# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Hiera model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices logger = logging.get_logger(__name__) class HieraConfig(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera 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 Hiera [facebook/hiera-base-224](https://huggingface.co/facebook/hiera-base-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: embed_dim (`int`, *optional*, defaults to 96): Dimensionality of patch embedding. image_size (`list(int)`, *optional*, defaults to `[224, 224]`): The size (resolution) of input in the format (height, width) for images and (frames, height, width) for videos. patch_size (`list(int)`, *optional*, defaults to `[7, 7]`): The size (resolution) of each patch. patch_stride (`list(int)`, *optional*, defaults to `[4, 4]`): The stride of the patch. patch_padding (`list(int)`, *optional*, defaults to `[3, 3]`): The padding of the patch. mlp_ratio (`float`, *optional*, defaults to 4.0): The ratio of mlp hidden dim to embedding dim. depths (`list(int)`, *optional*, defaults to `[2, 3, 16, 3]`): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to `[1, 2, 4, 8]`): Number of attention heads in each layer of the Transformer encoder. embed_dim_multiplier (`float`, *optional*, defaults to 2.0): The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder. num_query_pool (`int`, *optional*, defaults to 3): The number of query pool stages. query_stride (`list(int)`, *optional*, defaults to `[2, 2]`): The stride of the query pool. masked_unit_size (`list(int)`, *optional*, defaults to `[8, 8]`): The size of the masked unit. masked_unit_attention (`list(bool)`, *optional*, defaults to `[True, True, False, False]`): Whether to use masked unit attention in each layer of the Transformer encoder. drop_path_rate (`float`, *optional*, defaults to 0.0): The drop path rate. num_channels (`int`, *optional*, defaults to 3): The number of input channels. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices and the zero_initializer for initializing all bias vectors. layer_norm_init (`float`, *optional*, defaults to 1.0): The initial weight value for layer normalization layers. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. decoder_hidden_size (`int`, *optional*): Dimensionality of decoder embeddings for MAE pretraining. decoder_depth (`int`, *optional*): Depth of the decoder for MAE pretraining. decoder_num_heads (`int`, *optional*): Number of attention heads in each layer of the decoder for MAE pretraining. normalize_pixel_loss (`bool`, *optional*, defaults to `True`): Whether to normalize the pixel loss by the number of pixels. mask_ratio (`float`, *optional*, defaults to 0.6): The ratio of masked tokens in the input. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import HieraConfig, HieraModel >>> # Initializing a Hiera hiera-base-patch16-224 style configuration >>> configuration = HieraConfig() >>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration >>> model = HieraModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "hiera" attribute_map = {"num_hidden_layers": "num_layers"} def __init__( self, embed_dim=96, image_size=[224, 224], patch_size=[7, 7], patch_stride=[4, 4], patch_padding=[3, 3], mlp_ratio=4.0, depths=[2, 3, 16, 3], num_heads=[1, 2, 4, 8], embed_dim_multiplier=2.0, num_query_pool=3, query_stride=[2, 2], masked_unit_size=[8, 8], masked_unit_attention=[True, True, False, False], drop_path_rate=0.0, num_channels=3, hidden_act="gelu", initializer_range=0.02, layer_norm_init=1.0, layer_norm_eps=1e-6, decoder_hidden_size=None, decoder_depth=None, decoder_num_heads=None, normalize_pixel_loss=True, mask_ratio=0.6, out_features=None, out_indices=None, **kwargs, ): super().__init__(**kwargs) if masked_unit_size[0] % query_stride[0] ** (len(depths) - 1) != 0: raise ValueError( f"masked_unit_size[0] ({masked_unit_size[0]}) must be divisible by query_stride[0] ({query_stride[0]}) " f"raised to the power of the number of layers ({len(depths) - 1})" ) if num_query_pool >= len(depths): raise ValueError( f"num_query_pool ({num_query_pool}) must be less than the number of layers ({len(depths)})" ) self.embed_dim = embed_dim self.image_size = image_size self.patch_size = patch_size self.patch_stride = patch_stride self.patch_padding = patch_padding self.mlp_ratio = mlp_ratio self.depths = depths self.num_heads = num_heads self.num_layers = len(depths) self.embed_dim_multiplier = embed_dim_multiplier self.num_query_pool = num_query_pool self.query_stride = query_stride self.masked_unit_size = masked_unit_size self.masked_unit_attention = masked_unit_attention self.drop_path_rate = drop_path_rate self.num_channels = num_channels self.hidden_act = hidden_act self.initializer_range = initializer_range self.layer_norm_init = layer_norm_init self.layer_norm_eps = layer_norm_eps self.decoder_hidden_size = decoder_hidden_size self.decoder_depth = decoder_depth self.decoder_num_heads = decoder_num_heads self.normalize_pixel_loss = normalize_pixel_loss self.mask_ratio = mask_ratio # we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model self.hidden_size = int(embed_dim * embed_dim_multiplier ** (len(depths) - 1)) self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)] self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names ) __all__ = ["HieraConfig"]