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.gitattributes

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+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08d5dbabfa16593cce91a20c189cbd7730aea2cf9f75cc74a47396a89e31d921
+size 31265929

app.py

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+### 1. Imports 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 typing import Tuple, Dict
+
+# Setup class names
+class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model adn transforms preparation ###
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes = 3
+    )
+
+# Load save weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location = torch.device("cpu") # load the model to the CPU
+    )
+)
+
+### 3. Prediction function ###
+def predict(img) -> Tuple[Dict, float]:
+  #Start a timer
+  start_time = timer()
+
+  # Transform the input image for use with EffNetB2
+  transformed_img = effnetb2_transforms(img).unsqueeze(0) #unsqueeze = add batch dimension on 0th index
+
+  #Put model into eval mode, make prediciton
+  effnetb2.eval()
+  with torch.inference_mode():
+    # Pass the transformed image through the model and turn the prdiciton logits into probability
+    # pred_logit = effnetb2(transformed_img)
+    pred_probs = torch.softmax(effnetb2(transformed_img), dim = 1)
+    # pred_label = torch.argmax(pred_probs, dim = 1)
+    # class_name = class_names[pred_label]
+
+  # Create a prediction label and prediction probability dictionary
+  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+  # cAlculate pred time
+  end_time = timer()
+  pred_time = round(end_time - start_time, 4)
+
+  # Return pred dict and pred time
+  return pred_labels_and_probs, pred_time
+
+
+### 4. Gradio App ###
+
+
+# Create title, description and article
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An [EfficientNetB2 feature extractor] (https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
+article = "Created at PyTorch Model Deployment"
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio Demo
+demo = gr.Interface(fn = predict, #maps inputs to outputs
+                    inputs = gr.Image(type = "pil"),
+                    outputs = [gr.Label(num_top_classes = 3, label = "predictions"),
+                               gr.Number(label="Prediciton time (s)")],
+                    examples = example_list,
+                    title = title,
+                    description = description,
+                    article = article
+                    )
+
+#Launch the demo:
+demo.launch(debug = False, #print errors locally ?
+            share = True) # generate a publically shareable URL

model.py

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+
+import torch
+import torchvision
+
+from torch import nn
+
+def create_effnetb2_model(num_classes: int = 3, #default output classes = 3 (pizza, steak, sushi)
+                          seed: int = 42
+                          ):
+  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
+
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights = 'DEFAULT')
+
+  #4. Freeze all layers in the base model
+  for param in model.parameters():
+    param.requires_grad = False
+
+  #5. Change the classifier head with random seed for reproducibility
+  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 == 1.12.0
+torchvision == 0.13.0
+gradio == 3.6