| from transformers import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class UniFormerWithProjectionHeadConfig(PretrainedConfig): | |
| model_type = 'uniformer' | |
| def __init__( | |
| self, | |
| projection_size=None, | |
| embed_dim=[64, 128, 320, 512], | |
| image_size=384, | |
| in_chans=3, | |
| depth=[5, 8, 20, 7], | |
| patch_size=[4, 2, 2, 2], | |
| head_dim=64, | |
| mlp_ratio=4, | |
| qkv_bias=True, | |
| num_classes=1000, | |
| qk_scale=None, | |
| representation_size=None, | |
| drop_rate=0.0, | |
| drop_path_rate=0.3, | |
| attn_drop_rate=0.0, | |
| conv_stem=False, | |
| layer_norm_eps=1e-6, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| layer_norm_eps=layer_norm_eps, | |
| image_size=image_size, | |
| qkv_bias=qkv_bias, | |
| **kwargs, | |
| ) | |
| self.projection_size = projection_size | |
| self.embed_dim = embed_dim | |
| self.in_chans = in_chans | |
| self.depth = depth | |
| self.patch_size = patch_size | |
| self.head_dim = head_dim | |
| self.mlp_ratio = mlp_ratio | |
| self.num_classes = num_classes | |
| self.qk_scale = qk_scale | |
| self.representation_size = representation_size | |
| self.drop_rate = drop_rate | |
| self.drop_path_rate = drop_path_rate | |
| self.attn_drop_rate = attn_drop_rate | |
| self.conv_stem = conv_stem | |