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configuration_efficientformer.py
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configuration_efficientformer.py
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# 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