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CT Volume Encoder Wrapper for RepConTUR.
Wraps CTViT or NNUNet3DEncoder to encode full 3D CT volumes into
a flattened token sequence of shape (B, N, dim).
The encoder is loaded from a CT-CLIP checkpoint and fully frozen.
"""
from pathlib import Path
from typing import Optional, List
import torch
import torch.nn as nn
from ._compat import print_log
class CTVolumeEncoder(nn.Module):
"""
Wraps CTViT or NNUNet3DEncoder for 3D CT volume encoding.
Input: (B, 1, D, H, W) — full 3D CT volume
Output: (B, T*H'*W', dim) — flattened spatial/temporal tokens
The encoder is always frozen. Only downstream modules (attentional
pooler, MLP projection) are trainable.
"""
def __init__(
self,
encoder_type: str = 'ctvit',
checkpoint_path: Optional[str] = None,
ct_clip_source_path: Optional[str] = None,
# CTViT params
dim: int = 512,
codebook_size: int = 8192,
image_size: int = 360,
patch_size: int = 20,
temporal_patch_size: int = 10,
spatial_depth: int = 4,
temporal_depth: int = 4,
dim_head: int = 32,
heads: int = 8,
# NNUNet params
nnunet_depth: int = 5,
nnunet_channels: Optional[List[int]] = None,
nnunet_stride_list: Optional[List[List[int]]] = None,
):
super().__init__()
self.encoder_type = encoder_type
self.dim = dim
if nnunet_channels is None:
nnunet_channels = [32, 64, 128, 256, 320, 512]
if nnunet_stride_list is None:
nnunet_stride_list = [[1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]]
# transformer_maskgit is vendored alongside this module (ct_clip_source_path ignored).
if encoder_type == 'ctvit':
from .ctvit import CTViT
self.encoder = CTViT(
dim=dim,
codebook_size=codebook_size,
image_size=image_size,
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
spatial_depth=spatial_depth,
temporal_depth=temporal_depth,
dim_head=dim_head,
heads=heads,
)
elif encoder_type == 'nnunet':
from .nnunet import NNUNet3DEncoder
self.encoder = NNUNet3DEncoder(
depth=nnunet_depth,
channels=nnunet_channels,
stride_list=nnunet_stride_list,
)
self.dim = nnunet_channels[nnunet_depth] # output channel dim
else:
raise ValueError(f"Unknown encoder_type: {encoder_type}. Use 'ctvit' or 'nnunet'.")
# Load checkpoint
if checkpoint_path is not None:
self._load_checkpoint(checkpoint_path)
# Freeze the entire encoder
self.encoder.requires_grad_(False)
self.encoder.eval()
total_params = sum(p.numel() for p in self.encoder.parameters())
print_log(
f"CTVolumeEncoder ({encoder_type}): loaded with {total_params:,} params (all frozen)",
logger='current'
)
def _load_checkpoint(self, checkpoint_path: str):
"""Load encoder weights from a CT-CLIP checkpoint."""
path = Path(checkpoint_path)
if not path.exists():
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
if path.suffix == '.safetensors':
from safetensors import safe_open
f = safe_open(str(path), framework='pt', device='cpu')
all_keys = list(f.keys())
encoder_state = {}
for prefix in ['ctclip.visual_transformer.', 'visual_transformer.']:
for k in all_keys:
if k.startswith(prefix):
encoder_state[k[len(prefix):]] = f.get_tensor(k)
if encoder_state:
break
if not encoder_state:
for k in all_keys:
encoder_state[k] = f.get_tensor(k)
else:
# Standard PyTorch checkpoint
ckpt = torch.load(str(path), map_location='cpu')
if isinstance(ckpt, dict) and 'state_dict' in ckpt:
ckpt = ckpt['state_dict']
encoder_state = {}
for prefix in ['ctclip.visual_transformer.', 'visual_transformer.']:
for k, v in ckpt.items():
if k.startswith(prefix):
encoder_state[k[len(prefix):]] = v
if encoder_state:
break
if not encoder_state:
encoder_state = ckpt
# Load with strict=False to skip decoder keys (to_pixels*, dec_*)
missing, unexpected = self.encoder.load_state_dict(encoder_state, strict=False)
print_log(
f"CTVolumeEncoder: loaded checkpoint from {checkpoint_path}\n"
f" Matched keys: {len(encoder_state) - len(unexpected)}\n"
f" Missing keys (expected for encoder-only): {len(missing)}\n"
f" Unexpected keys (decoder, skipped): {len(unexpected)}",
logger='current'
)
def train(self, mode=True):
"""Override train to keep encoder always in eval mode."""
super().train(mode)
self.encoder.eval()
return self
def forward(self, volume: torch.Tensor) -> torch.Tensor:
"""
Args:
volume: (B, 1, D, H, W) — preprocessed 3D CT volume
Returns:
tokens: (B, N, dim) — flattened tokens where N = T*H'*W'
For CTViT with (180, 360, 360): N = 18*18*18 = 5832, dim = 512
For NNUNet with (180, 360, 360): N varies, dim = channels[-1]
"""
# Query the encoder's actual parameter dtype at runtime.
# .to(bf16) in __init__ may not persist (DeepSpeed can reset
# frozen-parameter dtypes), so we always match dynamically.
encoder_dtype = next(self.encoder.parameters()).dtype
volume = volume.to(dtype=encoder_dtype)
with torch.no_grad(), torch.cuda.amp.autocast(enabled=False):
tokens = self.encoder(volume, return_encoded_tokens=True)
# CTViT: (B, T, H', W', dim) e.g. (B, 18, 18, 18, 512)
# NNUNet: (B, D', H', W', dim) e.g. (B, 11, 11, 11, 512)
B = tokens.shape[0]
tokens = tokens.reshape(B, -1, self.dim) # (B, N, dim)
return tokens
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