202 lines
9.9 KiB
Python
202 lines
9.9 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License=, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing=, software
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# distributed under the License is distributed on an "AS IS" BASIS=,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" TVP model configuration"""
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import copy
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import TVP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class TvpConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Tvp
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[Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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backbone_config (`PretrainedConfig` or `dict`, *optional*):
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The configuration of the backbone model.
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backbone (`str`, *optional*):
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Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
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will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
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is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
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use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
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Whether to use pretrained weights for the backbone.
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use_timm_backbone (`bool`, *optional*, defaults to `False`):
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Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
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library.
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backbone_kwargs (`dict`, *optional*):
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Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
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e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
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distance_loss_weight (`float`, *optional*, defaults to 1.0):
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The weight of distance loss.
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duration_loss_weight (`float`, *optional*, defaults to 0.1):
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The weight of duration loss.
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visual_prompter_type (`str`, *optional*, defaults to `"framepad"`):
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Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad"
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or "framedownpad"
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visual_prompter_apply (`str`, *optional*, defaults to `"replace"`):
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The way of applying visual prompt. Replace means use the value of prompt to change the original value in
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visual inputs. Should be one of "replace", or "add", or "remove".
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visual_prompt_size (`int`, *optional*, defaults to 96):
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The size of visual prompt.
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max_img_size (`int`, *optional*, defaults to 448):
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The maximum size of frame.
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num_frames (`int`, *optional*, defaults to 48):
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The number of frames extracted from a video.
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`TvpModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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max_grid_col_position_embeddings (`int`, *optional*, defaults to 100):
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The largest number of horizontal patches from a video frame.
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max_grid_row_position_embeddings (`int`, *optional*, defaults to 100):
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The largest number of vertical patches from a video frame.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability of hidden layers.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability of attention layers.
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"""
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model_type = "tvp"
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def __init__(
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self,
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backbone_config=None,
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backbone=None,
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use_pretrained_backbone=False,
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use_timm_backbone=False,
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backbone_kwargs=None,
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distance_loss_weight=1.0,
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duration_loss_weight=0.1,
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visual_prompter_type="framepad",
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visual_prompter_apply="replace",
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visual_prompt_size=96,
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max_img_size=448,
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num_frames=48,
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vocab_size=30522,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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max_position_embeddings=512,
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max_grid_col_position_embeddings=100,
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max_grid_row_position_embeddings=100,
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hidden_dropout_prob=0.1,
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hidden_act="gelu",
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layer_norm_eps=1e-12,
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initializer_range=0.02,
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attention_probs_dropout_prob=0.1,
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**kwargs,
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):
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super().__init__(**kwargs)
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if use_pretrained_backbone:
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raise ValueError("Pretrained backbones are not supported yet.")
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if backbone_config is not None and backbone is not None:
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raise ValueError("You can't specify both `backbone` and `backbone_config`.")
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if backbone_config is None and backbone is None:
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logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
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backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
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elif isinstance(backbone_config, dict):
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backbone_model_type = backbone_config.get("model_type")
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config_class = CONFIG_MAPPING[backbone_model_type]
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backbone_config = config_class.from_dict(backbone_config)
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if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
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raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
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self.backbone_config = backbone_config
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self.backbone = backbone
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self.use_pretrained_backbone = use_pretrained_backbone
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self.use_timm_backbone = use_timm_backbone
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self.backbone_kwargs = backbone_kwargs
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self.distance_loss_weight = distance_loss_weight
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self.duration_loss_weight = duration_loss_weight
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self.visual_prompter_type = visual_prompter_type
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self.visual_prompter_apply = visual_prompter_apply
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self.visual_prompt_size = visual_prompt_size
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self.max_img_size = max_img_size
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self.num_frames = num_frames
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.max_grid_col_position_embeddings = max_grid_col_position_embeddings
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self.max_grid_row_position_embeddings = max_grid_row_position_embeddings
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self.layer_norm_eps = layer_norm_eps
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self.hidden_dropout_prob = hidden_dropout_prob
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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@classmethod
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def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
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"""Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration.
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Args:
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backbone_config ([`PretrainedConfig`]):
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The backbone configuration.
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Returns:
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[`TvpConfig`]: An instance of a configuration object
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"""
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return cls(backbone_config=backbone_config, **kwargs)
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
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Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = copy.deepcopy(self.__dict__)
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if output["backbone_config"] is not None:
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output["backbone_config"] = self.backbone_config.to_dict()
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output["model_type"] = self.__class__.model_type
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return output
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