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import torch
import torch.nn as nn
from vggt.heads.camera_head import CameraHead
from vggt.heads.dpt_head import DPTHead
from .aggregator import Aggregator
from .decoder import Decoder
def freeze_all_params(modules):
for module in modules:
try:
for n, param in module.named_parameters():
param.requires_grad = False
except AttributeError:
# module is directly a parameter
module.requires_grad = False
class VDPM(nn.Module):
def __init__(self, cfg, img_size=518, patch_size=14, embed_dim=1024):
super().__init__()
self.cfg = cfg
self.aggregator = Aggregator(
img_size=img_size,
patch_size=patch_size,
embed_dim=embed_dim,
)
self.decoder = Decoder(
cfg,
dim_in=2*embed_dim,
embed_dim=embed_dim,
depth=cfg.model.decoder_depth
)
self.point_head = DPTHead(dim_in=2 * embed_dim, output_dim=4, activation="inv_log", conf_activation="expp1")
self.camera_head = CameraHead(dim_in=2 * embed_dim)
self.profile = False
self.set_freeze()
def set_freeze(self):
to_be_frozen = [self.aggregator.patch_embed]
freeze_all_params(to_be_frozen)
def forward(
self,
views, autocast_dpt=None
):
images = torch.stack([view["img"] for view in views], dim=1)
aggregated_tokens_list, patch_start_idx = self.aggregator(images)
res_dynamic = dict()
if self.decoder is not None:
cond_view_idxs = torch.stack([view["view_idxs"][:, 1] for view in views], dim=1)
decoded_tokens = self.decoder(images, aggregated_tokens_list, patch_start_idx, cond_view_idxs)
if autocast_dpt is None:
autocast_dpt = torch.amp.autocast("cuda", enabled=False)
with autocast_dpt:
pts3d, pts3d_conf = self.point_head(
aggregated_tokens_list, images, patch_start_idx
)
padded_decoded_tokens = {
layer_idx: decoded_tokens[idx]
for idx, layer_idx in enumerate(self.point_head.intermediate_layer_idx)
}
pts3d_dyn, pts3d_dyn_conf = self.point_head(
padded_decoded_tokens, images, patch_start_idx
)
res_dynamic |= {
"pts3d": pts3d_dyn,
"conf": pts3d_dyn_conf
}
pose_enc_list = self.camera_head(aggregated_tokens_list)
res_dynamic |= {"pose_enc_list": pose_enc_list}
res_static = dict(
pts3d=pts3d,
conf=pts3d_conf
)
return res_static, res_dynamic
def inference(
self,
views,
images=None,
num_timesteps=None
):
profile = self.profile and torch.cuda.is_available()
if profile:
ev = lambda: torch.cuda.Event(enable_timing=True)
e_start, e_agg, e_cam_end = ev(), ev(), ev()
e_dec_starts, e_dec_ends = [], []
e_head_starts, e_head_ends = [], []
e_cam_start = ev()
mem_before = torch.cuda.memory_allocated() / 1024**3
e_start.record()
autocast_amp = torch.amp.autocast("cuda", enabled=True, dtype=torch.float16)
if images is None:
images = torch.stack([view["img"] for view in views], dim=1)
# If not profiling per-stage, measure a single total inference time (minimal overhead)
if not profile:
torch.cuda.synchronize()
_t_start = time.time()
with autocast_amp:
aggregated_tokens_list, patch_start_idx = self.aggregator(images)
if profile:
e_agg.record()
mem_after_agg = torch.cuda.memory_allocated() / 1024**3
S = images.shape[1]
# Determine number of timesteps to query
if num_timesteps is None:
if views is not None and "view_idxs" in views[0]:
try:
all_idxs = torch.cat([v["view_idxs"][:, 1] for v in views])
num_timesteps = int(all_idxs.max().item()) + 1
except:
num_timesteps = S
else:
num_timesteps = S
predictions = dict()
pointmaps = []
ones = torch.ones(1, S, dtype=torch.int64)
for time_ in range(num_timesteps):
cond_view_idxs = ones * time_
if profile:
e_ds = ev(); e_ds.record(); e_dec_starts.append(e_ds)
with autocast_amp:
decoded_tokens = self.decoder(images, aggregated_tokens_list, patch_start_idx, cond_view_idxs)
if profile:
e_de = ev(); e_de.record(); e_dec_ends.append(e_de)
padded_decoded_tokens = {
layer_idx: decoded_tokens[idx]
for idx, layer_idx in enumerate(self.point_head.intermediate_layer_idx)
}
if profile:
e_hs = ev(); e_hs.record(); e_head_starts.append(e_hs)
pts3d, pts3d_conf = self.point_head(
padded_decoded_tokens, images, patch_start_idx
)
if profile:
e_he = ev(); e_he.record(); e_head_ends.append(e_he)
pointmaps.append(dict(
pts3d=pts3d,
conf=pts3d_conf
))
if profile:
e_cam_start.record()
pose_enc_list = self.camera_head(aggregated_tokens_list)
if profile:
e_cam_end.record()
torch.cuda.synchronize() # single sync at the very end
mem_peak = torch.cuda.max_memory_allocated() / 1024**3
t_agg = e_start.elapsed_time(e_agg) / 1000
t_dec = sum(s.elapsed_time(e) / 1000 for s, e in zip(e_dec_starts, e_dec_ends))
t_head = sum(s.elapsed_time(e) / 1000 for s, e in zip(e_head_starts, e_head_ends))
t_cam = e_cam_start.elapsed_time(e_cam_end) / 1000
t_total = e_start.elapsed_time(e_cam_end) / 1000
print(f" [PROFILE] Aggregator: {t_agg:.3f}s | VRAM: {mem_before:.2f} -> {mem_after_agg:.2f} GB (+{mem_after_agg - mem_before:.2f})")
print(f" [PROFILE] Stored layers: {sorted(k for k in aggregated_tokens_list if k >= 0)}")
print(f" [PROFILE] Decoder: {t_dec:.3f}s ({num_timesteps} timesteps, {t_dec/max(num_timesteps,1)*1000:.0f}ms each)")
print(f" [PROFILE] Point Head: {t_head:.3f}s ({num_timesteps} timesteps, {t_head/max(num_timesteps,1)*1000:.0f}ms each)")
print(f" [PROFILE] Camera Head:{t_cam:.3f}s")
print(f" [PROFILE] Total: {t_total:.3f}s | Peak VRAM: {mem_peak:.2f} GB")
print(f" [PROFILE] Breakdown: Agg {t_agg/t_total*100:.0f}% | Dec {t_dec/t_total*100:.0f}% | PtHead {t_head/t_total*100:.0f}% | CamHead {t_cam/t_total*100:.0f}%")
predictions["pose_enc"] = pose_enc_list[-1]
predictions["pose_enc_list"] = pose_enc_list
predictions["pointmaps"] = pointmaps
if not profile:
# single final sync and lightweight wall-clock timing
torch.cuda.synchronize()
t_total = time.time() - _t_start
print(f" [PROFILE] Total inference time: {t_total:.3f}s")
return predictions
def load_state_dict(self, ckpt, is_VGGT_static=False, **kw):
# don't load these VGGT heads as not needed
exclude = ["depth_head", "track_head"]
ckpt = {k:v for k, v in ckpt.items() if k.split('.')[0] not in exclude}
res = super().load_state_dict(ckpt, **kw)
# Compile decoder blocks after weights are loaded so state_dict keys match the checkpoint.
if hasattr(self, "decoder") and hasattr(self.decoder, "compile_blocks"):
self.decoder.compile_blocks()
return res
def to_fp16(self, keep_norm_fp32: bool = False):
"""Convert model parameters and buffers to FP16 for inference.
Args:
keep_norm_fp32 (bool): If True, keep normalization layers (LayerNorm/BatchNorm)
in FP32 for numerical stability. If False, convert everything to FP16.
"""
# Convert whole model to half first
self.half()
if keep_norm_fp32:
for m in self.modules():
if isinstance(m, (torch.nn.LayerNorm, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.SyncBatchNorm)):
m.float()
# Ensure any stored dtype-sensitive tensors are converted appropriately
try:
# camera/register/time tokens are Parameters and are handled by self.half(),
# but ensure any other buffers are also cast
for name, buf in list(self._buffers.items()):
if isinstance(buf, torch.Tensor):
self.register_buffer(name, buf.half(), persistent=(getattr(buf, 'persistent', False)))
except Exception:
pass
return self
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