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Browse files- .gitattributes +1 -0
- 09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth +3 -0
- app.py +77 -0
- examples/2582289.jpg +0 -0
- examples/3622237.jpg +0 -0
- examples/592799.jpg +0 -0
- model.py +36 -0
- requirements.txt +3 -0
.gitattributes
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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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*.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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*.file_extension filter=lfs diff=lfs merge=lfs -text
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09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1c8ed70e28fb2998438814e4655e50bc9bfec79a7a85826da877becc61a6fe1
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size 31314554
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app.py
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### 1. Imports and class names setup ###
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import gradio as gr
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import os
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import torch
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from model import create_effnetb2_model
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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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### 2. Model and transforms preparation ###
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# Create EffNetB2 model
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effnetb2, effnetb2_transforms = create_effnetb2_model(
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num_classes=3, # len(class_names) would also work
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)
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# Load saved weights
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effnetb2.load_state_dict(
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torch.load(
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f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
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map_location=torch.device("cpu"), # load to CPU
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)
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)
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### 3. Predict function ###
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# Create predict function
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def predict(img) -> Tuple[Dict, float]:
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"""Transforms and performs a prediction on img and returns prediction and time taken.
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"""
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# Start the timer
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start_time = timer()
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# Transform the target image and add a batch dimension
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img = effnetb2_transforms(img).unsqueeze(0)
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# Put model into evaluation mode and turn on inference mode
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effnetb2.eval()
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with torch.inference_mode():
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# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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pred_probs = torch.softmax(effnetb2(img), dim=1)
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# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
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pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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# Calculate the prediction time
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pred_time = round(timer() - start_time, 5)
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# Return the prediction dictionary and prediction time
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return pred_labels_and_probs, pred_time
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### 4. Gradio app ###
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# Create title, description and article strings
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title = "FoodVision Mini ππ₯©π£"
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description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
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article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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# Create examples list from "examples/" directory
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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(fn=predict, # mapping function from input to output
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inputs=gr.Image(type="pil"), # what are the inputs?
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outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
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gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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# Create examples list from "examples/" directory
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examples=example_list,
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title=title,
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description=description,
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article=article)
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# Launch the demo!
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demo.launch()
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examples/2582289.jpg
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examples/3622237.jpg
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examples/592799.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_effnetb2_model(num_classes:int=3,
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seed:int=42):
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"""Creates an EfficientNetB2 feature extractor model and transforms.
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Args:
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num_classes (int, optional): number of classes in the classifier head.
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Defaults to 3.
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seed (int, optional): random seed value. Defaults to 42.
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Returns:
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model (torch.nn.Module): EffNetB2 feature extractor model.
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transforms (torchvision.transforms): EffNetB2 image transforms.
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"""
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# Create EffNetB2 pretrained weights, transforms and model
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weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
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transforms = weights.transforms()
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model = torchvision.models.efficientnet_b2(weights=weights)
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# Freeze all layers in base model
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for param in model.parameters():
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param.requires_grad = False
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# Change classifier head with random seed for reproducibility
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torch.manual_seed(seed)
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model.classifier = nn.Sequential(
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nn.Dropout(p=0.3, inplace=True),
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nn.Linear(in_features=1408, out_features=num_classes),
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)
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return model, transforms
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requirements.txt
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torch==2.1.0
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torchvision==0.16.0
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gradio==4.22.0
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