initial commit

.gitattributes

@@ -32,3 +32,4 @@ 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
+pretrained_effnetb2_feature_extractor.pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,77 @@
+
+import gradio as gr 
+import os 
+import torch 
+
+from model import create_effnet_b2 
+from timeit import default_timer as timer 
+from typing import Tuple, Dict 
+
+#setup class names 
+class_names = ['pizza', 'steak', 'sushi']
+
+#model and transforms preparation 
+effnetb2, effnetb2_transforms = create_effnet_b2(
+    num_classes = 3)
+
+#load saved weights 
+effnetb2.load_state_dict(
+    torch.load(f = 'pretrained_effnetb2_feature_extractor.pth',
+               map_location = torch.device('cpu')) #hardcoding to load state dict onto the cpu
+)
+
+#Predict function 
+
+def predict(img) -> Tuple[Dict, float]: 
+
+  #Start a timer
+  start_time = timer()
+
+  #transform the input image for use with effnetb2
+  transformed_image = effnetb2_transforms(img).unsqueeze(0)
+
+  #put model into deval mode, make preiction 
+  effnetb2.eval()
+  with torch.inference_mode(): 
+    pred_logits = effnetb2(transformed_image) 
+    pred_probs = torch.softmax(pred_logits, dim = 1)    
+
+  # create a prediction label and pred prob dictionary 
+  pred_labels_and_probs = {effnet_class_names[i]: float(pred_probs[0][i]) 
+                           for i in range(len(effnet_class_names))}
+  
+  #calculate pred time
+  end_time = timer() 
+  pred_time = end_time - start_time
+
+  #return pred dict and pred time 
+  print(pred_probs[0])
+  return pred_labels_and_probs, pred_time 
+
+
+# Gradio app 
+
+import gradio as gr 
+
+#Create title, description and article 
+title = 'FoodVision Mini'
+description = 'An EfficientNetB2 feature extractor to classify food as pizza, steak, and sushi'
+
+#Create example list 
+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)')], 
+                
+                    examples = example_list, 
+                    title = title,
+                    description = description)
+
+demo.launch(debug = False,
+            share = True)
+
+
+

foodvision_mini/app.py

@@ -0,0 +1,77 @@
+
+import gradio as gr 
+import os 
+import torch 
+
+from model import create_effnet_b2 
+from timeit import default_timer as timer 
+from typing import Tuple, Dict 
+
+#setup class names 
+class_names = ['pizza', 'steak', 'sushi']
+
+#model and transforms preparation 
+effnetb2, effnetb2_transforms = create_effnet_b2(
+    num_classes = 3)
+
+#load saved weights 
+effnetb2.load_state_dict(
+    torch.load(f = 'pretrained_effnetb2_feature_extractor.pth',
+               map_location = torch.device('cpu')) #hardcoding to load state dict onto the cpu
+)
+
+#Predict function 
+
+def predict(img) -> Tuple[Dict, float]: 
+
+  #Start a timer
+  start_time = timer()
+
+  #transform the input image for use with effnetb2
+  transformed_image = effnetb2_transforms(img).unsqueeze(0)
+
+  #put model into deval mode, make preiction 
+  effnetb2.eval()
+  with torch.inference_mode(): 
+    pred_logits = effnetb2(transformed_image) 
+    pred_probs = torch.softmax(pred_logits, dim = 1)    
+
+  # create a prediction label and pred prob dictionary 
+  pred_labels_and_probs = {effnet_class_names[i]: float(pred_probs[0][i]) 
+                           for i in range(len(effnet_class_names))}
+  
+  #calculate pred time
+  end_time = timer() 
+  pred_time = end_time - start_time
+
+  #return pred dict and pred time 
+  print(pred_probs[0])
+  return pred_labels_and_probs, pred_time 
+
+
+# Gradio app 
+
+import gradio as gr 
+
+#Create title, description and article 
+title = 'FoodVision Mini'
+description = 'An EfficientNetB2 feature extractor to classify food as pizza, steak, and sushi'
+
+#Create example list 
+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)')], 
+                
+                    examples = example_list, 
+                    title = title,
+                    description = description)
+
+demo.launch(debug = False,
+            share = True)
+
+
+

foodvision_mini/model.py

@@ -0,0 +1,29 @@
+
+import torch 
+import torchvision
+from torch import nn 
+
+#Function that creates an effnetb2 
+
+def create_effnet_b2(num_classes: int = 3, 
+                     seed: int = 42): 
+                      
+  #Get weights 
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights = weights) 
+
+
+  #Freeze parameters in features layer 
+  for param in model.parameters(): 
+    param.requires_grad = False
+
+  #Change classification layer 
+  torch.manual_seed(seed)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p = .3), 
+      nn.Linear(in_features = 1408, 
+                out_features = num_classes))
+      
+  return model, transforms
+

foodvision_mini/pretrained_effnetb2_feature_extractor.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4ab6ca54bf0bb2d03b2ef7a1487be135c47da838aba93d2bfd0df9c41d1632f
+size 31282061

foodvision_mini/requirements.txt

@@ -0,0 +1,6 @@
+torch == 1.12.0
+torchvision == .13.0 
+gradio ==3.1.4
+
+
+ 

model.py

@@ -0,0 +1,29 @@
+
+import torch 
+import torchvision
+from torch import nn 
+
+#Function that creates an effnetb2 
+
+def create_effnet_b2(num_classes: int = 3, 
+                     seed: int = 42): 
+                      
+  #Get weights 
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights = weights) 
+
+
+  #Freeze parameters in features layer 
+  for param in model.parameters(): 
+    param.requires_grad = False
+
+  #Change classification layer 
+  torch.manual_seed(seed)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p = .3), 
+      nn.Linear(in_features = 1408, 
+                out_features = num_classes))
+      
+  return model, transforms
+

pretrained_effnetb2_feature_extractor.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4ab6ca54bf0bb2d03b2ef7a1487be135c47da838aba93d2bfd0df9c41d1632f
+size 31282061

requirements.txt

@@ -0,0 +1,6 @@
+torch == 1.12.0
+torchvision == .13.0 
+gradio ==3.1.4
+
+
+