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import torch
import torch.nn as nn
import json
import torch.nn.functional as F
from optim.loss.loss import LOSS_REGISTRY
try:
import torch.distributed.nn
from torch import distributed as dist
has_distributed = True
except ImportError:
has_distributed = False
try:
import horovod.torch as hvd
except ImportError:
hvd = None
def load_json(path):
with open(path, 'r') as f:
return json.load(f)
def gather_features(
image_features,
text_features,
local_loss=False,
gather_with_grad=False,
rank=0,
world_size=1,
use_horovod=False
):
assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
if use_horovod:
assert hvd is not None, 'Please install horovod'
if gather_with_grad:
all_image_features = hvd.allgather(image_features)
all_text_features = hvd.allgather(text_features)
else:
with torch.no_grad():
all_image_features = hvd.allgather(image_features)
all_text_features = hvd.allgather(text_features)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
gathered_image_features[rank] = image_features
gathered_text_features[rank] = text_features
all_image_features = torch.cat(gathered_image_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
else:
# We gather tensors from all gpus
if gather_with_grad:
all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
else:
gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
dist.all_gather(gathered_image_features, image_features)
dist.all_gather(gathered_text_features, text_features)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_image_features[rank] = image_features
gathered_text_features[rank] = text_features
all_image_features = torch.cat(gathered_image_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
return all_image_features, all_text_features
class ClipLoss(nn.Module):
def __init__(
self,
local_loss=False,
gather_with_grad=False,
cache_labels=False,
rank=0,
world_size=1,
use_horovod=False,
):
super().__init__()
self.local_loss = local_loss
self.gather_with_grad = gather_with_grad
self.cache_labels = cache_labels
self.rank = rank
self.world_size = world_size
self.use_horovod = use_horovod
# cache state
self.prev_num_logits = 0
self.labels = {}
def get_ground_truth(self, device, num_logits) -> torch.Tensor:
# calculated ground-truth and cache if enabled
if self.prev_num_logits != num_logits or device not in self.labels:
labels = torch.arange(num_logits, device=device, dtype=torch.long)
if self.world_size > 1 and self.local_loss:
labels = labels + num_logits * self.rank
if self.cache_labels:
self.labels[device] = labels
self.prev_num_logits = num_logits
else:
labels = self.labels[device]
return labels
def get_logits(self, image_features, text_features, logit_scale, logit_bias=None):
if self.world_size > 1:
all_image_features, all_text_features = gather_features(
image_features,
text_features,
local_loss=self.local_loss,
gather_with_grad=self.gather_with_grad,
rank=self.rank,
world_size=self.world_size,
use_horovod=self.use_horovod,
)
if self.local_loss:
logits_per_image = logit_scale * image_features @ all_text_features.T
logits_per_text = logit_scale * text_features @ all_image_features.T
else:
logits_per_image = logit_scale * all_image_features @ all_text_features.T
logits_per_text = logits_per_image.T
else:
logits_per_image = logit_scale * image_features @ text_features.T
logits_per_text = logit_scale * text_features @ image_features.T
if logit_bias is not None:
logits_per_image += logit_bias
logits_per_text += logit_bias
return logits_per_image, logits_per_text
def forward(
self,
image_features,
text_features,
logit_scale,
logit_bias=None,
output_dict=False,
bidirectional = True
):
device = image_features.device
logits_per_image, logits_per_text = self.get_logits(
image_features,
text_features,
logit_scale,
logit_bias=logit_bias,
)
labels = self.get_ground_truth(device, logits_per_image.shape[0])
if bidirectional:
total_loss = (
F.cross_entropy(logits_per_image, labels) +
F.cross_entropy(logits_per_text, labels)
) / 2
else:
total_loss = F.cross_entropy(logits_per_image, labels)
return {"contrastive_loss": total_loss} if output_dict else total_loss
def soft_targets_from_dist(dist_bvv: torch.Tensor, tau_d: float, eye: torch.Tensor, eps=1e-8):
"""
dist_bvv: (B,V,V) distances (>=0). Smaller = closer.
eye: (V,V) bool diagonal mask
returns: (B,V,V) row-stochastic soft targets, diag=0
"""
# mask diagonal by setting huge distance (or directly zero later)
dist = dist_bvv.masked_fill(eye.unsqueeze(0), float("inf"))
# logits for target distribution (higher for closer)
tgt_logits = -dist / tau_d
tgt_logits = tgt_logits.masked_fill(eye.unsqueeze(0), -1e9)
p = torch.softmax(tgt_logits, dim=-1) # (B,V,V)
# numerical safety: ensure diag is exactly 0 then renormalize
p = p.masked_fill(eye.unsqueeze(0), 0.0)
p = p / (p.sum(dim=-1, keepdim=True) + eps)
return p
def soft_ce_from_logits(logits_bvv: torch.Tensor, p_bvv: torch.Tensor, eye: torch.Tensor):
"""
logits_bvv: (B,V,V) predicted logits
p_bvv: (B,V,V) soft targets (row-stochastic)
"""
log_q = F.log_softmax(logits_bvv, dim=-1) # (B,V,V)
# cross-entropy with soft labels: -sum p * log q
loss = -(p_bvv * log_q).sum(dim=-1) # (B,V)
return loss.mean()
@LOSS_REGISTRY.register()
class WarmUpPM_loss(nn.Module):
def __init__(self, cfg, accelerator):
super().__init__()
self.accelerator = accelerator
self.temperature = getattr(cfg, "contrastive_temperature", 1.0)
self.num_gpu = cfg.num_gpu
world_sz = self.accelerator.num_processes
rank = self.accelerator.process_index
self.contrast_loss = ClipLoss(rank=rank, world_size=world_sz)
def forward(self, data_dict):
logit_scale = data_dict['logit_scale']
view_rgb = data_dict['inter_view_rgb_embed'] # (B,V,D)
view_txt = data_dict['inter_view_txt_embed'] # (B,V,D)
view_pm = data_dict['inter_view_pm_embed'] # (B,V,D)
view_pm = F.normalize(view_pm, p=2, dim=1)
view_rgb = F.normalize(view_rgb, p=2, dim=1)
view_txt = F.normalize(view_txt, p=2, dim=1)
loss_rgb_pm_view = self.contrast_loss(view_pm, view_rgb, logit_scale)
loss_txt_pm_view = self.contrast_loss(view_pm, view_txt, logit_scale)
return {
'loss_rgb_pm_view': loss_rgb_pm_view,
'loss_txt_pm_view': loss_txt_pm_view
}
# @LOSS_REGISTRY.register()
# class SceneViewPM_loss(nn.Module):
# def __init__(self, cfg, accelerator):
# super().__init__()
# self.accelerator = accelerator
# world_sz = self.accelerator.num_processes
# rank = self.accelerator.process_index
# self.contrast_loss = ClipLoss(rank=rank, world_size=world_sz)
# self.chamfer_ranking = load_json(
# "/home/m50048399/transfered/ye_project/Project2/chamfer_rankings.json"
# )
# # Cache: (scan_id, V) -> pos_idx_cpu: LongTensor[V] (CPU)
# self._pos_idx_cache = {}
# # Per-scan caches
# self._view_keys_cache = {} # scan_id -> List[str] sorted view keys
# self._first_pos_cache = {} # scan_id -> Dict[str, Optional[str]] first non-self neighbor (string)
# # Cache diagonal masks per (V, device)
# self._eye_mask_cache = {} # (V, device) -> BoolTensor[V,V]
# self._dbg_every = int(getattr(cfg, "dbg_every", 10)) if cfg is not None else 10
# self._dbg_step = 0
# # -------------------------
# # Small helper: cached diag mask
# # -------------------------
# def _get_eye_mask(self, V: int, device: torch.device):
# key = (V, device)
# m = self._eye_mask_cache.get(key, None)
# if m is None:
# m = torch.eye(V, dtype=torch.bool, device=device)
# self._eye_mask_cache[key] = m
# return m
# # -------------------------
# # Per-scan cache build
# # -------------------------
# def _ensure_scan_cached(self, scan_id: str):
# scan_id = str(scan_id)
# if scan_id in self._view_keys_cache and scan_id in self._first_pos_cache:
# return
# rank_dict = self.chamfer_ranking[scan_id] # {view_key: [neighbors...]}
# # Normalize keys to str once (neighbors are accessed lazily)
# keys = [str(k) for k in rank_dict.keys()]
# try:
# keys_sorted = sorted(keys, key=lambda x: int(x))
# except Exception:
# keys_sorted = keys
# # Precompute first non-self neighbor per key (as str)
# first_pos = {}
# for k in keys_sorted:
# cands = rank_dict[k] if k in rank_dict else rank_dict[int(k)] # tolerate int keys if present
# fp = None
# ks = str(k)
# for v in cands:
# vs = str(v)
# if vs != ks:
# fp = vs
# break
# first_pos[ks] = fp
# self._view_keys_cache[scan_id] = keys_sorted
# self._first_pos_cache[scan_id] = first_pos
# def _get_view_keys(self, scan_id):
# scan_id = str(scan_id)
# vk = self._view_keys_cache.get(scan_id, None)
# if vk is not None:
# return vk
# self._ensure_scan_cached(scan_id)
# return self._view_keys_cache[scan_id]
# def soft_targets_from_dist(dist_bvv: torch.Tensor, tau_d: float, eye: torch.Tensor, eps=1e-8):
# """
# dist_bvv: (B,V,V) distances (>=0). Smaller = closer.
# eye: (V,V) bool diagonal mask
# returns: (B,V,V) row-stochastic soft targets, diag=0
# """
# # mask diagonal by setting huge distance (or directly zero later)
# dist = dist_bvv.masked_fill(eye.unsqueeze(0), float("inf"))
# # logits for target distribution (higher for closer)
# tgt_logits = -dist / tau_d
# tgt_logits = tgt_logits.masked_fill(eye.unsqueeze(0), -1e9)
# p = torch.softmax(tgt_logits, dim=-1) # (B,V,V)
# # numerical safety: ensure diag is exactly 0 then renormalize
# p = p.masked_fill(eye.unsqueeze(0), 0.0)
# p = p / (p.sum(dim=-1, keepdim=True) + eps)
# return p
# @torch.no_grad()
# def _get_pos_idx_cpu(self, scan_id: str, V: int) -> torch.LongTensor:
# """
# CPU LongTensor [V], where pos_idx[i] is index of closest *other* view for view i.
# Cached per (scan_id, V).
# """
# scan_id = str(scan_id)
# key = (scan_id, V)
# cached = self._pos_idx_cache.get(key, None)
# if cached is not None:
# return cached
# self._ensure_scan_cached(scan_id)
# view_keys = self._view_keys_cache[scan_id][:V]
# key2pos = {k: i for i, k in enumerate(view_keys)} # O(V)
# first_pos = self._first_pos_cache[scan_id]
# pos_idx = [0] * V
# for i, cur in enumerate(view_keys):
# cur = str(cur)
# pv = first_pos.get(cur, None) # already first non-self neighbor
# if pv is None or pv not in key2pos:
# pos_idx[i] = (i + 1) % V
# else:
# pos_idx[i] = key2pos[pv]
# out = torch.tensor(pos_idx, dtype=torch.long, device="cpu")
# self._pos_idx_cache[key] = out
# return out
# def forward(self, data_dict):
# scan_ids = list(data_dict["scan_id"])
# logit_scale = data_dict["logit_scale"]
# view_rgb = data_dict["inter_view_rgb_embed"] # (B,V,D)
# view_txt = data_dict["inter_view_txt_embed"] # (B,V,D)
# view_pm = data_dict["inter_view_pm_embed"] # (B,V,D)
# scene_rgb = data_dict["scene_rgb_embed"] # (B,D)
# scene_txt = data_dict["scene_text_embed"] # (B,D)
# scene_pm = data_dict["scene_pm_embed"] # (B,D)
# B, V, D = view_pm.shape
# device = view_pm.device
# # ---- Normalize ----
# view_pm_norm = F.normalize(view_pm, p=2, dim=-1) # (B,V,D)
# view_rgb_norm = F.normalize(view_rgb, p=2, dim=-1) # (B,V,D)
# view_txt_norm = F.normalize(view_txt, p=2, dim=-1) # (B,V,D)
# view_pm_f = view_pm_norm.reshape(-1, D) # (B*V,D)
# view_rgb_f = view_rgb_norm.reshape(-1, D)
# view_txt_f = view_txt_norm.reshape(-1, D)
# scene_rgb = F.normalize(scene_rgb, p=2, dim=-1)
# scene_txt = F.normalize(scene_txt, p=2, dim=-1)
# scene_pm = F.normalize(scene_pm, p=2, dim=-1)
# # temperature safety
# if torch.is_tensor(logit_scale):
# logit_scale = logit_scale.clamp(max=100)
# # ---- Cross-modal CLIP losses (unchanged) ----
# loss_rgb_pm_view = self.contrast_loss(view_pm_f, view_rgb_f, logit_scale)
# loss_txt_pm_view = self.contrast_loss(view_pm_f, view_txt_f, logit_scale)
# loss_rgb_pm_scene = self.contrast_loss(scene_pm, scene_rgb, logit_scale)
# loss_txt_pm_scene = self.contrast_loss(scene_pm, scene_txt, logit_scale)
# # ---- Targets: nearest neighbor indices per view (B,V) ----
# pos_idx_cpu = [self._get_pos_idx_cpu(str(s), V) for s in scan_ids]
# targets = torch.stack(pos_idx_cpu, dim=0).to(device, non_blocking=True) # (B,V)
# # ---- Common masks/constants ----
# eye = self._get_eye_mask(V, device) # (V,V) bool
# NEG = -1e4 if view_pm_f.dtype in (torch.float16, torch.bfloat16) else -1e9
# # ============================================================
# # 1) PM–PM neighbor matching (existing, but safer masking)
# # ============================================================
# logits_pm_pm = torch.bmm(view_pm_norm, view_pm_norm.transpose(1, 2)) * logit_scale # (B,V,V)
# logits_pm_pm = logits_pm_pm.masked_fill(eye.unsqueeze(0), NEG)
# loss_pm_pm_view = F.cross_entropy(
# logits_pm_pm.reshape(B * V, V),
# targets.reshape(B * V),
# )
# # ============================================================
# # 2) NEW: PM -> RGB neighbor matching
# # anchor: PM_i, candidates: RGB_j, target: chamfer NN index
# # ============================================================
# # logits_pm_rgb = torch.bmm(view_pm_norm, view_rgb_norm.transpose(1, 2)) * logit_scale # (B,V,V)
# # # mask j==i if your ranking excludes self and you don't want trivial matches
# # logits_pm_rgb = logits_pm_rgb.masked_fill(eye.unsqueeze(0), NEG)
# # loss_pm_rgb_nn = F.cross_entropy(
# # logits_pm_rgb.reshape(B * V, V),
# # targets.reshape(B * V),
# # )
# # # ============================================================
# # # 3) NEW: PM -> TXT neighbor matching
# # # ============================================================
# # logits_pm_txt = torch.bmm(view_pm_norm, view_txt_norm.transpose(1, 2)) * logit_scale # (B,V,V)
# # logits_pm_txt = logits_pm_txt.masked_fill(eye.unsqueeze(0), NEG)
# # loss_pm_txt_nn = F.cross_entropy(
# # logits_pm_txt.reshape(B * V, V),
# # targets.reshape(B * V),
# # )
# # ---- Weights (tune) ----
# geo_w = 1e-1 # your existing weight style
# loss_pm_pm_view = geo_w * loss_pm_pm_view
# # loss_pm_rgb_nn = geo_w * loss_pm_rgb_nn
# # loss_pm_txt_nn = geo_w * loss_pm_txt_nn
# return {
# "loss_rgb_pm_view": loss_rgb_pm_view,
# "loss_txt_pm_view": loss_txt_pm_view,
# "loss_rgb_pm_scene": loss_rgb_pm_scene,
# "loss_txt_pm_scene": loss_txt_pm_scene,
# # neighbor-matching regularizers
# "loss_geo_pm_nn": loss_pm_pm_view,
# # "loss_geo_pm_rgb_nn": loss_pm_rgb_nn,
# # "loss_geo_pm_txt_nn": loss_pm_txt_nn,
# }
import torch
import torch.nn as nn
import torch.nn.functional as F
# assumes you already have:
# - LOSS_REGISTRY
# - ClipLoss
# - load_json
@LOSS_REGISTRY.register()
class SceneViewPM_loss(nn.Module):
def __init__(self, cfg, accelerator):
super().__init__()
self.accelerator = accelerator
world_sz = self.accelerator.num_processes
rank = self.accelerator.process_index
self.contrast_loss = ClipLoss(rank=rank, world_size=world_sz)
self.chamfer_ranking = load_json(
"/home/m50048399/transfered/ye_project/Project2/chamfer_rankings.json"
)
# ---- caches ----
self._pos_idx_cache = {} # (scan_id, V) -> LongTensor[V] on CPU
self._dist_mat_cache = {} # (scan_id, V) -> FloatTensor[V,V] on CPU
self._view_keys_cache = {} # scan_id -> sorted List[str]
self._first_pos_cache = {} # scan_id -> Dict[str, Optional[str]]
self._eye_mask_cache = {} # (V, device) -> BoolTensor[V,V]
# ---- knobs for soft neighbor loss ----
# tau_d: softness over neighbor ranks/distances (smaller=sharper)
self.tau_d = float(getattr(cfg, "pm_nn_tau_d", 0.35)) if cfg is not None else 0.35
# alpha: mix hard NN one-hot with soft distribution
self.soft_alpha = float(getattr(cfg, "pm_nn_soft_alpha", 0.7)) if cfg is not None else 0.7
self._dbg_every = int(getattr(cfg, "dbg_every", 10)) if cfg is not None else 10
self._dbg_step = 0
# -------------------------
# cached diag mask
# -------------------------
def _get_eye_mask(self, V: int, device: torch.device):
key = (V, device)
m = self._eye_mask_cache.get(key, None)
if m is None:
m = torch.eye(V, dtype=torch.bool, device=device)
self._eye_mask_cache[key] = m
return m
# -------------------------
# per-scan cache build
# -------------------------
def _ensure_scan_cached(self, scan_id: str):
scan_id = str(scan_id)
if scan_id in self._view_keys_cache and scan_id in self._first_pos_cache:
return
rank_dict = self.chamfer_ranking[scan_id] # {view_key: [neighbors...]}
# normalize keys to str
keys = [str(k) for k in rank_dict.keys()]
try:
keys_sorted = sorted(keys, key=lambda x: int(x))
except Exception:
keys_sorted = keys
# first non-self neighbor per key
first_pos = {}
for k in keys_sorted:
ks = str(k)
# tolerate int keys in json
if ks in rank_dict:
cands = rank_dict[ks]
else:
try:
cands = rank_dict[int(ks)]
except Exception:
cands = []
fp = None
for v in cands:
vs = str(v)
if vs != ks:
fp = vs
break
first_pos[ks] = fp
self._view_keys_cache[scan_id] = keys_sorted
self._first_pos_cache[scan_id] = first_pos
# -------------------------
# soft target helpers
# -------------------------
@staticmethod
def _soft_targets_from_dist(dist_bvv: torch.Tensor, tau_d: float, eye: torch.Tensor, eps: float = 1e-8):
"""
dist_bvv: (B,V,V) distances (>=0). Smaller = closer.
eye: (V,V) bool diagonal mask
returns: (B,V,V) row-stochastic soft targets, diag=0
"""
# ensure diagonal excluded
dist = dist_bvv.masked_fill(eye.unsqueeze(0), float("inf"))
tgt_logits = -dist / max(tau_d, 1e-8)
tgt_logits = tgt_logits.masked_fill(eye.unsqueeze(0), -1e9)
p = torch.softmax(tgt_logits, dim=-1) # (B,V,V)
p = p.masked_fill(eye.unsqueeze(0), 0.0)
p = p / (p.sum(dim=-1, keepdim=True) + eps)
return p
@staticmethod
def _soft_ce_from_logits(logits_bvv: torch.Tensor, p_bvv: torch.Tensor):
"""Soft-label cross entropy: -sum p * log softmax(logits)"""
log_q = F.log_softmax(logits_bvv, dim=-1)
loss = -(p_bvv * log_q).sum(dim=-1) # (B,V)
return loss.mean()
# -------------------------
# hard NN targets (existing)
# -------------------------
@torch.no_grad()
def _get_pos_idx_cpu(self, scan_id: str, V: int) -> torch.LongTensor:
"""
CPU LongTensor [V], where pos_idx[i] is index of closest *other* view for view i.
Cached per (scan_id, V).
"""
scan_id = str(scan_id)
key = (scan_id, V)
cached = self._pos_idx_cache.get(key, None)
if cached is not None:
return cached
self._ensure_scan_cached(scan_id)
view_keys = self._view_keys_cache[scan_id][:V]
key2pos = {k: i for i, k in enumerate(view_keys)}
first_pos = self._first_pos_cache[scan_id]
pos_idx = [0] * V
for i, cur in enumerate(view_keys):
cur = str(cur)
pv = first_pos.get(cur, None)
if pv is None or pv not in key2pos:
pos_idx[i] = (i + 1) % V
else:
pos_idx[i] = key2pos[pv]
out = torch.tensor(pos_idx, dtype=torch.long, device="cpu")
self._pos_idx_cache[key] = out
return out
# -------------------------
# NEW: distance matrix from ranking (soft neighbors)
# -------------------------
@torch.no_grad()
def _get_rank_dist_cpu(self, scan_id: str, V: int) -> torch.FloatTensor:
"""
Build a (V,V) "distance" matrix from chamfer ranking list:
dist[i,j] = rank_position_of_view_j_in_view_i_neighbor_list
Smaller = closer. Missing entries get large distance.
Cached per (scan_id, V).
"""
scan_id = str(scan_id)
key = (scan_id, V)
cached = self._dist_mat_cache.get(key, None)
if cached is not None:
return cached
self._ensure_scan_cached(scan_id)
rank_dict = self.chamfer_ranking[scan_id]
view_keys = self._view_keys_cache[scan_id][:V]
key2pos = {k: i for i, k in enumerate(view_keys)}
# large distance for missing edges
BIG = float(V + 50)
dist = torch.full((V, V), BIG, dtype=torch.float32, device="cpu")
for i, ki in enumerate(view_keys):
kis = str(ki)
# read neighbor ranking list
if kis in rank_dict:
neigh = rank_dict[kis]
else:
try:
neigh = rank_dict[int(kis)]
except Exception:
neigh = []
# map neighbor -> rank (skip self)
# rank starts at 0 for closest neighbor
r = 0
for nb in neigh:
nbs = str(nb)
if nbs == kis:
continue
j = key2pos.get(nbs, None)
if j is None:
continue
dist[i, j] = float(r)
r += 1
if r >= V: # no need to go too deep
break
# diagonal zero (won't be used; will be masked)
dist.fill_diagonal_(0.0)
self._dist_mat_cache[key] = dist
return dist
def forward(self, data_dict):
scan_ids = list(data_dict["scan_id"])
logit_scale = data_dict["logit_scale"]
view_rgb = data_dict["inter_view_rgb_embed"] # (B,V,D)
view_txt = data_dict["inter_view_txt_embed"] # (B,V,D)
view_pm = data_dict["inter_view_pm_embed"] # (B,V,D)
# view_context_pm = data_dict["inter_view_context_pm_embed"]# (B,V,D)
view_ground_txt = data_dict["inter_view_ground_txt_embed"]# (B,V,D)
scene_rgb = data_dict["scene_rgb_embed"] # (B,D)
scene_txt = data_dict["scene_text_embed"] # (B,D)
scene_pm = data_dict["scene_pm_embed"] # (B,D)
B, V, D = view_pm.shape
device = view_pm.device
# ---- Normalize ----
view_pm_norm = F.normalize(view_pm, p=2, dim=-1) # (B,V,D)
view_rgb_norm = F.normalize(view_rgb, p=2, dim=-1)
view_txt_norm = F.normalize(view_txt, p=2, dim=-1)
# view_context_pm_norm = F.normalize(view_context_pm, p=2, dim=-1)
view_ground_txt_norm = F.normalize(view_ground_txt, p=2, dim=-1)
scene_rgb = F.normalize(scene_rgb, p=2, dim=-1)
scene_txt = F.normalize(scene_txt, p=2, dim=-1)
scene_pm = F.normalize(scene_pm, p=2, dim=-1)
# ---- Flatten for batch-wide CLIP losses (unchanged) ----
# view_pm_context_f = view_context_pm_norm.reshape(-1, D) # (B*V,D)
view_pm_f = view_pm_norm.reshape(-1, D) # (B*V,D)
view_rgb_f = view_rgb_norm.reshape(-1, D)
view_txt_f = view_txt_norm.reshape(-1, D)
# ---- Cross-modal CLIP losses (batch-wide, unchanged) ----
loss_rgb_pm_view = self.contrast_loss(view_pm_f, view_rgb_f, logit_scale)
loss_txt_pm_view = self.contrast_loss(view_pm_f, view_txt_f, logit_scale)
loss_rgb_pm_scene = self.contrast_loss(scene_pm, scene_rgb, logit_scale)
loss_txt_pm_scene = self.contrast_loss(scene_pm, scene_txt, logit_scale)
# ============================================================
# Grounded contrastive (WITHIN-SCENE ONLY):
# context PM (anchor) <-> grounded text, negatives are other views in the SAME scene
# ============================================================
gt_logits = torch.bmm(view_pm_norm, view_ground_txt_norm.transpose(1, 2)) * logit_scale # (B,V,V)
# mask for views that actually have grounded text (pass from dataloader ideally)
ground_mask = data_dict.get(
"inter_view_ground_txt_mask",
torch.ones((B, V), dtype=torch.bool, device=device)
) # (B,V) bool
targets_diag = torch.arange(V, device=device).unsqueeze(0).expand(B, V) # (B,V)
loss_ctxpm2gt = F.cross_entropy(
gt_logits.reshape(B * V, V),
targets_diag.reshape(B * V),
reduction="none",
).reshape(B, V)
loss_gt2ctxpm = F.cross_entropy(
gt_logits.transpose(1, 2).reshape(B * V, V),
targets_diag.reshape(B * V),
reduction="none",
).reshape(B, V)
den = ground_mask.float().sum().clamp_min(1.0)
loss_grounded_view = (
((loss_ctxpm2gt + loss_gt2ctxpm) * 0.5) * ground_mask.float()
).sum() / den
# ---- Targets: hard nearest neighbor indices per view (B,V) ----
pos_idx_cpu = [self._get_pos_idx_cpu(str(s), V) for s in scan_ids]
targets = torch.stack(pos_idx_cpu, dim=0).to(device, non_blocking=True) # (B,V)
# ---- masks/constants ----
eye = self._get_eye_mask(V, device) # (V,V) bool
NEG = -1e4 if view_pm_f.dtype in (torch.float16, torch.bfloat16) else -1e9
# ============================================================
# PM–PM neighbor matching (SOFT by ranking-distance)
# ============================================================
logits_pm_pm = torch.bmm(view_pm_norm, view_pm_norm.transpose(1, 2)) * logit_scale # (B,V,V)
logits_pm_pm = logits_pm_pm.masked_fill(eye.unsqueeze(0), NEG)
# build (B,V,V) distance matrix from ranking (CPU cache -> GPU)
dist_cpu = [self._get_rank_dist_cpu(str(s), V) for s in scan_ids] # list of (V,V) CPU
dist_bvv = torch.stack(dist_cpu, dim=0).to(device, non_blocking=True) # (B,V,V)
# soft targets from distances
p_soft = self._soft_targets_from_dist(dist_bvv, tau_d=self.tau_d, eye=eye)
# optional: mix with hard one-hot NN (stabilizes training)
if self.soft_alpha > 0:
hard = F.one_hot(targets, num_classes=V).float() # (B,V,V)
hard = hard.masked_fill(eye.unsqueeze(0), 0.0)
p_mix = self.soft_alpha * hard + (1.0 - self.soft_alpha) * p_soft
p_mix = p_mix / (p_mix.sum(dim=-1, keepdim=True) + 1e-8)
else:
p_mix = p_soft
loss_pm_pm_view = self._soft_ce_from_logits(logits_pm_pm, p_mix)
# ---- Weights (tune) ----
geo_w = 1e-1
loss_pm_pm_view = geo_w * loss_pm_pm_view
return {
"loss_rgb_pm_view": loss_rgb_pm_view,
"loss_txt_pm_view": loss_txt_pm_view,
"loss_rgb_pm_scene": loss_rgb_pm_scene,
"loss_txt_pm_scene": loss_txt_pm_scene,
"loss_geo_pm_nn": loss_pm_pm_view,
"loss_grounded_view": loss_grounded_view,
}
# def forward(self, data_dict):
# scan_ids = list(data_dict["scan_id"])
# logit_scale = data_dict["logit_scale"]
# view_rgb = data_dict["inter_view_rgb_embed"] # (B,V,D)
# view_txt = data_dict["inter_view_txt_embed"] # (B,V,D)
# view_pm = data_dict["inter_view_pm_embed"] # (B,V,D)
# view_context_pm = data_dict["inter_view_context_pm_embed"] # (B,V,D)
# view_ground_txt = data_dict["inter_view_ground_txt_embed"] # (B,V,D)
# scene_rgb = data_dict["scene_rgb_embed"] # (B,D)
# scene_txt = data_dict["scene_text_embed"] # (B,D)
# scene_pm = data_dict["scene_pm_embed"] # (B,D)
# B, V, D = view_pm.shape
# device = view_pm.device
# # ---- Normalize ----
# view_pm_norm = F.normalize(view_pm, p=2, dim=-1) # (B,V,D)
# view_rgb_norm = F.normalize(view_rgb, p=2, dim=-1)
# view_txt_norm = F.normalize(view_txt, p=2, dim=-1)
# view_context_pm_norm = F.normalize(view_context_pm, p=2, dim=-1)
# view_ground_txt_norm = F.normalize(view_ground_txt, p=2, dim=-1)
# scene_rgb = F.normalize(scene_rgb, p=2, dim=-1)
# scene_txt = F.normalize(scene_txt, p=2, dim=-1)
# scene_pm = F.normalize(scene_pm, p=2, dim=-1)
# # ============================================================
# # Cross-modal CLIP losses (WITHIN-SCENE ONLY):
# # context PM (anchor) <-> RGB/TXT, negatives are other views in SAME scene
# # ============================================================
# view_mask = data_dict.get(
# "inter_view_valid_mask",
# torch.ones((B, V), dtype=torch.bool, device=device)
# ) # (B,V) bool
# targets_diag = torch.arange(V, device=device) # (V,)
# targets_bv = targets_diag.unsqueeze(0).expand(B, V) # (B,V)
# # --- ctxPM <-> RGB ---
# logits_ctxpm_rgb = torch.bmm(
# view_context_pm_norm, view_rgb_norm.transpose(1, 2)
# ) * logit_scale # (B,V,V)
# loss_ctxpm2rgb = F.cross_entropy(
# logits_ctxpm_rgb.reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# loss_rgb2ctxpm = F.cross_entropy(
# logits_ctxpm_rgb.transpose(1, 2).reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# den_v = view_mask.float().sum().clamp_min(1.0)
# loss_rgb_pm_view = (
# ((loss_ctxpm2rgb + loss_rgb2ctxpm) * 0.5) * view_mask.float()
# ).sum() / den_v
# # --- ctxPM <-> TXT ---
# logits_ctxpm_txt = torch.bmm(
# view_context_pm_norm, view_txt_norm.transpose(1, 2)
# ) * logit_scale # (B,V,V)
# loss_ctxpm2txt = F.cross_entropy(
# logits_ctxpm_txt.reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# loss_txt2ctxpm = F.cross_entropy(
# logits_ctxpm_txt.transpose(1, 2).reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# loss_txt_pm_view = (
# ((loss_ctxpm2txt + loss_txt2ctxpm) * 0.5) * view_mask.float()
# ).sum() / den_v
# # ---- Scene-level CLIP losses (batch-wide is fine; it's already per-sample) ----
# loss_rgb_pm_scene = self.contrast_loss(scene_pm, scene_rgb, logit_scale)
# loss_txt_pm_scene = self.contrast_loss(scene_pm, scene_txt, logit_scale)
# # ============================================================
# # Grounded contrastive (WITHIN-SCENE ONLY):
# # context PM (anchor) <-> grounded text, negatives are other views in the SAME scene
# # ============================================================
# gt_logits = torch.bmm(
# view_context_pm_norm, view_ground_txt_norm.transpose(1, 2)
# ) * logit_scale # (B,V,V)
# ground_mask = data_dict.get(
# "inter_view_ground_txt_mask",
# torch.ones((B, V), dtype=torch.bool, device=device)
# ) # (B,V) bool
# # If you also have padding views, you typically want to AND them:
# ground_mask = ground_mask & view_mask
# loss_ctxpm2gt = F.cross_entropy(
# gt_logits.reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# loss_gt2ctxpm = F.cross_entropy(
# gt_logits.transpose(1, 2).reshape(B * V, V),
# targets_bv.reshape(B * V),
# reduction="none",
# ).reshape(B, V)
# den_g = ground_mask.float().sum().clamp_min(1.0)
# loss_grounded_view = (
# ((loss_ctxpm2gt + loss_gt2ctxpm) * 0.5) * ground_mask.float()
# ).sum() / den_g
# # ---- Targets: hard nearest neighbor indices per view (B,V) ----
# pos_idx_cpu = [self._get_pos_idx_cpu(str(s), V) for s in scan_ids]
# targets = torch.stack(pos_idx_cpu, dim=0).to(device, non_blocking=True) # (B,V)
# # ---- masks/constants ----
# eye = self._get_eye_mask(V, device) # (V,V) bool
# NEG = -1e4 if view_pm_norm.dtype in (torch.float16, torch.bfloat16) else -1e9
# # ============================================================
# # PM–PM neighbor matching (SOFT by ranking-distance)
# # ============================================================
# logits_pm_pm = torch.bmm(view_pm_norm, view_pm_norm.transpose(1, 2)) * logit_scale # (B,V,V)
# logits_pm_pm = logits_pm_pm.masked_fill(eye.unsqueeze(0), NEG)
# # build (B,V,V) distance matrix from ranking (CPU cache -> GPU)
# dist_cpu = [self._get_rank_dist_cpu(str(s), V) for s in scan_ids] # list of (V,V) CPU
# dist_bvv = torch.stack(dist_cpu, dim=0).to(device, non_blocking=True) # (B,V,V)
# # soft targets from distances
# p_soft = self._soft_targets_from_dist(dist_bvv, tau_d=self.tau_d, eye=eye)
# # optional: mix with hard one-hot NN (stabilizes training)
# if self.soft_alpha > 0:
# hard = F.one_hot(targets, num_classes=V).float() # (B,V,V)
# hard = hard.masked_fill(eye.unsqueeze(0), 0.0)
# p_mix = self.soft_alpha * hard + (1.0 - self.soft_alpha) * p_soft
# p_mix = p_mix / (p_mix.sum(dim=-1, keepdim=True) + 1e-8)
# else:
# p_mix = p_soft
# loss_pm_pm_view = self._soft_ce_from_logits(logits_pm_pm, p_mix)
# # ---- Weights (tune) ----
# geo_w = 1e-1
# loss_pm_pm_view = geo_w * loss_pm_pm_view
# return {
# "loss_rgb_pm_view": loss_rgb_pm_view,
# "loss_txt_pm_view": loss_txt_pm_view,
# "loss_rgb_pm_scene": loss_rgb_pm_scene,
# "loss_txt_pm_scene": loss_txt_pm_scene,
# "loss_geo_pm_nn": loss_pm_pm_view,
# "loss_grounded_view": loss_grounded_view,
# } |