# TextNet ## Overview The TextNet model was proposed in [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://huggingface.co/papers/2111.02394) by Zhe Chen, Jiahao Wang, Wenhai Wang, Guo Chen, Enze Xie, Ping Luo, Tong Lu. TextNet is a vision backbone useful for text detection tasks. It is the result of neural architecture search (NAS) on backbones with reward function as text detection task (to provide powerful features for text detection). drawing TextNet backbone as part of FAST. Taken from the original paper. This model was contributed by [Raghavan](https://huggingface.co/Raghavan), [jadechoghari](https://huggingface.co/jadechoghari) and [nielsr](https://huggingface.co/nielsr). ## Usage tips TextNet is mainly used as a backbone network for the architecture search of text detection. Each stage of the backbone network is comprised of a stride-2 convolution and searchable blocks. Specifically, we present a layer-level candidate set, defined as {conv3×3, conv1×3, conv3×1, identity}. As the 1×3 and 3×1 convolutions have asymmetric kernels and oriented structure priors, they may help to capture the features of extreme aspect-ratio and rotated text lines. TextNet is the backbone for Fast, but can also be used as an efficient text/image classification, we add a `TextNetForImageClassification` as is it would allow people to train an image classifier on top of the pre-trained textnet weights ## TextNetConfig[[transformers.TextNetConfig]] #### transformers.TextNetConfig[[transformers.TextNetConfig]] ```python transformers.TextNetConfig(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None, chunk_size_feed_forward: int = 0, is_encoder_decoder: bool = False, id2label: dict[int, str] | dict[str, str] | None = None, label2id: dict[str, int] | dict[str, str] | None = None, problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None, stem_kernel_size: int = 3, stem_stride: int = 2, stem_num_channels: int = 3, stem_out_channels: int = 64, stem_act_func: str = 'relu', image_size: list[int] | tuple[int, int] | int = (640, 640), conv_layer_kernel_sizes: list | None = None, conv_layer_strides: list | None = None, hidden_sizes: list[int] | tuple[int, ...] = (64, 64, 128, 256, 512), batch_norm_eps: float = 1e-05, initializer_range: float = 0.02, _out_features: list[str] | None = None, _out_indices: list[int] | None = None) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/configuration_textnet.py#L25) **Parameters:** stem_kernel_size (`int`, *optional*, defaults to 3) : The kernel size for the initial convolution layer. stem_stride (`int`, *optional*, defaults to 2) : The stride for the initial convolution layer. stem_num_channels (`int`, *optional*, defaults to 3) : The num of channels in input for the initial convolution layer. stem_out_channels (`int`, *optional*, defaults to 64) : The num of channels in out for the initial convolution layer. stem_act_func (`str`, *optional*, defaults to `"relu"`) : The activation function for the initial convolution layer. image_size (`Union[list[int], tuple[int, int], int]`, *optional*, defaults to `(640, 640)`) : The size (resolution) of each image. conv_layer_kernel_sizes (`list[list[list[int]]]`, *optional*) : A list of stage-wise kernel sizes. If `None`, defaults to: `[[[3, 3], [3, 3], [3, 3]], [[3, 3], [1, 3], [3, 3], [3, 1]], [[3, 3], [3, 3], [3, 1], [1, 3]], [[3, 3], [3, 1], [1, 3], [3, 3]]]`. conv_layer_strides (`list[list[int]]`, *optional*) : A list of stage-wise strides. If `None`, defaults to: `[[1, 2, 1], [2, 1, 1, 1], [2, 1, 1, 1], [2, 1, 1, 1]]`. hidden_sizes (`Union[list[int], tuple[int, ...]]`, *optional*, defaults to `(64, 64, 128, 256, 512)`) : Dimensionality (hidden size) at each stage of the model. batch_norm_eps (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the batch normalization layers. initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. This is the configuration class to store the configuration of a TextNetModel. It is used to instantiate a Textnet 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 [czczup/textnet-base](https://huggingface.co/czczup/textnet-base) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Examples: ```python >>> from transformers import TextNetConfig, TextNetBackbone >>> # Initializing a TextNetConfig >>> configuration = TextNetConfig() >>> # Initializing a model (with random weights) >>> model = TextNetBackbone(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` ## TextNetImageProcessor[[transformers.TextNetImageProcessor]] #### transformers.TextNetImageProcessor[[transformers.TextNetImageProcessor]] ```python transformers.TextNetImageProcessor(**kwargs: Unpack) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/image_processing_textnet.py#L44) **Parameters:** do_convert_rgb (`bool`, *kwargs*, *optional*, defaults to `True`) : 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*, defaults to `{'shortest_edge' : 640}`): Describes the maximum input dimensions to the model. default_to_square (`bool`, *kwargs*, *optional*, defaults to `False`) : 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*, defaults to `{'height' : 224, 'width': 224}`): 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*, defaults to `True`) : Whether to normalize the image. image_mean (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*, defaults to `[0.485, 0.456, 0.406]`) : 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*, defaults to `[0.229, 0.224, 0.225]`) : 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*, defaults to `False`) : 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 `self.size_divisor`) : Ensures height and width are rounded to a multiple of this value after resizing. Constructs a TextNetImageProcessor image processor. #### preprocess[[transformers.TextNetImageProcessor.preprocess]] ```python preprocess(images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']], **kwargs: Unpack) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/image_processing_textnet.py#L65) **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 `self.size_divisor`) : Ensures height and width are rounded to a multiple of this value after resizing. **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. ## TextNetImageProcessorPil[[transformers.TextNetImageProcessorPil]] #### transformers.TextNetImageProcessorPil[[transformers.TextNetImageProcessorPil]] ```python transformers.TextNetImageProcessorPil(**kwargs: Unpack) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/image_processing_pil_textnet.py#L45) **Parameters:** do_convert_rgb (`bool`, *kwargs*, *optional*, defaults to `True`) : 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*, defaults to `{'shortest_edge' : 640}`): Describes the maximum input dimensions to the model. default_to_square (`bool`, *kwargs*, *optional*, defaults to `False`) : 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*, defaults to `{'height' : 224, 'width': 224}`): 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*, defaults to `True`) : Whether to normalize the image. image_mean (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*, defaults to `[0.485, 0.456, 0.406]`) : 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*, defaults to `[0.229, 0.224, 0.225]`) : 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*, defaults to `False`) : 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 `self.size_divisor`) : Ensures height and width are rounded to a multiple of this value after resizing. Constructs a TextNetImageProcessor image processor. #### preprocess[[transformers.TextNetImageProcessorPil.preprocess]] ```python preprocess(images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']], **kwargs: Unpack) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/image_processing_pil_textnet.py#L66) **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 `self.size_divisor`) : Ensures height and width are rounded to a multiple of this value after resizing. **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. ## TextNetModel[[transformers.TextNetModel]] #### transformers.TextNetModel[[transformers.TextNetModel]] ```python transformers.TextNetModel(config) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/modeling_textnet.py#L224) **Parameters:** config ([TextNetModel](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetModel)) : 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()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. The bare Textnet Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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[[transformers.TextNetModel.forward]] ```python forward(pixel_values: Tensor, output_hidden_states: bool | None = None, return_dict: bool | None = None, **kwargs) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/modeling_textnet.py#L232) **Parameters:** pixel_values (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. Pixel values can be obtained using [TextNetImageProcessor](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetImageProcessor). See `TextNetImageProcessor.__call__()` for details (`processor_class` uses [TextNetImageProcessor](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetImageProcessor) for processing images). 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](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. **Returns:** `BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)` A `BaseModelOutputWithPoolingAndNoAttention` 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 ([TextNetConfig](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetConfig)) and inputs. The [TextNetModel](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetModel) 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, num_channels, height, width)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state after a pooling operation on the spatial dimensions. - **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. ## TextNetForImageClassification[[transformers.TextNetForImageClassification]] #### transformers.TextNetForImageClassification[[transformers.TextNetForImageClassification]] ```python transformers.TextNetForImageClassification(config) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/modeling_textnet.py#L271) **Parameters:** config ([TextNetForImageClassification](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetForImageClassification)) : 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()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. TextNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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[[transformers.TextNetForImageClassification.forward]] ```python forward(pixel_values: typing.Optional[torch.FloatTensor] = None, labels: typing.Optional[torch.LongTensor] = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, **kwargs) ``` [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/textnet/modeling_textnet.py#L286) **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) : The tensors corresponding to the input images. Pixel values can be obtained using [TextNetImageProcessor](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetImageProcessor). See `TextNetImageProcessor.__call__()` for details (`processor_class` uses [TextNetImageProcessor](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetImageProcessor) for processing images). labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*) : Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). 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](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. **Returns:** [ImageClassifierOutputWithNoAttention](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) or `tuple(torch.FloatTensor)` A [ImageClassifierOutputWithNoAttention](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) 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 ([TextNetConfig](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetConfig)) and inputs. The [TextNetForImageClassification](/docs/transformers/main/en/model_doc/textnet#transformers.TextNetForImageClassification) 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. - **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax). - **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 stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. Examples: ```python >>> import torch >>> import httpx >>> from io import BytesIO >>> from transformers import TextNetForImageClassification, TextNetImageProcessor >>> from PIL import Image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base") >>> model = TextNetForImageClassification.from_pretrained("czczup/textnet-base") >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> outputs.logits.shape torch.Size([1, 2]) ```