FlaxBloomForCausalLM

r, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].   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.   Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of
torch.Fl


FlaxBloomForCausalLM

oatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BloomConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + 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 initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (bat


FlaxBloomForCausalLM

ch_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 FlaxBloomPreTrainedModel 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.  Example:   Copied >>> from transformers import AutoTokenizer, FlaxBloomForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
>>> model = FlaxBloomForCausalLM.from_pretrained("bigscience/bloom")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
>>> outputs = model(**inputs)
>>> # retrieve logts for next token
>>> next_token_logits = outputs.logits[:


Overview

The DETR model was proposed in End-to-End Object Detection with Transformers by
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
The abstract from the paper is the following:
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
procedure or anchor generation that explicitly


Overview

 encode our prior knowledge about the task. The main ingredients of the
new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
DETR reasons about the relations of the objects and the global image context to directly output the final set of
predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
baselines.
This model was contributed by nielsr. The original code can be found here.
Here’


Overview

s a TLDR explaining how DetrForObjectDetection works:
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
ResNet-50/ResNet-101). Let’s assume we also add a batch dimension. This means that the input to the backbone is a
tensor of shape (batch_size, 3, height, width), assuming the image has 3 color channels (RGB). The CNN backbone
outputs a new lower-resolution feature map, typically of shape (batch_size, 2048, height/32, width/32). This is
then projected to match the hidden dimension of the Transformer of DETR, which is 256 by default, using a
nn.Conv2D layer. So now, we have a tensor of shape (batch_size, 256, height/32, width/32). Next, the
feature map is flattened and transposed to obtain a tensor of shape (batch_size, seq_len, d_model) =
(batch_size, width/32*height/32, 256). So a difference with NLP models is that the sequence length is actually
longer than usual, but with a smaller d_model (which in NLP is typic


Overview

ally 768 or higher).
Next, this is sent through the encoder, outputting encoder_hidden_states of the same shape (you can consider
these as image features). Next, so-called object queries are sent through the decoder. This is a tensor of shape
(batch_size, num_queries, d_model), with num_queries typically set to 100 and initialized with zeros.
These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
to output decoder_hidden_states of the same shape: (batch_size, num_queries, d_model). Next, two heads
are added on top for object detection: a linear layer for classifying each object query into one of the objects or “no
object”, and a MLP to predict bounding boxes for each que


Overview

ry.
The model is trained using a bipartite matching loss: so what we actually do is compare the predicted classes +
bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
(so if an image only contains 4 objects, 96 annotations will just have a “no object” as class and “no bounding box” as
bounding box). The Hungarian matching algorithm is used to find
an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
the classes) and a linear combination of the L1 and generalized IoU loss (for the
bounding boxes) are used to optimize the parameters of the model.
DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
segmentation). DetrForSegmentation adds a segmentation mask head on top of
DetrForObjectDetection. The mask head can be trained either jointly, or in a two steps process,
where one f


Overview

irst trains a DetrForObjectDetection model to detect bounding boxes around both
“things” (instances) and “stuff” (background things like trees, roads, sky), then freeze all the weights and train only
the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
Tips:
DETR uses so-called object queries to detect objects in an image. The number of queries determines the maximum
number of objects that can be detected in a single image, and is set to 100 by default (see parameter
num_queries of DetrConfig). Note that it’s good to have some slack (in COCO, the
authors used 100, while the maximum number of objects in a COCO image is ~70). The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
which use autoregressive decoding instead of parallel.


Overview

Hence, no causal attention mask is used. DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
absolute position embeddings. By default, the parameter position_embedding_type of
DetrConfig is set to "sine". During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
the model output the correct number of objects of each class. If you set the parameter auxiliary_loss of
DetrConfig to True, then prediction feedforward neural networks and Hungarian losses
are added after each decoder layer (with the FFNs sharing parameters). If you want to train the model in a distributed environment across multiple nodes, then one should update the
num_boxes variable in the DetrLoss class of modeling_detr.py. When training on multiple nodes, this should


Overview

be
set to the average number of target boxes across all nodes, as can be seen in the original implementation here. DetrForObjectDetection and DetrForSegmentation can be initialized with
any convolutional backbone available in the timm library.
Initializing with a MobileNet backbone for example can be done by setting the backbone attribute of
DetrConfig to "tf_mobilenetv3_small_075", and then initializing the model with that
config. DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
DetrImageProcessor to prepare images (and optional annotations in COCO format) for the
model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
larges


Overview

t size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
Alternatively, one can also define a custom collate_fn in order to batch images together, using
~transformers.DetrImageProcessor.pad_and_create_pixel_mask. The size of the images will determine the amount of memory being used, and will thus determine the batch_size.
It is advised to use a batch size of 2 per GPU. See this Github thread for more info.
There are three ways to instantiate a DETR model (depending on what you prefer):
Option 1: Instantiate DETR with pre-trained weights for entire model
  Copied >>> from transformers import DetrForObjectDetection
>>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
Option 2: Instantiate DETR with randomly initialized weights for Transformer, but pre-trained weights for backbone
  Copied >>> from transformers import DetrConfig, DetrForObjectDetection
>>> config = DetrConfig()
>>> model = DetrFor


Overview

ObjectDetection(config)
Option 3: Instantiate DETR with randomly initialized weights for backbone + Transformer
  Copied >>> config = DetrConfig(use_pretrained_backbone=False)
>>> model = DetrForObjectDetection(config)
As a summary, consider the following table:
| Task                                                                  | Object detection                                                                            | Instance segmentation                                                                                | Panoptic segmentation                                                                                |
|-----------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------


Overview

-------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
| Description                                                           | Predicting bounding boxes and class labels around objects in an image                       | Predicting masks around objects (i.e. instances) in an image                                                                                | Predicting masks around both objects (i.e. instances) as well as “stuff” (i.e. background things like trees and roads) in an image             |
| Model                                                                 | DetrForObjectDetection                                                                      | DetrForSegmentation


Overview

                                                                                | DetrForSegmentation                                                                                |
| Example dataset                                                       | COCO detection                                                                              | COCO detection, COCO panoptic                                                                                | COCO panoptic                                                                                |
| Format of annotations to provide to  DetrImageProcessor               | {‘image_id’:  int  , ‘annotations’:  List[Dict]  } each Dict being a COCO object annotation | {‘image_id’:  in


Overview

t  , ‘annotations’:  List[Dict]  } (in case of COCO detection) or {‘file_name’:  str  , ‘image_id’:  int  , ‘segments_info’:  List[Dict]  } (in case of COCO panoptic) | {‘file_name’:  str  , ‘image_id’:  int  , ‘segments_info’:  List[Dict]  } and masks_path (path to directory containing PNG files of the masks) |
| Postprocessing  (i.e. converting the output of the model to COCO API) | post_process()                                                                              | post_process_segmentation()                                                                                | post_process_segmentation()  ,  post_process_panoptic()                                                                                |
| evaluators                                                            | CocoEvaluator  with  iou_types="bbox"                                                       |


Overview

 CocoEvaluator  with  iou_types="bbox"  or  "segm"                                                                                | CocoEvaluator  with  iou_tupes="bbox"  or  "segm"  ,  PanopticEvaluator                                                                        |
In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
DetrImageProcessor to create pixel_values, pixel_mask and optional
labels, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
outputs of the model using one of the postprocessing methods of DetrImageProcessor. These can
be be provided to either CocoEvaluator or PanopticEvaluator, which allow you to calculate metrics like
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the original repository. See the example notebooks for more info regarding evaluatio


Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETR.
 Object Detection
All example notebooks illustrating fine-tuning DetrForObjectDetection and DetrForSegmentation on a custom dataset an be found here. See also: Object detection task guide
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.


DETR specific outputs

class transformers.models.detr.modeling_detr.DetrModelOutput  < source > ( last_hidden_state: FloatTensor = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None intermediate_hidden_states: typing.Optional[torch.FloatTensor] = None  )   Parameters   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 decoder of the model.   decoder_hidden


DETR specific outputs

_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.   decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.   cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch


DETR specific outputs

.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.   encoder_attentions (tuple(torch.FloatTensor), optional, returned when output


DETR specific outputs

_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.   intermediate_hidden_states (torch.FloatTensor of shape (config.decoder_layers, batch_size, sequence_length, hidden_size), optional, returned when config.auxiliary_loss=True) —
Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.    Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through a layernorm. This is useful when training the model with auxiliary decoding losses.
 class


DETR specific outputs

transformers.models.detr.modeling_detr.DetrObjectDetectionOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None loss_dict: typing.Optional[typing.Dict] = None logits: FloatTensor = None pred_boxes: FloatTensor = None auxiliary_outputs: typing.Optional[typing.List[typing.Dict]] = None last_hidden_state: typing.Optional[torch.FloatTensor] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels are provided)) —
Total loss as a l


DETR specific outputs

inear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.   loss_dict (Dict, optional) —
A dictionary containing the individual losses. Useful for logging.   logits (torch.FloatTensor of shape (batch_size, num_queries, num_classes + 1)) —
Classification logits (including no-object) for all queries.   pred_boxes (torch.FloatTensor of shape (batch_size, num_queries, 4)) —
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use post_process_object_detection() to retrieve the
unnormalized bounding boxes.   auxiliary_outputs (list[Dict], optional) —
Optional, only returned when auxilary losses


DETR specific outputs

 are activated (i.e. config.auxiliary_loss is set to True)
and labels are provided. It is a list of dictionaries containing the two above keys (logits and
pred_boxes) for each decoder layer.   last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the decoder of the model.   decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.   decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple


DETR specific outputs

of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.   cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_


DETR specific outputs

hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.   encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.    Output type of DetrForObjectDetection.
 class transformers.models.detr.modeling_detr.DetrSegmentationOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None loss_dict: typing.Optional[typing.Dict] = None logi


DETR specific outputs

ts: FloatTensor = None pred_boxes: FloatTensor = None pred_masks: FloatTensor = None auxiliary_outputs: typing.Optional[typing.List[typing.Dict]] = None last_hidden_state: typing.Optional[torch.FloatTensor] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels are provided)) —
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combin


DETR specific outputs

ation of the L1 loss and the generalized
scale-invariant IoU loss.   loss_dict (Dict, optional) —
A dictionary containing the individual losses. Useful for logging.   logits (torch.FloatTensor of shape (batch_size, num_queries, num_classes + 1)) —
Classification logits (including no-object) for all queries.   pred_boxes (torch.FloatTensor of shape (batch_size, num_queries, 4)) —
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use post_process_object_detection() to retrieve the
unnormalized bounding boxes.   pred_masks (torch.FloatTensor of shape (batch_size, num_queries, height/4, width/4)) —
Segmentation masks logits for all queries. See also
post_process_semantic_segmentation() or
post_process_instance_segmentation()
post_process_panop


DETR specific outputs

tic_segmentation() to evaluate semantic, instance and panoptic
segmentation masks respectively.   auxiliary_outputs (list[Dict], optional) —
Optional, only returned when auxiliary losses are activated (i.e. config.auxiliary_loss is set to True)
and labels are provided. It is a list of dictionaries containing the two above keys (logits and
pred_boxes) for each decoder layer.   last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the decoder of the model.   decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer pl


DETR specific outputs

us the initial embedding outputs.   decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.   cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_si


DETR specific outputs

ze), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.   encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.    Output type of DetrForSegmentation.


DetrConfig

class transformers.DetrConfig  < source > ( use_timm_backbone = True backbone_config = None num_channels = 3 num_queries = 100 encoder_layers = 6 encoder_ffn_dim = 2048 encoder_attention_heads = 8 decoder_layers = 6 decoder_ffn_dim = 2048 decoder_attention_heads = 8 encoder_layerdrop = 0.0 decoder_layerdrop = 0.0 is_encoder_decoder = True activation_function = 'relu' d_model = 256 dropout = 0.1 attention_dropout = 0.0 activation_dropout = 0.0 init_std = 0.02 init_xavier_std = 1.0 auxiliary_loss = False position_embedding_type = 'sine' backbone = 'resnet50' use_pretrained_backbone = True dilation = False class_cost = 1 bbox_cost = 5 giou_cost = 2 mask_loss_coefficient = 1 dice_loss_coefficient = 1 bbox_loss_coefficient = 5 giou_loss_coefficient = 2 eos_coefficient = 0.1 **kwargs  )   Parameters   use_timm_backbone (bool, optional, defaults to True) —
Whether or not to use the timm library for the backbone. If set to False, will use


DetrConfig

the AutoBackbone
API.   backbone_config (PretrainedConfig or dict, optional) —
The configuration of the backbone model. Only used in case use_timm_backbone is set to False in which
case it will default to ResNetConfig().   num_channels (int, optional, defaults to 3) —
The number of input channels.   num_queries (int, optional, defaults to 100) —
Number of object queries, i.e. detection slots. This is the maximal number of objects DetrModel can
detect in a single image. For COCO, we recommend 100 queries.   d_model (int, optional, defaults to 256) —
Dimension of the layers.   encoder_layers (int, optional, defaults to 6) —
Number of encoder layers.   decoder_layers (int, optional, defaults to 6) —
Number of decoder layers.   encoder_attention_heads (int, optional, defaults to 8) —
Number of attention heads for each attention layer in the Transformer encoder.   decoder_attention_heads (int, optional, defaults to 8) —
Number of attent


DetrConfig

ion heads for each attention layer in the Transformer decoder.   decoder_ffn_dim (int, optional, defaults to 2048) —
Dimension of the “intermediate” (often named feed-forward) layer in decoder.   encoder_ffn_dim (int, optional, defaults to 2048) —
Dimension of the “intermediate” (often named feed-forward) layer in decoder.   activation_function (str or function, optional, defaults to "relu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.   dropout (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.   activation_dropout (float, optional, defaults to 0.0) —
The dropout ratio for activations inside the fully connected layer.   init_std (float, option


DetrConfig

al, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   init_xavier_std (float, optional, defaults to 1) —
The scaling factor used for the Xavier initialization gain in the HM Attention map module.   encoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.   decoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.   auxiliary_loss (bool, optional, defaults to False) —
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.   position_embedding_type (str, optional, defaults to "sine") —
Type of position embeddings to be used on top of the image features. One of "sine" or "learned".   backbone (str, opti


DetrConfig

onal, defaults to "resnet50") —
Name of convolutional backbone to use in case use_timm_backbone = True. Supports any convolutional
backbone from the timm package. For a list of all available models, see this
page.   use_pretrained_backbone (bool, optional, defaults to True) —
Whether to use pretrained weights for the backbone. Only supported when use_timm_backbone = True.   dilation (bool, optional, defaults to False) —
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
use_timm_backbone = True.   class_cost (float, optional, defaults to 1) —
Relative weight of the classification error in the Hungarian matching cost.   bbox_cost (float, optional, defaults to 5) —
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.   giou_cost (float, optional, defaults to 2) —
Relative weight of the generalized IoU loss of the bounding box in the Hungarian m


DetrConfig

atching cost.   mask_loss_coefficient (float, optional, defaults to 1) —
Relative weight of the Focal loss in the panoptic segmentation loss.   dice_loss_coefficient (float, optional, defaults to 1) —
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.   bbox_loss_coefficient (float, optional, defaults to 5) —
Relative weight of the L1 bounding box loss in the object detection loss.   giou_loss_coefficient (float, optional, defaults to 2) —
Relative weight of the generalized IoU loss in the object detection loss.   eos_coefficient (float, optional, defaults to 0.1) —
Relative classification weight of the ‘no-object’ class in the object detection loss.    This is the configuration class to store the configuration of a DetrModel. It is used to instantiate a DETR
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur


DetrConfig

ation to that of the DETR
facebook/detr-resnet-50 architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Examples:   Copied >>> from transformers import DetrConfig, DetrModel
>>> # Initializing a DETR facebook/detr-resnet-50 style configuration
>>> configuration = DetrConfig()
>>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
>>> model = DetrModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config  from_backbone_config  < source > ( backbone_config: PretrainedConfig **kwargs  ) → DetrConfig  Parameters   backbone_config (PretrainedConfig) —
The backbone configuration.   Returns
DetrConfig

An instance of a configuration object
  Instantiate a DetrConfig (or a derived class) from a pre-trained backbone model configurati


DetrImageProcessor

class transformers.DetrImageProcessor  < source > ( format: typing.Union[str, transformers.models.detr.image_processing_detr.AnnotionFormat] = <AnnotionFormat.COCO_DETECTION: 'coco_detection'> do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BILINEAR: 2> do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float]] = None image_std: typing.Union[float, typing.List[float]] = None do_pad: bool = True **kwargs  )   Parameters   format (str, optional, defaults to "coco_detection") —
Data format of the annotations. One of “coco_detection” or “coco_panoptic”.   do_resize (bool, optional, defaults to True) —
Controls 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]


DetrImageProcessor

 optional, defaults to {"shortest_edge" -- 800, "longest_edge": 1333}):
Size of the image’s (height, width) dimensions after resizing. Can be overridden by the size parameter
in the preprocess method.   resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) —
Resampling filter to use if resizing the image.   do_rescale (bool, optional, defaults to True) —
Controls 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) —
Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in the
preprocess method.
do_normalize —
Controls whether to normalize the image. Can be overridden by the do_normalize parameter in the
preprocess method.   image_mean (float or List[float], optional, defaults to IMAGENET_DEFAULT_MEAN) —
Mean valu


DetrImageProcessor

es to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the image_mean parameter in the preprocess method.   image_std (float or List[float], optional, defaults to IMAGENET_DEFAULT_STD) —
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the image_std parameter in the preprocess method.   do_pad (bool, optional, defaults to True) —
Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
overridden by the do_pad parameter in the preprocess method.    Constructs a Detr image processor.  preprocess  < source > ( images: 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')]] annotati


DetrImageProcessor

ons: typing.Union[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]], typing.List[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]]], NoneType] = None return_segmentation_masks: bool = None masks_path: typing.Union[str, pathlib.Path, NoneType] = None do_resize: typing.Optional[bool] = None size: typing.Union[typing.Dict[str, int], NoneType] = None resample = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Union[int, float, NoneType] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_pad: typing.Optional[bool] = None format: typing.Union[str, transformers.models.detr.image_processing_detr.AnnotionFormat, NoneType] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Union[s


DetrImageProcessor

tr, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs  )   Parameters   images (ImageInput) —
Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.   annotations (AnnotationType or List[AnnotationType], optional) —
List of annotations associated with the image or batch of images. If annotation is for object
detection, the annotations should be a dictionary with the following keys:
“image_id” (int): The image id.
“annotations” (List[Dict]): List of annotations for an image. Each annotation should be a
dictionary. An image can have no annotations, in which case the list should be empty.
If annotation is for segmentation, the annotations should be


DetrImageProcessor

a dictionary with the following keys:
“image_id” (int): The image id.
“segments_info” (List[Dict]): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.
“file_name” (str): The file name of the image.
   return_segmentation_masks (bool, optional, defaults to self.return_segmentation_masks) —
Whether to return segmentation masks.   masks_path (str or pathlib.Path, optional) —
Path to the directory containing the segmentation masks.   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 resizing.   resample (PILImageResampling, optional, defaults to self.resample) —
Resampling filter to use when resizing the image.   do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image.   rescale_factor (float,


DetrImageProcessor

optional, defaults to self.rescale_factor) —
Rescale factor to use when rescaling the image.   do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.   image_mean (float or List[float], optional, defaults to self.image_mean) —
Mean to use when normalizing the image.   image_std (float or List[float], optional, defaults to self.image_std) —
Standard deviation to use when normalizing the image.   do_pad (bool, optional, defaults to self.do_pad) —
Whether to pad the image.   format (str or AnnotionFormat, optional, defaults to self.format) —
Format of the annotations.   return_tensors (str or TensorType, optional, defaults to self.return_tensors) —
Type of tensors to return. If None, will return the list of images.   data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
"channels_first" or Ch


DetrImageProcessor

annelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
Unset: Use the 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 a batch of images so that it can be used by the model.  post_process_object_detection  < source > ( outputs threshold: float = 0.5 target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None  ) → List[Dict]  Pa


DetrImageProcessor

rameters   outputs (DetrObjectDetectionOutput) —
Raw outputs of the model.   threshold (float, optional) —
Score threshold to keep object detection predictions.   target_sizes (torch.Tensor or List[Tuple[int, int]], optional) —
Tensor of shape (batch_size, 2) or list of tuples (Tuple[int, int]) containing the target size
(height, width) of each image in the batch. If unset, predictions will not be resized.   Returns
List[Dict]

A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
  Converts the raw output of DetrForObjectDetection into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.  post_process_semantic_segmentation  < source > ( outputs target_sizes: typing.List[typing.Tuple[int, int]] = None  ) → List[torch.Tensor]  Parameters   outputs (DetrForSegmentation) —
Raw outputs of t


DetrImageProcessor

he model.   target_sizes (List[Tuple[int, int]], optional) —
A list of tuples (Tuple[int, int]) containing the target size (height, width) of each image in the
batch. If unset, predictions will not be resized.   Returns
List[torch.Tensor]

A list of length batch_size, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if target_sizes is specified). Each entry of each
torch.Tensor correspond to a semantic class id.
  Converts the output of DetrForSegmentation into semantic segmentation maps. Only supports PyTorch.  post_process_instance_segmentation  < source > ( outputs threshold: float = 0.5 mask_threshold: float = 0.5 overlap_mask_area_threshold: float = 0.8 target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None return_coco_annotation: typing.Optional[bool] = False  ) → List[Dict]  Parameters   outputs (DetrForSegmentation) —
Raw out


DetrImageProcessor

puts of the model.   threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.   mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.   overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.   target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction. If unset, predictions will not be resized.   return_coco_annotation (bool, optional) —
Defaults to False. If set to True, segmentation maps are returned in COCO run-length encoding (RLE)
format.   Returns
List[Dict]

A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — A ten


DetrImageProcessor

sor of shape (height, width) where each pixel represents a segment_id or
List[List] run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
True. Set to None if no mask if found above threshold.
segments_info — A dictionary that contains additional information on each segment.
id — An integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
score — Prediction score of segment with segment_id.
  Converts the output of DetrForSegmentation into instance segmentation predictions. Only supports PyTorch.  post_process_panoptic_segmentation  < source > ( outputs threshold: float = 0.5 mask_threshold: float = 0.5 overlap_mask_area_threshold: float = 0.8 label_ids_to_fuse: typing.Optional[typing.Set[int]] = None target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None  ) → List[Dict]  Parameters   outputs (D


DetrImageProcessor

etrForSegmentation) —
The outputs from DetrForSegmentation.   threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.   mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.   overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.   label_ids_to_fuse (Set[int], optional) —
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.   target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction


DetrImageProcessor

 in batch. If unset, predictions will not be resized.   Returns
List[Dict]

A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — a tensor of shape (height, width) where each pixel represents a segment_id or
None if no mask if found above threshold. If target_sizes is specified, segmentation is resized to
the corresponding target_sizes entry.
segments_info — A dictionary that contains additional information on each segment.
id — an integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
was_fused — a boolean, True if label_id was in label_ids_to_fuse, False otherwise.
Multiple instances of the same class / label were fused and assigned a single segment_id.
score — Prediction score of segment with segment_id.
  Converts the output of DetrForSegmentation into image panoptic segmentation predictions. Only supports


DetrFeatureExtractor

class transformers.DetrFeatureExtractor  < source > ( *args **kwargs  )     __call__  < source > ( images **kwargs  )    Preprocess an image or a batch of images.  post_process_object_detection  < source > ( outputs threshold: float = 0.5 target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None  ) → List[Dict]  Parameters   outputs (DetrObjectDetectionOutput) —
Raw outputs of the model.   threshold (float, optional) —
Score threshold to keep object detection predictions.   target_sizes (torch.Tensor or List[Tuple[int, int]], optional) —
Tensor of shape (batch_size, 2) or list of tuples (Tuple[int, int]) containing the target size
(height, width) of each image in the batch. If unset, predictions will not be resized.   Returns
List[Dict]

A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.
  Converts the raw output of DetrForObjectDetection into final bounding b


DetrFeatureExtractor

oxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.  post_process_semantic_segmentation  < source > ( outputs target_sizes: typing.List[typing.Tuple[int, int]] = None  ) → List[torch.Tensor]  Parameters   outputs (DetrForSegmentation) —
Raw outputs of the model.   target_sizes (List[Tuple[int, int]], optional) —
A list of tuples (Tuple[int, int]) containing the target size (height, width) of each image in the
batch. If unset, predictions will not be resized.   Returns
List[torch.Tensor]

A list of length batch_size, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if target_sizes is specified). Each entry of each
torch.Tensor correspond to a semantic class id.
  Converts the output of DetrForSegmentation into semantic segmentation maps. Only supports PyTorch.  post_process_instance_segmentation  < source > ( outputs threshold: float = 0.5 mask_threshold: float = 0.5 overlap_m


DetrFeatureExtractor

ask_area_threshold: float = 0.8 target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None return_coco_annotation: typing.Optional[bool] = False  ) → List[Dict]  Parameters   outputs (DetrForSegmentation) —
Raw outputs of the model.   threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.   mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.   overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.   target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction. If unset, predictions will not be resized.   return_coco_annotation (bool, optional) —
Defaults to False. If set to True, segmentation maps a


DetrFeatureExtractor

re returned in COCO run-length encoding (RLE)
format.   Returns
List[Dict]

A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — A tensor of shape (height, width) where each pixel represents a segment_id or
List[List] run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
True. Set to None if no mask if found above threshold.
segments_info — A dictionary that contains additional information on each segment.
id — An integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
score — Prediction score of segment with segment_id.
  Converts the output of DetrForSegmentation into instance segmentation predictions. Only supports PyTorch.  post_process_panoptic_segmentation  < source > ( outputs threshold: float = 0.5 mask_threshold: float = 0.5 overlap_mask_area_threshold: float = 0.8 label_ids_to_fuse: typing.Optional[typing.Set[int]] = None target_


DetrFeatureExtractor

sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None  ) → List[Dict]  Parameters   outputs (DetrForSegmentation) —
The outputs from DetrForSegmentation.   threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.   mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.   overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.   label_ids_to_fuse (Set[int], optional) —
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.   target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the re


DetrFeatureExtractor

quested
final size (height, width) of each prediction in batch. If unset, predictions will not be resized.   Returns
List[Dict]

A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — a tensor of shape (height, width) where each pixel represents a segment_id or
None if no mask if found above threshold. If target_sizes is specified, segmentation is resized to
the corresponding target_sizes entry.
segments_info — A dictionary that contains additional information on each segment.
id — an integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
was_fused — a boolean, True if label_id was in label_ids_to_fuse, False otherwise.
Multiple instances of the same class / label were fused and assigned a single segment_id.
score — Prediction score of segment with segment_id.
  Converts the output of DetrForSegmentation into image panoptic segmentation predictions. Only supports
PyTorch.


DetrModel

class transformers.DetrModel  < source > ( config: DetrConfig  )   Parameters   config (DetrConfig) —
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 DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without
any specific head on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also 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 > ( pixel_values: FloatTensor pixe


DetrModel

l_mask: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.FloatTensor] = None encoder_outputs: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.detr.modeling_detr.DetrModelOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See DetrImageProcessor.call() for details.   pixel_mask (torch.LongTensor of shape (batch_size, height, width), optional) —
Mask to avoid performing attention on padding pixel values. Mask values


DetrModel

 selected in [0, 1]:
1 for pixels that are real (i.e. not masked),
0 for pixels that are padding (i.e. masked).
What are attention masks?   decoder_attention_mask (torch.FloatTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.   decoder_inputs_embeds (torch.Flo


DetrModel

atTensor of shape (batch_size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.   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.   Returns
transformers.models.detr.modeling_detr.DetrModelOutput or tuple(torch.FloatTensor)

A transformers.models.detr.modeling_detr.DetrModelOutput 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 (DetrConfig


DetrModel

) 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 decoder of the model.
decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted a


DetrModel

verage in the self-attention heads.
cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_s


DetrModel

ize). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
intermediate_hidden_states (torch.FloatTensor of shape (config.decoder_layers, batch_size, sequence_length, hidden_size), optional, returned when config.auxiliary_loss=True) — Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
  The DetrModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the


DetrModel

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:   Copied >>> from transformers import AutoImageProcessor, DetrModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
>>> model = DetrModel.from_pretrained("facebook/detr-resnet-50")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> # the last hidden states are the final query embeddings of the Transformer decoder
>>> # these are of shape (batch_size, num_queries, hidden_size)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 100


DetrForObjectDetection

class transformers.DetrForObjectDetection  < source > ( config: DetrConfig  )   Parameters   config (DetrConfig) —
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.    DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks
such as COCO detection. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also 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 > ( pixel_values: FloatTensor pixel_mask: typing.Option


DetrForObjectDetection

al[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.FloatTensor] = None encoder_outputs: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[typing.List[dict]] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.detr.modeling_detr.DetrObjectDetectionOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See DetrImageProcessor.call() for details.   pixel_mask (torch.LongTensor of shape (batch_size, height, width), optional) —
Mask to avoid performing attention on padding pixel values. Mask


DetrForObjectDetection

 values selected in [0, 1]:
1 for pixels that are real (i.e. not masked),
0 for pixels that are padding (i.e. masked).
What are attention masks?   decoder_attention_mask (torch.FloatTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_


DetrForObjectDetection

size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.   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.   labels (List[Dict] of len (batch_size,), optional) —
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: ‘class_labels’ and ‘boxes’ (the class labels and bounding boxes of an image in the batch
respectively). The class labels themselves should be a torch.LongTensor of len (number of bounding boxes in the


DetrForObjectDetection

image,) and the boxes a torch.FloatTensor of shape (number of bounding boxes in the image, 4).   Returns
transformers.models.detr.modeling_detr.DetrObjectDetectionOutput or tuple(torch.FloatTensor)

A transformers.models.detr.modeling_detr.DetrObjectDetectionOutput 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 (DetrConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels are provided)) — Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.
loss_dict (Dict, optional) — A dictionary containing the individual losses. Useful for logging.
logits (torch.FloatTensor of shape (batch_size, num_queries, num_classes + 1)) — Classification logits (including


DetrForObjectDetection

no-object) for all queries.
pred_boxes (torch.FloatTensor of shape (batch_size, num_queries, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use post_process_object_detection() to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (list[Dict], optional) — Optional, only returned when auxilary losses are activated (i.e. config.auxiliary_loss is set to True)
and labels are provided. It is a list of dictionaries containing the two above keys (logits and
pred_boxes) for each decoder layer.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=T


DetrForObjectDetection

rue is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_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 of the


DetrForObjectDetection

decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_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_le


DetrForObjectDetection

ngth). Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
  The DetrForObjectDetection 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:   Copied >>> from transformers import AutoImageProcessor, DetrForObjectDetection
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
>>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
>>> inputs = image_processor(images=image, return_tensors="p


DetrForObjectDetection

t")
>>> outputs = model(**inputs)
>>> # convert outputs (bounding boxes and class logits) to COCO API
>>> target_sizes = torch.tensor([image.size[::-1]])
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
...     0
... ]
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
...     box = [round(i, 2) for i in box.tolist()]
...     print(
...         f"Detected {model.config.id2label[label.item()]} with confidence "
...         f"{round(score.item(), 3)} at location {box}"
...     )
Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98]
Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66]
Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76]
Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93]
Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.7


DetrForSegmentation

class transformers.DetrForSegmentation  < source > ( config: DetrConfig  )   Parameters   config (DetrConfig) —
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.    DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, for tasks
such as COCO panoptic. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also 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  < sour


DetrForSegmentation

ce > ( pixel_values: FloatTensor pixel_mask: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.FloatTensor] = None encoder_outputs: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[typing.List[dict]] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.detr.modeling_detr.DetrSegmentationOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See DetrImageProcessor.call() for details.   pixel_mask (torch.LongT


DetrForSegmentation

ensor of shape (batch_size, height, width), optional) —
Mask to avoid performing attention on padding pixel values. Mask values selected in [0, 1]:
1 for pixels that are real (i.e. not masked),
0 for pixels that are padding (i.e. masked).
What are attention masks?   decoder_attention_mask (torch.FloatTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened fea


DetrForSegmentation

ture map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.   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.   labels (List[Dict] of len (batch_size,), optional) —
Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal


DetrForSegmentation

loss. List of dicts, each
dictionary containing at least the following 3 keys: ‘class_labels’, ‘boxes’ and ‘masks’ (the class labels,
bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves
should be a torch.LongTensor of len (number of bounding boxes in the image,), the boxes a
torch.FloatTensor of shape (number of bounding boxes in the image, 4) and the masks a
torch.FloatTensor of shape (number of bounding boxes in the image, height, width).   Returns
transformers.models.detr.modeling_detr.DetrSegmentationOutput or tuple(torch.FloatTensor)

A transformers.models.detr.modeling_detr.DetrSegmentationOutput 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 (DetrConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when la


DetrForSegmentation

bels are provided)) — Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.
loss_dict (Dict, optional) — A dictionary containing the individual losses. Useful for logging.
logits (torch.FloatTensor of shape (batch_size, num_queries, num_classes + 1)) — Classification logits (including no-object) for all queries.
pred_boxes (torch.FloatTensor of shape (batch_size, num_queries, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use post_process_object_detection() to retrieve the
unnormalized bounding boxes.
pred_masks (torch.FloatTensor of s


DetrForSegmentation

hape (batch_size, num_queries, height/4, width/4)) — Segmentation masks logits for all queries. See also
post_process_semantic_segmentation() or
post_process_instance_segmentation()
post_process_panoptic_segmentation() to evaluate semantic, instance and panoptic
segmentation masks respectively.
auxiliary_outputs (list[Dict], optional) — Optional, only returned when auxiliary losses are activated (i.e. config.auxiliary_loss is set to True)
and labels are provided. It is a list of dictionaries containing the two above keys (logits and
pred_boxes) for each decoder layer.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True)


DetrForSegmentation

 — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_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 weigh


DetrForSegmentation

ts of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one fo


DetrForSegmentation

r each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
  The DetrForSegmentation 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:   Copied >>> import io
>>> import requests
>>> from PIL import Image
>>> import torch
>>> import numpy
>>> from transformers import AutoImageProcessor, DetrForSegmentation
>>> from transformers.image_transforms import rgb_to_id
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>


DetrForSegmentation

>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
>>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
>>> # Segmentation results are returned as a list of dictionaries
>>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)])
>>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
>>> panoptic_seg = result[0]["segmentation"]
>>> # Get prediction score and segment_id to class_id mapping of each segment
>>> panoptic_segments_info = result[0]["segments


Overview

The MegatronGPT2 model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
approach does not require a new compiler or library chan


Overview

ges, is orthogonal and complimentary to pipeline model
parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
BERT-like models is critical to achieving


Overview

 increased performance as the model size grows. Using the GPT-2 model we
achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
of 89.4%).
Tips:
We have provided pretrained GPT2-345M checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first sign up for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentation for downloading models can be found in the NGC documentation.
Alternatively, you can directly download the checkpoints using:
  Copied wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O
megatron


Overview

_gpt2_345m_v0_0.zip
Once you have obtained the checkpoint from NVIDIA GPU Cloud (NGC), you have to convert it to a format that will easily
be loaded by Hugging Face Transformers GPT2 implementation.
The following command allows you to do the conversion. We assume that the folder models/megatron_gpt2 contains
megatron_gpt2_345m_v0_0.zip and that the command is run from that folder:
  Copied python3 $PATH_TO_TRANSFORMERS/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py megatron_gpt2_345m_v0_0.zip
This model was contributed by jdemouth. The original code can be found here. That repository contains a multi-GPU and multi-node implementation of the
Megatron Language models. In particular, it contains a hybrid model parallel approach using “tensor parallel” and
“pipeline parallel” technique


Overview

The Perceiver IO model was proposed in Perceiver IO: A General Architecture for Structured Inputs &
Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M.
Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
Perceiver IO is a generalization of Perceiver to handle arbitrary outputs in
addition to arbitrary inputs. The original Perceiver only produced a single classification label. In addition to
classification labels, Perceiver IO can produce (for example) language, optical flow, and multimodal videos with audio.
This is done using the same building blocks as the original Perceiver. The computational complexity of Perceiver IO is
linear in the input and output size and the bulk of the processing occurs in the latent space, allowing us to process
inputs and out


Overview

puts that are much larger than can be handled by standard Transformers. This means, for example,
Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs.
The abstract from the paper is the following:
The recently-proposed Perceiver model obtains good results on several domains (images, audio, multimodal, point
clouds) while scaling linearly in compute and memory with the input size. While the Perceiver supports many kinds of
inputs, it can only produce very simple outputs such as class scores. Perceiver IO overcomes this limitation without
sacrificing the original’s appealing properties by learning to flexibly query the model’s latent space to produce
outputs of arbitrary size and semantics. Perceiver IO still decouples model depth from data size and still scales
linearly with data size, but now with respect to both input and output sizes. The full Perceiver


Overview

 IO model achieves
strong results on tasks with highly structured output spaces, such as natural language and visual understanding,
StarCraft II, and multi-task and multi-modal domains. As highlights, 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.
Here’s a TLDR explaining how Perceiver works:
The main problem with the self-attention mechanism of the Transformer is that the time and memory requirements scale
quadratically with the sequence length. Hence, models like BERT and RoBERTa are limited to a max sequence length of 512
tokens. Perceiver aims to solve this issue by, instead of performing self-attention on the inputs, perform it on a set
of latent variables, and only use the inputs for cross-attention. In this way, the time and memory requirements d


Overview

on’t
depend on the length of the inputs anymore, as one uses a fixed amount of latent variables, like 256 or 512. These are
randomly initialized, after which they are trained end-to-end using backpropagation.
Internally, PerceiverModel will create the latents, which is a tensor of shape (batch_size, num_latents, d_latents). One must provide inputs (which could be text, images, audio, you name it!) to the model, which it will
use to perform cross-attention with the latents. The output of the Perceiver encoder is a tensor of the same shape. One
can then, similar to BERT, convert the last hidden states of the latents to classification logits by averaging along
the sequence dimension, and placing a linear layer on top of that to project the d_latents to num_labels.
This was the idea of the original Perceiver paper. However, it could only output classification logits. In a follow-up
work, PerceiverIO, they gene


Overview

ralized it to let the model also produce outputs of arbitrary size. How, you might ask? The
idea is actually relatively simple: one defines outputs of an arbitrary size, and then applies cross-attention with the
last hidden states of the latents, using the outputs as queries, and the latents as keys and values.
So let’s say one wants to perform masked language modeling (BERT-style) with the Perceiver. As the Perceiver’s input
length will not have an impact on the computation time of the self-attention layers, one can provide raw bytes,
providing inputs of length 2048 to the model. If one now masks out certain of these 2048 tokens, one can define the
outputs as being of shape: (batch_size, 2048, 768). Next, one performs cross-attention with the final hidden states
of the latents to update the outputs tensor. After cross-attention, one still has a tensor of shape (batch_size, 2048, 768). One can then place a


Overview

 regular language modeling head on top, to project the last dimension to the
vocabulary size of the model, i.e. creating logits of shape (batch_size, 2048, 262) (as Perceiver uses a vocabulary
size of 262 byte IDs).
Perceiver IO architecture. Taken from the original paper
This model was contributed by nielsr. The original code can be found
here.
Tips:
The quickest way to get started with the Perceiver is by checking the tutorial
notebooks. Refer to the blog post if you want to fully understand how the model works and
is implemented in the library. Note that the models available in the library only showcase some examples of what you can do
with the Perceiver. There are many more use cases, including question answering, named-entity recognition, object detection,
audio classification, video classification, etc.
Note:
Perceiver does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035


Perceiver specific outputs

class transformers.models.perceiver.modeling_perceiver.PerceiverModelOutput  < source > ( logits: FloatTensor = None last_hidden_state: FloatTensor = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   logits (torch.FloatTensor of shape (batch_size, num_labels)) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).   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.   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 + one for the output of each layer) of
shape (batch_


Perceiver specific outputs

size, sequence_length, hidden_size). Hidden-states of the model at the output of each layer
plus the 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.   cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.    Base class for Perceiver base model’s outputs


Perceiver specific outputs

, with potential hidden states, attentions and cross-attentions.
 class transformers.models.perceiver.modeling_perceiver.PerceiverDecoderOutput  < source > ( logits: FloatTensor = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   logits (torch.FloatTensor of shape (batch_size, num_labels)) —
Output of the basic decoder.   cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.    Base class for Perceiver decoder outputs, with potential cross-attentions.
 class transformers.models.perceiver.modeling_perceiver.PerceiverMaskedLMOutput  < source > ( los


Perceiver specific outputs

s: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Masked language modeling (MLM) loss.   logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the model at the output of e


Perceiver specific outputs

ach layer
plus the 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, num_latents, num_latents). Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.    Base class for Perceiver’s masked language model outputs.
 class transformers.models.perceiver.modeling_perceiver.PerceiverClassifierOu


Perceiver specific outputs

tput  < source > ( loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Classification (or regression if config.num_labels==1) loss.   logits (torch.FloatTensor of shape (batch_size, config.num_labels)) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).   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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the model at the o