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09_pretrained_effnetb2_feature_extractor__pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77e90b32d59d9cf33e5a15bf57e9c6d786272b7daa0ec937f2802357c1957e4c
+size 31334283

app.py

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+
+import gradio as gr
+import os
+import torch
+ 
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_names= ['pizza','steak','sushi']
+
+effnetb2, effnetb2_transforms= create_effnetb2_model(num_classes= 3)
+
+effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor__pizza_steak_sushi_20_percent.pth",map_location=
+ torch.device("cpu")
+)) 
+
+
+def predict(img)-> Tuple[Dict, float]:
+
+  start_time= timer()
+  img= effnetb2_transforms(img).unsqueeze(0)
+  effnetb2.eval()
+  with torch.inference_mode():
+    pred_probs= torch.softmax(effnetb2(img), dim= 1)
+
+  pred_labels_and_probs= {class_names[i]: float(pred_probs[0][i]) for i in range (len(class_names))}
+  pred_time=round(timer()- start_time, 5)
+
+  return pred_labels_and_probs, pred_time
+
+
+titile= "Foodvision Mini"
+description="An efficientnetb2 feature extractor computer vision model to classify images of pizza, steak and sushi."
+article= "Created at [09_Pytorch model deployment] (https://www.learnpytorch.io/09_pytorch_model_deployment/)"
+example_list= [["examples/"+ example] for example in os.listdir("examples")]
+
+demo= gr.Interface(fn= predict, inputs= gr.Image(type="pil"), outputs= [gr.Label(num_top_classes= 3, label= "predictions"),
+                                                                        gr.Number(label= "prediction time (s)")],
+                             example_list= example_list, title= title, description= description, article= article)
+
+
+demo.launch()

model.py

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+import torch
+import torchvision
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:int= 3,
+ seed:int= 40):
+
+ weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
+ transforms= weights.transforms()
+ model= torchvision.models.efficientnet_b2(weights= weights)
+
+ for param in model.parameters():
+    param.requires_grad= False
+
+ torch.manual_seed(seed)
+
+ model.classifier(nn.Sequential(
+
+   nn.Dropout(0.3, inplace=True),
+   nn.Linear(in_features= 1408, out_features= num_classes),
+
+ )
+
+ return model, transforms

requirements.txt

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+torch== 1.13.1
+torchvision== 0.14.1
+gradio== 3.23.0