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SegGPT

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This model was released on 2023-04-06 and added to Hugging Face Transformers on 2024-02-26.

SegGPT

PyTorch

Overview

The SegGPT model was proposed in SegGPT: Segmenting Everything In Context by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. SegGPT employs a decoder-only Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000.

The abstract from the paper is the following:

We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of

Tips:

  • One can use SegGptImageProcessor to prepare image input, prompt and mask to the model.
  • One can either use segmentation maps or RGB images as prompt masks. If using the latter make sure to set do_convert_rgb=False in the preprocess method.
  • It’s highly advisable to pass num_labels when using segmentation_maps (not considering background) during preprocessing and postprocessing with SegGptImageProcessor for your use case.
  • When doing inference with SegGptForImageSegmentation if your batch_size is greater than 1 you can use feature ensemble across your images by passing feature_ensemble=True in the forward method.

Here’s how to use the model for one-shot semantic segmentation:

import torch
from datasets import load_dataset
from transformers import SegGptImageProcessor, SegGptForImageSegmentation

checkpoint = "BAAI/seggpt-vit-large"
image_processor = SegGptImageProcessor.from_pretrained(checkpoint)
model = SegGptForImageSegmentation.from_pretrained(checkpoint)

dataset_id = "EduardoPacheco/FoodSeg103"
ds = load_dataset(dataset_id, split="train")
# Number of labels in FoodSeg103 (not including background)
num_labels = 103

image_input = ds[4]["image"]
ground_truth = ds[4]["label"]
image_prompt = ds[29]["image"]
mask_prompt = ds[29]["label"]

inputs = image_processor(
    images=image_input, 
    prompt_images=image_prompt,
    segmentation_maps=mask_prompt, 
    num_labels=num_labels,
    return_tensors="pt"
)

with torch.no_grad():
    outputs = model(**inputs)

target_sizes = [image_input.size[::-1]]
mask = image_processor.post_process_semantic_segmentation(outputs, target_sizes, num_labels=num_labels)[0]

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

SegGptConfig

class transformers.SegGptConfig

< >

( output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None tokenizer_class: str | transformers.tokenization_utils_base.PreTrainedTokenizerBase | None = None hidden_size: int = 1024 num_hidden_layers: int = 24 num_attention_heads: int = 16 hidden_act: str = 'gelu' hidden_dropout_prob: float = 0.0 initializer_range: float = 0.02 layer_norm_eps: float = 1e-06 image_size: int | list[int] | tuple[int, ...] = (896, 448) patch_size: int | list[int] | tuple[int, int] = 16 num_channels: int = 3 qkv_bias: bool = True mlp_dim: int | None = None drop_path_rate: float = 0.1 pretrain_image_size: int | list[int] | tuple[int, int] = 224 decoder_hidden_size: int = 64 use_relative_position_embeddings: bool = True merge_index: int = 2 intermediate_hidden_state_indices: list[int] | tuple[int, ...] = (5, 11, 17, 23) beta: float = 0.01 )

Parameters

  • output_hidden_states (bool, optional, defaults to False) — Whether or not the model should return all hidden-states.
  • return_dict (bool, optional, defaults to True) — Whether to return a ModelOutput (dataclass) instead of a plain tuple.
  • dtype (Union[str, torch.dtype], optional) — The chunk size of all feed forward layers in the residual attention blocks. A chunk size of 0 means that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time. For more information on feed forward chunking, see How does Feed Forward Chunking work?.
  • chunk_size_feed_forward (int, optional, defaults to 0) — The dtype of the weights. This attribute can be used to initialize the model to a non-default dtype (which is normally float32) and thus allow for optimal storage allocation. For example, if the saved model is float16, ideally we want to load it back using the minimal amount of memory needed to load float16 weights.
  • is_encoder_decoder (bool, optional, defaults to False) — Whether the model is used as an encoder/decoder or not.
  • id2label (Union[dict[int, str], dict[str, str]], optional) — A map from index (for instance prediction index, or target index) to label.
  • label2id (Union[dict[str, int], dict[str, str]], optional) — A map from label to index for the model.
  • problem_type (Literal[regression, single_label_classification, multi_label_classification], optional) — Problem type for XxxForSequenceClassification models. Can be one of "regression", "single_label_classification" or "multi_label_classification".
  • tokenizer_class (Union[str, ~tokenization_utils_base.PreTrainedTokenizerBase], optional) — The class name of model’s tokenizer.
  • hidden_size (int, optional, defaults to 1024) — Dimension of the hidden representations.
  • num_hidden_layers (int, optional, defaults to 24) — Number of hidden layers in the Transformer decoder.
  • num_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
  • hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • hidden_dropout_prob (float, optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • layer_norm_eps (float, optional, defaults to 1e-06) — The epsilon used by the layer normalization layers.
  • image_size (Union[int, list[int], tuple[int, ...]], optional, defaults to (896, 448)) — The size (resolution) of each image.
  • patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 16) — The size (resolution) of each patch.
  • num_channels (int, optional, defaults to 3) — The number of input channels.
  • qkv_bias (bool, optional, defaults to True) — Whether to add a bias to the queries, keys and values.
  • mlp_dim (int, optional) — The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to hidden_size * 4.
  • drop_path_rate (float, optional, defaults to 0.1) — Drop path rate for the patch fusion.
  • pretrain_image_size (int, optional, defaults to 224) — The pretrained size of the absolute position embeddings.
  • decoder_hidden_size (int, optional, defaults to 64) — Dimension of the hidden representations.
  • use_relative_position_embeddings (bool, optional, defaults to True) — Whether to use relative position embeddings in the attention layers.
  • merge_index (int, optional, defaults to 2) — The index of the encoder layer to merge the embeddings.
  • intermediate_hidden_state_indices (list[int], optional, defaults to [5, 11, 17, 23]) — The indices of the encoder layers which we store as features for the decoder.
  • beta (float, optional, defaults to 0.01) — Regularization factor for SegGptLoss (smooth-l1 loss).

This is the configuration class to store the configuration of a SegGptModel. It is used to instantiate a Seggpt 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 BAAI/seggpt-vit-large

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

Example:

>>> from transformers import SegGptConfig, SegGptModel

>>> # Initializing a SegGPT seggpt-vit-large style configuration
>>> configuration = SegGptConfig()

>>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration
>>> model = SegGptModel(configuration)

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

SegGptImageProcessor

class transformers.SegGptImageProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.seggpt.image_processing_seggpt.SegGptImageProcessorKwargs] )

Parameters

  • num_labels (int, kwargs, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the channel dimension when do_convert_rgb is True.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a SegGptImageProcessor image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None prompt_images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None prompt_masks: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.seggpt.image_processing_seggpt.SegGptImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) — Image 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.
  • prompt_images (ImageInput, optional) — Prompt images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.
  • prompt_masks (ImageInput, optional) — Prompt masks to preprocess. Can be in the format of segmentation maps (no channels) or RGB images. If in the format of RGB images, do_convert_rgb should be set to False. If in the format of segmentation maps, specifying num_labels is recommended to build a palette to map the prompt mask from a single channel to a 3-channel RGB. If num_labels is not specified, the prompt mask will be duplicated across the channel dimension.
  • num_labels (int, kwargs, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the channel dimension when do_convert_rgb is True.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

post_process_semantic_segmentation

< >

( outputs target_sizes: list[tuple[int, int]] | None = None num_labels: int | None = None )

Parameters

  • outputs (SegGptImageSegmentationOutput) — Raw outputs of the model.
  • target_sizes (list[tuple[int, int]], optional) — List of length batch_size, where each item corresponds to the requested final size (height, width) of each prediction. If left to None, predictions will not be resized.
  • num_labels (int, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built to map prediction masks from RGB values back to class indices. Should match the value used during preprocessing.

Converts the output of SegGptImageSegmentationOutput into segmentation maps. Only supports PyTorch.

SegGptImageProcessorPil

class transformers.SegGptImageProcessorPil

< >

( **kwargs: typing_extensions.Unpack[transformers.models.seggpt.image_processing_seggpt.SegGptImageProcessorKwargs] )

Parameters

  • num_labels (int, kwargs, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the channel dimension when do_convert_rgb is True.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a SegGptImageProcessor image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None prompt_images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None prompt_masks: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.seggpt.image_processing_seggpt.SegGptImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) — Image 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.
  • prompt_images (ImageInput, optional) — Prompt images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.
  • prompt_masks (ImageInput, optional) — Prompt masks to preprocess. Can be in the format of segmentation maps (no channels) or RGB images. If in the format of RGB images, do_convert_rgb should be set to False. If in the format of segmentation maps, specifying num_labels is recommended to build a palette to map the prompt mask from a single channel to a 3-channel RGB. If num_labels is not specified, the prompt mask will be duplicated across the channel dimension.
  • num_labels (int, kwargs, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the channel dimension when do_convert_rgb is True.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

post_process_semantic_segmentation

< >

( outputs target_sizes: list[tuple[int, int]] | None = None num_labels: int | None = None )

Parameters

  • outputs (SegGptImageSegmentationOutput) — Raw outputs of the model.
  • target_sizes (list[tuple[int, int]], optional) — List of length batch_size, where each item corresponds to the requested final size (height, width) of each prediction. If left to None, predictions will not be resized.
  • num_labels (int, optional) — Number of classes in the segmentation task (excluding the background). If specified, a palette will be built to map prediction masks from RGB values back to class indices. Should match the value used during preprocessing.

Converts the output of SegGptImageSegmentationOutput into segmentation maps. Only supports PyTorch.

SegGptModel

class transformers.SegGptModel

< >

( config: SegGptConfig )

Parameters

  • config (SegGptConfig) — 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 Seggpt Model 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

< >

( pixel_values: Tensor prompt_pixel_values: Tensor prompt_masks: Tensor bool_masked_pos: torch.BoolTensor | None = None feature_ensemble: bool | None = None embedding_type: str | None = None labels: torch.FloatTensor | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None return_dict: bool | None = None **kwargs ) → SegGptEncoderOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using SegGptImageProcessor. See SegGptImageProcessor.__call__() for details (processor_class uses SegGptImageProcessor for processing images).
  • prompt_pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Prompt pixel values. Prompt pixel values can be obtained using AutoImageProcessor. See SegGptImageProcessor.__call__() for details.
  • prompt_masks (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Prompt mask. Prompt mask can be obtained using AutoImageProcessor. See SegGptImageProcessor.__call__() for details.
  • bool_masked_pos (torch.BoolTensor of shape (batch_size, num_patches), optional) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
  • feature_ensemble (bool, optional) — Boolean indicating whether to use feature ensemble or not. If True, the model will use feature ensemble if we have at least two prompts. If False, the model will not use feature ensemble. This argument should be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image.
  • embedding_type (str, optional) — Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either instance or semantic.
  • labels (torch.FloatTensor of shape (batch_size, num_channels, height, width), optional) — Ground truth mask for input images.
  • 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

SegGptEncoderOutput or tuple(torch.FloatTensor)

A SegGptEncoderOutput 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 (SegGptConfig) and inputs.

The SegGptModel 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.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, patch_height, patch_width, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
  • hidden_states (tuple[torch.FloatTensor], optional, returned 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, patch_height, patch_width, hidden_size).
  • attentions (tuple[torch.FloatTensor], optional, returned when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, seq_len, seq_len).
  • intermediate_hidden_states (tuple[torch.FloatTensor], optional, returned when config.intermediate_hidden_state_indices is set) — Tuple of torch.FloatTensor of shape (batch_size, patch_height, patch_width, hidden_size). Each element in the Tuple corresponds to the output of the layer specified in config.intermediate_hidden_state_indices. Additionally, each feature passes through a LayerNorm.

Examples:

>>> from transformers import SegGptImageProcessor, SegGptModel
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO

>>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg"
>>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg"
>>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png"

>>> with httpx.stream("GET", image_input_url) as response:
...     image_input = Image.open(BytesIO(response.read()))

>>> with httpx.stream("GET", image_prompt_url) as response:
...     image_prompt = Image.open(BytesIO(response.read()))

>>> with httpx.stream("GET", mask_prompt_url) as response:
...     mask_prompt = Image.open(BytesIO(response.read())).convert("L")

>>> checkpoint = "BAAI/seggpt-vit-large"
>>> model = SegGptModel.from_pretrained(checkpoint)
>>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint)

>>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt")

>>> outputs = model(**inputs)
>>> list(outputs.last_hidden_state.shape)
[1, 56, 28, 1024]

SegGptForImageSegmentation

class transformers.SegGptForImageSegmentation

< >

( config: SegGptConfig )

Parameters

  • config (SegGptConfig) — 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.

SegGpt model with a decoder on top for one-shot image segmentation.

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

< >

( pixel_values: Tensor prompt_pixel_values: Tensor prompt_masks: Tensor bool_masked_pos: torch.BoolTensor | None = None feature_ensemble: bool | None = None embedding_type: str | None = None labels: torch.FloatTensor | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None return_dict: bool | None = None **kwargs ) → SegGptImageSegmentationOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using SegGptImageProcessor. See SegGptImageProcessor.__call__() for details (processor_class uses SegGptImageProcessor for processing images).
  • prompt_pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Prompt pixel values. Prompt pixel values can be obtained using AutoImageProcessor. See SegGptImageProcessor.__call__() for details.
  • prompt_masks (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Prompt mask. Prompt mask can be obtained using AutoImageProcessor. See SegGptImageProcessor.__call__() for details.
  • bool_masked_pos (torch.BoolTensor of shape (batch_size, num_patches), optional) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
  • feature_ensemble (bool, optional) — Boolean indicating whether to use feature ensemble or not. If True, the model will use feature ensemble if we have at least two prompts. If False, the model will not use feature ensemble. This argument should be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image.
  • embedding_type (str, optional) — Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either instance or semantic.
  • labels (torch.FloatTensor of shape (batch_size, num_channels, height, width), optional) — Ground truth mask for input images.
  • 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

SegGptImageSegmentationOutput or tuple(torch.FloatTensor)

A SegGptImageSegmentationOutput 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 (SegGptConfig) and inputs.

The SegGptForImageSegmentation 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.

  • loss (torch.FloatTensor, optional, returned when labels is provided) — The loss value.
  • pred_masks (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — The predicted masks.
  • hidden_states (tuple[torch.FloatTensor], optional, returned 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, patch_height, patch_width, hidden_size).
  • attentions (tuple[torch.FloatTensor], optional, returned when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, seq_len, seq_len).

Examples:

>>> from transformers import SegGptImageProcessor, SegGptForImageSegmentation
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO

>>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg"
>>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg"
>>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png"

>>> with httpx.stream("GET", image_input_url) as response:
...     image_input = Image.open(BytesIO(response.read()))

>>> with httpx.stream("GET", image_prompt_url) as response:
...     image_prompt = Image.open(BytesIO(response.read()))

>>> with httpx.stream("GET", mask_prompt_url) as response:
...     mask_prompt = Image.open(BytesIO(response.read())).convert("L")

>>> checkpoint = "BAAI/seggpt-vit-large"
>>> model = SegGptForImageSegmentation.from_pretrained(checkpoint)
>>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint)

>>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt")
>>> outputs = model(**inputs)
>>> result = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[(image_input.height, image_input.width)])[0]
>>> print(list(result.shape))
[170, 297]
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