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sam

mindnlp.transformers.models.sam.configuration_sam

SAM model configuration

mindnlp.transformers.models.sam.configuration_sam.SamConfig

Bases: PretrainedConfig

[SamConfig] is the configuration class to store the configuration of a [SamModel]. It is used to instantiate a SAM model according to the specified arguments, defining the vision model, prompt-encoder model and mask decoder configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the SAM-ViT-H facebook/sam-vit-huge architecture.

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

PARAMETER DESCRIPTION
vision_config

Dictionary of configuration options used to initialize [SamVisionConfig].

TYPE: Union[`dict`, `SamVisionConfig`], *optional* DEFAULT: None

prompt_encoder_config

Dictionary of configuration options used to initialize [SamPromptEncoderConfig].

TYPE: Union[`dict`, `SamPromptEncoderConfig`], *optional* DEFAULT: None

mask_decoder_config

Dictionary of configuration options used to initialize [SamMaskDecoderConfig].

TYPE: Union[`dict`, `SamMaskDecoderConfig`], *optional* DEFAULT: None

kwargs

Dictionary of keyword arguments.

TYPE: *optional* DEFAULT: {}

Example 
>>> from transformers import (
...     SamVisionConfig,
...     SamPromptEncoderConfig,
...     SamMaskDecoderConfig,
...     SamModel,
... )
...
>>> # Initializing a SamConfig with `"facebook/sam-vit-huge"` style configuration
>>> configuration = SamConfig()
...
>>> # Initializing a SamModel (with random weights) from the `"facebook/sam-vit-huge"` style configuration
>>> model = SamModel(configuration)
...
>>> # Accessing the model configuration
>>> configuration = model.config
...
>>> # We can also initialize a SamConfig from a SamVisionConfig, SamPromptEncoderConfig, and SamMaskDecoderConfig
...
>>> # Initializing SAM vision, SAM Q-Former and language model configurations
>>> vision_config = SamVisionConfig()
>>> prompt_encoder_config = SamPromptEncoderConfig()
>>> mask_decoder_config = SamMaskDecoderConfig()

>>> config = SamConfig(vision_config, prompt_encoder_config, mask_decoder_config)
Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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class SamConfig(PretrainedConfig):
    r"""
    [`SamConfig`] is the configuration class to store the configuration of a [`SamModel`]. It is used to instantiate a
    SAM model according to the specified arguments, defining the vision model, prompt-encoder model and mask decoder
    configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the
    SAM-ViT-H [facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) architecture.

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

    Args:
        vision_config (Union[`dict`, `SamVisionConfig`], *optional*):
            Dictionary of configuration options used to initialize [`SamVisionConfig`].
        prompt_encoder_config (Union[`dict`, `SamPromptEncoderConfig`], *optional*):
            Dictionary of configuration options used to initialize [`SamPromptEncoderConfig`].
        mask_decoder_config (Union[`dict`, `SamMaskDecoderConfig`], *optional*):
            Dictionary of configuration options used to initialize [`SamMaskDecoderConfig`].

        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:
        ```python
        >>> from transformers import (
        ...     SamVisionConfig,
        ...     SamPromptEncoderConfig,
        ...     SamMaskDecoderConfig,
        ...     SamModel,
        ... )
        ...
        >>> # Initializing a SamConfig with `"facebook/sam-vit-huge"` style configuration
        >>> configuration = SamConfig()
        ...
        >>> # Initializing a SamModel (with random weights) from the `"facebook/sam-vit-huge"` style configuration
        >>> model = SamModel(configuration)
        ...
        >>> # Accessing the model configuration
        >>> configuration = model.config
        ...
        >>> # We can also initialize a SamConfig from a SamVisionConfig, SamPromptEncoderConfig, and SamMaskDecoderConfig
        ...
        >>> # Initializing SAM vision, SAM Q-Former and language model configurations
        >>> vision_config = SamVisionConfig()
        >>> prompt_encoder_config = SamPromptEncoderConfig()
        >>> mask_decoder_config = SamMaskDecoderConfig()

        >>> config = SamConfig(vision_config, prompt_encoder_config, mask_decoder_config)
        ```
    """
    model_type = "sam"

    def __init__(
        self,
        vision_config=None,
        prompt_encoder_config=None,
        mask_decoder_config=None,
        initializer_range=0.02,
        **kwargs,
    ):
        """
        Initializes a new instance of the SamConfig class.

        Args:
            self: The current instance of the SamConfig class.
            vision_config (SamVisionConfig or None): The configuration for vision. If provided,
                it should be an instance of SamVisionConfig. Defaults to None.
            prompt_encoder_config (SamPromptEncoderConfig or None): The configuration for prompt encoder.
                If provided, it should be an instance of SamPromptEncoderConfig. Defaults to None.
            mask_decoder_config (SamMaskDecoderConfig or None): The configuration for mask decoder.
                If provided, it should be an instance of SamMaskDecoderConfig. Defaults to None.
            initializer_range (float): The range for weight initialization. Defaults to 0.02.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(**kwargs)
        vision_config = vision_config if vision_config is not None else {}
        prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {}
        mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {}

        if isinstance(vision_config, SamVisionConfig):
            vision_config = vision_config.to_dict()
        if isinstance(prompt_encoder_config, SamPromptEncoderConfig):
            prompt_encoder_config = prompt_encoder_config.to_dict()
        if isinstance(mask_decoder_config, SamMaskDecoderConfig):
            mask_decoder_config = mask_decoder_config.to_dict()

        self.vision_config = SamVisionConfig(**vision_config)
        self.prompt_encoder_config = SamPromptEncoderConfig(**prompt_encoder_config)
        self.mask_decoder_config = SamMaskDecoderConfig(**mask_decoder_config)
        self.initializer_range = initializer_range

mindnlp.transformers.models.sam.configuration_sam.SamConfig.__init__(vision_config=None, prompt_encoder_config=None, mask_decoder_config=None, initializer_range=0.02, **kwargs)

Initializes a new instance of the SamConfig class.

PARAMETER DESCRIPTION
self

The current instance of the SamConfig class.
vision_config

The configuration for vision. If provided, it should be an instance of SamVisionConfig. Defaults to None.

TYPE: SamVisionConfig or None DEFAULT: None

prompt_encoder_config

The configuration for prompt encoder. If provided, it should be an instance of SamPromptEncoderConfig. Defaults to None.

TYPE: SamPromptEncoderConfig or None DEFAULT: None

mask_decoder_config

The configuration for mask decoder. If provided, it should be an instance of SamMaskDecoderConfig. Defaults to None.

TYPE: SamMaskDecoderConfig or None DEFAULT: None

initializer_range

The range for weight initialization. Defaults to 0.02.

TYPE: float DEFAULT: 0.02

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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def __init__(
    self,
    vision_config=None,
    prompt_encoder_config=None,
    mask_decoder_config=None,
    initializer_range=0.02,
    **kwargs,
):
    """
    Initializes a new instance of the SamConfig class.

    Args:
        self: The current instance of the SamConfig class.
        vision_config (SamVisionConfig or None): The configuration for vision. If provided,
            it should be an instance of SamVisionConfig. Defaults to None.
        prompt_encoder_config (SamPromptEncoderConfig or None): The configuration for prompt encoder.
            If provided, it should be an instance of SamPromptEncoderConfig. Defaults to None.
        mask_decoder_config (SamMaskDecoderConfig or None): The configuration for mask decoder.
            If provided, it should be an instance of SamMaskDecoderConfig. Defaults to None.
        initializer_range (float): The range for weight initialization. Defaults to 0.02.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(**kwargs)
    vision_config = vision_config if vision_config is not None else {}
    prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {}
    mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {}

    if isinstance(vision_config, SamVisionConfig):
        vision_config = vision_config.to_dict()
    if isinstance(prompt_encoder_config, SamPromptEncoderConfig):
        prompt_encoder_config = prompt_encoder_config.to_dict()
    if isinstance(mask_decoder_config, SamMaskDecoderConfig):
        mask_decoder_config = mask_decoder_config.to_dict()

    self.vision_config = SamVisionConfig(**vision_config)
    self.prompt_encoder_config = SamPromptEncoderConfig(**prompt_encoder_config)
    self.mask_decoder_config = SamMaskDecoderConfig(**mask_decoder_config)
    self.initializer_range = initializer_range

mindnlp.transformers.models.sam.configuration_sam.SamMaskDecoderConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [SamMaskDecoder]. It is used to instantiate a SAM mask decoder to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the SAM-vit-h facebook/sam-vit-huge architecture.

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

PARAMETER DESCRIPTION
hidden_size

Dimensionality of the hidden states.

TYPE: `int`, *optional*, defaults to 256 DEFAULT: 256

hidden_act

The non-linear activation function used inside the SamMaskDecoder module.

TYPE: `str`, *optional*, defaults to `"relu"` DEFAULT: 'relu'

mlp_dim

Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 2048 DEFAULT: 2048

num_hidden_layers

Number of hidden layers in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 2 DEFAULT: 2

num_attention_heads

Number of attention heads for each attention layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 8 DEFAULT: 8

attention_downsample_rate

The downsampling rate of the attention layer.

TYPE: `int`, *optional*, defaults to 2 DEFAULT: 2

num_multimask_outputs

The number of outputs from the SamMaskDecoder module. In the Segment Anything paper, this is set to 3.

TYPE: `int`, *optional*, defaults to 3 DEFAULT: 3

iou_head_depth

The number of layers in the IoU head module.

TYPE: `int`, *optional*, defaults to 3 DEFAULT: 3

iou_head_hidden_dim

The dimensionality of the hidden states in the IoU head module.

TYPE: `int`, *optional*, defaults to 256 DEFAULT: 256

layer_norm_eps

The epsilon used by the layer normalization layers.

TYPE: `float`, *optional*, defaults to 1e-06 DEFAULT: 1e-06

Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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class SamMaskDecoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SamMaskDecoder`]. It is used to instantiate a SAM
    mask decoder to the specified arguments, defining the model architecture. Instantiating a configuration defaults
    will yield a similar configuration to that of the SAM-vit-h
    [facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the hidden states.
        hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function used inside the `SamMaskDecoder` module.
        mlp_dim (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 2):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        attention_downsample_rate (`int`, *optional*, defaults to 2):
            The downsampling rate of the attention layer.
        num_multimask_outputs (`int`, *optional*, defaults to 3):
            The number of outputs from the `SamMaskDecoder` module. In the Segment Anything paper, this is set to 3.
        iou_head_depth (`int`, *optional*, defaults to 3):
            The number of layers in the IoU head module.
        iou_head_hidden_dim (`int`, *optional*, defaults to 256):
            The dimensionality of the hidden states in the IoU head module.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.

    """
    def __init__(
        self,
        hidden_size=256,
        hidden_act="relu",
        mlp_dim=2048,
        num_hidden_layers=2,
        num_attention_heads=8,
        attention_downsample_rate=2,
        num_multimask_outputs=3,
        iou_head_depth=3,
        iou_head_hidden_dim=256,
        layer_norm_eps=1e-6,
        **kwargs,
    ):
        """
        Initializes a new instance of the SamMaskDecoderConfig class.

        Args:
            self: The object itself.
            hidden_size (int, optional): The size of the hidden layer. Default is 256.
            hidden_act (str, optional): The activation function to be used in the hidden layer. Default is 'relu'.
            mlp_dim (int, optional): The dimension of the Multi-Layer Perceptron (MLP). Default is 2048.
            num_hidden_layers (int, optional): The number of hidden layers. Default is 2.
            num_attention_heads (int, optional): The number of attention heads. Default is 8.
            attention_downsample_rate (int, optional): The downsample rate for attention. Default is 2.
            num_multimask_outputs (int, optional): The number of outputs for multimask. Default is 3.
            iou_head_depth (int, optional): The depth of the Intersection over Union (IoU) head. Default is 3.
            iou_head_hidden_dim (int, optional): The hidden dimension of the IoU head. Default is 256.
            layer_norm_eps (float, optional): The epsilon value for layer normalization. Default is 1e-06.

        Returns:
            None

        Raises:
            None
        """
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.hidden_act = hidden_act
        self.mlp_dim = mlp_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.attention_downsample_rate = attention_downsample_rate
        self.num_multimask_outputs = num_multimask_outputs
        self.iou_head_depth = iou_head_depth
        self.iou_head_hidden_dim = iou_head_hidden_dim
        self.layer_norm_eps = layer_norm_eps

mindnlp.transformers.models.sam.configuration_sam.SamMaskDecoderConfig.__init__(hidden_size=256, hidden_act='relu', mlp_dim=2048, num_hidden_layers=2, num_attention_heads=8, attention_downsample_rate=2, num_multimask_outputs=3, iou_head_depth=3, iou_head_hidden_dim=256, layer_norm_eps=1e-06, **kwargs)

Initializes a new instance of the SamMaskDecoderConfig class.

PARAMETER DESCRIPTION
self

The object itself.
hidden_size

The size of the hidden layer. Default is 256.

TYPE: int DEFAULT: 256

hidden_act

The activation function to be used in the hidden layer. Default is 'relu'.

TYPE: str DEFAULT: 'relu'

mlp_dim

The dimension of the Multi-Layer Perceptron (MLP). Default is 2048.

TYPE: int DEFAULT: 2048

num_hidden_layers

The number of hidden layers. Default is 2.

TYPE: int DEFAULT: 2

num_attention_heads

The number of attention heads. Default is 8.

TYPE: int DEFAULT: 8

attention_downsample_rate

The downsample rate for attention. Default is 2.

TYPE: int DEFAULT: 2

num_multimask_outputs

The number of outputs for multimask. Default is 3.

TYPE: int DEFAULT: 3

iou_head_depth

The depth of the Intersection over Union (IoU) head. Default is 3.

TYPE: int DEFAULT: 3

iou_head_hidden_dim

The hidden dimension of the IoU head. Default is 256.

TYPE: int DEFAULT: 256

layer_norm_eps

The epsilon value for layer normalization. Default is 1e-06.

TYPE: float DEFAULT: 1e-06

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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def __init__(
    self,
    hidden_size=256,
    hidden_act="relu",
    mlp_dim=2048,
    num_hidden_layers=2,
    num_attention_heads=8,
    attention_downsample_rate=2,
    num_multimask_outputs=3,
    iou_head_depth=3,
    iou_head_hidden_dim=256,
    layer_norm_eps=1e-6,
    **kwargs,
):
    """
    Initializes a new instance of the SamMaskDecoderConfig class.

    Args:
        self: The object itself.
        hidden_size (int, optional): The size of the hidden layer. Default is 256.
        hidden_act (str, optional): The activation function to be used in the hidden layer. Default is 'relu'.
        mlp_dim (int, optional): The dimension of the Multi-Layer Perceptron (MLP). Default is 2048.
        num_hidden_layers (int, optional): The number of hidden layers. Default is 2.
        num_attention_heads (int, optional): The number of attention heads. Default is 8.
        attention_downsample_rate (int, optional): The downsample rate for attention. Default is 2.
        num_multimask_outputs (int, optional): The number of outputs for multimask. Default is 3.
        iou_head_depth (int, optional): The depth of the Intersection over Union (IoU) head. Default is 3.
        iou_head_hidden_dim (int, optional): The hidden dimension of the IoU head. Default is 256.
        layer_norm_eps (float, optional): The epsilon value for layer normalization. Default is 1e-06.

    Returns:
        None

    Raises:
        None
    """
    super().__init__(**kwargs)
    self.hidden_size = hidden_size
    self.hidden_act = hidden_act
    self.mlp_dim = mlp_dim
    self.num_hidden_layers = num_hidden_layers
    self.num_attention_heads = num_attention_heads
    self.attention_downsample_rate = attention_downsample_rate
    self.num_multimask_outputs = num_multimask_outputs
    self.iou_head_depth = iou_head_depth
    self.iou_head_hidden_dim = iou_head_hidden_dim
    self.layer_norm_eps = layer_norm_eps

mindnlp.transformers.models.sam.configuration_sam.SamPromptEncoderConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [SamPromptEncoder]. The [SamPromptEncoder] module is used to encode the input 2D points and bounding boxes. Instantiating a configuration defaults will yield a similar configuration to that of the SAM-vit-h facebook/sam-vit-huge architecture.

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

PARAMETER DESCRIPTION
hidden_size

Dimensionality of the hidden states.

TYPE: `int`, *optional*, defaults to 256 DEFAULT: 256

image_size

The expected output resolution of the image.

TYPE: `int`, *optional*, defaults to 1024 DEFAULT: 1024

patch_size

The size (resolution) of each patch.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

mask_input_channels

The number of channels to be fed to the MaskDecoder module.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

num_point_embeddings

The number of point embeddings to be used.

TYPE: `int`, *optional*, defaults to 4 DEFAULT: 4

hidden_act

The non-linear activation function in the encoder and pooler.

TYPE: `str`, *optional*, defaults to `"gelu"` DEFAULT: 'gelu'

Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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class SamPromptEncoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SamPromptEncoder`]. The [`SamPromptEncoder`]
    module is used to encode the input 2D points and bounding boxes. Instantiating a configuration defaults will yield
    a similar configuration to that of the SAM-vit-h
    [facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the hidden states.
        image_size (`int`, *optional*, defaults to 1024):
            The expected output resolution of the image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        mask_input_channels (`int`, *optional*, defaults to 16):
            The number of channels to be fed to the `MaskDecoder` module.
        num_point_embeddings (`int`, *optional*, defaults to 4):
            The number of point embeddings to be used.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function in the encoder and pooler.
    """
    def __init__(
        self,
        hidden_size=256,
        image_size=1024,
        patch_size=16,
        mask_input_channels=16,
        num_point_embeddings=4,
        hidden_act="gelu",
        layer_norm_eps=1e-6,
        **kwargs,
    ):
        """
        Initializes an instance of the SamPromptEncoderConfig class.

        Args:
            self (SamPromptEncoderConfig): The instance of the class itself.
            hidden_size (int, optional): The size of the hidden state. Defaults to 256.
            image_size (int, optional): The size of the input image. Defaults to 1024.
            patch_size (int, optional): The size of each image patch. Defaults to 16.
            mask_input_channels (int, optional): The number of input channels for masking. Defaults to 16.
            num_point_embeddings (int, optional): The number of point embeddings. Defaults to 4.
            hidden_act (str, optional): The activation function for the hidden layers. Defaults to 'gelu'.
            layer_norm_eps (float, optional): The epsilon value for layer normalization. Defaults to 1e-06.

        Returns:
            None

        Raises:
            None
        """
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.image_embedding_size = image_size // patch_size
        self.mask_input_channels = mask_input_channels
        self.num_point_embeddings = num_point_embeddings
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps

mindnlp.transformers.models.sam.configuration_sam.SamPromptEncoderConfig.__init__(hidden_size=256, image_size=1024, patch_size=16, mask_input_channels=16, num_point_embeddings=4, hidden_act='gelu', layer_norm_eps=1e-06, **kwargs)

Initializes an instance of the SamPromptEncoderConfig class.

PARAMETER DESCRIPTION
self

The instance of the class itself.

TYPE: SamPromptEncoderConfig

hidden_size

The size of the hidden state. Defaults to 256.

TYPE: int DEFAULT: 256

image_size

The size of the input image. Defaults to 1024.

TYPE: int DEFAULT: 1024

patch_size

The size of each image patch. Defaults to 16.

TYPE: int DEFAULT: 16

mask_input_channels

The number of input channels for masking. Defaults to 16.

TYPE: int DEFAULT: 16

num_point_embeddings

The number of point embeddings. Defaults to 4.

TYPE: int DEFAULT: 4

hidden_act

The activation function for the hidden layers. Defaults to 'gelu'.

TYPE: str DEFAULT: 'gelu'

layer_norm_eps

The epsilon value for layer normalization. Defaults to 1e-06.

TYPE: float DEFAULT: 1e-06

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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def __init__(
    self,
    hidden_size=256,
    image_size=1024,
    patch_size=16,
    mask_input_channels=16,
    num_point_embeddings=4,
    hidden_act="gelu",
    layer_norm_eps=1e-6,
    **kwargs,
):
    """
    Initializes an instance of the SamPromptEncoderConfig class.

    Args:
        self (SamPromptEncoderConfig): The instance of the class itself.
        hidden_size (int, optional): The size of the hidden state. Defaults to 256.
        image_size (int, optional): The size of the input image. Defaults to 1024.
        patch_size (int, optional): The size of each image patch. Defaults to 16.
        mask_input_channels (int, optional): The number of input channels for masking. Defaults to 16.
        num_point_embeddings (int, optional): The number of point embeddings. Defaults to 4.
        hidden_act (str, optional): The activation function for the hidden layers. Defaults to 'gelu'.
        layer_norm_eps (float, optional): The epsilon value for layer normalization. Defaults to 1e-06.

    Returns:
        None

    Raises:
        None
    """
    super().__init__(**kwargs)
    self.hidden_size = hidden_size
    self.image_size = image_size
    self.patch_size = patch_size
    self.image_embedding_size = image_size // patch_size
    self.mask_input_channels = mask_input_channels
    self.num_point_embeddings = num_point_embeddings
    self.hidden_act = hidden_act
    self.layer_norm_eps = layer_norm_eps

mindnlp.transformers.models.sam.configuration_sam.SamVisionConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [SamVisionModel]. It is used to instantiate a SAM vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the SAM ViT-h facebook/sam-vit-huge architecture.

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

PARAMETER DESCRIPTION
hidden_size

Dimensionality of the encoder layers and the pooler layer.

TYPE: `int`, *optional*, defaults to 768 DEFAULT: 768

output_channels

Dimensionality of the output channels in the Patch Encoder.

TYPE: `int`, *optional*, defaults to 256 DEFAULT: 256

num_hidden_layers

Number of hidden layers in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 12 DEFAULT: 12

num_attention_heads

Number of attention heads for each attention layer in the Transformer encoder.

TYPE: `int`, *optional*, defaults to 12 DEFAULT: 12

num_channels

Number of channels in the input image.

TYPE: `int`, *optional*, defaults to 3 DEFAULT: 3

image_size

Expected resolution. Target size of the resized input image.

TYPE: `int`, *optional*, defaults to 1024 DEFAULT: 1024

patch_size

Size of the patches to be extracted from the input image.

TYPE: `int`, *optional*, defaults to 16 DEFAULT: 16

hidden_act

The non-linear activation function (function or string)

TYPE: `str`, *optional*, defaults to `"gelu"` DEFAULT: 'gelu'

layer_norm_eps

The epsilon used by the layer normalization layers.

TYPE: `float`, *optional*, defaults to 1e-06 DEFAULT: 1e-06

attention_dropout

The dropout ratio for the attention probabilities.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

initializer_range

The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

TYPE: `float`, *optional*, defaults to 1e-10 DEFAULT: 1e-10

qkv_bias

Whether to add a bias to query, key, value projections.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

mlp_ratio

Ratio of mlp hidden dim to embedding dim.

TYPE: `float`, *optional*, defaults to 4.0 DEFAULT: 4.0

use_abs_pos

Whether to use absolute position embedding.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

use_rel_pos

Whether to use relative position embedding.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

window_size

Window size for relative position.

TYPE: `int`, *optional*, defaults to 14 DEFAULT: 14

global_attn_indexes

The indexes of the global attention layers.

TYPE: `List[int]`, *optional*, defaults to `[2, 5, 8, 11]` DEFAULT: [2, 5, 8, 11]

num_pos_feats

The dimensionality of the position embedding.

TYPE: `int`, *optional*, defaults to 128 DEFAULT: 128

mlp_dim

The dimensionality of the MLP layer in the Transformer encoder. If None, defaults to mlp_ratio * hidden_size.

TYPE: `int`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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class SamVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SamVisionModel`]. It is used to instantiate a SAM
    vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
    defaults will yield a similar configuration to that of the SAM ViT-h
    [facebook/sam-vit-huge](https://huggingface.co/facebook/sam-vit-huge) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        output_channels (`int`, *optional*, defaults to 256):
            Dimensionality of the output channels in the Patch Encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input image.
        image_size (`int`, *optional*, defaults to 1024):
            Expected resolution. Target size of the resized input image.
        patch_size (`int`, *optional*, defaults to 16):
            Size of the patches to be extracted from the input image.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string)
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 1e-10):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to query, key, value projections.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            Ratio of mlp hidden dim to embedding dim.
        use_abs_pos (`bool`, *optional*, defaults to `True`):
            Whether to use absolute position embedding.
        use_rel_pos (`bool`, *optional*, defaults to `True`):
            Whether to use relative position embedding.
        window_size (`int`, *optional*, defaults to 14):
            Window size for relative position.
        global_attn_indexes (`List[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
            The indexes of the global attention layers.
        num_pos_feats (`int`, *optional*, defaults to 128):
            The dimensionality of the position embedding.
        mlp_dim (`int`, *optional*):
            The dimensionality of the MLP layer in the Transformer encoder. If `None`, defaults to `mlp_ratio *
            hidden_size`.
    """
    def __init__(
        self,
        hidden_size=768,
        output_channels=256,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=1024,
        patch_size=16,
        hidden_act="gelu",
        layer_norm_eps=1e-06,
        attention_dropout=0.0,
        initializer_range=1e-10,
        qkv_bias=True,
        mlp_ratio=4.0,
        use_abs_pos=True,
        use_rel_pos=True,
        window_size=14,
        global_attn_indexes=[2, 5, 8, 11],
        num_pos_feats=128,
        mlp_dim=None,
        **kwargs,
    ):
        """
        Initializes an instance of the SamVisionConfig class.

        Args:
            self: The object instance.
            hidden_size (int, optional): The size of the hidden state. Defaults to 768.
            output_channels (int, optional): The number of output channels. Defaults to 256.
            num_hidden_layers (int, optional): The number of hidden layers. Defaults to 12.
            num_attention_heads (int, optional): The number of attention heads. Defaults to 12.
            num_channels (int, optional): The number of input channels. Defaults to 3.
            image_size (int, optional): The size of the input image. Defaults to 1024.
            patch_size (int, optional): The size of each patch in the image. Defaults to 16.
            hidden_act (str, optional): The activation function for the hidden layers. Defaults to 'gelu'.
            layer_norm_eps (float, optional): The epsilon value for layer normalization. Defaults to 1e-06.
            attention_dropout (float, optional): The dropout rate for the attention mechanism. Defaults to 0.0.
            initializer_range (float, optional): The range for parameter initialization. Defaults to 1e-10.
            qkv_bias (bool, optional): Whether to include bias in the query, key, and value projections. Defaults to True.
            mlp_ratio (float, optional): The ratio of the hidden size to the feed-forward network size. Defaults to 4.0.
            use_abs_pos (bool, optional): Whether to use absolute position embeddings. Defaults to True.
            use_rel_pos (bool, optional): Whether to use relative position embeddings. Defaults to True.
            window_size (int, optional): The size of the attention window. Defaults to 14.
            global_attn_indexes (list[int], optional): The list of indexes for global attention. Defaults to [2, 5, 8, 11].
            num_pos_feats (int, optional): The number of positional features. Defaults to 128.
            mlp_dim (int, optional): The size of the hidden layer in the feed-forward network. If not provided,
                it is calculated as int(hidden_size * mlp_ratio).

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.output_channels = output_channels
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.qkv_bias = qkv_bias
        self.mlp_ratio = mlp_ratio
        self.use_abs_pos = use_abs_pos
        self.use_rel_pos = use_rel_pos
        self.window_size = window_size
        self.global_attn_indexes = global_attn_indexes
        self.num_pos_feats = num_pos_feats
        self.mlp_dim = int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim

mindnlp.transformers.models.sam.configuration_sam.SamVisionConfig.__init__(hidden_size=768, output_channels=256, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=1024, patch_size=16, hidden_act='gelu', layer_norm_eps=1e-06, attention_dropout=0.0, initializer_range=1e-10, qkv_bias=True, mlp_ratio=4.0, use_abs_pos=True, use_rel_pos=True, window_size=14, global_attn_indexes=[2, 5, 8, 11], num_pos_feats=128, mlp_dim=None, **kwargs)

Initializes an instance of the SamVisionConfig class.

PARAMETER DESCRIPTION
self

The object instance.
hidden_size

The size of the hidden state. Defaults to 768.

TYPE: int DEFAULT: 768

output_channels

The number of output channels. Defaults to 256.

TYPE: int DEFAULT: 256

num_hidden_layers

The number of hidden layers. Defaults to 12.

TYPE: int DEFAULT: 12

num_attention_heads

The number of attention heads. Defaults to 12.

TYPE: int DEFAULT: 12

num_channels

The number of input channels. Defaults to 3.

TYPE: int DEFAULT: 3

image_size

The size of the input image. Defaults to 1024.

TYPE: int DEFAULT: 1024

patch_size

The size of each patch in the image. Defaults to 16.

TYPE: int DEFAULT: 16

hidden_act

The activation function for the hidden layers. Defaults to 'gelu'.

TYPE: str DEFAULT: 'gelu'

layer_norm_eps

The epsilon value for layer normalization. Defaults to 1e-06.

TYPE: float DEFAULT: 1e-06

attention_dropout

The dropout rate for the attention mechanism. Defaults to 0.0.

TYPE: float DEFAULT: 0.0

initializer_range

The range for parameter initialization. Defaults to 1e-10.

TYPE: float DEFAULT: 1e-10

qkv_bias

Whether to include bias in the query, key, and value projections. Defaults to True.

TYPE: bool DEFAULT: True

mlp_ratio

The ratio of the hidden size to the feed-forward network size. Defaults to 4.0.

TYPE: float DEFAULT: 4.0

use_abs_pos

Whether to use absolute position embeddings. Defaults to True.

TYPE: bool DEFAULT: True

use_rel_pos

Whether to use relative position embeddings. Defaults to True.

TYPE: bool DEFAULT: True

window_size

The size of the attention window. Defaults to 14.

TYPE: int DEFAULT: 14

global_attn_indexes

The list of indexes for global attention. Defaults to [2, 5, 8, 11].

TYPE: list[int] DEFAULT: [2, 5, 8, 11]

num_pos_feats

The number of positional features. Defaults to 128.

TYPE: int DEFAULT: 128

mlp_dim

The size of the hidden layer in the feed-forward network. If not provided, it is calculated as int(hidden_size * mlp_ratio).

TYPE: int DEFAULT: None

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/configuration_sam.py 
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def __init__(
    self,
    hidden_size=768,
    output_channels=256,
    num_hidden_layers=12,
    num_attention_heads=12,
    num_channels=3,
    image_size=1024,
    patch_size=16,
    hidden_act="gelu",
    layer_norm_eps=1e-06,
    attention_dropout=0.0,
    initializer_range=1e-10,
    qkv_bias=True,
    mlp_ratio=4.0,
    use_abs_pos=True,
    use_rel_pos=True,
    window_size=14,
    global_attn_indexes=[2, 5, 8, 11],
    num_pos_feats=128,
    mlp_dim=None,
    **kwargs,
):
    """
    Initializes an instance of the SamVisionConfig class.

    Args:
        self: The object instance.
        hidden_size (int, optional): The size of the hidden state. Defaults to 768.
        output_channels (int, optional): The number of output channels. Defaults to 256.
        num_hidden_layers (int, optional): The number of hidden layers. Defaults to 12.
        num_attention_heads (int, optional): The number of attention heads. Defaults to 12.
        num_channels (int, optional): The number of input channels. Defaults to 3.
        image_size (int, optional): The size of the input image. Defaults to 1024.
        patch_size (int, optional): The size of each patch in the image. Defaults to 16.
        hidden_act (str, optional): The activation function for the hidden layers. Defaults to 'gelu'.
        layer_norm_eps (float, optional): The epsilon value for layer normalization. Defaults to 1e-06.
        attention_dropout (float, optional): The dropout rate for the attention mechanism. Defaults to 0.0.
        initializer_range (float, optional): The range for parameter initialization. Defaults to 1e-10.
        qkv_bias (bool, optional): Whether to include bias in the query, key, and value projections. Defaults to True.
        mlp_ratio (float, optional): The ratio of the hidden size to the feed-forward network size. Defaults to 4.0.
        use_abs_pos (bool, optional): Whether to use absolute position embeddings. Defaults to True.
        use_rel_pos (bool, optional): Whether to use relative position embeddings. Defaults to True.
        window_size (int, optional): The size of the attention window. Defaults to 14.
        global_attn_indexes (list[int], optional): The list of indexes for global attention. Defaults to [2, 5, 8, 11].
        num_pos_feats (int, optional): The number of positional features. Defaults to 128.
        mlp_dim (int, optional): The size of the hidden layer in the feed-forward network. If not provided,
            it is calculated as int(hidden_size * mlp_ratio).

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(**kwargs)

    self.hidden_size = hidden_size
    self.output_channels = output_channels
    self.num_hidden_layers = num_hidden_layers
    self.num_attention_heads = num_attention_heads
    self.num_channels = num_channels
    self.image_size = image_size
    self.patch_size = patch_size
    self.hidden_act = hidden_act
    self.layer_norm_eps = layer_norm_eps
    self.attention_dropout = attention_dropout
    self.initializer_range = initializer_range
    self.qkv_bias = qkv_bias
    self.mlp_ratio = mlp_ratio
    self.use_abs_pos = use_abs_pos
    self.use_rel_pos = use_rel_pos
    self.window_size = window_size
    self.global_attn_indexes = global_attn_indexes
    self.num_pos_feats = num_pos_feats
    self.mlp_dim = int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim

mindnlp.transformers.models.sam.image_processing_sam

Image processor class for SAM.

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor

Bases: BaseImageProcessor

Constructs a SAM image processor.

PARAMETER DESCRIPTION
do_resize

Whether to resize the image's (height, width) dimensions to the specified size. Can be overridden by the do_resize parameter in the preprocess method.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

size

Size of the output image after resizing. Resizes the longest edge of the image to match size["longest_edge"] while maintaining the aspect ratio. Can be overridden by the size parameter in the preprocess method.

TYPE: `dict`, *optional*, defaults to `{"longest_edge" -- 1024}` DEFAULT: None

mask_size

Size of the output segmentation map after resizing. Resizes the longest edge of the image to match size["longest_edge"] while maintaining the aspect ratio. Can be overridden by the mask_size parameter in the preprocess method.

TYPE: `dict`, *optional*, defaults to `{"longest_edge" -- 256}` DEFAULT: None

resample

Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the preprocess method.

TYPE: `PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR` DEFAULT: BILINEAR

do_rescale

Wwhether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale parameter in the preprocess method.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

rescale_factor

Scale factor to use if rescaling the image. Only has an effect if do_rescale is set to True. Can be overridden by the rescale_factor parameter in the preprocess method.

TYPE: `int` or `float`, *optional*, defaults to `1/255` DEFAULT: 1 / 255

do_normalize

Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess method. Can be overridden by the do_normalize parameter in the preprocess method.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

image_mean

Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can be overridden by the image_mean parameter in the preprocess method.

TYPE: `float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN` DEFAULT: None

image_std

Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_std parameter in the preprocess method. Can be overridden by the image_std parameter in the preprocess method.

TYPE: `float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD` DEFAULT: None

do_pad

Whether to pad the image to the specified pad_size. Can be overridden by the do_pad parameter in the preprocess method.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

pad_size

Size of the output image after padding. Can be overridden by the pad_size parameter in the preprocess method.

TYPE: `dict`, *optional*, defaults to `{"height" -- 1024, "width" -- 1024}` DEFAULT: None

mask_pad_size

Size of the output segmentation map after padding. Can be overridden by the mask_pad_size parameter in the preprocess method.

TYPE: `dict`, *optional*, defaults to `{"height" -- 256, "width" -- 256}` DEFAULT: None

do_convert_rgb

Whether to convert the image to RGB.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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class SamImageProcessor(BaseImageProcessor):
    r"""
    Constructs a SAM image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
            `do_resize` parameter in the `preprocess` method.
        size (`dict`, *optional*, defaults to `{"longest_edge" -- 1024}`):
            Size of the output image after resizing. Resizes the longest edge of the image to match
            `size["longest_edge"]` while maintaining the aspect ratio. Can be overridden by the `size` parameter in the
            `preprocess` method.
        mask_size (`dict`, *optional*, defaults to `{"longest_edge" -- 256}`):
            Size of the output segmentation map after resizing. Resizes the longest edge of the image to match
            `size["longest_edge"]` while maintaining the aspect ratio. Can be overridden by the `mask_size` parameter
            in the `preprocess` method.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
            `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
            `do_rescale` parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
            overridden by the `rescale_factor` parameter in the `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
            overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
            Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_pad (`bool`, *optional*, defaults to `True`):
            Whether to pad the image to the specified `pad_size`. Can be overridden by the `do_pad` parameter in the
            `preprocess` method.
        pad_size (`dict`, *optional*, defaults to `{"height" -- 1024, "width" -- 1024}`):
            Size of the output image after padding. Can be overridden by the `pad_size` parameter in the `preprocess`
            method.
        mask_pad_size (`dict`, *optional*, defaults to `{"height" -- 256, "width" -- 256}`):
            Size of the output segmentation map after padding. Can be overridden by the `mask_pad_size` parameter in
            the `preprocess` method.
        do_convert_rgb (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to RGB.
    """
    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        mask_size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: bool = True,
        pad_size: int = None,
        mask_pad_size: int = None,
        do_convert_rgb: bool = True,
        **kwargs,
    ) -> None:
        """
        Initializes an instance of the SamImageProcessor class.

        Args:
            self: The instance of the class.
            do_resize (bool): Determines whether resizing of images should be performed. Defaults to True.
            size (Dict[str, int]): The desired size of the images. Defaults to {'longest_edge': 1024}.
                The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'.
                If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.
            mask_size (Dict[str, int]): The desired size of the segmentation masks. Defaults to {'longest_edge': 256}.
                The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'.
                If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.
            resample (PILImageResampling): The resampling method to use during image resizing.
                Defaults to PILImageResampling.BILINEAR.
            do_rescale (bool): Determines whether rescaling of pixel values should be performed. Defaults to True.
            rescale_factor (Union[int, float]): The factor to divide pixel values by during rescaling.
                Defaults to 1 / 255.
            do_normalize (bool): Determines whether normalization of pixel values should be performed.
                Defaults to True.
            image_mean (Optional[Union[float, List[float]]]): The mean values to subtract from pixel values
                during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_MEAN.
            image_std (Optional[Union[float, List[float]]]): The standard deviation values to divide pixel values
                by during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_STD.
            do_pad (bool): Determines whether padding of images should be performed. Defaults to True.
            pad_size (int): The desired size of the padded images. Defaults to None,
                which uses {'height': 1024, 'width': 1024}. The size can be specified as a single integer, representing
                both height and width.
            mask_pad_size (int): The desired size of the padded segmentation masks. Defaults to None,
                which uses {'height': 256, 'width': 256}. The size can be specified as a single integer,
                representing both height and width.
            do_convert_rgb (bool): Determines whether conversion to RGB color space should be performed. Defaults to True.
            **kwargs: Additional keyword arguments to be passed to the parent class forwardor.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(**kwargs)
        size = size if size is not None else {"longest_edge": 1024}
        size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size

        pad_size = pad_size if pad_size is not None else {"height": 1024, "width": 1024}
        pad_size = get_size_dict(pad_size, default_to_square=True)

        mask_size = mask_size if mask_size is not None else {"longest_edge": 256}
        mask_size = (
            get_size_dict(max_size=mask_size, default_to_square=False)
            if not isinstance(mask_size, dict)
            else mask_size
        )

        mask_pad_size = mask_pad_size if mask_pad_size is not None else {"height": 256, "width": 256}
        mask_pad_size = get_size_dict(mask_pad_size, default_to_square=True)

        self.do_resize = do_resize
        self.size = size
        self.mask_size = mask_size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
        self.do_pad = do_pad
        self.pad_size = pad_size
        self.mask_pad_size = mask_pad_size
        self.do_convert_rgb = do_convert_rgb
        self._valid_processor_keys = [
            "images",
            "segmentation_maps",
            "do_resize",
            "size",
            "mask_size",
            "resample",
            "do_rescale",
            "rescale_factor",
            "do_normalize",
            "image_mean",
            "image_std",
            "do_pad",
            "pad_size",
            "mask_pad_size",
            "do_convert_rgb",
            "return_tensors",
            "data_format",
            "input_data_format",
        ]

    def pad_image(
        self,
        image: np.ndarray,
        pad_size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Pad an image to `(pad_size["height"], pad_size["width"])` with zeros to the right and bottom.

        Args:
            image (`np.ndarray`):
                Image to pad.
            pad_size (`Dict[str, int]`):
                Size of the output image after padding.
            data_format (`str` or `ChannelDimension`, *optional*):
                The data format of the image. Can be either "channels_first" or "channels_last". If `None`, the
                `data_format` of the `image` will be used.
            input_data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
        """
        output_height, output_width = pad_size["height"], pad_size["width"]
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)

        pad_width = output_width - input_width
        pad_height = output_height - input_height

        padded_image = pad(
            image,
            ((0, pad_height), (0, pad_width)),
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )
        return padded_image

    def _get_preprocess_shape(self, old_shape: Tuple[int, int], longest_edge: int):
        """
        Compute the output size given input size and target long side length.
        """
        oldh, oldw = old_shape
        scale = longest_edge * 1.0 / max(oldh, oldw)
        newh, neww = oldh * scale, oldw * scale
        newh = int(newh + 0.5)
        neww = int(neww + 0.5)
        return (newh, neww)

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"longest_edge": int}` specifying the size of the output image. The longest
                edge of the image will be resized to the specified size, while the other edge will be resized to
                maintain the aspect ratio.
            resample:
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        size = get_size_dict(size)
        if "longest_edge" not in size:
            raise ValueError(f"The `size` dictionary must contain the key `longest_edge`. Got {size.keys()}")
        input_size = get_image_size(image, channel_dim=input_data_format)
        output_height, output_width = self._get_preprocess_shape(input_size, size["longest_edge"])
        return resize(
            image,
            size=(output_height, output_width),
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def _preprocess(
        self,
        image: ImageInput,
        do_resize: bool,
        do_rescale: bool,
        do_normalize: bool,
        size: Optional[Dict[str, int]] = None,
        resample: PILImageResampling = None,
        rescale_factor: Optional[float] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: Optional[bool] = None,
        pad_size: Optional[Dict[str, int]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ):
        '''
        This method preprocesses the input image according to the specified operations such as resizing, rescaling,
        normalization, and padding.

        Args:
            self: The instance of the SamImageProcessor class.
            image (ImageInput): The input image to be preprocessed.
            do_resize (bool): A flag indicating whether to perform resizing on the input image.
            do_rescale (bool): A flag indicating whether to perform rescaling on the input image.
            do_normalize (bool): A flag indicating whether to perform normalization on the input image.
            size (Optional[Dict[str, int]]): The target size for resizing the image in the format
                {'width': int, 'height': int}. Default is None.
            resample (PILImageResampling): The resampling filter to be used during image resizing. Default is None.
            rescale_factor (Optional[float]): The factor by which the image should be rescaled. Default is None.
            image_mean (Optional[Union[float, List[float]]]): The mean value to be used for image normalization.
                It can be a single float value or a list of float values, depending on the input_data_format.
                Default is None.
            image_std (Optional[Union[float, List[float]]]):
                The standard deviation value to be used for image normalization.
                It can be a single float value or a list of float values, depending on the input_data_format.
                Default is None.
            do_pad (Optional[bool]): A flag indicating whether to perform padding on the input image. Default is None.
            pad_size (Optional[Dict[str, int]]): The size of the padding to be applied in the format
                {'top': int, 'bottom': int, 'left': int, 'right': int}. Default is None.
            input_data_format (Optional[Union[str, ChannelDimension]]): The data format of the input image,
                e.g., 'channels_first' or 'channels_last'. Default is None.

        Returns:
            Tuple[ImageInput, Tuple[int, int, int]]: The preprocessed image and the reshaped input size in the format
                (image, (height, width, channels)).

        Raises:
            ValueError: If the input_data_format is invalid or not supported.
            TypeError: If the input_data_format is not a string or ChannelDimension.
        '''
        if do_resize:
            image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
        reshaped_input_size = get_image_size(image, channel_dim=input_data_format)

        if do_rescale:
            image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)

        if do_normalize:
            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)

        if do_pad:
            image = self.pad_image(image=image, pad_size=pad_size, input_data_format=input_data_format)

        return image, reshaped_input_size

    def _preprocess_image(
        self,
        image: ImageInput,
        do_resize: Optional[bool] = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_rescale: bool = None,
        rescale_factor: Optional[float] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: Optional[bool] = None,
        pad_size: Optional[Dict[str, int]] = None,
        do_convert_rgb: Optional[bool] = None,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]:
        """
        This method preprocesses the input image with various transformations and returns the processed image,
        original size, and reshaped input size.

        Args:
            self: The instance of the SamImageProcessor class.
            image (ImageInput): The input image to be preprocessed.
            do_resize (Optional[bool]): A flag indicating whether to resize the image. Defaults to None.
            size (Optional[Dict[str, int]]): A dictionary containing the target width and height for resizing the image.
                Defaults to None.
            resample (PILImageResampling): The resampling filter to be used during image resizing.
            do_rescale (Optional[bool]): A flag indicating whether to rescale the image. Defaults to None.
            rescale_factor (Optional[float]): The factor by which to rescale the image. Defaults to None.
            do_normalize (Optional[bool]): A flag indicating whether to normalize the image. Defaults to None.
            image_mean (Optional[Union[float, List[float]]]): The mean values to be used for image normalization.
                Defaults to None.
            image_std (Optional[Union[float, List[float]]]): The standard deviation values to be used for
                image normalization. Defaults to None.
            do_pad (Optional[bool]): A flag indicating whether to pad the image. Defaults to None.
            pad_size (Optional[Dict[str, int]]): A dictionary containing the padding width and height.
                Defaults to None.
            do_convert_rgb (Optional[bool]): A flag indicating whether to convert the image to RGB format.
                Defaults to None.
            data_format (Optional[Union[str, ChannelDimension]]): The desired data format for the processed image.
            input_data_format (Optional[Union[str, ChannelDimension]]): The input data format of the image.

        Returns:
            Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]: A tuple containing the processed image as a numpy array,
                the original size of the input image, and the reshaped input size after preprocessing.

        Raises:
            None
        """
        image = to_numpy_array(image)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
            image = convert_to_rgb(image)

        # All transformations expect numpy arrays.
        image = to_numpy_array(image)

        if is_scaled_image(image) and do_rescale:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )

        if input_data_format is None:
            input_data_format = infer_channel_dimension_format(image)

        original_size = get_image_size(image, channel_dim=input_data_format)

        image, reshaped_input_size = self._preprocess(
            image=image,
            do_resize=do_resize,
            size=size,
            resample=resample,
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_pad=do_pad,
            pad_size=pad_size,
            input_data_format=input_data_format,
        )

        if data_format is not None:
            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)

        return image, original_size, reshaped_input_size

    def _preprocess_mask(
        self,
        segmentation_map: ImageInput,
        do_resize: Optional[bool] = None,
        mask_size: Dict[str, int] = None,
        do_pad: Optional[bool] = None,
        mask_pad_size: Optional[Dict[str, int]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        """
        Method to preprocess a segmentation mask.

        Args:
            self: The instance of the SamImageProcessor class.
            segmentation_map (ImageInput): The input segmentation map to be preprocessed.
            do_resize (Optional[bool]): Flag indicating whether resizing should be performed. Default is None.
            mask_size (Dict[str, int]): Dictionary containing the target size for the mask after resizing.
            do_pad (Optional[bool]): Flag indicating whether padding should be applied. Default is None.
            mask_pad_size (Optional[Dict[str, int]]): Dictionary containing the padding size for the mask.
            input_data_format (Optional[Union[str, ChannelDimension]]): Format of the input data. Default is None.

        Returns:
            np.ndarray: The preprocessed segmentation map as a NumPy array.
            original_size: The size of the original segmentation map.

        Raises:
            None
        """
        segmentation_map = to_numpy_array(segmentation_map)

        # Add channel dimension if missing - needed for certain transformations
        if segmentation_map.ndim == 2:
            added_channel_dim = True
            segmentation_map = segmentation_map[None, ...]
            input_data_format = ChannelDimension.FIRST
        else:
            added_channel_dim = False
            if input_data_format is None:
                input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)

        original_size = get_image_size(segmentation_map, channel_dim=input_data_format)

        segmentation_map, _ = self._preprocess(
            image=segmentation_map,
            do_resize=do_resize,
            size=mask_size,
            resample=PILImageResampling.NEAREST,
            do_rescale=False,
            do_normalize=False,
            do_pad=do_pad,
            pad_size=mask_pad_size,
            input_data_format=input_data_format,
        )

        # Remove extra channel dimension if added for processing
        if added_channel_dim:
            segmentation_map = segmentation_map.squeeze(0)
        segmentation_map = segmentation_map.astype(np.int64)

        return segmentation_map, original_size

    def preprocess(
        self,
        images: ImageInput,
        segmentation_maps: Optional[ImageInput] = None,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        mask_size: Optional[Dict[str, int]] = None,
        resample: Optional["PILImageResampling"] = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[Union[int, float]] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_pad: Optional[bool] = None,
        pad_size: Optional[Dict[str, int]] = None,
        mask_pad_size: Optional[Dict[str, int]] = None,
        do_convert_rgb: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ):
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                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`.
            segmentation_maps (`ImageInput`, *optional*):
                Segmentation map to preprocess.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Controls the size of the image after `resize`. The longest edge of the image is resized to
                `size["longest_edge"]` whilst preserving the aspect ratio.
            mask_size (`Dict[str, int]`, *optional*, defaults to `self.mask_size`):
                Controls the size of the segmentation map after `resize`. The longest edge of the image is resized to
                `size["longest_edge"]` whilst preserving the aspect ratio.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image pixel values by rescaling factor.
            rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to apply to the image pixel values.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean to normalize the image by if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
            do_pad (`bool`, *optional*, defaults to `self.do_pad`):
                Whether to pad the image.
            pad_size (`Dict[str, int]`, *optional*, defaults to `self.pad_size`):
                Controls the size of the padding applied to the image. The image is padded to `pad_size["height"]` and
                `pad_size["width"]` if `do_pad` is set to `True`.
            mask_pad_size (`Dict[str, int]`, *optional*, defaults to `self.mask_pad_size`):
                Controls the size of the padding applied to the segmentation map. The image is padded to
                `mask_pad_size["height"]` and `mask_pad_size["width"]` if `do_pad` is set to `True`.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:

                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `mindspore.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - Unset: Use the channel dimension format of the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:

                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size
        mask_size = mask_size if mask_size is not None else self.mask_size
        mask_size = (
            get_size_dict(max_size=mask_size, default_to_square=False)
            if not isinstance(mask_size, dict)
            else mask_size
        )
        resample = resample if resample is not None else self.resample
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        do_pad = do_pad if do_pad is not None else self.do_pad
        pad_size = pad_size if pad_size is not None else self.pad_size
        pad_size = get_size_dict(pad_size, default_to_square=True)
        mask_pad_size = mask_pad_size if mask_pad_size is not None else self.mask_pad_size
        mask_pad_size = get_size_dict(mask_pad_size, default_to_square=True)
        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        images = make_list_of_images(images)

        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "mindspore.Tensor, tf.Tensor or jax.ndarray."
            )

        if segmentation_maps is not None:
            segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)

            if not valid_images(segmentation_maps):
                raise ValueError(
                    "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "mindspore.Tensor, tf.Tensor or jax.ndarray."
                )
        validate_preprocess_arguments(
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_pad=do_pad,
            size_divisibility=pad_size,  # Here _preprocess needs do_pad and pad_size.
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        images, original_sizes, reshaped_input_sizes = zip(
            *(
                self._preprocess_image(
                    image=img,
                    do_resize=do_resize,
                    size=size,
                    resample=resample,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    do_pad=do_pad,
                    pad_size=pad_size,
                    do_convert_rgb=do_convert_rgb,
                    data_format=data_format,
                    input_data_format=input_data_format,
                )
                for img in images
            )
        )

        data = {
            "pixel_values": images,
            "original_sizes": original_sizes,
            "reshaped_input_sizes": reshaped_input_sizes,
        }

        if segmentation_maps is not None:
            segmentation_maps, original_mask_sizes = zip(
                *(
                    self._preprocess_mask(
                        segmentation_map=mask,
                        do_resize=do_resize,
                        mask_size=mask_size,
                        do_pad=do_pad,
                        mask_pad_size=mask_pad_size,
                        input_data_format=input_data_format,
                    )
                    for mask in segmentation_maps
                )
            )

            # masks should start out the same size as input images
            assert all(
                original_im_size == original_mask_size
                for original_im_size, original_mask_size in zip(original_sizes, original_mask_sizes)
            ), "Segmentation maps should be the same size as input images."

            data["labels"] = segmentation_maps

        return BatchFeature(data=data, tensor_type=return_tensors)

    def post_process_masks(
        self,
        masks,
        original_sizes,
        reshaped_input_sizes,
        mask_threshold=0.0,
        binarize=True,
        pad_size=None,
        return_tensors="ms",
    ):
        """
        Remove padding and upscale masks to the original image size.

        Args:
            masks (`Union[List[mindspore.Tensor], List[np.ndarray], List[tf.Tensor]]`):
                Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format.
            original_sizes (`Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`):
                The original sizes of each image before it was resized to the model's expected input shape, in (height,
                width) format.
            reshaped_input_sizes (`Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`):
                The size of each image as it is fed to the model, in (height, width) format. Used to remove padding.
            mask_threshold (`float`, *optional*, defaults to 0.0):
                The threshold to use for binarizing the masks.
            binarize (`bool`, *optional*, defaults to `True`):
                Whether to binarize the masks.
            pad_size (`int`, *optional*, defaults to `self.pad_size`):
                The target size the images were padded to before being passed to the model. If None, the target size is
                assumed to be the processor's `pad_size`.
            return_tensors (`str`, *optional*, defaults to `"ms"`):
                If `"ms"`, return PyTorch tensors. If `"tf"`, return TensorFlow tensors.

        Returns:
            (`Union[mindspore.Tensor, tf.Tensor]`): Batched masks in batch_size, num_channels, height, width) format, where
            (height, width) is given by original_size.
        """
        if return_tensors == "ms":
            return self._post_process_masks_ms(
                masks=masks,
                original_sizes=original_sizes,
                reshaped_input_sizes=reshaped_input_sizes,
                mask_threshold=mask_threshold,
                binarize=binarize,
                pad_size=pad_size,
            )
        else:
            raise ValueError("return_tensors must be 'ms'.")

    def _post_process_masks_ms(
        self, masks, original_sizes, reshaped_input_sizes, mask_threshold=0.0, binarize=True, pad_size=None
    ):
        """
        Remove padding and upscale masks to the original image size.

        Args:
            masks (`Union[List[mindspore.Tensor], List[np.ndarray]]`):
                Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format.
            original_sizes (`Union[mindspore.Tensor, List[Tuple[int,int]]]`):
                The original sizes of each image before it was resized to the model's expected input shape, in (height,
                width) format.
            reshaped_input_sizes (`Union[mindspore.Tensor, List[Tuple[int,int]]]`):
                The size of each image as it is fed to the model, in (height, width) format. Used to remove padding.
            mask_threshold (`float`, *optional*, defaults to 0.0):
                The threshold to use for binarizing the masks.
            binarize (`bool`, *optional*, defaults to `True`):
                Whether to binarize the masks.
            pad_size (`int`, *optional*, defaults to `self.pad_size`):
                The target size the images were padded to before being passed to the model. If None, the target size is
                assumed to be the processor's `pad_size`.

        Returns:
            (`mindspore.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width)
            is given by original_size.
        """
        requires_backends(self, ["torch"])
        pad_size = self.pad_size if pad_size is None else pad_size
        target_image_size = (pad_size["height"], pad_size["width"])
        if isinstance(original_sizes, (mindspore.Tensor, np.ndarray)):
            original_sizes = original_sizes.tolist()
        if isinstance(reshaped_input_sizes, (mindspore.Tensor, np.ndarray)):
            reshaped_input_sizes = reshaped_input_sizes.tolist()
        output_masks = []
        for i, original_size in enumerate(original_sizes):
            if isinstance(masks[i], np.ndarray):
                masks[i] = mindspore.Tensor.from_numpy(masks[i])
            elif not isinstance(masks[i], mindspore.Tensor):
                raise ValueError("Input masks should be a list of `mindspore.tensors` or a list of `np.ndarray`")
            interpolated_mask = ops.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False)
            interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]]
            interpolated_mask = ops.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False)
            if binarize:
                interpolated_mask = interpolated_mask > mask_threshold
            output_masks.append(interpolated_mask)

        return output_masks

    def post_process_for_mask_generation(
        self, all_masks, all_scores, all_boxes, crops_nms_thresh, return_tensors="ms"
    ):
        """
        Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks.

        Args:
            all_masks (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
                List of all predicted segmentation masks
            all_scores (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
                List of all predicted iou scores
            all_boxes (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
                List of all bounding boxes of the predicted masks
            crops_nms_thresh (`float`):
                Threshold for NMS (Non Maximum Suppression) algorithm.
            return_tensors (`str`, *optional*, defaults to `pt`):
                If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
        """
        if return_tensors == "ms":
            return _postprocess_for_mg(all_masks, all_scores, all_boxes, crops_nms_thresh)

    def generate_crop_boxes(
        self,
        image,
        target_size,
        crop_n_layers: int = 0,
        overlap_ratio: float = 512 / 1500,
        points_per_crop: Optional[int] = 32,
        crop_n_points_downscale_factor: Optional[List[int]] = 1,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        return_tensors: str = "ms",
    ):
        """
        Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

        Args:
            image (`np.array`):
                Input original image
            target_size (`int`):
                Target size of the resized image
            crop_n_layers (`int`, *optional*, defaults to 0):
                If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where
                each layer has 2**i_layer number of image crops.
            overlap_ratio (`float`, *optional*, defaults to 512/1500):
                Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of
                the image length. Later layers with more crops scale down this overlap.
            points_per_crop (`int`, *optional*, defaults to 32):
                Number of points to sample from each crop.
            crop_n_points_downscale_factor (`List[int]`, *optional*, defaults to 1):
                The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
            input_data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
            return_tensors (`str`, *optional*, defaults to `pt`):
                If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
        """
        crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes(
            image,
            target_size,
            crop_n_layers,
            overlap_ratio,
            points_per_crop,
            crop_n_points_downscale_factor,
            input_data_format,
        )
        if return_tensors == "ms":
            crop_boxes = mindspore.tensor(crop_boxes)
            points_per_crop = mindspore.tensor(points_per_crop)
            # cropped_images stays as np
            input_labels = mindspore.tensor(input_labels)
        else:
            raise ValueError("return_tensors must be 'ms'.")
        return crop_boxes, points_per_crop, cropped_images, input_labels

    def filter_masks(
        self,
        masks,
        iou_scores,
        original_size,
        cropped_box_image,
        pred_iou_thresh=0.88,
        stability_score_thresh=0.95,
        mask_threshold=0,
        stability_score_offset=1,
        return_tensors="ms",
    ):
        """
        Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being
        that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability
        score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to
        bounding boxes and pad the predicted masks if necessary.

        Args:
            masks (`Union[mindspore.Tensor, tf.Tensor]`):
                Input masks.
            iou_scores (`Union[mindspore.Tensor, tf.Tensor]`):
                List of IoU scores.
            original_size (`Tuple[int,int]`):
                Size of the orginal image.
            cropped_box_image (`np.array`):
                The cropped image.
            pred_iou_thresh (`float`, *optional*, defaults to 0.88):
                The threshold for the iou scores.
            stability_score_thresh (`float`, *optional*, defaults to 0.95):
                The threshold for the stability score.
            mask_threshold (`float`, *optional*, defaults to 0):
                The threshold for the predicted masks.
            stability_score_offset (`float`, *optional*, defaults to 1):
                The offset for the stability score used in the `_compute_stability_score` method.
            return_tensors (`str`, *optional*, defaults to `pt`):
                If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
        """
        if return_tensors == "ms":
            return self._filter_masks(
                masks=masks,
                iou_scores=iou_scores,
                original_size=original_size,
                cropped_box_image=cropped_box_image,
                pred_iou_thresh=pred_iou_thresh,
                stability_score_thresh=stability_score_thresh,
                mask_threshold=mask_threshold,
                stability_score_offset=stability_score_offset,
            )
        elif return_tensors == "tf":
            return self._filter_masks_tf(
                masks=masks,
                iou_scores=iou_scores,
                original_size=original_size,
                cropped_box_image=cropped_box_image,
                pred_iou_thresh=pred_iou_thresh,
                stability_score_thresh=stability_score_thresh,
                mask_threshold=mask_threshold,
                stability_score_offset=stability_score_offset,
            )

    def _filter_masks(
        self,
        masks,
        iou_scores,
        original_size,
        cropped_box_image,
        pred_iou_thresh=0.88,
        stability_score_thresh=0.95,
        mask_threshold=0,
        stability_score_offset=1,
    ):
        """
        Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being
        that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability
        score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to
        bounding boxes and pad the predicted masks if necessary.

        Args:
            masks (`mindspore.Tensor`):
                Input masks.
            iou_scores (`mindspore.Tensor`):
                List of IoU scores.
            original_size (`Tuple[int,int]`):
                Size of the orginal image.
            cropped_box_image (`np.array`):
                The cropped image.
            pred_iou_thresh (`float`, *optional*, defaults to 0.88):
                The threshold for the iou scores.
            stability_score_thresh (`float`, *optional*, defaults to 0.95):
                The threshold for the stability score.
            mask_threshold (`float`, *optional*, defaults to 0):
                The threshold for the predicted masks.
            stability_score_offset (`float`, *optional*, defaults to 1):
                The offset for the stability score used in the `_compute_stability_score` method.

        """
        requires_backends(self, ["torch"])
        original_height, original_width = original_size
        iou_scores = iou_scores.flatten(start_dim=0, end_dim=1)
        masks = masks.flatten(start_dim=0, end_dim=1)

        if masks.shape[0] != iou_scores.shape[0]:
            raise ValueError("masks and iou_scores must have the same batch size.")

        batch_size = masks.shape[0]

        keep_mask = ops.ones(batch_size, dtype=mindspore.bool_)

        if pred_iou_thresh > 0.0:
            keep_mask = keep_mask & (iou_scores > pred_iou_thresh)

        # compute stability score
        if stability_score_thresh > 0.0:
            stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset)
            keep_mask = keep_mask & (stability_scores > stability_score_thresh)

        scores = iou_scores[keep_mask]
        masks = masks[keep_mask]

        # binarize masks
        masks = masks > mask_threshold
        converted_boxes = _batched_mask_to_box(masks)

        keep_mask = ~_is_box_near_crop_edge(
            converted_boxes, cropped_box_image, [0, 0, original_width, original_height]
        )

        scores = scores[keep_mask]
        masks = masks[keep_mask]
        converted_boxes = converted_boxes[keep_mask]

        masks = _pad_masks(masks, cropped_box_image, original_height, original_width)
        # conversion to rle is necessary to run non-maximum suppresion
        masks = _mask_to_rle(masks)

        return masks, scores, converted_boxes

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.__init__(do_resize=True, size=None, mask_size=None, resample=PILImageResampling.BILINEAR, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=None, image_std=None, do_pad=True, pad_size=None, mask_pad_size=None, do_convert_rgb=True, **kwargs)

Initializes an instance of the SamImageProcessor class.

PARAMETER DESCRIPTION
self

The instance of the class.
do_resize

Determines whether resizing of images should be performed. Defaults to True.

TYPE: bool DEFAULT: True

size

The desired size of the images. Defaults to {'longest_edge': 1024}. The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'. If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.

TYPE: Dict[str, int] DEFAULT: None

mask_size

The desired size of the segmentation masks. Defaults to {'longest_edge': 256}. The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'. If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.

TYPE: Dict[str, int] DEFAULT: None

resample

The resampling method to use during image resizing. Defaults to PILImageResampling.BILINEAR.

TYPE: PILImageResampling DEFAULT: BILINEAR

do_rescale

Determines whether rescaling of pixel values should be performed. Defaults to True.

TYPE: bool DEFAULT: True

rescale_factor

The factor to divide pixel values by during rescaling. Defaults to 1 / 255.

TYPE: Union[int, float] DEFAULT: 1 / 255

do_normalize

Determines whether normalization of pixel values should be performed. Defaults to True.

TYPE: bool DEFAULT: True

image_mean

The mean values to subtract from pixel values during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_MEAN.

TYPE: Optional[Union[float, List[float]]] DEFAULT: None

image_std

The standard deviation values to divide pixel values by during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_STD.

TYPE: Optional[Union[float, List[float]]] DEFAULT: None

do_pad

Determines whether padding of images should be performed. Defaults to True.

TYPE: bool DEFAULT: True

pad_size

The desired size of the padded images. Defaults to None, which uses {'height': 1024, 'width': 1024}. The size can be specified as a single integer, representing both height and width.

TYPE: int DEFAULT: None

mask_pad_size

The desired size of the padded segmentation masks. Defaults to None, which uses {'height': 256, 'width': 256}. The size can be specified as a single integer, representing both height and width.

TYPE: int DEFAULT: None

do_convert_rgb

Determines whether conversion to RGB color space should be performed. Defaults to True.

TYPE: bool DEFAULT: True

**kwargs

Additional keyword arguments to be passed to the parent class forwardor.

DEFAULT: {}

RETURNS DESCRIPTION
None

None.
Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def __init__(
    self,
    do_resize: bool = True,
    size: Dict[str, int] = None,
    mask_size: Dict[str, int] = None,
    resample: PILImageResampling = PILImageResampling.BILINEAR,
    do_rescale: bool = True,
    rescale_factor: Union[int, float] = 1 / 255,
    do_normalize: bool = True,
    image_mean: Optional[Union[float, List[float]]] = None,
    image_std: Optional[Union[float, List[float]]] = None,
    do_pad: bool = True,
    pad_size: int = None,
    mask_pad_size: int = None,
    do_convert_rgb: bool = True,
    **kwargs,
) -> None:
    """
    Initializes an instance of the SamImageProcessor class.

    Args:
        self: The instance of the class.
        do_resize (bool): Determines whether resizing of images should be performed. Defaults to True.
        size (Dict[str, int]): The desired size of the images. Defaults to {'longest_edge': 1024}.
            The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'.
            If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.
        mask_size (Dict[str, int]): The desired size of the segmentation masks. Defaults to {'longest_edge': 256}.
            The size can be specified as a dictionary with keys 'longest_edge' or 'height' and 'width'.
            If not provided as a dictionary, it is converted to a dictionary with the 'longest_edge' key.
        resample (PILImageResampling): The resampling method to use during image resizing.
            Defaults to PILImageResampling.BILINEAR.
        do_rescale (bool): Determines whether rescaling of pixel values should be performed. Defaults to True.
        rescale_factor (Union[int, float]): The factor to divide pixel values by during rescaling.
            Defaults to 1 / 255.
        do_normalize (bool): Determines whether normalization of pixel values should be performed.
            Defaults to True.
        image_mean (Optional[Union[float, List[float]]]): The mean values to subtract from pixel values
            during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_MEAN.
        image_std (Optional[Union[float, List[float]]]): The standard deviation values to divide pixel values
            by during normalization. Defaults to None, which uses the IMAGENET_DEFAULT_STD.
        do_pad (bool): Determines whether padding of images should be performed. Defaults to True.
        pad_size (int): The desired size of the padded images. Defaults to None,
            which uses {'height': 1024, 'width': 1024}. The size can be specified as a single integer, representing
            both height and width.
        mask_pad_size (int): The desired size of the padded segmentation masks. Defaults to None,
            which uses {'height': 256, 'width': 256}. The size can be specified as a single integer,
            representing both height and width.
        do_convert_rgb (bool): Determines whether conversion to RGB color space should be performed. Defaults to True.
        **kwargs: Additional keyword arguments to be passed to the parent class forwardor.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(**kwargs)
    size = size if size is not None else {"longest_edge": 1024}
    size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size

    pad_size = pad_size if pad_size is not None else {"height": 1024, "width": 1024}
    pad_size = get_size_dict(pad_size, default_to_square=True)

    mask_size = mask_size if mask_size is not None else {"longest_edge": 256}
    mask_size = (
        get_size_dict(max_size=mask_size, default_to_square=False)
        if not isinstance(mask_size, dict)
        else mask_size
    )

    mask_pad_size = mask_pad_size if mask_pad_size is not None else {"height": 256, "width": 256}
    mask_pad_size = get_size_dict(mask_pad_size, default_to_square=True)

    self.do_resize = do_resize
    self.size = size
    self.mask_size = mask_size
    self.resample = resample
    self.do_rescale = do_rescale
    self.rescale_factor = rescale_factor
    self.do_normalize = do_normalize
    self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
    self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
    self.do_pad = do_pad
    self.pad_size = pad_size
    self.mask_pad_size = mask_pad_size
    self.do_convert_rgb = do_convert_rgb
    self._valid_processor_keys = [
        "images",
        "segmentation_maps",
        "do_resize",
        "size",
        "mask_size",
        "resample",
        "do_rescale",
        "rescale_factor",
        "do_normalize",
        "image_mean",
        "image_std",
        "do_pad",
        "pad_size",
        "mask_pad_size",
        "do_convert_rgb",
        "return_tensors",
        "data_format",
        "input_data_format",
    ]

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.filter_masks(masks, iou_scores, original_size, cropped_box_image, pred_iou_thresh=0.88, stability_score_thresh=0.95, mask_threshold=0, stability_score_offset=1, return_tensors='ms')

Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being that the iou scores needs to be greater than pred_iou_thresh. The second criterion is that the stability score needs to be greater than stability_score_thresh. The method also converts the predicted masks to bounding boxes and pad the predicted masks if necessary.

PARAMETER DESCRIPTION
masks

Input masks.

TYPE: `Union[mindspore.Tensor, tf.Tensor]`

iou_scores

List of IoU scores.

TYPE: `Union[mindspore.Tensor, tf.Tensor]`

original_size

Size of the orginal image.

TYPE: `Tuple[int,int]`

cropped_box_image

The cropped image.

TYPE: `np.array`

pred_iou_thresh

The threshold for the iou scores.

TYPE: `float`, *optional*, defaults to 0.88 DEFAULT: 0.88

stability_score_thresh

The threshold for the stability score.

TYPE: `float`, *optional*, defaults to 0.95 DEFAULT: 0.95

mask_threshold

The threshold for the predicted masks.

TYPE: `float`, *optional*, defaults to 0 DEFAULT: 0

stability_score_offset

The offset for the stability score used in the _compute_stability_score method.

TYPE: `float`, *optional*, defaults to 1 DEFAULT: 1

return_tensors

If pt, returns mindspore.Tensor. If tf, returns tf.Tensor.

TYPE: `str`, *optional*, defaults to `pt` DEFAULT: 'ms'

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def filter_masks(
    self,
    masks,
    iou_scores,
    original_size,
    cropped_box_image,
    pred_iou_thresh=0.88,
    stability_score_thresh=0.95,
    mask_threshold=0,
    stability_score_offset=1,
    return_tensors="ms",
):
    """
    Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being
    that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability
    score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to
    bounding boxes and pad the predicted masks if necessary.

    Args:
        masks (`Union[mindspore.Tensor, tf.Tensor]`):
            Input masks.
        iou_scores (`Union[mindspore.Tensor, tf.Tensor]`):
            List of IoU scores.
        original_size (`Tuple[int,int]`):
            Size of the orginal image.
        cropped_box_image (`np.array`):
            The cropped image.
        pred_iou_thresh (`float`, *optional*, defaults to 0.88):
            The threshold for the iou scores.
        stability_score_thresh (`float`, *optional*, defaults to 0.95):
            The threshold for the stability score.
        mask_threshold (`float`, *optional*, defaults to 0):
            The threshold for the predicted masks.
        stability_score_offset (`float`, *optional*, defaults to 1):
            The offset for the stability score used in the `_compute_stability_score` method.
        return_tensors (`str`, *optional*, defaults to `pt`):
            If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
    """
    if return_tensors == "ms":
        return self._filter_masks(
            masks=masks,
            iou_scores=iou_scores,
            original_size=original_size,
            cropped_box_image=cropped_box_image,
            pred_iou_thresh=pred_iou_thresh,
            stability_score_thresh=stability_score_thresh,
            mask_threshold=mask_threshold,
            stability_score_offset=stability_score_offset,
        )
    elif return_tensors == "tf":
        return self._filter_masks_tf(
            masks=masks,
            iou_scores=iou_scores,
            original_size=original_size,
            cropped_box_image=cropped_box_image,
            pred_iou_thresh=pred_iou_thresh,
            stability_score_thresh=stability_score_thresh,
            mask_threshold=mask_threshold,
            stability_score_offset=stability_score_offset,
        )

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.generate_crop_boxes(image, target_size, crop_n_layers=0, overlap_ratio=512 / 1500, points_per_crop=32, crop_n_points_downscale_factor=1, input_data_format=None, return_tensors='ms')

Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

PARAMETER DESCRIPTION
image

Input original image

TYPE: `np.array`

target_size

Target size of the resized image

TYPE: `int`

crop_n_layers

If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where each layer has 2**i_layer number of image crops.

TYPE: `int`, *optional*, defaults to 0 DEFAULT: 0

overlap_ratio

Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the image length. Later layers with more crops scale down this overlap.

TYPE: `float`, *optional*, defaults to 512/1500 DEFAULT: 512 / 1500

points_per_crop

Number of points to sample from each crop.

TYPE: `int`, *optional*, defaults to 32 DEFAULT: 32

crop_n_points_downscale_factor

The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.

TYPE: `List[int]`, *optional*, defaults to 1 DEFAULT: 1

input_data_format

The channel dimension format of the input image. If not provided, it will be inferred.

TYPE: `str` or `ChannelDimension`, *optional* DEFAULT: None

return_tensors

If pt, returns mindspore.Tensor. If tf, returns tf.Tensor.

TYPE: `str`, *optional*, defaults to `pt` DEFAULT: 'ms'

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def generate_crop_boxes(
    self,
    image,
    target_size,
    crop_n_layers: int = 0,
    overlap_ratio: float = 512 / 1500,
    points_per_crop: Optional[int] = 32,
    crop_n_points_downscale_factor: Optional[List[int]] = 1,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
    return_tensors: str = "ms",
):
    """
    Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

    Args:
        image (`np.array`):
            Input original image
        target_size (`int`):
            Target size of the resized image
        crop_n_layers (`int`, *optional*, defaults to 0):
            If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where
            each layer has 2**i_layer number of image crops.
        overlap_ratio (`float`, *optional*, defaults to 512/1500):
            Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of
            the image length. Later layers with more crops scale down this overlap.
        points_per_crop (`int`, *optional*, defaults to 32):
            Number of points to sample from each crop.
        crop_n_points_downscale_factor (`List[int]`, *optional*, defaults to 1):
            The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred.
        return_tensors (`str`, *optional*, defaults to `pt`):
            If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
    """
    crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes(
        image,
        target_size,
        crop_n_layers,
        overlap_ratio,
        points_per_crop,
        crop_n_points_downscale_factor,
        input_data_format,
    )
    if return_tensors == "ms":
        crop_boxes = mindspore.tensor(crop_boxes)
        points_per_crop = mindspore.tensor(points_per_crop)
        # cropped_images stays as np
        input_labels = mindspore.tensor(input_labels)
    else:
        raise ValueError("return_tensors must be 'ms'.")
    return crop_boxes, points_per_crop, cropped_images, input_labels

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.pad_image(image, pad_size, data_format=None, input_data_format=None, **kwargs)

Pad an image to (pad_size["height"], pad_size["width"]) with zeros to the right and bottom.

PARAMETER DESCRIPTION
image

Image to pad.

TYPE: `np.ndarray`

pad_size

Size of the output image after padding.

TYPE: `Dict[str, int]`

data_format

The data format of the image. Can be either "channels_first" or "channels_last". If None, the data_format of the image will be used.

TYPE: `str` or `ChannelDimension`, *optional* DEFAULT: None

input_data_format

The channel dimension format of the input image. If not provided, it will be inferred.

TYPE: `str` or `ChannelDimension`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def pad_image(
    self,
    image: np.ndarray,
    pad_size: Dict[str, int],
    data_format: Optional[Union[str, ChannelDimension]] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
    **kwargs,
) -> np.ndarray:
    """
    Pad an image to `(pad_size["height"], pad_size["width"])` with zeros to the right and bottom.

    Args:
        image (`np.ndarray`):
            Image to pad.
        pad_size (`Dict[str, int]`):
            Size of the output image after padding.
        data_format (`str` or `ChannelDimension`, *optional*):
            The data format of the image. Can be either "channels_first" or "channels_last". If `None`, the
            `data_format` of the `image` will be used.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred.
    """
    output_height, output_width = pad_size["height"], pad_size["width"]
    input_height, input_width = get_image_size(image, channel_dim=input_data_format)

    pad_width = output_width - input_width
    pad_height = output_height - input_height

    padded_image = pad(
        image,
        ((0, pad_height), (0, pad_width)),
        data_format=data_format,
        input_data_format=input_data_format,
        **kwargs,
    )
    return padded_image

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh, return_tensors='ms')

Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks.

PARAMETER DESCRIPTION
all_masks

List of all predicted segmentation masks

TYPE: `Union[List[mindspore.Tensor], List[tf.Tensor]]`

all_scores

List of all predicted iou scores

TYPE: `Union[List[mindspore.Tensor], List[tf.Tensor]]`

all_boxes

List of all bounding boxes of the predicted masks

TYPE: `Union[List[mindspore.Tensor], List[tf.Tensor]]`

crops_nms_thresh

Threshold for NMS (Non Maximum Suppression) algorithm.

TYPE: `float`

return_tensors

If pt, returns mindspore.Tensor. If tf, returns tf.Tensor.

TYPE: `str`, *optional*, defaults to `pt` DEFAULT: 'ms'

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def post_process_for_mask_generation(
    self, all_masks, all_scores, all_boxes, crops_nms_thresh, return_tensors="ms"
):
    """
    Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks.

    Args:
        all_masks (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
            List of all predicted segmentation masks
        all_scores (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
            List of all predicted iou scores
        all_boxes (`Union[List[mindspore.Tensor], List[tf.Tensor]]`):
            List of all bounding boxes of the predicted masks
        crops_nms_thresh (`float`):
            Threshold for NMS (Non Maximum Suppression) algorithm.
        return_tensors (`str`, *optional*, defaults to `pt`):
            If `pt`, returns `mindspore.Tensor`. If `tf`, returns `tf.Tensor`.
    """
    if return_tensors == "ms":
        return _postprocess_for_mg(all_masks, all_scores, all_boxes, crops_nms_thresh)

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.post_process_masks(masks, original_sizes, reshaped_input_sizes, mask_threshold=0.0, binarize=True, pad_size=None, return_tensors='ms')

Remove padding and upscale masks to the original image size.

PARAMETER DESCRIPTION
masks

Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format.

TYPE: `Union[List[mindspore.Tensor], List[np.ndarray], List[tf.Tensor]]`

original_sizes

The original sizes of each image before it was resized to the model's expected input shape, in (height, width) format.

TYPE: `Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`

reshaped_input_sizes

The size of each image as it is fed to the model, in (height, width) format. Used to remove padding.

TYPE: `Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`

mask_threshold

The threshold to use for binarizing the masks.

TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0

binarize

Whether to binarize the masks.

TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True

pad_size

The target size the images were padded to before being passed to the model. If None, the target size is assumed to be the processor's pad_size.

TYPE: `int`, *optional*, defaults to `self.pad_size` DEFAULT: None

return_tensors

If "ms", return PyTorch tensors. If "tf", return TensorFlow tensors.

TYPE: `str`, *optional*, defaults to `"ms"` DEFAULT: 'ms'

RETURNS DESCRIPTION
`Union[mindspore.Tensor, tf.Tensor]`

Batched masks in batch_size, num_channels, height, width) format, where

(height, width) is given by original_size.
Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def post_process_masks(
    self,
    masks,
    original_sizes,
    reshaped_input_sizes,
    mask_threshold=0.0,
    binarize=True,
    pad_size=None,
    return_tensors="ms",
):
    """
    Remove padding and upscale masks to the original image size.

    Args:
        masks (`Union[List[mindspore.Tensor], List[np.ndarray], List[tf.Tensor]]`):
            Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format.
        original_sizes (`Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`):
            The original sizes of each image before it was resized to the model's expected input shape, in (height,
            width) format.
        reshaped_input_sizes (`Union[mindspore.Tensor, tf.Tensor, List[Tuple[int,int]]]`):
            The size of each image as it is fed to the model, in (height, width) format. Used to remove padding.
        mask_threshold (`float`, *optional*, defaults to 0.0):
            The threshold to use for binarizing the masks.
        binarize (`bool`, *optional*, defaults to `True`):
            Whether to binarize the masks.
        pad_size (`int`, *optional*, defaults to `self.pad_size`):
            The target size the images were padded to before being passed to the model. If None, the target size is
            assumed to be the processor's `pad_size`.
        return_tensors (`str`, *optional*, defaults to `"ms"`):
            If `"ms"`, return PyTorch tensors. If `"tf"`, return TensorFlow tensors.

    Returns:
        (`Union[mindspore.Tensor, tf.Tensor]`): Batched masks in batch_size, num_channels, height, width) format, where
        (height, width) is given by original_size.
    """
    if return_tensors == "ms":
        return self._post_process_masks_ms(
            masks=masks,
            original_sizes=original_sizes,
            reshaped_input_sizes=reshaped_input_sizes,
            mask_threshold=mask_threshold,
            binarize=binarize,
            pad_size=pad_size,
        )
    else:
        raise ValueError("return_tensors must be 'ms'.")

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.preprocess(images, segmentation_maps=None, do_resize=None, size=None, mask_size=None, resample=None, do_rescale=None, rescale_factor=None, do_normalize=None, image_mean=None, image_std=None, do_pad=None, pad_size=None, mask_pad_size=None, do_convert_rgb=None, return_tensors=None, data_format=ChannelDimension.FIRST, input_data_format=None, **kwargs)

Preprocess an image or batch of images.

PARAMETER DESCRIPTION
images

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.

TYPE: `ImageInput`

segmentation_maps

Segmentation map to preprocess.

TYPE: `ImageInput`, *optional* DEFAULT: None

do_resize

Whether to resize the image.

TYPE: `bool`, *optional*, defaults to `self.do_resize` DEFAULT: None

size

Controls the size of the image after resize. The longest edge of the image is resized to size["longest_edge"] whilst preserving the aspect ratio.

TYPE: `Dict[str, int]`, *optional*, defaults to `self.size` DEFAULT: None

mask_size

Controls the size of the segmentation map after resize. The longest edge of the image is resized to size["longest_edge"] whilst preserving the aspect ratio.

TYPE: `Dict[str, int]`, *optional*, defaults to `self.mask_size` DEFAULT: None

resample

PILImageResampling filter to use when resizing the image e.g. PILImageResampling.BILINEAR.

TYPE: `PILImageResampling`, *optional*, defaults to `self.resample` DEFAULT: None

do_rescale

Whether to rescale the image pixel values by rescaling factor.

TYPE: `bool`, *optional*, defaults to `self.do_rescale` DEFAULT: None

rescale_factor

Rescale factor to apply to the image pixel values.

TYPE: `int` or `float`, *optional*, defaults to `self.rescale_factor` DEFAULT: None

do_normalize

Whether to normalize the image.

TYPE: `bool`, *optional*, defaults to `self.do_normalize` DEFAULT: None

image_mean

Image mean to normalize the image by if do_normalize is set to True.

TYPE: `float` or `List[float]`, *optional*, defaults to `self.image_mean` DEFAULT: None

image_std

Image standard deviation to normalize the image by if do_normalize is set to True.

TYPE: `float` or `List[float]`, *optional*, defaults to `self.image_std` DEFAULT: None

do_pad

Whether to pad the image.

TYPE: `bool`, *optional*, defaults to `self.do_pad` DEFAULT: None

pad_size

Controls the size of the padding applied to the image. The image is padded to pad_size["height"] and pad_size["width"] if do_pad is set to True.

TYPE: `Dict[str, int]`, *optional*, defaults to `self.pad_size` DEFAULT: None

mask_pad_size

Controls the size of the padding applied to the segmentation map. The image is padded to mask_pad_size["height"] and mask_pad_size["width"] if do_pad is set to True.

TYPE: `Dict[str, int]`, *optional*, defaults to `self.mask_pad_size` DEFAULT: None

do_convert_rgb

Whether to convert the image to RGB.

TYPE: `bool`, *optional*, defaults to `self.do_convert_rgb` DEFAULT: None

return_tensors

The type of tensors to return. Can be one of:

  • Unset: Return a list of np.ndarray.
  • TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
  • TensorType.PYTORCH or 'pt': Return a batch of type mindspore.Tensor.
  • TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
  • TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.

TYPE: `str` or `TensorType`, *optional* DEFAULT: None

data_format

The channel dimension format for the output image. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
  • Unset: Use the channel dimension format of the input image.

TYPE: `ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST` DEFAULT: FIRST

input_data_format

The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
  • "none" or ChannelDimension.NONE: image in (height, width) format.

TYPE: `ChannelDimension` or `str`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def preprocess(
    self,
    images: ImageInput,
    segmentation_maps: Optional[ImageInput] = None,
    do_resize: Optional[bool] = None,
    size: Optional[Dict[str, int]] = None,
    mask_size: Optional[Dict[str, int]] = None,
    resample: Optional["PILImageResampling"] = None,
    do_rescale: Optional[bool] = None,
    rescale_factor: Optional[Union[int, float]] = None,
    do_normalize: Optional[bool] = None,
    image_mean: Optional[Union[float, List[float]]] = None,
    image_std: Optional[Union[float, List[float]]] = None,
    do_pad: Optional[bool] = None,
    pad_size: Optional[Dict[str, int]] = None,
    mask_pad_size: Optional[Dict[str, int]] = None,
    do_convert_rgb: Optional[bool] = None,
    return_tensors: Optional[Union[str, TensorType]] = None,
    data_format: ChannelDimension = ChannelDimension.FIRST,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
    **kwargs,
):
    """
    Preprocess an image or batch of images.

    Args:
        images (`ImageInput`):
            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`.
        segmentation_maps (`ImageInput`, *optional*):
            Segmentation map to preprocess.
        do_resize (`bool`, *optional*, defaults to `self.do_resize`):
            Whether to resize the image.
        size (`Dict[str, int]`, *optional*, defaults to `self.size`):
            Controls the size of the image after `resize`. The longest edge of the image is resized to
            `size["longest_edge"]` whilst preserving the aspect ratio.
        mask_size (`Dict[str, int]`, *optional*, defaults to `self.mask_size`):
            Controls the size of the segmentation map after `resize`. The longest edge of the image is resized to
            `size["longest_edge"]` whilst preserving the aspect ratio.
        resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
            `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
        do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
            Whether to rescale the image pixel values by rescaling factor.
        rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`):
            Rescale factor to apply to the image pixel values.
        do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
            Whether to normalize the image.
        image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
            Image mean to normalize the image by if `do_normalize` is set to `True`.
        image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
            Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
        do_pad (`bool`, *optional*, defaults to `self.do_pad`):
            Whether to pad the image.
        pad_size (`Dict[str, int]`, *optional*, defaults to `self.pad_size`):
            Controls the size of the padding applied to the image. The image is padded to `pad_size["height"]` and
            `pad_size["width"]` if `do_pad` is set to `True`.
        mask_pad_size (`Dict[str, int]`, *optional*, defaults to `self.mask_pad_size`):
            Controls the size of the padding applied to the segmentation map. The image is padded to
            `mask_pad_size["height"]` and `mask_pad_size["width"]` if `do_pad` is set to `True`.
        do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
            Whether to convert the image to RGB.
        return_tensors (`str` or `TensorType`, *optional*):
            The type of tensors to return. Can be one of:

            - Unset: Return a list of `np.ndarray`.
            - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
            - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `mindspore.Tensor`.
            - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
            - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
        data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
            The channel dimension format for the output image. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            - Unset: Use the channel dimension format of the input image.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image. If unset, the channel dimension format is inferred
            from the input image. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
    """
    do_resize = do_resize if do_resize is not None else self.do_resize
    size = size if size is not None else self.size
    size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size
    mask_size = mask_size if mask_size is not None else self.mask_size
    mask_size = (
        get_size_dict(max_size=mask_size, default_to_square=False)
        if not isinstance(mask_size, dict)
        else mask_size
    )
    resample = resample if resample is not None else self.resample
    do_rescale = do_rescale if do_rescale is not None else self.do_rescale
    rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
    do_normalize = do_normalize if do_normalize is not None else self.do_normalize
    image_mean = image_mean if image_mean is not None else self.image_mean
    image_std = image_std if image_std is not None else self.image_std
    do_pad = do_pad if do_pad is not None else self.do_pad
    pad_size = pad_size if pad_size is not None else self.pad_size
    pad_size = get_size_dict(pad_size, default_to_square=True)
    mask_pad_size = mask_pad_size if mask_pad_size is not None else self.mask_pad_size
    mask_pad_size = get_size_dict(mask_pad_size, default_to_square=True)
    do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    images = make_list_of_images(images)

    validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)

    if not valid_images(images):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "mindspore.Tensor, tf.Tensor or jax.ndarray."
        )

    if segmentation_maps is not None:
        segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)

        if not valid_images(segmentation_maps):
            raise ValueError(
                "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "mindspore.Tensor, tf.Tensor or jax.ndarray."
            )
    validate_preprocess_arguments(
        do_rescale=do_rescale,
        rescale_factor=rescale_factor,
        do_normalize=do_normalize,
        image_mean=image_mean,
        image_std=image_std,
        do_pad=do_pad,
        size_divisibility=pad_size,  # Here _preprocess needs do_pad and pad_size.
        do_resize=do_resize,
        size=size,
        resample=resample,
    )

    images, original_sizes, reshaped_input_sizes = zip(
        *(
            self._preprocess_image(
                image=img,
                do_resize=do_resize,
                size=size,
                resample=resample,
                do_rescale=do_rescale,
                rescale_factor=rescale_factor,
                do_normalize=do_normalize,
                image_mean=image_mean,
                image_std=image_std,
                do_pad=do_pad,
                pad_size=pad_size,
                do_convert_rgb=do_convert_rgb,
                data_format=data_format,
                input_data_format=input_data_format,
            )
            for img in images
        )
    )

    data = {
        "pixel_values": images,
        "original_sizes": original_sizes,
        "reshaped_input_sizes": reshaped_input_sizes,
    }

    if segmentation_maps is not None:
        segmentation_maps, original_mask_sizes = zip(
            *(
                self._preprocess_mask(
                    segmentation_map=mask,
                    do_resize=do_resize,
                    mask_size=mask_size,
                    do_pad=do_pad,
                    mask_pad_size=mask_pad_size,
                    input_data_format=input_data_format,
                )
                for mask in segmentation_maps
            )
        )

        # masks should start out the same size as input images
        assert all(
            original_im_size == original_mask_size
            for original_im_size, original_mask_size in zip(original_sizes, original_mask_sizes)
        ), "Segmentation maps should be the same size as input images."

        data["labels"] = segmentation_maps

    return BatchFeature(data=data, tensor_type=return_tensors)

mindnlp.transformers.models.sam.image_processing_sam.SamImageProcessor.resize(image, size, resample=PILImageResampling.BICUBIC, data_format=None, input_data_format=None, **kwargs)

Resize an image to (size["height"], size["width"]).

PARAMETER DESCRIPTION
image

Image to resize.

TYPE: `np.ndarray`

size

Dictionary in the format {"longest_edge": int} specifying the size of the output image. The longest edge of the image will be resized to the specified size, while the other edge will be resized to maintain the aspect ratio.

TYPE: `Dict[str, int]`

resample

PILImageResampling filter to use when resizing the image e.g. PILImageResampling.BILINEAR.

TYPE: PILImageResampling DEFAULT: BICUBIC

data_format

The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.

TYPE: `ChannelDimension` or `str`, *optional* DEFAULT: None

input_data_format

The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:

  • "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.

TYPE: `ChannelDimension` or `str`, *optional* DEFAULT: None

RETURNS DESCRIPTION
ndarray

np.ndarray: The resized image.
Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def resize(
    self,
    image: np.ndarray,
    size: Dict[str, int],
    resample: PILImageResampling = PILImageResampling.BICUBIC,
    data_format: Optional[Union[str, ChannelDimension]] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
    **kwargs,
) -> np.ndarray:
    """
    Resize an image to `(size["height"], size["width"])`.

    Args:
        image (`np.ndarray`):
            Image to resize.
        size (`Dict[str, int]`):
            Dictionary in the format `{"longest_edge": int}` specifying the size of the output image. The longest
            edge of the image will be resized to the specified size, while the other edge will be resized to
            maintain the aspect ratio.
        resample:
            `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
        data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the output image. If unset, the channel dimension format of the input
            image is used. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
        input_data_format (`ChannelDimension` or `str`, *optional*):
            The channel dimension format for the input image. If unset, the channel dimension format is inferred
            from the input image. Can be one of:

            - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
            - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

    Returns:
        `np.ndarray`: The resized image.
    """
    size = get_size_dict(size)
    if "longest_edge" not in size:
        raise ValueError(f"The `size` dictionary must contain the key `longest_edge`. Got {size.keys()}")
    input_size = get_image_size(image, channel_dim=input_data_format)
    output_height, output_width = self._get_preprocess_shape(input_size, size["longest_edge"])
    return resize(
        image,
        size=(output_height, output_width),
        resample=resample,
        data_format=data_format,
        input_data_format=input_data_format,
        **kwargs,
    )

mindnlp.transformers.models.sam.image_processing_sam.batched_nms(boxes, scores, idxs, iou_threshold)

Performs non-maximum suppression in a batched fashion.

Each index value correspond to a category, and NMS will not be applied between elements of different categories.

PARAMETER DESCRIPTION
boxes

boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format with 0 <= x1 < x2 and 0 <= y1 < y2.

TYPE: Tensor[N, 4]

scores

scores for each one of the boxes

TYPE: Tensor[N]

idxs

indices of the categories for each one of the boxes.

TYPE: Tensor[N]

iou_threshold

discards all overlapping boxes with IoU > iou_threshold

TYPE: float

RETURNS DESCRIPTION
Tensor

int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores

TYPE: Tensor

Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def batched_nms(
    boxes: mindspore.Tensor,
    scores: mindspore.Tensor,
    idxs: mindspore.Tensor,
    iou_threshold: float,
) -> mindspore.Tensor:
    """
    Performs non-maximum suppression in a batched fashion.

    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.

    Args:
        boxes (Tensor[N, 4]): boxes where NMS will be performed. They
            are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
        scores (Tensor[N]): scores for each one of the boxes
        idxs (Tensor[N]): indices of the categories for each one of the boxes.
        iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold

    Returns:
        Tensor: int64 tensor with the indices of the elements that have been kept by NMS, sorted
            in decreasing order of scores
    """
    # Benchmarks that drove the following thresholds are at
    # https://github.com/pytorch/vision/issues/1311#issuecomment-781329339
    if boxes.numel() > (4000 if mindspore.get_context('device_target') == "CPU" else 20000):
        return _batched_nms_vanilla(boxes, scores, idxs, iou_threshold)
    else:
        return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold)

mindnlp.transformers.models.sam.image_processing_sam.nms(boxes, scores, iou_threshold)

Performs non-maximum suppression (NMS) on a set of bounding boxes.

PARAMETER DESCRIPTION
boxes

A tensor of shape (N, 4) representing the coordinates of the N bounding boxes. Each bounding box is defined by four values: (x_min, y_min, x_max, y_max).

TYPE: Tensor

scores

A tensor of shape (N,) representing the scores associated with each bounding box.

TYPE: Tensor

iou_threshold

The Intersection over Union (IoU) threshold used for NMS. Bounding boxes with IoU greater than or equal to this threshold will be suppressed.

TYPE: float

RETURNS DESCRIPTION

mindspore.Tensor: A tensor containing the indices of the selected bounding boxes after NMS. The shape of the returned tensor is (M,), where M is the number of selected bounding boxes.
RAISES DESCRIPTION
TypeError

If any of the input arguments are not of the expected type.
ValueError

If the shape of 'boxes' and 'scores' tensors are incompatible or if 'iou_threshold' is not within the valid range.
Source code in mindnlp/transformers/models/sam/image_processing_sam.py 
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def nms(boxes: mindspore.Tensor, scores: mindspore.Tensor, iou_threshold: float):
    """
    Performs non-maximum suppression (NMS) on a set of bounding boxes.

    Args:
        boxes (mindspore.Tensor): A tensor of shape (N, 4) representing the coordinates of the N bounding boxes. 
            Each bounding box is defined by four values: (x_min, y_min, x_max, y_max).
        scores (mindspore.Tensor): A tensor of shape (N,) representing the scores associated with each bounding box.
        iou_threshold (float): The Intersection over Union (IoU) threshold used for NMS. 
            Bounding boxes with IoU greater than or equal to this threshold will be suppressed.

    Returns:
        mindspore.Tensor: A tensor containing the indices of the selected bounding boxes after NMS. 
            The shape of the returned tensor is (M,), where M is the number of selected bounding boxes.

    Raises:
        TypeError: If any of the input arguments are not of the expected type.
        ValueError: If the shape of 'boxes' and 'scores' tensors are incompatible or if 'iou_threshold'
            is not within the valid range.
    """
    box_with_score = ops.stack((boxes, scores))
    _, _, selected_mask = _get_cache_prim(ops.NMSWithMask)(iou_threshold)(box_with_score)
    return ops.nonzero(selected_mask).reshape(-1)

mindnlp.transformers.models.sam.modeling_sam

MindSpore SAM model.

mindnlp.transformers.models.sam.modeling_sam.SamAttention

Bases: Module

SAM's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and values.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamAttention(nn.Module):
    """
    SAM's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and
    values.
    """
    def __init__(self, config, downsample_rate=None):
        """
        Initializes a new instance of the SamAttention class.

        Args:
            self: The object itself.
            config: An object of the configuration class containing various parameters.
            downsample_rate (optional): An integer representing the downsample rate.
                If not provided, it defaults to None. (default: None)

        Returns:
            None.

        Raises:
            ValueError: If the number of attention heads is not a divisor of hidden_size.
        """
        super().__init__()
        self.hidden_size = config.hidden_size

        downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate

        self.internal_dim = config.hidden_size // downsample_rate
        self.num_attention_heads = config.num_attention_heads
        if self.internal_dim % config.num_attention_heads != 0:
            raise ValueError("num_attention_heads must divide hidden_size.")

        self.q_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.k_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.v_proj = nn.Linear(self.hidden_size, self.internal_dim)
        self.out_proj = nn.Linear(self.internal_dim, self.hidden_size)

    def _separate_heads(self, hidden_states: Tensor, num_attention_heads: int) -> Tensor:
        """
        Method to separate heads in the attention mechanism.

        Args:
            self (SamAttention): The instance of the SamAttention class.
            hidden_states (Tensor): The input hidden states tensor of shape (batch, point_batch_size, n_tokens, channel).
                This tensor represents the input feature map.
            num_attention_heads (int): The number of attention heads to split the hidden states into.

        Returns:
            Tensor: The tensor resulting from splitting the hidden states into multiple heads.
                The shape of the returned tensor is (batch * point_batch_size, num_attention_heads, n_tokens, c_per_head),
                where c_per_head is the channel size divided by the number of attention heads.

        Raises:
            None
        """
        batch, point_batch_size, n_tokens, channel = hidden_states.shape
        c_per_head = channel // num_attention_heads
        hidden_states = hidden_states.reshape(batch * point_batch_size, n_tokens, num_attention_heads, c_per_head)
        return hidden_states.swapaxes(1, 2)

    def _recombine_heads(self, hidden_states: Tensor, point_batch_size: int) -> Tensor:
        """
        Recombines the heads in the hidden states tensor for self attention in the SamAttention class.

        Args:
            self (SamAttention): The instance of the SamAttention class.
            hidden_states (Tensor): A 4D tensor representing the hidden states with shape (batch, n_heads, n_tokens, c_per_head).
                It contains the intermediate representations of the input tokens.

                - batch: The number of sequences in the batch.
                - n_heads: The number of attention heads.
                - n_tokens: The number of input tokens.
                - c_per_head: The size of each head's output.
            point_batch_size (int): The size of the batch for each point.
                Determines how the hidden states should be reshaped.

        Returns:
            Tensor: A reshaped tensor of the hidden states after recombining the heads.
                The shape of the returned tensor is (batch // point_batch_size, point_batch_size, n_tokens,
                n_heads * c_per_head).
                This reshaped tensor is used for further processing in self attention mechanisms.

        Raises:
            None
        """
        batch, n_heads, n_tokens, c_per_head = hidden_states.shape
        hidden_states = hidden_states.swapaxes(1, 2)
        return hidden_states.reshape(batch // point_batch_size, point_batch_size, n_tokens, n_heads * c_per_head)

    def forward(self, query: Tensor, key: Tensor, value: Tensor, attention_similarity: Tensor = None) -> Tensor:
        '''
        Constructs a self-attention mechanism for the SamAttention class.

        Args:
            self (SamAttention): An instance of the SamAttention class.
            query (Tensor): The query tensor of shape (batch_size, seq_length, embedding_dim) representing
                the query values.
            key (Tensor): The key tensor of shape (batch_size, seq_length, embedding_dim) representing the key values.
            value (Tensor): The value tensor of shape (batch_size, seq_length, embedding_dim) representing
                the value values.
            attention_similarity (Tensor, optional): The attention similarity tensor of shape
                (batch_size, num_attention_heads, seq_length, seq_length) representing the similarity scores between
                tokens. Defaults to None.

        Returns:
            Tensor: The output tensor of shape (batch_size, seq_length, embedding_dim) representing the attended values.

        Raises:
            None.
        '''
        # Input projections
        query = self.q_proj(query)
        key = self.k_proj(key)
        value = self.v_proj(value)

        point_batch_size = query.shape[1]
        # Separate into heads
        query = self._separate_heads(query, self.num_attention_heads)
        key = self._separate_heads(key, self.num_attention_heads)
        value = self._separate_heads(value, self.num_attention_heads)

        # SamAttention
        _, _, _, c_per_head = query.shape
        attn = query @ key.permute(0, 1, 3, 2)  # batch_size * point_batch_size  x N_heads x N_tokens x N_tokens
        attn = attn / math.sqrt(c_per_head)
        attn = ops.softmax(attn, axis=-1)

        if attention_similarity is not None:
            attn = attn + attention_similarity
            attn = ops.softmax(attn, axis=-1)

        # Get output
        out = attn @ value
        out = self._recombine_heads(out, point_batch_size)
        out = self.out_proj(out)

        return out

mindnlp.transformers.models.sam.modeling_sam.SamAttention.__init__(config, downsample_rate=None)

Initializes a new instance of the SamAttention class.

PARAMETER DESCRIPTION
self

The object itself.
config

An object of the configuration class containing various parameters.
downsample_rate

An integer representing the downsample rate. If not provided, it defaults to None. (default: None)

TYPE: optional DEFAULT: None

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
ValueError

If the number of attention heads is not a divisor of hidden_size.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config, downsample_rate=None):
    """
    Initializes a new instance of the SamAttention class.

    Args:
        self: The object itself.
        config: An object of the configuration class containing various parameters.
        downsample_rate (optional): An integer representing the downsample rate.
            If not provided, it defaults to None. (default: None)

    Returns:
        None.

    Raises:
        ValueError: If the number of attention heads is not a divisor of hidden_size.
    """
    super().__init__()
    self.hidden_size = config.hidden_size

    downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate

    self.internal_dim = config.hidden_size // downsample_rate
    self.num_attention_heads = config.num_attention_heads
    if self.internal_dim % config.num_attention_heads != 0:
        raise ValueError("num_attention_heads must divide hidden_size.")

    self.q_proj = nn.Linear(self.hidden_size, self.internal_dim)
    self.k_proj = nn.Linear(self.hidden_size, self.internal_dim)
    self.v_proj = nn.Linear(self.hidden_size, self.internal_dim)
    self.out_proj = nn.Linear(self.internal_dim, self.hidden_size)

mindnlp.transformers.models.sam.modeling_sam.SamAttention.forward(query, key, value, attention_similarity=None)

Constructs a self-attention mechanism for the SamAttention class.

PARAMETER DESCRIPTION
self

An instance of the SamAttention class.

TYPE: SamAttention

query

The query tensor of shape (batch_size, seq_length, embedding_dim) representing the query values.

TYPE: Tensor

key

The key tensor of shape (batch_size, seq_length, embedding_dim) representing the key values.

TYPE: Tensor

value

The value tensor of shape (batch_size, seq_length, embedding_dim) representing the value values.

TYPE: Tensor

attention_similarity

The attention similarity tensor of shape (batch_size, num_attention_heads, seq_length, seq_length) representing the similarity scores between tokens. Defaults to None.

TYPE: Tensor DEFAULT: None

RETURNS DESCRIPTION
Tensor

The output tensor of shape (batch_size, seq_length, embedding_dim) representing the attended values.

TYPE: Tensor

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, query: Tensor, key: Tensor, value: Tensor, attention_similarity: Tensor = None) -> Tensor:
    '''
    Constructs a self-attention mechanism for the SamAttention class.

    Args:
        self (SamAttention): An instance of the SamAttention class.
        query (Tensor): The query tensor of shape (batch_size, seq_length, embedding_dim) representing
            the query values.
        key (Tensor): The key tensor of shape (batch_size, seq_length, embedding_dim) representing the key values.
        value (Tensor): The value tensor of shape (batch_size, seq_length, embedding_dim) representing
            the value values.
        attention_similarity (Tensor, optional): The attention similarity tensor of shape
            (batch_size, num_attention_heads, seq_length, seq_length) representing the similarity scores between
            tokens. Defaults to None.

    Returns:
        Tensor: The output tensor of shape (batch_size, seq_length, embedding_dim) representing the attended values.

    Raises:
        None.
    '''
    # Input projections
    query = self.q_proj(query)
    key = self.k_proj(key)
    value = self.v_proj(value)

    point_batch_size = query.shape[1]
    # Separate into heads
    query = self._separate_heads(query, self.num_attention_heads)
    key = self._separate_heads(key, self.num_attention_heads)
    value = self._separate_heads(value, self.num_attention_heads)

    # SamAttention
    _, _, _, c_per_head = query.shape
    attn = query @ key.permute(0, 1, 3, 2)  # batch_size * point_batch_size  x N_heads x N_tokens x N_tokens
    attn = attn / math.sqrt(c_per_head)
    attn = ops.softmax(attn, axis=-1)

    if attention_similarity is not None:
        attn = attn + attention_similarity
        attn = ops.softmax(attn, axis=-1)

    # Get output
    out = attn @ value
    out = self._recombine_heads(out, point_batch_size)
    out = self.out_proj(out)

    return out

mindnlp.transformers.models.sam.modeling_sam.SamFeedForward

Bases: Module

SamFeedForward is a class representing a feedforward neural network model with customizable parameters for input, hidden, and output dimensions, as well as the number of layers. The class allows for the option of applying a sigmoid activation function to the output layer.

PARAMETER DESCRIPTION
input_dim

The dimension of the input data.

TYPE: int

hidden_dim

The dimension of the hidden layers.

TYPE: int

output_dim

The dimension of the output data.

TYPE: int

num_layers

The number of hidden layers in the network.

TYPE: int

sigmoid_output

If True, applies a sigmoid activation function to the output layer. Defaults to False.

TYPE: bool DEFAULT: False

ATTRIBUTE DESCRIPTION
num_layers

The number of hidden layers in the network.

TYPE: int

activation

The rectified linear unit (ReLU) activation function.

TYPE: ReLU

proj_in

The linear transformation for input data to the hidden layer.

TYPE: Linear

proj_out

The linear transformation for the last hidden layer to the output data.

TYPE: Linear

layers

List of Dense layers for the hidden layers in the network.

TYPE: ModuleList

METHOD DESCRIPTION
forward

Constructs the feedforward network by applying linear transformations and activation functions to the input data through the hidden layers, and finally to the output data.
RETURNS DESCRIPTION
hidden_states

The output data after passing through the feedforward network, with optional sigmoid activation applied.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamFeedForward(nn.Module):

    """
    SamFeedForward is a class representing a feedforward neural network model with customizable parameters for input,
    hidden, and output dimensions, as well as the number of layers. The class allows for the option of applying a
    sigmoid activation function to the output layer.

    Parameters:
        input_dim (int): The dimension of the input data.
        hidden_dim (int): The dimension of the hidden layers.
        output_dim (int): The dimension of the output data.
        num_layers (int): The number of hidden layers in the network.
        sigmoid_output (bool, optional): If True, applies a sigmoid activation function to the output layer.
            Defaults to False.

    Attributes:
        num_layers (int): The number of hidden layers in the network.
        activation (nn.ReLU): The rectified linear unit (ReLU) activation function.
        proj_in (nn.Linear): The linear transformation for input data to the hidden layer.
        proj_out (nn.Linear): The linear transformation for the last hidden layer to the output data.
        layers (nn.ModuleList): List of Dense layers for the hidden layers in the network.

    Methods:
        forward: Constructs the feedforward network by applying linear transformations and activation functions
            to the input data through the hidden layers, and finally to the output data.

    Returns:
        hidden_states: The output data after passing through the feedforward network, with optional sigmoid activation
            applied.

    """
    def __init__(
        self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, sigmoid_output: bool = False
    ):
        """
        Initializes an instance of the SamFeedForward class.

        Args:
            self: The instance of the class.
            input_dim (int): The dimension of the input data.
            hidden_dim (int): The dimension of the hidden layers.
            output_dim (int): The dimension of the output data.
            num_layers (int): The number of hidden layers in the network.
            sigmoid_output (bool, optional): Flag to indicate whether the output should be passed through a sigmoid
                activation function. Default is False.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        self.num_layers = num_layers
        self.activation = nn.ReLU()
        self.proj_in = nn.Linear(input_dim, hidden_dim)
        self.proj_out = nn.Linear(hidden_dim, output_dim)
        self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)])
        self.sigmoid_output = sigmoid_output

    def forward(self, hidden_states):
        """
        This method forwards a feedforward neural network using the provided hidden states.

        Args:
            self (SamFeedForward): The instance of the SamFeedForward class.
            hidden_states (tensor): The input hidden states to be processed by the neural network.

        Returns:
            None.

        Raises:
            None.
        """
        hidden_states = self.proj_in(hidden_states)
        hidden_states = self.activation(hidden_states)
        for layer in self.layers:
            hidden_states = self.activation(layer(hidden_states))

        hidden_states = self.proj_out(hidden_states)
        if self.sigmoid_output:
            hidden_states = ops.sigmoid(hidden_states)
        return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamFeedForward.__init__(input_dim, hidden_dim, output_dim, num_layers, sigmoid_output=False)

Initializes an instance of the SamFeedForward class.

PARAMETER DESCRIPTION
self

The instance of the class.
input_dim

The dimension of the input data.

TYPE: int

hidden_dim

The dimension of the hidden layers.

TYPE: int

output_dim

The dimension of the output data.

TYPE: int

num_layers

The number of hidden layers in the network.

TYPE: int

sigmoid_output

Flag to indicate whether the output should be passed through a sigmoid activation function. Default is False.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(
    self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, sigmoid_output: bool = False
):
    """
    Initializes an instance of the SamFeedForward class.

    Args:
        self: The instance of the class.
        input_dim (int): The dimension of the input data.
        hidden_dim (int): The dimension of the hidden layers.
        output_dim (int): The dimension of the output data.
        num_layers (int): The number of hidden layers in the network.
        sigmoid_output (bool, optional): Flag to indicate whether the output should be passed through a sigmoid
            activation function. Default is False.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    self.num_layers = num_layers
    self.activation = nn.ReLU()
    self.proj_in = nn.Linear(input_dim, hidden_dim)
    self.proj_out = nn.Linear(hidden_dim, output_dim)
    self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)])
    self.sigmoid_output = sigmoid_output

mindnlp.transformers.models.sam.modeling_sam.SamFeedForward.forward(hidden_states)

This method forwards a feedforward neural network using the provided hidden states.

PARAMETER DESCRIPTION
self

The instance of the SamFeedForward class.

TYPE: SamFeedForward

hidden_states

The input hidden states to be processed by the neural network.

TYPE: tensor

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, hidden_states):
    """
    This method forwards a feedforward neural network using the provided hidden states.

    Args:
        self (SamFeedForward): The instance of the SamFeedForward class.
        hidden_states (tensor): The input hidden states to be processed by the neural network.

    Returns:
        None.

    Raises:
        None.
    """
    hidden_states = self.proj_in(hidden_states)
    hidden_states = self.activation(hidden_states)
    for layer in self.layers:
        hidden_states = self.activation(layer(hidden_states))

    hidden_states = self.proj_out(hidden_states)
    if self.sigmoid_output:
        hidden_states = ops.sigmoid(hidden_states)
    return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamImageSegmentationOutput dataclass

Bases: ModelOutput

Base class for Segment-Anything model's output

PARAMETER DESCRIPTION
iou_scores

The iou scores of the predicted masks.

TYPE: `mindspore.Tensor` of shape `(batch_size, num_masks)` DEFAULT: None

pred_masks

The predicted low resolutions masks. Needs to be post-processed by the processor

TYPE: `mindspore.Tensor` of shape `(batch_size, num_masks, height, width)` DEFAULT: None

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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@dataclass
class SamImageSegmentationOutput(ModelOutput):
    """
    Base class for Segment-Anything model's output

    Args:
        iou_scores (`mindspore.Tensor` of shape `(batch_size, num_masks)`):
            The iou scores of the predicted masks.
        pred_masks (`mindspore.Tensor` of shape `(batch_size, num_masks, height, width)`):
            The predicted low resolutions masks. Needs to be post-processed by the processor
        vision_hidden_states  (`tuple(mindspore.Tensor)`, *optional*, returned when `output_hidden_states=True`
            is passed or when `config.output_hidden_states=True`):
            Tuple of `mindspore.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the vision model at the output of each layer plus the optional initial embedding outputs.
        vision_attentions  (`tuple(mindspore.Tensor)`, *optional*, returned when `output_attentions=True` is passed
            or when `config.output_attentions=True`):
            Tuple of `mindspore.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        mask_decoder_attentions (`tuple(mindspore.Tensor)`, *optional*, returned when `output_attentions=True` is passed
            or when `config.output_attentions=True`):
            Tuple of `mindspore.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """
    iou_scores: mindspore.Tensor = None
    pred_masks: mindspore.Tensor = None
    vision_hidden_states: Optional[Tuple[mindspore.Tensor, ...]] = None
    vision_attentions: Optional[Tuple[mindspore.Tensor, ...]] = None
    mask_decoder_attentions: Optional[Tuple[mindspore.Tensor, ...]] = None

mindnlp.transformers.models.sam.modeling_sam.SamLayerNorm

Bases: Module

LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamLayerNorm(nn.Module):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        """
        Initializes a new instance of the SamLayerNorm class.

        Args:
            self: The object itself.
            normalized_shape (tuple): The shape of the input tensor, indicating the size of each dimension.
            eps (float, optional): A small value to prevent division by zero when normalizing the input tensor.
                Defaults to 1e-06.
            data_format (str, optional): The format of the input tensor. Accepted values are 'channels_last' and
                'channels_first'. Defaults to 'channels_last'.

        Returns:
            None

        Raises:
            NotImplementedError: If the specified data format is not supported.

        This method initializes the SamLayerNorm object with the provided parameters. It sets the weight and bias
        parameters as trainable variables, initializes the epsilon value for numerical stability, and validates the
        data format. The normalized_shape parameter represents the size of each dimension of the input tensor.
        The eps parameter is used to avoid division by zero when normalizing the input tensor. The data_format parameter
        specifies the layout of the input tensor, which can be either 'channels_last' or 'channels_first'.
        If an unsupported data format is provided, a NotImplementedError is raised.
        """
        super().__init__()
        self.weight = Parameter(ops.ones(normalized_shape))
        self.bias = Parameter(ops.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {self.data_format}")
        self.normalized_shape = (normalized_shape,)
        self.layer_norm = ops.LayerNorm(begin_norm_axis=-1,
                                      begin_params_axis=-1,
                                      epsilon=self.eps)

    def forward(self, x: mindspore.Tensor) -> mindspore.Tensor:
        """
        Constructs a layer normalization operation for the SamLayerNorm class.

        Args:
            self (SamLayerNorm): The instance of the SamLayerNorm class.
            x (mindspore.Tensor): The input tensor to be normalized.

        Returns:
            mindspore.Tensor: A normalized tensor based on the input tensor 'x'.

        Raises:
            ValueError: If the data format is not supported.
            TypeError: If the input tensor 'x' is of an unsupported type.
            RuntimeError: If any runtime error occurs during the normalization process.
        """
        if self.data_format == "channels_last":
            x, _, _ = self.layer_norm(x, self.weight, self.bias)
        elif self.data_format == "channels_first":
            input_dtype = x.dtype
            x = x.float()
            u = x.mean(1, keep_dims=True)
            s = (x - u).pow(2).mean(1, keep_dims=True)
            x = (x - u) / ops.sqrt(s + self.eps)
            x = x.to(dtype=input_dtype)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x

mindnlp.transformers.models.sam.modeling_sam.SamLayerNorm.__init__(normalized_shape, eps=1e-06, data_format='channels_last')

Initializes a new instance of the SamLayerNorm class.

PARAMETER DESCRIPTION
self

The object itself.
normalized_shape

The shape of the input tensor, indicating the size of each dimension.

TYPE: tuple

eps

A small value to prevent division by zero when normalizing the input tensor. Defaults to 1e-06.

TYPE: float DEFAULT: 1e-06

data_format

The format of the input tensor. Accepted values are 'channels_last' and 'channels_first'. Defaults to 'channels_last'.

TYPE: str DEFAULT: 'channels_last'

RETURNS DESCRIPTION

None
RAISES DESCRIPTION
NotImplementedError

If the specified data format is not supported.

This method initializes the SamLayerNorm object with the provided parameters. It sets the weight and bias parameters as trainable variables, initializes the epsilon value for numerical stability, and validates the data format. The normalized_shape parameter represents the size of each dimension of the input tensor. The eps parameter is used to avoid division by zero when normalizing the input tensor. The data_format parameter specifies the layout of the input tensor, which can be either 'channels_last' or 'channels_first'. If an unsupported data format is provided, a NotImplementedError is raised.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
    """
    Initializes a new instance of the SamLayerNorm class.

    Args:
        self: The object itself.
        normalized_shape (tuple): The shape of the input tensor, indicating the size of each dimension.
        eps (float, optional): A small value to prevent division by zero when normalizing the input tensor.
            Defaults to 1e-06.
        data_format (str, optional): The format of the input tensor. Accepted values are 'channels_last' and
            'channels_first'. Defaults to 'channels_last'.

    Returns:
        None

    Raises:
        NotImplementedError: If the specified data format is not supported.

    This method initializes the SamLayerNorm object with the provided parameters. It sets the weight and bias
    parameters as trainable variables, initializes the epsilon value for numerical stability, and validates the
    data format. The normalized_shape parameter represents the size of each dimension of the input tensor.
    The eps parameter is used to avoid division by zero when normalizing the input tensor. The data_format parameter
    specifies the layout of the input tensor, which can be either 'channels_last' or 'channels_first'.
    If an unsupported data format is provided, a NotImplementedError is raised.
    """
    super().__init__()
    self.weight = Parameter(ops.ones(normalized_shape))
    self.bias = Parameter(ops.zeros(normalized_shape))
    self.eps = eps
    self.data_format = data_format
    if self.data_format not in ["channels_last", "channels_first"]:
        raise NotImplementedError(f"Unsupported data format: {self.data_format}")
    self.normalized_shape = (normalized_shape,)
    self.layer_norm = ops.LayerNorm(begin_norm_axis=-1,
                                  begin_params_axis=-1,
                                  epsilon=self.eps)

mindnlp.transformers.models.sam.modeling_sam.SamLayerNorm.forward(x)

Constructs a layer normalization operation for the SamLayerNorm class.

PARAMETER DESCRIPTION
self

The instance of the SamLayerNorm class.

TYPE: SamLayerNorm

x

The input tensor to be normalized.

TYPE: Tensor

RETURNS DESCRIPTION
Tensor

mindspore.Tensor: A normalized tensor based on the input tensor 'x'.
RAISES DESCRIPTION
ValueError

If the data format is not supported.
TypeError

If the input tensor 'x' is of an unsupported type.
RuntimeError

If any runtime error occurs during the normalization process.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, x: mindspore.Tensor) -> mindspore.Tensor:
    """
    Constructs a layer normalization operation for the SamLayerNorm class.

    Args:
        self (SamLayerNorm): The instance of the SamLayerNorm class.
        x (mindspore.Tensor): The input tensor to be normalized.

    Returns:
        mindspore.Tensor: A normalized tensor based on the input tensor 'x'.

    Raises:
        ValueError: If the data format is not supported.
        TypeError: If the input tensor 'x' is of an unsupported type.
        RuntimeError: If any runtime error occurs during the normalization process.
    """
    if self.data_format == "channels_last":
        x, _, _ = self.layer_norm(x, self.weight, self.bias)
    elif self.data_format == "channels_first":
        input_dtype = x.dtype
        x = x.float()
        u = x.mean(1, keep_dims=True)
        s = (x - u).pow(2).mean(1, keep_dims=True)
        x = (x - u) / ops.sqrt(s + self.eps)
        x = x.to(dtype=input_dtype)
        x = self.weight[:, None, None] * x + self.bias[:, None, None]
    return x

mindnlp.transformers.models.sam.modeling_sam.SamMLPBlock

Bases: Module

This class represents a Multi-Layer Perceptron (MLP) block used in a neural network. It inherits from the nn.Module class, a base class for all neural network modules in MindSpore.

ATTRIBUTE DESCRIPTION
lin1

The first dense layer of the MLP block.

TYPE: Linear

lin2

The second dense layer of the MLP block.

TYPE: Linear

act

The activation function used in the hidden layer of the MLP block.

TYPE: function

METHOD DESCRIPTION
__init__

Initializes the SamMLPBlock instance.
forward

Constructs the forward pass of the MLP block.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamMLPBlock(nn.Module):

    """
    This class represents a Multi-Layer Perceptron (MLP) block used in a neural network.
    It inherits from the nn.Module class, a base class for all neural network modules in MindSpore.

    Attributes:
        lin1 (nn.Linear): The first dense layer of the MLP block.
        lin2 (nn.Linear): The second dense layer of the MLP block.
        act (function): The activation function used in the hidden layer of the MLP block.

    Methods:
        __init__: Initializes the SamMLPBlock instance.
        forward: Constructs the forward pass of the MLP block.

    """
    def __init__(self, config):
        """
        Initializes an instance of the SamMLPBlock class.

        Args:
            self: The instance of the SamMLPBlock class.
            config: An object containing configuration parameters for the MLP block.
                It is expected to have the following attributes:

                - hidden_size (int): The size of the hidden layer.
                - mlp_dim (int): The dimension of the MLP layer.
                - hidden_act (str): The activation function to be applied to the hidden layers.

        Returns:
            None.

        Raises:
            KeyError: If the 'hidden_act' attribute in the 'config' parameter does not correspond to
                any activation function in ACT2FN.
            AttributeError: If the 'config' parameter is missing any of the required attributes.
        """
        super().__init__()
        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
        self.act = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: mindspore.Tensor) -> mindspore.Tensor:
        """
        Constructs a multi-layer perceptron block.

        Args:
            self (SamMLPBlock): The instance of the SamMLPBlock class.
            hidden_states (mindspore.Tensor): The input hidden states tensor to be processed.

        Returns:
            mindspore.Tensor: The processed hidden states tensor after passing through the MLP block.

        Raises:
            None
        """
        hidden_states = self.lin1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.lin2(hidden_states)
        return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamMLPBlock.__init__(config)

Initializes an instance of the SamMLPBlock class.

PARAMETER DESCRIPTION
self

The instance of the SamMLPBlock class.
config

An object containing configuration parameters for the MLP block. It is expected to have the following attributes:

  • hidden_size (int): The size of the hidden layer.
  • mlp_dim (int): The dimension of the MLP layer.
  • hidden_act (str): The activation function to be applied to the hidden layers.
RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
KeyError

If the 'hidden_act' attribute in the 'config' parameter does not correspond to any activation function in ACT2FN.
AttributeError

If the 'config' parameter is missing any of the required attributes.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config):
    """
    Initializes an instance of the SamMLPBlock class.

    Args:
        self: The instance of the SamMLPBlock class.
        config: An object containing configuration parameters for the MLP block.
            It is expected to have the following attributes:

            - hidden_size (int): The size of the hidden layer.
            - mlp_dim (int): The dimension of the MLP layer.
            - hidden_act (str): The activation function to be applied to the hidden layers.

    Returns:
        None.

    Raises:
        KeyError: If the 'hidden_act' attribute in the 'config' parameter does not correspond to
            any activation function in ACT2FN.
        AttributeError: If the 'config' parameter is missing any of the required attributes.
    """
    super().__init__()
    self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
    self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
    self.act = ACT2FN[config.hidden_act]

mindnlp.transformers.models.sam.modeling_sam.SamMLPBlock.forward(hidden_states)

Constructs a multi-layer perceptron block.

PARAMETER DESCRIPTION
self

The instance of the SamMLPBlock class.

TYPE: SamMLPBlock

hidden_states

The input hidden states tensor to be processed.

TYPE: Tensor

RETURNS DESCRIPTION
Tensor

mindspore.Tensor: The processed hidden states tensor after passing through the MLP block.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, hidden_states: mindspore.Tensor) -> mindspore.Tensor:
    """
    Constructs a multi-layer perceptron block.

    Args:
        self (SamMLPBlock): The instance of the SamMLPBlock class.
        hidden_states (mindspore.Tensor): The input hidden states tensor to be processed.

    Returns:
        mindspore.Tensor: The processed hidden states tensor after passing through the MLP block.

    Raises:
        None
    """
    hidden_states = self.lin1(hidden_states)
    hidden_states = self.act(hidden_states)
    hidden_states = self.lin2(hidden_states)
    return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamMaskDecoder

Bases: Module

A class representing a Mask Decoder module for generating masks based on image and prompt embeddings.

This class inherits from nn.Module and contains methods for initializing the decoder and forwarding the masks based on input embeddings. The decoder architecture includes components such as transformers, convolutional layers, embeddings, and feedforward networks to generate masks with optional attentions and predictions.

ATTRIBUTE DESCRIPTION
hidden_size

The size of the hidden layers in the decoder.

TYPE: int

num_multimask_outputs

The number of multimask outputs to be generated.

TYPE: int

num_mask_tokens

The total number of mask tokens used in the decoder.

TYPE: int

iou_token

Embedding layer for IOU tokens.

TYPE: Embedding

mask_tokens

Embedding layer for mask tokens.

TYPE: Embedding

transformer

Transformer network used in the decoding process.

TYPE: SamTwoWayTransformer

upscale_conv1

Transposed convolutional layer for upscaling.

TYPE: Conv2dTranspose

upscale_conv2

Additional transposed convolutional layer for upscaling.

TYPE: Conv2dTranspose

upscale_layer_norm

Layer normalization applied after upscaling.

TYPE: SamLayerNorm

activation

Activation function used in the decoder.

TYPE: GELU

output_hypernetworks_mlps

List of feedforward networks for output hypernetworks.

TYPE: ModuleList

iou_prediction_head

Feedforward network for IOU prediction.

TYPE: SamFeedForward

METHOD DESCRIPTION
__init__

Initializes the Mask Decoder with the provided configuration.
forward

Predicts masks based on input embeddings and returns the generated masks along with optional attentions.

For more details on the functionality and usage of the Mask Decoder class, refer to the method descriptions and class attributes above.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamMaskDecoder(nn.Module):

    """
    A class representing a Mask Decoder module for generating masks based on image and prompt embeddings.

    This class inherits from nn.Module and contains methods for initializing the decoder and forwarding the masks
    based on input embeddings. The decoder architecture includes components such as transformers, convolutional layers,
    embeddings, and feedforward networks to generate masks with optional attentions and predictions.

    Attributes:
        hidden_size (int): The size of the hidden layers in the decoder.
        num_multimask_outputs (int): The number of multimask outputs to be generated.
        num_mask_tokens (int): The total number of mask tokens used in the decoder.
        iou_token (nn.Embedding): Embedding layer for IOU tokens.
        mask_tokens (nn.Embedding): Embedding layer for mask tokens.
        transformer (SamTwoWayTransformer): Transformer network used in the decoding process.
        upscale_conv1 (nn.Conv2dTranspose): Transposed convolutional layer for upscaling.
        upscale_conv2 (nn.Conv2dTranspose): Additional transposed convolutional layer for upscaling.
        upscale_layer_norm (SamLayerNorm): Layer normalization applied after upscaling.
        activation (nn.GELU): Activation function used in the decoder.
        output_hypernetworks_mlps (nn.ModuleList): List of feedforward networks for output hypernetworks.
        iou_prediction_head (SamFeedForward): Feedforward network for IOU prediction.

    Methods:
        __init__: Initializes the Mask Decoder with the provided configuration.
        forward: Predicts masks based on input embeddings and returns the generated masks along with optional attentions.

    For more details on the functionality and usage of the Mask Decoder class, refer to the method descriptions and class attributes above.
    """
    def __init__(self, config: SamMaskDecoderConfig):
        """
        Initialize the SamMaskDecoder class.

        Args:
            self: The instance of the SamMaskDecoder class.
            config (SamMaskDecoderConfig): An instance of the SamMaskDecoderConfig class containing the configuration
                parameters for the SamMaskDecoder.
                It includes the following attributes:

                - hidden_size (int): The size of the hidden state.
                - num_multimask_outputs (int): The number of multimask outputs.
                - num_mask_tokens (int): The number of mask tokens, which is calculated as
                config.num_multimask_outputs + 1.
                - iou_token (nn.Embedding): An embedding for the intersection over union (IOU) token with a shape of
                (1, hidden_size).
                - mask_tokens (nn.Embedding): An embedding for the mask tokens with a shape of
                (num_mask_tokens, hidden_size).
                - transformer (SamTwoWayTransformer): The SamTwoWayTransformer instance.
                - upscale_conv1 (nn.Conv2dTranspose): The first convolution layer for upscaling.
                - upscale_conv2 (nn.Conv2dTranspose): The second convolution layer for upscaling.
                - upscale_layer_norm (SamLayerNorm): The layer normalization for upscaling.
                - activation (nn.GELU): The activation function.
                - output_hypernetworks_mlps (nn.ModuleList): A list of SamFeedForward instances for output hypernetworks.
                - iou_prediction_head (SamFeedForward): The SamFeedForward instance for IOU prediction head.

        Returns:
            None.

        Raises:
            None
        """
        super().__init__()

        self.hidden_size = config.hidden_size

        self.num_multimask_outputs = config.num_multimask_outputs
        self.num_mask_tokens = config.num_multimask_outputs + 1

        self.iou_token = nn.Embedding(1, self.hidden_size)
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size)

        self.transformer = SamTwoWayTransformer(config)

        # should we create a new class for this?
        self.upscale_conv1 = nn.Conv2dTranspose(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2, pad_mode='valid', bias=True)
        self.upscale_conv2 = nn.Conv2dTranspose(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2, pad_mode='valid', bias=True)
        self.upscale_layer_norm = SamLayerNorm(self.hidden_size // 4, data_format="channels_first")
        self.activation = nn.GELU()

        mlps_list = []
        for _ in range(self.num_mask_tokens):
            mlps_list += [SamFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)]
        self.output_hypernetworks_mlps = nn.ModuleList(mlps_list)

        self.iou_prediction_head = SamFeedForward(
            self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth
        )

    def forward(
        self,
        image_embeddings: mindspore.Tensor,
        image_positional_embeddings: mindspore.Tensor,
        sparse_prompt_embeddings: mindspore.Tensor,
        dense_prompt_embeddings: mindspore.Tensor,
        multimask_output: bool,
        output_attentions: Optional[bool] = None,
        attention_similarity: mindspore.Tensor = None,
        target_embedding: mindspore.Tensor = None,
    ) -> Tuple[mindspore.Tensor, mindspore.Tensor]:
        """
        Predict masks given image and prompt embeddings.

        Args:
            image_embeddings (`mindspore.Tensor`):
                the embeddings from the image encoder
            image_positional_embedding (`mindspore.Tensor`):
                positional encoding with the shape of image_embeddings
            sparse_prompt_embeddings (`mindspore.Tensor`):
                The embeddings of the points and boxes
            dense_prompt_embeddings (`mindspore.Tensor`):
                the embeddings of the mask inputs
            multimask_output (bool):
                Whether to return multiple masks or a single mask.
            output_attentions (bool, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
        """
        batch_size, num_channels, height, width = image_embeddings.shape
        point_batch_size = sparse_prompt_embeddings.shape[1]
        # Concatenate output tokens
        output_tokens = ops.cat([self.iou_token.weight, self.mask_tokens.weight], axis=0)
        output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1)

        if sparse_prompt_embeddings.sum().item() != 0:
            tokens = ops.cat((output_tokens, sparse_prompt_embeddings), axis=2)
        else:
            tokens = output_tokens
        point_embeddings = tokens.to(self.iou_token.weight.dtype)

        # Expand per-image data in batch direction to be per-point
        image_embeddings = image_embeddings + dense_prompt_embeddings
        image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0)
        image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0)

        # Run the transformer, image_positional_embedding are consumed
        point_embedding, image_embeddings, attentions = self.transformer(
            point_embeddings=point_embeddings,
            image_embeddings=image_embeddings,
            image_positional_embeddings=image_positional_embeddings,
            attention_similarity=attention_similarity,
            target_embedding=target_embedding,
            output_attentions=output_attentions,
        )
        iou_token_out = point_embedding[:, :, 0, :]
        mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :]

        # Upscale mask embeddings and predict masks using the mask tokens
        image_embeddings = image_embeddings.swapaxes(2, 3).reshape(
            batch_size * point_batch_size, num_channels, height, width
        )

        upscaled_embedding = self.upscale_conv1(image_embeddings)
        upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding))
        upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding))

        hyper_in_list = []
        for i in range(self.num_mask_tokens):
            current_mlp = self.output_hypernetworks_mlps[i]
            hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])]
        hyper_in = ops.stack(hyper_in_list, axis=2)

        _, num_channels, height, width = upscaled_embedding.shape
        upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width)
        masks = (hyper_in @ upscaled_embedding).reshape(batch_size, point_batch_size, -1, height, width)

        # Generate mask quality predictions
        iou_pred = self.iou_prediction_head(iou_token_out)

        # Select the correct mask or masks for output
        if multimask_output:
            mask_slice = slice(1, None)
        else:
            mask_slice = slice(0, 1)
        masks = masks[:, :, mask_slice, :, :]
        iou_pred = iou_pred[:, :, mask_slice]

        outputs = (masks, iou_pred)

        if output_attentions:
            outputs = outputs + (attentions,)
        else:
            outputs = outputs + (None,)

        return outputs

mindnlp.transformers.models.sam.modeling_sam.SamMaskDecoder.__init__(config)

Initialize the SamMaskDecoder class.

PARAMETER DESCRIPTION
self

The instance of the SamMaskDecoder class.
config

An instance of the SamMaskDecoderConfig class containing the configuration parameters for the SamMaskDecoder. It includes the following attributes:

  • hidden_size (int): The size of the hidden state.
  • num_multimask_outputs (int): The number of multimask outputs.
  • num_mask_tokens (int): The number of mask tokens, which is calculated as config.num_multimask_outputs + 1.
  • iou_token (nn.Embedding): An embedding for the intersection over union (IOU) token with a shape of (1, hidden_size).
  • mask_tokens (nn.Embedding): An embedding for the mask tokens with a shape of (num_mask_tokens, hidden_size).
  • transformer (SamTwoWayTransformer): The SamTwoWayTransformer instance.
  • upscale_conv1 (nn.Conv2dTranspose): The first convolution layer for upscaling.
  • upscale_conv2 (nn.Conv2dTranspose): The second convolution layer for upscaling.
  • upscale_layer_norm (SamLayerNorm): The layer normalization for upscaling.
  • activation (nn.GELU): The activation function.
  • output_hypernetworks_mlps (nn.ModuleList): A list of SamFeedForward instances for output hypernetworks.
  • iou_prediction_head (SamFeedForward): The SamFeedForward instance for IOU prediction head.

TYPE: SamMaskDecoderConfig

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamMaskDecoderConfig):
    """
    Initialize the SamMaskDecoder class.

    Args:
        self: The instance of the SamMaskDecoder class.
        config (SamMaskDecoderConfig): An instance of the SamMaskDecoderConfig class containing the configuration
            parameters for the SamMaskDecoder.
            It includes the following attributes:

            - hidden_size (int): The size of the hidden state.
            - num_multimask_outputs (int): The number of multimask outputs.
            - num_mask_tokens (int): The number of mask tokens, which is calculated as
            config.num_multimask_outputs + 1.
            - iou_token (nn.Embedding): An embedding for the intersection over union (IOU) token with a shape of
            (1, hidden_size).
            - mask_tokens (nn.Embedding): An embedding for the mask tokens with a shape of
            (num_mask_tokens, hidden_size).
            - transformer (SamTwoWayTransformer): The SamTwoWayTransformer instance.
            - upscale_conv1 (nn.Conv2dTranspose): The first convolution layer for upscaling.
            - upscale_conv2 (nn.Conv2dTranspose): The second convolution layer for upscaling.
            - upscale_layer_norm (SamLayerNorm): The layer normalization for upscaling.
            - activation (nn.GELU): The activation function.
            - output_hypernetworks_mlps (nn.ModuleList): A list of SamFeedForward instances for output hypernetworks.
            - iou_prediction_head (SamFeedForward): The SamFeedForward instance for IOU prediction head.

    Returns:
        None.

    Raises:
        None
    """
    super().__init__()

    self.hidden_size = config.hidden_size

    self.num_multimask_outputs = config.num_multimask_outputs
    self.num_mask_tokens = config.num_multimask_outputs + 1

    self.iou_token = nn.Embedding(1, self.hidden_size)
    self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size)

    self.transformer = SamTwoWayTransformer(config)

    # should we create a new class for this?
    self.upscale_conv1 = nn.Conv2dTranspose(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2, pad_mode='valid', bias=True)
    self.upscale_conv2 = nn.Conv2dTranspose(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2, pad_mode='valid', bias=True)
    self.upscale_layer_norm = SamLayerNorm(self.hidden_size // 4, data_format="channels_first")
    self.activation = nn.GELU()

    mlps_list = []
    for _ in range(self.num_mask_tokens):
        mlps_list += [SamFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)]
    self.output_hypernetworks_mlps = nn.ModuleList(mlps_list)

    self.iou_prediction_head = SamFeedForward(
        self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth
    )

mindnlp.transformers.models.sam.modeling_sam.SamMaskDecoder.forward(image_embeddings, image_positional_embeddings, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output, output_attentions=None, attention_similarity=None, target_embedding=None)

Predict masks given image and prompt embeddings.

PARAMETER DESCRIPTION
image_embeddings

the embeddings from the image encoder

TYPE: `mindspore.Tensor`

image_positional_embedding

positional encoding with the shape of image_embeddings

TYPE: `mindspore.Tensor`

sparse_prompt_embeddings

The embeddings of the points and boxes

TYPE: `mindspore.Tensor`

dense_prompt_embeddings

the embeddings of the mask inputs

TYPE: `mindspore.Tensor`

multimask_output

Whether to return multiple masks or a single mask.

TYPE: bool

output_attentions

Whether or not to return the attentions tensors of all attention layers.

TYPE: bool, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    image_embeddings: mindspore.Tensor,
    image_positional_embeddings: mindspore.Tensor,
    sparse_prompt_embeddings: mindspore.Tensor,
    dense_prompt_embeddings: mindspore.Tensor,
    multimask_output: bool,
    output_attentions: Optional[bool] = None,
    attention_similarity: mindspore.Tensor = None,
    target_embedding: mindspore.Tensor = None,
) -> Tuple[mindspore.Tensor, mindspore.Tensor]:
    """
    Predict masks given image and prompt embeddings.

    Args:
        image_embeddings (`mindspore.Tensor`):
            the embeddings from the image encoder
        image_positional_embedding (`mindspore.Tensor`):
            positional encoding with the shape of image_embeddings
        sparse_prompt_embeddings (`mindspore.Tensor`):
            The embeddings of the points and boxes
        dense_prompt_embeddings (`mindspore.Tensor`):
            the embeddings of the mask inputs
        multimask_output (bool):
            Whether to return multiple masks or a single mask.
        output_attentions (bool, *optional*):
            Whether or not to return the attentions tensors of all attention layers.
    """
    batch_size, num_channels, height, width = image_embeddings.shape
    point_batch_size = sparse_prompt_embeddings.shape[1]
    # Concatenate output tokens
    output_tokens = ops.cat([self.iou_token.weight, self.mask_tokens.weight], axis=0)
    output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1)

    if sparse_prompt_embeddings.sum().item() != 0:
        tokens = ops.cat((output_tokens, sparse_prompt_embeddings), axis=2)
    else:
        tokens = output_tokens
    point_embeddings = tokens.to(self.iou_token.weight.dtype)

    # Expand per-image data in batch direction to be per-point
    image_embeddings = image_embeddings + dense_prompt_embeddings
    image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0)
    image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0)

    # Run the transformer, image_positional_embedding are consumed
    point_embedding, image_embeddings, attentions = self.transformer(
        point_embeddings=point_embeddings,
        image_embeddings=image_embeddings,
        image_positional_embeddings=image_positional_embeddings,
        attention_similarity=attention_similarity,
        target_embedding=target_embedding,
        output_attentions=output_attentions,
    )
    iou_token_out = point_embedding[:, :, 0, :]
    mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :]

    # Upscale mask embeddings and predict masks using the mask tokens
    image_embeddings = image_embeddings.swapaxes(2, 3).reshape(
        batch_size * point_batch_size, num_channels, height, width
    )

    upscaled_embedding = self.upscale_conv1(image_embeddings)
    upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding))
    upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding))

    hyper_in_list = []
    for i in range(self.num_mask_tokens):
        current_mlp = self.output_hypernetworks_mlps[i]
        hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])]
    hyper_in = ops.stack(hyper_in_list, axis=2)

    _, num_channels, height, width = upscaled_embedding.shape
    upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width)
    masks = (hyper_in @ upscaled_embedding).reshape(batch_size, point_batch_size, -1, height, width)

    # Generate mask quality predictions
    iou_pred = self.iou_prediction_head(iou_token_out)

    # Select the correct mask or masks for output
    if multimask_output:
        mask_slice = slice(1, None)
    else:
        mask_slice = slice(0, 1)
    masks = masks[:, :, mask_slice, :, :]
    iou_pred = iou_pred[:, :, mask_slice]

    outputs = (masks, iou_pred)

    if output_attentions:
        outputs = outputs + (attentions,)
    else:
        outputs = outputs + (None,)

    return outputs

mindnlp.transformers.models.sam.modeling_sam.SamMaskEmbedding

Bases: Module

This class represents a mask embedding module used for generating dense embeddings from input masks. It consists of several convolutional and normalization layers for processing the input masks and producing dense embeddings. The class inherits from nn.Module.

ATTRIBUTE DESCRIPTION
mask_input_channels

Number of input channels for the mask

TYPE: int

activation

Activation function used in the module

TYPE: function

conv1

Convolutional layer 1

TYPE: Conv2d

conv2

Convolutional layer 2

TYPE: Conv2d

conv3

Convolutional layer 3

TYPE: Conv2d

layer_norm1

Layer normalization for the first layer

TYPE: SamLayerNorm

layer_norm2

Layer normalization for the second layer

TYPE: SamLayerNorm

METHOD DESCRIPTION
forward

Processes the input masks through the convolutional and normalization layers to generate dense embeddings
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamMaskEmbedding(nn.Module):

    """
    This class represents a mask embedding module used for generating dense embeddings from input masks.
    It consists of several convolutional and normalization layers for processing the input masks and
    producing dense embeddings. The class inherits from nn.Module.

    Attributes:
        mask_input_channels (int): Number of input channels for the mask
        activation (function): Activation function used in the module
        conv1 (nn.Conv2d): Convolutional layer 1
        conv2 (nn.Conv2d): Convolutional layer 2
        conv3 (nn.Conv2d): Convolutional layer 3
        layer_norm1 (SamLayerNorm): Layer normalization for the first layer
        layer_norm2 (SamLayerNorm): Layer normalization for the second layer

    Methods:
        forward: Processes the input masks through the convolutional and normalization layers to generate dense
            embeddings
    """
    def __init__(self, config: SamPromptEncoderConfig):
        """
        Initializes the SamMaskEmbedding object with the provided configuration.

        Args:
            self: The instance of the SamMaskEmbedding class.
            config (SamPromptEncoderConfig): An instance of the SamPromptEncoderConfig class containing the
                configuration settings for the SamMaskEmbedding.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        self.mask_input_channels = config.mask_input_channels // 4
        self.activation = ACT2FN[config.hidden_act]
        self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2, pad_mode='valid', bias=True)
        self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2, pad_mode='valid', bias=True)
        self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1, pad_mode='valid', bias=True)
        self.layer_norm1 = SamLayerNorm(
            self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first"
        )
        self.layer_norm2 = SamLayerNorm(
            self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first"
        )

    def forward(self, masks):
        """
        Constructs dense embeddings from masks using convolutional layers.

        Args:
            self: An instance of the SamMaskEmbedding class.
            masks: A tensor of shape (batch_size, channels, height, width) representing the input masks.

        Returns:
            None: The method modifies the state of the object by updating the dense embeddings attribute.

        Raises:
            None.

        This method applies a series of convolutional layers to the input masks to generate dense embeddings.
        The process involves the following steps:

        1. Convolution 1: Applies a 2D convolutional operation to the masks tensor, resulting in hidden states.
        2. Layer Normalization 1: Performs layer normalization on the hidden states.
        3. Activation: Applies an activation function to the normalized hidden states.
        4. Convolution 2: Applies another 2D convolutional operation to the activated hidden states.
        5. Layer Normalization 2: Performs layer normalization on the hidden states from the second convolution.
        6. Activation: Applies the activation function to the normalized hidden states from the second convolution.
        7. Convolution 3: Applies a final 2D convolutional operation to the normalized hidden states from the
        second convolution.

        After these steps, the method returns the dense embeddings.
        """
        hidden_states = self.conv1(masks)
        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.activation(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.activation(hidden_states)
        dense_embeddings = self.conv3(hidden_states)
        return dense_embeddings

mindnlp.transformers.models.sam.modeling_sam.SamMaskEmbedding.__init__(config)

Initializes the SamMaskEmbedding object with the provided configuration.

PARAMETER DESCRIPTION
self

The instance of the SamMaskEmbedding class.
config

An instance of the SamPromptEncoderConfig class containing the configuration settings for the SamMaskEmbedding.

TYPE: SamPromptEncoderConfig

RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamPromptEncoderConfig):
    """
    Initializes the SamMaskEmbedding object with the provided configuration.

    Args:
        self: The instance of the SamMaskEmbedding class.
        config (SamPromptEncoderConfig): An instance of the SamPromptEncoderConfig class containing the
            configuration settings for the SamMaskEmbedding.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    self.mask_input_channels = config.mask_input_channels // 4
    self.activation = ACT2FN[config.hidden_act]
    self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2, pad_mode='valid', bias=True)
    self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2, pad_mode='valid', bias=True)
    self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1, pad_mode='valid', bias=True)
    self.layer_norm1 = SamLayerNorm(
        self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first"
    )
    self.layer_norm2 = SamLayerNorm(
        self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first"
    )

mindnlp.transformers.models.sam.modeling_sam.SamMaskEmbedding.forward(masks)

Constructs dense embeddings from masks using convolutional layers.

PARAMETER DESCRIPTION
self

An instance of the SamMaskEmbedding class.
masks

A tensor of shape (batch_size, channels, height, width) representing the input masks.
RETURNS DESCRIPTION
None

The method modifies the state of the object by updating the dense embeddings attribute.

This method applies a series of convolutional layers to the input masks to generate dense embeddings. The process involves the following steps:

  1. Convolution 1: Applies a 2D convolutional operation to the masks tensor, resulting in hidden states.
  2. Layer Normalization 1: Performs layer normalization on the hidden states.
  3. Activation: Applies an activation function to the normalized hidden states.
  4. Convolution 2: Applies another 2D convolutional operation to the activated hidden states.
  5. Layer Normalization 2: Performs layer normalization on the hidden states from the second convolution.
  6. Activation: Applies the activation function to the normalized hidden states from the second convolution.
  7. Convolution 3: Applies a final 2D convolutional operation to the normalized hidden states from the second convolution.

After these steps, the method returns the dense embeddings.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, masks):
    """
    Constructs dense embeddings from masks using convolutional layers.

    Args:
        self: An instance of the SamMaskEmbedding class.
        masks: A tensor of shape (batch_size, channels, height, width) representing the input masks.

    Returns:
        None: The method modifies the state of the object by updating the dense embeddings attribute.

    Raises:
        None.

    This method applies a series of convolutional layers to the input masks to generate dense embeddings.
    The process involves the following steps:

    1. Convolution 1: Applies a 2D convolutional operation to the masks tensor, resulting in hidden states.
    2. Layer Normalization 1: Performs layer normalization on the hidden states.
    3. Activation: Applies an activation function to the normalized hidden states.
    4. Convolution 2: Applies another 2D convolutional operation to the activated hidden states.
    5. Layer Normalization 2: Performs layer normalization on the hidden states from the second convolution.
    6. Activation: Applies the activation function to the normalized hidden states from the second convolution.
    7. Convolution 3: Applies a final 2D convolutional operation to the normalized hidden states from the
    second convolution.

    After these steps, the method returns the dense embeddings.
    """
    hidden_states = self.conv1(masks)
    hidden_states = self.layer_norm1(hidden_states)
    hidden_states = self.activation(hidden_states)

    hidden_states = self.conv2(hidden_states)
    hidden_states = self.layer_norm2(hidden_states)
    hidden_states = self.activation(hidden_states)
    dense_embeddings = self.conv3(hidden_states)
    return dense_embeddings

mindnlp.transformers.models.sam.modeling_sam.SamModel

Bases: SamPreTrainedModel

The SamModel class is a Python class that represents a model for image segmentation tasks. It is a subclass of the SamPreTrainedModel class.

The SamModel class is typically used for image segmentation tasks. An example of how to use the SamModel class is provided in the docstring.

Note

This docstring provides an overview of the SamModel class and its methods. For more detailed information on the parameters and return types of each method, please refer to the method docstrings.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamModel(SamPreTrainedModel):

    """
    The `SamModel` class is a Python class that represents a model for image segmentation tasks.
    It is a subclass of the `SamPreTrainedModel` class.

    The `SamModel` class is typically used for image segmentation tasks. An example of how to use the `SamModel`
    class is provided in the docstring.

    Note:
        This docstring provides an overview of the `SamModel` class and its methods. For more detailed information
        on the parameters and return types of each method, please refer to the method docstrings.
    """
    _tied_weights_keys = ["prompt_encoder.shared_embedding.positional_embedding"]

    def __init__(self, config):
        """
        Initializes a new instance of the SamModel class.

        Args:
            self (SamModel): The current instance of the SamModel class.
            config (object): Configuration object containing various settings for the model.

        Returns:
            None.

        Raises:
            TypeError: If the provided 'config' parameter is not of type 'object'.
            ValueError: If the 'config' parameter is missing required settings or contains invalid values.
            RuntimeError: If any unexpected runtime error occurs during initialization.
        """
        super().__init__(config)
        self.shared_image_embedding = SamPositionalEmbedding(config.vision_config)

        self.vision_encoder = SamVisionEncoder(config.vision_config)
        self.prompt_encoder = SamPromptEncoder(config.prompt_encoder_config, self.shared_image_embedding)
        self.mask_decoder = SamMaskDecoder(config.mask_decoder_config)

        self.post_init()

    def get_input_embeddings(self):
        """
        This method 'get_input_embeddings' in the class 'SamModel' retrieves the input embeddings from the vision encoder.

        Args:
            self: SamModel instance. Represents the current instance of the class.

        Returns:
            None: This method returns None as it simply retrieves the input embeddings from the vision encoder.

        Raises:
            None
        """
        return self.vision_encoder.get_input_embeddings()

    def get_image_wide_positional_embeddings(self):
        """
        This method calculates wide positional embeddings for an image in the SamModel class.

        Args:
            self: An instance of the SamModel class. It is used to access configuration parameters and shared image embeddings.

        Returns:
            positional_embedding (torch.Tensor): A tensor representing the positional embeddings for the image.
                The tensor is permuted and unsqueezed before returning.

        Raises:
            None
        """
        size = self.config.prompt_encoder_config.image_embedding_size
        target_dtype = self.shared_image_embedding.positional_embedding.dtype
        grid = ops.ones((size, size), dtype=target_dtype)
        y_embed = grid.cumsum(axis=0) - 0.5
        x_embed = grid.cumsum(axis=1) - 0.5
        y_embed = y_embed / size
        x_embed = x_embed / size

        positional_embedding = self.shared_image_embedding(ops.stack([x_embed, y_embed], axis=-1))
        return positional_embedding.permute(2, 0, 1).unsqueeze(0)  # channel x height x width

    def get_image_embeddings(
        self,
        pixel_values,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Returns the image embeddings by passing the pixel values through the vision encoder.

        Args:
            pixel_values (`mindspore.Tensor` of shape `(batch_size, num_channels, height, width)`):
                Input pixel values
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

        """
        vision_output = self.vision_encoder(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        image_embeddings = vision_output[0]
        return image_embeddings

    def get_prompt_embeddings(
        self,
        input_points: Optional[mindspore.Tensor] = None,
        input_labels: Optional[mindspore.Tensor] = None,
        input_boxes: Optional[mindspore.Tensor] = None,
        input_masks: Optional[mindspore.Tensor] = None,
    ):
        r"""
        Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder.

        Args:
            input_points (`mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`):
                Optional input points for the prompt encoder. The padding of the point is automatically done by the
                processor. `point_batch_size` refers to the number of masks that we want the model to predict per
                point. The model will output `point_batch_size` times 3 masks in total.
            input_labels (`mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image)`):
                Optional input labels for the prompt encoder. The padding of the labels is automatically done by the
                processor, or can be fed by the user.
            input_boxes (`mindspore.Tensor` of shape `(batch_size, num_boxes_per_image, 4)`):
                Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the
                processor. users can also pass manually the input boxes.
            input_masks (`mindspore.Tensor` of shape `(batch_size, image_size, image_size)`):
                Optional input masks for the prompt encoder.
        """
        prompt_output = self.prompt_encoder(
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
            input_masks=input_masks,
        )
        return prompt_output

    def forward(
        self,
        pixel_values: Optional[mindspore.Tensor] = None,
        input_points: Optional[mindspore.Tensor] = None,
        input_labels: Optional[mindspore.Tensor] = None,
        input_boxes: Optional[mindspore.Tensor] = None,
        input_masks: Optional[mindspore.Tensor] = None,
        image_embeddings: Optional[mindspore.Tensor] = None,
        multimask_output: bool = True,
        attention_similarity: Optional[mindspore.Tensor] = None,
        target_embedding: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> List[Dict[str, mindspore.Tensor]]:
        r"""

        Example:
            ```python
            >>> from PIL import Image
            >>> import requests
            >>> from transformers import AutoModel, AutoProcessor
            ...
            >>> model = AutoModel.from_pretrained("facebook/sam-vit-base")
            >>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
            ...
            >>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
            >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
            >>> input_points = [[[400, 650]]]  # 2D location of a window on the car
            >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt")
            ...
            >>> # Get segmentation mask
            >>> outputs = model(**inputs)
            ...
            >>> # Postprocess masks
            >>> masks = processor.post_process_masks(
            ...     outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
            ... )
            ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None and image_embeddings is None:
            raise ValueError("Either pixel_values or image_embeddings must be provided.")

        if pixel_values is not None and image_embeddings is not None:
            raise ValueError("Only one of pixel_values and image_embeddings can be provided.")

        if input_points is not None and len(input_points.shape) != 4:
            raise ValueError(
                "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.",
                " got {}.".format(input_points.shape),
            )
        if input_boxes is not None and len(input_boxes.shape) != 3:
            raise ValueError(
                "The input_points must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`.",
                " got {}.".format(input_boxes.shape),
            )
        if input_points is not None and input_boxes is not None:
            point_batch_size = input_points.shape[1]
            box_batch_size = input_boxes.shape[1]
            if point_batch_size != box_batch_size:
                raise ValueError(
                    "You should provide as many bounding boxes as input points per box. Got {} and {}.".format(
                        point_batch_size, box_batch_size
                    )
                )

        image_positional_embeddings = self.get_image_wide_positional_embeddings()
        # repeat with batch size
        batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0]
        image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1)

        vision_attentions = None
        vision_hidden_states = None

        if pixel_values is not None:
            vision_outputs = self.vision_encoder(
                pixel_values,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
            image_embeddings = vision_outputs[0]

            if output_hidden_states:
                vision_hidden_states = vision_outputs[1]
            if output_attentions:
                vision_attentions = vision_outputs[-1]

        if input_points is not None and input_labels is None:
            input_labels = ops.ones_like(input_points[:, :, :, 0], dtype=mindspore.int32)

        if input_points is not None and image_embeddings.shape[0] != input_points.shape[0]:
            raise ValueError(
                "The batch size of the image embeddings and the input points must be the same. ",
                "Got {} and {} respectively.".format(image_embeddings.shape[0], input_points.shape[0]),
                " if you want to pass multiple points for the same image, make sure that you passed ",
                " input_points of shape (batch_size, point_batch_size, num_points_per_image, 3) and ",
                " input_labels of shape (batch_size, point_batch_size, num_points_per_image)",
            )

        sparse_embeddings, dense_embeddings = self.prompt_encoder(
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
            input_masks=input_masks,
        )

        low_res_masks, iou_predictions, mask_decoder_attentions = self.mask_decoder(
            image_embeddings=image_embeddings,
            image_positional_embeddings=image_positional_embeddings,
            sparse_prompt_embeddings=sparse_embeddings,
            dense_prompt_embeddings=dense_embeddings,
            multimask_output=multimask_output,
            attention_similarity=attention_similarity,
            target_embedding=target_embedding,
            output_attentions=output_attentions,
        )

        if not return_dict:
            output = (iou_predictions, low_res_masks)
            if output_hidden_states:
                output = output + (vision_hidden_states,)

            if output_attentions:
                output = output + (vision_attentions, mask_decoder_attentions)
            return output

        return SamImageSegmentationOutput(
            iou_scores=iou_predictions,
            pred_masks=low_res_masks,
            vision_hidden_states=vision_hidden_states,
            vision_attentions=vision_attentions,
            mask_decoder_attentions=mask_decoder_attentions,
        )

mindnlp.transformers.models.sam.modeling_sam.SamModel.__init__(config)

Initializes a new instance of the SamModel class.

PARAMETER DESCRIPTION
self

The current instance of the SamModel class.

TYPE: SamModel

config

Configuration object containing various settings for the model.

TYPE: object

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
TypeError

If the provided 'config' parameter is not of type 'object'.
ValueError

If the 'config' parameter is missing required settings or contains invalid values.
RuntimeError

If any unexpected runtime error occurs during initialization.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config):
    """
    Initializes a new instance of the SamModel class.

    Args:
        self (SamModel): The current instance of the SamModel class.
        config (object): Configuration object containing various settings for the model.

    Returns:
        None.

    Raises:
        TypeError: If the provided 'config' parameter is not of type 'object'.
        ValueError: If the 'config' parameter is missing required settings or contains invalid values.
        RuntimeError: If any unexpected runtime error occurs during initialization.
    """
    super().__init__(config)
    self.shared_image_embedding = SamPositionalEmbedding(config.vision_config)

    self.vision_encoder = SamVisionEncoder(config.vision_config)
    self.prompt_encoder = SamPromptEncoder(config.prompt_encoder_config, self.shared_image_embedding)
    self.mask_decoder = SamMaskDecoder(config.mask_decoder_config)

    self.post_init()

mindnlp.transformers.models.sam.modeling_sam.SamModel.forward(pixel_values=None, input_points=None, input_labels=None, input_boxes=None, input_masks=None, image_embeddings=None, multimask_output=True, attention_similarity=None, target_embedding=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs)

Example 
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoModel, AutoProcessor
...
>>> model = AutoModel.from_pretrained("facebook/sam-vit-base")
>>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
...
>>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
>>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
>>> input_points = [[[400, 650]]]  # 2D location of a window on the car
>>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt")
...
>>> # Get segmentation mask
>>> outputs = model(**inputs)
...
>>> # Postprocess masks
>>> masks = processor.post_process_masks(
...     outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
... )
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    pixel_values: Optional[mindspore.Tensor] = None,
    input_points: Optional[mindspore.Tensor] = None,
    input_labels: Optional[mindspore.Tensor] = None,
    input_boxes: Optional[mindspore.Tensor] = None,
    input_masks: Optional[mindspore.Tensor] = None,
    image_embeddings: Optional[mindspore.Tensor] = None,
    multimask_output: bool = True,
    attention_similarity: Optional[mindspore.Tensor] = None,
    target_embedding: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    **kwargs,
) -> List[Dict[str, mindspore.Tensor]]:
    r"""

    Example:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoModel, AutoProcessor
        ...
        >>> model = AutoModel.from_pretrained("facebook/sam-vit-base")
        >>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base")
        ...
        >>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png"
        >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
        >>> input_points = [[[400, 650]]]  # 2D location of a window on the car
        >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt")
        ...
        >>> # Get segmentation mask
        >>> outputs = model(**inputs)
        ...
        >>> # Postprocess masks
        >>> masks = processor.post_process_masks(
        ...     outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
        ... )
        ```
    """
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if pixel_values is None and image_embeddings is None:
        raise ValueError("Either pixel_values or image_embeddings must be provided.")

    if pixel_values is not None and image_embeddings is not None:
        raise ValueError("Only one of pixel_values and image_embeddings can be provided.")

    if input_points is not None and len(input_points.shape) != 4:
        raise ValueError(
            "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.",
            " got {}.".format(input_points.shape),
        )
    if input_boxes is not None and len(input_boxes.shape) != 3:
        raise ValueError(
            "The input_points must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`.",
            " got {}.".format(input_boxes.shape),
        )
    if input_points is not None and input_boxes is not None:
        point_batch_size = input_points.shape[1]
        box_batch_size = input_boxes.shape[1]
        if point_batch_size != box_batch_size:
            raise ValueError(
                "You should provide as many bounding boxes as input points per box. Got {} and {}.".format(
                    point_batch_size, box_batch_size
                )
            )

    image_positional_embeddings = self.get_image_wide_positional_embeddings()
    # repeat with batch size
    batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0]
    image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1)

    vision_attentions = None
    vision_hidden_states = None

    if pixel_values is not None:
        vision_outputs = self.vision_encoder(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        image_embeddings = vision_outputs[0]

        if output_hidden_states:
            vision_hidden_states = vision_outputs[1]
        if output_attentions:
            vision_attentions = vision_outputs[-1]

    if input_points is not None and input_labels is None:
        input_labels = ops.ones_like(input_points[:, :, :, 0], dtype=mindspore.int32)

    if input_points is not None and image_embeddings.shape[0] != input_points.shape[0]:
        raise ValueError(
            "The batch size of the image embeddings and the input points must be the same. ",
            "Got {} and {} respectively.".format(image_embeddings.shape[0], input_points.shape[0]),
            " if you want to pass multiple points for the same image, make sure that you passed ",
            " input_points of shape (batch_size, point_batch_size, num_points_per_image, 3) and ",
            " input_labels of shape (batch_size, point_batch_size, num_points_per_image)",
        )

    sparse_embeddings, dense_embeddings = self.prompt_encoder(
        input_points=input_points,
        input_labels=input_labels,
        input_boxes=input_boxes,
        input_masks=input_masks,
    )

    low_res_masks, iou_predictions, mask_decoder_attentions = self.mask_decoder(
        image_embeddings=image_embeddings,
        image_positional_embeddings=image_positional_embeddings,
        sparse_prompt_embeddings=sparse_embeddings,
        dense_prompt_embeddings=dense_embeddings,
        multimask_output=multimask_output,
        attention_similarity=attention_similarity,
        target_embedding=target_embedding,
        output_attentions=output_attentions,
    )

    if not return_dict:
        output = (iou_predictions, low_res_masks)
        if output_hidden_states:
            output = output + (vision_hidden_states,)

        if output_attentions:
            output = output + (vision_attentions, mask_decoder_attentions)
        return output

    return SamImageSegmentationOutput(
        iou_scores=iou_predictions,
        pred_masks=low_res_masks,
        vision_hidden_states=vision_hidden_states,
        vision_attentions=vision_attentions,
        mask_decoder_attentions=mask_decoder_attentions,
    )

mindnlp.transformers.models.sam.modeling_sam.SamModel.get_image_embeddings(pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None)

Returns the image embeddings by passing the pixel values through the vision encoder.

PARAMETER DESCRIPTION
pixel_values

Input pixel values

TYPE: `mindspore.Tensor` of shape `(batch_size, num_channels, height, width)`

output_attentions

Whether or not to return the attentions tensors of all attention layers.

TYPE: `bool`, *optional* DEFAULT: None

output_hidden_states

Whether or not to return the hidden states of all layers.

TYPE: `bool`, *optional* DEFAULT: None

return_dict

Whether or not to return a [~utils.ModelOutput] instead of a plain tuple.

TYPE: `bool`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_image_embeddings(
    self,
    pixel_values,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
):
    r"""
    Returns the image embeddings by passing the pixel values through the vision encoder.

    Args:
        pixel_values (`mindspore.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Input pixel values
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

    """
    vision_output = self.vision_encoder(
        pixel_values,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    image_embeddings = vision_output[0]
    return image_embeddings

mindnlp.transformers.models.sam.modeling_sam.SamModel.get_image_wide_positional_embeddings()

This method calculates wide positional embeddings for an image in the SamModel class.

PARAMETER DESCRIPTION
self

An instance of the SamModel class. It is used to access configuration parameters and shared image embeddings.
RETURNS DESCRIPTION
positional_embedding

A tensor representing the positional embeddings for the image. The tensor is permuted and unsqueezed before returning.

TYPE: Tensor

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_image_wide_positional_embeddings(self):
    """
    This method calculates wide positional embeddings for an image in the SamModel class.

    Args:
        self: An instance of the SamModel class. It is used to access configuration parameters and shared image embeddings.

    Returns:
        positional_embedding (torch.Tensor): A tensor representing the positional embeddings for the image.
            The tensor is permuted and unsqueezed before returning.

    Raises:
        None
    """
    size = self.config.prompt_encoder_config.image_embedding_size
    target_dtype = self.shared_image_embedding.positional_embedding.dtype
    grid = ops.ones((size, size), dtype=target_dtype)
    y_embed = grid.cumsum(axis=0) - 0.5
    x_embed = grid.cumsum(axis=1) - 0.5
    y_embed = y_embed / size
    x_embed = x_embed / size

    positional_embedding = self.shared_image_embedding(ops.stack([x_embed, y_embed], axis=-1))
    return positional_embedding.permute(2, 0, 1).unsqueeze(0)  # channel x height x width

mindnlp.transformers.models.sam.modeling_sam.SamModel.get_input_embeddings()

This method 'get_input_embeddings' in the class 'SamModel' retrieves the input embeddings from the vision encoder.

PARAMETER DESCRIPTION
self

SamModel instance. Represents the current instance of the class.
RETURNS DESCRIPTION
None

This method returns None as it simply retrieves the input embeddings from the vision encoder.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_input_embeddings(self):
    """
    This method 'get_input_embeddings' in the class 'SamModel' retrieves the input embeddings from the vision encoder.

    Args:
        self: SamModel instance. Represents the current instance of the class.

    Returns:
        None: This method returns None as it simply retrieves the input embeddings from the vision encoder.

    Raises:
        None
    """
    return self.vision_encoder.get_input_embeddings()

mindnlp.transformers.models.sam.modeling_sam.SamModel.get_prompt_embeddings(input_points=None, input_labels=None, input_boxes=None, input_masks=None)

Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder.

PARAMETER DESCRIPTION
input_points

Optional input points for the prompt encoder. The padding of the point is automatically done by the processor. point_batch_size refers to the number of masks that we want the model to predict per point. The model will output point_batch_size times 3 masks in total.

TYPE: `mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)` DEFAULT: None

input_labels

Optional input labels for the prompt encoder. The padding of the labels is automatically done by the processor, or can be fed by the user.

TYPE: `mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image)` DEFAULT: None

input_boxes

Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the processor. users can also pass manually the input boxes.

TYPE: `mindspore.Tensor` of shape `(batch_size, num_boxes_per_image, 4)` DEFAULT: None

input_masks

Optional input masks for the prompt encoder.

TYPE: `mindspore.Tensor` of shape `(batch_size, image_size, image_size)` DEFAULT: None

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_prompt_embeddings(
    self,
    input_points: Optional[mindspore.Tensor] = None,
    input_labels: Optional[mindspore.Tensor] = None,
    input_boxes: Optional[mindspore.Tensor] = None,
    input_masks: Optional[mindspore.Tensor] = None,
):
    r"""
    Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder.

    Args:
        input_points (`mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`):
            Optional input points for the prompt encoder. The padding of the point is automatically done by the
            processor. `point_batch_size` refers to the number of masks that we want the model to predict per
            point. The model will output `point_batch_size` times 3 masks in total.
        input_labels (`mindspore.Tensor` of shape `(batch_size, point_batch_size, num_points_per_image)`):
            Optional input labels for the prompt encoder. The padding of the labels is automatically done by the
            processor, or can be fed by the user.
        input_boxes (`mindspore.Tensor` of shape `(batch_size, num_boxes_per_image, 4)`):
            Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the
            processor. users can also pass manually the input boxes.
        input_masks (`mindspore.Tensor` of shape `(batch_size, image_size, image_size)`):
            Optional input masks for the prompt encoder.
    """
    prompt_output = self.prompt_encoder(
        input_points=input_points,
        input_labels=input_labels,
        input_boxes=input_boxes,
        input_masks=input_masks,
    )
    return prompt_output

mindnlp.transformers.models.sam.modeling_sam.SamPatchEmbeddings

Bases: Module

This class turns pixel_values of shape (batch_size, num_channels, height, width) into the initial hidden_states (patch embeddings) of shape (batch_size, seq_length, hidden_size) to be consumed by a Transformer.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """
    def __init__(self, config):
        """
        Initializes an instance of the SamPatchEmbeddings class.

        Args:
            self: The object instance.
            config:
                An object that stores configuration parameters for the SamPatchEmbeddings class.

                - image_size: The size of the input image as a tuple or a single integer.
                - patch_size: The size of each patch as a tuple or a single integer.
                - num_channels: The number of channels in the input image.
                - hidden_size: The size of the hidden layer in the projection operation.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size, pad_mode='valid', bias=True)

    def forward(self, pixel_values):
        """
        Construct method in the SamPatchEmbeddings class.

        This method forwards embeddings for a given set of pixel values.

        Args:
            self: An instance of the SamPatchEmbeddings class.
            pixel_values (ndarray):
                A 4-dimensional array representing the pixel values of the input images.

                - The shape of the array should be (batch_size, num_channels, height, width).
                - The batch_size represents the number of images in the batch.
                - The num_channels represents the number of color channels in each image.
                - The height and width represent the dimensions of each image.

        Returns:
            None

        Raises:
            ValueError:
                - If the number of channels in the pixel values does not match the number of channels set in the
                configuration.
                - If the height or width of the input images do not match the expected image size defined in the model.
        """
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if height != self.image_size[0] or width != self.image_size[1]:
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
        return embeddings

mindnlp.transformers.models.sam.modeling_sam.SamPatchEmbeddings.__init__(config)

Initializes an instance of the SamPatchEmbeddings class.

PARAMETER DESCRIPTION
self

The object instance.
config

An object that stores configuration parameters for the SamPatchEmbeddings class.

  • image_size: The size of the input image as a tuple or a single integer.
  • patch_size: The size of each patch as a tuple or a single integer.
  • num_channels: The number of channels in the input image.
  • hidden_size: The size of the hidden layer in the projection operation.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config):
    """
    Initializes an instance of the SamPatchEmbeddings class.

    Args:
        self: The object instance.
        config:
            An object that stores configuration parameters for the SamPatchEmbeddings class.

            - image_size: The size of the input image as a tuple or a single integer.
            - patch_size: The size of each patch as a tuple or a single integer.
            - num_channels: The number of channels in the input image.
            - hidden_size: The size of the hidden layer in the projection operation.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    image_size, patch_size = config.image_size, config.patch_size
    num_channels, hidden_size = config.num_channels, config.hidden_size
    image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
    patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
    num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.image_size = image_size
    self.patch_size = patch_size
    self.num_channels = num_channels
    self.num_patches = num_patches

    self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size, pad_mode='valid', bias=True)

mindnlp.transformers.models.sam.modeling_sam.SamPatchEmbeddings.forward(pixel_values)

Construct method in the SamPatchEmbeddings class.

This method forwards embeddings for a given set of pixel values.

PARAMETER DESCRIPTION
self

An instance of the SamPatchEmbeddings class.
pixel_values

A 4-dimensional array representing the pixel values of the input images.

  • The shape of the array should be (batch_size, num_channels, height, width).
  • The batch_size represents the number of images in the batch.
  • The num_channels represents the number of color channels in each image.
  • The height and width represent the dimensions of each image.

TYPE: ndarray

RETURNS DESCRIPTION

None
RAISES DESCRIPTION
ValueError
  • If the number of channels in the pixel values does not match the number of channels set in the configuration.
  • If the height or width of the input images do not match the expected image size defined in the model.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, pixel_values):
    """
    Construct method in the SamPatchEmbeddings class.

    This method forwards embeddings for a given set of pixel values.

    Args:
        self: An instance of the SamPatchEmbeddings class.
        pixel_values (ndarray):
            A 4-dimensional array representing the pixel values of the input images.

            - The shape of the array should be (batch_size, num_channels, height, width).
            - The batch_size represents the number of images in the batch.
            - The num_channels represents the number of color channels in each image.
            - The height and width represent the dimensions of each image.

    Returns:
        None

    Raises:
        ValueError:
            - If the number of channels in the pixel values does not match the number of channels set in the
            configuration.
            - If the height or width of the input images do not match the expected image size defined in the model.
    """
    batch_size, num_channels, height, width = pixel_values.shape
    if num_channels != self.num_channels:
        raise ValueError(
            "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
        )
    if height != self.image_size[0] or width != self.image_size[1]:
        raise ValueError(
            f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
        )
    embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
    return embeddings

mindnlp.transformers.models.sam.modeling_sam.SamPositionalEmbedding

Bases: Module

The SamPositionalEmbedding class represents a positional encoding module that inherits from nn.Module. It provides functionality to positionally encode points normalized to the range [0,1] using sinusoidal and cosine functions.

ATTRIBUTE DESCRIPTION
scale

The scale value calculated as config.hidden_size // 2.

TYPE: int

positional_embedding

The positional embedding parameter calculated using random values with specified shape and no gradient.

TYPE: Parameter

METHOD DESCRIPTION
forward

Positionally encodes normalized points and returns the encoded coordinates as a concatenation of sinusoidal and cosine functions.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamPositionalEmbedding(nn.Module):

    """
    The SamPositionalEmbedding class represents a positional encoding module that inherits from nn.Module.
    It provides functionality to positionally encode points normalized to the range [0,1] using sinusoidal
    and cosine functions.

    Attributes:
        scale (int): The scale value calculated as config.hidden_size // 2.
        positional_embedding (Parameter): The positional embedding parameter calculated using random values with
            specified shape and no gradient.

    Methods:
        forward: Positionally encodes normalized points and returns the encoded
            coordinates as a concatenation of sinusoidal and cosine functions.
    """
    def __init__(self, config):
        """
        Initializes an instance of the SamPositionalEmbedding class.

        Args:
            self (SamPositionalEmbedding): The current instance of the SamPositionalEmbedding class.
            config (object): The configuration object that holds various settings.

        Returns:
            None

        Raises:
            None
        """
        super().__init__()
        self.scale = config.hidden_size // 2
        self.positional_embedding = Parameter(self.scale * ops.randn((2, config.num_pos_feats)), requires_grad=False)

    def forward(self, input_coords, input_shape=None):
        """Positionally encode points that are normalized to [0,1]."""
        coordinates = input_coords.copy()

        if input_shape is not None:
            coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1]
            coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0]

        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
        coordinates = 2 * coordinates - 1
        coordinates = coordinates.to(self.positional_embedding.dtype)
        coordinates = coordinates @ self.positional_embedding
        coordinates = 2 * np.pi * coordinates
        # outputs d_1 x ... x d_n x channel shape
        return ops.cat([ops.sin(coordinates), ops.cos(coordinates)], axis=-1)

mindnlp.transformers.models.sam.modeling_sam.SamPositionalEmbedding.__init__(config)

Initializes an instance of the SamPositionalEmbedding class.

PARAMETER DESCRIPTION
self

The current instance of the SamPositionalEmbedding class.

TYPE: SamPositionalEmbedding

config

The configuration object that holds various settings.

TYPE: object

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config):
    """
    Initializes an instance of the SamPositionalEmbedding class.

    Args:
        self (SamPositionalEmbedding): The current instance of the SamPositionalEmbedding class.
        config (object): The configuration object that holds various settings.

    Returns:
        None

    Raises:
        None
    """
    super().__init__()
    self.scale = config.hidden_size // 2
    self.positional_embedding = Parameter(self.scale * ops.randn((2, config.num_pos_feats)), requires_grad=False)

mindnlp.transformers.models.sam.modeling_sam.SamPositionalEmbedding.forward(input_coords, input_shape=None)

Positionally encode points that are normalized to [0,1].

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, input_coords, input_shape=None):
    """Positionally encode points that are normalized to [0,1]."""
    coordinates = input_coords.copy()

    if input_shape is not None:
        coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1]
        coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0]

    # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
    coordinates = 2 * coordinates - 1
    coordinates = coordinates.to(self.positional_embedding.dtype)
    coordinates = coordinates @ self.positional_embedding
    coordinates = 2 * np.pi * coordinates
    # outputs d_1 x ... x d_n x channel shape
    return ops.cat([ops.sin(coordinates), ops.cos(coordinates)], axis=-1)

mindnlp.transformers.models.sam.modeling_sam.SamPreTrainedModel

Bases: PreTrainedModel

A class representing a pretrained model in the Sam library.

This class, 'SamPreTrainedModel', is a subclass of the 'PreTrainedModel' class in the Sam library. It provides functionality for initializing the weights of different types of cells in the model. The weights are initialized using a normal distribution with a standard deviation specified in the configuration. If a bias term is present in the cell, it is initialized to zeros. For embedding cells, the weights are initialized using a normal distribution and a padding index, if provided, is set to zero.

ATTRIBUTE DESCRIPTION
config

The configuration object for the pretrained model.

TYPE: PretrainedConfig

METHOD DESCRIPTION
_init_weights

Initializes the weights of different types of cells in the model.
Note

This class assumes that the model is built using cells from the Sam library, such as nn.Linear, nn.Conv2d, nn.Conv2dTranspose, and nn.Embedding.

Example 
>>> model = SamPreTrainedModel(config)
>>> model._init_weights(cell)
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamPreTrainedModel(PreTrainedModel):

    """A class representing a pretrained model in the Sam library.

    This class, 'SamPreTrainedModel', is a subclass of the 'PreTrainedModel' class in the Sam library.
    It provides functionality for initializing the weights of different types of cells in the model.
    The weights are initialized using a normal distribution with a standard deviation specified in the configuration.
    If a bias term is present in the cell, it is initialized to zeros.
    For embedding cells, the weights are initialized using a normal distribution and a padding index,
    if provided, is set to zero.

    Attributes:
        config (PretrainedConfig): The configuration object for the pretrained model.

    Methods:
        _init_weights: Initializes the weights of different types of cells in the model.

    Note:
        This class assumes that the model is built using cells from the Sam library, such as nn.Linear, nn.Conv2d,
        nn.Conv2dTranspose, and nn.Embedding.

    Example:
        ```python
        >>> model = SamPreTrainedModel(config)
        >>> model._init_weights(cell)
        ```
    """
    config_class = SamConfig
    base_model_prefix = "sam"
    main_input_name = "pixel_values"

    def _init_weights(self, cell):
        '''
        This method initializes the weights and biases of the provided cell based on the specified configuration.

        Args:
            self (SamPreTrainedModel): The instance of the SamPreTrainedModel class.
            cell: The neural network cell for which the weights and biases are initialized.
                It can be an instance of nn.Linear, nn.Conv2d, nn.Conv2dTranspose, or nn.Embedding.

        Returns:
            None.

        Raises:
            TypeError: If the cell parameter is not an instance of supported cell types
                (nn.Linear, nn.Conv2d, nn.Conv2dTranspose, or nn.Embedding).
            ValueError: If the cell parameter is an instance of nn.Embedding and the padding index
                (cell.padding_idx) is out of range.
        '''
        std = self.config.initializer_range
        if isinstance(cell, (nn.Linear, nn.Conv2d, nn.Conv2dTranspose)):
            cell.weight.initialize(Normal(std))
            if cell.bias is not None:
                cell.bias.initialize('zeros')
        elif isinstance(cell, nn.Embedding):
            data = np.random.normal(0.0, std, cell.weight.shape)
            if cell.padding_idx is not None:
                data[cell.padding_idx] = 0
            cell.weight.set_data(Tensor(data, cell.weight.dtype))

mindnlp.transformers.models.sam.modeling_sam.SamPromptEncoder

Bases: Module

A prompt encoder for sparse and dense embeddings.

This class represents a prompt encoder that embeds different types of prompts, returning both sparse and dense embeddings.

PARAMETER DESCRIPTION
config

The configuration for the prompt encoder.

TYPE: SamPromptEncoderConfig

shared_patch_embedding

A shared patch embedding.
ATTRIBUTE DESCRIPTION
shared_embedding

The shared patch embedding for the prompt encoder.
mask_embed

The mask embedding for the prompt encoder.
no_mask_embed

A tensor for no mask embedding.
image_embedding_size

The size of the image embedding.
input_image_size

The size of the input image.
point_embed

A list of point embeddings.
hidden_size

The hidden size for the prompt encoder.
not_a_point_embed

The embedding for non-point prompts.
METHOD DESCRIPTION
_embed_points

Embeds point prompts.
_embed_boxes

Embeds box prompts.
forward

Embeds different types of prompts,
RAISES DESCRIPTION
ValueError

If points are provided, labels must also be provided.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamPromptEncoder(nn.Module):

    """
    A prompt encoder for sparse and dense embeddings.

    This class represents a prompt encoder that embeds different types of prompts, returning both sparse
    and dense embeddings.

    Args:
        config (SamPromptEncoderConfig): The configuration for the prompt encoder.
        shared_patch_embedding : A shared patch embedding.

    Attributes:
        shared_embedding: The shared patch embedding for the prompt encoder.
        mask_embed: The mask embedding for the prompt encoder.
        no_mask_embed: A tensor for no mask embedding.
        image_embedding_size: The size of the image embedding.
        input_image_size: The size of the input image.
        point_embed: A list of point embeddings.
        hidden_size: The hidden size for the prompt encoder.
        not_a_point_embed: The embedding for non-point prompts.

    Methods:
        _embed_points(: Embeds point prompts.
        _embed_boxes: Embeds box prompts.
        forward: Embeds different types of prompts,
        returning both sparse and dense embeddings.

    Raises:
        ValueError: If points are provided, labels must also be provided.

    """
    def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedding):
        """
        Initializes a new instance of SamPromptEncoder.

        Args:
            self: The instance of the class.
            config (SamPromptEncoderConfig): An instance of SamPromptEncoderConfig containing configuration parameters.
            shared_patch_embedding: The shared patch embedding used in the encoder.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        self.shared_embedding = shared_patch_embedding
        self.mask_embed = SamMaskEmbedding(config)
        self.no_mask_embed = nn.Embedding(1, config.hidden_size)

        self.image_embedding_size = (config.image_embedding_size, config.image_embedding_size)
        self.input_image_size = config.image_size

        self.point_embed = nn.ModuleList(
            [nn.Embedding(1, config.hidden_size) for i in range(config.num_point_embeddings)]
        )
        self.hidden_size = config.hidden_size
        self.not_a_point_embed = nn.Embedding(1, config.hidden_size)

    def _embed_points(self, points: mindspore.Tensor, labels: mindspore.Tensor, pad: bool) -> mindspore.Tensor:
        """Embeds point prompts."""
        points = points + 0.5  # Shift to center of pixel
        if pad:
            target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1])
            target_labels_shape = (points.shape[0], points.shape[1], 1)
            padding_point = ops.zeros(target_point_shape)
            padding_label = -ops.ones(target_labels_shape)
            points = ops.cat([points, padding_point], axis=2)
            labels = ops.cat([labels, padding_label.type_as(labels)], axis=2)
        input_shape = (self.input_image_size, self.input_image_size)
        point_embedding = self.shared_embedding(points, input_shape)

        # torch.where and expanding the labels tensor is required by the ONNX export
        point_embedding = ops.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding)

        # specificed as otherwise torch.onnx.export interprets as double
        point_embedding = ops.where(
            labels[..., None] != -10,
            point_embedding,
            mindspore.tensor(0.0, dtype=point_embedding.dtype),
        )

        point_embedding = ops.where(
            (labels == 0)[:, :, :, None],
            point_embedding + self.point_embed[0].weight[None, None, :, :],
            point_embedding,
        )

        point_embedding = ops.where(
            (labels == 1)[:, :, :, None],
            point_embedding + self.point_embed[1].weight[None, None, :, :],
            point_embedding,
        )

        return point_embedding

    def _embed_boxes(self, boxes: mindspore.Tensor) -> mindspore.Tensor:
        """Embeds box prompts."""
        boxes = boxes + 0.5  # Shift to center of pixel
        batch_size, nb_boxes = boxes.shape[:2]
        coords = boxes.reshape(batch_size, nb_boxes, 2, 2)
        input_shape = (self.input_image_size, self.input_image_size)
        corner_embedding = self.shared_embedding(coords, input_shape)
        corner_embedding[:, :, 0, :] += self.point_embed[2].weight
        corner_embedding[:, :, 1, :] += self.point_embed[3].weight
        return corner_embedding

    def forward(
        self,
        input_points: Optional[Tuple[mindspore.Tensor, mindspore.Tensor]],
        input_labels: Optional[mindspore.Tensor],
        input_boxes: Optional[mindspore.Tensor],
        input_masks: Optional[mindspore.Tensor],
    ) -> Tuple[mindspore.Tensor, mindspore.Tensor]:
        """
        Embeds different types of prompts, returning both sparse and dense embeddings.

        Args:
            points (`mindspore.Tensor`, *optional*):
                point coordinates and labels to embed.
            boxes (`mindspore.Tensor`, *optional*):
                boxes to embed
            masks (`mindspore.Tensor`, *optional*):
                masks to embed
        """
        sparse_embeddings = None
        batch_size = 1

        if input_points is not None:
            batch_size, point_batch_size = input_points.shape[:2]
            if input_labels is None:
                raise ValueError("If points are provided, labels must also be provided.")
            point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
            sparse_embeddings = point_embeddings

        if input_boxes is not None:
            batch_size = input_boxes.shape[0]
            box_embeddings = self._embed_boxes(input_boxes)
            if sparse_embeddings is None:
                sparse_embeddings = box_embeddings
            else:
                sparse_embeddings = ops.cat([sparse_embeddings, box_embeddings], axis=2)

        if input_masks is not None:
            dense_embeddings = self.mask_embed(input_masks)
        else:
            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
                batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1]
            )

        if sparse_embeddings is None:
            sparse_embeddings = ops.zeros((batch_size, 1, 1, self.hidden_size))

        return sparse_embeddings, dense_embeddings

mindnlp.transformers.models.sam.modeling_sam.SamPromptEncoder.__init__(config, shared_patch_embedding)

Initializes a new instance of SamPromptEncoder.

PARAMETER DESCRIPTION
self

The instance of the class.
config

An instance of SamPromptEncoderConfig containing configuration parameters.

TYPE: SamPromptEncoderConfig

shared_patch_embedding

The shared patch embedding used in the encoder.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedding):
    """
    Initializes a new instance of SamPromptEncoder.

    Args:
        self: The instance of the class.
        config (SamPromptEncoderConfig): An instance of SamPromptEncoderConfig containing configuration parameters.
        shared_patch_embedding: The shared patch embedding used in the encoder.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    self.shared_embedding = shared_patch_embedding
    self.mask_embed = SamMaskEmbedding(config)
    self.no_mask_embed = nn.Embedding(1, config.hidden_size)

    self.image_embedding_size = (config.image_embedding_size, config.image_embedding_size)
    self.input_image_size = config.image_size

    self.point_embed = nn.ModuleList(
        [nn.Embedding(1, config.hidden_size) for i in range(config.num_point_embeddings)]
    )
    self.hidden_size = config.hidden_size
    self.not_a_point_embed = nn.Embedding(1, config.hidden_size)

mindnlp.transformers.models.sam.modeling_sam.SamPromptEncoder.forward(input_points, input_labels, input_boxes, input_masks)

Embeds different types of prompts, returning both sparse and dense embeddings.

PARAMETER DESCRIPTION
points

point coordinates and labels to embed.

TYPE: `mindspore.Tensor`, *optional*

boxes

boxes to embed

TYPE: `mindspore.Tensor`, *optional*

masks

masks to embed

TYPE: `mindspore.Tensor`, *optional*

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    input_points: Optional[Tuple[mindspore.Tensor, mindspore.Tensor]],
    input_labels: Optional[mindspore.Tensor],
    input_boxes: Optional[mindspore.Tensor],
    input_masks: Optional[mindspore.Tensor],
) -> Tuple[mindspore.Tensor, mindspore.Tensor]:
    """
    Embeds different types of prompts, returning both sparse and dense embeddings.

    Args:
        points (`mindspore.Tensor`, *optional*):
            point coordinates and labels to embed.
        boxes (`mindspore.Tensor`, *optional*):
            boxes to embed
        masks (`mindspore.Tensor`, *optional*):
            masks to embed
    """
    sparse_embeddings = None
    batch_size = 1

    if input_points is not None:
        batch_size, point_batch_size = input_points.shape[:2]
        if input_labels is None:
            raise ValueError("If points are provided, labels must also be provided.")
        point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
        sparse_embeddings = point_embeddings

    if input_boxes is not None:
        batch_size = input_boxes.shape[0]
        box_embeddings = self._embed_boxes(input_boxes)
        if sparse_embeddings is None:
            sparse_embeddings = box_embeddings
        else:
            sparse_embeddings = ops.cat([sparse_embeddings, box_embeddings], axis=2)

    if input_masks is not None:
        dense_embeddings = self.mask_embed(input_masks)
    else:
        dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
            batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1]
        )

    if sparse_embeddings is None:
        sparse_embeddings = ops.zeros((batch_size, 1, 1, self.hidden_size))

    return sparse_embeddings, dense_embeddings

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayAttentionBlock

Bases: Module

A transformer block with four layers:

  1. self-attention of sparse inputs
  2. cross attention of sparse inputs -> dense inputs
  3. mlp block on sparse inputs
  4. cross attention of dense inputs -> sparse inputs

This class represents a SamTwoWayAttentionBlock that implements a transformer block with the above-described layers. It inherits from nn.Module and is designed to handle attention mechanisms between sparse and dense inputs.

PARAMETER DESCRIPTION
config

The configuration file used to instantiate the block.

TYPE: `SamMaskDecoderConfig`

attention_downsample_rate

The downsample ratio of the block used to reduce the inner dimension of the attention.

TYPE: *optional*, int, defaults to 2 DEFAULT: 2

skip_first_layer_pe

Whether or not to skip the addition of the query_point_embedding on the first layer.

TYPE: *optional*, bool, defaults to `False` DEFAULT: False

ATTRIBUTE DESCRIPTION
hidden_size

The size of the hidden layers in the block.

TYPE: int

layer_norm_eps

The epsilon value for layer normalization.

TYPE: float

self_attn

The self-attention mechanism for sparse inputs.

TYPE: SamAttention

layer_norm1

Layer normalization for the first layer.

TYPE: LayerNorm

cross_attn_token_to_image

Cross-attention from token to image inputs.

TYPE: SamAttention

layer_norm2

Layer normalization for the second layer.

TYPE: LayerNorm

mlp

Multi-Layer Perceptron block for processing inputs.

TYPE: SamMLPBlock

layer_norm3

Layer normalization for the third layer.

TYPE: LayerNorm

layer_norm4

Layer normalization for the fourth layer.

TYPE: LayerNorm

cross_attn_image_to_token

Cross-attention from image to token inputs.

TYPE: SamAttention

Note

This class is specialized for two-way attention mechanisms in transformer architectures and is used to process sparse and dense inputs efficiently.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamTwoWayAttentionBlock(nn.Module):

    """
    A transformer block with four layers:

    1. self-attention of sparse inputs
    2. cross attention of sparse inputs -> dense inputs
    3. mlp block on sparse inputs
    4. cross attention of dense inputs -> sparse inputs

    This class represents a SamTwoWayAttentionBlock that implements a transformer block with the above-described layers.
    It inherits from nn.Module and is designed to handle attention mechanisms between sparse and dense inputs.

    Arguments:
        config (`SamMaskDecoderConfig`): The configuration file used to instantiate the block.
        attention_downsample_rate (*optional*, int, defaults to 2): The downsample ratio of the block used to reduce
            the inner dimension of the attention.
        skip_first_layer_pe (*optional*, bool, defaults to `False`): Whether or not to skip the addition of the
            query_point_embedding on the first layer.

    Attributes:
        hidden_size (int): The size of the hidden layers in the block.
        layer_norm_eps (float): The epsilon value for layer normalization.
        self_attn (SamAttention): The self-attention mechanism for sparse inputs.
        layer_norm1 (nn.LayerNorm): Layer normalization for the first layer.
        cross_attn_token_to_image (SamAttention): Cross-attention from token to image inputs.
        layer_norm2 (nn.LayerNorm): Layer normalization for the second layer.
        mlp (SamMLPBlock): Multi-Layer Perceptron block for processing inputs.
        layer_norm3 (nn.LayerNorm): Layer normalization for the third layer.
        layer_norm4 (nn.LayerNorm): Layer normalization for the fourth layer.
        cross_attn_image_to_token (SamAttention): Cross-attention from image to token inputs.

    Note:
        This class is specialized for two-way attention mechanisms in transformer architectures and is used to process
        sparse and dense inputs efficiently.
    """
    def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False):
        """
        A transformer block with four layers:
            (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on
            sparse inputs (4) cross attention of dense inputs -> sparse inputs

        Arguments:
            config (`SamMaskDecoderConfig`):
                The configuration file used to instantiate the block
            attention_downsample_rate (*optionalk*, int, defaults to 2):
                The downsample ratio of the block used to reduce the inner dim of the attention.
            skip_first_layer_pe (*optional*, bool, defaults to `False`):
                Whether or not to skip the addition of the query_point_embedding on the first layer.
        """
        super().__init__()

        self.hidden_size = config.hidden_size
        self.layer_norm_eps = config.layer_norm_eps

        self.self_attn = SamAttention(config, downsample_rate=1)
        self.layer_norm1 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

        self.cross_attn_token_to_image = SamAttention(config, downsample_rate=attention_downsample_rate)
        self.layer_norm2 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

        self.mlp = SamMLPBlock(config)
        self.layer_norm3 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

        self.layer_norm4 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
        self.cross_attn_image_to_token = SamAttention(config, downsample_rate=attention_downsample_rate)

        self.skip_first_layer_pe = skip_first_layer_pe

    def forward(
        self,
        queries: Tensor,
        keys: Tensor,
        query_point_embedding: Tensor,
        key_point_embedding: Tensor,
        attention_similarity: Tensor,
        output_attentions: bool = False,
    ):
        """
        This method forwards a two-way attention block for processing queries and keys in a neural network model.

        Args:
            self: The instance of the class.
            queries (Tensor): The input tensor representing queries for attention mechanism.
            keys (Tensor): The input tensor representing keys for attention mechanism.
            query_point_embedding (Tensor): The embedding tensor for query points.
            key_point_embedding (Tensor): The embedding tensor for key points.
            attention_similarity (Tensor): The tensor representing attention similarity scores.
            output_attentions (bool, optional): A flag indicating whether to output attention values. Defaults to False.

        Returns:
            Tuple[Tensor, Tensor, Tensor]: A tuple containing processed queries and keys, and optionally attention values.

        Raises:
            None
        """
        # Self attention block
        if self.skip_first_layer_pe:
            queries = self.self_attn(query=queries, key=queries, value=queries)
        else:
            query = queries + query_point_embedding
            attn_out = self.self_attn(query=query, key=query, value=queries)
            queries = queries + attn_out
        queries = self.layer_norm1(queries)

        # Cross attention block, tokens attending to image embedding
        query = queries + query_point_embedding
        key = keys + key_point_embedding

        attn_out = self.cross_attn_token_to_image(
            query=query, key=key, value=keys, attention_similarity=attention_similarity
        )
        queries = queries + attn_out

        queries = self.layer_norm2(queries)

        # MLP block
        mlp_out = self.mlp(queries)
        queries = queries + mlp_out
        queries = self.layer_norm3(queries)

        # Cross attention block, image embedding attending to tokens
        query = queries + query_point_embedding
        key = keys + key_point_embedding

        attn_out = self.cross_attn_image_to_token(query=key, key=query, value=queries)
        keys = keys + attn_out

        keys = self.layer_norm4(keys)

        outputs = (queries, keys)

        if output_attentions:
            outputs = outputs + (attn_out,)
        else:
            outputs = outputs + (None,)

        return outputs

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayAttentionBlock.__init__(config, attention_downsample_rate=2, skip_first_layer_pe=False)

A transformer block with four layers

(1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on sparse inputs (4) cross attention of dense inputs -> sparse inputs

PARAMETER DESCRIPTION
config

The configuration file used to instantiate the block

TYPE: `SamMaskDecoderConfig`

attention_downsample_rate

The downsample ratio of the block used to reduce the inner dim of the attention.

TYPE: *optionalk*, int, defaults to 2 DEFAULT: 2

skip_first_layer_pe

Whether or not to skip the addition of the query_point_embedding on the first layer.

TYPE: *optional*, bool, defaults to `False` DEFAULT: False

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False):
    """
    A transformer block with four layers:
        (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on
        sparse inputs (4) cross attention of dense inputs -> sparse inputs

    Arguments:
        config (`SamMaskDecoderConfig`):
            The configuration file used to instantiate the block
        attention_downsample_rate (*optionalk*, int, defaults to 2):
            The downsample ratio of the block used to reduce the inner dim of the attention.
        skip_first_layer_pe (*optional*, bool, defaults to `False`):
            Whether or not to skip the addition of the query_point_embedding on the first layer.
    """
    super().__init__()

    self.hidden_size = config.hidden_size
    self.layer_norm_eps = config.layer_norm_eps

    self.self_attn = SamAttention(config, downsample_rate=1)
    self.layer_norm1 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

    self.cross_attn_token_to_image = SamAttention(config, downsample_rate=attention_downsample_rate)
    self.layer_norm2 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

    self.mlp = SamMLPBlock(config)
    self.layer_norm3 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)

    self.layer_norm4 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
    self.cross_attn_image_to_token = SamAttention(config, downsample_rate=attention_downsample_rate)

    self.skip_first_layer_pe = skip_first_layer_pe

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayAttentionBlock.forward(queries, keys, query_point_embedding, key_point_embedding, attention_similarity, output_attentions=False)

This method forwards a two-way attention block for processing queries and keys in a neural network model.

PARAMETER DESCRIPTION
self

The instance of the class.
queries

The input tensor representing queries for attention mechanism.

TYPE: Tensor

keys

The input tensor representing keys for attention mechanism.

TYPE: Tensor

query_point_embedding

The embedding tensor for query points.

TYPE: Tensor

key_point_embedding

The embedding tensor for key points.

TYPE: Tensor

attention_similarity

The tensor representing attention similarity scores.

TYPE: Tensor

output_attentions

A flag indicating whether to output attention values. Defaults to False.

TYPE: bool DEFAULT: False

RETURNS DESCRIPTION

Tuple[Tensor, Tensor, Tensor]: A tuple containing processed queries and keys, and optionally attention values.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    queries: Tensor,
    keys: Tensor,
    query_point_embedding: Tensor,
    key_point_embedding: Tensor,
    attention_similarity: Tensor,
    output_attentions: bool = False,
):
    """
    This method forwards a two-way attention block for processing queries and keys in a neural network model.

    Args:
        self: The instance of the class.
        queries (Tensor): The input tensor representing queries for attention mechanism.
        keys (Tensor): The input tensor representing keys for attention mechanism.
        query_point_embedding (Tensor): The embedding tensor for query points.
        key_point_embedding (Tensor): The embedding tensor for key points.
        attention_similarity (Tensor): The tensor representing attention similarity scores.
        output_attentions (bool, optional): A flag indicating whether to output attention values. Defaults to False.

    Returns:
        Tuple[Tensor, Tensor, Tensor]: A tuple containing processed queries and keys, and optionally attention values.

    Raises:
        None
    """
    # Self attention block
    if self.skip_first_layer_pe:
        queries = self.self_attn(query=queries, key=queries, value=queries)
    else:
        query = queries + query_point_embedding
        attn_out = self.self_attn(query=query, key=query, value=queries)
        queries = queries + attn_out
    queries = self.layer_norm1(queries)

    # Cross attention block, tokens attending to image embedding
    query = queries + query_point_embedding
    key = keys + key_point_embedding

    attn_out = self.cross_attn_token_to_image(
        query=query, key=key, value=keys, attention_similarity=attention_similarity
    )
    queries = queries + attn_out

    queries = self.layer_norm2(queries)

    # MLP block
    mlp_out = self.mlp(queries)
    queries = queries + mlp_out
    queries = self.layer_norm3(queries)

    # Cross attention block, image embedding attending to tokens
    query = queries + query_point_embedding
    key = keys + key_point_embedding

    attn_out = self.cross_attn_image_to_token(query=key, key=query, value=queries)
    keys = keys + attn_out

    keys = self.layer_norm4(keys)

    outputs = (queries, keys)

    if output_attentions:
        outputs = outputs + (attn_out,)
    else:
        outputs = outputs + (None,)

    return outputs

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayTransformer

Bases: Module

This class represents a two-way transformer model called SamTwoWayTransformer. It is a subclass of nn.Module.

SamTwoWayTransformer is designed to perform two-way attention between point embeddings and image embeddings. It consists of multiple layers of SamTwoWayAttentionBlock, followed by a final attention step using SamAttention. The class also includes a layer normalization step.

The main purpose of this class is to forward the transformer model and generate the outputs based on the given inputs. The inputs include point embeddings, image embeddings, image positional embeddings, attention similarity, target embeddings (optional), and various optional parameters to control the output format.

The forwardor (init) initializes the SamTwoWayTransformer instance with a configuration object (config) of type SamMaskDecoderConfig. It sets the configuration, number of hidden layers, and initializes the list of layers.

The forward method takes the point embeddings, image embeddings, image positional embeddings, attention similarity, target embedding, and optional parameters as inputs. It performs the necessary computations to generate the outputs of the transformer model. The method supports optional arguments to control the output format, such as output_attentions, output_hidden_states, and return_dict. The method returns a tuple containing the queries, keys, and optionally, all the attention outputs.

Please note that this class requires the image_embeddings parameter to be specified. If it is not provided, a ValueError will be raised.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamTwoWayTransformer(nn.Module):

    """
    This class represents a two-way transformer model called SamTwoWayTransformer. It is a subclass of nn.Module.

    SamTwoWayTransformer is designed to perform two-way attention between point embeddings and image embeddings.
    It consists of multiple layers of SamTwoWayAttentionBlock, followed by a final attention step using SamAttention.
    The class also includes a layer normalization step.

    The main purpose of this class is to forward the transformer model and generate the outputs based on the given
    inputs. The inputs include point embeddings, image embeddings, image positional embeddings, attention similarity,
    target embeddings (optional), and various optional parameters to control the output format.

    The forwardor (__init__) initializes the SamTwoWayTransformer instance with a configuration object (config) of
    type SamMaskDecoderConfig. It sets the configuration, number of hidden layers, and initializes the list of layers.

    The forward method takes the point embeddings, image embeddings, image positional embeddings, attention similarity,
    target embedding, and optional parameters as inputs. It performs the necessary computations to generate the outputs
    of the transformer model. The method supports optional arguments to control the output format, such as
    output_attentions, output_hidden_states, and return_dict. The method returns a tuple containing the queries, keys,
    and optionally, all the attention outputs.

    Please note that this class requires the image_embeddings parameter to be specified. If it is not provided,
    a ValueError will be raised.

    """
    def __init__(self, config: SamMaskDecoderConfig):
        """
        Initializes a new instance of the SamTwoWayTransformer class.

        Args:
            self: The instance of the class.
            config (SamMaskDecoderConfig): The configuration object containing the parameters for the transformer.

        Returns:
            None

        Raises:
            None
        """
        super().__init__()
        self.config = config

        self.num_hidden_layers = config.num_hidden_layers
        self.layers = nn.ModuleList()

        for i in range(self.num_hidden_layers):
            self.layers.append(SamTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0)))

        self.final_attn_token_to_image = SamAttention(config)
        self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size)

    def forward(
        self,
        point_embeddings: Tensor,
        image_embeddings: Tensor,
        image_positional_embeddings: Tensor,
        attention_similarity: Tensor,
        target_embedding=None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        """
        Constructs the SamTwoWayTransformer.

        This method initializes and forwards the SamTwoWayTransformer model by taking in various input parameters.

        Args:
            self: The object instance.
            point_embeddings (Tensor): The tensor representing the point embeddings.
            image_embeddings (Tensor): The tensor representing the image embeddings.
            image_positional_embeddings (Tensor): The tensor representing the positional embeddings of the images.
            attention_similarity (Tensor): The tensor representing the attention similarity.
            target_embedding (Optional[Tensor]): The tensor representing the target embedding (default: None).
            output_attentions (Optional[bool]): Whether to output attentions (default: None).
            output_hidden_states (Optional[bool]): Whether to output hidden states (default: None).
            return_dict (Optional[bool]): Whether to use return dict (default: None).

        Returns:
            Union[Tuple, BaseModelOutput]: The output of the SamTwoWayTransformer model.

        Raises:
            ValueError: This exception is raised if the image_embeddings parameter is not specified.

        Note:
            - The output_attentions, output_hidden_states, and return_dict parameters will take values from the self.config
              object if not explicitly provided.
            - This method performs various computations and transformations on the input tensors to forward the
              SamTwoWayTransformer model.
            - The forwarded model is returned as an output.

        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        all_attentions = ()

        if image_embeddings is None:
            raise ValueError("You have to specify an image_embedding")

        image_embeddings = image_embeddings.flatten(start_dim=2).permute(0, 2, 1).unsqueeze(1)
        image_positional_embeddings = image_positional_embeddings.flatten(start_dim=2).permute(0, 2, 1).unsqueeze(1)

        # Prepare queries
        queries = point_embeddings
        keys = image_embeddings

        # Apply transformer blocks and final layernorm
        for layer in self.layers:
            if target_embedding is not None:
                queries += target_embedding

            queries, keys, attention_outputs = layer(
                queries=queries,
                keys=keys,
                query_point_embedding=point_embeddings,
                key_point_embedding=image_positional_embeddings,
                attention_similarity=attention_similarity,
                output_attentions=output_attentions,
            )

            if output_attentions:
                all_attentions = all_attentions + (attention_outputs,)

        # Apply the final attenion layer from the points to the image
        query = queries + point_embeddings
        key = keys + image_positional_embeddings

        attn_out = self.final_attn_token_to_image(query=query, key=key, value=keys)

        queries = queries + attn_out
        queries = self.layer_norm_final_attn(queries)
        return queries, keys, all_attentions

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayTransformer.__init__(config)

Initializes a new instance of the SamTwoWayTransformer class.

PARAMETER DESCRIPTION
self

The instance of the class.
config

The configuration object containing the parameters for the transformer.

TYPE: SamMaskDecoderConfig

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamMaskDecoderConfig):
    """
    Initializes a new instance of the SamTwoWayTransformer class.

    Args:
        self: The instance of the class.
        config (SamMaskDecoderConfig): The configuration object containing the parameters for the transformer.

    Returns:
        None

    Raises:
        None
    """
    super().__init__()
    self.config = config

    self.num_hidden_layers = config.num_hidden_layers
    self.layers = nn.ModuleList()

    for i in range(self.num_hidden_layers):
        self.layers.append(SamTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0)))

    self.final_attn_token_to_image = SamAttention(config)
    self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size)

mindnlp.transformers.models.sam.modeling_sam.SamTwoWayTransformer.forward(point_embeddings, image_embeddings, image_positional_embeddings, attention_similarity, target_embedding=None, output_attentions=None, output_hidden_states=None, return_dict=None)

Constructs the SamTwoWayTransformer.

This method initializes and forwards the SamTwoWayTransformer model by taking in various input parameters.

PARAMETER DESCRIPTION
self

The object instance.
point_embeddings

The tensor representing the point embeddings.

TYPE: Tensor

image_embeddings

The tensor representing the image embeddings.

TYPE: Tensor

image_positional_embeddings

The tensor representing the positional embeddings of the images.

TYPE: Tensor

attention_similarity

The tensor representing the attention similarity.

TYPE: Tensor

target_embedding

The tensor representing the target embedding (default: None).

TYPE: Optional[Tensor] DEFAULT: None

output_attentions

Whether to output attentions (default: None).

TYPE: Optional[bool] DEFAULT: None

output_hidden_states

Whether to output hidden states (default: None).

TYPE: Optional[bool] DEFAULT: None

return_dict

Whether to use return dict (default: None).

TYPE: Optional[bool] DEFAULT: None

RETURNS DESCRIPTION
Union[Tuple, BaseModelOutput]

Union[Tuple, BaseModelOutput]: The output of the SamTwoWayTransformer model.
RAISES DESCRIPTION
ValueError

This exception is raised if the image_embeddings parameter is not specified.
Note
  • The output_attentions, output_hidden_states, and return_dict parameters will take values from the self.config object if not explicitly provided.
  • This method performs various computations and transformations on the input tensors to forward the SamTwoWayTransformer model.
  • The forwarded model is returned as an output.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    point_embeddings: Tensor,
    image_embeddings: Tensor,
    image_positional_embeddings: Tensor,
    attention_similarity: Tensor,
    target_embedding=None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
    """
    Constructs the SamTwoWayTransformer.

    This method initializes and forwards the SamTwoWayTransformer model by taking in various input parameters.

    Args:
        self: The object instance.
        point_embeddings (Tensor): The tensor representing the point embeddings.
        image_embeddings (Tensor): The tensor representing the image embeddings.
        image_positional_embeddings (Tensor): The tensor representing the positional embeddings of the images.
        attention_similarity (Tensor): The tensor representing the attention similarity.
        target_embedding (Optional[Tensor]): The tensor representing the target embedding (default: None).
        output_attentions (Optional[bool]): Whether to output attentions (default: None).
        output_hidden_states (Optional[bool]): Whether to output hidden states (default: None).
        return_dict (Optional[bool]): Whether to use return dict (default: None).

    Returns:
        Union[Tuple, BaseModelOutput]: The output of the SamTwoWayTransformer model.

    Raises:
        ValueError: This exception is raised if the image_embeddings parameter is not specified.

    Note:
        - The output_attentions, output_hidden_states, and return_dict parameters will take values from the self.config
          object if not explicitly provided.
        - This method performs various computations and transformations on the input tensors to forward the
          SamTwoWayTransformer model.
        - The forwarded model is returned as an output.

    """
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    all_attentions = ()

    if image_embeddings is None:
        raise ValueError("You have to specify an image_embedding")

    image_embeddings = image_embeddings.flatten(start_dim=2).permute(0, 2, 1).unsqueeze(1)
    image_positional_embeddings = image_positional_embeddings.flatten(start_dim=2).permute(0, 2, 1).unsqueeze(1)

    # Prepare queries
    queries = point_embeddings
    keys = image_embeddings

    # Apply transformer blocks and final layernorm
    for layer in self.layers:
        if target_embedding is not None:
            queries += target_embedding

        queries, keys, attention_outputs = layer(
            queries=queries,
            keys=keys,
            query_point_embedding=point_embeddings,
            key_point_embedding=image_positional_embeddings,
            attention_similarity=attention_similarity,
            output_attentions=output_attentions,
        )

        if output_attentions:
            all_attentions = all_attentions + (attention_outputs,)

    # Apply the final attenion layer from the points to the image
    query = queries + point_embeddings
    key = keys + image_positional_embeddings

    attn_out = self.final_attn_token_to_image(query=query, key=key, value=keys)

    queries = queries + attn_out
    queries = self.layer_norm_final_attn(queries)
    return queries, keys, all_attentions

mindnlp.transformers.models.sam.modeling_sam.SamVisionAttention

Bases: Module

Multi-head Attention block with relative position embeddings.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamVisionAttention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""
    def __init__(self, config, window_size):
        """
        Initializes a SamVisionAttention object.

        Args:
            self: The object itself.
            config: An instance of a configuration class containing various parameters.
                It specifies the configuration settings for the attention mechanism.
            window_size: An integer representing the size of the window. If set to 0, the window size
                is determined based on the config's image size. It determines the size of the attention window.

        Returns:
            None

        Raises:
            ValueError: If input_size is None and use_rel_pos is True.
        """
        super().__init__()
        input_size = (
            (config.image_size // config.patch_size, config.image_size // config.patch_size)
            if window_size == 0
            else (window_size, window_size)
        )

        self.num_attention_heads = config.num_attention_heads
        head_dim = config.hidden_size // config.num_attention_heads
        self.scale = head_dim**-0.5
        self.dropout = config.attention_dropout

        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
        self.proj = nn.Linear(config.hidden_size, config.hidden_size)

        self.use_rel_pos = config.use_rel_pos
        if self.use_rel_pos:
            if input_size is None:
                raise ValueError("Input size must be provided if using relative positional encoding.")

            # initialize relative positional embeddings
            self.rel_pos_h = Parameter(ops.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = Parameter(ops.zeros(2 * input_size[1] - 1, head_dim))

    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: mindspore.Tensor) -> mindspore.Tensor:
        """
        Get relative positional embeddings according to the relative positions of
            query and key sizes.

        Args:
            q_size (int):
                size of the query.
            k_size (int):
                size of key k.
            rel_pos (`mindspore.Tensor`):
                relative position embeddings (L, channel).

        Returns:
            Extracted positional embeddings according to relative positions.
        """
        max_rel_dist = int(2 * max(q_size, k_size) - 1)
        # Interpolate rel pos.
        rel_pos_resized = ops.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)

        # Scale the coords with short length if shapes for q and k are different.
        q_coords = ops.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
        k_coords = ops.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

        return rel_pos_resized[relative_coords.long()]

    def add_decomposed_rel_pos(
        self,
        attn: mindspore.Tensor,
        query: mindspore.Tensor,
        rel_pos_h: mindspore.Tensor,
        rel_pos_w: mindspore.Tensor,
        q_size: Tuple[int, int],
        k_size: Tuple[int, int],
    ) -> mindspore.Tensor:
        """
        Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
        https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py

        Args:
            attn (`mindspore.Tensor`):
                attention map.
            query (`mindspore.Tensor`):
                query q in the attention layer with shape (batch_size, query_height * query_width, channel).
            rel_pos_h (`mindspore.Tensor`):
                relative position embeddings (Lh, channel) for height axis.
            rel_pos_w (`mindspore.Tensor`):
                relative position embeddings (Lw, channel) for width axis.
            q_size (tuple):
                spatial sequence size of query q with (query_height, query_width).
            k_size (tuple):
                spatial sequence size of key k with (key_height, key_width).

        Returns:
            attn (`mindspore.Tensor`):
                attention map with added relative positional embeddings.
        """
        query_height, query_width = q_size
        key_height, key_width = k_size
        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)

        batch_size, _, dim = query.shape
        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
        rel_h = ops.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
        rel_w = ops.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)
        attn = attn.reshape(batch_size, query_height, query_width, key_height, key_width)
        attn = attn + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
        attn = attn.reshape(batch_size, query_height * query_width, key_height * key_width)
        return attn

    def forward(self, hidden_states: mindspore.Tensor, output_attentions=False) -> mindspore.Tensor:
        """
        Method 'forward' in the class 'SamVisionAttention'.

        Args:
            self: SamVisionAttention object. Represents the instance of the SamVisionAttention class.
            hidden_states: mindspore.Tensor. Input tensor of shape (batch_size, height, width, _),
                where _ represents a dimension. Contains the hidden states to be processed.
            output_attentions: bool. Indicates whether to output the attention weights. Default is False.
                If True, the attention weights will be included in the return value.

        Returns:
            Tuple[Tensor]:
                Tuple of two elements - attn_output: mindspore.Tensor. Output tensor after attention mechanism processing.
                If output_attentions is True, the second element is attn_weights: mindspore.Tensor. Attention weights tensor.
                The return value represents the result of applying the attention mechanism on the hidden_states input.

        Raises:
            None.
        """
        batch_size, height, width, _ = hidden_states.shape
        # qkv with shape (3, batch_size, nHead, height * width, channel)
        qkv = (
            self.qkv(hidden_states)
            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
            .permute(2, 0, 3, 1, 4)
        )
        # q, k, v with shape (batch_size * nHead, height * width, channel)
        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)

        attn_weights = (query * self.scale) @ key.swapaxes(-2, -1)

        if self.use_rel_pos:
            attn_weights = self.add_decomposed_rel_pos(
                attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
            )

        attn_weights = ops.softmax(attn_weights, dtype=mindspore.float32, axis=-1).to(query.dtype)

        attn_probs = ops.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)

        attn_output = self.proj(attn_output)

        if output_attentions:
            outputs = (attn_output, attn_weights)
        else:
            outputs = (attn_output, None)

        return outputs

mindnlp.transformers.models.sam.modeling_sam.SamVisionAttention.__init__(config, window_size)

Initializes a SamVisionAttention object.

PARAMETER DESCRIPTION
self

The object itself.
config

An instance of a configuration class containing various parameters. It specifies the configuration settings for the attention mechanism.
window_size

An integer representing the size of the window. If set to 0, the window size is determined based on the config's image size. It determines the size of the attention window.
RETURNS DESCRIPTION

None
RAISES DESCRIPTION
ValueError

If input_size is None and use_rel_pos is True.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config, window_size):
    """
    Initializes a SamVisionAttention object.

    Args:
        self: The object itself.
        config: An instance of a configuration class containing various parameters.
            It specifies the configuration settings for the attention mechanism.
        window_size: An integer representing the size of the window. If set to 0, the window size
            is determined based on the config's image size. It determines the size of the attention window.

    Returns:
        None

    Raises:
        ValueError: If input_size is None and use_rel_pos is True.
    """
    super().__init__()
    input_size = (
        (config.image_size // config.patch_size, config.image_size // config.patch_size)
        if window_size == 0
        else (window_size, window_size)
    )

    self.num_attention_heads = config.num_attention_heads
    head_dim = config.hidden_size // config.num_attention_heads
    self.scale = head_dim**-0.5
    self.dropout = config.attention_dropout

    self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
    self.proj = nn.Linear(config.hidden_size, config.hidden_size)

    self.use_rel_pos = config.use_rel_pos
    if self.use_rel_pos:
        if input_size is None:
            raise ValueError("Input size must be provided if using relative positional encoding.")

        # initialize relative positional embeddings
        self.rel_pos_h = Parameter(ops.zeros(2 * input_size[0] - 1, head_dim))
        self.rel_pos_w = Parameter(ops.zeros(2 * input_size[1] - 1, head_dim))

mindnlp.transformers.models.sam.modeling_sam.SamVisionAttention.add_decomposed_rel_pos(attn, query, rel_pos_h, rel_pos_w, q_size, k_size)

Calculate decomposed Relative Positional Embeddings from :paper:mvitv2. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py

PARAMETER DESCRIPTION
attn

attention map.

TYPE: `mindspore.Tensor`

query

query q in the attention layer with shape (batch_size, query_height * query_width, channel).

TYPE: `mindspore.Tensor`

rel_pos_h

relative position embeddings (Lh, channel) for height axis.

TYPE: `mindspore.Tensor`

rel_pos_w

relative position embeddings (Lw, channel) for width axis.

TYPE: `mindspore.Tensor`

q_size

spatial sequence size of query q with (query_height, query_width).

TYPE: tuple

k_size

spatial sequence size of key k with (key_height, key_width).

TYPE: tuple

RETURNS DESCRIPTION
attn

attention map with added relative positional embeddings.

TYPE: `mindspore.Tensor`

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def add_decomposed_rel_pos(
    self,
    attn: mindspore.Tensor,
    query: mindspore.Tensor,
    rel_pos_h: mindspore.Tensor,
    rel_pos_w: mindspore.Tensor,
    q_size: Tuple[int, int],
    k_size: Tuple[int, int],
) -> mindspore.Tensor:
    """
    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py

    Args:
        attn (`mindspore.Tensor`):
            attention map.
        query (`mindspore.Tensor`):
            query q in the attention layer with shape (batch_size, query_height * query_width, channel).
        rel_pos_h (`mindspore.Tensor`):
            relative position embeddings (Lh, channel) for height axis.
        rel_pos_w (`mindspore.Tensor`):
            relative position embeddings (Lw, channel) for width axis.
        q_size (tuple):
            spatial sequence size of query q with (query_height, query_width).
        k_size (tuple):
            spatial sequence size of key k with (key_height, key_width).

    Returns:
        attn (`mindspore.Tensor`):
            attention map with added relative positional embeddings.
    """
    query_height, query_width = q_size
    key_height, key_width = k_size
    relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
    relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)

    batch_size, _, dim = query.shape
    reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
    rel_h = ops.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
    rel_w = ops.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)
    attn = attn.reshape(batch_size, query_height, query_width, key_height, key_width)
    attn = attn + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
    attn = attn.reshape(batch_size, query_height * query_width, key_height * key_width)
    return attn

mindnlp.transformers.models.sam.modeling_sam.SamVisionAttention.forward(hidden_states, output_attentions=False)

Method 'forward' in the class 'SamVisionAttention'.

PARAMETER DESCRIPTION
self

SamVisionAttention object. Represents the instance of the SamVisionAttention class.
hidden_states

mindspore.Tensor. Input tensor of shape (batch_size, height, width, _), where _ represents a dimension. Contains the hidden states to be processed.

TYPE: Tensor

output_attentions

bool. Indicates whether to output the attention weights. Default is False. If True, the attention weights will be included in the return value.

DEFAULT: False

RETURNS DESCRIPTION
Tensor

Tuple[Tensor]: Tuple of two elements - attn_output: mindspore.Tensor. Output tensor after attention mechanism processing. If output_attentions is True, the second element is attn_weights: mindspore.Tensor. Attention weights tensor. The return value represents the result of applying the attention mechanism on the hidden_states input.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, hidden_states: mindspore.Tensor, output_attentions=False) -> mindspore.Tensor:
    """
    Method 'forward' in the class 'SamVisionAttention'.

    Args:
        self: SamVisionAttention object. Represents the instance of the SamVisionAttention class.
        hidden_states: mindspore.Tensor. Input tensor of shape (batch_size, height, width, _),
            where _ represents a dimension. Contains the hidden states to be processed.
        output_attentions: bool. Indicates whether to output the attention weights. Default is False.
            If True, the attention weights will be included in the return value.

    Returns:
        Tuple[Tensor]:
            Tuple of two elements - attn_output: mindspore.Tensor. Output tensor after attention mechanism processing.
            If output_attentions is True, the second element is attn_weights: mindspore.Tensor. Attention weights tensor.
            The return value represents the result of applying the attention mechanism on the hidden_states input.

    Raises:
        None.
    """
    batch_size, height, width, _ = hidden_states.shape
    # qkv with shape (3, batch_size, nHead, height * width, channel)
    qkv = (
        self.qkv(hidden_states)
        .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
        .permute(2, 0, 3, 1, 4)
    )
    # q, k, v with shape (batch_size * nHead, height * width, channel)
    query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)

    attn_weights = (query * self.scale) @ key.swapaxes(-2, -1)

    if self.use_rel_pos:
        attn_weights = self.add_decomposed_rel_pos(
            attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
        )

    attn_weights = ops.softmax(attn_weights, dtype=mindspore.float32, axis=-1).to(query.dtype)

    attn_probs = ops.dropout(attn_weights, p=self.dropout, training=self.training)

    attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
    attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)

    attn_output = self.proj(attn_output)

    if output_attentions:
        outputs = (attn_output, attn_weights)
    else:
        outputs = (attn_output, None)

    return outputs

mindnlp.transformers.models.sam.modeling_sam.SamVisionAttention.get_rel_pos(q_size, k_size, rel_pos)

Get relative positional embeddings according to the relative positions of query and key sizes.

PARAMETER DESCRIPTION
q_size

size of the query.

TYPE: int

k_size

size of key k.

TYPE: int

rel_pos

relative position embeddings (L, channel).

TYPE: `mindspore.Tensor`

RETURNS DESCRIPTION
Tensor

Extracted positional embeddings according to relative positions.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_rel_pos(self, q_size: int, k_size: int, rel_pos: mindspore.Tensor) -> mindspore.Tensor:
    """
    Get relative positional embeddings according to the relative positions of
        query and key sizes.

    Args:
        q_size (int):
            size of the query.
        k_size (int):
            size of key k.
        rel_pos (`mindspore.Tensor`):
            relative position embeddings (L, channel).

    Returns:
        Extracted positional embeddings according to relative positions.
    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos.
    rel_pos_resized = ops.interpolate(
        rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
        size=max_rel_dist,
        mode="linear",
    )
    rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = ops.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
    k_coords = ops.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

    return rel_pos_resized[relative_coords.long()]

mindnlp.transformers.models.sam.modeling_sam.SamVisionEncoder

Bases: Module

The SamVisionEncoder class represents a vision encoder for processing image data using the SAM (Self-Attention Model) architecture. It inherits from the nn.Module class and is designed to be used within the MindSpore framework for deep learning applications.

The class initializes with a SamVisionConfig object and sets various attributes based on the provided configuration. It includes methods for retrieving input embeddings and forwarding the encoder output based on the input pixel values. The forwardion process involves passing the input through the patch embeddings, applying positional embeddings if configured, processing the input through multiple vision layers, and finally passing the output through a vision neck module.

The class also provides options for controlling the output of attentions and hidden states, as well as the ability to return the output as a dictionary. Additionally, it supports gradient checkpointing during training for efficient memory usage.

Overall, the SamVisionEncoder class encapsulates the functionality for encoding image data using the SAM architecture, providing a flexible and configurable interface for vision processing tasks within the MindSpore framework.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamVisionEncoder(nn.Module):

    """
    The SamVisionEncoder class represents a vision encoder for processing image data using the SAM
    (Self-Attention Model) architecture. It inherits from the nn.Module class and is designed to be used within the
    MindSpore framework for deep learning applications.

    The class initializes with a SamVisionConfig object and sets various attributes based on the provided configuration.
    It includes methods for retrieving input embeddings and forwarding the encoder output based on the input pixel
    values. The forwardion process involves passing the input through the patch embeddings, applying positional
    embeddings if configured, processing the input through multiple vision layers, and finally passing the output
    through a vision neck module.

    The class also provides options for controlling the output of attentions and hidden states, as well as the ability
    to return the output as a dictionary. Additionally, it supports gradient checkpointing during training for efficient
    memory usage.

    Overall, the SamVisionEncoder class encapsulates the functionality for encoding image data using the SAM architecture,
    providing a flexible and configurable interface for vision processing tasks within the MindSpore framework.
    """
    def __init__(self, config: SamVisionConfig):
        """
        Initializes a SamVisionEncoder object.

        Args:
            self: The object itself.
            config (SamVisionConfig): An instance of SamVisionConfig containing configuration parameters.

        Returns:
            None

        Raises:
            None
        """
        super().__init__()
        self.config = config
        self.image_size = config.image_size

        self.patch_embed = SamPatchEmbeddings(config)

        self.pos_embed = None
        if config.use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = Parameter(
                ops.zeros(
                    1,
                    config.image_size // config.patch_size,
                    config.image_size // config.patch_size,
                    config.hidden_size,
                )
            )

        self.layers = nn.ModuleList()
        for i in range(config.num_hidden_layers):
            layer = SamVisionLayer(
                config,
                window_size=config.window_size if i not in config.global_attn_indexes else 0,
            )
            self.layers.append(layer)

        self.neck = SamVisionNeck(config)

        self.gradient_checkpointing = False

    def get_input_embeddings(self):
        """
        Retrieves the input embeddings of the SamVisionEncoder.

        Args:
            self: An instance of the SamVisionEncoder class.

        Returns:
            None.

        Raises:
            None.

        This method retrieves the input embeddings used by the SamVisionEncoder.
        The input embeddings are obtained from the patch embedding process performed by the 'patch_embed' method.
        """
        return self.patch_embed

    def forward(
        self,
        pixel_values: Optional[mindspore.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SamVisionEncoderOutput]:
        """
        Constructs the SamVisionEncoder.

        Args:
            self (SamVisionEncoder): The instance of the SamVisionEncoder class.
            pixel_values (Optional[mindspore.Tensor]): The input pixel values. Default is None.
            output_attentions (Optional[bool]): Whether to output attentions. Default is None.
            output_hidden_states (Optional[bool]): Whether to output hidden states. Default is None.
            return_dict (Optional[bool]): Whether to return a dictionary. Default is None.

        Returns:
            Union[Tuple, SamVisionEncoderOutput]: The output of the SamVisionEncoder.
            If return_dict is False, returns a tuple containing the hidden states,
            all_hidden_states (if output_hidden_states is True), and all_self_attentions (if output_attentions is True).
            If return_dict is True, returns a SamVisionEncoderOutput object containing the last hidden state,
            all_hidden_states, and all_self_attentions.

        Raises:
            ValueError: If pixel_values is None.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        hidden_states = self.patch_embed(pixel_values)
        if self.pos_embed is not None:
            hidden_states = hidden_states + self.pos_embed

        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                )
            else:
                layer_outputs = layer_module(hidden_states, output_attentions=output_attentions)

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        hidden_states = self.neck(hidden_states)

        if not return_dict:
            outputs = (hidden_states,)
            if output_hidden_states:
                outputs = outputs + (all_hidden_states,)
            if output_attentions:
                outputs = outputs + (all_self_attentions,)
            return outputs

        return SamVisionEncoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )

mindnlp.transformers.models.sam.modeling_sam.SamVisionEncoder.__init__(config)

Initializes a SamVisionEncoder object.

PARAMETER DESCRIPTION
self

The object itself.
config

An instance of SamVisionConfig containing configuration parameters.

TYPE: SamVisionConfig

RETURNS DESCRIPTION

None
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamVisionConfig):
    """
    Initializes a SamVisionEncoder object.

    Args:
        self: The object itself.
        config (SamVisionConfig): An instance of SamVisionConfig containing configuration parameters.

    Returns:
        None

    Raises:
        None
    """
    super().__init__()
    self.config = config
    self.image_size = config.image_size

    self.patch_embed = SamPatchEmbeddings(config)

    self.pos_embed = None
    if config.use_abs_pos:
        # Initialize absolute positional embedding with pretrain image size.
        self.pos_embed = Parameter(
            ops.zeros(
                1,
                config.image_size // config.patch_size,
                config.image_size // config.patch_size,
                config.hidden_size,
            )
        )

    self.layers = nn.ModuleList()
    for i in range(config.num_hidden_layers):
        layer = SamVisionLayer(
            config,
            window_size=config.window_size if i not in config.global_attn_indexes else 0,
        )
        self.layers.append(layer)

    self.neck = SamVisionNeck(config)

    self.gradient_checkpointing = False

mindnlp.transformers.models.sam.modeling_sam.SamVisionEncoder.forward(pixel_values=None, output_attentions=None, output_hidden_states=None, return_dict=None)

Constructs the SamVisionEncoder.

PARAMETER DESCRIPTION
self

The instance of the SamVisionEncoder class.

TYPE: SamVisionEncoder

pixel_values

The input pixel values. Default is None.

TYPE: Optional[Tensor] DEFAULT: None

output_attentions

Whether to output attentions. Default is None.

TYPE: Optional[bool] DEFAULT: None

output_hidden_states

Whether to output hidden states. Default is None.

TYPE: Optional[bool] DEFAULT: None

return_dict

Whether to return a dictionary. Default is None.

TYPE: Optional[bool] DEFAULT: None

RETURNS DESCRIPTION
Union[Tuple, SamVisionEncoderOutput]

Union[Tuple, SamVisionEncoderOutput]: The output of the SamVisionEncoder.
Union[Tuple, SamVisionEncoderOutput]

If return_dict is False, returns a tuple containing the hidden states,
Union[Tuple, SamVisionEncoderOutput]

all_hidden_states (if output_hidden_states is True), and all_self_attentions (if output_attentions is True).
Union[Tuple, SamVisionEncoderOutput]

If return_dict is True, returns a SamVisionEncoderOutput object containing the last hidden state,
Union[Tuple, SamVisionEncoderOutput]

all_hidden_states, and all_self_attentions.
RAISES DESCRIPTION
ValueError

If pixel_values is None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    pixel_values: Optional[mindspore.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, SamVisionEncoderOutput]:
    """
    Constructs the SamVisionEncoder.

    Args:
        self (SamVisionEncoder): The instance of the SamVisionEncoder class.
        pixel_values (Optional[mindspore.Tensor]): The input pixel values. Default is None.
        output_attentions (Optional[bool]): Whether to output attentions. Default is None.
        output_hidden_states (Optional[bool]): Whether to output hidden states. Default is None.
        return_dict (Optional[bool]): Whether to return a dictionary. Default is None.

    Returns:
        Union[Tuple, SamVisionEncoderOutput]: The output of the SamVisionEncoder.
        If return_dict is False, returns a tuple containing the hidden states,
        all_hidden_states (if output_hidden_states is True), and all_self_attentions (if output_attentions is True).
        If return_dict is True, returns a SamVisionEncoderOutput object containing the last hidden state,
        all_hidden_states, and all_self_attentions.

    Raises:
        ValueError: If pixel_values is None.
    """
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if pixel_values is None:
        raise ValueError("You have to specify pixel_values")

    hidden_states = self.patch_embed(pixel_values)
    if self.pos_embed is not None:
        hidden_states = hidden_states + self.pos_embed

    all_hidden_states = () if output_hidden_states else None
    all_self_attentions = () if output_attentions else None

    for i, layer_module in enumerate(self.layers):
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if self.gradient_checkpointing and self.training:
            layer_outputs = self._gradient_checkpointing_func(
                layer_module.__call__,
                hidden_states,
            )
        else:
            layer_outputs = layer_module(hidden_states, output_attentions=output_attentions)

        hidden_states = layer_outputs[0]

        if output_attentions:
            all_self_attentions = all_self_attentions + (layer_outputs[1],)

    if output_hidden_states:
        all_hidden_states = all_hidden_states + (hidden_states,)

    hidden_states = self.neck(hidden_states)

    if not return_dict:
        outputs = (hidden_states,)
        if output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if output_attentions:
            outputs = outputs + (all_self_attentions,)
        return outputs

    return SamVisionEncoderOutput(
        last_hidden_state=hidden_states,
        hidden_states=all_hidden_states,
        attentions=all_self_attentions,
    )

mindnlp.transformers.models.sam.modeling_sam.SamVisionEncoder.get_input_embeddings()

Retrieves the input embeddings of the SamVisionEncoder.

PARAMETER DESCRIPTION
self

An instance of the SamVisionEncoder class.
RETURNS DESCRIPTION

None.

This method retrieves the input embeddings used by the SamVisionEncoder. The input embeddings are obtained from the patch embedding process performed by the 'patch_embed' method.

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def get_input_embeddings(self):
    """
    Retrieves the input embeddings of the SamVisionEncoder.

    Args:
        self: An instance of the SamVisionEncoder class.

    Returns:
        None.

    Raises:
        None.

    This method retrieves the input embeddings used by the SamVisionEncoder.
    The input embeddings are obtained from the patch embedding process performed by the 'patch_embed' method.
    """
    return self.patch_embed

mindnlp.transformers.models.sam.modeling_sam.SamVisionEncoderOutput dataclass

Bases: ModelOutput

Base class for sam vision model's outputs that also contains image embeddings obtained by applying the projection layer to the pooler_output.

PARAMETER DESCRIPTION
last_hidden_state

Sequence of hidden-states at the output of the last layer of the model.

TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length, hidden_size)` DEFAULT: None

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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@dataclass
class SamVisionEncoderOutput(ModelOutput):
    """
    Base class for sam vision model's outputs that also contains image embeddings obtained by applying the projection
    layer to the pooler_output.

    Args:
        image_embeds (`mindspore.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model
            is initialized with `with_projection=True`):
            The image embeddings obtained by applying the projection layer to the pooler_output.
        last_hidden_state (`mindspore.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (`tuple(mindspore.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed
            or when `config.output_hidden_states=True`):
            Tuple of `mindspore.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(mindspore.Tensor)`, *optional*, returned when `output_attentions=True` is passed
            or when `config.output_attentions=True`):
            Tuple of `mindspore.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """
    image_embeds: Optional[mindspore.Tensor] = None
    last_hidden_state: mindspore.Tensor = None
    hidden_states: Optional[Tuple[mindspore.Tensor, ...]] = None
    attentions: Optional[Tuple[mindspore.Tensor, ...]] = None

mindnlp.transformers.models.sam.modeling_sam.SamVisionLayer

Bases: Module

This class represents a vision layer in the SamVision model. It inherits from the nn.Module class and implements the necessary methods and functionality for performing attention-based operations on input image tokens.

ATTRIBUTE DESCRIPTION
layer_norm1

An instance of nn.LayerNorm which applies layer normalization to the input hidden states.
attn

An instance of the SamVisionAttention class which performs attention computation on the hidden states.
layer_norm2

An instance of nn.LayerNorm which applies layer normalization to the output hidden states.
mlp

An instance of the SamMLPBlock class which applies multi-layer perceptron operations to the output hidden states.
window_size

An integer representing the size of the attention windows.
METHOD DESCRIPTION
window_partition

Partitions the input hidden states into non-overlapping windows with padding if needed.
window_unpartition

Unpartitions the windows into original sequences, removing padding.
forward

Constructs the output hidden states by applying layer normalization, attention, and MLP operations.
Example 
>>> config = Configuration()
>>> window_size = 16
>>> vision_layer = SamVisionLayer(config, window_size)
>>> hidden_states = torch.randn(batch_size, height, width, channel)
>>> output = vision_layer.forward(hidden_states)
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamVisionLayer(nn.Module):

    """
    This class represents a vision layer in the SamVision model. It inherits from the nn.Module class and implements
    the necessary methods and functionality for performing attention-based operations on input image tokens.

    Attributes:
        layer_norm1: An instance of nn.LayerNorm which applies layer normalization to the input hidden states.
        attn: An instance of the SamVisionAttention class which performs attention computation on the hidden states.
        layer_norm2: An instance of nn.LayerNorm which applies layer normalization to the output hidden states.
        mlp: An instance of the SamMLPBlock class which applies multi-layer perceptron operations to the output hidden states.
        window_size: An integer representing the size of the attention windows.

    Methods:
        window_partition(hidden_states, window_size): Partitions the input hidden states into non-overlapping windows
            with padding if needed.
        window_unpartition(windows, window_size, padding_shape, original_shape): Unpartitions the windows into original
            sequences, removing padding.
        forward(hidden_states, output_attentions=False): Constructs the output hidden states by applying
            layer normalization, attention, and MLP operations.

    Example:
        ```python
        >>> config = Configuration()
        >>> window_size = 16
        >>> vision_layer = SamVisionLayer(config, window_size)
        >>> hidden_states = torch.randn(batch_size, height, width, channel)
        >>> output = vision_layer.forward(hidden_states)
        ```
    """
    def __init__(self, config, window_size):
        """
        Initializes a new instance of SamVisionLayer.

        Args:
            self: The instance of the class.
            config:
                A configuration object containing the hidden size and layer normalization epsilon.

                - Type: object
                - Purpose: Specifies the configuration for the vision layer.
                - Restrictions: Must contain a 'hidden_size' property and a 'layer_norm_eps' property.
            window_size:
                An integer specifying the size of the vision window.

                - Type: int
                - Purpose: Specifies the size of the vision window used for attention mechanism.
                - Restrictions: Must be a positive integer.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__()
        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attn = SamVisionAttention(config, window_size)
        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = SamMLPBlock(config)
        self.window_size = window_size

    def window_partition(self, hidden_states: mindspore.Tensor, window_size: int) -> Tuple[mindspore.Tensor, Tuple[int, int]]:
        """
        Partition into non-overlapping windows with padding if needed.

        Args:
            hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
                size.

        Returns:
            windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
                (pad_height, pad_width): padded height and width before partition
        """
        batch_size, height, width, channel = hidden_states.shape

        pad_h = (window_size - height % window_size) % window_size
        pad_w = (window_size - width % window_size) % window_size
        hidden_states = ops.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
        pad_height, pad_width = height + pad_h, width + pad_w

        hidden_states = hidden_states.reshape(
            batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
        )
        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).reshape(-1, window_size, window_size, channel)
        return windows, (pad_height, pad_width)

    def window_unpartition(
        self, windows: mindspore.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]
    ) -> mindspore.Tensor:
        """
        Window unpartition into original sequences and removing padding.

        Args:
            hidden_states (tensor):
                input tokens with [batch_size * num_windows, window_size, window_size, channel].
            window_size (int):
                window size.
            padding_shape (Tuple):
                padded height and width (pad_height, pad_width).
            original_shape (Tuple): original height and width (height, width) before padding.

        Returns:
            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
        """
        pad_height, pad_width = padding_shape
        height, width = original_shape
        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
        hidden_states = windows.reshape(
            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
        )
        hidden_states = (
            hidden_states.permute(0, 1, 3, 2, 4, 5).reshape(batch_size, pad_height, pad_width, -1)
        )

        hidden_states = hidden_states[:, :height, :width, :]
        return hidden_states

    def forward(
        self,
        hidden_states: mindspore.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[mindspore.Tensor]:
        '''
        This method forwards the SamVisionLayer by applying attention mechanism and multi-layer perceptron (MLP) to
        the input hidden states.

        Args:
            self: The instance of the SamVisionLayer class.
            hidden_states (mindspore.Tensor): The input tensor representing the hidden states.
                It is expected to be a tensor of shape (batch_size, sequence_length, hidden_size).
            output_attentions (Optional[bool]): A flag indicating whether to output the attention weights.
                Default is False.

        Returns:
            Tuple[mindspore.Tensor]: A tuple containing the output hidden states tensor after applying attention
                mechanism and MLP.

        Raises:
            None
        '''
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        # Window partition
        if self.window_size > 0:
            height, width = hidden_states.shape[1], hidden_states.shape[2]
            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)

        hidden_states, attn_weights = self.attn(
            hidden_states=hidden_states,
            output_attentions=output_attentions,
        )
        # Reverse window partition
        if self.window_size > 0:
            hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))

        hidden_states = residual + hidden_states
        layernorm_output = self.layer_norm2(hidden_states)
        hidden_states = hidden_states + self.mlp(layernorm_output)

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (attn_weights,)

        return outputs

mindnlp.transformers.models.sam.modeling_sam.SamVisionLayer.__init__(config, window_size)

Initializes a new instance of SamVisionLayer.

PARAMETER DESCRIPTION
self

The instance of the class.
config

A configuration object containing the hidden size and layer normalization epsilon.

  • Type: object
  • Purpose: Specifies the configuration for the vision layer.
  • Restrictions: Must contain a 'hidden_size' property and a 'layer_norm_eps' property.
window_size

An integer specifying the size of the vision window.

  • Type: int
  • Purpose: Specifies the size of the vision window used for attention mechanism.
  • Restrictions: Must be a positive integer.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config, window_size):
    """
    Initializes a new instance of SamVisionLayer.

    Args:
        self: The instance of the class.
        config:
            A configuration object containing the hidden size and layer normalization epsilon.

            - Type: object
            - Purpose: Specifies the configuration for the vision layer.
            - Restrictions: Must contain a 'hidden_size' property and a 'layer_norm_eps' property.
        window_size:
            An integer specifying the size of the vision window.

            - Type: int
            - Purpose: Specifies the size of the vision window used for attention mechanism.
            - Restrictions: Must be a positive integer.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__()
    self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    self.attn = SamVisionAttention(config, window_size)
    self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    self.mlp = SamMLPBlock(config)
    self.window_size = window_size

mindnlp.transformers.models.sam.modeling_sam.SamVisionLayer.forward(hidden_states, output_attentions=False)

This method forwards the SamVisionLayer by applying attention mechanism and multi-layer perceptron (MLP) to the input hidden states.

PARAMETER DESCRIPTION
self

The instance of the SamVisionLayer class.
hidden_states

The input tensor representing the hidden states. It is expected to be a tensor of shape (batch_size, sequence_length, hidden_size).

TYPE: Tensor

output_attentions

A flag indicating whether to output the attention weights. Default is False.

TYPE: Optional[bool] DEFAULT: False

RETURNS DESCRIPTION
Tuple[Tensor]

Tuple[mindspore.Tensor]: A tuple containing the output hidden states tensor after applying attention mechanism and MLP.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(
    self,
    hidden_states: mindspore.Tensor,
    output_attentions: Optional[bool] = False,
) -> Tuple[mindspore.Tensor]:
    '''
    This method forwards the SamVisionLayer by applying attention mechanism and multi-layer perceptron (MLP) to
    the input hidden states.

    Args:
        self: The instance of the SamVisionLayer class.
        hidden_states (mindspore.Tensor): The input tensor representing the hidden states.
            It is expected to be a tensor of shape (batch_size, sequence_length, hidden_size).
        output_attentions (Optional[bool]): A flag indicating whether to output the attention weights.
            Default is False.

    Returns:
        Tuple[mindspore.Tensor]: A tuple containing the output hidden states tensor after applying attention
            mechanism and MLP.

    Raises:
        None
    '''
    residual = hidden_states

    hidden_states = self.layer_norm1(hidden_states)
    # Window partition
    if self.window_size > 0:
        height, width = hidden_states.shape[1], hidden_states.shape[2]
        hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)

    hidden_states, attn_weights = self.attn(
        hidden_states=hidden_states,
        output_attentions=output_attentions,
    )
    # Reverse window partition
    if self.window_size > 0:
        hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))

    hidden_states = residual + hidden_states
    layernorm_output = self.layer_norm2(hidden_states)
    hidden_states = hidden_states + self.mlp(layernorm_output)

    outputs = (hidden_states,)
    if output_attentions:
        outputs += (attn_weights,)

    return outputs

mindnlp.transformers.models.sam.modeling_sam.SamVisionLayer.window_partition(hidden_states, window_size)

Partition into non-overlapping windows with padding if needed.

PARAMETER DESCRIPTION
hidden_states

input tokens with [batch_size, height, width, channel]. window_size (int): window size.

TYPE: tensor

RETURNS DESCRIPTION
windows

windows after partition with [batch_size * num_windows, window_size, window_size, channel]. (pad_height, pad_width): padded height and width before partition

TYPE: Tuple[Tensor, Tuple[int, int]]

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def window_partition(self, hidden_states: mindspore.Tensor, window_size: int) -> Tuple[mindspore.Tensor, Tuple[int, int]]:
    """
    Partition into non-overlapping windows with padding if needed.

    Args:
        hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
            size.

    Returns:
        windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
            (pad_height, pad_width): padded height and width before partition
    """
    batch_size, height, width, channel = hidden_states.shape

    pad_h = (window_size - height % window_size) % window_size
    pad_w = (window_size - width % window_size) % window_size
    hidden_states = ops.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
    pad_height, pad_width = height + pad_h, width + pad_w

    hidden_states = hidden_states.reshape(
        batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
    )
    windows = hidden_states.permute(0, 1, 3, 2, 4, 5).reshape(-1, window_size, window_size, channel)
    return windows, (pad_height, pad_width)

mindnlp.transformers.models.sam.modeling_sam.SamVisionLayer.window_unpartition(windows, window_size, padding_shape, original_shape)

Window unpartition into original sequences and removing padding.

PARAMETER DESCRIPTION
hidden_states

input tokens with [batch_size * num_windows, window_size, window_size, channel].

TYPE: tensor

window_size

window size.

TYPE: int

padding_shape

padded height and width (pad_height, pad_width).

TYPE: Tuple

original_shape

original height and width (height, width) before padding.

TYPE: Tuple

RETURNS DESCRIPTION
hidden_states

unpartitioned sequences with [batch_size, height, width, channel].

TYPE: Tensor

Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def window_unpartition(
    self, windows: mindspore.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]
) -> mindspore.Tensor:
    """
    Window unpartition into original sequences and removing padding.

    Args:
        hidden_states (tensor):
            input tokens with [batch_size * num_windows, window_size, window_size, channel].
        window_size (int):
            window size.
        padding_shape (Tuple):
            padded height and width (pad_height, pad_width).
        original_shape (Tuple): original height and width (height, width) before padding.

    Returns:
        hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
    """
    pad_height, pad_width = padding_shape
    height, width = original_shape
    batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
    hidden_states = windows.reshape(
        batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
    )
    hidden_states = (
        hidden_states.permute(0, 1, 3, 2, 4, 5).reshape(batch_size, pad_height, pad_width, -1)
    )

    hidden_states = hidden_states[:, :height, :width, :]
    return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamVisionNeck

Bases: Module

This class represents the SamVisionNeck module, which is a component of a vision model that performs operations on hidden states.

SamVisionNeck inherits from the nn.Module class and includes two convolutional layers with layer normalization. The hidden states are passed through these layers to extract relevant features.

ATTRIBUTE DESCRIPTION
config

The configuration object that defines the parameters for the SamVisionNeck module.

TYPE: SamVisionConfig

conv1

The first convolutional layer that processes the hidden states.

TYPE: Conv2d

layer_norm1

The first layer normalization module that normalizes the output of the first convolutional layer.

TYPE: SamLayerNorm

conv2

The second convolutional layer that further processes the hidden states.

TYPE: Conv2d

layer_norm2

The second layer normalization module that normalizes the output of the second convolutional layer.

TYPE: SamLayerNorm

METHOD DESCRIPTION
__init__

Initializes a new instance of the SamVisionNeck class with the given configuration.
forward

Processes the hidden states through the convolutional and layer normalization layers.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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class SamVisionNeck(nn.Module):

    """
    This class represents the SamVisionNeck module, which is a component of a vision model that performs operations
    on hidden states.

    SamVisionNeck inherits from the nn.Module class and includes two convolutional layers with layer normalization.
    The hidden states are passed through these layers to extract relevant features.

    Attributes:
        config (SamVisionConfig): The configuration object that defines the parameters for the SamVisionNeck module.
        conv1 (nn.Conv2d): The first convolutional layer that processes the hidden states.
        layer_norm1 (SamLayerNorm): The first layer normalization module that normalizes the output of the
            first convolutional layer.
        conv2 (nn.Conv2d): The second convolutional layer that further processes the hidden states.
        layer_norm2 (SamLayerNorm): The second layer normalization module that normalizes the output of the
            second convolutional layer.

    Methods:
        __init__: Initializes a new instance of the SamVisionNeck class with the given configuration.
        forward: Processes the hidden states through the convolutional and layer normalization layers.

    """
    def __init__(self, config: SamVisionConfig):
        """
        Initialize the SamVisionNeck class.

        Args:
            self: The instance of the class.
            config (SamVisionConfig): An instance of SamVisionConfig containing the configuration for the SamVisionNeck.
                It defines the parameters required for the layers in the network.

        Returns:
            None.

        Raises:
            ValueError: If the configuration provided is invalid or incomplete.
            TypeError: If the configuration data type is not as expected.
            RuntimeError: If there is an issue during the initialization of the layers.
        """
        super().__init__()
        self.config = config

        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False, pad_mode='valid')
        self.layer_norm1 = SamLayerNorm(config.output_channels, data_format="channels_first")
        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False, pad_mode='pad')
        self.layer_norm2 = SamLayerNorm(config.output_channels, data_format="channels_first")

    def forward(self, hidden_states):
        """Constructs the hidden states in the SamVisionNeck class.

        This method takes in two parameters: self and hidden_states. The hidden_states parameter represents the
        input hidden states and should be a tensor. The purpose of this parameter is to provide the input
        for forwarding the hidden states. There are no restrictions on the shape or size of the hidden_states tensor.

        The method performs the following operations on the hidden_states:

        1. Permute the dimensions of the hidden_states tensor using the permute() function,
        with the dimensions permuted as (0, 3, 1, 2).
        2. Apply the conv1 layer to the permuted hidden_states tensor.
        3. Apply the layer_norm1 layer to the output of the conv1 layer.
        4. Apply the conv2 layer to the output of the layer_norm1 layer.
        5. Apply the layer_norm2 layer to the output of the conv2 layer.

        The method returns the final forwarded hidden states tensor.

        Args:
            self: An instance of the SamVisionNeck class.
            hidden_states: A tensor representing the input hidden states.

        Returns:
            hidden_states: The method returns the forwarded hidden states as a tensor.

        Raises:
            None.
        """
        hidden_states = hidden_states.permute(0, 3, 1, 2)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm1(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        return hidden_states

mindnlp.transformers.models.sam.modeling_sam.SamVisionNeck.__init__(config)

Initialize the SamVisionNeck class.

PARAMETER DESCRIPTION
self

The instance of the class.
config

An instance of SamVisionConfig containing the configuration for the SamVisionNeck. It defines the parameters required for the layers in the network.

TYPE: SamVisionConfig

RETURNS DESCRIPTION

None.
RAISES DESCRIPTION
ValueError

If the configuration provided is invalid or incomplete.
TypeError

If the configuration data type is not as expected.
RuntimeError

If there is an issue during the initialization of the layers.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def __init__(self, config: SamVisionConfig):
    """
    Initialize the SamVisionNeck class.

    Args:
        self: The instance of the class.
        config (SamVisionConfig): An instance of SamVisionConfig containing the configuration for the SamVisionNeck.
            It defines the parameters required for the layers in the network.

    Returns:
        None.

    Raises:
        ValueError: If the configuration provided is invalid or incomplete.
        TypeError: If the configuration data type is not as expected.
        RuntimeError: If there is an issue during the initialization of the layers.
    """
    super().__init__()
    self.config = config

    self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False, pad_mode='valid')
    self.layer_norm1 = SamLayerNorm(config.output_channels, data_format="channels_first")
    self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False, pad_mode='pad')
    self.layer_norm2 = SamLayerNorm(config.output_channels, data_format="channels_first")

mindnlp.transformers.models.sam.modeling_sam.SamVisionNeck.forward(hidden_states)

Constructs the hidden states in the SamVisionNeck class.

This method takes in two parameters: self and hidden_states. The hidden_states parameter represents the input hidden states and should be a tensor. The purpose of this parameter is to provide the input for forwarding the hidden states. There are no restrictions on the shape or size of the hidden_states tensor.

The method performs the following operations on the hidden_states:

  1. Permute the dimensions of the hidden_states tensor using the permute() function, with the dimensions permuted as (0, 3, 1, 2).
  2. Apply the conv1 layer to the permuted hidden_states tensor.
  3. Apply the layer_norm1 layer to the output of the conv1 layer.
  4. Apply the conv2 layer to the output of the layer_norm1 layer.
  5. Apply the layer_norm2 layer to the output of the conv2 layer.

The method returns the final forwarded hidden states tensor.

PARAMETER DESCRIPTION
self

An instance of the SamVisionNeck class.
hidden_states

A tensor representing the input hidden states.
RETURNS DESCRIPTION
hidden_states

The method returns the forwarded hidden states as a tensor.
Source code in mindnlp/transformers/models/sam/modeling_sam.py 
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def forward(self, hidden_states):
    """Constructs the hidden states in the SamVisionNeck class.

    This method takes in two parameters: self and hidden_states. The hidden_states parameter represents the
    input hidden states and should be a tensor. The purpose of this parameter is to provide the input
    for forwarding the hidden states. There are no restrictions on the shape or size of the hidden_states tensor.

    The method performs the following operations on the hidden_states:

    1. Permute the dimensions of the hidden_states tensor using the permute() function,
    with the dimensions permuted as (0, 3, 1, 2).
    2. Apply the conv1 layer to the permuted hidden_states tensor.
    3. Apply the layer_norm1 layer to the output of the conv1 layer.
    4. Apply the conv2 layer to the output of the layer_norm1 layer.
    5. Apply the layer_norm2 layer to the output of the conv2 layer.

    The method returns the final forwarded hidden states tensor.

    Args:
        self: An instance of the SamVisionNeck class.
        hidden_states: A tensor representing the input hidden states.

    Returns:
        hidden_states: The method returns the forwarded hidden states as a tensor.

    Raises:
        None.
    """
    hidden_states = hidden_states.permute(0, 3, 1, 2)
    hidden_states = self.conv1(hidden_states)
    hidden_states = self.layer_norm1(hidden_states)

    hidden_states = self.conv2(hidden_states)
    hidden_states = self.layer_norm2(hidden_states)
    return hidden_states

mindnlp.transformers.models.sam.processing_sam

Processor class for SAM.

mindnlp.transformers.models.sam.processing_sam.SamProcessor

Bases: ProcessorMixin

Constructs a SAM processor which wraps a SAM image processor and an 2D points & Bounding boxes processor into a single processor.

[SamProcessor] offers all the functionalities of [SamImageProcessor]. See the docstring of [~SamImageProcessor.__call__] for more information.

PARAMETER DESCRIPTION
image_processor

An instance of [SamImageProcessor]. The image processor is a required input.

TYPE: `SamImageProcessor`

Source code in mindnlp/transformers/models/sam/processing_sam.py 
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class SamProcessor(ProcessorMixin):
    r"""
    Constructs a SAM processor which wraps a SAM image processor and an 2D points & Bounding boxes processor into a
    single processor.

    [`SamProcessor`] offers all the functionalities of [`SamImageProcessor`]. See the docstring of
    [`~SamImageProcessor.__call__`] for more information.

    Args:
        image_processor (`SamImageProcessor`):
            An instance of [`SamImageProcessor`]. The image processor is a required input.
    """
    attributes = ["image_processor"]
    image_processor_class = "SamImageProcessor"

    def __init__(self, image_processor):
        """
        Initializes a new instance of the SamProcessor class.

        Args:
            self: The instance of the SamProcessor class.
            image_processor: An image processor object used for image processing operations.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(image_processor)
        self.current_processor = self.image_processor
        self.point_pad_value = -10
        self.target_size = self.image_processor.size["longest_edge"]

    def __call__(
        self,
        images=None,
        segmentation_maps=None,
        input_points=None,
        input_labels=None,
        input_boxes=None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchEncoding:
        """
        This method uses [`SamImageProcessor.__call__`] method to prepare image(s) for the model. It also prepares 2D
        points and bounding boxes for the model if they are provided.
        """
        encoding_image_processor = self.image_processor(
            images,
            segmentation_maps=segmentation_maps,
            return_tensors=return_tensors,
            **kwargs,
        )

        # pop arguments that are not used in the foward but used nevertheless
        original_sizes = encoding_image_processor["original_sizes"]

        if hasattr(original_sizes, "asnumpy"):  # Checks if MindSpore tensor
            original_sizes = original_sizes.asnumpy()

        input_points, input_labels, input_boxes = self._check_and_preprocess_points(
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
        )
        encoding_image_processor = self._normalize_and_convert(
            encoding_image_processor,
            original_sizes,
            input_points=input_points,
            input_labels=input_labels,
            input_boxes=input_boxes,
            return_tensors=return_tensors,
        )

        return encoding_image_processor

    def _normalize_and_convert(
        self,
        encoding_image_processor,
        original_sizes,
        input_points=None,
        input_labels=None,
        input_boxes=None,
        return_tensors="ms",
    ):
        """Normalize and convert input data for encoding image processing.

        Args:
            self: SamProcessor
                The instance of the SamProcessor class.
            encoding_image_processor: object
                The encoding image processor object.
            original_sizes: list
                A list containing the original sizes of the input data.
            input_points: list, optional
                A list of input points to be processed. Defaults to None.
            input_labels: list, optional
                A list of input labels to be processed. Defaults to None.
            input_boxes: list, optional
                A list of input boxes to be processed. Defaults to None.
            return_tensors: str
                A string indicating the type of return tensors. Allowed values are: 'ms' for MindSpore tensors.

        Returns:
            None: This method does not return a value. The input encoding_image_processor is updated with
                the processed input data.

        Raises:
            ValueError: If the length of original_sizes does not match the length of input_points or input_boxes.
            ValueError: If input_points and input_labels are not of the same shape.
        """
        if input_points is not None:
            if len(original_sizes) != len(input_points):
                input_points = [
                    self._normalize_coordinates(self.target_size, point, original_sizes[0]) for point in input_points
                ]
            else:
                input_points = [
                    self._normalize_coordinates(self.target_size, point, original_size)
                    for point, original_size in zip(input_points, original_sizes)
                ]
            # check that all arrays have the same shape
            if not all(point.shape == input_points[0].shape for point in input_points):
                if input_labels is not None:
                    input_points, input_labels = self._pad_points_and_labels(input_points, input_labels)

            input_points = np.array(input_points)

        if input_labels is not None:
            input_labels = np.array(input_labels)

        if input_boxes is not None:
            if len(original_sizes) != len(input_boxes):
                input_boxes = [
                    self._normalize_coordinates(self.target_size, box, original_sizes[0], is_bounding_box=True)
                    for box in input_boxes
                ]
            else:
                input_boxes = [
                    self._normalize_coordinates(self.target_size, box, original_size, is_bounding_box=True)
                    for box, original_size in zip(input_boxes, original_sizes)
                ]
            input_boxes = np.array(input_boxes)

        if input_boxes is not None:
            if return_tensors == "ms":
                input_boxes = mindspore.tensor(input_boxes)
                # boxes batch size of 1 by default
                input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
            encoding_image_processor.update({"input_boxes": input_boxes})
        if input_points is not None:
            if return_tensors == "ms":
                input_points = mindspore.tensor(input_points, mindspore.float32)
                # point batch size of 1 by default
                input_points = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
            encoding_image_processor.update({"input_points": input_points})
        if input_labels is not None:
            if return_tensors == "ms":
                input_labels = mindspore.tensor(input_labels)
                # point batch size of 1 by default
                input_labels = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
            encoding_image_processor.update({"input_labels": input_labels})

        return encoding_image_processor

    def _pad_points_and_labels(self, input_points, input_labels):
        r"""
        The method pads the 2D points and labels to the maximum number of points in the batch.
        """
        expected_nb_points = max(point.shape[0] for point in input_points)
        processed_input_points = []
        for i, point in enumerate(input_points):
            if point.shape[0] != expected_nb_points:
                point = np.concatenate(
                    [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value], axis=0
                )
                input_labels[i] = np.append(input_labels[i], [self.point_pad_value])
            processed_input_points.append(point)
        input_points = processed_input_points
        return input_points, input_labels

    def _normalize_coordinates(
        self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False
    ) -> np.ndarray:
        """
        Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format.
        """
        old_h, old_w = original_size
        new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size)
        coords = deepcopy(coords).astype(float)

        if is_bounding_box:
            coords = coords.reshape(-1, 2, 2)

        coords[..., 0] = coords[..., 0] * (new_w / old_w)
        coords[..., 1] = coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
            coords = coords.reshape(-1, 4)

        return coords

    def _check_and_preprocess_points(
        self,
        input_points=None,
        input_labels=None,
        input_boxes=None,
    ):
        r"""
        Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they
        are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`,
        it is converted to a `numpy.ndarray` and then to a `list`.
        """
        if input_points is not None:
            if hasattr(input_points, "asnumpy"):  # Checks for MindSpore tensor
                input_points = input_points.asnumpy().tolist()

            if not isinstance(input_points, list) or not isinstance(input_points[0], list):
                raise ValueError("Input points must be a list of list of floating points.")
            input_points = [np.array(input_point) for input_point in input_points]
        else:
            input_points = None

        if input_labels is not None:
            if hasattr(input_labels, "numpy"):
                input_labels = input_labels.numpy().tolist()

            if not isinstance(input_labels, list) or not isinstance(input_labels[0], list):
                raise ValueError("Input labels must be a list of list integers.")
            input_labels = [np.array(label) for label in input_labels]
        else:
            input_labels = None

        if input_boxes is not None:
            if hasattr(input_boxes, "numpy"):
                input_boxes = input_boxes.numpy().tolist()

            if (
                not isinstance(input_boxes, list)
                or not isinstance(input_boxes[0], list)
                or not isinstance(input_boxes[0][0], list)
            ):
                raise ValueError("Input boxes must be a list of list of list of floating points.")
            input_boxes = [np.array(box).astype(np.float32) for box in input_boxes]
        else:
            input_boxes = None

        return input_points, input_labels, input_boxes

    @property
    def model_input_names(self):
        """
        This method returns a list of unique model input names used in the SamProcessor class.

        Args:
            self (SamProcessor): The instance of the SamProcessor class.

        Returns:
            list: A list of unique model input names extracted from the image processor.

        Raises:
            None.
        """
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(image_processor_input_names))

    def post_process_masks(self, *args, **kwargs):
        """
        Post-processes masks using the image processor.

        Args:
            self: The instance of the SamProcessor class.

        Returns:
            None.

        Raises:
            Any exceptions raised by the image_processor.post_process_masks method may be propagated from this method.
        """
        return self.image_processor.post_process_masks(*args, **kwargs)

mindnlp.transformers.models.sam.processing_sam.SamProcessor.model_input_names property

This method returns a list of unique model input names used in the SamProcessor class.

PARAMETER DESCRIPTION
self

The instance of the SamProcessor class.

TYPE: SamProcessor

RETURNS DESCRIPTION
list

A list of unique model input names extracted from the image processor.

mindnlp.transformers.models.sam.processing_sam.SamProcessor.__call__(images=None, segmentation_maps=None, input_points=None, input_labels=None, input_boxes=None, return_tensors=None, **kwargs)

This method uses [SamImageProcessor.__call__] method to prepare image(s) for the model. It also prepares 2D points and bounding boxes for the model if they are provided.

Source code in mindnlp/transformers/models/sam/processing_sam.py 
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def __call__(
    self,
    images=None,
    segmentation_maps=None,
    input_points=None,
    input_labels=None,
    input_boxes=None,
    return_tensors: Optional[Union[str, TensorType]] = None,
    **kwargs,
) -> BatchEncoding:
    """
    This method uses [`SamImageProcessor.__call__`] method to prepare image(s) for the model. It also prepares 2D
    points and bounding boxes for the model if they are provided.
    """
    encoding_image_processor = self.image_processor(
        images,
        segmentation_maps=segmentation_maps,
        return_tensors=return_tensors,
        **kwargs,
    )

    # pop arguments that are not used in the foward but used nevertheless
    original_sizes = encoding_image_processor["original_sizes"]

    if hasattr(original_sizes, "asnumpy"):  # Checks if MindSpore tensor
        original_sizes = original_sizes.asnumpy()

    input_points, input_labels, input_boxes = self._check_and_preprocess_points(
        input_points=input_points,
        input_labels=input_labels,
        input_boxes=input_boxes,
    )
    encoding_image_processor = self._normalize_and_convert(
        encoding_image_processor,
        original_sizes,
        input_points=input_points,
        input_labels=input_labels,
        input_boxes=input_boxes,
        return_tensors=return_tensors,
    )

    return encoding_image_processor

mindnlp.transformers.models.sam.processing_sam.SamProcessor.__init__(image_processor)

Initializes a new instance of the SamProcessor class.

PARAMETER DESCRIPTION
self

The instance of the SamProcessor class.
image_processor

An image processor object used for image processing operations.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/processing_sam.py 
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def __init__(self, image_processor):
    """
    Initializes a new instance of the SamProcessor class.

    Args:
        self: The instance of the SamProcessor class.
        image_processor: An image processor object used for image processing operations.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(image_processor)
    self.current_processor = self.image_processor
    self.point_pad_value = -10
    self.target_size = self.image_processor.size["longest_edge"]

mindnlp.transformers.models.sam.processing_sam.SamProcessor.post_process_masks(*args, **kwargs)

Post-processes masks using the image processor.

PARAMETER DESCRIPTION
self

The instance of the SamProcessor class.
RETURNS DESCRIPTION

None.
Source code in mindnlp/transformers/models/sam/processing_sam.py 
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def post_process_masks(self, *args, **kwargs):
    """
    Post-processes masks using the image processor.

    Args:
        self: The instance of the SamProcessor class.

    Returns:
        None.

    Raises:
        Any exceptions raised by the image_processor.post_process_masks method may be propagated from this method.
    """
    return self.image_processor.post_process_masks(*args, **kwargs)