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09_pretrained_effnetb2_feature_extractor__pizza_steak_sushi_20_percent.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:77e90b32d59d9cf33e5a15bf57e9c6d786272b7daa0ec937f2802357c1957e4c
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+ size 31334283
2582289.jpg ADDED
3622237.jpg ADDED
592799.jpg ADDED
app.py ADDED
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+
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+ import gradio as gr
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+ import os
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+ import torch
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+
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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 typing import Tuple, Dict
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+
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+ class_names= ['pizza','steak','sushi']
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+
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+ effnetb2, effnetb2_transforms= create_effnetb2_model(num_classes= 3)
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+
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+ effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor__pizza_steak_sushi_20_percent.pth",map_location=
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+ torch.device("cpu")
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+ ))
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+
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+
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+ def predict(img)-> Tuple[Dict, float]:
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+
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+ start_time= timer()
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+ img= effnetb2_transforms(img).unsqueeze(0)
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+ effnetb2.eval()
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+ with torch.inference_mode():
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+ pred_probs= torch.softmax(effnetb2(img), dim= 1)
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+
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+ pred_labels_and_probs= {class_names[i]: float(pred_probs[0][i]) for i in range (len(class_names))}
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+ pred_time=round(timer()- start_time, 5)
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+
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+ return pred_labels_and_probs, pred_time
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+
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+
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+ titile= "Foodvision Mini"
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+ description="An efficientnetb2 feature extractor computer vision model to classify images of pizza, steak and sushi."
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+ article= "Created at [09_Pytorch model deployment] (https://www.learnpytorch.io/09_pytorch_model_deployment/)"
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+ example_list= [["examples/"+ example] for example in os.listdir("examples")]
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+
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+ demo= gr.Interface(fn= predict, inputs= gr.Image(type="pil"), outputs= [gr.Label(num_top_classes= 3, label= "predictions"),
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+ gr.Number(label= "prediction time (s)")],
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+ example_list= example_list, title= title, description= description, article= article)
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+
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+
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+ demo.launch()
model.py ADDED
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+ import torch
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+ import torchvision
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+ from torch import nn
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+
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+
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+ def create_effnetb2_model(num_classes:int= 3,
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+ seed:int= 40):
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+
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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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+
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+ for param in model.parameters():
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+ param.requires_grad= False
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+
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+ torch.manual_seed(seed)
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+
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+ model.classifier(nn.Sequential(
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+
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+ nn.Dropout(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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+ )
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+
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+ return model, transforms
requirements.txt ADDED
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+ torch== 1.13.1
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+ torchvision== 0.14.1
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+ gradio== 3.23.0