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from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class PretrainingWrapper(nn.Module):
"""Wrapper that owns DINO/SimCLR pretraining routines and delegates
feature extraction to a backbone model.
Note: KoLeo (`koleo_loss`) and DGCL (`dgcl_loss`) remain on the backbone.
"""
def __init__(self, backbone, simclr=False, simclr_temp=0.1,
dino=False, dino_student_temp=0.1, dino_teacher_temp=0.04,
dino_center_momentum=0.9, dino_momentum=0.996):
super().__init__()
self.backbone = backbone
self.simclr = simclr
self.simclr_temp = simclr_temp
self.dino = dino
self.dino_student_temp = dino_student_temp
self.dino_teacher_temp = dino_teacher_temp
self.dino_center_momentum = dino_center_momentum
self.dino_momentum = dino_momentum
self.use_micro = getattr(backbone, 'use_micro', False)
self.bidi = getattr(backbone, 'bidi', False)
self.cycle_len = getattr(backbone, 'cycle_len', 180)
self.use_transformer = getattr(backbone, 'use_transformer', False)
# teacher modules (set by init_teacher)
self.teacher_encoder = None
self.teacher_fuser = None
self.teacher_proj = None
self.teacher_sequence_encoder = None
self.teacher_contr_head = None
if self.dino:
# try to infer seq_dim from backbone.proj
try:
seq_dim = self.backbone.proj.out_features
except Exception:
seq_dim = 512
self.register_buffer('dino_center_f', torch.zeros(1, seq_dim))
self.register_buffer('dino_center_r', torch.zeros(1, seq_dim))
def forward(self, sig, mask=None, demo=None):
if self.dino:
return self._forward_dino(sig, mask)
if self.simclr:
return self._forward_simclr(sig, mask)
return self.backbone(sig, mask=mask, demo=demo)
def __getattr__(self, name):
"""Delegate unknown attributes to backbone to preserve legacy access
patterns like model.encoder/model.fuser/model.revin.
"""
try:
return super().__getattr__(name)
except AttributeError as exc:
if self.backbone is not None and hasattr(self.backbone, name):
return getattr(self.backbone, name)
raise exc
def state_dict(self, *args, **kwargs):
"""Expose original backbone key names (without `backbone.` prefix)."""
sd = super().state_dict(*args, **kwargs)
out = OrderedDict()
for k, v in sd.items():
if k.startswith('backbone.'):
out[k[len('backbone.'):]] = v
else:
out[k] = v
return out
def load_state_dict(self, state_dict, strict=True):
"""Accept both legacy keys and wrapped keys."""
own_keys = super().state_dict().keys()
converted = OrderedDict()
for k, v in state_dict.items():
if k in own_keys:
converted[k] = v
elif f'backbone.{k}' in own_keys:
converted[f'backbone.{k}'] = v
else:
converted[k] = v
return super().load_state_dict(converted, strict=strict)
def init_teacher(self):
if not self.dino:
return
self.teacher_encoder = copy.deepcopy(self.backbone.encoder)
self.teacher_fuser = copy.deepcopy(self.backbone.fuser)
self.teacher_proj = copy.deepcopy(self.backbone.proj) if hasattr(self.backbone, 'proj') else None
self.teacher_sequence_encoder = copy.deepcopy(self.backbone.sequence_encoder) if hasattr(self.backbone, 'sequence_encoder') else None
self.teacher_contr_head = copy.deepcopy(self.backbone.contr_head) if hasattr(self.backbone, 'contr_head') else None
self._freeze_teacher()
def _freeze_teacher(self):
for module in [self.teacher_encoder, self.teacher_fuser, self.teacher_proj, self.teacher_sequence_encoder, self.teacher_contr_head]:
if module is None:
continue
for p in module.parameters():
p.requires_grad = False
module.eval()
# helper that mirrors previous behaviour in-class
def _forward_features_with_modules(self, inputs, mask, encoder, fuser, proj, sequence_encoder):
modalities_to_use = sorted(list(inputs.keys()))
m = modalities_to_use[0]
N, L = mask.shape
if mask is not None:
nonmask_idx = torch.nonzero(mask.view(-1,)).squeeze(-1)
latents_shared, latents_specific, _ = encoder(inputs, modalities_to_use)
latents_fused, _ = fuser(latents_shared, latents_specific)
latents = torch.stack(latents_fused, dim=0) # (M, N, L, D)
latents = rearrange(latents, 'm n l d -> n l (m d)')
# if getattr(self.backbone, 'use_micro', False):
# return None, latents_micro
latents = proj(latents)
latents = latents.mean(1)
dummy = torch.zeros((N * L, latents.shape[-1]), device=latents.device, dtype=latents.dtype)
dummy.scatter_(dim=0, index=nonmask_idx.unsqueeze(-1).repeat(1, latents.shape[-1]), src=latents)
latents = dummy
latents = latents.view(N, -1, latents.shape[-1])
latents = sequence_encoder(latents, mask=mask)
return latents, None
def _get_cycle_values(self, xf, mask, mode='last'):
if hasattr(self.backbone, '_get_cycle_values'):
return self.backbone._get_cycle_values(xf, mask, mode=mode)
if mask is not None:
xf = xf.masked_fill(~mask.unsqueeze(-1), 0.0)
min_length = mask.sum(dim=1).min()
num_seg = min_length // self.cycle_len
if num_seg < 1:
seq1_last = xf[torch.arange(xf.shape[0], device=xf.device), mask.sum(dim=1) - 1]
return seq1_last.unsqueeze(1)
seq1 = xf.view(xf.shape[0], -1, self.cycle_len, xf.shape[-1]).mean(2)
return seq1
def _simclr_representation(self, sig, mask):
feats = self.backbone.forward_features(sig, mask=mask)
x_macro, _ = feats
if self.bidi:
xf, xr = x_macro
xf = self._get_cycle_values(xf, mask)
xr = self._get_cycle_values(xr, mask)
return xf, xr
else:
return self._get_cycle_values(x_macro, mask)
def _simclr_representation_simple(self, sig, mask):
# alias kept for clarity
return self._simclr_representation(sig, mask)
def _simclr_loss(self, z1, z2):
z1 = F.normalize(z1, p=2, dim=-1)
z2 = F.normalize(z2, p=2, dim=-1)
z = torch.cat([z1, z2], dim=0)
logits = torch.matmul(z, z.t()) / (self.simclr_temp + 1e-8)
logits = logits - torch.eye(logits.shape[0], device=logits.device, dtype=logits.dtype) * 1e9
batch_size = z1.shape[0]
targets = torch.arange(2 * batch_size, device=logits.device)
targets = (targets + batch_size) % (2 * batch_size)
return F.cross_entropy(logits, targets)
def _forward_simclr(self, sig, mask):
if mask is None:
raise ValueError("SimCLR pretraining requires a valid mask.")
if isinstance(sig, (tuple, list)):
if len(sig) != 2:
raise ValueError("SimCLR expects exactly two views in a list/tuple.")
sig1, sig2 = sig
elif isinstance(sig, dict) and 'view1' in sig and 'view2' in sig:
sig1, sig2 = sig['view1'], sig['view2']
else:
raise ValueError("SimCLR expects inputs as (view1, view2) or a dict with 'view1'/'view2'.")
if self.bidi:
z1_f, z1_r = self._simclr_representation(sig1, mask)
z2_f, z2_r = self._simclr_representation(sig2, mask)
z1_f = self.backbone.contr_head(z1_f)
z1_r = self.backbone.contr_head(z1_r)
z2_f = self.backbone.contr_head(z2_f)
z2_r = self.backbone.contr_head(z2_r)
num_seg = z1_f.shape[1]
loss_fwd, loss_bwd = 0.0, 0.0
for s in range(num_seg):
loss_fwd += self._simclr_loss(z1_f[:,s], z2_f[:,s])
loss_bwd += self._simclr_loss(z1_r[:,s], z2_r[:,s])
loss = (loss_fwd + loss_bwd) / 2
else:
z1 = self._simclr_representation(sig1, mask)
z2 = self._simclr_representation(sig2, mask)
loss = self._simclr_loss(self.backbone.contr_head(z1), self.backbone.contr_head(z2))
return loss
def _dino_representation(self, sig, mask, use_teacher=False):
if use_teacher:
feats = self._forward_features_with_modules(sig, mask, self.teacher_encoder, self.teacher_fuser, self.teacher_proj, self.teacher_sequence_encoder)
else:
feats = self.backbone.forward_features(sig, mask=mask)
x_macro, _ = feats
if self.bidi:
xf, xr = x_macro
xf = self._get_cycle_values(xf, mask)
xr = self._get_cycle_values(xr, mask)
return xf, xr
else:
return self._get_cycle_values(x_macro, mask)
def _forward_dino(self, sig, mask):
if self.simclr:
raise ValueError("DINO and SimCLR cannot be enabled at the same time.")
if self.teacher_encoder is None:
raise ValueError("DINO teacher modules are not initialized. Call init_teacher() after loading checkpoints.")
if mask is None:
raise ValueError("DINO pretraining requires a valid mask.")
if isinstance(sig, (tuple, list)):
if len(sig) != 2:
raise ValueError("DINO expects exactly two views in a list/tuple.")
sig1, sig2 = sig
elif isinstance(sig, dict) and 'view1' in sig and 'view2' in sig:
sig1, sig2 = sig['view1'], sig['view2']
else:
raise ValueError("DINO expects inputs as (view1, view2) or a dict with 'view1'/'view2'.")
z1_f_s, z1_r_s = self._dino_representation(sig1, mask, use_teacher=False)
z2_f_s, z2_r_s = self._dino_representation(sig2, mask, use_teacher=False)
z1_f_s = self.backbone.contr_head(z1_f_s)
z1_r_s = self.backbone.contr_head(z1_r_s)
z2_f_s = self.backbone.contr_head(z2_f_s)
z2_r_s = self.backbone.contr_head(z2_r_s)
with torch.no_grad():
z1_f_t, z1_r_t = self._dino_representation(sig1, mask, use_teacher=True)
z2_f_t, z2_r_t = self._dino_representation(sig2, mask, use_teacher=True)
z1_f_t = self.teacher_contr_head(z1_f_t)
z1_r_t = self.teacher_contr_head(z1_r_t)
z2_f_t = self.teacher_contr_head(z2_f_t)
z2_r_t = self.teacher_contr_head(z2_r_t)
self._update_dino_center(torch.cat([z1_f_t.mean(1), z2_f_t.mean(1)], dim=0), torch.cat([z1_r_t.mean(1), z2_r_t.mean(1)], dim=0))
num_seg = z2_f_s.shape[1]
loss_fwd, loss_bwd = 0.0, 0.0
for s in range(num_seg):
loss_fwd += self.dino_loss(z1_f_s[:,s], z2_f_t[:,s], fwd=True)
loss_fwd += self.dino_loss(z2_f_s[:,s], z1_f_t[:,s], fwd=True)
loss_bwd += self.dino_loss(z1_r_s[:,s], z2_r_t[:,s], fwd=False)
loss_bwd += self.dino_loss(z2_r_s[:,s], z1_r_t[:,s], fwd=False)
loss = (loss_fwd + loss_bwd) / 2 / 2
return loss
def dino_loss(self, student_out, teacher_out, fwd=False):
student_out = student_out / (self.dino_student_temp + 1e-8)
center = self.dino_center_f if fwd else self.dino_center_r
teacher_out = F.softmax((teacher_out - center) / (self.dino_teacher_temp + 1e-8), dim=-1)
return torch.sum(-teacher_out * F.log_softmax(student_out, dim=-1), dim=-1).mean()
@torch.no_grad()
def _update_dino_center(self, teacher_out_f, teacher_out_r):
batch_center_f = teacher_out_f.mean(dim=0, keepdim=True)
batch_center_r = teacher_out_r.mean(dim=0, keepdim=True)
self.dino_center_f.mul_(self.dino_center_momentum).add_(batch_center_f * (1.0 - self.dino_center_momentum))
self.dino_center_r.mul_(self.dino_center_momentum).add_(batch_center_r * (1.0 - self.dino_center_momentum))
@torch.no_grad()
def update_teacher(self, momentum=None):
if not self.dino or self.teacher_encoder is None:
return
m = self.dino_momentum if momentum is None else momentum
pairs = [
(self.backbone.encoder, self.teacher_encoder),
(self.backbone.fuser, self.teacher_fuser),
]
if self.teacher_proj is not None:
pairs.append((self.backbone.proj, self.teacher_proj))
if self.teacher_sequence_encoder is not None:
pairs.append((self.backbone.sequence_encoder, self.teacher_sequence_encoder))
pairs.append((self.backbone.contr_head, self.teacher_contr_head))
for student, teacher in pairs:
for param_s, param_t in zip(student.parameters(), teacher.parameters()):
param_t.data.mul_(m).add_(param_s.data * (1.0 - m))
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