def _new_model(self, x_dim, n_classes, **kwargs):
if self.common_model is None:
# This is the first task
self.n_classes = n_classes
if len(x_dim) == 1:
n_convs = 0
else:
k = 3
stride = 2
second_layer_map_size = [(s - (k - 1) + 1) // stride
for s in x_dim[1:]]
self.hidden_size[0] = (self.hidden_size[0],
*second_layer_map_size)
n_convs = 1
self.all_dims = [x_dim, *self.hidden_size]
common_trunc = self.base_model_func(self.hidden_size,
n_convs=n_convs)
head = MultiHead(self.all_dims[-1], n_classes)
self.common_model = make_model(common_trunc, head)
assert n_classes == self.n_classes, 'Number of classes should always ' \
'be the same with NewLegLLModel'
new_leg = self.base_model_func(self.all_dims[:2])
return make_model(new_leg, self.common_model)
def _new_model(self, x_dim, n_classes, task_id, **kwargs):
if self.all_dims is None:
if len(x_dim) == 1:
self.n_convs = 0
else:
k = 3
stride = 2
second_layer_map_size = [(s - (k - 1) + 1) // stride
for s in x_dim[1:]]
self.hidden_size[0] = (self.hidden_size[0],
*second_layer_map_size)
self.n_convs = 1
self.all_dims = [x_dim, *self.hidden_size]
if self.source_ll_model is None:
raise ValueError('Source model should be set before creating the '
'first model for FineTuneLegLLModel !')
head = MultiHead(self.all_dims[-1], n_classes)
common_trunc = self.base_model_func(self.all_dims[1:],
n_convs=self.n_convs)
new_trunc = make_model(common_trunc, head)
new_leg = self.base_model_func(self.all_dims[:2])
new_model = make_model(new_leg, new_trunc, seq=FineTuneLegMultiTask)
return new_model
def _new_model(self, x_dim, n_classes, **kwargs):
all_dims = [x_dim, *self.hidden_size]
new_base = self.base_model_func(all_dims)
new_head = MultiHead(all_dims[-1], n_classes)
new_model = make_model(new_base, new_head)
return new_model
def _new_model(self, x_dim, n_classes, task_id, **kwargs):
if self.all_dims is None:
self.all_dims = [x_dim, *self.hidden_size]
if self.source_ll_model is None:
raise ValueError('Source model should be set before creating the '
'first model for FineTuneHeadLLModel !')
new_base = self.base_model_func(self.all_dims)
new_head = MultiHead(self.all_dims[-1], n_classes)
new_model = make_model(new_base, new_head, seq=FineTuneHeadMultiTask)
return new_model
def _new_model(self, x_dim, n_classes, **kwargs):
if self.common_model is None:
self.all_dims = [x_dim, *self.hidden_size]
self.common_model = self.base_model_func(self.all_dims)
assert x_dim == self.all_dims[0], 'SingleHead model can only be used ' \
'with the same input dimensions. ' \
'Was initialized with {} but got {}' \
' on the last task'.format(
self.all_dims[0], x_dim)
new_head = MultiHead(self.all_dims[-1], n_classes)
new_model = make_model(self.common_model, new_head)
return new_model
def _new_model(self, x_dim, n_classes, **kwargs):
if self.model is None:
self.x_dim = x_dim
self.n_classes = n_classes
all_dims = [x_dim, *self.hidden_size]
base_model = self.base_model_func(all_dims)
new_head = MultiHead(all_dims[-1], n_classes)
self.model = make_model(base_model, new_head)
if n_classes != self.n_classes:
raise ValueError(
'SingleHead model can only be used with the same number of '
'classes. Was initialized with {} but got {} on the last '
'task'.format(self.n_classes, n_classes))
if x_dim != self.x_dim:
raise ValueError(
'SingleHead model can only be used with the same input '
'dimensions. Was initialized with '
'{} but got {} on the last task'.format(self.x_dim, x_dim))
return self.model