CHSTR's picture
Upload src
265ae36 verified
raw
history blame
29.3 kB
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
import copy
from functools import partial
import math
import warnings
import torch
import torch.nn as nn
from .ops import resize
# XXX: (Untested) replacement for mmcv.imdenormalize()
def _imdenormalize(img, mean, std, to_bgr=True):
import numpy as np
mean = mean.reshape(1, -1).astype(np.float64)
std = std.reshape(1, -1).astype(np.float64)
img = (img * std) + mean
if to_bgr:
img = img[::-1]
return img
class DepthBaseDecodeHead(nn.Module):
"""Base class for BaseDecodeHead.
Args:
in_channels (List): Input channels.
channels (int): Channels after modules, before conv_depth.
conv_layer (nn.Module): Conv layers. Default: None.
act_layer (nn.Module): Activation layers. Default: nn.ReLU.
loss_decode (dict): Config of decode loss.
Default: ().
sampler (dict|None): The config of depth map sampler.
Default: None.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
min_depth (int): Min depth in dataset setting.
Default: 1e-3.
max_depth (int): Max depth in dataset setting.
Default: None.
norm_layer (dict|None): Norm layers.
Default: None.
classify (bool): Whether predict depth in a cls.-reg. manner.
Default: False.
n_bins (int): The number of bins used in cls. step.
Default: 256.
bins_strategy (str): The discrete strategy used in cls. step.
Default: 'UD'.
norm_strategy (str): The norm strategy on cls. probability
distribution. Default: 'linear'
scale_up (str): Whether predict depth in a scale-up manner.
Default: False.
"""
def __init__(
self,
in_channels,
conv_layer=None,
act_layer=nn.ReLU,
channels=96,
loss_decode=(),
sampler=None,
align_corners=False,
min_depth=1e-3,
max_depth=None,
norm_layer=None,
classify=False,
n_bins=256,
bins_strategy="UD",
norm_strategy="linear",
scale_up=False,
):
super(DepthBaseDecodeHead, self).__init__()
self.in_channels = in_channels
self.channels = channels
self.conf_layer = conv_layer
self.act_layer = act_layer
self.loss_decode = loss_decode
self.align_corners = align_corners
self.min_depth = min_depth
self.max_depth = max_depth
self.norm_layer = norm_layer
self.classify = classify
self.n_bins = n_bins
self.scale_up = scale_up
if self.classify:
assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID"
assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
self.bins_strategy = bins_strategy
self.norm_strategy = norm_strategy
self.softmax = nn.Softmax(dim=1)
self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1)
else:
self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, inputs, img_metas):
"""Placeholder of forward function."""
pass
def forward_train(self, img, inputs, img_metas, depth_gt):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`depth/datasets/pipelines/formatting.py:Collect`.
depth_gt (Tensor): GT depth
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
depth_pred = self.forward(inputs, img_metas)
losses = self.losses(depth_pred, depth_gt)
log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0])
losses.update(**log_imgs)
return losses
def forward_test(self, inputs, img_metas):
"""Forward function for testing.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`depth/datasets/pipelines/formatting.py:Collect`.
Returns:
Tensor: Output depth map.
"""
return self.forward(inputs, img_metas)
def depth_pred(self, feat):
"""Prediction each pixel."""
if self.classify:
logit = self.conv_depth(feat)
if self.bins_strategy == "UD":
bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
elif self.bins_strategy == "SID":
bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
# following Adabins, default linear
if self.norm_strategy == "linear":
logit = torch.relu(logit)
eps = 0.1
logit = logit + eps
logit = logit / logit.sum(dim=1, keepdim=True)
elif self.norm_strategy == "softmax":
logit = torch.softmax(logit, dim=1)
elif self.norm_strategy == "sigmoid":
logit = torch.sigmoid(logit)
logit = logit / logit.sum(dim=1, keepdim=True)
output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
else:
if self.scale_up:
output = self.sigmoid(self.conv_depth(feat)) * self.max_depth
else:
output = self.relu(self.conv_depth(feat)) + self.min_depth
return output
def losses(self, depth_pred, depth_gt):
"""Compute depth loss."""
loss = dict()
depth_pred = resize(
input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False
)
if not isinstance(self.loss_decode, nn.ModuleList):
losses_decode = [self.loss_decode]
else:
losses_decode = self.loss_decode
for loss_decode in losses_decode:
if loss_decode.loss_name not in loss:
loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt)
else:
loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt)
return loss
def log_images(self, img_path, depth_pred, depth_gt, img_meta):
import numpy as np
show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
show_img = show_img.numpy().astype(np.float32)
show_img = _imdenormalize(
show_img,
img_meta["img_norm_cfg"]["mean"],
img_meta["img_norm_cfg"]["std"],
img_meta["img_norm_cfg"]["to_rgb"],
)
show_img = np.clip(show_img, 0, 255)
show_img = show_img.astype(np.uint8)
show_img = show_img[:, :, ::-1]
show_img = show_img.transpose(0, 2, 1)
show_img = show_img.transpose(1, 0, 2)
depth_pred = depth_pred / torch.max(depth_pred)
depth_gt = depth_gt / torch.max(depth_gt)
depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color}
class BNHead(DepthBaseDecodeHead):
"""Just a batchnorm."""
def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs):
super().__init__(**kwargs)
self.input_transform = input_transform
self.in_index = in_index
self.upsample = upsample
# self.bn = nn.SyncBatchNorm(self.in_channels)
if self.classify:
self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1)
else:
self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1)
def _transform_inputs(self, inputs):
"""Transform inputs for decoder.
Args:
inputs (list[Tensor]): List of multi-level img features.
Returns:
Tensor: The transformed inputs
"""
if "concat" in self.input_transform:
inputs = [inputs[i] for i in self.in_index]
if "resize" in self.input_transform:
inputs = [
resize(
input=x,
size=[s * self.upsample for s in inputs[0].shape[2:]],
mode="bilinear",
align_corners=self.align_corners,
)
for x in inputs
]
inputs = torch.cat(inputs, dim=1)
elif self.input_transform == "multiple_select":
inputs = [inputs[i] for i in self.in_index]
else:
inputs = inputs[self.in_index]
return inputs
def _forward_feature(self, inputs, img_metas=None, **kwargs):
"""Forward function for feature maps before classifying each pixel with
``self.cls_seg`` fc.
Args:
inputs (list[Tensor]): List of multi-level img features.
Returns:
feats (Tensor): A tensor of shape (batch_size, self.channels,
H, W) which is feature map for last layer of decoder head.
"""
# accept lists (for cls token)
inputs = list(inputs)
for i, x in enumerate(inputs):
if len(x) == 2:
x, cls_token = x[0], x[1]
if len(x.shape) == 2:
x = x[:, :, None, None]
cls_token = cls_token[:, :, None, None].expand_as(x)
inputs[i] = torch.cat((x, cls_token), 1)
else:
x = x[0]
if len(x.shape) == 2:
x = x[:, :, None, None]
inputs[i] = x
x = self._transform_inputs(inputs)
# feats = self.bn(x)
return x
def forward(self, inputs, img_metas=None, **kwargs):
"""Forward function."""
output = self._forward_feature(inputs, img_metas=img_metas, **kwargs)
output = self.depth_pred(output)
return output
class ConvModule(nn.Module):
"""A conv block that bundles conv/norm/activation layers.
This block simplifies the usage of convolution layers, which are commonly
used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
It is based upon three build methods: `build_conv_layer()`,
`build_norm_layer()` and `build_activation_layer()`.
Besides, we add some additional features in this module.
1. Automatically set `bias` of the conv layer.
2. Spectral norm is supported.
3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
supports zero and circular padding, and we add "reflect" padding mode.
Args:
in_channels (int): Number of channels in the input feature map.
Same as that in ``nn._ConvNd``.
out_channels (int): Number of channels produced by the convolution.
Same as that in ``nn._ConvNd``.
kernel_size (int | tuple[int]): Size of the convolving kernel.
Same as that in ``nn._ConvNd``.
stride (int | tuple[int]): Stride of the convolution.
Same as that in ``nn._ConvNd``.
padding (int | tuple[int]): Zero-padding added to both sides of
the input. Same as that in ``nn._ConvNd``.
dilation (int | tuple[int]): Spacing between kernel elements.
Same as that in ``nn._ConvNd``.
groups (int): Number of blocked connections from input channels to
output channels. Same as that in ``nn._ConvNd``.
bias (bool | str): If specified as `auto`, it will be decided by the
norm_layer. Bias will be set as True if `norm_layer` is None, otherwise
False. Default: "auto".
conv_layer (nn.Module): Convolution layer. Default: None,
which means using conv2d.
norm_layer (nn.Module): Normalization layer. Default: None.
act_layer (nn.Module): Activation layer. Default: nn.ReLU.
inplace (bool): Whether to use inplace mode for activation.
Default: True.
with_spectral_norm (bool): Whether use spectral norm in conv module.
Default: False.
padding_mode (str): If the `padding_mode` has not been supported by
current `Conv2d` in PyTorch, we will use our own padding layer
instead. Currently, we support ['zeros', 'circular'] with official
implementation and ['reflect'] with our own implementation.
Default: 'zeros'.
order (tuple[str]): The order of conv/norm/activation layers. It is a
sequence of "conv", "norm" and "act". Common examples are
("conv", "norm", "act") and ("act", "conv", "norm").
Default: ('conv', 'norm', 'act').
"""
_abbr_ = "conv_block"
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias="auto",
conv_layer=nn.Conv2d,
norm_layer=None,
act_layer=nn.ReLU,
inplace=True,
with_spectral_norm=False,
padding_mode="zeros",
order=("conv", "norm", "act"),
):
super(ConvModule, self).__init__()
official_padding_mode = ["zeros", "circular"]
self.conv_layer = conv_layer
self.norm_layer = norm_layer
self.act_layer = act_layer
self.inplace = inplace
self.with_spectral_norm = with_spectral_norm
self.with_explicit_padding = padding_mode not in official_padding_mode
self.order = order
assert isinstance(self.order, tuple) and len(self.order) == 3
assert set(order) == set(["conv", "norm", "act"])
self.with_norm = norm_layer is not None
self.with_activation = act_layer is not None
# if the conv layer is before a norm layer, bias is unnecessary.
if bias == "auto":
bias = not self.with_norm
self.with_bias = bias
if self.with_explicit_padding:
if padding_mode == "zeros":
padding_layer = nn.ZeroPad2d
else:
raise AssertionError(f"Unsupported padding mode: {padding_mode}")
self.pad = padding_layer(padding)
# reset padding to 0 for conv module
conv_padding = 0 if self.with_explicit_padding else padding
# build convolution layer
self.conv = self.conv_layer(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=conv_padding,
dilation=dilation,
groups=groups,
bias=bias,
)
# export the attributes of self.conv to a higher level for convenience
self.in_channels = self.conv.in_channels
self.out_channels = self.conv.out_channels
self.kernel_size = self.conv.kernel_size
self.stride = self.conv.stride
self.padding = padding
self.dilation = self.conv.dilation
self.transposed = self.conv.transposed
self.output_padding = self.conv.output_padding
self.groups = self.conv.groups
if self.with_spectral_norm:
self.conv = nn.utils.spectral_norm(self.conv)
# build normalization layers
if self.with_norm:
# norm layer is after conv layer
if order.index("norm") > order.index("conv"):
norm_channels = out_channels
else:
norm_channels = in_channels
norm = partial(norm_layer, num_features=norm_channels)
self.add_module("norm", norm)
if self.with_bias:
from torch.nnModules.batchnorm import _BatchNorm
from torch.nnModules.instancenorm import _InstanceNorm
if isinstance(norm, (_BatchNorm, _InstanceNorm)):
warnings.warn("Unnecessary conv bias before batch/instance norm")
else:
self.norm_name = None
# build activation layer
if self.with_activation:
# nn.Tanh has no 'inplace' argument
# (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.HSigmoid, nn.Swish, nn.GELU)
if not isinstance(act_layer, (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.GELU)):
act_layer = partial(act_layer, inplace=inplace)
self.activate = act_layer()
# Use msra init by default
self.init_weights()
@property
def norm(self):
if self.norm_name:
return getattr(self, self.norm_name)
else:
return None
def init_weights(self):
# 1. It is mainly for customized conv layers with their own
# initialization manners by calling their own ``init_weights()``,
# and we do not want ConvModule to override the initialization.
# 2. For customized conv layers without their own initialization
# manners (that is, they don't have their own ``init_weights()``)
# and PyTorch's conv layers, they will be initialized by
# this method with default ``kaiming_init``.
# Note: For PyTorch's conv layers, they will be overwritten by our
# initialization implementation using default ``kaiming_init``.
if not hasattr(self.conv, "init_weights"):
if self.with_activation and isinstance(self.act_layer, nn.LeakyReLU):
nonlinearity = "leaky_relu"
a = 0.01 # XXX: default negative_slope
else:
nonlinearity = "relu"
a = 0
if hasattr(self.conv, "weight") and self.conv.weight is not None:
nn.init.kaiming_normal_(self.conv.weight, a=a, mode="fan_out", nonlinearity=nonlinearity)
if hasattr(self.conv, "bias") and self.conv.bias is not None:
nn.init.constant_(self.conv.bias, 0)
if self.with_norm:
if hasattr(self.norm, "weight") and self.norm.weight is not None:
nn.init.constant_(self.norm.weight, 1)
if hasattr(self.norm, "bias") and self.norm.bias is not None:
nn.init.constant_(self.norm.bias, 0)
def forward(self, x, activate=True, norm=True):
for layer in self.order:
if layer == "conv":
if self.with_explicit_padding:
x = self.pad(x)
x = self.conv(x)
elif layer == "norm" and norm and self.with_norm:
x = self.norm(x)
elif layer == "act" and activate and self.with_activation:
x = self.activate(x)
return x
class Interpolate(nn.Module):
def __init__(self, scale_factor, mode, align_corners=False):
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
return x
class HeadDepth(nn.Module):
def __init__(self, features):
super(HeadDepth, self).__init__()
self.head = nn.Sequential(
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
)
def forward(self, x):
x = self.head(x)
return x
class ReassembleBlocks(nn.Module):
"""ViTPostProcessBlock, process cls_token in ViT backbone output and
rearrange the feature vector to feature map.
Args:
in_channels (int): ViT feature channels. Default: 768.
out_channels (List): output channels of each stage.
Default: [96, 192, 384, 768].
readout_type (str): Type of readout operation. Default: 'ignore'.
patch_size (int): The patch size. Default: 16.
"""
def __init__(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16):
super(ReassembleBlocks, self).__init__()
assert readout_type in ["ignore", "add", "project"]
self.readout_type = readout_type
self.patch_size = patch_size
self.projects = nn.ModuleList(
[
ConvModule(
in_channels=in_channels,
out_channels=out_channel,
kernel_size=1,
act_layer=None,
)
for out_channel in out_channels
]
)
self.resize_layers = nn.ModuleList(
[
nn.ConvTranspose2d(
in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
),
nn.ConvTranspose2d(
in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
),
nn.Identity(),
nn.Conv2d(
in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
),
]
)
if self.readout_type == "project":
self.readout_projects = nn.ModuleList()
for _ in range(len(self.projects)):
self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
def forward(self, inputs):
assert isinstance(inputs, list)
out = []
for i, x in enumerate(inputs):
assert len(x) == 2
x, cls_token = x[0], x[1]
feature_shape = x.shape
if self.readout_type == "project":
x = x.flatten(2).permute((0, 2, 1))
readout = cls_token.unsqueeze(1).expand_as(x)
x = self.readout_projects[i](torch.cat((x, readout), -1))
x = x.permute(0, 2, 1).reshape(feature_shape)
elif self.readout_type == "add":
x = x.flatten(2) + cls_token.unsqueeze(-1)
x = x.reshape(feature_shape)
else:
pass
x = self.projects[i](x)
x = self.resize_layers[i](x)
out.append(x)
return out
class PreActResidualConvUnit(nn.Module):
"""ResidualConvUnit, pre-activate residual unit.
Args:
in_channels (int): number of channels in the input feature map.
act_layer (nn.Module): activation layer.
norm_layer (nn.Module): norm layer.
stride (int): stride of the first block. Default: 1
dilation (int): dilation rate for convs layers. Default: 1.
"""
def __init__(self, in_channels, act_layer, norm_layer, stride=1, dilation=1):
super(PreActResidualConvUnit, self).__init__()
self.conv1 = ConvModule(
in_channels,
in_channels,
3,
stride=stride,
padding=dilation,
dilation=dilation,
norm_layer=norm_layer,
act_layer=act_layer,
bias=False,
order=("act", "conv", "norm"),
)
self.conv2 = ConvModule(
in_channels,
in_channels,
3,
padding=1,
norm_layer=norm_layer,
act_layer=act_layer,
bias=False,
order=("act", "conv", "norm"),
)
def forward(self, inputs):
inputs_ = inputs.clone()
x = self.conv1(inputs)
x = self.conv2(x)
return x + inputs_
class FeatureFusionBlock(nn.Module):
"""FeatureFusionBlock, merge feature map from different stages.
Args:
in_channels (int): Input channels.
act_layer (nn.Module): activation layer for ResidualConvUnit.
norm_layer (nn.Module): normalization layer.
expand (bool): Whether expand the channels in post process block.
Default: False.
align_corners (bool): align_corner setting for bilinear upsample.
Default: True.
"""
def __init__(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True):
super(FeatureFusionBlock, self).__init__()
self.in_channels = in_channels
self.expand = expand
self.align_corners = align_corners
self.out_channels = in_channels
if self.expand:
self.out_channels = in_channels // 2
self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_layer=None, bias=True)
self.res_conv_unit1 = PreActResidualConvUnit(
in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
)
self.res_conv_unit2 = PreActResidualConvUnit(
in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
)
def forward(self, *inputs):
x = inputs[0]
if len(inputs) == 2:
if x.shape != inputs[1].shape:
res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False)
else:
res = inputs[1]
x = x + self.res_conv_unit1(res)
x = self.res_conv_unit2(x)
x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
x = self.project(x)
return x
class DPTHead(DepthBaseDecodeHead):
"""Vision Transformers for Dense Prediction.
This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
Args:
embed_dims (int): The embed dimension of the ViT backbone.
Default: 768.
post_process_channels (List): Out channels of post process conv
layers. Default: [96, 192, 384, 768].
readout_type (str): Type of readout operation. Default: 'ignore'.
patch_size (int): The patch size. Default: 16.
expand_channels (bool): Whether expand the channels in post process
block. Default: False.
"""
def __init__(
self,
embed_dims=768,
post_process_channels=[96, 192, 384, 768],
readout_type="ignore",
patch_size=16,
expand_channels=False,
**kwargs,
):
super(DPTHead, self).__init__(**kwargs)
self.in_channels = self.in_channels
self.expand_channels = expand_channels
self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size)
self.post_process_channels = [
channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels)
]
self.convs = nn.ModuleList()
for channel in self.post_process_channels:
self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_layer=None, bias=False))
self.fusion_blocks = nn.ModuleList()
for _ in range(len(self.convs)):
self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_layer, self.norm_layer))
self.fusion_blocks[0].res_conv_unit1 = None
self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_layer=self.norm_layer)
self.num_fusion_blocks = len(self.fusion_blocks)
self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
self.num_post_process_channels = len(self.post_process_channels)
assert self.num_fusion_blocks == self.num_reassemble_blocks
assert self.num_reassemble_blocks == self.num_post_process_channels
self.conv_depth = HeadDepth(self.channels)
def forward(self, inputs, img_metas):
assert len(inputs) == self.num_reassemble_blocks
x = [inp for inp in inputs]
x = self.reassemble_blocks(x)
x = [self.convs[i](feature) for i, feature in enumerate(x)]
out = self.fusion_blocks[0](x[-1])
for i in range(1, len(self.fusion_blocks)):
out = self.fusion_blocks[i](out, x[-(i + 1)])
out = self.project(out)
out = self.depth_pred(out)
return out