models/dcgan.py

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Module):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

        main = nn.Sequential()
        # input is nc x isize x isize
        main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
                        nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
        main.add_module('initial.relu.{0}'.format(ndf),
                        nn.LeakyReLU(0.2, inplace=True))
        csize, cndf = isize / 2, ndf

        # Extra layers
        for t in range(n_extra_layers):
            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
                            nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cndf),
                            nn.BatchNorm2d(cndf))
            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
                            nn.LeakyReLU(0.2, inplace=True))

        while csize > 4:
            in_feat = cndf
            out_feat = cndf * 2
            main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
                            nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
            main.add_module('pyramid.{0}.batchnorm'.format(out_feat),
                            nn.BatchNorm2d(out_feat))
            main.add_module('pyramid.{0}.relu'.format(out_feat),
                            nn.LeakyReLU(0.2, inplace=True))
            cndf = cndf * 2
            csize = csize / 2

        # state size. K x 4 x 4
        cls = nn.Conv2d(cndf, 1, 4, 1, 0, bias=False)
        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
                        cls)

        self.main = main

        # assign cls flag
        for p in self.main.parameters():
            p.cls        = False
            p.cls_weight = False
            p.cls_bias   = False
        for p in cls.parameters():
            p.cls = True
            if p.data.ndimension() == cls.weight.data.ndimension():
                p.cls_weight = True
            else:
                p.cls_bias   = True

    def forward(self, input):
        #gpu_ids = None
        #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        #    gpu_ids = range(self.ngpu)
        #return nn.parallel.data_parallel(self.main, input, gpu_ids)
        return self.main(input)

class DCGAN_G(nn.Module):
    def __init__(self, isize, nz, nc, ngf, ngpu, n_extra_layers=0):
        super(DCGAN_G, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

        cngf, tisize = ngf//2, 4
        while tisize != isize:
            cngf = cngf * 2
            tisize = tisize * 2

        main = nn.Sequential()
        # input is Z, going into a convolution
        main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
                        nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
        main.add_module('initial.{0}.batchnorm'.format(cngf),
                        nn.BatchNorm2d(cngf))
        main.add_module('initial.{0}.relu'.format(cngf),
                        nn.ReLU(True))

        csize, cndf = 4, cngf
        while csize < isize//2:
            main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
                            nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
            main.add_module('pyramid.{0}.batchnorm'.format(cngf//2),
                            nn.BatchNorm2d(cngf//2))
            main.add_module('pyramid.{0}.relu'.format(cngf//2),
                            nn.ReLU(True))
            cngf = cngf // 2
            csize = csize * 2

        # Extra layers
        for t in range(n_extra_layers):
            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
                            nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cngf),
                            nn.BatchNorm2d(cngf))
            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
                            nn.ReLU(True))

        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
                        nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
        main.add_module('final.{0}.tanh'.format(nc),
                        nn.Tanh())
        self.main = main

    def forward(self, input):
        #gpu_ids = None
        #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        #    gpu_ids = range(self.ngpu)
        #return nn.parallel.data_parallel(self.main, input, gpu_ids)
        return self.main(input)


###############################################################################
class DCGAN_D_nobn(nn.Module):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D_nobn, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

        main = nn.Sequential()
        # input is nc x isize x isize
        # input is nc x isize x isize
        main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
                        nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
        main.add_module('initial.relu.{0}'.format(ndf),
                        nn.LeakyReLU(0.2, inplace=True))
        csize, cndf = isize / 2, ndf

        # Extra layers
        for t in range(n_extra_layers):
            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
                            nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
                            nn.LeakyReLU(0.2, inplace=True))

        while csize > 4:
            in_feat = cndf
            out_feat = cndf * 2
            main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
                            nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
            main.add_module('pyramid.{0}.relu'.format(out_feat),
                            nn.LeakyReLU(0.2, inplace=True))
            cndf = cndf * 2
            csize = csize / 2

        # state size. K x 4 x 4
        cls = nn.Conv2d(cndf, 1, 4, 1, 0, bias=False)
        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
                        cls)

        self.main = main

        # assign cls flag
        for p in self.main.parameters():
            p.cls        = False
            p.cls_weight = False
            p.cls_bias   = False
        for p in cls.parameters():
            p.cls = True
            if p.data.ndimension() == cls.weight.data.ndimension():
                p.cls_weight = True
            else:
                p.cls_bias   = True

    def forward(self, input):
        #gpu_ids = None
        #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        #    gpu_ids = range(self.ngpu)
        ##output = nn.parallel.data_parallel(self.main, input, gpu_ids)
        ##output = output.mean(0)
        ##return output.view(1)
        #return nn.parallel.data_parallel(self.main, input, gpu_ids)
        return self.main(input)

class DCGAN_G_nobn(nn.Module):
    def __init__(self, isize, nz, nc, ngf, ngpu, n_extra_layers=0):
        super(DCGAN_G_nobn, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

        cngf, tisize = ngf//2, 4
        while tisize != isize:
            cngf = cngf * 2
            tisize = tisize * 2

        main = nn.Sequential()
        main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
                        nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
        main.add_module('initial.{0}.relu'.format(cngf),
                        nn.ReLU(True))

        csize, cndf = 4, cngf
        while csize < isize//2:
            main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
                            nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
            main.add_module('pyramid.{0}.relu'.format(cngf//2),
                            nn.ReLU(True))
            cngf = cngf // 2
            csize = csize * 2

        # Extra layers
        for t in range(n_extra_layers):
            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
                            nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
                            nn.ReLU(True))

        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
                        nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
        main.add_module('final.{0}.tanh'.format(nc),
                        nn.Tanh())
        self.main = main

    def forward(self, input):
        #gpu_ids = None
        #if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        #    gpu_ids = range(self.ngpu)
        #return nn.parallel.data_parallel(self.main, input, gpu_ids)
        return self.main(input)