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import sys
from os.path import abspath, dirname
sys.path.append(abspath(dirname(__name__)))
import gradio as gr
import torch
from os.path import dirname
from torchvision import transforms
from PIL import Image
import yaml
from torch import nn
def image_classification():
with open(f"{dirname(abspath(__file__))}/config.yaml", 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
labels = config["labels"]
class DinoVisionTransformerClassifier(nn.Module):
def __init__(self):
super(DinoVisionTransformerClassifier, self).__init__()
self.transformer = torch.hub.load("facebookresearch/dinov2", "dinov2_vits14")
self.classifier = nn.Sequential(nn.Linear(384, 256), nn.ReLU(), nn.Linear(256, 2))
def forward(self, x):
x = self.transformer(x)
x = self.transformer.norm(x)
x = self.classifier(x)
return x
dino = DinoVisionTransformerClassifier()
model_path = f"{dirname(abspath(__file__))}/model.pth"
state_dict = torch.load(model_path)
dino.load_state_dict(state_dict)
def preprocess(img_path):
data_transforms = {
"test": transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
]
)
}
img = Image.open(img_path).convert('RGB')
img_transformed = data_transforms['test'](img)
return img_transformed
def predict(img_path):
img = preprocess(img_path)
img = img.unsqueeze(0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dino.to(device)
dino.eval()
with torch.no_grad():
output = dino(img.to(device))
_, predicted = torch.max(output.data, 1)
print("Predicted", predicted[0])
return labels[predicted[0].item()]
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath", label="Classify Image"),
outputs=gr.Textbox(label="Label"),
title="Person classifier",
)
demo.launch(share=True, debug=True)
if __name__ == "__main__":
image_classification() |