def __init__(self, file_path, graph_params = None):
self.mcp = IConfigParser(dict_type=OrderedDict)
self.mcp.readfp(open(file_path))
self.file_path = file_path
self.layers = dict()
self.network_config = dict()
self.parse(self.mcp)
def test1():
file_path = '/home/grads/sijinli2/Projects/DHMLPE/doc/netdef/dhmlpe-layer-def-t0.cfg'
a = IConfigParser(dict_type=OrderedDict)
a.readfp(open(file_path))
for name in a.sections():
print 'Section name {}'.format(name)
if a.has_option(name, 'type'):
e = a.safe_get(name, 'type')
print ' type is {}'.format(e)
def test2():
file_path = '/opt/visal/tmp/for_sijin/tmp/t.cfg'
a = IConfigParser(dict_type=OrderedDict)
a.readfp(open(file_path))
for name in a.sections():
print 'Section name {}'.format(name)
e = a.safe_get_tuple_list(name, 'L')
print e
print '----------'
e1 = a.safe_get_tuple_int_list(name, 'L')
print e1
def test1():
file_path = '/home/grads/sijinli2/Projects/DHMLPE/doc/netdef/dhmlpe-layer-def-t0.cfg'
a = IConfigParser(dict_type=OrderedDict)
a.readfp(open(file_path))
for name in a.sections():
print 'Section name {}'.format(name)
if a.has_option(name, 'type'):
e = a.safe_get(name, 'type')
print ' type is {}'.format(e)
def test2():
file_path = '/opt/visal/tmp/for_sijin/tmp/t.cfg'
a = IConfigParser(dict_type=OrderedDict)
a.readfp(open(file_path))
for name in a.sections():
print 'Section name {}'.format(name)
e = a.safe_get_tuple_list(name, 'L')
print e
print '----------'
e1 = a.safe_get_tuple_int_list(name, 'L')
print e1
class GraphParser(object):
"""
Read the cfg and build the network graph
"""
def __init__(self, file_path, graph_params = None):
self.mcp = IConfigParser(dict_type=OrderedDict)
self.mcp.readfp(open(file_path))
self.file_path = file_path
self.layers = dict()
self.network_config = dict()
self.parse(self.mcp)
def get_input_data(self, name, mcp, input_dims):
"""
"""
input_names = mcp.safe_get_list(name, 'inputs')
l = []
for e,dim in zip(input_names, input_dims):
if e in self.symbolic_var_dic:
print(' use symbolic variable {}'.format(e))
l += [self.symbolic_var_dic[e]]
else:
try:
if isinstance(dim, int):
sym, descr = tensor.fmatrix(e), 'fmatrix'
elif isinstance(dim, tuple):
t = len(dim)
if t != 2 and t != 3:
raise Exception('Unsupported dimension {}'.format(t))
if t == 2:
sym, descr = tensor.ftensor3(e), 'tensor3'
else:
sym, descr = tensor.ftensor4(e), 'tensor4'
print(' create symbolic variable {} as {}'.format(e, descr))
self.symbolic_var_dic[e] = sym
l += [sym]
except Exception as err:
print err
sys.exit(1)
return input_names, l
@classmethod
def parse_name_idx(self,s):
p = s.find('[')
return [s,0] if p == -1 else [s[:p],int(s[p+1:s.rfind(']')])]
def get_shared_weights(self, w_s, layers, dic):
l = [self.parse_name_idx(s) for s in w_s]
dic['weights'] = [layers[name][2].W_list[idx] for name, idx in l]
dic['weights_inc'] = [layers[name][2].W_inc_list[idx] for name, idx in l]
def get_shared_biases(self, b_s, layers, dic):
l = [self.parse_name_idx(s) for s in b_s]
# assert(len(l) == 1)
# assert(l[0][1] == 0)
dic['biases'] = [layers[name][2].b_list[idx] for name, idx in l]
dic['biases_inc'] = [layers[name][2].b_inc_list[idx] for name, idx in l]
def add_network_config(self, name, mcp):
# Move to Network Parser latter
net_config = {'layer_def_path':self.file_path}
default_config = {'dropout_layer_names': None, 'data_idx':None,
'dual_mode_layer_names':None, 'additional_update_layer':None}
for e in ['cost_layer_names', 'data_layer_names', 'output_layer_names',
'layer_with_weights', 'dropout_layer_names', 'data_idx',
'dual_mode_layer_names', 'additional_update_layer_names']:
if mcp.has_option(name, e):
if e in ['data_idx']:
net_config[e] = mcp.safe_get_int_list(name, e)
else:
t = mcp.safe_get_list(name, e)
net_config[e] = t if (len(t)>1 or t[0]!='') else []
print '{}:\t{}\t{}'.format(name, e, net_config[e])
elif e in default_config:
net_config[e] = default_config[e]
else:
net_config[e] = []
self.network_config[name] = net_config
@classmethod
def print_graph_connections(cls, layers):
for name in layers:
l = layers[name]
cur_l = l[2]
print('Layer: {} :input layer {} \n \t output layers {}'.format(l[2].param_dic['name'], l[0], l[1]))
print (' input_var {}'.format(l[2].inputs))
def parse(self,mcp):
self.layers = OrderedDict()
self.network_config = OrderedDict()
self.symbolic_var_dic = OrderedDict()
for name in mcp.sections(): # Ensure the iterator is ordered
mcp.check_options(name, ['type'])
layer_type= mcp.safe_get(name, 'type')
advanced_params = OrderedDict()
if layer_type == 'network':
self.add_network_config(name, mcp)
continue
elif layer_type == 'data':
mcp.check_options(name, ['inputs','input_dims'])
str_dim = mcp.safe_get(name, 'input_dims')
input_dims = mcp.safe_get_int_list(name, 'input_dims') if str_dim.find('(') == -1 else mcp.safe_get_tuple_int_list(name, 'input_dims')
input_names,input_var = self.get_input_data(name, mcp, input_dims)
else:
mcp.check_options(name, ['inputs'])
input_names = mcp.safe_get_list(name, 'inputs')
input_var = sum([self.layers[lname][2].outputs for lname in input_names], [])
input_dims = sum([self.layers[lname][2].param_dic['output_dims'] for lname in input_names],[])
if mcp.has_option(name, 'weightSource'):
w_s = mcp.safe_get_list(name, 'weightSource')
self.get_shared_weights(w_s, self.layers, advanced_params)
if mcp.has_option(name, 'biasSource'):
b_s = mcp.safe_get_list(name, 'biasSource')
self.get_shared_biases(b_s, self.layers,advanced_params)
if name in self.layers:
raise Exception('Layer [{}] has been defined'.format(name))
lp = layer_parser_dic[layer_type](name, input_var, mcp, input_dims, advanced_params)
self.layers[name] = [input_names, [], lp.parse()]
for lname in input_names:
if lname in self.layers:
self.layers[lname][1] += [name]
out_dims = self.layers[name][2].param_dic['output_dims'] if ('output_dims' in self.layers[name][2].param_dic) else None
print 'Init {} finished: input_dims{}\t output_dims {}'.format(name,self.layers[name][2].param_dic['input_dims'], out_dims)
# class NetworkParser(object):
# def __init__(self, file_path, graph_params = None):
# self.mcp = IConfigParser(dict_type=OrderedDict)
# self.mcp.readfp(open(file_path))
# self.file_path = file_path
