numpyAc

testTorchac.py

@@ -1,50 +0,0 @@
-'''
-LastEditors: fcy
-'''
-import torchac
-import torch
-import numpy as np
-# Encode to bytestream.
-
-seed=6
-torch.manual_seed(seed)
-np.random.seed(seed)
-
-dim = 500
-symsNum = 40000
-pdf = np.random.rand(symsNum,dim)
-pdf = pdf / (np.sum(pdf,1,keepdims=True))
-sym = torch.ShortTensor(np.random.randint(0,dim,symsNum,dtype=np.int16))
-
-def pdf_convert_to_cdf_and_normalize(pdf):
-    assert pdf.ndim==2
-    pdf = pdf / (np.sum(pdf,1,keepdims=True))/(1+10**(-10))
-    cdfF = np.cumsum( pdf, axis=1)
-    cdfF = np.hstack((np.zeros((pdf.shape[0],1)),cdfF))
-    return cdfF
-
-
-output_cdf = torch.Tensor(pdf_convert_to_cdf_and_normalize(pdf)) # Get CDF from your model, shape B, C, H, W, Lp
-
-byte_stream = torchac.encode_float_cdf(output_cdf, sym, check_input_bounds=True)
-
-
-# pdf = np.diff(cdfF)
-# print( -np.log2(pdf[range(0,oct_len),sym]).sum())
-
-# Number of bits taken by the stream
-real_bits = len(byte_stream) * 8
-print(real_bits)
-# Write to a file.
-with open('outfile.b', 'wb') as fout:
-    fout.write(byte_stream)
-
-# Read from a file.
-with open('outfile.b', 'rb') as fin:
-    byte_stream = fin.read()
-
-# Decode from bytestream.
-sym_out = torchac.decode_float_cdf(output_cdf, byte_stream)
-
-# Output will be equal to the input.
-assert sym_out.equal(sym)