class Module(object):
"""Base Module to adapter tf Module."""
def __init__(self):
self.name = ''
self.data_format = General.data_format
self._modules = Config()
self._parameters = OrderedDict()
self._weights_buffer = OrderedDict()
self._init_configs()
def _init_configs(self):
self._training = True
self._trainable = True
self.weight_file = None
self.from_weight_type = None
self._is_load_pretrained = False
self._is_adaptive_weight = False
self.exclude_weight_prefix = None
def add_module(self, name, model):
"""Add models into self._models."""
setattr(self, str(name), model)
def build(self):
"""Build model or params."""
pass
def named_modules(self):
"""Return names spaces."""
self._apply_names()
_modules = []
for module in self.children():
_modules.append((module.name, module))
_modules.extend(module.named_modules())
return _modules
def named_children(self):
"""Return names children."""
return [(name, module) for name, module in self._modules.items()]
def children(self):
"""Get child models of current Module."""
for model in self._modules.values():
yield model
def load_checkpoint(self, weight_file):
"""Load weight state dict from last checkpoint file."""
if not weight_file:
return
logging.info("Load checkpoint form file ({}).".format(weight_file))
# model_file = tf.train.latest_checkpoint(weight_file)
reader = tf.train.NewCheckpointReader(weight_file)
variables = reader.get_variable_to_shape_map()
states = {v: reader.get_tensor(v) for v in variables}
self.load_checkpoint_from_numpy(states)
def load_checkpoint_from_numpy(self, states):
"""Load checkpoint from numpy."""
states = self._exclude_checkpoint_by_prefix(states)
for name, module in self.named_modules():
child_state = [(k, v) for k, v in states.items() if k.startswith(module.name + '/')]
for k, v in child_state:
module.set_weights(k, v)
def _exclude_checkpoint_by_prefix(self, states):
if self.exclude_weight_prefix:
if not isinstance(self.exclude_weight_prefix, list):
self.exclude_weight_prefix = [self.exclude_weight_prefix]
for prefix in self.exclude_weight_prefix:
states = {k: v for k, v in states.items() if not k.startswith(prefix)}
return states
def set_weights(self, name, value):
"""Set weights into weights buffer."""
self._weights_buffer[name] = value
@property
def training(self):
"""Get training flag."""
return self._training
@training.setter
def training(self, value):
"""Set training flag."""
self._training = value
for module in self.children():
module.training = value
@property
def is_adaptive_weight(self):
"""Get _is_adaptive_weight flag."""
return self._is_adaptive_weight
@is_adaptive_weight.setter
def is_adaptive_weight(self, value):
"""Set _is_adaptive_weight flag."""
self._is_adaptive_weight = value
for module in self.children():
module.is_adaptive_weight = value
def freeze(self):
"""Set training flag."""
self._trainable = False
for module in self.children():
module.freeze()
def __setattr__(self, key, value):
"""Set name to modules."""
super().__setattr__(key, value)
if isinstance(value, Module):
self._modules[key] = value
def set_parameters(self, name, value):
"""Set Parameters."""
self._parameters[name] = value
setattr(self, name, value)
return self.name
def get_weights(self, name=None):
"""Get weights by name."""
if self._weights_buffer:
return self._weights_buffer
return tf.get_default_graph().get_tensor_by_name('{}:0'.format(name))
def get_all_weights(self):
"""Get all weights."""
all_weights = OrderedDict()
for child in self.children():
all_weights.update(child._weights_buffer)
if isinstance(child, Module):
all_weights.update(child.get_all_weights())
return all_weights
def get_weight_ops(self, name):
"""Get weight ops."""
all_weight = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
weight_ops = [t for t in all_weight if not t.name.startswith(name)]
return weight_ops
def call(self, inputs, *args, **kwarg):
"""Call inputs."""
output = inputs
for model in self.children():
output = model(output)
return output
def adaptive_weight(self, inputs):
"""Adaptive weight."""
return {}
def _apply_names(self, parent_name=''):
"""Apply names spaces."""
for scope_name, module in self._modules.items():
scope_name = '{}.{}'.format(parent_name, scope_name) if parent_name else scope_name
module.name = module.name or scope_name + '/' + module.__class__.__name__
module._apply_names(scope_name)
def _apply_parameters(self):
"""Apply names spaces."""
for name, params in self._parameters.items():
setattr(self, name, tf.Variable(params, name='{}.{}'.format(self.name, name) if self.name else name))
def __call__(self, inputs, *args, **kwargs):
"""Call call function."""
self.build()
self._apply_parameters()
self._apply_names()
for module in self.children():
module._is_load_pretrained = True
out = self.call(inputs, *args, **kwargs)
self._apply_weights(inputs)
return out
def _apply_weights(self, inputs):
if not self._weights_buffer:
return
variables = tf.get_collection(tf.GraphKeys.VARIABLES)
if self.is_adaptive_weight:
self._weights_buffer.update(self.adaptive_weight(inputs))
values = [(var, self._weights_buffer.get(var.name.replace(':0', ''))) for var in variables if
var.name.replace(':0', '') in self._weights_buffer]
for v, weight in values:
v._initializer_op = state_ops.assign(v, weight)
self._weights_buffer.clear()
def modules(self):
"""Get the current modules."""
if self._modules.values():
return self._modules.values()
else:
return [self]
class Module(object):
"""Base Module to adapter tf Module."""
data_format = 'channels_first'
def __init__(self):
self.parent_scope_name = ''
self._scope_name = ''
self._modules = Config()
self._training = True
self.enable_scope_name = enable_scope_name
self.data_format = General.data_format
self.pretrained_model_file = None
self._is_load_pretrained = False
self.load_pretrained_type = None
self._trainable = True
self.pretrained_prefix = None
def add_module(self, name, model):
"""Add models into self._models."""
setattr(self, str(name), model)
def named_modules(self):
"""Return names spaces."""
_names_modules = []
for model in self.children():
if isinstance(model, Module):
_names_modules.append(((model._scope_name, model)))
child_modules = model.named_modules()
_names_modules.extend(child_modules)
return _names_modules
def named_children(self):
"""Return names children."""
return [(name, module) for name, module in self._modules.items()]
def children(self):
"""Get child models of current Module."""
for model in self._modules.values():
if isinstance(model, Module):
model._scope_name = "{}.{}".format(
self._scope_name, model.parent_scope_name) if self._scope_name else model.parent_scope_name
yield model
def pretrained(self, pretrained_model_file=None):
"""Load Pretrained weights."""
if self._is_load_pretrained:
return []
assign_vars = []
checkpoint_path = pretrained_model_file or self.pretrained_model_file
if not checkpoint_path:
return
pretrained_prefix = self.pretrained_prefix or {self._scope_name: self._scope_name}
if self.load_pretrained_type == 'pytorch':
assign_vars = assign_pytorch_weights(checkpoint_path, pretrained_prefix)
else:
tf.train.init_from_checkpoint(checkpoint_path, pretrained_prefix)
self._is_load_pretrained = True
return assign_vars
@property
def training(self):
"""Get training flag."""
return self._training
@training.setter
def training(self, value):
"""Set training flag."""
self._training = value
for module in self.children():
module.training = value
@property
def freeze(self):
"""Get training flag."""
return self.freeze
@freeze.setter
def freeze(self, value):
"""Set training flag."""
self._trainable = not value
for module in self.children():
module.freeze = value
def __setattr__(self, key, value):
"""Set name to modules."""
self.__dict__[key] = value
# super().__setattr__(key, value)
if isinstance(value, Module):
if self.enable_scope_name:
value.parent_scope_name = key
self._modules[key] = value
def __getattribute__(self, name):
"""Get modules by name."""
value = object.__getattribute__(self, name)
if isinstance(value, Module) and self.enable_scope_name:
value._scope_name = "{}.{}".format(
self._scope_name, value.parent_scope_name) if self._scope_name else value.parent_scope_name
return value
def set_parameters(self, name, value):
"""Set Parameters."""
with tf.variable_scope('', reuse=tf.AUTO_REUSE):
setattr(self, name, tf.get_variable(name, initializer=value))
def get_weights(self, name):
"""Get weights by name."""
return tf.get_default_graph().get_tensor_by_name('{}:0'.format(name))
def get_weight_ops(self, name):
"""Get weight ops."""
all_weight = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
weight_ops = [t for t in all_weight if not t.name.startswith(name)]
return weight_ops
def call(self, inputs, *args, **kwarg):
"""Call inputs."""
output = inputs
for model in self.children():
output = model(output)
return output
def __call__(self, inputs, *args, **kwargs):
"""Call call function."""
return self.call(inputs, *args, **kwargs)
def modules(self):
"""Get the current modules."""
if self._modules.values():
return self._modules.values()
else:
return [self]
