entropy coding

README.md

@@ -1,7 +1,3 @@
----
-datasets:
-- danjacobellis/LSDIR_540
----
 ```python
 !pip install walloc PyWavelets pytorch-wavelets
 ```
@@ -13,8 +9,10 @@ datasets:
 
 
 ```python
+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
@@ -25,14 +23,14 @@ class Args: pass
 
 ```python
 device = "cpu"
-checkpoint = torch.load("v0.5.1.pth",map_location="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 = 6
+    latent_bits = 5
 )
 codec.load_state_dict(checkpoint['model_state_dict'])
 codec = codec.to(device)
@@ -84,7 +82,7 @@ with torch.no_grad():
 print(f"dimensionality reduction: {x.numel()/Y.numel()}x")
 ```
 
-    dimensionality reduction: 16.0x
+    dimensionality reduction: 12.0x
 
 
 
@@ -95,18 +93,15 @@ Y.unique()
 
 
 
-    tensor([-31., -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20.,
-            -19., -18., -17., -16., -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.,  16.,
-             17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,
-             29.,  30.,  31.])
+    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.])
 
 
 
 
 ```python
-plt.hist(Y.flatten().numpy(),range=(-32,32),bins=65);
+plt.hist(Y.flatten().numpy(),range=(-17.5,17.5),bins=35);
 ```
 
 
@@ -117,28 +112,33 @@ plt.hist(Y.flatten().numpy(),range=(-32,32),bins=65);
 
 
 ```python
-for latent in Y[0,:4,:,:]:
-    display(ToPILImage()(0.5*latent/codec.latent_max + 0.5).resize((192,128),resample=Image.Resampling.NEAREST))
+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
 ```
 
 
-    
-![png](README_files/README_9_0.png)
-    
-
 
 
     
-![png](README_files/README_9_1.png)
+![png](README_files/README_9_0.png)
     
 
 
 
-    
-![png](README_files/README_9_2.png)
-    
 
+```python
+combined_image.save('tmp.png')
+print("compression_ratio: ", x.numel()/os.path.getsize("tmp.png"))
+```
 
+    compression_ratio:  20.75383532723434
 
-    
-![png](README_files/README_9_3.png)