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09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:6058a3c3818ff75e8467ce92923d9b0ac7080c3143a28f9f1325d0f3f207c4f2
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+ size 31313869
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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+ class_names = ["pizza", "steak", "sushi"]
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+
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+ ### 2. Model and transforms preparation ###
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+
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+ # Create EffNetB2 model
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+ effnetb2, effnetb2_transforms = create_effnetb2_model(
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+ num_classes=3, # len(class_names) would also work
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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_pizza_steak_sushi_20_percent.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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+ title = "FoodVision Mini πŸ•πŸ₯©πŸ£"
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+ description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
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+
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+
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+
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+ example_list = [["examples/" + example] for example in os.listdir("examples")]
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+
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+
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+ demo = gr.Interface(fn=predict,
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+ inputs=gr.Image(type="pil"),
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+ outputs=[gr.Label(num_top_classes=3, label="Predictions"),
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+ gr.Number(label="Prediction time (s)")],
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+ examples=example_list,
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+ title=title,
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+ description=description)
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+
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+ # Launch the demo!
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+ demo.launch()
examples/2582289.jpg ADDED
examples/3622237.jpg ADDED
examples/592799.jpg ADDED
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==2.0.1
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+ torchvision==0.15.2
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+ gradio==3.35.2