def _network(proto, default_context, batch_size, all_variables, rng):
network = Network()
network.name = proto.name
# Read Repeat Info
network.repeat_info = {}
for r in proto.repeat_info:
network.repeat_info[r.id] = r.times
network.variables = OrderedDict()
if batch_size is None:
network.batch_size = proto.batch_size
else:
network.batch_size = batch_size
for v in proto.variable:
for variable_index in itertools.product(*map(
tuple,
map(range, [network.repeat_info[id] for id in v.repeat_id]))):
name = v.name
for index, i in enumerate(variable_index):
if ('{' + v.repeat_id[index] + '}' in name):
name = name.replace('{' + v.repeat_id[index] + '}',
'[' + str(i) + ']')
else:
name += '_' + v.repeat_id[index] + '[' + str(i) + ']'
if name in all_variables:
variable = all_variables[name]
else:
shape = tuple(
[d if d >= 1 else network.batch_size for d in v.shape.dim])
variable = _create_variable(v, name, shape, rng)
all_variables[name] = variable
network.variables[name] = variable
logger.debug('{}'.format(
(name, variable.shape,
v.initializer.type if v.initializer.type else '-',
v.initializer.multiplier)))
network.functions = OrderedDict()
network.function_inputs = OrderedDict()
network.function_outputs = OrderedDict()
network.variable_inputs = OrderedDict()
network.variable_outputs = OrderedDict()
for f in proto.function:
ctx = default_context if not f.context.backends else _context(
f.context)
for variable_index in itertools.product(*map(
tuple,
map(range, [network.repeat_info[id] for id in f.repeat_id]))):
function, input_variable_names, output_variable_names = _create_function(
ctx, network, f, variable_index)
if function is not None:
network.functions[function.name] = function
for v_name in output_variable_names:
network.variable_inputs[network.variables[v_name]] = [
function
]
for v_name in input_variable_names:
if not network.variables[
v_name] in network.variable_outputs:
network.variable_outputs[
network.variables[v_name]] = []
network.variable_outputs[network.variables[v_name]].append(
function)
network.setup(optimize=True)
return network
def _network(proto, default_context, all_variables):
network = Network()
network.name = proto.name
# Read Repeat Info
network.repeat_info = {}
for r in proto.repeat_info:
network.repeat_info[r.id] = r.times
network.variables = OrderedDict()
network.batch_size = proto.batch_size
for v in proto.variable:
for variable_index in itertools.product(*map(tuple, map(range, [network.repeat_info[id] for id in v.repeat_id]))):
name = v.name + ''.join(['_' + v.repeat_id[index] + '[' +
str(i) + ']' for index, i in enumerate(variable_index)])
if name in all_variables:
variable = all_variables[name]
else:
shape = tuple(
[d if d >= 1 else network.batch_size for d in v.shape.dim])
variable = _create_variable(v, name, shape)
all_variables[name] = variable
network.variables[name] = variable
logger.debug('{}'.format(
(name, variable.shape, v.initializer.type if v.initializer.type else '-', v.initializer.multiplier)))
network.functions = OrderedDict()
network.function_inputs = OrderedDict()
network.function_outputs = OrderedDict()
network.variable_inputs = OrderedDict()
network.variable_outputs = OrderedDict()
for f in proto.function:
ctx = default_context if f.context.backend == "" else context(
f.context)
for variable_index in itertools.product(*map(tuple, map(range, [network.repeat_info[id] for id in f.repeat_id]))):
function, input_variable_names, output_variable_names = _create_function(
ctx, network, f, variable_index)
if function is not None:
network.functions[function.name] = function
for v_name in output_variable_names:
network.variable_inputs[
network.variables[v_name]] = [function]
for v_name in input_variable_names:
if not network.variables[v_name] in network.variable_outputs:
network.variable_outputs[
network.variables[v_name]] = []
network.variable_outputs[
network.variables[v_name]].append(function)
network.setup(optimize=True)
return network
