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#!/usr/bin/env python3
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import torch.nn as nn
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
if len(in_shape) >= 4:
out_shape4 = out_shape
if expand:
out_shape1 = out_shape
out_shape2 = out_shape * 2
out_shape3 = out_shape * 4
if len(in_shape) >= 4:
out_shape4 = out_shape * 8
scratch.layer1_rn = nn.Conv2d(
in_shape[0],
out_shape1,
kernel_size=3,
stride=1,
padding=1,
bias=False,
groups=groups,
)
scratch.layer2_rn = nn.Conv2d(
in_shape[1],
out_shape2,
kernel_size=3,
stride=1,
padding=1,
bias=False,
groups=groups,
)
scratch.layer3_rn = nn.Conv2d(
in_shape[2],
out_shape3,
kernel_size=3,
stride=1,
padding=1,
bias=False,
groups=groups,
)
if len(in_shape) >= 4:
scratch.layer4_rn = nn.Conv2d(
in_shape[3],
out_shape4,
kernel_size=3,
stride=1,
padding=1,
bias=False,
groups=groups,
)
return scratch
class ResidualConvUnit(nn.Module):
"""Residual convolution module."""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups = 1
self.conv1 = nn.Conv2d(
features,
features,
kernel_size=3,
stride=1,
padding=1,
bias=True,
groups=self.groups,
)
self.conv2 = nn.Conv2d(
features,
features,
kernel_size=3,
stride=1,
padding=1,
bias=True,
groups=self.groups,
)
if self.bn:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
class FeatureFusionBlock(nn.Module):
"""Feature fusion block."""
def __init__(
self,
features,
activation,
deconv=False,
bn=False,
expand=False,
align_corners=True,
size=None,
):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups = 1
self.expand = expand
out_features = features
if self.expand:
out_features = features // 2
self.out_conv = nn.Conv2d(
features,
out_features,
kernel_size=1,
stride=1,
padding=0,
bias=True,
groups=1,
)
self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
self.size = size
def forward(self, *xs, size=None):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
output = self.resConfUnit2(output)
if (size is None) and (self.size is None):
modifier = {"scale_factor": 2}
elif size is None:
modifier = {"size": self.size}
else:
modifier = {"size": size}
output = nn.functional.interpolate(
output, **modifier, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
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