3 Perceiver-Actor PerAct is a language-conditioned behavior-cloning agent for 6-DoF manipulation. logits: FloatTensor = None size = 224 Comparable or better performance than SOTA models in ImageNet, AudioSet, and ModelNet-40. The implementation in HuggingFace Transformers is based on the original JAX/Haiku implementation which can be found here. output_shape = [1, 16, 224, 224] This experiment shows the challenges involved in using CNNs and ViT with certain modalities or cross-modalities. **kwargs can then, similar to BERT, convert the last hidden states of the latents to classification logits by averaging along hidden_act = 'gelu' is_split_into_words: bool = False The values of Fourier transform at these frequencies gives us the embeddings which are then concatenated with the inputs. overflowing_tokens List of overflowing tokens sequences (when a max_length is specified and input to the forward pass of PerceiverForMultimodalAutoencoding. conv_after_patching: bool = False NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass For the class label modality, there's no need to subsample. creating logits of shape (batch_size, 2048, 262) (as Perceiver uses a vocabulary Current vision transformers use pixel grid structure or aggressive subsampling techniques to reduce the computation cost of the self-attention network. ( image_mean = None The authors use 512 latents for all image models, and set the dimensionality of the latents to 1024. sequences. The PerceiverForImageClassificationFourier forward method, overrides the __call__ special method. Language-Conditioned Manipulation Tasks: PERACT is a language-conditioned multi-task agent capable of imitating a wide range of 6-DoF manipulation tasks. Perceivers build on the Transformer, an architecture that uses an operation called "attention" to map inputs into outputs. attention_mask: typing.Optional[torch.Tensor] = None In an essence, Perceiver is composed of two types of layers: The Cross-Attention layer and the Latent Transformer layer. transformers.models.perceiver.modeling_perceiver.PerceiverModelOutput or tuple(torch.FloatTensor), transformers.models.perceiver.modeling_perceiver.PerceiverModelOutput or tuple(torch.FloatTensor). n is predetermined by us. The Perceiver consists of two main components: the cross attention and the latent transformer. special_tokens_mask List of 0s and 1s, with 1 specifying added special tokens and 0 specifying Baseline projection decoder (no cross-attention). Understanding different kinds of data and extracting patterns in it requires algorithms and models specific to the modality of the data. PIL images. [1] The images - actually a sequence of frames - of shape (batch_size, 16, 3, 224, 224) are turned into a tensor of shape (batch_size, 50176, 243) using, The audio has shape (batch_size, 30720, 1) and is turned into a tensor of shape (batch_size, 1920, 401) using, The class label of shape (batch_size, 700) is turned into a tensor of shape (batch_size, 1, 700) using. ) out_channels: int = 64 However, it is necessary to note that PointNet++ uses sophisticated data augmentation and feature engineering methods. position_encoding_type: str = 'fourier' return_overflowing_tokens=True). [2], DeepMind Perceiver and Perceiver IO | Paper Explained, Perceiver: General Perception with Iterative Attention (Google DeepMind Research Paper Explained), "Google's Supermodel: DeepMind Perceiver is a step on the road to an AI machine that could process anything and everything", https://en.wikipedia.org/w/index.php?title=Perceiver&oldid=1120031475, This page was last edited on 4 November 2022, at 18:35. Not long after that, AI researchers started to apply the idea of BERT to other domains. The shape of the label output query is (batch_size, 1, 1024). Currently, all of them are implemented in PyTorch. As highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the GLUE language benchmark despite removing input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation with no explicit mechanisms for multiscale correspondence. import torch from perceiver_pytorch import Perceiver model = Perceiver ( input_channels = 3, # number of channels for each token of the input input_axis = 2, # number of axis for input data (2 for images, 3 for video) num_freq_bands = 6, # number of freq bands, with original value (2 * K + 1) max_freq = 10., # maximum frequency, hyperparameter depending on how fine the data is depth = 6 . output_num_channels = 2 Based on the Transformer architecture, the Perceiver makes no assumptions on the modality of the input data and also solves the long-standing quadratic bottleneck problem. The Video Vision Transformer (ViViT) extracts non-overlapping, spatio-temporal Perceiver's performance is comparable to strong vision models like ResNet-50 on ImageNet, state-of-the-art performance on the AudioSet sound event classification benchmark, and strong performance on ModelNet-40 point cloud classification. Therefore, we make use of parameterized Fourier feature positional encodings in the input sequence. of latent variables, and only use the inputs for cross-attention. use_query_residual = True Perceiver IO is a general-purpose multi-modal architecture that can handle wide variety of inputs as well as outputs. In the original Perceiver paper, the authors showed that the architecture can be used to process 3D point clouds a common concern for self-driving cars equipped with Lidar sensors. use to perform cross-attention with the latents. Unlike frameworks that operate on 2D images, the voxelized . This model is a PyTorch torch.nn.Module sub-class. The idea is to utilize Cross-Attention Layers (we will see in a bit what they are) to compress the input data into latent space vectors that can be processed by Latent Transformer layers. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents . Since transformers cannot handle the 224x224 long sequence, the image had to be downsampled to 64x64 before testing. Discover special offers, top stories, upcoming events, and more. turn each byte ID into a corresponding vector), as well as adding absolute position embeddings. For each of the 2 frames, the authors extract a 3 x 3 patch around each pixel, leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels). The authors use 2048 latents for the optical flow model (yes, 2048! The complexity of the query(Q)-key(K)-value(V) self-attention softmax[(QKT)V] isO(M2), where M is the sequence length. If left unset or set to None, this will use the predefined model maximum length if a maximum length Real-World Data comes in several modalities such as audio, text, video and images. regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True). spatial_downsample: int = 4 position encoding kwargs are set equal to the out_channels. A cross-modal transformer-based model that performs well in several tasks. Light-weight wrapper of PerceiverBasicDecoder for logit output. : typing.Optional[typing.Tuple[torch.FloatTensor]] = None, : typing.Optional[torch.FloatTensor] = None, # Initializing a Perceiver deepmind/language-perceiver style configuration, # Initializing a model from the deepmind/language-perceiver style configuration, : typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None, : typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None, : typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False, : typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None, : typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None, : typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]], : typing.Mapping[str, typing.Callable[, typing.Tuple[torch.Tensor, typing.Optional[torch.Tensor], torch.Tensor]]], : typing.Union[typing.Mapping[str, float], NoneType] = None, : typing.Dict[str, transformers.models.perceiver.modeling_perceiver.PerceiverAbstractDecoder], : typing.Union[typing.Dict[str, transformers.models.perceiver.modeling_perceiver.PerceiverAbstractDecoder], NoneType] = None, : typing.Mapping[str, typing.Callable[, typing.Any]], : typing.Callable[, typing.Tuple[torch.Tensor, typing.Optional[torch.Tensor], torch.Tensor]] = None, : typing.Callable[, typing.Any] = None, : typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None, # EXAMPLE 1: using the Perceiver to classify texts, # - we define a TextPreprocessor, which can be used to embed tokens, # - we define a ClassificationDecoder, which can be used to decode the, # final hidden states of the latents to classification logits. 3. The shape of the output of this class depends on how one defines the output queries (also called decoder queries). ( After concatenation, the final decoder query has shape (batch_size, 6272 + 15 + 1, 1026) = (batch_size, 6288, 1026). This model is a PyTorch torch.nn.Module sub-class. In this paper we introduce the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to. But now, the latent variables will produce keys and values, and one provides a tensor of whatever shape we'd like - in this case we'll provide a tensor of shape (batch_size, 1, num_labels) which will act as queries (the authors refer to these as "decoder queries", because they are used in the decoder). Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None output_image_shape head_mask: typing.Optional[torch.Tensor] = None Hence, the latents have shape (batch_size, 784, 512). The positional encodings are generated by using the (x,y) positions of the 224x224 input crop. self_attention_widening_factor = 1 As shown in the paper, this model can achieve a top-1 accuracy of 72.7 on ImageNet. ( Outputs, transformers.models.perceiver.modeling_perceiver.PerceiverModelOutput, transformers.models.perceiver.modeling_perceiver.PerceiverMaskedLMOutput, transformers.models.perceiver.modeling_perceiver.PerceiverClassifierOutput, The quickest way to get started with the Perceiver is by checking the. [2], Perceiver uses cross-attention to produce linear complexity layers and to detach network depth from input size. Without the downsampling, the input sequence would have 32x256x256 i.e. The Per-ceiver encoder consists of an initial cross-attention . position_encoding_type: typing.Optional[str] = 'trainable' Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs. The Transformer, originally introduced by AI enthusiast with a flair for NLP. The computational complexity of Perceiver IO is Audio postprocessing for Perceiver. [2], It associates position and modality-specific features with every input element (e.g. Perceiver has been deployed in several domains like long-context auto-regressive generation, vision-language models for few-shot learning, image and audio classification, and optical flow prediction. Each of these are different instances of PerceiverModel, just with a different preprocessor and/or decoder (and optionally, a postprocessor as is the case for multimodal autoencoding). After cross-attention, one still has a tensor of shape (batch_size, 2048, 768). ( output_num_channels: int conv_after_patching_in_channels: int = 54 We conduct several experiments and compare the Perceiver to models like ResNet-50, ViT-B, and the stack of transformers across three different domains: vision, sound and audio, and point clouds. Perceiver IO matches a Transformer-based BERT baseline on the GLUE language benchmark without the need for input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation. This model uses a 2D conv+maxpool preprocessing network. Transformers are the rage nowadays, but how do they work? Here N is much smaller compared to M. This is especially helpful for data modalities with high bandwidth. config: PerceiverConfig In the sections below, we explain in detail - in terms of shapes of tensors - how the Perceiver actually pre and post processes modalities of any kind. **decoder_kwargs The query(Q) is derived from a latent array (NxD), instead of the byte input array(MxC). attention_mask: typing.Optional[torch.Tensor] = None In 2018, BERT The latent array is much smaller with a size of about 1024 for ImageNet. config: PerceiverConfig Note that the output after these 26 self-attention layers still has the same shape as what one initially provided as input to the encoder: (batch_size, 256, 1280). # in the Perceiver IO paper, the authors extract a 3 x 3 patch around each pixel, # leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels), # patches have shape (batch_size, num_frames, num_channels, height, width), # the authors train on resolutions of 368 x 496, # in the Perceiver IO paper, videos are auto-encoded in chunks, # each chunk subsamples different index dimensions of the image and audio modality decoder queries, Load pretrained instances with an AutoClass. applying a 2D convolutional + maxpool layer and adding fixed 2D Fourier position embeddings. Pythons tokenizer, this method will raise NotImplementedError. The Perceiver aims to solve this limitation by employing the self-attention mechanism on a set of latent variables, rather than on the inputs. # you can then do a forward pass as follows: # to train, one can train the model using standard cross-entropy: # EXAMPLE 2: using the Perceiver to classify images, # - we define an ImagePreprocessor, which can be used to embed images, "http://images.cocodataset.org/val2017/000000039769.jpg", "This is an incomplete sentence where some words are missing.". size of 262 byte IDs). But biological systems do not use disparate models to process data of diverse modalities. If parameters are shared across Transformer blocks and cross-attention layers, the Perceiver can essentially be seen as an RNN with a Transformer at its core. concatenated along the time dimension. Will turn the output of the Perceiver encoder which is of shape (batch_size, num_latents, d_latents) to a tensor of Perceiver combines a now-standard Transformer neural network with a trick called "inducing points," as a summary of the data, to reduce how much raw data from pixels or audio or video needs to. attention_mask: typing.Optional[torch.Tensor] = None , 'http://images.cocodataset.org/val2017/000000039769.jpg'. logits: FloatTensor = None The authors show that it's straightforward to make the Perceiver also work on optical flow, which is a decades-old problem in computer vision, with many broader applications. Perceiver can be applied to for example image-text classification . text_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None After this operation, the Perceiver encoder employs a (repeatable) block of self-attention layers to update the embeddings of the latents. However, as it is not possible to decode an entire video in a single forward pass, the authors instead auto-encode in chunks. config it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and the bytes) one provided, as these were only used during the cross-attention operation. The authors also used the Perceiver to replace the original Transformer in AlphaStar, the state-of-the-art reinforcement learning system for the complex game of StarCraft II. DeepMind's New Super Model: Perceiver IO is a Transformer that can Handle Any Dataset #mw https://bit.ly/3DE44wy #latest. ). ). [3], Perceiver is designed without modality-specific elements. Once fine-tuned however, the Performer quickly recovers accuracy in a small fraction of the original number of gradient steps. We found that using image coordinates instead of crop coordinates causes overfitting. **decoder_kwargs num_outputs: int conv1x1 or conv. PerceiverModel into classification logits. elements depending on the configuration (PerceiverConfig) and inputs. However, autoencoding an entire video in a single forward pass is v_channels: typing.Optional[int] = None Base class for Perceiver decoder outputs, with potential cross-attentions. As one can see, one just provides a dummy sequence length dimension of 1. behavior. The audio was sampled at 48 kHz with 61,400 inputs over 1.28s of video. In order to ensure that the model does not miss necessary details and is not stuck with redundant signals, the Perceiver uses multiple byte-attend layers that iteratively extract information from the input sequence before each cross-attention layer, and hold it in the latent array as required. crop_size = 256 We also tested the Perceiver on the AudioSet dataset: a large dataset with 10 second long 1.7M training videos and 527 classes. ( hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Audio preprocessing for Perceiver Encoder. cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Light-weight wrapper of [PerceiverBasicDecoder] with video Suppose one has a video of 16 frames of resolution 224x224 and 30,720 audio samples, then the modalities are preprocessed as follows: Next, PerceiverMultimodalPreprocessor will pad the preprocessed modalities with modality-specific trainable embeddings to make concatenation along the time dimension possible. In this work, the authors introduce the Perceiver architecture, able to leverage arbitrary modalities by iteratively projecting each one to a latent space using attention mechanisms and transformers. Perceiver is able to receive and classify input multiple data types, namely point cloud, audio and images. Hence, the latents have shape (batch_size, 2048, 512). One can use PerceiverTokenizer to turn a text into a sequence of byte IDs, padded up to a length of 2048: In this case, one provides PerceiverTextPreprocessor as preprocessor to the model, which will take care of embedding the inputs (i.e. the last hidden states of the latents) into something more useful, such as classification logits or optical flow. Top 5 predicted labels for the video above. ( In the ImageNet experiments, parameter-sharing reduces the number of parameters by 10 times. text_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None In this case, the modality with the highest channel dimension is the class label (it has 700 channels). although slow. **kwargs For the videos, a 2x4x4 shaped bounding box was used to downsample the 32 frame clip at 256x256 pixels giving a total of 65,536 inputs. num_channels: typing.Optional[int] = 128 output_attentions: typing.Optional[bool] = None In this blog post, we went over the architecture of Perceiver IO, an extension of the Perceiver by Google Deepmind, and showed its generality of handling all kinds of modalities. As shown in the paper, this model can achieve a top-1 accuracy Stay up to date with our latest news, receive exclusive deals, and more. input_ids: typing.Optional[torch.Tensor] = None Can be used to project the channels of the decoder output to a lower ( PerceiverMultimodalDecoder also pads the decoder queries of the different If the model has no specific maximum input transformers.models.perceiver.modeling_perceiver.PerceiverClassifierOutput or tuple(torch.FloatTensor). Modality-specific queries are padded with trainable modality-specific parameters, after which they are Since the self-attention operation is permutation invariant, the Perceiver model lacks the ability to exploit spatial relationships in input data like CNNs can. and get access to the augmented documentation experience. The authors explain in their paper (page 8) why concatenation along the channel dimension makes sense. strong results on tasks with highly structured output spaces, such as natural language and visual understanding, One can use PerceiverFeatureExtractor for this, as follows: PerceiverImagePreprocessor (with the settings defined above) will first apply a convolutional layer with kernel size (1, 1) to turn the inputs into a tensor of shape (batch_size, 256, 224, 224) - hence increasing the channel dimension. Next, there's an optional decoder, which can be used to decode the final hidden states of the latents into something more useful, such as classification logits. input_preprocessor: typing.Callable[, typing.Tuple[torch.Tensor, typing.Optional[torch.Tensor], torch.Tensor]] = None prep_type = 'conv' processing (NLP), most famously the GLUE benchmark. this superclass for more information regarding those methods. (with prep_type="conv1x1") to preprocess the input images, and inputs: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None ( Vaswani et al. Unlike frameworks that operate. output_attentions: typing.Optional[bool] = None The PerceiverForMultimodalAutoencoding forward method, overrides the __call__ special method. attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Suppose that one provides a batch of images to the model. flattening the pixel values and adding fixed 2D Fourier position embeddings. image) has M elements (i.e. config Poll Campaigns Get Interesting with Deepfakes, Chatbots & AI Candidates, Decentralised, Distributed, Transparent: Blockchain to Disrupt Ad Industry, A Case for IT Professionals Switching Jobs Frequently, Council Post: Moving From A Contributor To An AI Leader, A Guide to Automated String Cleaning and Encoding in Python, Hands-On Guide to Building Knowledge Graph for Named Entity Recognition, Version 3 Of StyleGAN Released: Major Updates & Features, Why Did Alphabet Launch A Separate Company For Drug Discovery. The PerceiverForSequenceClassification forward method, overrides the __call__ special method. People can come up with new preprocessors, decoders and postprocessors to make the model solve different problems. As decoder, one provides PerceiverClassificationDecoder to the model (which will turn the last hidden states of the latents into classification logits). about 2 million inputs, the model would still work! Multimodal preprocessing for Perceiver Encoder. labels: typing.Optional[torch.Tensor] = None In this operation, the latent variables produce queries (Q), while the preprocessed inputs produce keys and values (KV). The PerceiverForMaskedLM forward method, overrides the __call__ special method. The output will have drawn some information from the byte array and this output is again used(after passing through a Latent Transformer block) to query the byte array again. However, one only subsamples the first 30720/128/16 = 15 values. A latent array is used to extract information from the input byte array using top-down or feedback processing i.e. The Vision Transformer (ViT) divides an image into a sequence of non-overlapping patches, which serve as "tokens". elements depending on the configuration (PerceiverConfig) and inputs. NOTE: if you are not familiar with HuggingFace and/or Transformers, I highly recommend to check out our free course, which introduces you to several Transformer architectures (such as BERT, GPT-2, T5, BART, etc. transformers.models.perceiver.modeling_perceiver. They process data from different modalities simultaneously. An overview of the architecture is depicted below. using the same encoding used for the input. This means, for example, is required by one of the truncation/padding parameters. ) performance on Sintel optical flow estimation. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. ). qk_channels = None preprocess the 3 modalities: images, audio and class labels. To turn these into classification logits, PerceiverClassificationDecoder is used, which works similarly to the one for text classification: it uses the latents as keys + values, and uses trainable position embeddings of shape (batch_size, 1, num_labels) as queries. To decode, one queries the latent representation As one auto-encodes an entire video in chunks, one needs to concatenate the reconstruction of each chunk to have a final reconstruction of an entire video. If one adds absolute position embeddings, one must make sure the num_channels of the modalities: typing.Dict[str, transformers.models.perceiver.modeling_perceiver.PerceiverAbstractDecoder] inputs: typing.Optional[torch.Tensor] = None As the memory and time requirements of the Perceiver's self-attention mechanism don't depend on the size of the inputs, one can directly provide raw UTF-8 bytes to the model. return_attention_mask=True or if attention_mask is in self.model_input_names). deepmind/language-perceiver architecture. out_channels = 64 inputs: typing.Optional[torch.Tensor] = None temporal_downsample: int = 1 configuration with the defaults will yield a similar configuration to that of the Perceiver The decoder then simply squeezes this tensor to have shape (batch_size, num_labels) and boom, one has classification logits1. In the Perceiver IO paper, a single block of 26 self-attention layers (each of which has 8 attention heads) were used to update the representations of the latents of the text model. Inspired by this biological perception approach Perceiver, a transformer-based model was introduced by Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals and Joao Carreira on 4th March 2021. However, it could only output classification logits. ) head_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None Also, it is to be noted that no masks are used in the attention layers. Hi there! PerAct encodes language goals and RGB-D voxel observations with a Perceiver Transformer, and outputs discretized actions by "detecting the next best voxel action". Ok, so now one has final hidden states of shape (batch_size, 256, 1280). One benefit is to eliminate the quadratic scaling problem found in early transformers. 07 Nov 2022 13:11:49 PerceiverForImageClassificationLearned uses PerceiverImagePreprocessor These feature-based positional encodings allow the model to learn how to exploit the positional dependencies. In a follow-up paper, called Perceiver IO, the authors extend this idea to let the Perceiver also handle arbitrary outputs. Therefore, we make use of parameterized Fourier feature positional encodings in the input sequence. This tensor produces queries in the cross-attention operation, while the latents act as keys and values. However, given the increasing sizes of multi-modal datasets, it is wise to question whether such biases limit the capabilities of the models. it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and Lets see how we can train a Perceiver model and run inference on it.. A PyTorch implementation of the model was made available by authors. patches), where M is large. This makes it possible to build very deep networks even when using large inputs like images or videos. As Karpathy puts it, it may well be that this architecture can unify all modalities into a shared space, with a library of encoders/decoders. The inputs contain the byte IDs (similar to the input_ids of BERT) for a single piece of text. # mask bytes corresponding to " missing.". Image preprocessing for Perceiver Encoder. of channels. Although the recipe for forward pass needs to be defined within this function, one should call the Module Take a look at the following Spaces to view some examples: predicting optical flow between images classifying images. ModelNet40 is a dataset of point clouds derived from 3D triangular meshes and given the coordinates of 2000 points in 3D space, the model makes predictions from 40 man-made categories. Given two images of the same scene (e.g. In other words, the output of the cross-attention operation is of shape (batch_size, 256, 1280). Speech-to-Text Perceiver The Speech-to-Text Perceiver (Fig.1) employs a Perceiver encoder [9] coupled with a Transformer decoder [3]. ). Lets use the CIFAR10 image dataset for training the model. This preprocessor uses modality-specific preprocessors to PROPOSED METHODOLOGY 3.1. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. The Perceiver authors also show that it is straightforward to pre-train the Perceiver for masked language modeling, similar to BERT. Given a voxelized reconstruction of a scene, we use a Perceiver Transformer [ 1] to learn per-voxel features. Well, let's take a look in detail at how Perceiver IO works. 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