^jOdZddlmZddlmZddlmZddlmZddl m Z ed eGd d eZ d gZ y )zDPT model configuration)strict)%consolidate_backbone_kwargs_to_config)PreTrainedConfig)auto_docstring) AutoConfigzIntel/dpt-large) checkpointc eZdZUdZdZdeiZdZee d<dZ deze d<dZ edze d <d Z edze d <d Z ee d <dZeezdze d<dZeezdze d<dZee d<dZedze d<dZeeezeeefzdze d<dZeeezeeefzdze d<dZedze d<dZee d<dZedze d<dZeeeed fzdze d!<d"Zee d#<d$Zeeezeeezd fze d%<d&Zeeeed fze d'<d(Z ee d)<d*Z!ee d+<dZ"edze d,<dZ#edze d-<dZ$ee d.<dZ%edze d/<d0Z&ee d1<d2Z'ee d3<d4Z(eeze d5<d6Z)eeeed fzdze d7<d8Z*eeeed fze d9<dZ+e,e-zdze d<dZ.edze d:<d;Z/ee d<<fd=Z0xZ1S)> DPTConfiga is_hybrid (`bool`, *optional*, defaults to `False`): Whether to use a hybrid backbone. Useful in the context of loading DPT-Hybrid models. backbone_out_indices (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): Indices of the intermediate hidden states to use from backbone. readout_type (`str`, *optional*, defaults to `"project"`): The readout type to use when processing the readout token (CLS token) of the intermediate hidden states of the ViT backbone. Can be one of [`"ignore"`, `"add"`, `"project"`]. - "ignore" simply ignores the CLS token. - "add" passes the information from the CLS token to all other tokens by adding the representations. - "project" passes information to the other tokens by concatenating the readout to all other tokens before projecting the representation to the original feature dimension D using a linear layer followed by a GELU non-linearity. reassemble_factors (`list[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`): The up/downsampling factors of the reassemble layers. neck_hidden_sizes (`list[str]`, *optional*, defaults to `[96, 192, 384, 768]`): The hidden sizes to project to for the feature maps of the backbone. fusion_hidden_size (`int`, *optional*, defaults to 256): The number of channels before fusion. head_in_index (`int`, *optional*, defaults to -1): The index of the features to use in the heads. use_batch_norm_in_fusion_residual (`bool`, *optional*, defaults to `False`): Whether to use batch normalization in the pre-activate residual units of the fusion blocks. use_bias_in_fusion_residual (`bool`, *optional*, defaults to `True`): Whether to use bias in the pre-activate residual units of the fusion blocks. add_projection (`bool`, *optional*, defaults to `False`): Whether to add a projection layer before the depth estimation head. use_auxiliary_head (`bool`, *optional*, defaults to `True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. semantic_classifier_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the semantic classification head. backbone_featmap_shape (`list[int]`, *optional*, defaults to `[1, 1024, 24, 24]`): Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone. neck_ignore_stages (`list[int]`, *optional*, defaults to `[0, 1]`): Used only for the `hybrid` embedding type. The stages of the readout layers to ignore. pooler_output_size (`int`, *optional*): Dimensionality of the pooler layer. If None, defaults to `hidden_size`. pooler_act (`str`, *optional*, defaults to `"tanh"`): The activation function to be used by the pooler. Example: ```python >>> from transformers import DPTModel, DPTConfig >>> # Initializing a DPT dpt-large style configuration >>> configuration = DPTConfig() >>> # Initializing a model from the dpt-large style configuration >>> model = DPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```dptbackbone_config hidden_size Nnum_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actghidden_dropout_probattention_probs_dropout_probg{Gz?initializer_rangeg-q=layer_norm_eps image_size patch_sizer num_channelsF is_hybridTqkv_bias)r .backbone_out_indicesproject readout_type)rg?reassemble_factors)`rrneck_hidden_sizesfusion_hidden_size head_in_index!use_batch_norm_in_fusion_residualuse_bias_in_fusion_residualadd_projectionuse_auxiliary_headg?auxiliary_loss_weightsemantic_loss_ignore_indexg?semantic_classifier_dropout)r)ir:backbone_featmap_shape)rr)neck_ignore_stagespooler_output_sizetanh pooler_actc |jdvr td|jr{t|jt r|jj ddtd|jdddgdgdd d d |\|_}|jd k7rNtd |jd |j&tdd|ji|\|_}d|_ |jr |jnd|_ |jr |jng|_ |jr |jn |j|_ t|<di|y)N)ignoreaddr&z8Readout_type must be one of ['ignore', 'add', 'project'] model_typebitsame bottleneck)rr( )stage1stage2stage3T)global_padding layer_typedepths out_featuresembedding_dynamic_padding)rdefault_config_typedefault_config_kwargsr&z>$..5$$// eD+P , $ 4 4$)&,".'$B15 ' , , (D &  I- !_`` ZZ # /43G3G3S+P, $ 4 4,, (D &)-D %EI^^d&A&AY]#=A^^$"9"9QS=A=T=T$"9"9Z^ZjZj '')2__name__ __module__ __qualname____doc__rCr sub_configsrint__annotations__rrrrstrrfloatrrrrlisttuplerrr boolr!r%r'r*r-r/r1r2r3r4r5r6r8r9r;r<rrVrr=r?rZ __classcell__)r]s@r^r r s`7 rJ$j1K K$&tcz&&(t($(sTz(J.1t+17: %#+"4:#u##(NEDL(;>Jd3i%S/1D8>;=Jd3i%S/1D8= L#* It HdTk ?L$s)eCHo5<L!L#!FTS5[)E#+s2B,CCT5HtCy5c?2H!!M35:%td{:/33 ND &*t *#&5&&))/22ARDIc3h7$>R6<S E#s(O3<6:OT,,t3:%)d )J ( (r_r N) rchuggingface_hub.dataclassesrbackbone_utilsrconfiguration_utilsrutilsrauto.configuration_autor r __all__rSr_r^rssP.C3#0,-A( A(.A(H -r_