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### 1. Imports and class names setup ### | |
import gradio as gr | |
import os | |
import torch | |
from torch import nn | |
from model import create_resnet50_model | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
import torch.nn.functional as F | |
# Setup class names | |
class_names = ['CRVO', | |
'Choroidal Nevus', | |
'Diabetic Retinopathy', | |
'Laser Spots', | |
'Macular Degeneration', | |
'Macular Hole', | |
'Myelinated Nerve Fiber', | |
'Normal', | |
'Pathological Mypoia', | |
'Retinitis Pigmentosa'] | |
### 2. Model and transforms preparation ### | |
# Create ResNet50 model | |
resnet50, resnet50_transforms = create_resnet50_model( | |
num_classes=len(class_names), # actual value would also work | |
) | |
resnet50.fc = nn.Linear(2048, 10) | |
# Load saved weights | |
resnet50.load_state_dict( | |
torch.load( | |
f="pretrained_resnet50_feature_extractor_drappcompressed.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 = resnet50_transforms(img).unsqueeze(0) | |
# # Put model into evaluation mode and turn on inference mode | |
# resnet50.eval() | |
# with torch.inference_mode(): | |
# # Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
# pred_probs = torch.softmax(resnet50(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 | |
def predict(img): | |
"""Transforms and performs a prediction on img and returns prediction and time taken.""" | |
start_time = timer() | |
try: | |
img = resnet50_transforms(img).unsqueeze(0) | |
resnet50.eval() | |
with torch.inference_mode(): | |
pred_probs = torch.softmax(resnet50(img), dim=1) | |
# Calculate entropy for OOD detection | |
entropy = -torch.sum(pred_probs * torch.log(pred_probs + 1e-8)).item() | |
max_prob = torch.max(pred_probs).item() | |
# Create base prediction dictionary | |
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
# OOD Detection - modify existing probabilities instead of adding new keys | |
if (max_prob > 0.95 and entropy < 0.2) or entropy > 2.0: | |
# Boost the probability of the first class and add a marker | |
pred_labels_and_probs[class_names[0]] = 0.99 # Use existing class | |
# You could also just print a warning or log it | |
print("May not be retina scan") | |
pred_time = round(timer() - start_time, 5) | |
return pred_labels_and_probs, pred_time | |
except Exception as e: | |
# Return dictionary with same structure as normal case | |
pred_labels_and_probs = {class_names[i]: 0.0 for i in range(len(class_names))} | |
pred_labels_and_probs[class_names[0]] = 1.0 # Show error in first class | |
return pred_labels_and_probs, 0.0 | |
### 4. Gradio app ### | |
# Create title, description and article strings | |
#title = "DeepFundus π" | |
#description = "A ResNet50 feature extractor computer vision model to classify funduscopic images." | |
#article = "Created with the help from [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() | |