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# 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,
]
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