#
TVP

##
Overview

The text-visual prompting (TVP) framework was proposed in the paper Text-Visual Prompting for Efficient 2D Temporal Video Grounding by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding.

The abstract from the paper is the following:

In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. However, the high complexity of 3D convolutional neural networks (CNNs) makes extracting dense 3D visual features time-consuming, which calls for intensive memory and computing resources. Towards efficient TVG, we propose a novel text-visual prompting (TVP) framework, which incorporates optimized perturbation patterns (that we call ‘prompts’) into both visual inputs and textual features of a TVG model. In sharp contrast to 3D CNNs, we show that TVP allows us to effectively co-train vision encoder and language encoder in a 2D TVG model and improves the performance of cross-modal feature fusion using only low-complexity sparse 2D visual features. Further, we propose a Temporal-Distance IoU (TDIoU) loss for efficient learning of TVG. Experiments on two benchmark datasets, Charades-STA and ActivityNet Captions datasets, empirically show that the proposed TVP significantly boosts the performance of 2D TVG (e.g., 9.79% improvement on Charades-STA and 30.77% improvement on ActivityNet Captions) and achieves 5× inference acceleration over TVG using 3D visual features.

This research addresses temporal video grounding (TVG), which is the process of pinpointing the start and end times of specific events in a long video, as described by a text sentence. Text-visual prompting (TVP), is proposed to enhance TVG. TVP involves integrating specially designed patterns, known as ‘prompts’, into both the visual (image-based) and textual (word-based) input components of a TVG model. These prompts provide additional spatial-temporal context, improving the model’s ability to accurately determine event timings in the video. The approach employs 2D visual inputs in place of 3D ones. Although 3D inputs offer more spatial-temporal detail, they are also more time-consuming to process. The use of 2D inputs with the prompting method aims to provide similar levels of context and accuracy more efficiently.

TVP architecture. Taken from the original paper.

This model was contributed by Jiqing Feng. The original code can be found here.

##
Usage tips and examples

Prompts are optimized perturbation patterns, which would be added to input video frames or text features. Universal set refers to using the same exact set of prompts for any input, this means that these prompts are added consistently to all video frames and text features, regardless of the input’s content.

TVP consists of a visual encoder and cross-modal encoder. A universal set of visual prompts and text prompts to be integrated into sampled video frames and textual features, respectively. Specially, a set of different visual prompts are applied to uniformly-sampled frames of one untrimmed video in order.

The goal of this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems. In principle, one can apply any visual, cross-modal encoder in the proposed architecture.

The TvpProcessor wraps BertTokenizer and TvpImageProcessor into a single instance to both encode the text and prepare the images respectively.

The following example shows how to run temporal video grounding using TvpProcessor and TvpForVideoGrounding.

    import av
    import cv2
    import numpy as np
    import torch
    from huggingface_hub import hf_hub_download
    from transformers import AutoProcessor, TvpForVideoGrounding

    def pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
        '''
        Convert the video from its original fps to the target_fps and decode the video with PyAV decoder.
        Args:
            container (container): pyav container.
            sampling_rate (int): frame sampling rate (interval between two sampled frames).
            num_frames (int): number of frames to sample.
            clip_idx (int): if clip_idx is -1, perform random temporal sampling.
                If clip_idx is larger than -1, uniformly split the video to num_clips
                clips, and select the clip_idx-th video clip.
            num_clips (int): overall number of clips to uniformly sample from the given video.
            target_fps (int): the input video may have different fps, convert it to
                the target video fps before frame sampling.
        Returns:
            frames (tensor): decoded frames from the video. Return None if the no
                video stream was found.
            fps (float): the number of frames per second of the video.
        '''
        video = container.streams.video[0]
        fps = float(video.average_rate)
        clip_size = sampling_rate * num_frames / target_fps * fps
        delta = max(num_frames - clip_size, 0)
        start_idx = delta * clip_idx / num_clips
        end_idx = start_idx + clip_size - 1
        timebase = video.duration / num_frames
        video_start_pts = int(start_idx * timebase)
        video_end_pts = int(end_idx * timebase)
        seek_offset = max(video_start_pts - 1024, 0)
        container.seek(seek_offset, any_frame=False, backward=True, stream=video)
        frames = {}
        for frame in container.decode(video=0):
            if frame.pts < video_start_pts:
                continue
            frames[frame.pts] = frame
            if frame.pts > video_end_pts:
                break
        frames = [frames[pts] for pts in sorted(frames)]
        return frames, fps

    def decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
        '''
        Decode the video and perform temporal sampling.
        Args:
            container (container): pyav container.
            sampling_rate (int): frame sampling rate (interval between two sampled frames).
            num_frames (int): number of frames to sample.
            clip_idx (int): if clip_idx is -1, perform random temporal sampling.
                If clip_idx is larger than -1, uniformly split the video to num_clips
                clips, and select the clip_idx-th video clip.
            num_clips (int): overall number of clips to uniformly sample from the given video.
            target_fps (int): the input video may have different fps, convert it to
                the target video fps before frame sampling.
        Returns:
            frames (tensor): decoded frames from the video.
        '''
        assert clip_idx >= -2, "Not a valid clip_idx {}".format(clip_idx)
        frames, fps = pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps)
        clip_size = sampling_rate * num_frames / target_fps * fps
        index = np.linspace(0, clip_size - 1, num_frames)
        index = np.clip(index, 0, len(frames) - 1).astype(np.int64)
        frames = np.array([frames[idx].to_rgb().to_ndarray() for idx in index])
        frames = frames.transpose(0, 3, 1, 2)
        return frames

    file = hf_hub_download(repo_id="Intel/tvp_demo", filename="AK2KG.mp4", repo_type="dataset")
    model = TvpForVideoGrounding.from_pretrained("Intel/tvp-base")

    decoder_kwargs = dict(
        container=av.open(file, metadata_errors="ignore"),
        sampling_rate=1,
        num_frames=model.config.num_frames,
        clip_idx=0,
        num_clips=1,
        target_fps=3,
    )
    raw_sampled_frms = decode(**decoder_kwargs)

    text = "a person is sitting on a bed."
    processor = AutoProcessor.from_pretrained("Intel/tvp-base")
    model_inputs = processor(
        text=[text], videos=list(raw_sampled_frms), return_tensors="pt", max_text_length=100#, size=size
    )

    model_inputs["pixel_values"] = model_inputs["pixel_values"].to(model.dtype)
    output = model(**model_inputs)

    def get_video_duration(filename):
        cap = cv2.VideoCapture(filename)
        if cap.isOpened():
            rate = cap.get(5)
            frame_num = cap.get(7)
            duration = frame_num/rate
            return duration
        return -1

    duration = get_video_duration(file)
    start, end = processor.post_process_video_grounding(output.logits, duration)

    print(f"The time slot of the video corresponding to the text \"{text}\" is from {start}s to {end}s")

Tips:

- This implementation of TVP uses BertTokenizer to generate text embeddings and Resnet-50 model to compute visual embeddings.
- Checkpoints for pre-trained tvp-base is released.
- Please refer to Table 2 for TVP’s performance on Temporal Video Grounding task.

##
TvpConfig

###
class transformers.TvpConfig

< source >

( 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 )

Parameters

- 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.

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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

####
from_backbone_config

< source >

( backbone_config: PretrainedConfig **kwargs ) →
TvpConfig

Parameters

- backbone_config (PretrainedConfig) — The backbone configuration.

Returns

TvpConfig

An instance of a configuration object

Instantiate a TvpConfig (or a derived class) from a pre-trained backbone model configuration.

####
to_dict

< source >

( ) →
`Dict[str, any]`

Returns

`Dict[str, any]`

Dictionary of all the attributes that make up this configuration instance,

Serializes this instance to a Python dictionary. Override the default to_dict().

##
TvpImageProcessor

###
class transformers.TvpImageProcessor

< source >

( do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BILINEAR: 2> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_pad: bool = True pad_size: typing.Dict[str, int] = None constant_values: typing.Union[float, typing.Iterable[float]] = 0 pad_mode: PaddingMode = <PaddingMode.CONSTANT: 'constant'> do_normalize: bool = True do_flip_channel_order: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None **kwargs )

Parameters

- do_resize (`bool`, optional, defaults to `True`) — Whether to resize the image’s (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
- size (`Dict[str, int]` optional, defaults to `{"longest_edge" -- 448}`): Size of the output image after resizing. The longest edge of the image will be resized to `size["longest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by `size` in the `preprocess` method.
- resample (`PILImageResampling`, optional, defaults to `Resampling.BILINEAR`) — Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method.
- do_center_crop (`bool`, optional, defaults to `True`) — Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method.
- crop_size (`Dict[str, int]`, optional, defaults to `{"height" -- 448, "width": 448}`): Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the `preprocess` method.
- do_rescale (`bool`, optional, defaults to `True`) — Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method.
- rescale_factor (`int` or `float`, optional, defaults to `1/255`) — Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method.
- do_pad (`bool`, optional, defaults to `True`) — Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
- pad_size (`Dict[str, int]`, optional, defaults to `{"height" -- 448, "width": 448}`): Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the `preprocess` method.
- constant_values (`Union[float, Iterable[float]]`, optional, defaults to 0) — The fill value to use when padding the image.
- pad_mode (`PaddingMode`, optional, defaults to `PaddingMode.CONSTANT`) — Use what kind of mode in padding.
- do_normalize (`bool`, optional, defaults to `True`) — Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
- do_flip_channel_order (`bool`, optional, defaults to `True`) — Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order` parameter in the `preprocess` method.
- image_mean (`float` or `List[float]`, optional, defaults to `IMAGENET_STANDARD_MEAN`) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
- image_std (`float` or `List[float]`, optional, defaults to `IMAGENET_STANDARD_STD`) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.

Constructs a Tvp image processor.

####
preprocess

< source >

( videos: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], typing.List[typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]], typing.List[typing.List[typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]]]] do_resize: typing.Optional[bool] = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Dict[str, int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_pad: typing.Optional[bool] = None pad_size: typing.Dict[str, int] = None constant_values: typing.Union[float, typing.Iterable[float]] = None pad_mode: PaddingMode = None do_normalize: typing.Optional[bool] = None do_flip_channel_order: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

Parameters

- videos (`ImageInput` or `List[ImageInput]` or `List[List[ImageInput]]`) — Frames to preprocess.
- do_resize (`bool`, optional, defaults to `self.do_resize`) — Whether to resize the image.
- size (`Dict[str, int]`, optional, defaults to `self.size`) — Size of the image after applying resize.
- resample (`PILImageResampling`, optional, defaults to `self.resample`) — Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`.
- do_center_crop (`bool`, optional, defaults to `self.do_centre_crop`) — Whether to centre crop the image.
- crop_size (`Dict[str, int]`, optional, defaults to `self.crop_size`) — Size of the image after applying the centre crop.
- do_rescale (`bool`, optional, defaults to `self.do_rescale`) — Whether to rescale the image values between [0 - 1].
- rescale_factor (`float`, optional, defaults to `self.rescale_factor`) — Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- do_pad (`bool`, optional, defaults to `True`) — Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method.
- pad_size (`Dict[str, int]`, optional, defaults to `{"height" -- 448, "width": 448}`): Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the `preprocess` method.
- constant_values (`Union[float, Iterable[float]]`, optional, defaults to 0) — The fill value to use when padding the image.
- pad_mode (`PaddingMode`, optional, defaults to “PaddingMode.CONSTANT”) — Use what kind of mode in padding.
- do_normalize (`bool`, optional, defaults to `self.do_normalize`) — Whether to normalize the image.
- do_flip_channel_order (`bool`, optional, defaults to `self.do_flip_channel_order`) — Whether to flip the channel order of the image.
- image_mean (`float` or `List[float]`, optional, defaults to `self.image_mean`) — Image mean.
- image_std (`float` or `List[float]`, optional, defaults to `self.image_std`) — Image standard deviation.
- return_tensors (`str` or `TensorType`, optional) — The type of tensors to return. Can be one of:
  - Unset: Return a list of `np.ndarray`.
  - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
  - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
  - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
  - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
- data_format (`ChannelDimension` or `str`, optional, defaults to `ChannelDimension.FIRST`) — The channel dimension format for the output image. Can be one of:
  - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - Unset: Use the inferred channel dimension format of the input image.
- input_data_format (`ChannelDimension` or `str`, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

Preprocess an image or batch of images.

##
TvpProcessor

###
class transformers.TvpProcessor

< source >

( image_processor = None tokenizer = None **kwargs )

Parameters

- image_processor (TvpImageProcessor, optional) — The image processor is a required input.
- tokenizer (BertTokenizerFast, optional) — The tokenizer is a required input.

Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor.

TvpProcessor offers all the functionalities of TvpImageProcessor and BertTokenizerFast. See the call() and `decode()` for more information.

####
__call__

< source >

( text = None videos = None return_tensors = None **kwargs ) →
BatchEncoding

Parameters

- text (`str`, `List[str]`, `List[List[str]]`) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarray]]`, — `List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors, each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of channels.
- return_tensors (`str` or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:
  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
  - `'jax'`: Return JAX `jnp.ndarray` objects.

Returns

BatchEncoding

A BatchEncoding with the following fields:

- input_ids — List of token ids to be fed to a model. Returned when `text` is not `None`.
- attention_mask — List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if “attention_mask” is in `self.model_input_names` and if `text` is not `None`).
- pixel_values — Pixel values to be fed to a model. Returned when `videos` is not `None`.

Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to BertTokenizerFast’s call() if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to TvpImageProcessor’s call() if `videos` is not `None`. Please refer to the docstring of the above two methods for more information.

##
TvpModel

###
class transformers.TvpModel

< source >

( config )

Parameters

- config (TvpConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Tvp Model transformer outputting BaseModelOutputWithPooling object without any specific head on top. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False ) →
transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input IDs?
- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using TvpImageProcessor. See TvpImageProcessor.call() for details.
- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked. What are attention masks?
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, optional, defaults to `False`) — Whether to interpolate the pre-trained image pad prompter encodings and positional encodings.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`<class 'transformers.models.tvp.configuration_tvp.TvpConfig'>`) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TvpModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> import torch
    >>> from transformers import AutoConfig, AutoTokenizer, TvpModel

    >>> model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp")

    >>> tokenizer = AutoTokenizer.from_pretrained("Jiqing/tiny-random-tvp")

    >>> pixel_values = torch.rand(1, 1, 3, 448, 448)
    >>> text_inputs = tokenizer("This is an example input", return_tensors="pt")
    >>> output = model(text_inputs.input_ids, pixel_values, text_inputs.attention_mask)

##
TvpForVideoGrounding

###
class transformers.TvpForVideoGrounding

< source >

( config )

Parameters

- config (TvpConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

Tvp Model with a video grounding head on top computing IoU, distance, and duration loss.

This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

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( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None labels: typing.Tuple[torch.Tensor] = None head_mask: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False ) →
`transformers.models.tvp.modeling_tvp.TvpVideoGroundingOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input IDs?
- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using TvpImageProcessor. See TvpImageProcessor.call() for details.
- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked. What are attention masks?
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, optional, defaults to `False`) — Whether to interpolate the pre-trained image pad prompter encodings and positional encodings.
- labels (`torch.FloatTensor` of shape `(batch_size, 3)`, optional) — The labels contains duration, start time, and end time of the video corresponding to the text.

Returns

`transformers.models.tvp.modeling_tvp.TvpVideoGroundingOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.tvp.modeling_tvp.TvpVideoGroundingOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`<class 'transformers.models.tvp.configuration_tvp.TvpConfig'>`) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`) — Temporal-Distance IoU loss for video grounding.
- logits (`torch.FloatTensor` of shape `(batch_size, 2)`) — Contains start_time/duration and end_time/duration. It is the time slot of the videos corresponding to the input texts.
- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

The TvpForVideoGrounding forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> import torch
    >>> from transformers import AutoConfig, AutoTokenizer, TvpForVideoGrounding

    >>> model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp")

    >>> tokenizer = AutoTokenizer.from_pretrained("Jiqing/tiny-random-tvp")

    >>> pixel_values = torch.rand(1, 1, 3, 448, 448)
    >>> text_inputs = tokenizer("This is an example input", return_tensors="pt")
    >>> output = model(text_inputs.input_ids, pixel_values, text_inputs.attention_mask)

< > Update on GitHub