Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,912 Bytes
4845d25 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict as TyDict
from typing import List, Sequence
import torch
import torch.nn as nn
from depth_anything_3.model.dpt import DPT
from depth_anything_3.model.utils.head_utils import activate_head_gs, custom_interpolate
class GSDPT(DPT):
def __init__(
self,
dim_in: int,
patch_size: int = 14,
output_dim: int = 4,
activation: str = "linear",
conf_activation: str = "sigmoid",
features: int = 256,
out_channels: Sequence[int] = (256, 512, 1024, 1024),
pos_embed: bool = True,
feature_only: bool = False,
down_ratio: int = 1,
conf_dim: int = 1,
norm_type: str = "idt", # use to match legacy GS-DPT head, "idt" / "layer"
fusion_block_inplace: bool = False,
) -> None:
super().__init__(
dim_in=dim_in,
patch_size=patch_size,
output_dim=output_dim,
activation=activation,
conf_activation=conf_activation,
features=features,
out_channels=out_channels,
pos_embed=pos_embed,
down_ratio=down_ratio,
head_name="raw_gs",
use_sky_head=False,
norm_type=norm_type,
fusion_block_inplace=fusion_block_inplace,
)
self.conf_dim = conf_dim
if conf_dim and conf_dim > 1:
assert (
conf_activation == "linear"
), "use linear prediction when using view-dependent opacity"
merger_out_dim = features if feature_only else features // 2
self.images_merger = nn.Sequential(
nn.Conv2d(3, merger_out_dim // 4, 3, 1, 1), # fewer channels first
nn.GELU(),
nn.Conv2d(merger_out_dim // 4, merger_out_dim // 2, 3, 1, 1),
nn.GELU(),
nn.Conv2d(merger_out_dim // 2, merger_out_dim, 3, 1, 1),
nn.GELU(),
)
# -------------------------------------------------------------------------
# Internal forward (single chunk)
# -------------------------------------------------------------------------
def _forward_impl(
self,
feats: List[torch.Tensor],
H: int,
W: int,
patch_start_idx: int,
images: torch.Tensor,
) -> TyDict[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:] # [B*S, N_patch, C]
x = self.norm(x)
x = x.permute(0, 2, 1).reshape(B, C, ph, pw) # [B*S, 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) # Align scale
resized_feats.append(x)
# 2) Fusion pyramid (main branch only)
fused = self._fuse(resized_feats)
fused = self.scratch.output_conv1(fused)
# 3) Upsample to target resolution, optionally add position encoding again
h_out = int(ph * self.patch_size / self.down_ratio)
w_out = int(pw * self.patch_size / self.down_ratio)
fused = custom_interpolate(fused, (h_out, w_out), mode="bilinear", align_corners=True)
# inject the image information here
fused = fused + self.images_merger(images)
if self.pos_embed:
fused = self._add_pos_embed(fused, W, H)
# 4) Shared neck1
# feat = self.scratch.output_conv1(fused)
feat = fused
# 5) Main head: logits -> activate_head or single channel activation
main_logits = self.scratch.output_conv2(feat)
outs: TyDict[str, torch.Tensor] = {}
if self.has_conf:
pred, conf = activate_head_gs(
main_logits,
activation=self.activation,
conf_activation=self.conf_activation,
conf_dim=self.conf_dim,
)
outs[self.head_main] = pred.squeeze(1)
outs[f"{self.head_main}_conf"] = conf.squeeze(1)
else:
outs[self.head_main] = self._apply_activation_single(main_logits).squeeze(1)
return outs
|