|
|
| from typing import List |
| from transformers import PretrainedConfig, T5Config |
|
|
|
|
| class SwinVilmaConfig(PretrainedConfig): |
| model_type = "swin_vilma" |
|
|
| def __init__( |
| self, |
| patch_size: int = 4, |
| in_chans: int = 3, |
| embed_dim: int = 96, |
| depths: List[int] = [2, 2, 18, 2], |
| num_heads: List[int] = [3, 6, 12, 24], |
| window_size: int = 24, |
| mlp_ratio: float = 4.0, |
| qkv_bias: bool = True, |
| ape: bool = False, |
| patch_norm: bool = True, |
| pretrained_window_sizes: List[int] = [0, 0, 0, 0], |
| vl_cross_attn_layers: List[int] = [3], |
| vl_alpha: float = 0.5, |
| lm_d_model: int = 768, |
| text_embedder: str = "t5-base", |
| downsampling_method: str = "merge_attention_v3", |
| vision_name: str = "swin_small_patch4_window7_224_22k", |
| image_size: List[int] = [1536, 768], |
| drop_path_rate: float = 0.3, |
| drop_rate: float = 0.0, |
| resume_from: str = "", |
| use_checkpoint: bool = False, |
| do_shift: bool = True, |
| input_type: str = "rgb", |
| vl_learned_ape: bool = True, |
| **kwargs |
| ): |
| super().__init__(**kwargs) |
| self.patch_size = patch_size |
| self.in_chans = in_chans |
| self.embed_dim = embed_dim |
| self.depths = depths |
| self.num_heads = num_heads |
| self.window_size = window_size |
| self.mlp_ratio = mlp_ratio |
| self.qkv_bias = qkv_bias |
| self.ape = ape |
| self.patch_norm = patch_norm |
| self.pretrained_window_sizes = pretrained_window_sizes |
| self.vl_cross_attn_layers = vl_cross_attn_layers |
| self.vl_alpha = vl_alpha |
| self.lm_d_model = lm_d_model |
| self.text_embedder = text_embedder |
| self.downsampling_method = downsampling_method |
| self.vision_name = vision_name |
| self.image_size = image_size |
| self.drop_path_rate = drop_path_rate |
| self.drop_rate = drop_rate |
| self.resume_from = resume_from |
| self.use_checkpoint = use_checkpoint |
| self.do_shift = do_shift |
| self.input_type = input_type |
| self.vl_learned_ape = vl_learned_ape |
|
|
| class VisFocusConfig(PretrainedConfig): |
| model_type = "visfocus" |
|
|
| def __init__( |
| self, |
| initializer_factor: float = 1.0, |
| initializer_range: float = 0.02, |
| max_seq_length: int = 2048, |
| generate_max_new_tokens_len: int = 256, |
| model_name_or_path: str = "", |
| variant: str = "vf-base", |
| image_size: List[int] = [1536, 768], |
| seed: int = 42, |
| do_lower_case: bool = True, |
| hidden_dropout_prob: float = .1, |
| **kwargs |
| ): |
| super().__init__(**kwargs) |
| self.initializer_factor = initializer_factor |
| self.initializer_range = initializer_range |
| self.max_seq_length = max_seq_length |
| self.generate_max_new_tokens_len = generate_max_new_tokens_len |
| self.model_name_or_path = model_name_or_path |
| self.variant = variant |
| self.image_size = image_size |
| self.seed = seed |
| self.do_lower_case = do_lower_case |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.vision_config = SwinVilmaConfig() |
| self.lm_config = T5Config() |
|
|
