innital commit

.gitattributes

@@ -33,3 +33,7 @@ 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
+pretrained_effnetb3_flowers102_10_epochs.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text
+*.pth
+ filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,81 @@
+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+
+from model import effnet_feature_extractor
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+with open("class_names.txt", "r") as f: # reading them in from class_names.txt
+    class_names = [food_name.strip() for food_name in  f.readlines()]
+    
+### 2. Model and transforms preparation ###    
+
+# Create model
+effnetb3, effnetb3_transforms = effnet_feature_extractor(
+    num_classes=102, # could also use len(class_names)
+)
+
+# Load saved weights
+effnetb3.load_state_dict(
+    torch.load(
+        f="pretrained_effnetb3_flowers102.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+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 = auto_transform(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    effnetb3.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb3(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. Gradio app ###
+
+# Create title, description and article strings
+title = "Flowers102 🌷🌺🪷🌸"
+description = "An EfficientNetB3 feature extractor computer vision model to classify images of flowers into [102 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
+article = "Created at "
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create Gradio interface 
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Predictions"),
+        gr.Number(label="Prediction time (s)"),
+    ],
+    examples=example_list,
+    title=title,
+    description=description,
+    article=article,
+)
+
+# Launch the app!
+demo.launch()

model.py

@@ -0,0 +1,25 @@
+
+import torch
+import torchvision
+from torch import nn
+
+def effnet_feature_extractor(
+                             num_classes: int=102,
+                             seed: int=42):
+  # 1, 2, 3,
+  weights= torchvision.models.EfficientNet_B3_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b3(weights=weights)
+
+  # 4. freeze
+  for param in model.parameters():
+    param.requires_grad = False
+
+  # 5. CHange head
+  torch.manual_seed(seed)
+  model.classifier= nn.Sequential(
+      nn.Dropout(p=0.2, inplace=True),
+      nn.Linear(in_features=1536,
+                out_features=num_classes)
+  )
+  return model, transforms

pretrained_effnetb3_flowers102_10_epochs.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4ef667ac404e6d169d37dbce9aa83ed8c3356e39b7df0656f2fbc84d9c6719d
+size 43991736

requirements.txt

@@ -0,0 +1,3 @@
+torch==2.2.0
+torchvision==0.17.0
+gradio==4.28.3