firt_commit

.gitattributes

@@ -33,3 +33,5 @@ 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
+*.file_extension filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.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:eff07ee6a9faf1b1cbaf25837bd5990025f46ac083ea629919de57c82a86c157
+size 31314554

app.py

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+###1. Import classnames 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 and transforms preparation
+effnetb2,effnetb2_transforms=create_effnetb2_model(
+    num_classes=3)
+
+#load save weight
+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. Predict function (predict())###
+def predict(img) -> Tuple[Dict,float]:
+    #start a timer
+    start_time=timer()
+
+    # transform the input image for use with EffNetB2
+    img=effnetb2_transforms(img).unsqueeze(0)#unsqueeze = add batch dimension on 0th
+
+    #Put model into eval mode, make prediction
+    effnetb2.eval()
+    with torch.inference_mode():
+        #pass transformed image through the model and turn the prediction logits into probabilities
+        pred_probs=torch.softmax(effnetb2(img),dim=1)
+
+
+    #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/efficientnet.html) computer vision model to classify images as pizza,steak or sushi"
+article="Create at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_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="Prediction time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article
+                    )
+
+#Launch the demo!
+demo.launch(debug=False)#print errors locally?

foodvision_mini

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+Subproject commit e5e10bb2ef9727760930f348653752f16daba772

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
+                          seed:int=42):
+    weights=torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms=weights.transforms()
+    model=torchvision.models.efficientnet_b2(weights=weights)
+
+    # 4. Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # 5. change 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 == 3.1.4