first 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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c01101ba959b24724487b8c84e45ec9d0cc8b2b1de2f6e2fa7eb02be566dfab
+size 31313869

app.py

@@ -0,0 +1,89 @@
+#!usr/bin/env python
+import os
+import torch
+
+import gradio as gr
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+from PIL import Image
+
+class_names = ["pizza", "steak", "sushi"]
+
+# create effnetb2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=len(class_names),
+)
+
+# 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"),
+    )
+)
+
+# predict function
+def predict(img: Image) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on an image and returns the prediction
+    and the time taken
+
+    Parameters
+    ----------
+    img : Image
+        Image to classify
+
+    Returns
+    -------
+    Tuple[Dict, float]
+        tuple with a dictionary that contains the probability that img belongs to
+        each class and the time taken to make the prediction
+        
+        Example: ({"class1": 0.95, "class2": 0.02, "class3": 0.03}, 0.026)
+    """
+    start = timer()
+    
+    # transform target image and add batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    # put model into eval mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        preds_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # create a prediction label and pred prob dictionary
+    pred_labels_and_probs = {
+        class_names[i]: float(preds_probs[0][i]) for i in range(len(class_names))
+    }
+    
+    # get prediction time
+    pred_time = round(timer() - start, 5)
+    
+    return pred_labels_and_probs, pred_time
+
+### Gradio app ###
+title = "FoodVision Mini"
+description = "An EfficientNetB2 feature extractor computer vision model to classify\
+    images of pizza, steak and sushi"
+article = "test"
+
+# create exmaples list from "examples" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+def main():
+    # create Gradio demo
+    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)
+
+    # launch demo
+    demo.launch()
+
+if __name__ == '__main__':
+    main()

model.py

@@ -0,0 +1,40 @@
+import torch
+import torchvision
+
+from torch import nn
+from typing import Tuple
+
+def create_effnetb2_model(
+    num_classes: int = 3, 
+    seed: int = 42) -> Tuple[torch.nn.Module, torchvision.transforms.Compose]:
+    """Creates an EfficientNetB2 feature extractor model and transforms
+
+    Parameters
+    ----------
+    num_classes : int, optional
+        Number of classes in the classifier head, by default 3
+    seed : int, optional
+        random seed value, by default 42
+
+    Returns
+    -------
+    Tuple[torch.nn.Module, torchvision.transforms.Compose]
+        Tuple[EffnetB2 feature extractor model, 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 parameters
+    for param in model.parameters():
+        param.requires_grad = False
+    
+    # change classifier head
+    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