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import gradio as gr
import torch
import torchvision
from PIL import Image
import numpy as np
import random
from einops import rearrange
import matplotlib.pyplot as plt
from torchvision.transforms import v2
from model import MAE_ViT, MAE_Encoder, MAE_Decoder, MAE_Encoder_FeatureExtractor
path = [['images/cat.jpg'], ['images/dog.jpg']]
model_name = "vit-t-mae-pretrain.pt"
model = torch.load(model_name, map_location='cpu')
model.eval()
device = torch.device("cpu")
model.to(device)
transform = v2.Compose([
v2.Resize((32, 32)),
v2.ToTensor(),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
# Load and Preprocess the Image
def load_image(image_path, transform):
img = Image.open(image_path).convert('RGB')
img = transform(img).unsqueeze(0) # Add batch dimension
return img
def show_image(img, title):
img = rearrange(img, "c h w -> h w c")
img = (img.cpu().detach().numpy() + 1) / 2 # Normalize to [0, 1]
plt.imshow(img)
plt.axis('off')
plt.title(title)
# Visualize a Single Image
def visualize_single_image(image_path):
img = load_image(image_path, transform).to(device)
# Run inference
model.eval()
with torch.no_grad():
predicted_img, mask = model(img)
# Convert the tensor back to a displayable image
# masked image
im_masked = img * (1 - mask)
# MAE reconstruction pasted with visible patches
im_paste = img * (1 - mask) + predicted_img * mask
# make the plt figure larger
plt.figure(figsize=(18, 8))
plt.subplot(1, 4, 1)
show_image(img[0], "original")
plt.subplot(1, 4, 2)
show_image(im_masked[0], "masked")
plt.subplot(1, 4, 3)
show_image(predicted_img[0], "reconstruction")
plt.subplot(1, 4, 4)
show_image(im_paste[0], "reconstruction + visible")
plt.tight_layout()
# convert the plt figure to a numpy array
plt.savefig("output.png")
return np.array(plt.imread("output.png"))
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
gr.Interface(
fn=visualize_single_image,
inputs=inputs_image,
outputs=outputs_image,
examples=path,
title="MAE-ViT Image Reconstruction",
description="This is a demo of the MAE-ViT model for image reconstruction.",
).launch() |