initial commit

.gitattributes

@@ -33,3 +33,4 @@ 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
+effnetb2_model.pth filter=lfs diff=lfs merge=lfs -text

app.py

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+
+### 1. Imports and class names setup
+import gradio as gr
+import os 
+import torch
+import gradio as gr
+import torchvision
+
+#from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Dict, Tuple
+
+class_names = ['butterfly',  'cat',  'chicken',  'cow', 'dog',
+               'elephant',  'horse',   'sheep',  'spider', 'squirrel']
+
+### 2. Model and transforms prepartaion ###
+effnetb2, effnetb2_transforms = create_effnetb2_model()
+
+# Loade the save weights.
+effnetb2.load_state_dict(torch.load(f = "effnetb2_model.pth",
+                                    map_location = torch.device("cpu")))
+
+### 3. Predict Function ###
+effnetb2 = effnetb2.to('cpu')
+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. ### 
+
+# Create title, description and article strings
+title = "AnimalsClassification "
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article = "ModelDeployment"
+
+# Create example list.
+example_list = [["animal_classification/examples/" + example] for example in os.listdir(animal_example)]
+
+# 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
+                    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?

effnetb2_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23f6d18d2f24ee08fddab5dbedbdc4ddc4f4ec88d7729dde671ef105e42dfd3c
+size 31316986

model.py

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+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model():
+    # 1. Setup the pretrained EffNetB2 weights
+    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+
+    # 2. Setup the transforms
+    effnetb2_transforms = effnetb2_weights.transforms()
+
+    # 3. Setup pretrained model instance
+    effnetb2 = torchvision.models.efficientnet_b2(weights = effnetb2_weights)
+
+    # 4. Freeze the layers
+    for param in effnetb2.parameters():
+        param.requires_grad = False
+
+    # 5. Change the classifier of the model
+    effnetb2.classifier = nn.Sequential(
+        nn.Dropout(p = 0.3, inplace = True),
+        nn.Linear(in_features = 1408, out_features = 10, bias = True)
+    )
+
+    return effnetb2, effnetb2_transforms

requirements.txt

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+