first commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.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 sha256:29e76c4da4682eb81e3e08537864fc811bb0d8cd1ca2690b359f074bfe0c8da6
+size 31313869

app.py

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+import gradio as gr
+import os, torch
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_names = ['pizza', 'steak', 'sushi']
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device('cpu')
+    )
+)
+
+def predict(img) -> Tuple[Dict, float]:
+    """
+    Transforms and performs a prediction on img and
+    returns prediction and time taken.
+    """
+    start_time = timer()
+    img = effnetb2_transforms(img).unsqueeze(0)
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(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, 5)
+    return pred_labels_and_probs, pred_time
+
+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/)."
+
+example_list = [['examples/' + example] for example in os.listdir('examples')]
+
+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=example_list,
+    title=title,
+    description=description,
+    article=article
+)
+
+demo.launch()

model.py

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+import torch, 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.
+    """
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    for param in model.parameters():
+        param.requires_grad = False
+    
+    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>=1.12.0
+torchvision>=0.13.0
+gradio>=3.1.4