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import gradio as gr
import torch
import numpy as np
import matplotlib.pyplot as plt
from transformers import ViTMAEForPreTraining, ViTFeatureExtractor
from PIL import Image
import uuid
feature_extractor = ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base")
model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
imagenet_mean = np.array(feature_extractor.image_mean)
imagenet_std = np.array(feature_extractor.image_std)
def show_image(image, title=''):
# image is [H, W, 3]
assert image.shape[2] == 3
unique_id = str(uuid.uuid4())
plt.imshow(torch.clip((image * imagenet_std + imagenet_mean) * 255, 0, 255).int())
plt.axis('off')
plt.savefig(f"{unique_id}.png", bbox_inches='tight', pad_inches=0)
return f"{unique_id}.png"
def visualize(image):
pixel_values = feature_extractor(image, return_tensors="pt").pixel_values
# forward pass
outputs = model(pixel_values)
y = model.unpatchify(outputs.logits)
y = torch.einsum('nchw->nhwc', y).detach().cpu()
# visualize the mask
mask = outputs.mask.detach()
mask = mask.unsqueeze(-1).repeat(1, 1, model.config.patch_size**2 *3) # (N, H*W, p*p*3)
mask = model.unpatchify(mask) # 1 is removing, 0 is keeping
mask = torch.einsum('nchw->nhwc', mask).detach().cpu()
x = torch.einsum('nchw->nhwc', pixel_values)
# masked image
im_masked = x * (1 - mask)
# MAE reconstruction pasted with visible patches
im_paste = x * (1 - mask) + y * mask
gallery_labels = ["Original Image", "Masked Image", "Reconstruction", "Reconstruction with Patches"]
gallery_out = [show_image(out) for out in [x[0], im_masked[0], y[0], im_paste[0]]]
return [(k,v) for k,v in zip(gallery_out, gallery_labels)]
with gr.Blocks() as demo:
gr.Markdown("## ViTMAE Demo")
gr.Markdown("**ViTMAE is an architecture that combine masked autoencoder and Vision Transformer (ViT) for self-supervised pre-training.**")
gr.Markdown("**By pre-training a ViT to reconstruct pixel values for masked patches, one can get results after fine-tuning that outperform supervised pre-training.**")
gr.Markdown("**This application demonstrates the reconstruction. To start, simply upload an image.**")
with gr.Row():
input_img = gr.Image()
output = gr.Gallery()
input_img.change(visualize, inputs=input_img, outputs=output)
gr.Examples([["./cat.png"]], inputs=input_img, outputs=output, fn=visualize)
demo.launch(debug=True)
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