README.md

!pip install walloc PyWavelets pytorch-wavelets

!wget "https://r0k.us/graphics/kodak/kodak/kodim05.png"

import os
import torch
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from IPython.display import display
from torchvision.transforms import ToPILImage, PILToTensor
from walloc.walloc import Walloc
class Args: pass

device = "cpu"
checkpoint = torch.load("v0.6.1.pth",map_location="cpu")
args = checkpoint['args']
codec = Walloc(
    channels = args.channels,
    J = args.J,
    N = args.N,
    latent_dim = args.latent_dim,
    latent_bits = 5
)
codec.load_state_dict(checkpoint['model_state_dict'])
codec = codec.to(device)

img = Image.open("kodim05.png")
img

with torch.no_grad():
    codec.eval()
    x = PILToTensor()(img).to(torch.float)
    x = (x/255 - 0.5).unsqueeze(0).to(device)
    x_hat, _, _ = codec(x)
ToPILImage()(x_hat[0]+0.5)

with torch.no_grad():
    codec.eval()
    X = codec.wavelet_analysis(x,J=codec.J)
    Y = codec.encoder(X)
    X_hat = codec.decoder(Y)
    x_hat = codec.wavelet_synthesis(X_hat,J=codec.J)

print(f"dimensionality reduction: {x.numel()/Y.numel()}x")

dimensionality reduction: 12.0x

Y.unique()

tensor([-15., -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,
         -3.,  -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,
          9.,  10.,  11.,  12.,  13.,  14.,  15.])

plt.hist(Y.flatten().numpy(),range=(-17.5,17.5),bins=35);

grid_size = 4
n_channels, H, W = Y[0].shape
combined_image = Image.new('L', (W * grid_size, H * grid_size))
size_bytes = 0
for i, channel in enumerate(Y[0]):
    channel = (channel+16).to(torch.uint8)
    row = i // grid_size
    col = i % grid_size
    channel = ToPILImage()(channel)
    combined_image.paste(channel, (col * W, row * H))
combined_image

combined_image.save('tmp.png')
print("compression_ratio: ", x.numel()/os.path.getsize("tmp.png"))

compression_ratio:  20.75383532723434