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Deformable DETR

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Deformable DETR

Overview

The Deformable DETR model was proposed in Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai. Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original DETR by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.

The abstract from the paper is the following:

DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.

Tips:

One can use the AutoFeatureExtractor API to prepare images (and optional targets) for the model. This will instantiate a DetrFeatureExtractor behind the scenes.
Training Deformable DETR is equivalent to training the original DETR model. Demo notebooks can be found here.

Deformable DETR architecture. Taken from the original paper.

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

DeformableDetrFeatureExtractor

class transformers.DeformableDetrFeatureExtractor

< source >

( format = 'coco_detection' do_resize = True size = 800 max_size = 1333 do_normalize = True image_mean = None image_std = None **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) — Whether to resize the input to a certain size.
size (int, optional, defaults to 800) — Resize the input to the given size. Only has an effect if do_resize is set to True. If size is a sequence like (width, height), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
max_size (int, optional, defaults to 1333) — The largest size an image dimension can have (otherwise it’s capped). Only has an effect if do_resize is set to True.
do_normalize (bool, optional, defaults to True) — Whether or not to normalize the input with mean and standard deviation.
image_mean (int, optional, defaults to [0.485, 0.456, 0.406]) — The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
image_std (int, optional, defaults to [0.229, 0.224, 0.225]) — The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the ImageNet std.

Constructs a Deformable DETR feature extractor. Differs only in the postprocessing of object detection compared to DETR.

This feature extractor inherits from FeatureExtractionMixin which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

call

< source >

( images: typing.Union[PIL.Image.Image, numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] annotations: typing.Union[typing.List[typing.Dict], typing.List[typing.List[typing.Dict]]] = None return_segmentation_masks: typing.Optional[bool] = False masks_path: typing.Optional[pathlib.Path] = None pad_and_return_pixel_mask: typing.Optional[bool] = True return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs ) → BatchFeature

Parameters

images (PIL.Image.Image, np.ndarray, torch.Tensor, List[PIL.Image.Image], List[np.ndarray], List[torch.Tensor]) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width.
annotations (Dict, List[Dict], optional) — The corresponding annotations in COCO format.

In case DetrFeatureExtractor was initialized with format = "coco_detection", the annotations for each image should have the following format: {‘image_id’: int, ‘annotations’: [annotation]}, with the annotations being a list of COCO object annotations.

In case DetrFeatureExtractor was initialized with format = "coco_panoptic", the annotations for each image should have the following format: {‘image_id’: int, ‘file_name’: str, ‘segments_info’: [segment_info]} with segments_info being a list of COCO panoptic annotations.
return_segmentation_masks (Dict, List[Dict], optional, defaults to False) — Whether to also include instance segmentation masks as part of the labels in case format = "coco_detection".
masks_path (pathlib.Path, optional) — Path to the directory containing the PNG files that store the class-agnostic image segmentations. Only relevant in case DetrFeatureExtractor was initialized with format = "coco_panoptic".
pad_and_return_pixel_mask (bool, optional, defaults to True) — Whether or not to pad images up to the largest image in a batch and create a pixel mask.

If left to the default, will return a pixel mask that is:
- 1 for pixels that are real (i.e. not masked),
- 0 for pixels that are padding (i.e. masked).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of NumPy arrays. If set to 'pt', return PyTorch torch.Tensor objects.

Returns

BatchFeature

A BatchFeature with the following fields:

pixel_values — Pixel values to be fed to a model.
pixel_mask — Pixel mask to be fed to a model (when pad_and_return_pixel_mask=True or if “pixel_mask” is in self.model_input_names).
labels — Optional labels to be fed to a model (when annotations are provided)

Main method to prepare for the model one or several image(s) and optional annotations. Images are by default padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are real/which are padding.

NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass PIL images.

pad_and_create_pixel_mask

< source >

( pixel_values_list: typing.List[ForwardRef('torch.Tensor')] return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None ) → BatchFeature

Parameters

pixel_values_list (List[torch.Tensor]) — List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of NumPy arrays. If set to 'pt', return PyTorch torch.Tensor objects.

Returns

BatchFeature

A BatchFeature with the following fields:

pixel_values — Pixel values to be fed to a model.
pixel_mask — Pixel mask to be fed to a model (when pad_and_return_pixel_mask=True or if “pixel_mask” is in self.model_input_names).

Pad images up to the largest image in a batch and create a corresponding pixel_mask.

post_process

< source >

( outputs target_sizes ) → List[Dict]

Parameters

outputs (DeformableDetrObjectDetectionOutput) — Raw outputs of the model.
target_sizes (torch.Tensor of shape (batch_size, 2)) — Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the original image size (before any data augmentation). For visualization, this should be the image size after data augment, but before padding.

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 output of DeformableDetrForObjectDetection into the format expected by the COCO api. Only supports PyTorch.

post_process_segmentation

< source >

( outputs target_sizes threshold = 0.9 mask_threshold = 0.5 ) → List[Dict]

Parameters

outputs (DeformableDetrSegmentationOutput) — Raw outputs of the model.
target_sizes (torch.Tensor of shape (batch_size, 2) or List[Tuple] of length batch_size) — Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction.
threshold (float, optional, defaults to 0.9) — Threshold to use to filter out queries.
mask_threshold (float, optional, defaults to 0.5) — Threshold to use when turning the predicted masks into binary values.

Returns

List[Dict]

A list of dictionaries, each dictionary containing the scores, labels, and masks for an image in the batch as predicted by the model.

Converts the output of DeformableDetrForSegmentation into image segmentation predictions. Only supports PyTorch.

post_process_panoptic

< source >

( outputs processed_sizes target_sizes = None is_thing_map = None threshold = 0.85 ) → List[Dict]

Parameters

outputs (DeformableDetrSegmentationOutput) — Raw outputs of the model.
processed_sizes (torch.Tensor of shape (batch_size, 2) or List[Tuple] of length batch_size) — Torch Tensor (or list) containing the size (h, w) of each image of the batch, i.e. the size after data augmentation but before batching.
target_sizes (torch.Tensor of shape (batch_size, 2) or List[Tuple] of length batch_size, optional) — Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction. If left to None, it will default to the processed_sizes.
is_thing_map (torch.Tensor of shape (batch_size, 2), optional) — Dictionary mapping class indices to either True or False, depending on whether or not they are a thing. If not set, defaults to the is_thing_map of COCO panoptic.
threshold (float, optional, defaults to 0.85) — Threshold to use to filter out queries.

Returns

List[Dict]

A list of dictionaries, each dictionary containing a PNG string and segments_info values for an image in the batch as predicted by the model.

Converts the output of DeformableDetrForSegmentation into actual panoptic predictions. Only supports PyTorch.

DeformableDetrConfig

class transformers.DeformableDetrConfig

< source >

( num_queries = 300 max_position_embeddings = 1024 encoder_layers = 6 encoder_ffn_dim = 1024 encoder_attention_heads = 8 decoder_layers = 6 decoder_ffn_dim = 1024 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 return_intermediate = True auxiliary_loss = False position_embedding_type = 'sine' backbone = 'resnet50' dilation = False num_feature_levels = 4 encoder_n_points = 4 decoder_n_points = 4 two_stage = False two_stage_num_proposals = 300 with_box_refine = 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

num_queries (int, optional, defaults to 300) — Number of object queries, i.e. detection slots. This is the maximal number of objects DeformableDetrModel can detect in a single image. In case two_stage is set to True, we use two_stage_num_proposals instead.
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 attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (int, optional, defaults to 1024) — Dimension of the “intermediate” (often named feed-forward) layer in decoder.
encoder_ffn_dim (int, optional, defaults to 1024) — 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, optional, 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, optional, defaults to "resnet50") — Name of convolutional backbone to use. Supports any convolutional backbone from the timm package. For a list of all available models, see this page.
dilation (bool, optional, defaults to False) — Whether to replace stride with dilation in the last convolutional block (DC5).
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 matching 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.
num_feature_levels (int, optional, defaults to 4) — The number of input feature levels.
encoder_n_points (int, optional, defaults to 4) — The number of sampled keys in each feature level for each attention head in the encoder.
decoder_n_points (int, optional, defaults to 4) — The number of sampled keys in each feature level for each attention head in the decoder.
two_stage (bool, optional, defaults to False) — Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of Deformable DETR, which are further fed into the decoder for iterative bounding box refinement.
two_stage_num_proposals (int, optional, defaults to 300) — The number of region proposals to be generated, in case two_stage is set to True.
with_box_refine (bool, optional, defaults to False) — Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes based on the predictions from the previous layer.

This is the configuration class to store the configuration of a DeformableDetrModel. It is used to instantiate a Deformable DETR model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Deformable DETR SenseTime/deformable-detr architecture.

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

Examples:

>>> from transformers import DeformableDetrModel, DeformableDetrConfig

>>> # Initializing a Deformable DETR SenseTime/deformable-detr style configuration
>>> configuration = DeformableDetrConfig()

>>> # Initializing a model from the SenseTime/deformable-detr style configuration
>>> model = DeformableDetrModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

DeformableDetrModel

class transformers.DeformableDetrModel

< source >

( config: DeformableDetrConfig )

Parameters

config (DeformableDetrConfig) — 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 Deformable 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 pixel_mask = None decoder_attention_mask = None encoder_outputs = None inputs_embeds = None decoder_inputs_embeds = None output_attentions = None output_hidden_states = None return_dict = None ) → transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModelOutput 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 AutoFeatureExtractor. See AutoFeatureExtractor.__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 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.LongTensor 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_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.deformable_detr.modeling_deformable_detr.DeformableDetrModelOutput or tuple(torch.FloatTensor)

A transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModelOutput 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 (DeformableDetrConfig) and inputs.

init_reference_points (torch.FloatTensor of shape (batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder.
last_hidden_state (torch.FloatTensor of shape (batch_size, num_queries, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
intermediate_hidden_states (torch.FloatTensor of shape (config.decoder_layers, batch_size, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder).
intermediate_reference_points (torch.FloatTensor of shape (config.decoder_layers, batch_size, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder).
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, num_queries, 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, num_queries, num_queries). 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_queries, num_heads, 4, 4). 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_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_queries, num_heads, 4, 4). Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
enc_outputs_class (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels), optional, returned when config.with_box_refine=True and config.two_stage=True) — Predicted bounding boxes scores where the top config.two_stage_num_proposals scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background).
enc_outputs_coord_logits (torch.FloatTensor of shape (batch_size, sequence_length, 4), optional, returned when config.with_box_refine=True and config.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage.

The DeformableDetrModel 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:

>>> from transformers import AutoFeatureExtractor, DeformableDetrModel
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("SenseTime/deformable-detr")
>>> model = DeformableDetrModel.from_pretrained("SenseTime/deformable-detr")

>>> inputs = feature_extractor(images=image, return_tensors="pt")

>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 300, 256]

DeformableDetrForObjectDetection

class transformers.DeformableDetrForObjectDetection

< source >

( config: DeformableDetrConfig )

Parameters

config (DeformableDetrConfig) — 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.

Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection.

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 pixel_mask = None decoder_attention_mask = None encoder_outputs = None inputs_embeds = None decoder_inputs_embeds = None labels = None output_attentions = None output_hidden_states = None return_dict = None ) → transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput 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 AutoFeatureExtractor. See AutoFeatureExtractor.__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 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.LongTensor 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_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 image,) and the boxes a torch.FloatTensor of shape (number of bounding boxes in the image, 4).

Returns

transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput or tuple(torch.FloatTensor)

A transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput 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 (DeformableDetrConfig) 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 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 ~AutoFeatureExtractor.post_process 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, num_queries, 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, num_queries, 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, num_queries, num_queries). 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_queries, num_heads, 4, 4). 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_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_heads, 4, 4). 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, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder).
intermediate_reference_points (torch.FloatTensor of shape (config.decoder_layers, batch_size, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder).
init_reference_points (torch.FloatTensor of shape (batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder.
enc_outputs_class (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels), optional, returned when config.with_box_refine=True and config.two_stage=True) — Predicted bounding boxes scores where the top config.two_stage_num_proposals scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background).
enc_outputs_coord_logits (torch.FloatTensor of shape (batch_size, sequence_length, 4), optional, returned when config.with_box_refine=True and config.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage.

The DeformableDetrForObjectDetection forward method, overrides the __call__ special method.

Examples:

>>> from transformers import AutoFeatureExtractor, DeformableDetrForObjectDetection
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("SenseTime/deformable-detr")
>>> model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr")

>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)

>>> # convert outputs (bounding boxes and class logits) to COCO API
>>> target_sizes = torch.tensor([image.size[::-1]])
>>> results = feature_extractor.post_process(outputs, 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()]
...     # let's only keep detections with score > 0.5
...     if score > 0.5:
...         print(
...             f"Detected {model.config.id2label[label.item()]} with confidence "
...             f"{round(score.item(), 3)} at location {box}"
...         )
Detected cat with confidence 0.8 at location [16.5, 52.84, 318.25, 470.78]
Detected cat with confidence 0.789 at location [342.19, 24.3, 640.02, 372.25]
Detected remote with confidence 0.633 at location [40.79, 72.78, 176.76, 117.25]

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