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100sports.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:286c72f304b5bb5615f8d0592d12fb511fe2ef6791c3df2cc22b7dad429f0f92
+size 43955834

app.py

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+### 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 = ['air hockey',
+  'ampute football',
+  'archery',
+  'arm wrestling',
+  'axe throwing',
+  'balance beam',
+  'barell racing',
+  'baseball',
+  'basketball',
+  'baton twirling',
+  'bike polo',
+  'billiards',
+  'bmx',
+  'bobsled',
+  'bowling',
+  'boxing',
+  'bull riding',
+  'bungee jumping',
+  'canoe slamon',
+  'cheerleading',
+  'chuckwagon racing',
+  'cricket',
+  'croquet',
+  'curling',
+  'disc golf',
+  'fencing',
+  'field hockey',
+  'figure skating men',
+  'figure skating pairs',
+  'figure skating women',
+  'fly fishing',
+  'football',
+  'formula 1 racing',
+  'frisbee',
+  'gaga',
+  'giant slalom',
+  'golf',
+  'hammer throw',
+  'hang gliding',
+  'harness racing',
+  'high jump',
+  'hockey',
+  'horse jumping',
+  'horse racing',
+  'horseshoe pitching',
+  'hurdles',
+  'hydroplane racing',
+  'ice climbing',
+  'ice yachting',
+  'jai alai',
+  'javelin',
+  'jousting',
+  'judo',
+  'lacrosse',
+  'log rolling',
+  'luge',
+  'motorcycle racing',
+  'mushing',
+  'nascar racing',
+  'olympic wrestling',
+  'parallel bar',
+  'pole climbing',
+  'pole dancing',
+  'pole vault',
+  'polo',
+  'pommel horse',
+  'rings',
+  'rock climbing',
+  'roller derby',
+  'rollerblade racing',
+  'rowing',
+  'rugby',
+  'sailboat racing',
+  'shot put',
+  'shuffleboard',
+  'sidecar racing',
+  'ski jumping',
+  'sky surfing',
+  'skydiving',
+  'snow boarding',
+  'snowmobile racing',
+  'speed skating',
+  'steer wrestling',
+  'sumo wrestling',
+  'surfing',
+  'swimming',
+  'table tennis',
+  'tennis',
+  'track bicycle',
+  'trapeze',
+  'tug of war',
+  'ultimate',
+  'uneven bars',
+  'volleyball',
+  'water cycling',
+  'water polo',
+  'weightlifting',
+  'wheelchair basketball',
+  'wheelchair racing',
+  'wingsuit flying']
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# 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 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 = 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. Gradio app ###
+
+# Create title, description and article strings
+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/)."
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# 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
+                    # Create examples list from "examples/" directory
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:int=100, 
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): 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 all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # 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),
+    """
+    model = torch.load('100sports.pth')
+
+    )
+    
+    return model, transforms

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4