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"""Wrapper for conv2d."""
from __future__ import annotations
from typing import NamedTuple
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from vis4d.common.typing import ArgsType
from .weight_init import constant_init
class Conv2d(nn.Conv2d):
"""Wrapper around Conv2d to support empty inputs and norm/activation."""
def __init__(
self,
*args: ArgsType,
norm: nn.Module | None = None,
activation: nn.Module | None = None,
**kwargs: ArgsType,
) -> None:
"""Creates an instance of the class.
If norm is specified, it is initialized with 1.0 and bias with 0.0.
"""
super().__init__(*args, **kwargs)
self.norm = norm
self.activation = activation
if self.norm is not None:
constant_init(self.norm, 1.0, bias=0.0)
def forward( # pylint: disable=arguments-renamed
self, x: Tensor
) -> Tensor:
"""Forward pass."""
if not torch.jit.is_scripting(): # type: ignore
# https://github.com/pytorch/pytorch/issues/12013
if (
x.numel() == 0
and self.training
and isinstance(self.norm, nn.SyncBatchNorm)
):
raise ValueError(
"SyncBatchNorm does not support empty inputs!"
)
x = F.conv2d( # pylint: disable=not-callable
x,
self.weight,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
if self.norm is not None:
x = self.norm(x)
if self.activation is not None:
x = self.activation(x)
return x
def add_conv_branch(
num_branch_convs: int,
last_layer_dim: int,
conv_out_dim: int,
conv_has_bias: bool,
norm_cfg: str | None,
num_groups: int | None,
) -> tuple[nn.ModuleList, int]:
"""Init conv branch for head."""
convs = nn.ModuleList()
if norm_cfg is not None:
norm = getattr(nn, norm_cfg)
else:
norm = None
if norm == nn.GroupNorm:
assert num_groups is not None, "num_groups must be specified"
norm = lambda x: nn.GroupNorm( # pylint: disable=unnecessary-lambda-assignment
num_groups, x
)
if num_branch_convs > 0:
for i in range(num_branch_convs):
conv_in_dim = last_layer_dim if i == 0 else conv_out_dim
convs.append(
Conv2d(
conv_in_dim,
conv_out_dim,
kernel_size=3,
padding=1,
bias=conv_has_bias,
norm=norm(conv_out_dim) if norm is not None else norm,
activation=nn.ReLU(inplace=True),
)
)
last_layer_dim = conv_out_dim
return convs, last_layer_dim
class UnetDownConvOut(NamedTuple):
"""Output of the UnetDownConv operator.
features: Features before applying the pooling operator
pooled_features: Features after applying the pooling operator
"""
features: Tensor
pooled_features: Tensor
class UnetDownConv(nn.Module):
"""Downsamples a feature map by applying two convolutions and maxpool."""
def __init__(
self,
in_channels: int,
out_channels: int,
pooling: bool = True,
activation: str = "ReLU",
):
"""Creates a new downsampling convolution operator.
This operator consists of two convolutions followed by a maxpool
operator.
Args:
in_channels (int): input channesl
out_channels (int): output channesl
pooling (bool): If pooling should be applied
activation (str): Activation that should be applied
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.pooling = pooling
activation = getattr(nn, activation)()
self.conv1 = nn.Conv2d(
self.in_channels,
self.out_channels,
kernel_size=3,
padding=1,
stride=1,
bias=True,
)
self.conv2 = nn.Conv2d(
self.out_channels,
self.out_channels,
kernel_size=3,
padding=1,
stride=1,
bias=True,
)
if self.pooling:
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
def __call__(self, data: Tensor) -> UnetDownConvOut:
"""Applies the operator.
Args:
data (Tensor): Input data.
Returns:
UnetDownConvOut: Containing the features before the pooling
operation (features) and after (pooled_features).
"""
return self._call_impl(data)
def forward(self, data: Tensor) -> UnetDownConvOut:
"""Applies the operator.
Args:
data (Tensor): Input data.
Returns:
UnetDownConvOut: containing the features before the pooling
operation (features) and after (pooled_features).
"""
x = F.relu(self.conv1(data))
x = F.relu(self.conv2(x))
before_pool = x
if self.pooling:
x = self.pool(x)
return UnetDownConvOut(features=before_pool, pooled_features=x)
class UnetUpConv(nn.Module):
"""An operator that performs 2 convolutions and 1 UpConvolution.
A ReLU activation follows each convolution.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
merge_mode: str = "concat",
up_mode: str = "transpose",
):
"""Creates a new UpConv operator.
This operator merges two inputs by upsampling one and combining it with
the other.
Args:
in_channels: Number of input channels (low res)
out_channels: Number of output channels (high res)
merge_mode: How to merge both input channels
up_mode: How to upsample the channel with lower resolution
Raises:
ValueError: If upsampling mode is unknown
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.merge_mode = merge_mode
self.up_mode = up_mode
# Upsampling
if self.up_mode == "transpose":
self.upconv: nn.Module = nn.ConvTranspose2d(
in_channels, out_channels, kernel_size=2, stride=2
)
elif self.up_mode == "upsample":
self.upconv = nn.Sequential(
nn.Upsample(mode="bilinear", scale_factor=2),
nn.Conv2d(in_channels, out_channels, kernel_size=1),
)
else:
raise ValueError(f"Unknown upsampling mode: {up_mode}")
if self.merge_mode == "concat":
self.conv1 = nn.Conv2d(
2 * self.out_channels, self.out_channels, 3, padding=1
)
else:
# num of input channels to conv2 is same
self.conv1 = nn.Conv2d(
self.out_channels, self.out_channels, 3, padding=1
)
self.conv2 = nn.Conv2d(
self.out_channels, self.out_channels, 3, padding=1
)
def __call__(self, from_down: Tensor, from_up: Tensor) -> Tensor:
"""Forward pass.
Arguments:
from_down (Tensor): Tensor from the encoder pathway. Assumed to
have dimension 'out_channels'
from_up (Tensor): Upconv'd tensor from the decoder pathway. Assumed
to have dimension 'in_channels'
"""
return self._call_impl(from_down, from_up)
def forward(self, from_down: Tensor, from_up: Tensor) -> Tensor:
"""Forward pass.
Arguments:
from_down (Tensor): Tensor from the encoder pathway. Assumed to
have dimension 'out_channels'
from_up (Tensor): Upconv'd tensor from the decoder pathway. Assumed
to have dimension 'in_channels'
"""
from_up = self.upconv(from_up)
if self.merge_mode == "concat":
x = torch.cat((from_up, from_down), 1)
else:
x = from_up + from_down
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
return x
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