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9882c88 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class ResidualConvUnit(nn.Module):
def __init__(self, in_channels: int):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, in_channels, 3, padding=1),
nn.ReLU(True),
nn.Conv2d(in_channels, in_channels, 3, padding=1),
nn.ReLU(True),
)
def forward(self, x: Tensor) -> Tensor:
return self.conv(x) + x
class FeatureFusionBlock(nn.Module):
def __init__(self, in_channels: int, does_fusion: bool = True):
super().__init__()
self.does_fusion = does_fusion
if does_fusion:
self.res_conv_unit1 = ResidualConvUnit(in_channels)
self.res_conv_unit2 = ResidualConvUnit(in_channels)
def forward(self, x: Tensor, x_from_top: Tensor | None = None) -> Tensor:
if x_from_top is not None:
assert self.does_fusion and x.shape == x_from_top.shape
x = self.res_conv_unit1(x) + x_from_top
x = self.res_conv_unit2(x)
return x
class ReassembleBlocks(nn.Module):
"""Reassemble block with 'ignore" readout and 2x bilinear up-sampling"""
def __init__(self, embed_dim: int, post_process_channels: list[int]):
super().__init__()
self.projects = nn.ModuleList(
[
nn.Conv2d(embed_dim, out_channel, 1)
for out_channel in post_process_channels
]
)
def forward(self, inputs: list[Tensor]) -> list[Tensor]:
return [
F.interpolate(
project(x), scale_factor=2, mode="bilinear", align_corners=False
)
for x, project in zip(inputs, self.projects)
]
class LightDPTDecoder(nn.Module):
"""DPT decoder https://arxiv.org/pdf/2103.13413
Based on DINOv2 and DPT implementations:
* https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/eval/depth/models/decode_heads/dpt_head.py#L227
* https://github.com/isl-org/DPT/blob/f43ef9e08d70a752195028a51be5e1aff227b913/dpt/vit.py#L221
* https://github.com/isl-org/DPT/blob/f43ef9e08d70a752195028a51be5e1aff227b913/dpt/blocks.py
This implementation differs from the one of DINOv2 in that we return a prediction
at (H/7, W/7) spatial resolution (instead of (4/7 H, 4/7 W) as DINOv2's DPT---prior
to depth estimation).
Args:
embed_dim: dimension of the encoded features (e.g. 768 for vitb).
readout_type: readout type, only "ignore" is currently supported.
post_process_channels: list of output channels for each reassemble block.
dim_dhat: intermediate feature dimension (D^hat in DPT's paper).
"""
def __init__(
self,
embed_dim: int = 768,
readout_type: str = "ignore",
post_process_channels: list[int] | None = None,
dim_dhat: int = 256,
):
super().__init__()
if readout_type != "ignore":
raise NotImplementedError
if post_process_channels is None:
# e.g. [96, 192, 384, 768] for vitb (embed_dim=768)
post_process_channels = [embed_dim // 2 ** (3 - i) for i in range(4)]
post_process_channels = (
post_process_channels * 4
if len(post_process_channels) == 1
else post_process_channels
)
# just "resample"'s 1x1 convs -> 2x upsampling -> projection to D^ dims
self.reassemble_blocks = ReassembleBlocks(embed_dim, post_process_channels)
# projection to D^ dims
self.convs = nn.ModuleList(
[
nn.Conv2d(out_channel, dim_dhat, 3, padding=1)
for out_channel in post_process_channels
]
)
# fusion between intermediate layers without upsampling
self.fusion_blocks = nn.ModuleList(
[FeatureFusionBlock(dim_dhat, does_fusion=(i != 0)) for i in range(4)]
)
self.project = nn.Sequential(
nn.Conv2d(dim_dhat, dim_dhat, 3, padding=1), nn.ReLU(True)
)
# self.project = nn.Conv2d(dim_dhat, dim_dhat, 3, padding=1)
# info
self.out_channels = dim_dhat
self.post_process_channels = post_process_channels
self.num_post_process_channels = len(post_process_channels)
self.num_reassemble_blocks = len(self.reassemble_blocks.projects)
self.num_fusion_blocks = len(self.fusion_blocks)
assert self.num_post_process_channels == self.num_reassemble_blocks
assert self.num_post_process_channels == self.num_fusion_blocks
def forward(self, inputs: dict[str, list[Tensor]]) -> Tensor:
"""Forward pass
Args:
inputs: Dict with the following key-value pairs:
- outputs: list of (B, embdedding_dim, H // 14, W // 14) dinov2 embeddings.
- class_tokens: list of (B, embdedding_dim) class tokens.
Returns:
(B, dim_dhat, H/7, W/7) tensor
"""
x = inputs["outputs"] # ignore class tokens
assert len(x) == self.num_reassemble_blocks
x = self.reassemble_blocks(x)
x = [conv(x_) for conv, x_ in zip(self.convs, x)]
out = self.fusion_blocks[0](x[-1])
for i in range(1, self.num_fusion_blocks):
out = self.fusion_blocks[i](x[-i - 1], out)
out = self.project(out)
return out
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