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import types
import numpy as np
from core.interact import interact as io
from core.leras import nn
tf = nn.tf
class ModelBase(nn.Saveable):
def __init__(self, *args, name=None, **kwargs):
super().__init__(name=name)
self.layers = []
self.layers_by_name = {}
self.built = False
self.args = args
self.kwargs = kwargs
self.run_placeholders = None
def _build_sub(self, layer, name):
if isinstance (layer, list):
for i,sublayer in enumerate(layer):
self._build_sub(sublayer, f"{name}_{i}")
elif isinstance (layer, dict):
for subname in layer.keys():
sublayer = layer[subname]
self._build_sub(sublayer, f"{name}_{subname}")
elif isinstance (layer, nn.LayerBase) or \
isinstance (layer, ModelBase):
if layer.name is None:
layer.name = name
if isinstance (layer, nn.LayerBase):
with tf.variable_scope(layer.name):
layer.build_weights()
elif isinstance (layer, ModelBase):
layer.build()
self.layers.append (layer)
self.layers_by_name[layer.name] = layer
def xor_list(self, lst1, lst2):
return [value for value in lst1+lst2 if (value not in lst1) or (value not in lst2) ]
def build(self):
with tf.variable_scope(self.name):
current_vars = []
generator = None
while True:
if generator is None:
generator = self.on_build(*self.args, **self.kwargs)
if not isinstance(generator, types.GeneratorType):
generator = None
if generator is not None:
try:
next(generator)
except StopIteration:
generator = None
v = vars(self)
new_vars = self.xor_list (current_vars, list(v.keys()) )
for name in new_vars:
self._build_sub(v[name],name)
current_vars += new_vars
if generator is None:
break
self.built = True
#override
def get_weights(self):
if not self.built:
self.build()
weights = []
for layer in self.layers:
weights += layer.get_weights()
return weights
def get_layer_by_name(self, name):
return self.layers_by_name.get(name, None)
def get_layers(self):
if not self.built:
self.build()
layers = []
for layer in self.layers:
if isinstance (layer, nn.LayerBase):
layers.append(layer)
else:
layers += layer.get_layers()
return layers
#override
def on_build(self, *args, **kwargs):
"""
init model layers here
return 'yield' if build is not finished
therefore dependency models will be initialized
"""
pass
#override
def forward(self, *args, **kwargs):
#flow layers/models/tensors here
pass
def __call__(self, *args, **kwargs):
if not self.built:
self.build()
return self.forward(*args, **kwargs)
# def compute_output_shape(self, shapes):
# if not self.built:
# self.build()
# not_list = False
# if not isinstance(shapes, list):
# not_list = True
# shapes = [shapes]
# with tf.device('/CPU:0'):
# # CPU tensors will not impact any performance, only slightly RAM "leakage"
# phs = []
# for dtype,sh in shapes:
# phs += [ tf.placeholder(dtype, sh) ]
# result = self.__call__(phs[0] if not_list else phs)
# if not isinstance(result, list):
# result = [result]
# result_shapes = []
# for t in result:
# result_shapes += [ t.shape.as_list() ]
# return result_shapes[0] if not_list else result_shapes
def build_for_run(self, shapes_list):
if not isinstance(shapes_list, list):
raise ValueError("shapes_list must be a list.")
self.run_placeholders = []
for dtype,sh in shapes_list:
self.run_placeholders.append ( tf.placeholder(dtype, sh) )
self.run_output = self.__call__(self.run_placeholders)
def run (self, inputs):
if self.run_placeholders is None:
raise Exception ("Model didn't build for run.")
if len(inputs) != len(self.run_placeholders):
raise ValueError("len(inputs) != self.run_placeholders")
feed_dict = {}
for ph, inp in zip(self.run_placeholders, inputs):
feed_dict[ph] = inp
return nn.tf_sess.run ( self.run_output, feed_dict=feed_dict)
def summary(self):
layers = self.get_layers()
layers_names = []
layers_params = []
max_len_str = 0
max_len_param_str = 0
delim_str = "-"
total_params = 0
#Get layers names and str lenght for delim
for l in layers:
if len(str(l))>max_len_str:
max_len_str = len(str(l))
layers_names+=[str(l).capitalize()]
#Get params for each layer
layers_params = [ int(np.sum(np.prod(w.shape) for w in l.get_weights())) for l in layers ]
total_params = np.sum(layers_params)
#Get str lenght for delim
for p in layers_params:
if len(str(p))>max_len_param_str:
max_len_param_str=len(str(p))
#Set delim
for i in range(max_len_str+max_len_param_str+3):
delim_str += "-"
output = "\n"+delim_str+"\n"
#Format model name str
model_name_str = "| "+self.name.capitalize()
len_model_name_str = len(model_name_str)
for i in range(len(delim_str)-len_model_name_str):
model_name_str+= " " if i!=(len(delim_str)-len_model_name_str-2) else " |"
output += model_name_str +"\n"
output += delim_str +"\n"
#Format layers table
for i in range(len(layers_names)):
output += delim_str +"\n"
l_name = layers_names[i]
l_param = str(layers_params[i])
l_param_str = ""
if len(l_name)<=max_len_str:
for i in range(max_len_str - len(l_name)):
l_name+= " "
if len(l_param)<=max_len_param_str:
for i in range(max_len_param_str - len(l_param)):
l_param_str+= " "
l_param_str += l_param
output +="| "+l_name+"|"+l_param_str+"| \n"
output += delim_str +"\n"
#Format sum of params
total_params_str = "| Total params count: "+str(total_params)
len_total_params_str = len(total_params_str)
for i in range(len(delim_str)-len_total_params_str):
total_params_str+= " " if i!=(len(delim_str)-len_total_params_str-2) else " |"
output += total_params_str +"\n"
output += delim_str +"\n"
io.log_info(output)
nn.ModelBase = ModelBase
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