def get_out_data_from_opts(cls,
name,
sources,
repetitions,
n_out=None,
**kwargs):
"""
:param str name:
:param list[LayerBase] sources:
:param int repetitions:
:param int|None|returnn.util.basic.NotSpecified n_out:
:rtype: Data
"""
input_data = get_concat_sources_data_template(sources)
assert not input_data.sparse
return Data(
name="%s_output" % name,
shape=[input_data.shape[1], input_data.shape[2] * repetitions],
dtype=input_data.dtype,
sparse=False,
size_placeholder={
0:
input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch]
},
batch_dim_axis=0,
time_dim_axis=1)
def get_out_data_from_opts(cls,
name,
sources,
nr_of_channels,
n_out=None,
**kwargs):
"""
:param str name:
:param list[LayerBase] sources:
:param int nr_of_channels:
:param int|None|returnn.util.basic.NotSpecified n_out:
:rtype: Data
"""
input_data = get_concat_sources_data_template(
sources).copy_as_batch_major()
assert not input_data.sparse
return Data(name="%s_output" % name,
shape=[
input_data.batch_shape[1],
input_data.batch_shape[2] // nr_of_channels
],
dtype=input_data.dtype,
sparse=False,
batch_dim_axis=0,
time_dim_axis=1)
def get_out_data_from_opts(cls,
name,
sources,
pool_size,
n_out=None,
**kwargs):
"""
:param str name:
:param list[LayerBase] sources:
:param int pool_size:
:param int|None|returnn.util.basic.NotSpecified n_out:
:rtype: Data
"""
input_data = get_concat_sources_data_template(sources)
assert not input_data.sparse
return Data(
name="%s_output" % name,
shape=[
input_data.get_placeholder_as_batch_major().shape[1].value,
input_data.get_placeholder_as_batch_major().shape[2].value
],
dtype=input_data.dtype,
size_placeholder={
0:
tf.strided_slice(
input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch], [0],
tf.shape(input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch]),
[pool_size])
},
sparse=False,
batch_dim_axis=0,
time_dim_axis=1)
def get_out_data_from_opts(cls,
name,
sources,
pool_size,
n_out=None,
**kwargs):
input_data = get_concat_sources_data_template(sources)
assert not input_data.sparse
return Data(
name="%s_output" % name,
shape=[
input_data.get_placeholder_as_batch_major().shape[1].value,
input_data.get_placeholder_as_batch_major().shape[2].value
],
dtype=input_data.dtype,
size_placeholder={
0:
tf.strided_slice(
input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch], [0],
tf.shape(input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch]),
[pool_size])
},
sparse=False,
batch_dim_axis=0,
time_dim_axis=1)
def create_state_var(self, name, initial_value=None, data_shape=None):
"""
A state var is a variable where the initial value is given by the encoder, or a constant,
and the final value is determined by one step of this rec layer (usually called the decoder).
:param str name:
:param tf.Tensor|None initial_value: assumes batch-major, if data_shape is not given
:param Data|None data_shape:
:rtype: tf.Tensor
"""
assert name not in self.state_vars
assert data_shape or initial_value is not None
if data_shape:
assert isinstance(data_shape, Data)
elif initial_value.shape.ndims == 0:
data_shape = Data(name=name,
batch_dim_axis=None,
shape=(),
dtype=initial_value.dtype.name)
else:
assert initial_value.shape.dims[
0].value is None # first is batch dim
data_shape = Data(name=name,
batch_dim_axis=0,
shape=initial_value.shape.as_list()[1:],
dtype=initial_value.dtype.name)
if initial_value is not None:
# initial_value might have dim 1 in variable dimensions (which are not the batch-dim-axis),
# see get_rec_initial_output, which should be fine for broadcasting.
initial_value.set_shape(data_shape.batch_shape)
var = self.StateVar(parent=self,
name=name,
initial_value=initial_value,
data_shape=data_shape)
self.state_vars[name] = var
return var.read()
def get_out_data_from_opts(cls,
name,
sources,
nr_of_channels,
n_out=None,
**kwargs):
input_data = get_concat_sources_data_template(
sources).copy_as_batch_major()
assert not input_data.sparse
return Data(name="%s_output" % name,
shape=[
input_data.batch_shape[1],
input_data.batch_shape[2] // nr_of_channels
],
dtype=input_data.dtype,
sparse=False,
batch_dim_axis=0,
time_dim_axis=1)
def get_out_data_from_opts(cls,
name,
sources,
repetitions,
n_out=None,
**kwargs):
input_data = get_concat_sources_data_template(sources)
assert not input_data.sparse
return Data(
name="%s_output" % name,
shape=[
input_data.get_placeholder_as_batch_major().shape[1].value,
input_data.get_placeholder_as_batch_major().shape[2].value *
repetitions
],
dtype=input_data.dtype,
sparse=False,
size_placeholder={
0:
input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch]
},
batch_dim_axis=0,
time_dim_axis=1)
def get_out_data_from_opts(cls, name, **kwargs):
from returnn.tf.util.basic import Data
return Data(name="%s_output" % name,
batch_dim_axis=None,
shape=(),
dtype="float32") # scalar