def decompress(self, strings, min_v, max_v, shape, channels=None, device='cpu'):
"""Decompress values from their compressed string representations.
Arguments:
strings: A string `Tensor` vector containing the compressed data.(numpy)
min_v & max_v.(numpy)
shape: A `Tensor` vector of int32 type. Contains the shape of the tensor to be
decompressed, excluding the batch dimension. [points, channels] (numpy)
Returns:
The decompressed `Tensor`. (torch) # TODO
"""
# To Tensorflow
if min_v == max_v:
values = torch.zeros(tuple(shape)).to(device)
return values
strings = tf.convert_to_tensor(strings, dtype=tf.string)
shape = tf.convert_to_tensor(shape)# [points, channels]
cdf = self._get_cdf(min_v, max_v, device)
# range decode
values = coder_ops.range_decode(
strings, shape, cdf, precision=self._range_coder_precision) + min_v
values = tf.reshape(values, shape)
values = tf.cast(values, tf.float32)
values = values.numpy()
values = torch.from_numpy(values).to(device)
return values
def decompress(self, strings, min_v, max_v, shape, channels=None):
"""Decompress values from their compressed string representations.
Arguments:
strings: A string `Tensor` vector containing the compressed data.
shape: A `Tensor` vector of int32 type. Contains the shape of the tensor to be
decompressed. [batch size, length, width, height, channels]
min_v & max_v: minimum & maximum values.
Returns:
The decompressed `Tensor`. tf.float32.
"""
with tf.name_scope(self._name_scope()):
strings = tf.convert_to_tensor(strings, dtype=tf.string)
shape = tf.convert_to_tensor(
shape,
dtype='int32') # [batch size, length, width, height, channels]
min_v = tf.convert_to_tensor(min_v, dtype='int32')
max_v = tf.convert_to_tensor(max_v, dtype='int32')
if self.built:
ndim = self.input_spec.ndim
if channels is None:
channels = self.input_spec.axes[ndim - 1]
else:
channels = int(channels)
ndim = shape.shape[0].value # ndim=5
input_shape = ndim * [None]
input_shape[-1] = channels
self.build(input_shape)
code_length = tf.reduce_prod(shape)
code_shape = (tf.constant(code_length // channels, dtype='int32'),
tf.constant(channels,
dtype='int32')) # shape=[-1, channel]
# get cdf.
cdf = self._get_cdf(min_v, max_v)
# range decode.
values = coder_ops.range_decode(
strings,
code_shape,
cdf,
precision=self._range_coder_precision)
values = tf.cast(values, tf.int32)
values = values + min_v
values = tf.reshape(values, shape)
values = tf.cast(values, tf.float32)
return values
def testReadmeExample(self):
data = random_ops.random_uniform((128, 128), 0, 10, dtype=dtypes.int32)
histogram = math_ops.bincount(data, minlength=10, maxlength=10)
cdf = math_ops.cumsum(histogram, exclusive=False)
cdf = array_ops.pad(cdf, [[1, 0]])
cdf = array_ops.reshape(cdf, [1, 1, -1])
data = math_ops.cast(data, dtypes.int16)
encoded = coder_ops.range_encode(data, cdf, precision=14)
decoded = coder_ops.range_decode(
encoded, array_ops.shape(data), cdf, precision=14)
with self.test_session() as sess:
self.assertAllEqual(*sess.run((data, decoded)))
def decompress(self, strings, loc, scale, min_v, max_v, datashape):
"""Decompress values from their compressed string representations.
Arguments:
strings: A string `Tensor` vector containing the compressed data.
shape: A `Tensor` vector of int32 type. Contains the shape of the tensor to be
decompressed. [batch size, length, width, height, channels]
loc & scale: parameters of distributions.
min_v & max_v: minimum & maximum values.
Return: outputs [BatchSize, H, W, D, C]
"""
strings = tf.convert_to_tensor(strings, dtype=tf.string)
datashape = tf.convert_to_tensor(datashape, dtype='int32')
loc = tf.convert_to_tensor(loc, dtype=self.dtype)
scale = tf.convert_to_tensor(scale, dtype=self.dtype)
min_v = tf.convert_to_tensor(min_v, dtype='int32')
max_v = tf.convert_to_tensor(max_v, dtype='int32')
# reshape.
channels = datashape[-1]
loc = tf.reshape(loc, [-1, channels])
scale = tf.reshape(scale, [-1, channels])
# get cdf.
cdf = self._get_cdf(loc, scale, min_v, max_v,
datashape) # [BatchSizexHxWxD, C, N]
# range decode.
code_shape = (tf.reduce_prod(datashape) / channels, channels
) # shape=[-1, channel]
values = coder_ops.range_decode(strings,
code_shape,
cdf,
precision=self._range_coder_precision)
values = tf.cast(values, tf.int32)
values = values + min_v
values = tf.reshape(values, datashape)
values = tf.cast(values, "float32")
return values
def loop_body(string):
return coder_ops.range_decode(
string, shape, cdf, precision=self.range_coder_precision)
def loop_body(string):
return coder_ops.range_decode(
string, shape, cdf, precision=self.range_coder_precision)