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