DeepFundus / app.py
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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 πŸ‘€"
#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()