Initial commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+effnet_b2_model.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text
+*pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,72 @@
+
+# Import and class names setup
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typings import Tuple, Dict
+
+# Setup class names
+class_names= ['pizza', 'steak', 'sushi']
+
+# Model and transforms preparation
+effnetb2_model, effnetb2_transform= create_effnetb2_model()
+# Load state dict
+effnetb2_model.load_state_dict(torch.load(
+    f= 'effnet_b2_model.pth',
+    map_location= torch.device('cpu')
+    )
+)
+
+# Predict function
+
+def predict(img)-> Tuple[Dict, float]:
+  # start a timer
+  start_time= timer()
+
+  #transform the input image for use with effnet b2
+  transform_image= effnetb2_transform(img).unsqueeze(0)
+
+  #put model into eval mode, make pred
+  effnetb2_model.eval()
+  with torch.inference_mode():
+    pred_logits= effnetb2_model(transform_image)
+    pred_prob= torch.softmax(pred_logits, dim=1)
+
+  # create a pred label and pred prob dict
+  pred_label_and_prob= {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))}
+
+
+  # calc pred time
+  stop_time= timer()
+  pred_time= round(stop_time - start_time, 4)
+
+
+  # return pred dict and pred time
+  return pred_label_and_prob, pred_time
+
+# create example list
+example_list= [['example/'+example] for example in os.listdir('example')]
+
+# create gradio app
+title= 'FoodVision Mini 🍕🥩🍣 '
+description= 'An EfficientnetB2 feature extractor Computer vision model to classify image as pizza, steak or sushi'
+article= 'Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).'
+
+# Create the gradio demo
+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)')],
+                   examples= example_list,
+                   title= title,
+                   description= description,
+                   article= article
+                   )
+
+# Launch the demo
+#demo.launch()
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL?

effnet_b2_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bcd7636f134dff8f3606ab07122e3dcc393ce42b1531a032311566e4d9da6bd
+size 31277689

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3,
+                          seed:int=42):
+  # Create Effnet pretrained model
+  weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms= weights.transforms()
+  model= torchvision.models.efficientnet_b2(weights=weights)
+
+  # Freeze all layers in the base model
+  for param in model.parameters():
+    param.requires_grad= False
+
+  # Change the classifier layer
+  torch.manual_seed(seed)
+  model.classifier= nn.Sequential(
+      nn.Dropout(p=0.3, inplace= True),
+      nn.Linear(in_features=1408, out_features= num_classes)
+  )
+
+  return model, transforms

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+gradio==3.35.2