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# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from sapiens.registry import MODELS
from torch import Tensor
@MODELS.register_module()
class PointmapHead(nn.Module):
def __init__(
self,
in_channels: int = 768,
channels: int = 16,
upsample_channels: List[int] = [768, 384, 192, 96],
conv_out_channels: Optional[Sequence[int]] = None,
conv_kernel_sizes: Optional[Sequence[int]] = None,
scale_conv_out_channels: Optional[Sequence[int]] = (1536, 512, 128),
scale_conv_kernel_sizes: Optional[Sequence[int]] = (1, 1, 1),
scale_final_layer: Optional[Sequence[int]] = (48 * 128, 512, 64, 1),
loss_decode=dict(type="L1Loss", loss_weight=1.0),
**kwargs,
):
super().__init__(**kwargs)
self.in_channels = in_channels
self.channels = channels
self._build_network(upsample_channels, conv_out_channels, conv_kernel_sizes)
if scale_conv_out_channels is not None:
self.scale_conv_layers = self._make_regression_conv_layers(
in_channels=self.in_channels,
layer_out_channels=scale_conv_out_channels,
layer_kernel_sizes=scale_conv_kernel_sizes,
)
self.scale_final_layer = self._make_final_layer(scale_final_layer)
else:
self.scale_conv_layers = None
self.scale_final_layer = None
# final conv layer to predict pointmap
in_channels = (
conv_out_channels[-1] if conv_out_channels else upsample_channels[-1]
)
self.conv_pointmap = nn.Conv2d(in_channels, 3, kernel_size=1)
if isinstance(loss_decode, dict):
self.loss_decode = MODELS.build(loss_decode)
elif isinstance(loss_decode, (list, tuple)):
self.loss_decode = nn.ModuleList()
for loss in loss_decode:
self.loss_decode.append(MODELS.build(loss))
else:
raise TypeError(
f"loss_decode must be a dict or sequence of dict,\
but got {type(loss_decode)}"
)
self._init_weights()
def _build_network(
self,
upsample_channels: List[int],
conv_out_channels: Optional[Sequence[int]],
conv_kernel_sizes: Optional[Sequence[int]],
) -> None:
in_channels = self.in_channels
self.input_conv = nn.Sequential(
nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1),
nn.InstanceNorm2d(in_channels), # Normalize first
nn.SiLU(inplace=True),
)
# Progressive upsampling blocks
up_blocks = []
cur_ch = in_channels
for out_ch in upsample_channels:
up_blocks.append(
nn.Sequential(
nn.Conv2d(cur_ch, out_ch * 4, kernel_size=3, padding=1),
nn.PixelShuffle(2), # ↑ spatial ×2
nn.InstanceNorm2d(out_ch),
nn.SiLU(inplace=True),
)
)
cur_ch = out_ch
self.upsample_blocks = nn.Sequential(*up_blocks)
# optional extra conv layers
conv_layers = []
if conv_out_channels and conv_kernel_sizes:
for out_ch, k in zip(conv_out_channels, conv_kernel_sizes):
conv_layers.extend(
[
nn.Conv2d(cur_ch, out_ch, k, padding=(k - 1) // 2),
nn.InstanceNorm2d(out_ch),
nn.SiLU(inplace=True),
]
)
cur_ch = out_ch
self.conv_layers = nn.Sequential(*conv_layers)
def _make_final_layer(self, final_layer: Sequence[int]) -> nn.Module:
"""Create final layer by given parameters."""
layers = [nn.Flatten()]
in_features = final_layer[0]
for i in range(1, len(final_layer)):
layers.append(nn.Linear(in_features, final_layer[i]))
if i < len(final_layer) - 1: # No activation after the last layer
layers.append(nn.SiLU())
in_features = final_layer[i]
return nn.Sequential(*layers)
def _make_regression_conv_layers(
self,
in_channels: int,
layer_out_channels: Sequence[int],
layer_kernel_sizes: Sequence[int],
) -> nn.Module:
"""Create convolutional layers by given parameters."""
layers = []
for out_channels, kernel_size in zip(layer_out_channels, layer_kernel_sizes):
stride = 2 # Set stride to 2 to reduce resolution by half
padding = (kernel_size - 1) // 2
layers.append(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
)
)
layers.append(nn.InstanceNorm2d(out_channels))
layers.append(nn.SiLU(inplace=True))
in_channels = out_channels
return nn.Sequential(*layers)
def _init_weights(self) -> None:
"""Initialize network weights."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
weight_dtype = m.weight.dtype
weight = nn.init.kaiming_normal_(
m.weight.float(), mode="fan_out", nonlinearity="relu"
)
m.weight.data = weight.to(weight_dtype)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
weight_dtype = m.weight.dtype
weight = nn.init.kaiming_normal_(
m.weight.float(), mode="fan_in", nonlinearity="linear"
)
m.weight.data = weight.to(weight_dtype)
if m.bias is not None:
nn.init.zeros_(m.bias)
def forward(self, x: Union[Tensor, Tuple[Tensor]]) -> Tensor:
x_pointmap = self.input_conv(x)
x_pointmap = self.upsample_blocks(x_pointmap)
x_pointmap = self.conv_layers(x_pointmap)
pointmap = self.conv_pointmap(x_pointmap)
if self.scale_conv_layers is not None:
x_scale = self.scale_conv_layers(x)
scale = self.scale_final_layer(
x_scale
) ## B x 1. scale = f_c / f_actual. in pixel spac of fx
else:
scale = None
return pointmap, scale
def loss(
self,
outputs: Tuple[Tensor],
data_samples: dict,
) -> dict:
pred_pointmap, pred_scale = outputs
gt_pointmap = data_samples["gt_pointmap"] ## B x 3 x H x W
gt_mean_depth = data_samples["gt_mean_depth"] ## B x 1 x 1 x 1
# gt_K = data_samples["meta"]["K"] ## B x 3 x 3
gt_original_K = data_samples["meta"]["original_K"] ## B x 3 x 3
gt_scale = data_samples["meta"]["scale"].view(-1, 1) ## B x 1
gt_mask = data_samples["mask"] ## B x 1 x H x W
if pred_pointmap.shape[2:] != gt_pointmap.shape[2:]:
print(
"Warning: this is not recommended in pointmap, you may get artifacts!"
)
print(
f"pred_pointmap size: {pred_pointmap.shape}, gt_pointmap size: {gt_pointmap.shape}"
)
pred_pointmap = F.interpolate(
input=pred_pointmap,
size=gt_pointmap.shape[2:],
mode="bilinear",
align_corners=False,
antialias=False,
)
##---------------------------------
loss = dict()
if not isinstance(self.loss_decode, nn.ModuleList):
losses_decode = [self.loss_decode]
else:
losses_decode = self.loss_decode
## B x 1 x H x W
pred_depth = pred_pointmap[:, 2].unsqueeze(dim=1) ## B x 1 x H x W
gt_depth = gt_pointmap[:, 2].unsqueeze(dim=1) ## B x 1 x H x W
for loss_decode in losses_decode:
## pointmap consistency loss
if loss_decode.loss_name == "loss_K_consistency":
this_loss = loss_decode(
pred_pointmap,
gt_pointmap,
valid_mask=gt_mask,
intrinsics=gt_original_K, ## Caution: using original K for consistency loss. since X/Z and Y/Z ratio is the same
)
elif loss_decode.loss_name == "loss_silog":
this_loss = loss_decode(
pred_depth,
gt_depth,
valid_mask=gt_mask,
)
elif loss_decode.loss_name == "loss_normal":
this_loss = loss_decode(
pred_pointmap,
gt_pointmap,
valid_mask=gt_mask,
scale=gt_scale,
)
elif loss_decode.loss_name == "loss_scale_l1":
this_loss = loss_decode(pred_scale, gt_scale)
elif loss_decode.loss_name in [
"loss_l1",
"loss_shift_invariant",
"loss_multiscale_l1_2",
"loss_multiscale_l1_4",
]:
this_loss = loss_decode(
pred_pointmap / gt_mean_depth,
gt_pointmap / gt_mean_depth,
valid_mask=gt_mask,
)
this_loss = torch.clamp(this_loss, max=4.0)
else:
raise NotImplementedError(
f"loss {loss_decode.loss_name} is not implemented"
)
if loss_decode.loss_name not in loss:
loss[loss_decode.loss_name] = this_loss
else:
loss[loss_decode.loss_name] += this_loss
return loss, (pred_pointmap, pred_scale)
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