def _process_variable_info(self):
var_infos = self.config.desc.extra_info.map.data['var_infos']
for var_info in var_infos.map.data:
idx = from_flexible_data_to_pyobj(
var_infos.map.data[var_info].map.data['block_id'])
stop_gradient = from_flexible_data_to_pyobj(
var_infos.map.data[var_info].map.data['stop_gradient'])
block = self.inference_program.blocks[idx]
var_name = HUB_VAR_PREFIX + var_info
if var_name in block.vars:
var = block.vars[var_name]
var.stop_gradient = stop_gradient
def from_flexible_data_to_param(flexible_data):
param = {'gradient_clip_attr': None, 'regularizer': None}
param['trainable'] = from_flexible_data_to_pyobj(
flexible_data.map.data['trainable'])
param['do_model_average'] = from_flexible_data_to_pyobj(
flexible_data.map.data['do_model_average'])
param['optimize_attr'] = from_flexible_data_to_pyobj(
flexible_data.map.data['optimize_attr'])
if flexible_data.map.data['regularizer'].type != module_desc_pb2.NONE:
regularizer_type = flexible_data.map.data['regularizer'].name
regularization_coeff = from_flexible_data_to_pyobj(
flexible_data.map.data['regularizer'].object.
data['_regularization_coeff'])
param['regularizer'] = eval(
"fluid.regularizer.%s(regularization_coeff = %f)" %
(regularizer_type, regularization_coeff))
if flexible_data.map.data[
'gradient_clip_attr'].type != module_desc_pb2.NONE:
clip_type = flexible_data.map.data['gradient_clip_attr'].name
if clip_type == "ErrorClipByValue" or clip_type == "GradientClipByValue":
max = from_flexible_data_to_pyobj(
flexible_data.map.data['gradient_clip_attr'].object.data['max']
)
min = from_flexible_data_to_pyobj(
flexible_data.map.data['gradient_clip_attr'].object.data['min']
)
param['gradient_clip_attr'] = eval(
"fluid.clip.%s(max = %f, min = %f)" % (clip_type, max, min))
if clip_type == "GradientClipByNorm":
clip_norm = from_flexible_data_to_pyobj(
flexible_data.map.data['gradient_clip_attr'].object.
data['clip_norm'])
param['gradient_clip_attr'] = eval(
"fluid.clip.%s(clip_norm = %f)" % (clip_type, clip_norm))
if clip_type == "GradientClipByGlobalNorm":
clip_norm = from_flexible_data_to_pyobj(
flexible_data.map.data['gradient_clip_attr'].object.
data['clip_norm'])
group_name = from_flexible_data_to_pyobj(
flexible_data.map.data['gradient_clip_attr'].object.
data['group_name'])
param['gradient_clip_attr'] = eval(
"fluid.clip.%s(clip_norm = %f, group_name = \"%s\")" %
(clip_type, clip_norm, group_name))
return param
def test_convert_compound_object(self):
input = {
False: 1,
'2': 3,
4.0: [5, 6.0, ['7', {
8: 9
}]],
'set': {10},
'dict': {
11: 12
}
}
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert input == output, "dict convesion error"
def test_convert_dict(self):
input = {1: 1, 2: 2, 3: 3}
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert input == output, "dict convesion error"
def test_convert_tuple(self):
input = (1, 2, 3)
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert list(input) == output, "tuple convesion error"
def test_convert_list(self):
input = [1, 2, 3]
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert input == output, "list convesion error"
def test_convert_bool(self):
input = False
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert input == output, "bool convesion error"
def test_convert_str(self):
input = "123"
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert input == output, "str convesion error"
def test_convert_float(self):
input = 2.012
flexible_data = module_desc_pb2.FlexibleData()
from_pyobj_to_flexible_data(input, flexible_data)
output = from_flexible_data_to_pyobj(flexible_data)
assert _compare_float(input, output), "float convesion error"
