# coding=utf-8 # Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License=, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing=, software # distributed under the License is distributed on an "AS IS" BASIS=, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TVP model configuration""" import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import verify_backbone_config_arguments from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) class TvpConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Tvp [Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: backbone_config (`PretrainedConfig` or `dict`, *optional*): The configuration of the backbone model. backbone (`str`, *optional*): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `False`): Whether to use pretrained weights for the backbone. use_timm_backbone (`bool`, *optional*, defaults to `False`): Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. distance_loss_weight (`float`, *optional*, defaults to 1.0): The weight of distance loss. duration_loss_weight (`float`, *optional*, defaults to 0.1): The weight of duration loss. visual_prompter_type (`str`, *optional*, defaults to `"framepad"`): Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad" or "framedownpad" visual_prompter_apply (`str`, *optional*, defaults to `"replace"`): The way of applying visual prompt. Replace means use the value of prompt to change the original value in visual inputs. Should be one of "replace", or "add", or "remove". visual_prompt_size (`int`, *optional*, defaults to 96): The size of visual prompt. max_img_size (`int`, *optional*, defaults to 448): The maximum size of frame. num_frames (`int`, *optional*, defaults to 48): The number of frames extracted from a video. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`TvpModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). max_grid_col_position_embeddings (`int`, *optional*, defaults to 100): The largest number of horizontal patches from a video frame. max_grid_row_position_embeddings (`int`, *optional*, defaults to 100): The largest number of vertical patches from a video frame. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of hidden layers. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of attention layers. """ model_type = "tvp" def __init__( self, backbone_config=None, backbone=None, use_pretrained_backbone=False, use_timm_backbone=False, backbone_kwargs=None, distance_loss_weight=1.0, duration_loss_weight=0.1, visual_prompter_type="framepad", visual_prompter_apply="replace", visual_prompt_size=96, max_img_size=448, num_frames=48, vocab_size=30522, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, max_position_embeddings=512, max_grid_col_position_embeddings=100, max_grid_row_position_embeddings=100, hidden_dropout_prob=0.1, hidden_act="gelu", layer_norm_eps=1e-12, initializer_range=0.02, attention_probs_dropout_prob=0.1, **kwargs, ): super().__init__(**kwargs) if backbone_config is None and backbone is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) elif isinstance(backbone_config, dict): backbone_model_type = backbone_config.get("model_type") config_class = CONFIG_MAPPING[backbone_model_type] backbone_config = config_class.from_dict(backbone_config) verify_backbone_config_arguments( use_timm_backbone=use_timm_backbone, use_pretrained_backbone=use_pretrained_backbone, backbone=backbone, backbone_config=backbone_config, backbone_kwargs=backbone_kwargs, ) self.backbone_config = backbone_config self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.use_timm_backbone = use_timm_backbone self.backbone_kwargs = backbone_kwargs self.distance_loss_weight = distance_loss_weight self.duration_loss_weight = duration_loss_weight self.visual_prompter_type = visual_prompter_type self.visual_prompter_apply = visual_prompter_apply self.visual_prompt_size = visual_prompt_size self.max_img_size = max_img_size self.num_frames = num_frames self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.max_grid_col_position_embeddings = max_grid_col_position_embeddings self.max_grid_row_position_embeddings = max_grid_row_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_dropout_prob = hidden_dropout_prob self.hidden_act = hidden_act self.initializer_range = initializer_range self.attention_probs_dropout_prob = attention_probs_dropout_prob @classmethod def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs): """Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration. Args: backbone_config ([`PretrainedConfig`]): The backbone configuration. Returns: [`TvpConfig`]: An instance of a configuration object """ return cls(backbone_config=backbone_config, **kwargs) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: output["backbone_config"] = self.backbone_config.to_dict() output["model_type"] = self.__class__.model_type return output __all__ = ["TvpConfig"]