3x3 convolution with padding

3x3 convolution with padding

3x3 convolution with padding

3x3 convolution with padding

convert a batch of data into a sequence of input tensors

convert a batch of data into a sequence of input tensors

Converts `text` to Unicode (if it's not already), assuming utf-8 input.

Crop the input image into given size at given position. Args: patch(tuple): width and height of the patch position(list(str)): le

Crop at positions from [left_top, left_bottom, right_top, right_bottom, and center]. Args: patch(tuple): width and height

Same as clone(). Returns: a new Tensor

Elementi-wise division. Args: lhs (Tensor): lhs tensor rhs (Tensor): rhs tensor ret (Tensor, optional): if not None, the

Sets the model in evaluation mode.

Score each architecture with predefined architecture and data :param arch: architecture to be scored :param device: cpu or g

:param run_id_m: run_id 100 max :param B1_m: number of models evaluted :return:

Randomly flip a img left to right. Args: num_case: num of cases, must be in {1,2}; if 2, then add the orignal

Defines the computation performed in every forward propagation. Should be overridden by all subclasses. Args: *input: th

Defines the computation performed in every forward propagation. Should be overridden by all subclasses. Args: *input: th

Defines the computation performed in every forward propagation. Should be overridden by all subclasses. Args: *input: th

Defines the computation performed in every forward propagation. Should be overridden by all subclasses. Args: *input: th

Only for the MLP Returns:

ml_data_score_dic: { model_id: {alg: score1, alg2: score2} } return: { model_id: {alg1_alg2: rank_score} }

cudnn requires tensor dimensions to fulfill 1 requirement: 1.) Dimensions to be set to a minimum of 4 for 4d and lower dimensional tensors if

Default use 108 epoch for c10, this is the largest epoch number. :param dataset: :param arch_id: architecture id :par

The model is already scored by: low -> high :param k: :return:

r""" Args: base_model: the base model peft_config: the config of peft Returns: a peft model based on peft config

deserialize training data file args: data_dir: dir of data file return: train_raw: list of QnA pair, length o

hanlde some special operations Args: name (str): name of the state node (ONNXNode): ONNX node ten

Init the instance

Create a new circle that includes only this instance.

=============Element-wise operations====================================

Each layer sub-class would optionaly have a type name. Used for debugging and logging.