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| from pydoc import describe | |
| import re | |
| import numpy as np | |
| from PIL import Image | |
| import torch | |
| from torchvision import transforms | |
| import gradio as gr | |
| from model import TransformerNet | |
| style_model = TransformerNet() | |
| device=torch.device("cpu") | |
| styles_map = {"Kandinsky, Several circles": "kand_circles.model", | |
| "Haring, Dance": "haring_dance.model", | |
| "Picasso, The weeping woman": "picasso_weeping.model", | |
| "Van Gogh, Wheatfield with crows": "vangogh_crows.model"} | |
| content_transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Lambda(lambda x: x.mul(255)) | |
| ]) | |
| def run(content_image, style): | |
| content_image.thumbnail((1080, 1080)) | |
| img = content_transform(content_image) | |
| img = img.unsqueeze(0).to(device) | |
| model = styles_map[style] | |
| state_dict = torch.load(f"./models/{model}") | |
| for k in list(state_dict.keys()): | |
| if re.search(r'in\d+\.running_(mean|var)$', k): | |
| del state_dict[k] | |
| style_model.load_state_dict(state_dict) | |
| style_model.to(device) | |
| with torch.no_grad(): | |
| output = style_model(img) | |
| img = output[0].clone().clamp(0, 255).numpy() | |
| img = img.transpose(1, 2, 0).astype("uint8") | |
| img = Image.fromarray(img) | |
| return img | |
| content_image_input = gr.inputs.Image(label="Content Image", type="pil") | |
| style_input = gr.inputs.Dropdown(list(styles_map.keys()), type="value", default="Kandinsky, Several circles", label="Style") | |
| description="Fast Neural Style Transfer demo (trained from scratch!). Upload a content image. Select an artwork. Enjoy." | |
| article=""" | |
| **References**\n\n | |
| You can find <a href='https://francescopochetti.com/fast-neural-style-transfer-deploying-pytorch-models-to-aws-lambda/' target='_blank'>here</a> a post I put together | |
| describing the approach I used to train models and deploy them on <a href='http://visualneurons.com/fast.html' target='_blank'>visualneurons.com</a> using AWS Lambda. \n | |
| <a href='https://github.com/FraPochetti/examples/blob/master/fast_neural_style/neural_style/FastStyleTransferPytorch.ipynb' target='_blank'>Here</a> is instead the Jupyter notebook | |
| with the training logic. \n | |
| <br> | |
| <hr> | |
| **Kandinsky, Several circles** | |
| <img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Several_Circles.jpeg'> | |
| <hr> | |
| **Haring, Dance** | |
| <img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Haring.jpeg'> | |
| <hr> | |
| **Picasso, The weeping woman** | |
| <img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/weeping.png'> | |
| <hr> | |
| **Van Gogh, Wheatfield with crows** | |
| <img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Wheatfield_with_crows.jpeg'> | |
| """ | |
| example = ["dog.jpeg", "Kandinsky, Several circles"] | |
| app_interface = gr.Interface(fn=run, | |
| inputs=[content_image_input, style_input], | |
| outputs="image", | |
| title="Fast Neural Style Transfer", | |
| description=description, | |
| examples=[example], | |
| article=article) | |
| app_interface.launch() |