initial commit

.gitattributes

@@ -29,3 +29,4 @@ 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 sha256:24a803b0e458a9949a7725d651f780c5c77592042d159c7dcd3e658e95e5b96d
+size 31273033

app.py

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+import gradio as gr
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+# Create model
+model, transforms = create_effnetb2_model(
+    num_classes=3,
+)
+
+# Load saved weights
+model.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
+    )
+)
+
+# Create prediction code
+def predict(img):
+    start_time = timer()
+    img = transforms(img).unsqueeze(0)
+    model.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(model(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
+
+
+# Create Gradio app
+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_dir = "demo/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="demo/foodvision_mini/examples",
+    interpretation="default",
+    title=title,
+    description=description,
+    article=article,
+)
+
+demo.launch()

model.py

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+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes: int):
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Freeze base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head
+    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