def num_inputs_outputs_from_config(cls, config):
"""
:type config: Config.Config
:returns (num_inputs, num_outputs),
where num_inputs is like num_outputs["data"][0],
and num_outputs is a dict of data_key -> (dim, ndim),
where data_key is e.g. "classes" or "data",
dim is the feature dimension or the number of classes,
and ndim is the ndim counted without batch-dim,
i.e. ndim=1 means usually sparse data and ndim=2 means dense data.
:rtype: (int,dict[str,(int,int)])
"""
num_inputs = config.int('num_inputs', 0)
target = config.value('target', 'classes')
if config.is_typed('num_outputs'):
num_outputs = config.typed_value('num_outputs')
if not isinstance(num_outputs, dict):
num_outputs = {target: num_outputs}
num_outputs = num_outputs.copy()
from Dataset import convert_data_dims
from Util import BackendEngine
num_outputs = convert_data_dims(num_outputs, leave_dict_as_is=BackendEngine.is_tensorflow_selected())
if "data" in num_outputs:
num_inputs = num_outputs["data"][0]
elif config.has('num_outputs'):
num_outputs = {target: [config.int('num_outputs', 0), 1]}
else:
num_outputs = None
dataset = None
if config.list('train') and ":" not in config.value('train', ''):
dataset = config.list('train')[0]
if not config.is_typed('num_outputs') and dataset:
try:
_num_inputs = hdf5_dimension(dataset, 'inputCodeSize') * config.int('window', 1)
except Exception:
_num_inputs = hdf5_dimension(dataset, 'inputPattSize') * config.int('window', 1)
try:
_num_outputs = {target: [hdf5_dimension(dataset, 'numLabels'), 1]}
except Exception:
_num_outputs = hdf5_group(dataset, 'targets/size')
for k in _num_outputs:
_num_outputs[k] = [_num_outputs[k], len(hdf5_shape(dataset, 'targets/data/' + k))]
if num_inputs: assert num_inputs == _num_inputs
if num_outputs: assert num_outputs == _num_outputs
num_inputs = _num_inputs
num_outputs = _num_outputs
if not num_inputs and not num_outputs and config.has("load"):
from Network import LayerNetwork
import h5py
model = h5py.File(config.value("load", ""), "r")
num_inputs, num_outputs = LayerNetwork._n_in_out_from_hdf_model(model)
assert num_inputs and num_outputs, "provide num_inputs/num_outputs directly or via train"
return num_inputs, num_outputs
def num_inputs_outputs_from_config(cls, config):
"""
:type config: Config.Config
:rtype: (int,dict[str,(int,int)])
"""
num_inputs = config.int('num_inputs', 0)
target = config.value('target', 'classes')
if config.is_typed('num_outputs'):
num_outputs = config.typed_value('num_outputs')
if not isinstance(num_outputs, dict):
num_outputs = {target: num_outputs}
num_outputs = num_outputs.copy()
from Dataset import convert_data_dims
num_outputs = convert_data_dims(num_outputs)
if "data" in num_outputs:
num_inputs = num_outputs["data"][0]
elif config.has('num_outputs'):
num_outputs = {target: [config.int('num_outputs', 0), 1]}
else:
num_outputs = None
if not config.is_typed('num_outputs') and config.list('train') and ":" not in config.value('train', ''):
try:
_num_inputs = hdf5_dimension(config.list('train')[0], 'inputCodeSize') * config.int('window', 1)
except Exception:
_num_inputs = hdf5_dimension(config.list('train')[0], 'inputPattSize') * config.int('window', 1)
try:
_num_outputs = {target: [hdf5_dimension(config.list('train')[0], 'numLabels'), 1]}
except Exception:
_num_outputs = hdf5_group(config.list('train')[0], 'targets/size')
for k in _num_outputs:
_num_outputs[k] = [_num_outputs[k], len(hdf5_shape(config.list('train')[0], 'targets/data/' + k))]
if num_inputs: assert num_inputs == _num_inputs
if num_outputs: assert num_outputs == _num_outputs
num_inputs = _num_inputs
num_outputs = _num_outputs
assert num_inputs and num_outputs, "provide num_inputs/num_outputs directly or via train"
loss = cls.loss_from_config(config)
#if loss in ('ctc', 'ce_ctc') or config.bool('add_blank', False):
# for k in num_outputs:
# num_outputs[k][0] += 1 # add blank
return num_inputs, num_outputs
def num_inputs_outputs_from_config(cls, config):
"""
:type config: Config.Config
:returns (num_inputs, num_outputs),
where num_inputs is like num_outputs["data"][0],
and num_outputs is a dict of data_key -> (dim, ndim),
where data_key is e.g. "classes" or "data",
dim is the feature dimension or the number of classes,
and ndim is the ndim counted without batch-dim,
i.e. ndim=1 means usually sparse data and ndim=2 means dense data.
:rtype: (int,dict[str,(int,int)])
"""
from Util import BackendEngine
num_inputs = config.int('num_inputs', 0)
target = config.value('target', 'classes')
if config.is_typed('num_outputs'):
num_outputs = config.typed_value('num_outputs')
if not isinstance(num_outputs, dict):
num_outputs = {target: num_outputs}
num_outputs = num_outputs.copy()
from Dataset import convert_data_dims
num_outputs = convert_data_dims(num_outputs, leave_dict_as_is=BackendEngine.is_tensorflow_selected())
if "data" in num_outputs:
num_inputs = num_outputs["data"]
if isinstance(num_inputs, (list, tuple)):
num_inputs = num_inputs[0]
elif isinstance(num_inputs, dict):
if "dim" in num_inputs:
num_inputs = num_inputs["dim"]
else:
num_inputs = num_inputs["shape"][-1]
else:
raise TypeError("data key %r" % num_inputs)
elif config.has('num_outputs'):
num_outputs = {target: [config.int('num_outputs', 0), 1]}
else:
num_outputs = None
dataset = None
if config.list('train') and ":" not in config.value('train', ''):
dataset = config.list('train')[0]
if not config.is_typed('num_outputs') and dataset:
# noinspection PyBroadException
try:
_num_inputs = hdf5_dimension(dataset, 'inputCodeSize') * config.int('window', 1)
except Exception:
_num_inputs = hdf5_dimension(dataset, 'inputPattSize') * config.int('window', 1)
# noinspection PyBroadException
try:
_num_outputs = {target: [hdf5_dimension(dataset, 'numLabels'), 1]}
except Exception:
_num_outputs = hdf5_group(dataset, 'targets/size')
for k in _num_outputs:
_num_outputs[k] = [_num_outputs[k], len(hdf5_shape(dataset, 'targets/data/' + k))]
if num_inputs:
assert num_inputs == _num_inputs
if num_outputs:
assert num_outputs == _num_outputs
num_inputs = _num_inputs
num_outputs = _num_outputs
if not num_inputs and not num_outputs and config.has("load") and BackendEngine.is_theano_selected():
from Network import LayerNetwork
import h5py
model = h5py.File(config.value("load", ""), "r")
# noinspection PyProtectedMember
num_inputs, num_outputs = LayerNetwork._n_in_out_from_hdf_model(model)
assert num_inputs and num_outputs, "provide num_inputs/num_outputs directly or via train"
return num_inputs, num_outputs