| from transformers import PretrainedConfig |
|
|
| class PATHViTConfig(PretrainedConfig): |
| model_type = "vit-b16" |
|
|
| def __init__( |
| self, |
| img_size=224, |
| patch_size=16, |
| in_chans=3, |
| num_classes=80, |
| embed_dim=768, |
| depth=12, |
| num_heads=12, |
| mlp_ratio=4.0, |
| qkv_bias=True, |
| drop_path_rate=0.1, |
| norm_layer=None, |
| norm_layer_eps=1e-6, |
| window=True, |
| use_abs_pos_emb=True, |
| interval=3, |
| test_pos_mode=False, |
| task_sp_list=(), |
| neck_sp_list=(), |
| learnable_pos=False, |
| rel_pos_spatial=False, |
| lms_checkpoint_train=False, |
| prompt=None, |
| pad_attn_mask=False, |
| freeze_iters=0, |
| act_layer="GELU", |
| pre_ln=False, |
| mask_input=False, |
| ending_norm=True, |
| round_padding=False, |
| compat=False, |
| use_cls_token=False, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
| self.img_size = img_size |
| self.patch_size = patch_size |
| self.in_chans = in_chans |
| self.num_classes = num_classes |
| self.embed_dim = embed_dim |
| self.depth = depth |
| self.num_heads = num_heads |
| self.mlp_ratio = mlp_ratio |
| self.qkv_bias = qkv_bias |
| self.drop_path_rate = drop_path_rate |
| |
| self.norm_layer = norm_layer |
| self.norm_layer_eps = norm_layer_eps |
| |
| self.window = window |
| self.use_abs_pos_emb = use_abs_pos_emb |
| self.interval = interval |
| self.test_pos_mode = test_pos_mode |
| self.task_sp_list = task_sp_list |
| self.neck_sp_list = neck_sp_list |
| self.learnable_pos = learnable_pos |
| self.rel_pos_spatial = rel_pos_spatial |
| self.lms_checkpoint_train = lms_checkpoint_train |
| self.prompt = prompt |
| self.pad_attn_mask = pad_attn_mask |
| self.freeze_iters = freeze_iters |
| self.act_layer = act_layer |
| self.pre_ln = pre_ln |
| self.mask_input = mask_input |
| self.ending_norm = ending_norm |
| self.round_padding = round_padding |
| self.compat = compat |
| self.use_cls_token = use_cls_token |
|
|
|
|
