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62762da 3758fa2 62762da 3758fa2 62762da 3758fa2 62762da 3758fa2 62762da 3758fa2 62762da 3758fa2 62762da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | # Copyright 2025 AI for Oncology Research Group. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
from transformers import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPooling
import torch
from .configuration_tapct import TAPCTConfig
from .vision_transformer import vit_small, vit_base
from .vision_transformer_3d import vit_3d_small, vit_3d_base
from .vision_transformer_base import DinoVisionTransformerBase
class TAPCTPreTrainedModel(PreTrainedModel):
config_class = TAPCTConfig
base_model_prefix = "tapct"
class TAPCTModel(TAPCTPreTrainedModel):
"""
TAP-CT Vision Transformer model based on DINOv2: https://github.com/facebookresearch/dinov2.
This model outputs raw hidden states and does not include any task-specific head.
"""
def __init__(self, config: TAPCTConfig) -> None:
super().__init__(config)
self.config = config
self.model: DinoVisionTransformerBase
match config.model_variant:
case "2d":
if config.model_size == "small":
self.model = vit_small(
img_size=config.img_size,
patch_size=config.patch_size,
num_register_tokens=config.num_register_tokens,
in_chans=config.in_chans,
init_values=config.init_values,
block_chunks=config.block_chunks
)
elif config.model_size == "base":
self.model = vit_base(
img_size=config.img_size,
patch_size=config.patch_size,
num_register_tokens=config.num_register_tokens,
in_chans=config.in_chans,
init_values=config.init_values,
block_chunks=config.block_chunks
)
else:
raise ValueError(f"Model size '{config.model_size}' not supported for 2D")
case "2.5d" | "3d":
if config.model_size == "small":
self.model = vit_3d_small(
img_size=config.img_size,
patch_size=config.patch_size,
num_register_tokens=config.num_register_tokens,
in_chans=config.in_chans,
init_values=config.init_values,
block_chunks=config.block_chunks
)
elif config.model_size == "base":
self.model = vit_3d_base(
img_size=config.img_size,
patch_size=config.patch_size,
num_register_tokens=config.num_register_tokens,
in_chans=config.in_chans,
init_values=config.init_values,
block_chunks=config.block_chunks
)
else:
raise ValueError(f"Model size '{config.model_size}' not supported for 3D")
case _:
raise ValueError(f"Model variant '{config.model_variant}' not supported. Use '2d', '2.5d', or '3d'.")
# Initialize weights
self.post_init()
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
reshape: bool = False
) -> BaseModelOutputWithPooling:
"""
Forward pass of the TAP-CT model.
Parameters
----------
pixel_values : torch.Tensor
Input images. Shape (B, C, H, W) for 2D or (B, C, D, H, W) for 3D.
output_hidden_states : Optional[bool], optional
Whether to return hidden states from all layers.
return_dict : Optional[bool], optional
Whether to return a ModelOutput instead of a plain tuple.
reshape : bool, default=False
Whether to reshape output features to spatial dimensions. If True,
returns shape (B, H, W, C) for 2D or (B, D, H, W, C) for 3D instead
of flattened (B, N, C) where N is the number of patches.
Returns
-------
BaseModelOutputWithPooling
Contains:
- last_hidden_state: Patch token features from the last layer
- pooler_output: CLS token from the last layer
- hidden_states: (optional) All hidden states if output_hidden_states=True
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
if output_hidden_states:
outputs_tuple = self.model.get_intermediate_layers(
pixel_values,
n=self.model.n_blocks,
return_class_token=True,
reshape=reshape
)
outputs = tuple(o[0] for o in outputs_tuple)
class_tokens = tuple(o[1] for o in outputs_tuple)
last_hidden_state = outputs[-1]
pooler_output = class_tokens[-1]
hidden_states = outputs
else:
outputs_tuple = self.model.get_intermediate_layers(
pixel_values,
n=1,
return_class_token=True,
reshape=reshape
)
last_hidden_state = outputs_tuple[0][0]
pooler_output = outputs_tuple[0][1]
hidden_states = None
if not return_dict:
return tuple(
v for v in [last_hidden_state, pooler_output, hidden_states]
if v is not None
)
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooler_output,
hidden_states=hidden_states
) |