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configuration_focalnet.py
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configuration_focalnet.py
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# coding=utf-8
# Copyright 2023 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.
""" FocalNet 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 FocalNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FocalNetModel`]. It is used to instantiate a
FocalNet 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 FocalNet
[microsoft/focalnet-tiny](https://huggingface.co/microsoft/focalnet-tiny) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch in the embeddings layer.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 96):
Dimensionality of patch embedding.
use_conv_embed (`bool`, *optional*, defaults to `False`):
Whether to use convolutional embedding. The authors noted that using convolutional embedding usually
improve the performance, but it's not used by default.
hidden_sizes (`List[int]`, *optional*, defaults to `[192, 384, 768, 768]`):
Dimensionality (hidden size) at each stage.
depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
Depth (number of layers) of each stage in the encoder.
focal_levels (`list(int)`, *optional*, defaults to `[2, 2, 2, 2]`):
Number of focal levels in each layer of the respective stages in the encoder.
focal_windows (`list(int)`, *optional*, defaults to `[3, 3, 3, 3]`):
Focal window size in each layer of the respective stages in the encoder.
hidden_act (`str` or `function`, *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.
mlp_ratio (`float`, *optional*, defaults to 4.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
use_layerscale (`bool`, *optional*, defaults to `False`):
Whether to use layer scale in the encoder.
layerscale_value (`float`, *optional*, defaults to 0.0001):
The initial value of the layer scale.
use_post_layernorm (`bool`, *optional*, defaults to `False`):
Whether to use post layer normalization in the encoder.
use_post_layernorm_in_modulation (`bool`, *optional*, defaults to `False`):
Whether to use post layer normalization in the modulation layer.
normalize_modulator (`bool`, *optional*, defaults to `False`):
Whether to normalize the modulator.
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-05):
The epsilon used by the layer normalization layers.
encoder_stride (`int`, *optional*, defaults to 32):
Factor to increase the spatial resolution by in the decoder head for masked image modeling.
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 FocalNetConfig, FocalNetModel
>>> # Initializing a FocalNet microsoft/focalnet-tiny style configuration
>>> configuration = FocalNetConfig()
>>> # Initializing a model (with random weights) from the microsoft/focalnet-tiny style configuration
>>> model = FocalNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "focalnet"
def __init__(
self,
image_size=224,
patch_size=4,
num_channels=3,
embed_dim=96,
use_conv_embed=False,
hidden_sizes=[192, 384, 768, 768],
depths=[2, 2, 6, 2],
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
hidden_act="gelu",
mlp_ratio=4.0,
hidden_dropout_prob=0.0,
drop_path_rate=0.1,
use_layerscale=False,
layerscale_value=1e-4,
use_post_layernorm=False,
use_post_layernorm_in_modulation=False,
normalize_modulator=False,
initializer_range=0.02,
layer_norm_eps=1e-5,
encoder_stride=32,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.use_conv_embed = use_conv_embed
self.hidden_sizes = hidden_sizes
self.depths = depths
self.focal_levels = focal_levels
self.focal_windows = focal_windows
self.hidden_act = hidden_act
self.mlp_ratio = mlp_ratio
self.hidden_dropout_prob = hidden_dropout_prob
self.drop_path_rate = drop_path_rate
self.use_layerscale = use_layerscale
self.layerscale_value = layerscale_value
self.use_post_layernorm = use_post_layernorm
self.use_post_layernorm_in_modulation = use_post_layernorm_in_modulation
self.normalize_modulator = normalize_modulator
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.encoder_stride = encoder_stride
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.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
)