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312 | class DualDPT(nn.Module):
"""
Dual-head DPT for dense prediction with an auxiliary head.
Simplified for single-view depth estimation - only depth output is used.
"""
def __init__(
self,
dim_in: int,
*,
patch_size: int = 14,
output_dim: int = 2,
activation: str = "exp",
conf_activation: str = "expp1",
features: int = 256,
out_channels: Sequence[int] = (256, 512, 1024, 1024),
pos_embed: bool = True,
down_ratio: int = 1,
aux_pyramid_levels: int = 4,
aux_out1_conv_num: int = 5,
head_names: Tuple[str, str] = ("depth", "ray"),
) -> None:
super().__init__()
self.patch_size = patch_size
self.activation = activation
self.conf_activation = conf_activation
self.pos_embed = pos_embed
self.down_ratio = down_ratio
self.aux_levels = aux_pyramid_levels
self.aux_out1_conv_num = aux_out1_conv_num
self.head_main, self.head_aux = head_names
self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
self.norm = nn.LayerNorm(dim_in)
self.projects = nn.ModuleList(
[
nn.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0)
for oc in out_channels
]
)
self.resize_layers = nn.ModuleList(
[
nn.ConvTranspose2d(
out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0
),
nn.ConvTranspose2d(
out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0
),
nn.Identity(),
nn.Conv2d(
out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1
),
]
)
self.scratch = _make_scratch(list(out_channels), features, expand=False)
# Main fusion chain
self.scratch.refinenet1 = _make_fusion_block(features)
self.scratch.refinenet2 = _make_fusion_block(features)
self.scratch.refinenet3 = _make_fusion_block(features)
self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False)
head_features_1 = features
head_features_2 = 32
self.scratch.output_conv1 = nn.Conv2d(
head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1
)
self.scratch.output_conv2 = nn.Sequential(
nn.Conv2d(
head_features_1 // 2,
head_features_2,
kernel_size=3,
stride=1,
padding=1,
),
nn.ReLU(inplace=True),
nn.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0),
)
# Auxiliary fusion chain (for ray head - not used for inference but needed for weight loading)
self.scratch.refinenet1_aux = _make_fusion_block(features)
self.scratch.refinenet2_aux = _make_fusion_block(features)
self.scratch.refinenet3_aux = _make_fusion_block(features)
self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False)
self.scratch.output_conv1_aux = nn.ModuleList(
[self._make_aux_out1_block(head_features_1) for _ in range(self.aux_levels)]
)
use_ln = True
ln_seq = (
[
Permute((0, 2, 3, 1)),
nn.LayerNorm(head_features_2),
Permute((0, 3, 1, 2)),
]
if use_ln
else []
)
self.scratch.output_conv2_aux = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(
head_features_1 // 2,
head_features_2,
kernel_size=3,
stride=1,
padding=1,
),
*ln_seq,
nn.ReLU(inplace=True),
nn.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0),
)
for _ in range(self.aux_levels)
]
)
def forward(
self,
feats: List[torch.Tensor],
H: int,
W: int,
patch_start_idx: int,
chunk_size: int = 8,
) -> Dict[str, torch.Tensor]:
B, S, N, C = feats[0][0].shape
feats = [feat[0].reshape(B * S, N, C) for feat in feats]
if chunk_size is None or chunk_size >= S:
out_dict = self._forward_impl(feats, H, W, patch_start_idx)
out_dict = {k: v.reshape(B, S, *v.shape[1:]) for k, v in out_dict.items()}
return out_dict
out_dicts = []
for s0 in range(0, B * S, chunk_size):
s1 = min(s0 + chunk_size, B * S)
out_dict = self._forward_impl(
[feat[s0:s1] for feat in feats],
H,
W,
patch_start_idx,
)
out_dicts.append(out_dict)
out_dict = {
k: torch.cat([out_dict[k] for out_dict in out_dicts], dim=0)
for k in out_dicts[0].keys()
}
out_dict = {k: v.view(B, S, *v.shape[1:]) for k, v in out_dict.items()}
return out_dict
def _forward_impl(
self,
feats: List[torch.Tensor],
H: int,
W: int,
patch_start_idx: int,
) -> Dict[str, torch.Tensor]:
B, _, C = feats[0].shape
ph, pw = H // self.patch_size, W // self.patch_size
resized_feats = []
for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
x = feats[take_idx][:, patch_start_idx:]
x = self.norm(x)
x = x.permute(0, 2, 1).reshape(B, C, ph, pw)
x = self.projects[stage_idx](x)
if self.pos_embed:
x = self._add_pos_embed(x, W, H)
x = self.resize_layers[stage_idx](x)
resized_feats.append(x)
# Only compute main fusion for depth (skip aux for inference)
fused_main, _ = self._fuse(resized_feats)
h_out = int(ph * self.patch_size / self.down_ratio)
w_out = int(pw * self.patch_size / self.down_ratio)
fused_main = custom_interpolate(
fused_main, (h_out, w_out), mode="bilinear", align_corners=True
)
if self.pos_embed:
fused_main = self._add_pos_embed(fused_main, W, H)
main_logits = self.scratch.output_conv2(fused_main)
fmap = main_logits.permute(0, 2, 3, 1)
main_pred = self._apply_activation_single(fmap[..., :-1], self.activation)
main_conf = self._apply_activation_single(fmap[..., -1], self.conf_activation)
return {
self.head_main: main_pred.squeeze(-1),
f"{self.head_main}_conf": main_conf,
}
def _fuse(
self, feats: List[torch.Tensor]
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
l1, l2, l3, l4 = feats
l1_rn = self.scratch.layer1_rn(l1)
l2_rn = self.scratch.layer2_rn(l2)
l3_rn = self.scratch.layer3_rn(l3)
l4_rn = self.scratch.layer4_rn(l4)
out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
aux_out = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:])
aux_list: List[torch.Tensor] = []
if self.aux_levels >= 4:
aux_list.append(aux_out)
out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:])
aux_out = self.scratch.refinenet3_aux(aux_out, l3_rn, size=l2_rn.shape[2:])
if self.aux_levels >= 3:
aux_list.append(aux_out)
out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:])
aux_out = self.scratch.refinenet2_aux(aux_out, l2_rn, size=l1_rn.shape[2:])
if self.aux_levels >= 2:
aux_list.append(aux_out)
out = self.scratch.refinenet1(out, l1_rn)
aux_out = self.scratch.refinenet1_aux(aux_out, l1_rn)
aux_list.append(aux_out)
out = self.scratch.output_conv1(out)
aux_list = [
self.scratch.output_conv1_aux[i](aux) for i, aux in enumerate(aux_list)
]
return out, aux_list
def _add_pos_embed(
self, x: torch.Tensor, W: int, H: int, ratio: float = 0.1
) -> torch.Tensor:
pw, ph = x.shape[-1], x.shape[-2]
pe = create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device)
pe = position_grid_to_embed(pe, x.shape[1]) * ratio
pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1)
return x + pe.to(x.dtype)
def _make_aux_out1_block(self, in_ch: int) -> nn.Sequential:
if self.aux_out1_conv_num == 5:
return nn.Sequential(
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
)
if self.aux_out1_conv_num == 3:
return nn.Sequential(
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
)
if self.aux_out1_conv_num == 1:
return nn.Sequential(nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1))
raise ValueError(f"aux_out1_conv_num {self.aux_out1_conv_num} not supported")
def _apply_activation_single(
self, x: torch.Tensor, activation: str = "linear"
) -> torch.Tensor:
act = activation.lower() if isinstance(activation, str) else activation
if act == "exp":
return torch.exp(x)
if act == "expm1":
return torch.expm1(x)
if act == "expp1":
return torch.exp(x) + 1
if act == "relu":
return torch.relu(x)
if act == "sigmoid":
return torch.sigmoid(x)
if act == "softplus":
return torch.nn.functional.softplus(x)
if act == "tanh":
return torch.tanh(x)
return x
|