TruFor_train_test/lib/models/DnCNN.py

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
#
# All rights reserved.
# This work should only be used for nonprofit purposes.
#
# By downloading and/or using any of these files, you implicitly agree to all the
# terms of the license, as specified in the document LICENSE.txt
# (included in this package) and online at
# http://www.grip.unina.it/download/LICENSE_OPEN.txt

"""
Created in September 2020
@author: davide.cozzolino
"""

import math
import torch.nn as nn

def conv_with_padding(in_planes, out_planes, kernelsize, stride=1, dilation=1, bias=False, padding = None):
    if padding is None:
        padding = kernelsize//2
    return nn.Conv2d(in_planes, out_planes, kernel_size=kernelsize, stride=stride, dilation=dilation, padding=padding, bias=bias)

def conv_init(conv, act='linear'):
    r"""
    Reproduces conv initialization from DnCNN
    """
    n = conv.kernel_size[0] * conv.kernel_size[1] * conv.out_channels
    conv.weight.data.normal_(0, math.sqrt(2. / n))

def batchnorm_init(m, kernelsize=3):
    r"""
    Reproduces batchnorm initialization from DnCNN
    """
    n = kernelsize**2 * m.num_features
    m.weight.data.normal_(0, math.sqrt(2. / (n)))
    m.bias.data.zero_()

def make_activation(act):
    if act is None:
        return None
    elif act == 'relu':
        return nn.ReLU(inplace=True)
    elif act == 'tanh':
        return nn.Tanh()
    elif act == 'leaky_relu':
        return nn.LeakyReLU(inplace=True)
    elif act == 'softmax':
        return nn.Softmax()
    elif act == 'linear':
        return None
    else:
        assert(False)

def make_net(nplanes_in, kernels, features, bns, acts, dilats, bn_momentum = 0.1, padding=None):
    r"""
    :param nplanes_in: number of of input feature channels
    :param kernels: list of kernel size for convolution layers
    :param features: list of hidden layer feature channels
    :param bns: list of whether to add batchnorm layers
    :param acts: list of activations
    :param dilats: list of dilation factors
    :param bn_momentum: momentum of batchnorm
    :param padding: integer for padding (None for same padding)
    """

    depth = len(features)
    assert(len(features)==len(kernels))

    layers = list()
    for i in range(0,depth):
        if i==0:
            in_feats = nplanes_in
        else:
            in_feats = features[i-1]

        elem = conv_with_padding(in_feats, features[i], kernelsize=kernels[i], dilation=dilats[i], padding=padding, bias=not(bns[i]))
        conv_init(elem, act=acts[i])
        layers.append(elem)

        if bns[i]:
            elem = nn.BatchNorm2d(features[i], momentum = bn_momentum)
            batchnorm_init(elem, kernelsize=kernels[i])
            layers.append(elem)

        elem = make_activation(acts[i])
        if elem is not None:
            layers.append(elem)

    return nn.Sequential(*layers)

class DnCNN(nn.Module):
    r"""
    Implements a DnCNN network
    """
    def __init__(self, nplanes_in, nplanes_out, features, kernel, depth, activation, residual, bn, lastact=None, bn_momentum = 0.10, padding=None):
        r"""
        :param nplanes_in: number of of input feature channels
        :param nplanes_out: number of of output feature channels
        :param features: number of of hidden layer feature channels
        :param kernel: kernel size of convolution layers
        :param depth: number of convolution layers (minimum 2)
        :param bn:  whether to add batchnorm layers
        :param residual: whether to add a residual connection from input to output
        :param bn_momentum: momentum of batchnorm
        :param padding: inteteger for padding
        """
        super(DnCNN, self).__init__()

        self.residual = residual
        self.nplanes_out = nplanes_out
        self.nplanes_in = nplanes_in

        kernels = [kernel, ] * depth
        features = [features, ] * (depth-1) + [nplanes_out, ]
        bns = [False, ] + [bn,] * (depth - 2) + [False, ]
        dilats = [1, ] * depth
        acts = [activation, ] * (depth - 1) + [lastact, ]
        self.layers = make_net(nplanes_in, kernels, features, bns, acts, dilats=dilats, bn_momentum = bn_momentum, padding=padding)


    def forward(self, x):
        shortcut = x

        x = self.layers(x)

        if self.residual:
            nshortcut = min(self.nplanes_in, self.nplanes_out)
            x[:, :nshortcut, :, :] = x[:, :nshortcut, :, :] + shortcut[:, :nshortcut, :, :]

        return x


def add_commandline_networkparams(parser, name, features, depth, kernel, activation, bn):
    parser.add_argument("--{}.{}".format(name, "features"  ), type=int, default=features  )
    parser.add_argument("--{}.{}".format(name, "depth"     ), type=int, default=depth     )
    parser.add_argument("--{}.{}".format(name, "kernel"    ), type=int, default=kernel    )
    parser.add_argument("--{}.{}".format(name, "activation"), type=str, default=activation)

    bnarg = "{}.{}".format(name, "bn")
    parser.add_argument("--"+bnarg   , action="store_true", dest=bnarg)
    parser.add_argument("--{}.{}".format(name, "no-bn"), action="store_false", dest=bnarg)
    parser.set_defaults(**{bnarg: bn})