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# Copyright (c) EPFL VILAB.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# Based on timm, DPT and ConvNeXt code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/isl-org/DPT
# https://github.com/facebookresearch/ConvNeXt
# --------------------------------------------------------
import torch
import torch.nn as nn
from .multimae_utils import DropPath
class ConvNeXtBlock(nn.Module):
r"""ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path: Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 0 (disabled for isotropic ConvNeXt).
Code from: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
"""
def __init__(self, dim, drop_path=0., layer_scale_init_value=0.):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.norm = nn.LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
requires_grad=True) if layer_scale_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class ResidualConvUnit_custom(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=not self.bn,
groups=self.groups,
)
self.conv2 = nn.Conv2d(
features,
features,
kernel_size=3,
stride=1,
padding=1,
bias=not self.bn,
groups=self.groups,
)
if self.bn == True:
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 == True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn == True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
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
out_shape4 = out_shape
if expand == True:
out_shape1 = out_shape
out_shape2 = out_shape * 2
out_shape3 = out_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,
)
scratch.layer4_rn = nn.Conv2d(
in_shape[3],
out_shape4,
kernel_size=3,
stride=1,
padding=1,
bias=False,
groups=groups,
)
scratch.layer_rn = nn.ModuleList([
scratch.layer1_rn,
scratch.layer2_rn,
scratch.layer3_rn,
scratch.layer4_rn,
])
return scratch
class FeatureFusionBlock_custom(nn.Module):
"""Feature fusion block."""
def __init__(
self,
features,
activation,
deconv=False,
bn=False,
expand=False,
align_corners=True,
):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock_custom, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups = 1
self.expand = expand
out_features = features
if self.expand == True:
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_custom(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
"""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 += res
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
def make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, scale_factor, mode, align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
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):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x,
scale_factor=self.scale_factor,
mode=self.mode,
align_corners=self.align_corners,
)
return x
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