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.gitattributes

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+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d19770ada64e5a76b25a703a2b1a2a2a67cdf479d11f38876a968166add3274
+size 31313869

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 Dict, Tuple
+
+
+# Setup class names
+class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model and transforms perparation ###
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+
+# Load save 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 the model to the CPU
+
+### 3. Predict function ###
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+
+    # Transform the input image for use with EffNetB2
+    transformed_img = effnetb2_transforms(img).unsqueeze(dim=0)  # unsqueeze = add batch dimension on 0th
+
+    # Put model into eval mode, make prediction
+    with torch.inference_mode():
+        effnetb2.eval()
+        
+        # Pass the transformed image through the model and turn the prediction logits into probabilities
+        pred_prob = effnetb2(transformed_img).softmax(dim=1)
+
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {class_names[i]: pred_prob[0][i].item() for i in range(len(class_names))}
+
+    # Calcualte pred time
+    end_time = timer()
+    inference_time = round(end_time - start_time, 4)
+
+    # Return pred dict and pred time
+    return pred_labels_and_probs, inference_time
+
+### 4. Gradio app ###
+# Create title, description and aritcle
+title = 'FoodVision Mini 🍕🥩🍣'
+description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/0.16/models/generated/torchvision.models.efficientnet_b2.html#efficientnet-b2) computer vision model to classify images as pizza, steak, sushi.'
+article = 'Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface).'
+
+# Create example list
+example_list = [['examples/' + example] for example in os.listdir('examples')]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict,  # maps inputs to outputs
+                    inputs=gr.Image(type='pil'),
+                    outputs=[gr.Label(num_top_classes=3, label='Predictions'),
+                             gr.Number(label='Prediction time (s)')],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False,  # print errors locally?
+            share=True)  # generate a publically shareable URL

model.py

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+import torch
+import torchvision
+from torch import nn
+
+
+def create_effnetb2_model(num_classese: int = 3,  # default output classes = 3 (pizza, steak , sushi)
+                          seed: int = 42):
+    # 1, 2, 3 Create EffNetB2 pretained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # 4. Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # 5. Change classifier head with random seed for reproducibility
+        torch.manual_seed(seed)
+        model.classifier = nn.Sequential(
+            nn.Dropout(p=.3, inplace=True),
+            nn.Linear(in_features=1408, out_features=num_classese)
+        )
+
+    return model, transforms

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+gradio==4.14.0