. Upload an image or click on one of the examples to try it out!"
+article = """
+From the [Zero to Mastery PyTorch tutorial](https://www.learnpytorch.io/09_pytorch_model_deployment/), using the
+[Food-101 dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
+"""
+examples = [[example] for example in Path("examples").glob("*.jpg")]
+
+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=examples,
+    title=title,
+    description=description,
+    article=article,
+)
+
+demo.launch()

effnetb3_full_food101.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:221dfc2c8bcb2664081e0c57fffcb04001e77b523613538aa29f1ed2870c5c79
+size 43989701

model.py

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+from typing import Tuple
+
+import torch
+from torch import nn
+import torchvision
+
+def create_effnetb3_model(num_classes: int = 101,
+                          seed: int = 4,
+                         ) -> Tuple[nn.Module, torchvision.transforms.Compose]:
+    """Create an EfficientNetB2 feature extractor model and transforms.
+    
+    Args:
+      num_classes: Number of classes to use for classification (default 3).
+      seed: Random seed for reproducibility (default 4).
+      
+    Returns:
+      A tuple (model, transforms) of the model and its image transforms.
+    """
+    weights = torchvision.models.EfficientNet_B3_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b3(weights=weights)
+    
+    # Freeze parameters below the head
+    for param in model.parameters():
+        param.requires_grad = False
+    # Replace the classifier head with one of appropriate size for the problem
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1536, out_features=num_classes)
+    )
+    return model, transforms

requirements.txt

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+gradio==3.37.0
+torch==2.0.1
+torchvision==0.15.2