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adeca79 d419eb9 adeca79 124875f bad0af8 adeca79 124875f adeca79 124875f ff18d9c 124875f adeca79 124875f bad0af8 124875f d419eb9 adeca79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_resnet_model, create_custom_model
from timeit import default_timer as timer
import torchvision
import torchvision.transforms as transforms
transformer = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])
model_name = 'resnet'
### 2. Model and transforms preparation ###
if model_name == 'custom':
# Create model
model = create_custom_model()
# Load saved weights
model.load_state_dict(
torch.load(
f="./cnn-custom-model-version-4.pt",
map_location=torch.device("cpu"), # load to CPU
)
)
elif model_name == 'resnet':
model = create_resnet_model()
# Load saved weights
model.load_state_dict(
torch.load(
f="./cnn-resnet-version-1.pt",
map_location=torch.device("cpu"), # load to CPU
)
)
# else:
### 3. Predict function ###
def predict(img):
"""Transforms and performs a prediction on img and returns prediction and time taken.
"""
# Transform the target image and add a batch dimension
img = transformer(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
model.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_prob = torch.sigmoid(model(img))
pred_probs = {'Covid' : float(pred_prob), 'Non Covid' : (1-float(pred_prob))}
# Return the prediction dictionary and prediction time
return pred_probs
### 4. Gradio app ###
# Create title, description and article strings
title = "Corona Prediction"
description = "A Convolutional Neural Network To classify whether a person have Corona or not using CT Scans."
article = "Created by Thenujan Nagaratnam for DNN module at UoM"
# 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=2, label="Predictions")], # our fn has two outputs, therefore we have two outputs
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch()
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