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11_model_deployment_effnetb2_full_dataset_food101.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a90a9a1e236b1746bcbfbee2ea0772e3bf453afc50f27d8df82abcf402c3020f
+size 31851995

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 List, Dict,Tuple
+
+with open("class_name.txt", "r") as f:
+    class_names = [food.strip("\n") for food in f.readlines()]
+
+### 2 model and transform preparation###
+effnetb2_loaded, transforms = create_effnetb2_model(num_classes=len(class_names))
+effnetb2_loaded.load_state_dict(torch.load("11_model_deployment_effnetb2_full_dataset_food101.pth",map_location="cpu"))
+
+
+### 3 we need a predict function###
+def predict(img) -> Tuple[Dict,float]:
+    #start a timer
+    start_time = timer()
+
+    # transform the image
+    transformed_image = effnet_transform(img).unsqueeze(0)
+    
+    # putting the model in eval mode and make the prediction  
+    effnetb2_loaded.eval()
+    with torch.inference_mode():
+        logit = effnetb2_loaded(transformed_image)
+
+        probs = torch.softmax(logit, dim=1)
+        # Create a prediction label and prediction probability dictionary
+        pred_label_dict ={class_names[i] : probs[0][i].item() for i in range(len(class_names))}
+
+    # calculate the pred time 
+    end_time = timer()
+    inference_time = round(end_time - start_time, 4)
+    # return the label dict and inference time
+    return pred_label_dict, inference_time
+###Grad###
+title = "FoodVision big models trained on Food101 Dataset"
+description = "An EfficientnetB2 feature extraction model is used to classify images of food101 dataset"
+example_list =[["examples/"+example] for example in os.listdir("examples")]
+
+# create a gradio demo
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes = 5,label= "prediction"),
+             gr.Number(label=" Prediction time in second")],
+    examples=example_list,
+    title=title,
+    description=description,
+    cache_examples=False
+)
+demo.launch(share= False)
+

class_name.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

foodvision_big.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35d653329c5352cd128249887389a7b366dbe2a44c65f8054fb9a9cd0be0c826
+size 29602137

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 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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+torch
+torchvision 
+gradio