Why after huffman_encode, why the parameters increase? about deep-compression-pytorch HOT 1 CLOSED

For the first part (where the all alive parameters equals to 112422),
the nonzero numbers mean the number of nonzero weight values.
for the second part (after Huffman encoding),
the numbers mean the number of Bytes needed to store weights.
Note that I didn't encode the biases so they use precisely 4 Bytes for each weight.
However, for normal weights (fc1.weight, fc2.weight, fc3.weight),
I used either CSC or CSR matrix representation (https://en.wikipedia.org/wiki/Sparse_matrix).
(The paper also uses these representations with a minor variation)
So they use a bit fewer Bytes per each weight.
For example, for fc1 layer, 858868/235200 ~= 3.65. It uses approximately 3.65 bytes per weight
for fc2 layer, 35108/30000 ~= 1.17. It uses approximately 1.17 bytes per weight.
(Note that this efficiency comes from the sparsity of fc2 weights)

Maybe I'll add more information about the numbers in the future.

from deep-compression-pytorch.