Delete foodvision_mini

foodvision_mini/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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-version https://git-lfs.github.com/spec/v1
-oid sha256:eff07ee6a9faf1b1cbaf25837bd5990025f46ac083ea629919de57c82a86c157
-size 31314554

foodvision_mini/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 Tuple, Dict
-
-# Setup class names
-class_names = ["pizza", "steak", "sushi"]
-
-### 2. Model and transforms preparation ###
-
-# Create EffNetB2 model
-effnetb2, effnetb2_transforms = create_effnetb2_model(
-    num_classes=3, # len(class_names) would also work
-)
-
-# Load saved weights
-effnetb2.load_state_dict(
-    torch.load(
-        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
-        map_location=torch.device("cpu"),  # load to CPU
-    )
-)
-
-### 3. Predict function ###
-
-# Create predict function
-def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
-    start_time = timer()
-
-    # Transform the target image and add a batch dimension
-    img = effnetb2_transforms(img).unsqueeze(0)
-
-    # Put model into evaluation mode and turn on inference mode
-    effnetb2.eval()
-    with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(effnetb2(img), dim=1)
-
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-    # Calculate the prediction time
-    pred_time = round(timer() - start_time, 5)
-
-    # Return the prediction dictionary and prediction time
-    return pred_labels_and_probs, pred_time
-
-### 4. Gradio app ###
-
-# Create title, description and article strings
-title = "FoodVision Mini 🍕🥩🍣"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
-
-# Create examples list from "examples/" directory
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
-                    inputs=gr.Image(type="pil"), # what are the inputs?
-                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
-                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
-                    # Create examples list from "examples/" directory
-                    examples=example_list,
-                    title=title,
-                    description=description,
-                    article=article)
-
-# Launch the demo!
-demo.launch()

foodvision_mini/model.py

@@ -1,37 +0,0 @@
-
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3,
-                          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

foodvision_mini/requirements.txt

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-torch==2.2.1
-torchvision==0.17.1
-gradio==3.1.4
-httpx==0.24.1