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mnist_compress.py
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mnist_compress.py
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from utils.torch.rand import *
from utils.torch.modules import ImageNet
from model.mnist_train import Model
from torch.utils.data import *
from discretization import *
from torchvision import datasets, transforms
import random
import time
import argparse
from tqdm import tqdm
import pickle
class ANS:
def __init__(self, pmfs, bits=31, quantbits=8):
self.device = pmfs.device
self.bits = bits
self.quantbits = quantbits
# mask of 2**bits - 1 bits
self.mask = (1 << bits) - 1
# normalization constants
self.lbound = 1 << 32
self.tail_bits = (1 << 32) - 1
self.seq_len, self.support = pmfs.shape
# compute pmf's and cdf's scaled up by 2**n
multiplier = (1 << self.bits) - (1 << self.quantbits)
self.pmfs = (pmfs * multiplier).long()
# add ones to counter zero probabilities
self.pmfs += torch.ones_like(self.pmfs)
# add remnant to the maximum value of the probabilites
self.pmfs[torch.arange(0, self.seq_len),torch.argmax(self.pmfs, dim=1)] += ((1 << self.bits) - self.pmfs.sum(1))
# compute cdf's
self.cdfs = torch.cumsum(self.pmfs, dim=1) # compute CDF (scaled up to 2**n)
self.cdfs = torch.cat([torch.zeros([self.cdfs.shape[0], 1], dtype=torch.long, device=self.device), self.cdfs], dim=1) # pad with 0 at the beginning
# move cdf's and pmf's the cpu for faster encoding and decoding
self.cdfs = self.cdfs.cpu().numpy()
self.pmfs = self.pmfs.cpu().numpy()
assert self.cdfs.shape == (self.seq_len, self.support + 1)
assert np.all(self.cdfs[:,-1] == (1 << bits))
def encode(self, x, symbols):
for i, s in enumerate(symbols):
pmf = int(self.pmfs[i,s])
if x[-1] >= ((self.lbound >> self.bits) << 32) * pmf:
x.append(x[-1] >> 32)
x[-2] = x[-2] & self.tail_bits
x[-1] = ((x[-1] // pmf) << self.bits) + (x[-1] % pmf) + int(self.cdfs[i, s])
return x
def decode(self, x):
sequence = np.zeros((self.seq_len,), dtype=np.int64)
for i in reversed(range(self.seq_len)):
masked_x = x[-1] & self.mask
s = np.searchsorted(self.cdfs[i,:-1], masked_x, 'right') - 1
sequence[i] = s
x[-1] = int(self.pmfs[i,s]) * (x[-1] >> self.bits) + masked_x - int(self.cdfs[i, s])
if x[-1] < self.lbound:
x[-1] = (x[-1] << 32) | x.pop(-2)
sequence = torch.from_numpy(sequence).to(self.device)
return x, sequence
def compress(quantbits, nz, bitswap, gpu):
# model and compression params
zdim = 1 * 16 * 16
zrange = torch.arange(zdim)
xdim = 32 ** 2 * 1
xrange = torch.arange(xdim)
ansbits = 31 # ANS precision
type = torch.float64 # datatype throughout compression
device = f"cuda:{gpu}" # gpu
# set up the different channel dimension for different latent depths
if nz == 8:
reswidth = 61
elif nz == 4:
reswidth = 62
elif nz == 2:
reswidth = 63
else:
reswidth = 64
assert nz > 0
print(f"{'Bit-Swap' if bitswap else 'BB-ANS'} - MNIST - {nz} latent layers - {quantbits} bits quantization")
# seed for replicating experiment and stability
np.random.seed(100)
random.seed(50)
torch.manual_seed(50)
torch.cuda.manual_seed(50)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
# compression experiment params
experiments = 100
ndatapoints = 100
decompress = False
# <=== MODEL ===>
model = Model(xs = (1, 32, 32), nz=nz, zchannels=1, nprocessing=4, kernel_size=3, resdepth=8, reswidth=reswidth).to(device)
model.load_state_dict(
torch.load(f'model/params/mnist/nz{nz}',
map_location=lambda storage, location: storage
)
)
model.eval()
print("Discretizing")
# get discretization bins for latent variables
zendpoints, zcentres = discretize(nz, quantbits, type, device, model, "mnist")
# get discretization bins for discretized logistic
xbins = ImageBins(type, device, xdim)
xendpoints = xbins.endpoints()
xcentres = xbins.centres()
print("Load data..")
# <=== DATA ===>
class ToInt:
def __call__(self, pic):
return pic * 255
transform_ops = transforms.Compose([transforms.Pad(2), transforms.ToTensor(), ToInt()])
test_set = datasets.MNIST(root="model/data/mnist", train=False, transform=transform_ops, download=True)
# sample (experiments, ndatapoints) from test set with replacement
if not os.path.exists("bitstreams/mnist/indices"):
randindices = np.random.choice(len(test_set.data), size=(experiments, ndatapoints), replace=False)
np.save("bitstreams/mnist/indices", randindices)
else:
randindices = np.load("bitstreams/mnist/indices")
print("Setting up metrics..")
# metrics for the results
nets = np.zeros((experiments, ndatapoints), dtype=np.float)
elbos = np.zeros((experiments, ndatapoints), dtype=np.float)
cma = np.zeros((experiments, ndatapoints), dtype=np.float)
total = np.zeros((experiments, ndatapoints), dtype=np.float)
print("Compression..")
for ei in range(experiments):
print(f"Experiment {ei + 1}")
subset = Subset(test_set, randindices[ei])
test_loader = DataLoader(
dataset=subset,
batch_size=1, shuffle=False, drop_last=True)
datapoints = list(test_loader)
# < ===== COMPRESSION ===>
# initialize compression
model.compress()
state = list(map(int, np.random.randint(low=1 << 16, high=(1 << 32) - 1, size=10000, dtype=np.uint32))) # fill state list with 'random' bits
state[-1] = state[-1] << 32
initialstate = state.copy()
restbits = None
# <===== SENDER =====>
iterator = tqdm(range(len(datapoints)), desc="Sender")
for xi in iterator:
(x, _) = datapoints[xi]
x = x.to(device).view(xdim)
# calculate ELBO
with torch.no_grad():
model.compress(False)
logrecon, logdec, logenc, _ = model.loss(x.view((-1,) + model.xs))
elbo = -logrecon + torch.sum(-logdec + logenc)
model.compress(True)
if bitswap:
# < ===== Bit-Swap ====>
# inference and generative model
for zi in range(nz):
# inference model
input = zcentres[zi - 1, zrange, zsym] if zi > 0 else xcentres[xrange, x.long()]
mu, scale = model.infer(zi)(given=input)
cdfs = logistic_cdf(zendpoints[zi].t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# decode z
state, zsymtop = ANS(pmfs, bits=ansbits, quantbits=quantbits).decode(state)
# save excess bits for calculations
if xi == zi == 0:
restbits = state.copy()
assert len(restbits) > 1, "too few initial bits" # otherwise initial state consists of too few bits
# generative model
z = zcentres[zi, zrange, zsymtop]
mu, scale = model.generate(zi)(given=z)
cdfs = logistic_cdf((zendpoints[zi - 1] if zi > 0 else xendpoints).t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# encode z or x
state = ANS(pmfs, bits=ansbits, quantbits=(quantbits if zi > 0 else 8)).encode(state, zsym if zi > 0 else x.long())
zsym = zsymtop
else:
# < ===== BB-ANS ====>
# inference and generative model
zs = []
for zi in range(nz):
# inference model
input = zcentres[zi - 1, zrange, zsym] if zi > 0 else xcentres[xrange, x.long()]
mu, scale = model.infer(zi)(given=input)
cdfs = logistic_cdf(zendpoints[zi].t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# decode z
state, zsymtop = ANS(pmfs, bits=ansbits, quantbits=quantbits).decode(state)
zs.append(zsymtop)
zsym = zsymtop
# save excess bits for calculations
if xi == 0:
restbits = state.copy()
assert len(restbits) > 1 # otherwise initial state consists of too few bits
for zi in range(nz):
# generative model
zsymtop = zs.pop(0)
z = zcentres[zi, zrange, zsymtop]
mu, scale = model.generate(zi)(given=z)
cdfs = logistic_cdf((zendpoints[zi - 1] if zi > 0 else xendpoints).t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# encode z or x
state = ANS(pmfs, bits=ansbits, quantbits=(quantbits if zi > 0 else 8)).encode(state, zsym if zi > 0 else x.long())
zsym = zsymtop
assert zs == []
# prior
cdfs = logistic_cdf(zendpoints[-1].t(), torch.zeros(1, device=device, dtype=type), torch.ones(1, device=device, dtype=type)).t()
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# encode prior
state = ANS(pmfs, bits=ansbits, quantbits=quantbits).encode(state, zsymtop)
# calculating bits
totaladdedbits = (len(state) - len(initialstate)) * 32
totalbits = (len(state) - (len(restbits) - 1)) * 32
# logging
nets[ei, xi] = (totaladdedbits / xdim) - nets[ei, :xi].sum()
elbos[ei, xi] = elbo.item() / xdim
cma[ei, xi] = totalbits / (xdim * (xi + 1))
total[ei, xi] = totalbits
iterator.set_postfix_str(s=f"N:{nets[ei,:xi+1].mean():.2f}±{nets[ei,:xi+1].std():.2f}, D:{nets[ei,:xi+1].mean()-elbos[ei,:xi+1].mean():.4f}, C: {cma[ei,:xi+1].mean():.2f}, T: {totalbits:.0f}", refresh=False)
# write state to file
with open(f"bitstreams/mnist/nz{nz}/{'Bit-Swap' if bitswap else 'BB-ANS'}/{'Bit-Swap' if bitswap else 'BB-ANS'}_{quantbits}bits_nz{nz}_experiment{ei + 1}", "wb") as fp:
pickle.dump(state, fp)
state = None
# open state file
with open(f"bitstreams/mnist/nz{nz}/{'Bit-Swap' if bitswap else 'BB-ANS'}/{'Bit-Swap' if bitswap else 'BB-ANS'}_{quantbits}bits_nz{nz}_experiment{ei + 1}", "rb") as fp:
state = pickle.load(fp)
if not decompress:
continue
# <===== RECEIVER =====>
datapoints.reverse()
iterator = tqdm(range(len(datapoints)), desc="Receiver", postfix=f"decoded {None}")
for xi in iterator:
(x, _) = datapoints[xi]
x = x.to(device).view(xdim)
# prior
cdfs = logistic_cdf(zendpoints[-1].t(), torch.zeros(1, device=device, dtype=type),
torch.ones(1, device=device, dtype=type)).t()
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# decode z
state, zsymtop = ANS(pmfs, bits=ansbits, quantbits=quantbits).decode(state)
if bitswap:
# < ===== Bit-Swap ====>
# inference and generative model
for zi in reversed(range(nz)):
# generative model
z = zcentres[zi, zrange, zsymtop]
mu, scale = model.generate(zi)(given=z)
cdfs = logistic_cdf((zendpoints[zi - 1] if zi > 0 else xendpoints).t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# decode z or x
state, sym = ANS(pmfs, bits=ansbits, quantbits=quantbits if zi > 0 else 8).decode(state)
# inference model
input = zcentres[zi - 1, zrange, sym] if zi > 0 else xcentres[xrange, sym]
mu, scale = model.infer(zi)(given=input)
cdfs = logistic_cdf(zendpoints[zi].t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# encode z
state = ANS(pmfs, bits=ansbits, quantbits=quantbits).encode(state, zsymtop)
zsymtop = sym
assert torch.all(x.long() == zsymtop), f"decoded datapoint does not match {xi + 1}"
else:
# < ===== BB-ANS ====>
# inference and generative model
zs = [zsymtop]
for zi in reversed(range(nz)):
# generative model
z = zcentres[zi, zrange, zsymtop]
mu, scale = model.generate(zi)(given=z)
cdfs = logistic_cdf((zendpoints[zi - 1] if zi > 0 else xendpoints).t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# decode z or x
state, sym = ANS(pmfs, bits=ansbits, quantbits=quantbits if zi > 0 else 8).decode(state)
zs.append(sym)
zsymtop = sym
zsymtop = zs.pop(0)
for zi in reversed(range(nz)):
# inference model
sym = zs.pop(0) if zi > 0 else zs[0]
input = zcentres[zi - 1, zrange, sym] if zi > 0 else xcentres[xrange, sym]
mu, scale = model.infer(zi)(given=input)
cdfs = logistic_cdf(zendpoints[zi].t(), mu, scale).t() # most expensive calculation?
pmfs = cdfs[:, 1:] - cdfs[:, :-1]
pmfs = torch.cat((cdfs[:, 0].unsqueeze(1), pmfs, 1. - cdfs[:, -1].unsqueeze(1)), dim=1)
# encode z
state = ANS(pmfs, bits=ansbits, quantbits=quantbits).encode(state, zsymtop)
zsymtop = sym
# check if decoded datapoint matches the real datapoint
assert torch.all(x.long() == zs[0]), f"decoded datapoint does not match {xi + 1}"
iterator.set_postfix_str(s=f"decoded {len(datapoints) - xi}")
# check if the initial state matches the output state
assert initialstate == state
print(f"N:{nets.mean():.4f}±{nets.std():.2f}, E:{elbos.mean():.4f}±{elbos.std():.2f}, D:{nets.mean() - elbos.mean():.6f}")
# save experiments
np.save(f"plots/mnist{nz}/{'bitswap' if bitswap else 'bbans'}_{quantbits}bits_nets",nets)
np.save(f"plots/mnist{nz}/{'bitswap' if bitswap else 'bbans'}_{quantbits}bits_elbos", elbos)
np.save(f"plots/mnist{nz}/{'bitswap' if bitswap else 'bbans'}_{quantbits}bits_cmas",cma)
np.save(f"plots/mnist{nz}/{'bitswap' if bitswap else 'bbans'}_{quantbits}bits_total", total)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default=0, type=int) # assign to gpu
parser.add_argument('--nz', default=2, type=int) # choose number of latent variables
parser.add_argument('--quantbits', default=10, type=int) # choose discretization precision
parser.add_argument('--bitswap', default=1, type=int) # choose whether to use Bit-Swap or not
args = parser.parse_args()
print(args)
gpu = args.gpu
nz = args.nz
quantbits = args.quantbits
bitswap = args.bitswap
for nz in [nz]:
for bits in [quantbits]:
for bitswap in [bitswap]:
compress(bits, nz, bitswap, gpu)