# coding=utf-8 # Copyright 2022 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. """ EfficientFormer model configuration""" from typing import List from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "snap-research/efficientformer-l1-300": ( "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json" ), } class EfficientFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of an [`EfficientFormerModel`]. It is used to instantiate an EfficientFormer 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 EfficientFormer [snap-research/efficientformer-l1](https://huggingface.co/snap-research/efficientformer-l1) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: depths (`List(int)`, *optional*, defaults to `[3, 2, 6, 4]`) Depth of each stage. hidden_sizes (`List(int)`, *optional*, defaults to `[48, 96, 224, 448]`) Dimensionality of each stage. downsamples (`List(bool)`, *optional*, defaults to `[True, True, True, True]`) Whether or not to downsample inputs between two stages. dim (`int`, *optional*, defaults to 448): Number of channels in Meta3D layers key_dim (`int`, *optional*, defaults to 32): The size of the key in meta3D block. attention_ratio (`int`, *optional*, defaults to 4): Ratio of the dimension of the query and value to the dimension of the key in MSHA block resolution (`int`, *optional*, defaults to 7) Size of each patch num_hidden_layers (`int`, *optional*, defaults to 5): 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 3D MetaBlock. mlp_expansion_ratio (`int`, *optional*, defaults to 4): Ratio of size of the hidden dimensionality of an MLP to the dimensionality of its input. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings and encoder. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. pool_size (`int`, *optional*, defaults to 3): Kernel size of pooling layers. downsample_patch_size (`int`, *optional*, defaults to 3): The size of patches in downsampling layers. downsample_stride (`int`, *optional*, defaults to 2): The stride of convolution kernels in downsampling layers. downsample_pad (`int`, *optional*, defaults to 1): Padding in downsampling layers. drop_path_rate (`int`, *optional*, defaults to 0): Rate at which to increase dropout probability in DropPath. num_meta3d_blocks (`int`, *optional*, defaults to 1): The number of 3D MetaBlocks in the last stage. distillation (`bool`, *optional*, defaults to `True`): Whether to add a distillation head. use_layer_scale (`bool`, *optional*, defaults to `True`): Whether to scale outputs from token mixers. layer_scale_init_value (`float`, *optional*, defaults to 1e-5): Factor by which outputs from token mixers are scaled. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. 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. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to `224`): The size (resolution) of each image. Example: ```python >>> from transformers import EfficientFormerConfig, EfficientFormerModel >>> # Initializing a EfficientFormer efficientformer-l1 style configuration >>> configuration = EfficientFormerConfig() >>> # Initializing a EfficientFormerModel (with random weights) from the efficientformer-l3 style configuration >>> model = EfficientFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "efficientformer" def __init__( self, depths: List[int] = [3, 2, 6, 4], hidden_sizes: List[int] = [48, 96, 224, 448], downsamples: List[bool] = [True, True, True, True], dim: int = 448, key_dim: int = 32, attention_ratio: int = 4, resolution: int = 7, num_hidden_layers: int = 5, num_attention_heads: int = 8, mlp_expansion_ratio: int = 4, hidden_dropout_prob: float = 0.0, patch_size: int = 16, num_channels: int = 3, pool_size: int = 3, downsample_patch_size: int = 3, downsample_stride: int = 2, downsample_pad: int = 1, drop_path_rate: float = 0.0, num_meta3d_blocks: int = 1, distillation: bool = True, use_layer_scale: bool = True, layer_scale_init_value: float = 1e-5, hidden_act: str = "gelu", initializer_range: float = 0.02, layer_norm_eps: float = 1e-12, image_size: int = 224, batch_norm_eps: float = 1e-05, **kwargs, ) -> None: super().__init__(**kwargs) self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.hidden_sizes = hidden_sizes self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.patch_size = patch_size self.num_channels = num_channels self.depths = depths self.mlp_expansion_ratio = mlp_expansion_ratio self.downsamples = downsamples self.dim = dim self.key_dim = key_dim self.attention_ratio = attention_ratio self.resolution = resolution self.pool_size = pool_size self.downsample_patch_size = downsample_patch_size self.downsample_stride = downsample_stride self.downsample_pad = downsample_pad self.drop_path_rate = drop_path_rate self.num_meta3d_blocks = num_meta3d_blocks self.distillation = distillation self.use_layer_scale = use_layer_scale self.layer_scale_init_value = layer_scale_init_value self.image_size = image_size self.batch_norm_eps = batch_norm_eps