RT-DETRv2
Overview
The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu.
RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance.
The abstract from the paper is the following:
In this report, we present RT-DETRv2, an improved Real-Time DEtection TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR, and opens up a set of bag-of-freebies for flexibility and practicality, as well as optimizing the training strategy to achieve enhanced performance. To improve the flexibility, we suggest setting a distinct number of sampling points for features at different scales in the deformable attention to achieve selective multi-scale feature extraction by the decoder. To enhance practicality, we propose an optional discrete sampling operator to replace the grid_sample operator that is specific to RT-DETR compared to YOLOs. This removes the deployment constraints typically associated with DETRs. For the training strategy, we propose dynamic data augmentation and scale-adaptive hyperparameters customization to improve performance without loss of speed.
This model was contributed by jadechoghari. The original code can be found here.
Usage tips
This second version of RT-DETR improves how the decoder finds objects in an image.
- better sampling – adjusts offsets so the model looks at the right areas
- flexible attention – can use smooth (bilinear) or fixed (discrete) sampling
- optimized processing – improves how attention weights mix information
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
>>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)
>>> for result in results:
... for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
... score, label = score.item(), label_id.item()
... box = [round(i, 2) for i in box.tolist()]
... print(f"{model.config.id2label[label]}: {score:.2f} {box}")
cat: 0.97 [341.14, 25.11, 639.98, 372.89]
cat: 0.96 [12.78, 56.35, 317.67, 471.34]
remote: 0.95 [39.96, 73.12, 175.65, 117.44]
sofa: 0.86 [-0.11, 2.97, 639.89, 473.62]
sofa: 0.82 [-0.12, 1.78, 639.87, 473.52]
remote: 0.79 [333.65, 76.38, 370.69, 187.48]Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RT-DETRv2.
- Scripts for finetuning RTDetrV2ForObjectDetection with Trainer or Accelerate can be found here.
- See also: Object detection task guide.
- Notebooks for inference and fine-tuning RT-DETRv2 on a custom dataset (🌎).
RTDetrV2Config
class transformers.RTDetrV2Config
< source >( initializer_range = 0.01 initializer_bias_prior_prob = None layer_norm_eps = 1e-05 batch_norm_eps = 1e-05 backbone_config = None backbone = None use_pretrained_backbone = False use_timm_backbone = False freeze_backbone_batch_norms = True backbone_kwargs = None encoder_hidden_dim = 256 encoder_in_channels = [512, 1024, 2048] feat_strides = [8, 16, 32] encoder_layers = 1 encoder_ffn_dim = 1024 encoder_attention_heads = 8 dropout = 0.0 activation_dropout = 0.0 encode_proj_layers = [2] positional_encoding_temperature = 10000 encoder_activation_function = 'gelu' activation_function = 'silu' eval_size = None normalize_before = False hidden_expansion = 1.0 d_model = 256 num_queries = 300 decoder_in_channels = [256, 256, 256] decoder_ffn_dim = 1024 num_feature_levels = 3 decoder_n_points = 4 decoder_layers = 6 decoder_attention_heads = 8 decoder_activation_function = 'relu' attention_dropout = 0.0 num_denoising = 100 label_noise_ratio = 0.5 box_noise_scale = 1.0 learn_initial_query = False anchor_image_size = None disable_custom_kernels = True with_box_refine = True is_encoder_decoder = True matcher_alpha = 0.25 matcher_gamma = 2.0 matcher_class_cost = 2.0 matcher_bbox_cost = 5.0 matcher_giou_cost = 2.0 use_focal_loss = True auxiliary_loss = True focal_loss_alpha = 0.75 focal_loss_gamma = 2.0 weight_loss_vfl = 1.0 weight_loss_bbox = 5.0 weight_loss_giou = 2.0 eos_coefficient = 0.0001 decoder_n_levels = 3 decoder_offset_scale = 0.5 decoder_method = 'default' **kwargs )
Parameters
- initializer_range (
float, optional, defaults to 0.01) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - initializer_bias_prior_prob (
float, optional) — The prior probability used by the bias initializer to initialize biases forenc_score_headandclass_embed. IfNone,prior_probcomputed asprior_prob = 1 / (num_labels + 1)while initializing model weights. - layer_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers. - batch_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the batch normalization layers. - backbone_config (
Dict, optional, defaults toRTDetrV2ResNetConfig()) — The configuration of the backbone model. - backbone (
str, optional) — Name of backbone to use whenbackbone_configisNone. Ifuse_pretrained_backboneisTrue, this will load the corresponding pretrained weights from the timm or transformers library. Ifuse_pretrained_backboneisFalse, this loads the backbone’s config and uses that to initialize the backbone with random weights. - use_pretrained_backbone (
bool, optional, defaults toFalse) — Whether to use pretrained weights for the backbone. - use_timm_backbone (
bool, optional, defaults toFalse) — Whether to loadbackbonefrom the timm library. IfFalse, the backbone is loaded from the transformers library. - freeze_backbone_batch_norms (
bool, optional, defaults toTrue) — Whether to freeze the batch normalization layers in the backbone. - backbone_kwargs (
dict, optional) — Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g.{'out_indices': (0, 1, 2, 3)}. Cannot be specified ifbackbone_configis set. - encoder_hidden_dim (
int, optional, defaults to 256) — Dimension of the layers in hybrid encoder. - encoder_in_channels (
list, optional, defaults to[512, 1024, 2048]) — Multi level features input for encoder. - feat_strides (
List[int], optional, defaults to[8, 16, 32]) — Strides used in each feature map. - encoder_layers (
int, optional, defaults to 1) — Total of layers to be used by the encoder. - encoder_ffn_dim (
int, optional, defaults to 1024) — Dimension of the “intermediate” (often named feed-forward) layer in decoder. - encoder_attention_heads (
int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer encoder. - dropout (
float, optional, defaults to 0.0) — The ratio for all dropout layers. - activation_dropout (
float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer. - encode_proj_layers (
List[int], optional, defaults to[2]) — Indexes of the projected layers to be used in the encoder. - positional_encoding_temperature (
int, optional, defaults to 10000) — The temperature parameter used to create the positional encodings. - encoder_activation_function (
str, optional, defaults to"gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu","relu","silu"and"gelu_new"are supported. - activation_function (
str, optional, defaults to"silu") — The non-linear activation function (function or string) in the general layer. If string,"gelu","relu","silu"and"gelu_new"are supported. - eval_size (
Tuple[int, int], optional) — Height and width used to compute the effective height and width of the position embeddings after taking into account the stride. - normalize_before (
bool, optional, defaults toFalse) — Determine whether to apply layer normalization in the transformer encoder layer before self-attention and feed-forward modules. - hidden_expansion (
float, optional, defaults to 1.0) — Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. - d_model (
int, optional, defaults to 256) — Dimension of the layers exclude hybrid encoder. - num_queries (
int, optional, defaults to 300) — Number of object queries. - decoder_in_channels (
list, optional, defaults to[256, 256, 256]) — Multi level features dimension for decoder - decoder_ffn_dim (
int, optional, defaults to 1024) — Dimension of the “intermediate” (often named feed-forward) layer in decoder. - num_feature_levels (
int, optional, defaults to 3) — The number of input feature levels. - decoder_n_points (
int, optional, defaults to 4) — The number of sampled keys in each feature level for each attention head in the decoder. - decoder_layers (
int, optional, defaults to 6) — Number of decoder layers. - decoder_attention_heads (
int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer decoder. - decoder_activation_function (
str, optional, defaults to"relu") — The non-linear activation function (function or string) in the decoder. If string,"gelu","relu","silu"and"gelu_new"are supported. - attention_dropout (
float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities. - num_denoising (
int, optional, defaults to 100) — The total number of denoising tasks or queries to be used for contrastive denoising. - label_noise_ratio (
float, optional, defaults to 0.5) — The fraction of denoising labels to which random noise should be added. - box_noise_scale (
float, optional, defaults to 1.0) — Scale or magnitude of noise to be added to the bounding boxes. - learn_initial_query (
bool, optional, defaults toFalse) — Indicates whether the initial query embeddings for the decoder should be learned during training - anchor_image_size (
Tuple[int, int], optional) — Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. - disable_custom_kernels (
bool, optional, defaults toTrue) — Whether to disable custom kernels. - with_box_refine (
bool, optional, defaults toTrue) — Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes based on the predictions from the previous layer. - is_encoder_decoder (
bool, optional, defaults toTrue) — Whether the architecture has an encoder decoder structure. - matcher_alpha (
float, optional, defaults to 0.25) — Parameter alpha used by the Hungarian Matcher. - matcher_gamma (
float, optional, defaults to 2.0) — Parameter gamma used by the Hungarian Matcher. - matcher_class_cost (
float, optional, defaults to 2.0) — The relative weight of the class loss used by the Hungarian Matcher. - matcher_bbox_cost (
float, optional, defaults to 5.0) — The relative weight of the bounding box loss used by the Hungarian Matcher. - matcher_giou_cost (
float, optional, defaults to 2.0) — The relative weight of the giou loss of used by the Hungarian Matcher. - use_focal_loss (
bool, optional, defaults toTrue) — Parameter informing if focal loss should be used. - auxiliary_loss (
bool, optional, defaults toTrue) — Whether auxiliary decoding losses (loss at each decoder layer) are to be used. - focal_loss_alpha (
float, optional, defaults to 0.75) — Parameter alpha used to compute the focal loss. - focal_loss_gamma (
float, optional, defaults to 2.0) — Parameter gamma used to compute the focal loss. - weight_loss_vfl (
float, optional, defaults to 1.0) — Relative weight of the varifocal loss in the object detection loss. - weight_loss_bbox (
float, optional, defaults to 5.0) — Relative weight of the L1 bounding box loss in the object detection loss. - weight_loss_giou (
float, optional, defaults to 2.0) — Relative weight of the generalized IoU loss in the object detection loss. - eos_coefficient (
float, optional, defaults to 0.0001) — Relative classification weight of the ‘no-object’ class in the object detection loss. - decoder_n_levels (
int, optional, defaults to 3) — The number of feature levels used by the decoder. - decoder_offset_scale (
float, optional, defaults to 0.5) — Scaling factor applied to the attention offsets in the decoder. - decoder_method (
str, optional, defaults to"default") — The method to use for the decoder:"default"or"discrete".
This is the configuration class to store the configuration of a RTDetrV2Model. It is used to instantiate a RT-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 RT-DETR architecture.
e.g. PekingU/rtdetr_r18vd
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 RTDetrV2Config, RTDetrV2Model
>>> # Initializing a RT-DETR configuration
>>> configuration = RTDetrV2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = RTDetrV2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configfrom_backbone_configs
< source >( backbone_config: PretrainedConfig **kwargs ) → RTDetrV2Config
Parameters
- backbone_config (PretrainedConfig) — The backbone configuration.
Returns
An instance of a configuration object
Instantiate a RTDetrV2Config (or a derived class) from a pre-trained backbone model configuration and DETR model configuration.
RTDetrV2Model
class transformers.RTDetrV2Model
< source >( config: RTDetrV2Config )
Parameters
- config (RTDetrV2Config) — 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.
RT-DETR Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any 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 pixel_mask: typing.Optional[torch.LongTensor] = 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.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ModelOutput or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof 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. SeeRTDetrV2ImageProcessor.__call__for details. - pixel_mask (
torch.LongTensorof 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).
- encoder_outputs (
tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional:hidden_states, optional:attentions)last_hidden_stateof 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.FloatTensorof 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.FloatTensorof 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. - 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 atorch.LongTensorof len(number of bounding boxes in the image,)and the boxes atorch.FloatTensorof shape(number of bounding boxes in the image, 4). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ModelOutput or tuple(torch.FloatTensor)
A transformers.models.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ModelOutput 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 (RTDetrV2Config) and inputs.
- last_hidden_state (
torch.FloatTensorof 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.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder). - intermediate_logits (
torch.FloatTensorof shape(batch_size, config.decoder_layers, sequence_length, config.num_labels)) — Stacked intermediate logits (logits of each layer of the decoder). - intermediate_reference_points (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder). - decoder_hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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. - init_reference_points (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder. - enc_topk_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels)) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring bounding boxes are picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. foreground and background). - enc_topk_bboxes (
torch.FloatTensorof shape(batch_size, sequence_length, 4)) — Logits of predicted bounding boxes coordinates in the encoder stage. - enc_outputs_class (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring 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.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage. - denoising_meta_values (
dict) — Extra dictionary for the denoising related values
The RTDetrV2Model 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 AutoImageProcessor, RTDetrV2Model
>>> 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("PekingU/RTDetrV2_r50vd")
>>> model = RTDetrV2Model.from_pretrained("PekingU/RTDetrV2_r50vd")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 300, 256]RTDetrV2ForObjectDetection
class transformers.RTDetrV2ForObjectDetection
< source >( config: RTDetrV2Config )
Parameters
- config (RTDetrV2Config) — 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.
RT-DETR Model (consisting of a backbone and encoder-decoder) outputting bounding boxes and logits to be further decoded into scores and classes.
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.Optional[torch.LongTensor] = 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 **loss_kwargs ) → transformers.models.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof 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. SeeRTDetrV2ImageProcessor.__call__for details. - pixel_mask (
torch.LongTensorof 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).
- encoder_outputs (
tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional:hidden_states, optional:attentions)last_hidden_stateof 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.FloatTensorof 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.FloatTensorof 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. - 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 atorch.LongTensorof len(number of bounding boxes in the image,)and the boxes atorch.FloatTensorof shape(number of bounding boxes in the image, 4). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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 atorch.LongTensorof len(number of bounding boxes in the image,)and the boxes atorch.FloatTensorof shape(number of bounding boxes in the image, 4).
Returns
transformers.models.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.rt_detr_v2.modeling_rt_detr_v2.RTDetrV2ObjectDetectionOutput 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 (RTDetrV2Config) and inputs.
- loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsare 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.FloatTensorof shape(batch_size, num_queries, num_classes + 1)) — Classification logits (including no-object) for all queries. - pred_boxes (
torch.FloatTensorof 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~RTDetrV2ImageProcessor.post_process_object_detectionto retrieve the unnormalized (absolute) bounding boxes. - auxiliary_outputs (
list[Dict], optional) — Optional, only returned when auxiliary losses are activated (i.e.config.auxiliary_lossis set toTrue) and labels are provided. It is a list of dictionaries containing the two above keys (logitsandpred_boxes) for each decoder layer. - last_hidden_state (
torch.FloatTensorof 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.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder). - intermediate_logits (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, config.num_labels)) — Stacked intermediate logits (logits of each layer of the decoder). - intermediate_reference_points (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder). - decoder_hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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. - init_reference_points (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder. - enc_topk_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the encoder. - enc_topk_bboxes (
torch.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the encoder. - enc_outputs_class (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring 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.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage. - denoising_meta_values (
dict) — Extra dictionary for the denoising related values
The RTDetrV2ForObjectDetection 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 RTDetrV2ImageProcessor, RTDetrV2ForObjectDetection
>>> from PIL import Image
>>> import requests
>>> import torch
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = RTDetrV2ImageProcessor.from_pretrained("PekingU/RTDetrV2_r50vd")
>>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/RTDetrV2_r50vd")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> list(logits.shape)
[1, 300, 80]
>>> boxes = outputs.pred_boxes
>>> list(boxes.shape)
[1, 300, 4]
>>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> 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 sofa with confidence 0.97 at location [0.14, 0.38, 640.13, 476.21]
Detected cat with confidence 0.96 at location [343.38, 24.28, 640.14, 371.5]
Detected cat with confidence 0.958 at location [13.23, 54.18, 318.98, 472.22]
Detected remote with confidence 0.951 at location [40.11, 73.44, 175.96, 118.48]
Detected remote with confidence 0.924 at location [333.73, 76.58, 369.97, 186.99]