def forward(self, observation=[]):
states = [pyprob.sample(init_dist)]
for o in observation:
state = pyprob.sample(self.trans_dists[int(states[-1])])
pyprob.observe(self.obs_dists[int(state)], o)
states.append(state)
return torch.stack([util.one_hot(3, int(s)) for s in states])
def __init__(self, *args, **kwargs):
class MiniCaptcha(Model):
def __init__(self, alphabet=['A', 'B', 'C', 'D', 'E', 'F'], noise=0.1):
self._alphabet = alphabet
self._probs = [1/len(alphabet) for i in range(len(alphabet))]
self._noise = noise
super().__init__('MiniCaptcha')
def render(self, text, size=18, height=28, width=28, x=6, y=6):
pil_font = ImageFont.truetype('Ubuntu-B.ttf', size=size)
text_width, text_height = pil_font.getsize(text)
canvas = Image.new('RGB', [height, width], (255, 255, 255))
draw = ImageDraw.Draw(canvas)
draw.text((x, y), text, font=pil_font, fill='#000000')
return torch.from_numpy(1 - (np.asarray(canvas) / 255.0))[:, :, 0].unsqueeze(0).float()
def forward(self):
letter_id = pyprob.sample(Categorical(self._probs))
image = self.render(self._alphabet[letter_id]).view(-1)
likelihood = Normal(image, self._noise)
pyprob.observe(likelihood, name='query_image')
return letter_id
self._model = MiniCaptcha()
self._test_images = [self._model.render(letter).view(-1) for letter in self._model._alphabet]
self._true_posteriors = [Categorical(util.one_hot(len(self._model._alphabet), i) + util._epsilon) for i in range(len(self._model._alphabet))]
super().__init__(*args, **kwargs)
def forward(self):
states = [pyprob.sample(init_dist)]
for i in range(self.obs_length):
state = pyprob.sample(self.trans_dists[int(states[-1])])
pyprob.observe(self.obs_dists[int(state)], name='obs{}'.format(i))
states.append(state)
return torch.stack([util.one_hot(3, int(s)) for s in states])
def get_sample(s):
address = s.Address().decode("utf-8")
distribution = None
# sample.instance = s.Instance()
value = NDArray_to_Tensor(s.Value())
distribution_type = s.DistributionType()
if distribution_type != infcomp.protocol.Distribution.Distribution().NONE:
if distribution_type == infcomp.protocol.Distribution.Distribution(
).UniformDiscrete:
p = infcomp.protocol.UniformDiscrete.UniformDiscrete()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = UniformDiscrete(p.PriorMin(), p.PriorSize())
if value.dim() > 0:
value = util.one_hot(distribution.prior_size,
int(value[0]) - distribution.prior_min)
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).MultivariateNormal:
p = infcomp.protocol.MultivariateNormal.MultivariateNormal()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = MultivariateNormal(NDArray_to_Tensor(p.PriorMean()),
NDArray_to_Tensor(p.PriorCov()))
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Normal:
p = infcomp.protocol.Normal.Normal()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = Normal(p.PriorMean(), p.PriorStd())
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Flip:
distribution = Flip()
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Discrete:
p = infcomp.protocol.Discrete.Discrete()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = Discrete(p.PriorSize())
if value.dim() > 0:
value = util.one_hot(distribution.prior_size, int(value[0]))
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Categorical:
p = infcomp.protocol.Categorical.Categorical()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = Categorical(p.PriorSize())
if value.dim() > 0:
value = util.one_hot(distribution.prior_size, int(value[0]))
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).UniformContinuous:
p = infcomp.protocol.UniformContinuous.UniformContinuous()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = UniformContinuous(p.PriorMin(), p.PriorMax())
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).UniformContinuousAlt:
p = infcomp.protocol.UniformContinuousAlt.UniformContinuousAlt()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = UniformContinuousAlt(p.PriorMin(), p.PriorMax())
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Laplace:
p = infcomp.protocol.Laplace.Laplace()
p.Init(s.Distribution().Bytes, s.Distribution().Pos)
distribution = Laplace(p.PriorLocation(), p.PriorScale())
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Gamma:
distribution = Gamma()
elif distribution_type == infcomp.protocol.Distribution.Distribution(
).Beta:
distribution = Beta()
else:
util.logger.log(
'get_sample: Unknown distribution:Distribution id: {0}.'.
format(distribution_type))
sample = Sample(address, distribution, value)
return sample