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Runtime error
| from transformers import PretrainedConfig | |
| from typing import List | |
| class FocusOnDepthConfig(PretrainedConfig): | |
| model_type = "focusondepth" | |
| def __init__( | |
| self, | |
| image_size = (3, 384, 384), | |
| patch_size = 16, | |
| emb_dim = 768, | |
| resample_dim = 256, | |
| read = 'projection', | |
| num_layers_encoder = 24, | |
| hooks = [2, 5, 8, 11], | |
| reassemble_s = [4, 8, 16, 32], | |
| transformer_dropout= 0, | |
| nclasses = 2, | |
| type_ = "full", | |
| model_timm = "vit_base_patch16_384", | |
| **kwargs, | |
| ): | |
| if type_ not in ["full", "depth", "segmentation"]: | |
| raise ValueError(f"`type_` must be 'full' or depth' or 'segmentation, got {type_}.") | |
| if read not in ["ignore", "add", "projection"]: | |
| raise ValueError(f"`read` must be '', 'ignore' or 'add' or 'projection, got {read}.") | |
| if image_size[0] != 3 and image_size[1] != 384 and image_size[2] != 384: | |
| raise ValueError(f"`image_size` must be 3, 384, 384, got {image_size}.") | |
| if patch_size != 16: | |
| raise ValueError(f"`patch_size` must be 16, got {patch_size}.") | |
| self.image_size = image_size | |
| self.patch_size = patch_size | |
| self.emb_dim = emb_dim | |
| self.resample_dim = resample_dim | |
| self.read = read | |
| self.num_layers_encoder = num_layers_encoder | |
| self.hooks = hooks | |
| self.reassemble_s = reassemble_s | |
| self.transformer_dropout = transformer_dropout | |
| self.nclasses = nclasses | |
| self.type_ = type_ | |
| self.model_timm = model_timm | |
| super().__init__(**kwargs) |