OnabajoMonsurat
commited on
Commit
β’
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Parent(s):
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Initial commit
Browse files- .gitattributes +3 -0
- app.py +72 -0
- effnet_b2_model.pth +3 -0
- example/2582289.jpg +0 -0
- example/3622237.jpg +0 -0
- example/592799.jpg +0 -0
- model.py +24 -0
- requirements.txt +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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effnet_b2_model.pth filter=lfs diff=lfs merge=lfs -text
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.pth filter=lfs diff=lfs merge=lfs -text
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*pth filter=lfs diff=lfs merge=lfs -text
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app.py
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# Import and class names setup
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import gradio as gr
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import os
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import torch
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from model import create_effnetb2_model
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from timeit import default_timer as timer
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from typings import Tuple, Dict
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# Setup class names
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class_names= ['pizza', 'steak', 'sushi']
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# Model and transforms preparation
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effnetb2_model, effnetb2_transform= create_effnetb2_model()
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# Load state dict
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effnetb2_model.load_state_dict(torch.load(
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f= 'effnet_b2_model.pth',
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map_location= torch.device('cpu')
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)
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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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#transform the input image for use with effnet b2
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transform_image= effnetb2_transform(img).unsqueeze(0)
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#put model into eval mode, make pred
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effnetb2_model.eval()
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with torch.inference_mode():
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pred_logits= effnetb2_model(transform_image)
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pred_prob= torch.softmax(pred_logits, dim=1)
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# create a pred label and pred prob dict
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pred_label_and_prob= {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))}
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# calc pred time
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stop_time= timer()
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pred_time= round(stop_time - start_time, 4)
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# return pred dict and pred time
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return pred_label_and_prob, pred_time
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# create example list
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example_list= [['example/'+example] for example in os.listdir('example')]
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# create gradio app
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title= 'FoodVision Mini ππ₯©π£ '
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description= 'An EfficientnetB2 feature extractor Computer vision model to classify image as pizza, steak or sushi'
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article= 'Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).'
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# Create the gradio demo
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demo= gr.Interface(fn= predict,
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inputs=gr.Image(type='pil'),
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outputs= [gr.Label(num_top_classes=3, label= 'predictions'),
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gr.Number(label= 'Prediction time (S)')],
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examples= example_list,
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title= title,
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description= description,
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article= article
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)
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# Launch the demo
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#demo.launch()
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demo.launch(debug=False, # print errors locally?
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share=True) # generate a publically shareable URL?
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effnet_b2_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bcd7636f134dff8f3606ab07122e3dcc393ce42b1531a032311566e4d9da6bd
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size 31277689
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example/2582289.jpg
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example/3622237.jpg
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example/592799.jpg
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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_effnetb2_model(num_classes:int=3,
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seed:int=42):
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# Create Effnet pretrained model
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weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
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transforms= weights.transforms()
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model= torchvision.models.efficientnet_b2(weights=weights)
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# Freeze all layers in the base model
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for param in model.parameters():
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param.requires_grad= False
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# Change the classifier layer
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torch.manual_seed(seed)
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model.classifier= nn.Sequential(
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nn.Dropout(p=0.3, inplace= True),
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nn.Linear(in_features=1408, out_features= num_classes)
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)
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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==3.35.2
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