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from torchvision import transforms | |
import torch | |
import urllib | |
from PIL import Image | |
import gradio as gr | |
import torch | |
# Images | |
torch.hub.download_url_to_file('https://images.pexels.com/photos/17811/pexels-photo.jpg', 'bird.jpg') | |
model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, | |
**{'topN': 6, 'device':'cpu', 'num_classes': 200}) | |
transform_test = transforms.Compose([ | |
transforms.Resize((600, 600), Image.BILINEAR), | |
transforms.CenterCrop((448, 448)), | |
# transforms.RandomHorizontalFlip(), # only if train | |
transforms.ToTensor(), | |
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), | |
]) | |
model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200}) | |
def birds(img): | |
scaled_img = transform_test(img) | |
torch_images = scaled_img.unsqueeze(0) | |
with torch.no_grad(): | |
top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images) | |
_, predict = torch.max(concat_logits, 1) | |
pred_id = predict.item() | |
return model.bird_classes[pred_id].split('.')[1] | |
inputs = gr.inputs.Image(type='pil', label="Original Image") | |
outputs = gr.outputs.Textbox(label="bird class") | |
title = "ntsnet" | |
description = "demo for ntsnet to classify birds. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
article = "<p style='text-align: center'><a href='http://artelab.dista.uninsubria.it/res/research/papers/2019/2019-IVCNZ-Nawaz-Birds.pdf'>Are These Birds Similar: Learning Branched Networks for Fine-grained Representations</a> | <a href='https://github.com/nicolalandro/ntsnet-cub200'>Github Repo</a></p>" | |
examples = [ | |
['bird.jpg'] | |
] | |
gr.Interface(birds, inputs, outputs, title=title, description=description, | |
article=article, examples=examples, analytics_enabled=False).launch() |