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import torch
import torch.nn as nn
import torch.nn.functional as F
from .vision_encoders import build_vision_encoder
logger = logging.getLogger("glint")
def build_projector(config):
if config.model_type == "mlp":
model = MLP(config)
else:
raise NotImplementedError()
return model
def get_activation(activation_name):
activation_map = {"silu": nn.SiLU(), "relu": nn.ReLU(), "gelu": nn.GELU(), "tanh": nn.Tanh()}
activation_name = activation_name.lower()
if activation_name not in activation_map:
raise ValueError(f"Unknown activation: {activation_name}. Available: {list(activation_map.keys())}")
return activation_map[activation_name]
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
activation = get_activation(getattr(config, "activation", "silu"))
dropout = getattr(config, "dropout", 0.0)
num_layers = getattr(config, "num_layers", 3)
layers = []
layers.append(nn.Linear(config.input_dim, config.hidden_dim))
layers.append(activation)
if dropout > 0:
layers.append(nn.Dropout(dropout))
for _ in range(num_layers - 2):
layers.append(nn.Linear(config.hidden_dim, config.hidden_dim))
layers.append(activation)
if dropout > 0:
layers.append(nn.Dropout(dropout))
layers.append(nn.Linear(config.hidden_dim, config.out_dim))
self.mlp = nn.Sequential(*layers)
def forward(self, x):
return self.mlp(x)
class DFRLoss(nn.Module):
def __init__(
self,
use_token_wise=True,
**kwargs,
):
super().__init__()
self.use_token_wise = use_token_wise
def forward(
self,
proj_student_features,
teacher_features,
):
outputs = dict()
if isinstance(proj_student_features, list):
total_loss = 0.0
n = 0
for ps, ts in zip(proj_student_features, teacher_features):
if isinstance(ps, dict):
for k in ps.keys():
total_loss += self._compute_loss(ps[k], ts[k])
n += 1
else:
total_loss += self._compute_loss(ps, ts)
n += 1
loss = total_loss / max(n, 1)
elif isinstance(proj_student_features, dict):
total_loss = 0
num_pairs = len(proj_student_features)
for key in proj_student_features.keys():
proj_student = proj_student_features[key]
teacher = teacher_features[key]
total_loss += self._compute_loss(proj_student, teacher)
loss = total_loss / num_pairs
else:
loss = self._compute_loss(proj_student_features, teacher_features)
losses = {
"dfr_loss": loss,
"loss": loss,
}
outputs["losses"] = losses
return outputs
def _compute_loss(self, proj_student, teacher):
proj_student = F.normalize(proj_student, p=2, dim=-1)
teacher = F.normalize(teacher, p=2, dim=-1)
if self.use_token_wise:
similarity = torch.sum(proj_student * teacher, dim=-1)
loss = (1.0 - similarity).mean()
else:
proj_student_global = proj_student.mean(dim=1)
teacher_global = teacher.mean(dim=1)
similarity = torch.sum(proj_student_global * teacher_global, dim=-1)
loss = (1.0 - similarity).mean()
loss = loss.mean()
return loss
class DFRModule(nn.Module):
def __init__(self, dfr_config):
super().__init__()
self.config = dfr_config
self.use_cache = getattr(dfr_config, "use_cache", False)
if not self.use_cache:
self.vision_teacher_model = build_vision_encoder(dfr_config.vfm_teacher_config)
else:
logger.info("DFRModule: use_cache=True, skipping teacher model loading")
self.layer_mapping = dfr_config.layer_mapping
student_layers = self.layer_mapping["student_layers"]
teacher_layers = self.layer_mapping["teacher_layers"]
self._validate_layer_mapping(student_layers, teacher_layers)
self.num_layer_pairs = len(student_layers)
self._build_projectors(dfr_config.projector_config, student_layers, teacher_layers)
def _validate_layer_mapping(self, student_layers, teacher_layers):
if len(student_layers) != len(teacher_layers):
raise ValueError(
f"student_layer and teacher_layer must have same length. "
f"Got {len(student_layers)} and {len(teacher_layers)}"
)
def _build_projectors(self, projector_config, student_layers, teacher_layers):
use_multiple_projectors = self.layer_mapping.get("use_multiple_projectors", False)
if use_multiple_projectors and self.num_layer_pairs > 1:
self.projector = nn.ModuleDict()
for s_layer, t_layer in zip(student_layers, teacher_layers):
projector_key = f"projector_{s_layer}_{t_layer}"
self.projector[projector_key] = build_projector(projector_config)
self.use_multiple_projectors = True
else:
self.projector = build_projector(projector_config)
self.use_multiple_projectors = False
def forward(self, pixel_values, student_hidden_states, cached_teacher_features=None):
student_layers = self.layer_mapping["student_layers"]
teacher_layers = self.layer_mapping["teacher_layers"]
if cached_teacher_features is not None:
teacher_hidden_states = cached_teacher_features
else:
with torch.no_grad():
teacher_outputs = self.vision_teacher_model(pixel_values, output_hidden_states=True)
results = {
"teacher_features": {},
"proj_student_features": {},
}
for s_layer, t_layer in zip(student_layers, teacher_layers):
student_feat = student_hidden_states[s_layer]
if cached_teacher_features is not None:
teacher_feat = teacher_hidden_states[t_layer]
teacher_feat = teacher_feat.to(dtype=student_feat.dtype)
else:
teacher_feat = teacher_outputs.hidden_states[t_layer]
proj_student_feat = self._project_student_features(student_feat, s_layer, t_layer)
results["teacher_features"][f"layer_{s_layer}_{t_layer}"] = teacher_feat
results["proj_student_features"][f"layer_{s_layer}_{t_layer}"] = proj_student_feat
return results
def _project_student_features(self, student_feat, s_layer, t_layer):
if self.use_multiple_projectors:
projector_key = f"projector_{s_layer}_{t_layer}"
return self.projector[projector_key](student_feat)
else:
return self.projector(student_feat)
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