def _prepare_distill(self):
if self.distill_config.teacher_model == None:
_logger.error(
'If you want to add distill, please input instance of teacher model'
)
### instance model by user can input super-param easily.
assert isinstance(self.distill_config.teacher_model, Layer)
# load teacher parameter
if self.distill_config.teacher_model_path != None:
param_state_dict, _ = paddle.load_dygraph(
self.distill_config.teacher_model_path)
self.distill_config.teacher_model.set_dict(param_state_dict)
self.ofa_teacher_model = OFABase(self.distill_config.teacher_model)
self.ofa_teacher_model.model.eval()
# add hook if mapping layers is not None
# if mapping layer is None, return the output of the teacher model,
# if mapping layer is NOT None, add hook and compute distill loss about mapping layers.
mapping_layers = getattr(self.distill_config, 'mapping_layers', None)
if mapping_layers != None:
if isinstance(self.model, DataParallel):
for idx, name in enumerate(mapping_layers):
if name[:7] != '_layers':
mapping_layers[idx] = '_layers.' + name
self._mapping_layers = mapping_layers
self.netAs = []
for name, sublayer in self.model.named_sublayers():
if name in self._mapping_layers:
if self.distill_config.mapping_op != None:
if self.distill_config.mapping_op.lower() == 'conv2d':
netA = SuperConv2D(
getattr(sublayer, '_num_filters',
sublayer._out_channels),
getattr(sublayer, '_num_filters',
sublayer._out_channels), 1)
elif self.distill_config.mapping_op.lower(
) == 'linear':
netA = SuperLinear(
getattr(sublayer, '_output_dim',
sublayer._out_features),
getattr(sublayer, '_output_dim',
sublayer._out_features))
else:
raise NotImplementedError(
"Not Support Op: {}".format(
self.distill_config.mapping_op.lower()))
else:
netA = None
if netA != None:
self.netAs_param.extend(netA.parameters())
self.netAs.append(netA)
def _prepare_distill(self):
self.Tacts, self.Sacts = {}, {}
if self.distill_config.teacher_model == None:
logging.error(
'If you want to add distill, please input instance of teacher model'
)
### instance model by user can input super-param easily.
assert isinstance(self.distill_config.teacher_model,
paddle.fluid.dygraph.Layer)
# load teacher parameter
if self.distill_config.teacher_model_path != None:
param_state_dict, _ = paddle.load_dygraph(
self.distill_config.teacher_model_path)
self.distill_config.teacher_model.set_dict(param_state_dict)
self.ofa_teacher_model = OFABase(self.distill_config.teacher_model)
self.ofa_teacher_model.model.eval()
# add hook if mapping layers is not None
# if mapping layer is None, return the output of the teacher model,
# if mapping layer is NOT None, add hook and compute distill loss about mapping layers.
mapping_layers = getattr(self.distill_config, 'mapping_layers', None)
if mapping_layers != None:
self.netAs = []
for name, sublayer in self.model.named_sublayers():
if name in mapping_layers:
netA = SuperConv2D(sublayer._num_filters,
sublayer._num_filters,
filter_size=1)
self.netAs_param.extend(netA.parameters())
self.netAs.append(netA)
def get_activation(mem, name):
def get_output_hook(layer, input, output):
mem[name] = output
return get_output_hook
def add_hook(net, mem, mapping_layers):
for idx, (n, m) in enumerate(net.named_sublayers()):
if n in mapping_layers:
m.register_forward_post_hook(get_activation(mem, n))
add_hook(self.model, self.Sacts, mapping_layers)
add_hook(self.ofa_teacher_model.model, self.Tacts, mapping_layers)
def _prepare_distill(self):
self.Tacts, self.Sacts = {}, {}
if self.distill_config.teacher_model == None:
logging.error(
'If you want to add distill, please input instance of teacher model'
)
### instance model by user can input super-param easily.
assert isinstance(self.distill_config.teacher_model, Layer)
# load teacher parameter
if self.distill_config.teacher_model_path != None:
param_state_dict, _ = paddle.load_dygraph(
self.distill_config.teacher_model_path)
self.distill_config.teacher_model.set_dict(param_state_dict)
self.ofa_teacher_model = OFABase(self.distill_config.teacher_model)
self.ofa_teacher_model.model.eval()
# add hook if mapping layers is not None
# if mapping layer is None, return the output of the teacher model,
# if mapping layer is NOT None, add hook and compute distill loss about mapping layers.
mapping_layers = getattr(self.distill_config, 'mapping_layers', None)
if mapping_layers != None:
self.netAs = []
for name, sublayer in self.model.named_sublayers():
if name in mapping_layers:
if self.distill_config.mapping_op != None:
if self.distill_config.mapping_op.lower() == 'conv2d':
netA = SuperConv2D(
getattr(sublayer, '_num_filters',
sublayer._out_channels),
getattr(sublayer, '_num_filters',
sublayer._out_channels), 1)
elif self.distill_config.mapping_op.lower(
) == 'linear':
netA = SuperLinear(
getattr(sublayer, '_output_dim',
sublayer._out_features),
getattr(sublayer, '_output_dim',
sublayer._out_features))
else:
raise NotImplementedError(
"Not Support Op: {}".format(
self.distill_config.mapping_op.lower()))
else:
netA = None
if netA != None:
self.netAs_param.extend(netA.parameters())
self.netAs.append(netA)
def get_activation(mem, name):
def get_output_hook(layer, input, output):
mem[name] = output
return get_output_hook
def add_hook(net, mem, mapping_layers):
for idx, (n, m) in enumerate(net.named_sublayers()):
if n in mapping_layers:
m.register_forward_post_hook(get_activation(mem, n))
add_hook(self.model, self.Sacts, mapping_layers)
add_hook(self.ofa_teacher_model.model, self.Tacts, mapping_layers)
