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# --------------------------------------------------------
# InternImage
# Copyright (c) 2025 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from transformers import PretrainedConfig
class InternImageConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~InternImageModel`].
It is used to instantiate an internimage 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 internimage [OpenGVLab/internimage](https://huggingface.co/OpenGVLab/internimage) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used
to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
core_op (`str`, *optional*, defaults to `"DCNv3"`):
Core operation used in the InternImageModel.
depths (`tuple`, *optional*, defaults to `(4, 4, 18, 4)`):
Tuple specifying the depth of layers in the InternImageModel.
groups (`tuple`, *optional*, defaults to `(4, 8, 16, 32)`):
Tuple specifying the group of layers in the InternImageModel.
channels (`int`, *optional*, defaults to `64`):
Number of channels in the InternImageModel.
dw_kernel_size (`int`, *optional*, defaults to `None`):
Kernel size for depthwise convolutions.
layer_scale (`float`, *optional*, defaults to `None`):
Scale of the layers in the model.
offset_scale (`float`, *optional*, defaults to `1.0`):
Offset scale in the model.
mlp_ratio (`float`, *optional*, defaults to `4.0`):
Ratio of mlp layers in the InternImageModel.
post_norm (`bool`, *optional*, defaults to `False`):
Whether to use post normalization in the model.
level2_post_norm (`bool`, *optional*, defaults to `False`):
Whether to use level 2 post normalization.
level2_post_norm_block_ids (`list`, *optional*, defaults to `None`):
Specific block IDs for level 2 post normalization.
center_feature_scale (`bool`, *optional*, defaults to `False`):
Whether to apply center feature scaling.
use_clip_projector (`bool`, *optional*, defaults to `False`):
Whether to use CLIP projector.
remove_center (`bool`, *optional*, defaults to `False`):
Whether to remove center pixels in some operations.
num_classes (`int`, *optional*, defaults to `1000`):
Number of classes for the model output.
drop_rate (`float`, *optional*, defaults to `0.0`):
Dropout rate in the model.
drop_path_rate (`float`, *optional*, defaults to `0.0`):
Dropout path rate in the model.
drop_path_type (`str`, *optional*, defaults to `"linear"`):
Type of dropout path used in the model.
act_layer (`str`, *optional*, defaults to `"GELU"`):
Activation function used in the model.
norm_layer (`str`, *optional*, defaults to `"LN"`):
Normalization layer used in the model.
cls_scale (`float`, *optional*, defaults to `1.5`):
Scale of the classification layer in the model.
with_cp (`bool`, *optional*, defaults to `False`):
Whether to use checkpointing in the model.
"""
model_type = 'internimage'
def __init__(
self,
core_op='DCNv3',
depths=(4, 4, 18, 4),
groups=(4, 8, 16, 32),
channels=64,
dw_kernel_size=None,
layer_scale=None,
offset_scale=1.0,
mlp_ratio=4.0,
post_norm=False,
res_post_norm=False,
level2_post_norm=False,
level2_post_norm_block_ids=None,
center_feature_scale=False,
use_clip_projector=False,
remove_center=False,
num_classes=1000,
drop_rate=0.0,
drop_path_rate=0.0,
drop_path_type='linear',
act_layer='GELU',
norm_layer='LN',
cls_scale=1.5,
with_cp=False,
**kwargs,
):
super().__init__(**kwargs)
# Model configuration parameters
self.core_op = core_op
self.depths = depths
self.groups = groups
self.channels = channels
self.dw_kernel_size = dw_kernel_size
self.layer_scale = layer_scale
self.offset_scale = offset_scale
self.mlp_ratio = mlp_ratio
self.post_norm = post_norm
self.res_post_norm = res_post_norm
self.level2_post_norm = level2_post_norm
self.level2_post_norm_block_ids = level2_post_norm_block_ids
self.center_feature_scale = center_feature_scale
self.use_clip_projector = use_clip_projector
self.remove_center = remove_center
self.num_classes = num_classes
self.drop_rate = drop_rate
self.drop_path_rate = drop_path_rate
self.drop_path_type = drop_path_type
self.act_layer = act_layer
self.norm_layer = norm_layer
self.cls_scale = cls_scale
self.with_cp = with_cp
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