def _make_recovery_model_include_top(recovery_model:Layer,default_shape=None,input_shape=None, include_top=True, classes=1000, freeze_features=False):
size_change=False
if default_shape is None:
if recovery_model.built:
default_shape=tuple(recovery_model._input_shape.dims[1:] if isinstance(recovery_model._input_shape,TensorShape) else recovery_model._input_shape)
else:
default_shape=(3,224,224) if get_backend() == 'pytorch' else (224,224,3)
if input_shape is not None and input_shape !=default_shape:
size_change=True
dims = list(input_shape)
dims.insert(0, None)
if isinstance(recovery_model.signature, Signature):
recovery_model._input_shape = TensorShape(dims)
recovery_model.signature.inputs.value_list[0].shape = TensorShape(dims)
recovery_model.signature.inputs.value_list[0].object_type=ObjectType.rgb
if freeze_features:
recovery_model.trainable=False
idx=-1
while (len(recovery_model[idx]._parameters) == 0 or isinstance(recovery_model[idx], Dense))and len(recovery_model[idx].output_shape) >= 2:
layer=recovery_model[idx]
if layer.output_shape.rank>2:
break
if len(recovery_model[idx]._parameters) >0:
recovery_model[idx].trainable=True
idx-=1
if not include_top:
while len(recovery_model[-1]._parameters)==0 or isinstance(recovery_model[-1],Dense) and len(recovery_model[-1].output_shape)>=2:
layer = recovery_model[-1]
if layer.output_shape.rank > 2:
break
recovery_model.remove_at(-1)
recovery_model.class_names = []
elif size_change:
new_layers=[]
dims = list(input_shape)
dims.insert(0, None)
shp=TensorShape(dims)
while len(recovery_model[-1]._parameters) == 0 or isinstance(recovery_model[-1], Dense) and len(recovery_model[-1].output_shape) >= 2:
layer = recovery_model[-1]
if layer.output_shape.rank > 2:
break
new_layer=copy.deepcopy(layer)
if isinstance(layer,Dense) :
if layer.num_filters==1000 and classes != 1000:
new_layer=Dense((classes))
recovery_model.class_names = []
else:
num_filters=new_layer.num_filters
new_layer=Dense((num_filters))
new_layers.insert(0,new_layer)
recovery_model.remove_at(-1)
out=recovery_model(to_tensor(shp.get_dummy_tensor()))
recovery_model[-1].output_shape=tensor_to_shape(out,need_exclude_batch_axis=True)
fc_seq=0
for ly in new_layers:
if isinstance(ly, Dense):
recovery_model.add_module('fc' if fc_seq==0 else 'fc{0}'.format(fc_seq),ly)
fc_seq += 1
else:
recovery_model.add_module(ly.name, ly)
if isinstance(recovery_model.signature, Signature):
recovery_model.output_shape = TensorShape([None, classes])
recovery_model.signature.outputs.value_list[0].shape = TensorShape([None, classes])
recovery_model.signature.outputs.value_list[0].object_type = ObjectType.classification_label
else:
#include_top=True
if classes != 1000:
while len(recovery_model[-1]._parameters)==0 or isinstance(recovery_model[-1],Dense) and len(recovery_model[-1].output_shape)>=2:
m=recovery_model[-1]
if isinstance(m,Dense):
recovery_model[-1]=Dense((classes))
recovery_model.add_module('softmax',SoftMax())
break
else:
recovery_model.remove_at(-1)
if isinstance(recovery_model.signature, Signature):
recovery_model.output_shape= TensorShape([None,classes])
recovery_model.signature.outputs.value_list[0].shape = TensorShape([None,classes])
recovery_model.signature.outputs.value_list[0].object_type = ObjectType.classification_label
recovery_model.class_names = []
return recovery_model
def _make_recovery_model_include_top(recovery_model: Layer,
default_shape=None,
input_shape=None,
include_top=True,
classes=1000,
freeze_features=True):
size_change = False
if default_shape is None:
if recovery_model.built:
default_shape = tuple(
recovery_model._input_shape.
dims[1:] if isinstance(recovery_model._input_shape, TensorShape
) else recovery_model._input_shape)
else:
default_shape = (3, 224,
224) if get_backend() == 'pytorch' else (224, 224,
3)
if input_shape is not None and input_shape != default_shape:
size_change = True
if freeze_features:
recovery_model.trainable = False
idx = -1
is_last_dense = True
while (len(recovery_model[idx]._parameters) == 0
or isinstance(recovery_model[idx], Dense)) and len(
recovery_model[idx].output_shape) >= 2:
layer = recovery_model[idx]
if layer.output_shape.rank > 2:
break
elif len(recovery_model[idx]._parameters) > 0:
if not include_top:
recovery_model.remove_at(idx)
idx += 1
elif size_change or (is_last_dense and classes != 1000 and
isinstance(recovery_model[idx], Dense)):
if hasattr(
recovery_model[idx], 'num_filters'
) and recovery_model[idx].num_filters != classes:
recovery_model[idx].num_filters = classes
recovery_model[idx]._built = False
recovery_model[idx]._parameters.clear()
else:
recovery_model[idx].trainable = True
else:
if not include_top:
recovery_model.remove_at(idx)
idx += 1
idx -= 1
dims = list(default_shape)
dims.insert(0, None)
new_tensorshape = TensorShape(dims)
if size_change:
dims = list(input_shape)
dims.insert(0, None)
new_tensorshape = TensorShape(dims)
for module in recovery_model.modules():
module._input_shape = None
module._output_shape = None
recovery_model.to(get_device())
out = recovery_model(
to_tensor(new_tensorshape.get_dummy_tensor(), device=get_device()))
if isinstance(recovery_model.signature, Signature):
recovery_model.signature.inputs.value_list[0].shape = TensorShape(dims)
recovery_model.signature.inputs.value_list[
0].object_type = ObjectType.rgb
recovery_model.to(get_device())
return recovery_model