def get_out_data_from_opts(cls,
name,
window_size,
axis="T",
sources=(),
**kwargs):
"""
:param str name:
:param list[LayerBase] sources:
:param int window_size:
:param str axis:
:rtype: Data
"""
data = get_concat_sources_data_template(sources)
data = data.copy_template(name="%s_output" % name)
data = data.copy_as_batch_major()
if axis == "T" and data.time_dim_axis is None:
# Assume inside RecLayer.
axis = 0
else:
axis = data.get_axis_from_description(axis)
data = data.copy_add_spatial_dim(
spatial_dim_axis=axis + 1,
dim=window_size) # add new axis right after
return data
def get_out_data_from_opts(cls, name, sources=(), **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name)
out.batch_dim_axis = 0
out.time_dim_axis = 1
out.dim = None
out.size_placeholder = {}
return out
def get_out_data_from_opts(cls, name, sources=(), **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name)
out.batch_dim_axis = 0
out.time_dim_axis = 1
out.dim = None
out.size_placeholder = {}
return out
def get_out_data_from_opts(cls,
name,
sources,
nr_of_channels,
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 //
nr_of_channels
],
dtype=input_data.dtype,
size_placeholder={
0:
tf.reshape(
tf.tile(
tf.reshape(
input_data.size_placeholder[
input_data.time_dim_axis_excluding_batch],
[-1, 1]), [1, nr_of_channels]), [-1])
},
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 get_out_data_from_opts(cls, name, sources, **kwargs):
out = get_concat_sources_data_template(sources,
name="%s_output" %
name).copy_as_batch_major()
out.dim = None
out.shape = (1, None)
out.size_placeholder = {}
return out
def get_out_data_from_opts(cls, n_out, **kwargs):
data = get_concat_sources_data_template(kwargs["sources"], name="%s_output" % kwargs["name"])
data = data.copy_as_time_major() # type: Data
data.shape = (None, n_out)
data.time_dim_axis = 0
data.batch_dim_axis = 1
data.dim = n_out
return data
def get_out_data_from_opts(cls, name, sources, window, broadcast_axis='time', **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name).copy_as_batch_major()
out.size_placeholder = {}
out.size_placeholder[0] = None
out.shape = (1, window) if broadcast_axis == 'time' else (window, 1)
out.dim = window if broadcast_axis == 'time' else 1
out.sparse = False
out.dtype = 'float32'
return out
def get_out_data_from_opts(cls, name, sources, window, broadcast_axis='time', **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name).copy_as_batch_major()
out.size_placeholder = {}
out.size_placeholder[0] = None
out.shape = (1, window) if broadcast_axis == 'time' else (window, 1)
out.dim = window if broadcast_axis == 'time' else 1
out.sparse = False
out.dtype = 'float32'
return out
def get_out_data_from_opts(cls, name, sources, num_classes, window, **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name).copy_as_batch_major()
out.size_placeholder = {}
out.size_placeholder[0] = None
out.shape = (window, num_classes)
out.dim = num_classes
out.sparse = False
out.dtype = 'float32'
return out
def get_out_data_from_opts(cls, name, sources, num_classes, window, **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name).copy_as_batch_major()
out.size_placeholder = {}
out.size_placeholder[0] = None
out.shape = (window, num_classes)
out.dim = num_classes
out.sparse = False
out.dtype = 'float32'
return out
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, 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 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_rec_initial_extra_outputs(cls,
batch_dim,
rec_layer,
window_size,
axis="T",
sources=(),
**kwargs):
"""
:param tf.Tensor batch_dim:
:param TFNetworkRecLayer.RecLayer|LayerBase rec_layer:
:param int window_size:
:param str axis:
:param list[LayerBase] sources:
:rtype: dict[str,tf.Tensor]
"""
data = get_concat_sources_data_template(sources)
data = data.copy_as_batch_major()
if axis == "T" and data.time_dim_axis is None:
# Assume inside RecLayer.
shape = list(data.batch_shape)
shape[0] = batch_dim
shape.insert(1, window_size)
return {"state": tf.zeros(shape, dtype=data.dtype)}
return {}
def get_out_data_from_opts(cls, name, sources=(), **kwargs): return get_concat_sources_data_template(sources, name="%s_output" % name)
def get_out_data_from_opts(cls, name, sources, **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name).copy_as_batch_major()
out.dim = None
out.shape = (1, None)
out.size_placeholder = {}
return out
def get_out_data_from_opts(cls,
name,
sources,
switch_axes=None,
size_base=None,
set_axes=None,
enforce_batch_major=False,
enforce_time_major=False,
set_sparse=None,
set_sparse_dim=NotSpecified,
increase_sparse_dim=None,
**kwargs):
"""
:param str name:
:param list[LayerBase] sources:
:param str|list[str] switch_axes: e.g. "bt" to switch batch and time axes
:param LayerBase|None size_base: similar as size_target
:param dict[str,int] set_axes:
:param bool enforce_batch_major:
:param bool enforce_time_major:
:param bool|None set_sparse: if bool, set sparse value to this
:param int|None|NotSpecified set_sparse_dim: set sparse dim to this. assumes that it is sparse
:param int|None increase_sparse_dim: add this to the dim. assumes that it is sparse
"""
out = get_concat_sources_data_template(sources,
name="%s_output" % name)
assert not (enforce_batch_major and enforce_time_major)
if enforce_batch_major:
out = out.copy_as_batch_major()
if enforce_time_major:
out = out.copy_as_time_major()
def map_axis_name(s):
"""
:param str s:
:rtype: str
"""
if s.upper() == "B":
return "batch_dim_axis"
if s.upper() == "T":
return "time_dim_axis"
if s.upper() == "F":
return "feature_dim_axis"
assert s in ["batch_dim_axis", "time_dim_axis", "feature_dim_axis"]
return s
if switch_axes:
assert len(switch_axes) == 2
axes_s = list(map(map_axis_name, switch_axes))
axes = [getattr(out, s) for s in axes_s]
for i in range(len(axes)):
setattr(out, axes_s[i], axes[(i + 1) % len(axes)])
if set_axes:
for s, i in sorted(set_axes.items()):
s = map_axis_name(s)
if isinstance(i, int):
assert enforce_batch_major or enforce_time_major, "%r: explicit set_axes %r" % (
name, set_axes)
i = out.get_axis_from_description(i)
setattr(out, s, i)
if s == "feature_dim_axis":
out.dim = out.batch_shape[out.feature_dim_axis]
if size_base:
out.size_placeholder = size_base.output.size_placeholder.copy()
if set_sparse is not None:
assert isinstance(set_sparse, bool)
out.sparse = set_sparse
if set_sparse_dim is not NotSpecified:
assert set_sparse_dim is None or isinstance(set_sparse_dim, int)
out.dim = set_sparse_dim
if increase_sparse_dim:
assert out.sparse
out.dim += increase_sparse_dim
return out
def get_out_data_from_opts(cls, name, sources, window, **kwargs):
out = get_concat_sources_data_template(sources, name="%s_output" % name)
out.size_placeholder = {}
out.size_placeholder[0] = None
return out
def get_out_data_from_opts(cls, name, sources, window, **kwargs):
out = get_concat_sources_data_template(sources,
name="%s_output" % name)
out.size_placeholder = {}
out.size_placeholder[0] = None
return out
def get_out_data_from_opts(cls, name, sources=(), **kwargs):
return get_concat_sources_data_template(sources,
name="%s_output" % name)
