Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,332 Bytes
de1b1de |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 |
# Copyright (C) 2024 Apple Inc. All Rights Reserved.
# DepthProEncoder combining patch and image encoders.
from __future__ import annotations
import math
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class DepthProEncoder(nn.Module):
"""DepthPro Encoder.
An encoder aimed at creating multi-resolution encodings from Vision Transformers.
"""
def __init__(
self,
dims_encoder: Iterable[int],
patch_encoder: nn.Module,
image_encoder: nn.Module,
hook_block_ids: Iterable[int],
decoder_features: int,
):
"""Initialize DepthProEncoder.
The framework
1. creates an image pyramid,
2. generates overlapping patches with a sliding window at each pyramid level,
3. creates batched encodings via vision transformer backbones,
4. produces multi-resolution encodings.
Args:
----
img_size: Backbone image resolution.
dims_encoder: Dimensions of the encoder at different layers.
patch_encoder: Backbone used for patches.
image_encoder: Backbone used for global image encoder.
hook_block_ids: Hooks to obtain intermediate features for the patch encoder model.
decoder_features: Number of feature output in the decoder.
"""
super().__init__()
self.dims_encoder = list(dims_encoder)
self.patch_encoder = patch_encoder
self.image_encoder = image_encoder
self.hook_block_ids = list(hook_block_ids)
patch_encoder_embed_dim = patch_encoder.embed_dim
image_encoder_embed_dim = image_encoder.embed_dim
self.out_size = int(
patch_encoder.patch_embed.img_size[0] // patch_encoder.patch_embed.patch_size[0]
)
def _create_project_upsample_block(
dim_in: int,
dim_out: int,
upsample_layers: int,
dim_int: Optional[int] = None,
) -> nn.Module:
if dim_int is None:
dim_int = dim_out
# Projection.
blocks = [
nn.Conv2d(
in_channels=dim_in,
out_channels=dim_int,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
]
# Upsampling.
blocks += [
nn.ConvTranspose2d(
in_channels=dim_int if i == 0 else dim_out,
out_channels=dim_out,
kernel_size=2,
stride=2,
padding=0,
bias=False,
)
for i in range(upsample_layers)
]
return nn.Sequential(*blocks)
self.upsample_latent0 = _create_project_upsample_block(
dim_in=patch_encoder_embed_dim,
dim_int=self.dims_encoder[0],
dim_out=decoder_features,
upsample_layers=3,
)
self.upsample_latent1 = _create_project_upsample_block(
dim_in=patch_encoder_embed_dim, dim_out=self.dims_encoder[0], upsample_layers=2
)
self.upsample0 = _create_project_upsample_block(
dim_in=patch_encoder_embed_dim, dim_out=self.dims_encoder[1], upsample_layers=1
)
self.upsample1 = _create_project_upsample_block(
dim_in=patch_encoder_embed_dim, dim_out=self.dims_encoder[2], upsample_layers=1
)
self.upsample2 = _create_project_upsample_block(
dim_in=patch_encoder_embed_dim, dim_out=self.dims_encoder[3], upsample_layers=1
)
self.upsample_lowres = nn.ConvTranspose2d(
in_channels=image_encoder_embed_dim,
out_channels=self.dims_encoder[3],
kernel_size=2,
stride=2,
padding=0,
bias=True,
)
self.fuse_lowres = nn.Conv2d(
in_channels=(self.dims_encoder[3] + self.dims_encoder[3]),
out_channels=self.dims_encoder[3],
kernel_size=1,
stride=1,
padding=0,
bias=True,
)
# Obtain intermediate outputs of the blocks.
self.patch_encoder.blocks[self.hook_block_ids[0]].register_forward_hook(
self._hook0
)
self.patch_encoder.blocks[self.hook_block_ids[1]].register_forward_hook(
self._hook1
)
def _hook0(self, model, input, output):
self.backbone_highres_hook0 = output
def _hook1(self, model, input, output):
self.backbone_highres_hook1 = output
@property
def img_size(self) -> int:
"""Return the full image size of the SPN network."""
return self.patch_encoder.patch_embed.img_size[0] * 4
def _create_pyramid(
self, x: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Create a 3-level image pyramid."""
# Original resolution: 1536 by default.
x0 = x
# Middle resolution: 768 by default.
x1 = F.interpolate(
x, size=None, scale_factor=0.5, mode="bilinear", align_corners=False
)
# Low resolution: 384 by default, corresponding to the backbone resolution.
x2 = F.interpolate(
x, size=None, scale_factor=0.25, mode="bilinear", align_corners=False
)
return x0, x1, x2
def split(self, x: torch.Tensor, overlap_ratio: float = 0.25) -> torch.Tensor:
"""Split the input into small patches with sliding window."""
patch_size = 384
patch_stride = int(patch_size * (1 - overlap_ratio))
image_size = x.shape[-1]
steps = int(math.ceil((image_size - patch_size) / patch_stride)) + 1
x_patch_list = []
for j in range(steps):
j0 = j * patch_stride
j1 = j0 + patch_size
for i in range(steps):
i0 = i * patch_stride
i1 = i0 + patch_size
x_patch_list.append(x[..., j0:j1, i0:i1])
return torch.cat(x_patch_list, dim=0)
def merge(self, x: torch.Tensor, batch_size: int, padding: int = 3) -> torch.Tensor:
"""Merge the patched input into a image with sliding window."""
steps = int(math.sqrt(x.shape[0] // batch_size))
idx = 0
output_list = []
for j in range(steps):
output_row_list = []
for i in range(steps):
output = x[batch_size * idx : batch_size * (idx + 1)]
if j != 0:
output = output[..., padding:, :]
if i != 0:
output = output[..., :, padding:]
if j != steps - 1:
output = output[..., :-padding, :]
if i != steps - 1:
output = output[..., :, :-padding]
output_row_list.append(output)
idx += 1
output_row = torch.cat(output_row_list, dim=-1)
output_list.append(output_row)
output = torch.cat(output_list, dim=-2)
return output
def reshape_feature(
self, embeddings: torch.Tensor, width, height, cls_token_offset=1
):
"""Discard class token and reshape 1D feature map to a 2D grid."""
b, hw, c = embeddings.shape
# Remove class token.
if cls_token_offset > 0:
embeddings = embeddings[:, cls_token_offset:, :]
# Shape: (batch, height, width, dim) -> (batch, dim, height, width)
embeddings = embeddings.reshape(b, height, width, c).permute(0, 3, 1, 2)
return embeddings
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
"""Encode input at multiple resolutions.
Args:
----
x (torch.Tensor): Input image.
Returns:
-------
Multi resolution encoded features.
"""
batch_size = x.shape[0]
# Step 0: create a 3-level image pyramid.
x0, x1, x2 = self._create_pyramid(x)
# Step 1: split to create batched overlapped mini-images at the backbone (BeiT/ViT/Dino)
# resolution.
# 5x5 @ 384x384 at the highest resolution (1536x1536).
x0_patches = self.split(x0, overlap_ratio=0.25)
# 3x3 @ 384x384 at the middle resolution (768x768).
x1_patches = self.split(x1, overlap_ratio=0.5)
# 1x1 # 384x384 at the lowest resolution (384x384).
x2_patches = x2
# Concatenate all the sliding window patches and form a batch of size (35=5x5+3x3+1x1).
x_pyramid_patches = torch.cat(
(x0_patches, x1_patches, x2_patches),
dim=0,
)
# Step 2: Run the backbone (BeiT) model and get the result of large batch size.
x_pyramid_encodings = self.patch_encoder(x_pyramid_patches)
x_pyramid_encodings = self.reshape_feature(
x_pyramid_encodings, self.out_size, self.out_size
)
# Step 3: merging.
# Merge highres latent encoding.
x_latent0_encodings = self.reshape_feature(
self.backbone_highres_hook0,
self.out_size,
self.out_size,
)
x_latent0_features = self.merge(
x_latent0_encodings[: batch_size * 5 * 5], batch_size=batch_size, padding=3
)
x_latent1_encodings = self.reshape_feature(
self.backbone_highres_hook1,
self.out_size,
self.out_size,
)
x_latent1_features = self.merge(
x_latent1_encodings[: batch_size * 5 * 5], batch_size=batch_size, padding=3
)
# Split the 35 batch size from pyramid encoding back into 5x5+3x3+1x1.
x0_encodings, x1_encodings, x2_encodings = torch.split(
x_pyramid_encodings,
[len(x0_patches), len(x1_patches), len(x2_patches)],
dim=0,
)
# 96x96 feature maps by merging 5x5 @ 24x24 patches with overlaps.
x0_features = self.merge(x0_encodings, batch_size=batch_size, padding=3)
# 48x84 feature maps by merging 3x3 @ 24x24 patches with overlaps.
x1_features = self.merge(x1_encodings, batch_size=batch_size, padding=6)
# 24x24 feature maps.
x2_features = x2_encodings
# Apply the image encoder model.
x_global_features = self.image_encoder(x2_patches)
x_global_features = self.reshape_feature(
x_global_features, self.out_size, self.out_size
)
# Upsample feature maps.
x_latent0_features = self.upsample_latent0(x_latent0_features)
x_latent1_features = self.upsample_latent1(x_latent1_features)
x0_features = self.upsample0(x0_features)
x1_features = self.upsample1(x1_features)
x2_features = self.upsample2(x2_features)
x_global_features = self.upsample_lowres(x_global_features)
x_global_features = self.fuse_lowres(
torch.cat((x2_features, x_global_features), dim=1)
)
return [
x_latent0_features,
x_latent1_features,
x0_features,
x1_features,
x_global_features,
]
|