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

app.py

@@ -0,0 +1,69 @@
+
+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model and transforms preparation
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3)
+
+# 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") # load the model to the CPU
+    )
+)
+
+### 3. Predict function ###
+def predict(img) -> Tuple[Dict, float]:
+    # Start a timer
+    start_time = timer()
+    
+    # Transform the input image for use with EffNetB2
+    img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
+    
+    # Put model into eval mode, make prediction
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate pred time
+    end_time = timer()
+    pred_time = round(end_time-start_time, 4)
+    
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+# Create a title, description, article
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b1.html#torchvision.models.efficientnet_b1)"
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # maps inputs to outputs
+                    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 the demo!
+demo.launch()

model.py

@@ -0,0 +1,22 @@
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)
+                         seed:int=42):
+    # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+    
+    # 4 Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+    
+    # 5. Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes, bias=True)
+    )
+    return model, transforms

requirements.txt

@@ -0,0 +1,4 @@
+
+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4