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Initial Commit
Browse files- EffNetB0_data_auto_10_epochs.pth +3 -0
- app.py +103 -0
- examples/NORMAL2-IM-0035-0001.jpeg +0 -0
- examples/SARS-10.1148rg.242035193-g04mr34g0-Fig8a-day0.jpeg +0 -0
- model.py +29 -0
- requirements.txt +3 -0
EffNetB0_data_auto_10_epochs.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:bbec879cdf158c1d6a55dd56baa97521d201ad8cde2e80f48d1c0985ace221d8
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size 16340835
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app.py
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# Load model
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import torch
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import torchvision
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import os
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import gradio as gr
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from torchvision import transforms
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from model import create_effnet
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from typing import Tuple, Dict
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from timeit import default_timer as timer
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# Device agnostic code
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if torch.backends.mps.is_available():
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device = "mps"
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elif torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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class_name = ["NORMAL", "COVID"]
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EffNetB0_load_model, EffNetB0_transforms = create_effnet(
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pretrained_weights=torchvision.models.EfficientNet_B0_Weights.DEFAULT,
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model=torchvision.models.efficientnet_b0,
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in_features=1280,
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dropout=0.2,
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out_features=2,
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)
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# Write a transform for image
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data_transform = transforms.Compose(
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[
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# Resize our images to 64x64
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transforms.Resize(size=(64, 64)),
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# Flip the images randomly on the horizontal
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transforms.RandomHorizontalFlip(p=0.5),
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# Turns image into grayscale
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transforms.Grayscale(num_output_channels=3),
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# Turn the image into a torch.Tensor
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transforms.ToTensor()
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# Permute the channel height and width
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]
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)
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EffNetB0_load_model.load_state_dict(
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torch.load("./EffNetB0_data_auto_10_epochs.pth"), map_location=torch.device("cpu")
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)
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### Predict function ---------------------------------------------------- ###
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def predict(img) -> Tuple[Dict, float]:
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# Start a timer
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start_time = timer()
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class_names = ["normal", "covid"]
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# Transform the input image for use with ViT Model
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img = EffNetB0_transforms(img).unsqueeze(
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0
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) # unsqueeze = add batch dimension on 0th index (3, 224, 224) into (1, 3, 224, 224)
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# Put model into eval mode, make prediction
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EffNetB0_load_model.eval()
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with torch.inference_mode():
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# Pass transformed image through the model and turn the prediction logits into probabilities
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pred_logits = EffNetB0_load_model(img)
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pred_probs = torch.softmax(pred_logits, dim=1)
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# Create a prediction label and prediction probability dictionary
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pred_labels_and_probs = {
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class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
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}
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# Calculate pred time
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end_timer = timer()
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pred_time = round(end_timer - start_time, 4)
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# Return pred dict and pred time
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return pred_labels_and_probs, pred_time
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# Create title and description
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title = "Covid Prediction: EfficientNetB0 Model"
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description = (
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"An EfficientNet model trained on Covid-19 Dataset to classify X-RAY images"
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)
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# Create example list
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example_list = [
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["Covid-19-Detection/examples" + example]
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for example in os.listdir("Covid-19-Detection/examples")
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]
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# Create the Gradio demo
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demo = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=[
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gr.Label(num_top_classes=2, label="Predictions"),
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gr.Number(label="Prediction time(s)"),
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],
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title=title,
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description=description,
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examples=example_list,
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)
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demo.launch()
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examples/NORMAL2-IM-0035-0001.jpeg
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examples/SARS-10.1148rg.242035193-g04mr34g0-Fig8a-day0.jpeg
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model.py
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import torch
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import torchvision
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from torch import nn
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def create_effnet(
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pretrained_weights: torchvision.models.Weights,
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model: torchvision.models,
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in_features: int,
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dropout: int,
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out_features: int,
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device: torch.device,
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):
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# Get the weights and setup the model
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model = model(weights=pretrained_weights).to(device)
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transforms = pretrained_weights.transforms()
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# Freeze the base model layers
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for param in model.features.parameters():
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param.requires_grad = False
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# Change the classifier head
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model.classifier = nn.Sequential(
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nn.Dropout(p=dropout, inplace=True),
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nn.Linear(in_features=in_features, out_features=out_features),
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).to(device)
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return model, transforms
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requirements.txt
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torch==2.0.1
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torchvision==0.15.2
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gradio==3.23.0
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