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Initial Commit
Browse files- .gitattributes +1 -0
- __pycache__/model.cpython-311.pyc +0 -0
- app.py +87 -0
- examples/3177743.jpg +0 -0
- examples/61656.jpg +0 -0
- examples/730464.jpg +0 -0
- model.py +28 -0
- pretrained_vit_foodvision.pth +3 -0
- requirements.txt +4 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pretrained_vit_foodvision.pth filter=lfs diff=lfs merge=lfs -text
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__pycache__/model.cpython-311.pyc
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Binary file (1.35 kB). View file
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app.py
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### Imports and class names setup ---------------------------------------------------- ###
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import os
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import torch
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import torchvision
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import gradio as gr
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from model import create_vit
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from timeit import default_timer as timer
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from typing import Tuple, Dict
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# Setup class names
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class_names = ["pizza", "steak", "sushi"]
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# Device agnostic code
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if torch.backends.mps.is_available():
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device = "mps"
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elif torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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### Model and transforms preparation ---------------------------------------------------- ###
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vit_model, vit_transforms = create_vit(
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pretrained_weights=torchvision.models.ViT_B_16_Weights.DEFAULT,
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model=torchvision.models.vit_b_16,
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in_features=768,
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out_features=3,
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device="cpu",
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)
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# Load save weights
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vit_model.load_state_dict(
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torch.load(f="pretrained_vit_foodvision.pth", map_location=torch.device("cpu"))
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) # load the model to the CPU
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### Predict function ---------------------------------------------------- ###
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def predict(img) -> Tuple[Dict, float]:
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# Start a timer
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start_time = timer()
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# Transform the input image for use with ViT Model
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img = vit_transforms(img).unsqueeze(
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0
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) # unsqueeze = add batch dimension on 0th index (3, 224, 224) into (1, 3, 224, 224)
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# Put model into eval mode, make prediction
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vit_model.eval()
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with torch.inference_mode():
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# Pass transformed image through the model and turn the prediction logits into probabilities
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pred_logits = vit_model(img)
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pred_probs = torch.softmax(pred_logits, dim=1)
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# Create a prediction label and prediction probability dictionary
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pred_labels_and_probs = {
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class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
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}
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# Calculate pred time
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end_timer = timer()
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pred_time = round(end_timer - start_time, 4)
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# Return pred dict and pred time
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return pred_labels_and_probs, pred_time
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### Gradio interface and launch ------------------------------------------------------------------ ###
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# Create title and description
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title = "FoodVision Mini: ViT Model"
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description = "A ViT model trained on 20% of the Food101 dataset to classify images of pizza, steak or sushi."
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# Create example list
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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# Create the Gradio demo
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demo = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=[
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gr.Label(num_top_classes=3, label="Predictions"),
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gr.Number(label="Prediction time(s)"),
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],
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title=title,
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description=description,
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examples=example_list,
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)
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demo.launch(
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debug=False, share=True # print errors locally
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) # generate a publically shareable URL
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examples/3177743.jpg
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examples/61656.jpg
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examples/730464.jpg
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model.py
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import torch
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import torchvision
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from torch import nn
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def create_vit(pretrained_weights: torchvision.models.Weights,
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model: torchvision.models,
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in_features: int,
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out_features: int,
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device: torch.device):
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"""Creates a Vision Transformer (ViT) instance from torchvision
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and returns it.
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"""
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# Create a pretrained ViT model
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model = torchvision.models.vit_b_16(weights=pretrained_weights).to(device)
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transforms = pretrained_weights.transforms()
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# Freeze the feature extractor
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for param in model.parameters():
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param.requires_grad = False
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# Change the head of the ViT
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model.heads = nn.Sequential(
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nn.Linear(in_features=in_features, out_features=out_features)
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).to(device)
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return model, transforms
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pretrained_vit_foodvision.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c9067188086ff537cdb76de31c205acb865cca5ee0a25ed2d7ffe6b05376fea
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size 343264485
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requirements.txt
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torch==2.0.1
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torchvision==0.15.2
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gradio==3.23.0
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