def get_param_type_and_layer_index(self, param_name):
type = None
layer_index = -1
block_index = -1
tensor_index = -1
if not LAYER_STR in param_name:
type = self.conv_or_bn_type(param_name)
if type != ParameterType.FC_WEIGHTS:
tensor_index = int_from_str(param_name)[0]
else:
layer_index, block_index, tensor_index = int_from_str(param_name)
type = self.conv_or_bn_type(param_name)
return type, tensor_index, layer_index, block_index
def get_param_type_and_layer_index(self, param_name):
type = None
if RCNN_BASE_STR in param_name:
if WEIGHT_STR in param_name:
type = ParameterType.CNN_WEIGHTS
else:
type = ParameterType.CNN_BIAS
tensor_index = -1
if (type == ParameterType.CNN_WEIGHTS or type == ParameterType.CNN_BIAS
) and len(int_from_str(param_name)) != 0:
tensor_index = int_from_str(param_name)[0]
return type, tensor_index, -1, -1
def get_param_type_and_layer_index(self, param_name):
type = None
if WEIGHT_STR in param_name:
if FEATURES_STR in param_name:
type = ParameterType.CNN_WEIGHTS
else:
type = ParameterType.FC_WEIGHTS
elif BIAS_STR in param_name:
if FEATURES_STR in param_name:
type = ParameterType.CNN_BIAS
else:
type = ParameterType.FC_BIAS
tensor_index = -1
if len(int_from_str(param_name)) != 0:
tensor_index = int_from_str(param_name)[0]
if tensor_index in self.batch_norm_index:
if WEIGHT_STR in param_name:
type = ParameterType.BN_WEIGHT
else:
type = ParameterType.BN_BIAS
return type, tensor_index, -1, -1
def conv_or_bn_type(self, param_name):
if CONV_STR in param_name:
return ParameterType.CNN_WEIGHTS
elif BN_STR in param_name:
if WEIGHT_STR in param_name:
return ParameterType.BN_WEIGHT
else:
return ParameterType.BN_BIAS
elif DOWNSAMPLE_STR in param_name:
downsample_index = int_from_str(param_name)[2]
if downsample_index == 0:
if WEIGHT_STR in param_name:
return ParameterType.DOWNSAMPLE_WEIGHTS
else:
return ParameterType.DOWNSAMPLE_BIAS
else:
if WEIGHT_STR in param_name:
return ParameterType.DOWNSAMPLE_BN_W
else:
return ParameterType.DOWNSAMPLE_BN_B
elif FC_STR in param_name:
return ParameterType.FC_WEIGHTS
