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GLPN

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This model was published in HF papers on 2022-01-19 and contributed to Hugging Face Transformers on 2022-03-22.

GLPN

This is a recently introduced model so the API hasn’t been tested extensively. There may be some bugs or slight breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Overview

The GLPN model was proposed in Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim. GLPN combines SegFormer’s hierarchical mix-Transformer with a lightweight decoder for monocular depth estimation. The proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity.

The abstract from the paper is the following:

Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks. In this paper, we propose a novel structure and training strategy for monocular depth estimation to further improve the prediction accuracy of the network. We deploy a hierarchical transformer encoder to capture and convey the global context, and design a lightweight yet powerful decoder to generate an estimated depth map while considering local connectivity. By constructing connected paths between multi-scale local features and the global decoding stream with our proposed selective feature fusion module, the network can integrate both representations and recover fine details. In addition, the proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity. Furthermore, we improve the depth-specific augmentation method by utilizing an important observation in depth estimation to enhance the model. Our network achieves state-of-the-art performance over the challenging depth dataset NYU Depth V2. Extensive experiments have been conducted to validate and show the effectiveness of the proposed approach. Finally, our model shows better generalisation ability and robustness than other comparative models.

Summary of the approach. Taken from the original paper.

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

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GLPN.

Demo notebooks for GLPNForDepthEstimation can be found here.
Monocular depth estimation task guide

GLPNConfig

class transformers.GLPNConfig

< source >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonenum_channels: int = 3num_encoder_blocks: int = 4depths: list[int] | tuple[int, ...] = (2, 2, 2, 2)sr_ratios: list[int] | tuple[int, ...] = (8, 4, 2, 1)hidden_sizes: list[int] | tuple[int, ...] = (32, 64, 160, 256)patch_sizes: list[int] | tuple[int, ...] = (7, 3, 3, 3)strides: list[int] | tuple[int, ...] = (4, 2, 2, 2)num_attention_heads: list[int] | tuple[int, ...] = (1, 2, 5, 8)mlp_ratios: list[int] | tuple[int, ...] = (4, 4, 4, 4)hidden_act: str = 'gelu'hidden_dropout_prob: float | int = 0.0attention_probs_dropout_prob: float | int = 0.0initializer_range: float = 0.02drop_path_rate: float | int = 0.1layer_norm_eps: float = 1e-06decoder_hidden_size: int = 64max_depth: int = 10head_in_index: int = -1 )

Parameters

num_channels (int, optional, defaults to 3) — The number of input channels.
num_encoder_blocks (int, optional, defaults to 4) — The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (list[int], optional, defaults to [2, 2, 2, 2]) — The number of layers in each encoder block.
sr_ratios (list[int], optional, defaults to [8, 4, 2, 1]) — Sequence reduction ratios in each encoder block.
hidden_sizes (Union[list[int], tuple[int, ...]], optional, defaults to (32, 64, 160, 256)) — Dimensionality (hidden size) at each stage of the model.
patch_sizes (list[int], optional, defaults to [7, 3, 3, 3]) — Patch size before each encoder block.
strides (list[int], optional, defaults to [4, 2, 2, 2]) — Stride before each encoder block.
num_attention_heads (list[int], optional, defaults to [1, 2, 5, 8]) — Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (list[int], optional, defaults to [4, 4, 4, 4]) — Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks.
hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
hidden_dropout_prob (Union[float, int], optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (Union[float, int], optional, defaults to 0.1) — Drop path rate for the patch fusion.
layer_norm_eps (float, optional, defaults to 1e-06) — The epsilon used by the layer normalization layers.
decoder_hidden_size (int, optional, defaults to 64) — The dimension of the decoder.
max_depth (int, optional, defaults to 10) — The maximum depth of the decoder.
head_in_index (int, optional, defaults to -1) — The index of the features to use in the head.

This is the configuration class to store the configuration of a GLPNModel. It is used to instantiate a Glpn model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vinvino02/glpn-kitti

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

Example:

>>> from transformers import GLPNModel, GLPNConfig

>>> # Initializing a GLPN vinvino02/glpn-kitti style configuration
>>> configuration = GLPNConfig()

>>> # Initializing a model from the vinvino02/glpn-kitti style configuration
>>> model = GLPNModel(configuration)

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

GLPNImageProcessor

class transformers.GLPNImageProcessor

< source >

( **kwargs: Unpack )

Parameters

do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional, defaults to True) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional, defaults to True) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BILINEAR) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
size_divisor (int, kwargs, optional, defaults to 32) — When do_resize is True, images are resized so their height and width are rounded down to the closest multiple of size_divisor.

Constructs a GLPNImageProcessor image processor.

preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]**kwargs: Unpack ) → ~image_processing_base.BatchFeature

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — 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.
do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_rescale (bool, kwargs, optional) — Whether to rescale the image.
rescale_factor (float, kwargs, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
size_divisor (int, kwargs, optional, defaults to 32) — When do_resize is True, images are resized so their height and width are rounded down to the closest multiple of size_divisor.

Returns

~image_processing_base.BatchFeature

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

GLPNImageProcessorPil

class transformers.GLPNImageProcessorPil

< source >

( **kwargs: Unpack )

Parameters

do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional, defaults to True) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional, defaults to True) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BILINEAR) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
size_divisor (int, kwargs, optional, defaults to 32) — When do_resize is True, images are resized so their height and width are rounded down to the closest multiple of size_divisor.

Constructs a GLPNImageProcessor image processor.

preprocess

< source >

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — 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.
do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_rescale (bool, kwargs, optional) — Whether to rescale the image.
rescale_factor (float, kwargs, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
size_divisor (int, kwargs, optional, defaults to 32) — When do_resize is True, images are resized so their height and width are rounded down to the closest multiple of size_divisor.

Returns

~image_processing_base.BatchFeature

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

GLPNModel

class transformers.GLPNModel

< source >

( config )

Parameters

config (GLPNModel) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Glpn Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( pixel_values: FloatTensoroutput_attentions: bool | None = Noneoutput_hidden_states: bool | None = Nonereturn_dict: bool | None = None**kwargs ) → BaseModelOutput or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using GLPNImageProcessor. See GLPNImageProcessor.__call__() for details (processor_class uses GLPNImageProcessor for processing images).
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

BaseModelOutput or tuple(torch.FloatTensor)

A BaseModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (GLPNConfig) and inputs.

The GLPNModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

GLPNForDepthEstimation

class transformers.GLPNForDepthEstimation

< source >

( config )

Parameters

config (GLPNForDepthEstimation) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

GLPN Model transformer with a lightweight depth estimation head on top e.g. for KITTI, NYUv2.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( pixel_values: FloatTensorlabels: typing.Optional[torch.FloatTensor] = Noneoutput_attentions: bool | None = Noneoutput_hidden_states: bool | None = Nonereturn_dict: bool | None = None**kwargs ) → DepthEstimatorOutput or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using GLPNImageProcessor. See GLPNImageProcessor.__call__() for details (processor_class uses GLPNImageProcessor for processing images).
labels (torch.FloatTensor of shape (batch_size, height, width), optional) — Ground truth depth estimation maps for computing the loss.
output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

DepthEstimatorOutput or tuple(torch.FloatTensor)

A DepthEstimatorOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (GLPNConfig) and inputs.

The GLPNForDepthEstimation forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
predicted_depth (torch.FloatTensor of shape (batch_size, height, width)) — Predicted depth for each pixel.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, num_channels, height, width).

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Examples:

>>> from transformers import AutoImageProcessor, GLPNForDepthEstimation
>>> import torch
>>> import numpy as np
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti")
>>> model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti")

>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")

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

>>> # interpolate to original size
>>> post_processed_output = image_processor.post_process_depth_estimation(
...     outputs,
...     target_sizes=[(image.height, image.width)],
... )

>>> # visualize the prediction
>>> predicted_depth = post_processed_output[0]["predicted_depth"]
>>> depth = predicted_depth * 255 / predicted_depth.max()
>>> depth = depth.detach().cpu().numpy()
>>> depth = Image.fromarray(depth.astype("uint8"))

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