From cf7a0bcabbf191b028cfa5ba662676222dc98f7e Mon Sep 17 00:00:00 2001 From: Muhammad Haris Date: Mon, 30 Mar 2020 06:56:32 +0700 Subject: [PATCH] init --- .gitignore | 5 + autoencoder_v4.py | 116 + base_networks.py | 491 + data.py | 34 + dataset.py | 304 + dbpns_rbpn.py | 57 + eval.py | 200 + eval_star_t_hr.py | 200 + evaluate.m | 56 + fbpn_sr_rbpn_v1.py | 186 + fbpn_sr_rbpn_v1_ref.py | 69 + fbpn_sr_rbpn_v2.py | 182 + fbpn_sr_rbpn_v2_ref.py | 69 + fbpn_sr_rbpn_v3.py | 181 + fbpn_sr_rbpn_v3_ref.py | 69 + fbpn_sr_rbpn_v4.py | 192 + fbpn_sr_rbpn_v4_ref.py | 62 + inter_error.m | 4 + main.py | 207 + main_refinement_flow.py | 254 + main_refinement_t_sr_hr.py | 203 + middlebury/eval-data/test.txt | 12 + middlebury/other-data/test.txt | 12 + niqe_release/computefeature.m | 24 + niqe_release/computemean.m | 3 + niqe_release/computequality.m | 91 + niqe_release/estimateaggdparam.m | 20 + niqe_release/estimatemodelparam.m | 116 + niqe_release/modelparameters.mat | Bin 0 -> 8347 bytes niqe_release/readme.txt | 127 + pyflow.cpython-35m-x86_64-linux-gnu.so | Bin 0 -> 759480 bytes rbpn.py | 97 + ucf101_interp_ours/test.txt | 379 + utils.py | 46 + vimeo_triplet/sequences/tri_testlist.txt | 3782 ++ vimeo_triplet/sequences/tri_trainlist.txt | 51313 ++++++++++++++++++ weights/put the pretrained weights here.txt | 0 37 files changed, 59163 insertions(+) create mode 100644 .gitignore create mode 100644 autoencoder_v4.py create mode 100644 base_networks.py create mode 100644 data.py create mode 100644 dataset.py create mode 100644 dbpns_rbpn.py create mode 100644 eval.py create mode 100644 eval_star_t_hr.py create mode 100644 evaluate.m create mode 100644 fbpn_sr_rbpn_v1.py create mode 100644 fbpn_sr_rbpn_v1_ref.py create mode 100644 fbpn_sr_rbpn_v2.py create mode 100644 fbpn_sr_rbpn_v2_ref.py create mode 100644 fbpn_sr_rbpn_v3.py create mode 100644 fbpn_sr_rbpn_v3_ref.py create mode 100644 fbpn_sr_rbpn_v4.py create mode 100644 fbpn_sr_rbpn_v4_ref.py create mode 100644 inter_error.m create mode 100644 main.py create mode 100644 main_refinement_flow.py create mode 100644 main_refinement_t_sr_hr.py create mode 100644 middlebury/eval-data/test.txt create mode 100644 middlebury/other-data/test.txt create mode 100644 niqe_release/computefeature.m create mode 100644 niqe_release/computemean.m create mode 100644 niqe_release/computequality.m create mode 100644 niqe_release/estimateaggdparam.m create mode 100644 niqe_release/estimatemodelparam.m create mode 100644 niqe_release/modelparameters.mat create mode 100644 niqe_release/readme.txt create mode 100644 pyflow.cpython-35m-x86_64-linux-gnu.so create mode 100644 rbpn.py create mode 100644 ucf101_interp_ours/test.txt create mode 100644 utils.py create mode 100644 vimeo_triplet/sequences/tri_testlist.txt create mode 100644 vimeo_triplet/sequences/tri_trainlist.txt create mode 100644 weights/put the pretrained weights here.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3149953 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ + +FBPN_NEW/.DS_Store +weights/.DS_Store +ucf101_interp_ours/.DS_Store +.DS_Store diff --git a/autoencoder_v4.py b/autoencoder_v4.py new file mode 100644 index 0000000..823c9e2 --- /dev/null +++ b/autoencoder_v4.py @@ -0,0 +1,116 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class UNet(nn.Module): + def __init__(self, in_channels, out_classes): + super(UNet, self).__init__() + self.inc = inconv(in_channels, 64) + self.down1 = down(64, 128) + self.down2 = down(128, 256) + self.down3 = down(256, 512) + self.down4 = down(512, 1024) + self.down5 = down(1024, 1024) + self.up1 = up(2048, 512) + self.up2 = up(1024, 256) + self.up3 = up(512, 128) + self.up4 = up(256, 64) + self.up5 = up(128, 64) + self.outc = outconv(64, out_classes) + + def forward(self, x): + x1 = self.inc(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + x = self.up1(x6, x5) + x = self.up2(x, x4) + x = self.up3(x, x3) + x = self.up4(x, x2) + x = self.up5(x, x1) + x = self.outc(x) + return torch.sigmoid(x) + +class double_conv(nn.Module): + '''(conv => BN => ReLU) * 2''' + def __init__(self, in_ch, out_ch): + super(double_conv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_ch, out_ch, 3, padding=1), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + nn.Conv2d(out_ch, out_ch, 3, padding=1), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + x = self.conv(x) + return x + + +class inconv(nn.Module): + def __init__(self, in_ch, out_ch): + super(inconv, self).__init__() + self.conv = double_conv(in_ch, out_ch) + + def forward(self, x): + x = self.conv(x) + return x + + +class down(nn.Module): + def __init__(self, in_ch, out_ch): + super(down, self).__init__() + self.mpconv = nn.Sequential( + nn.MaxPool2d(2), + double_conv(in_ch, out_ch) + ) + + def forward(self, x): + x = self.mpconv(x) + return x + + +class up(nn.Module): + def __init__(self, in_ch, out_ch, bilinear=True): + super(up, self).__init__() + + # would be a nice idea if the upsampling could be learned too, + # but my machine do not have enough memory to handle all those weights + if bilinear: + self.up = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True) + else: + self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2) + + self.conv = double_conv(in_ch, out_ch) + + def forward(self, x1, x2): + x1 = self.up(x1) + + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, (diffX // 2, diffX - diffX//2, + diffY // 2, diffY - diffY//2)) + + # for padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + + x = torch.cat([x2, x1], dim=1) + x = self.conv(x) + return x + + +class outconv(nn.Module): + def __init__(self, in_ch, out_ch): + super(outconv, self).__init__() + self.conv = nn.Conv2d(in_ch, out_ch, 1) + + def forward(self, x): + x = self.conv(x) + return x \ No newline at end of file diff --git a/base_networks.py b/base_networks.py new file mode 100644 index 0000000..c3918c2 --- /dev/null +++ b/base_networks.py @@ -0,0 +1,491 @@ +import torch +import math +import torch.nn as nn +import torch.nn.functional as F + +class DenseBlock(nn.Module): + def __init__(self, input_size, output_size, bias=True, activation='relu', norm='batch'): + super(DenseBlock, self).__init__() + self.fc = nn.Linear(input_size, output_size, bias=bias) + + self.norm = norm + if self.norm =='batch': + self.bn = nn.BatchNorm1d(output_size) + elif self.norm == 'instance': + self.bn = nn.InstanceNorm1d(output_size) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + if self.norm is not None: + out = self.bn(self.fc(x)) + else: + out = self.fc(x) + + if self.activation is not None: + return self.act(out) + else: + return out + + +class ConvBlock(nn.Module): + def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None): + super(ConvBlock, self).__init__() + self.conv = nn.Conv2d(input_size, output_size, kernel_size, stride, padding, bias=bias) + + self.norm = norm + if self.norm =='batch': + self.bn = nn.BatchNorm2d(output_size) + elif self.norm == 'instance': + self.bn = nn.InstanceNorm2d(output_size) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + if self.norm is not None: + out = self.bn(self.conv(x)) + else: + out = self.conv(x) + + if self.activation is not None: + return self.act(out) + else: + return out + +class ConvBlock3D(nn.Module): + def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None): + super(ConvBlock3D, self).__init__() + self.conv = nn.Conv3d(input_size, output_size, kernel_size, stride, padding) + + self.norm = norm + if self.norm =='batch': + self.bn = nn.BatchNorm2d(output_size) + elif self.norm == 'instance': + self.bn = nn.InstanceNorm2d(output_size) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + if self.norm is not None: + out = self.bn(self.conv(x)) + else: + out = self.conv(x) + + if self.activation is not None: + return self.act(out) + else: + return out + +class DeconvBlock(nn.Module): + def __init__(self, input_size, output_size, kernel_size=4, stride=2, padding=1, bias=True, activation='prelu', norm=None): + super(DeconvBlock, self).__init__() + self.deconv = nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding, bias=bias) + + self.norm = norm + if self.norm == 'batch': + self.bn = nn.BatchNorm2d(output_size) + elif self.norm == 'instance': + self.bn = nn.InstanceNorm2d(output_size) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + if self.norm is not None: + out = self.bn(self.deconv(x)) + else: + out = self.deconv(x) + + if self.activation is not None: + return self.act(out) + else: + return out + + +class ResnetBlock(nn.Module): + def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'): + super(ResnetBlock, self).__init__() + self.conv1 = nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding, bias=bias) + self.conv2 = nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding, bias=bias) + + self.norm = norm + if self.norm == 'batch': + self.bn = nn.BatchNorm2d(num_filter) + elif norm == 'instance': + self.bn = nn.InstanceNorm2d(num_filter) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + + def forward(self, x): + residual = x + if self.norm is not None: + out = self.bn(self.conv1(x)) + else: + out = self.conv1(x) + + if self.activation is not None: + out = self.act(out) + + if self.norm is not None: + out = self.bn(self.conv2(out)) + else: + out = self.conv2(out) + + out = torch.add(out, residual) + + if self.activation is not None: + out = self.act(out) + + return out + +class ResnetBlock3D(nn.Module): + def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'): + super(ResnetBlock3D, self).__init__() + self.conv1 = nn.Conv3d(num_filter, num_filter, kernel_size, stride, padding) + self.conv2 = nn.Conv3d(num_filter, num_filter, kernel_size, stride, padding) + + self.norm = norm + if self.norm == 'batch': + self.bn = nn.BatchNorm2d(num_filter) + elif norm == 'instance': + self.bn = nn.InstanceNorm2d(num_filter) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + + def forward(self, x): + residual = x + if self.norm is not None: + out = self.bn(self.conv1(x)) + else: + out = self.conv1(x) + + if self.activation is not None: + out = self.act(out) + + if self.norm is not None: + out = self.bn(self.conv2(out)) + else: + out = self.conv2(out) + + out = torch.add(out, residual) + + if self.activation is not None: + out = self.act(out) + + return out + +class UpBlock(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None): + super(UpBlock, self).__init__() + self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + h0 = self.up_conv1(x) + l0 = self.up_conv2(h0) + h1 = self.up_conv3(l0 - x) + return h1 + h0 + +class UpBlockPix(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, scale=4, bias=True, activation='prelu', norm=None): + super(UpBlockPix, self).__init__() + self.up_conv1 = Upsampler(scale,num_filter) + self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv3 = Upsampler(scale,num_filter) + + def forward(self, x): + h0 = self.up_conv1(x) + l0 = self.up_conv2(h0) + h1 = self.up_conv3(l0 - x) + return h1 + h0 + +class D_UpBlock(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, bias=True, activation='prelu', norm=None): + super(D_UpBlock, self).__init__() + self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, activation, norm=None) + self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + x = self.conv(x) + h0 = self.up_conv1(x) + l0 = self.up_conv2(h0) + h1 = self.up_conv3(l0 - x) + return h1 + h0 + +class D_UpBlockPix(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, scale=4, bias=True, activation='prelu', norm=None): + super(D_UpBlockPix, self).__init__() + self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, activation, norm=None) + self.up_conv1 = Upsampler(scale,num_filter) + self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.up_conv3 = Upsampler(scale,num_filter) + + def forward(self, x): + x = self.conv(x) + h0 = self.up_conv1(x) + l0 = self.up_conv2(h0) + h1 = self.up_conv3(l0 - x) + return h1 + h0 + +class DownBlock(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None): + super(DownBlock, self).__init__() + self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + l0 = self.down_conv1(x) + h0 = self.down_conv2(l0) + l1 = self.down_conv3(h0 - x) + return l1 + l0 + +class DownBlockPix(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, scale=4,bias=True, activation='prelu', norm=None): + super(DownBlockPix, self).__init__() + self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv2 = Upsampler(scale,num_filter) + self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + l0 = self.down_conv1(x) + h0 = self.down_conv2(l0) + l1 = self.down_conv3(h0 - x) + return l1 + l0 + +class D_DownBlock(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, bias=True, activation='prelu', norm=None): + super(D_DownBlock, self).__init__() + self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, bias, activation, norm=None) + self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + x = self.conv(x) + l0 = self.down_conv1(x) + h0 = self.down_conv2(l0) + l1 = self.down_conv3(h0 - x) + return l1 + l0 + +class D_DownBlockPix(nn.Module): + def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, scale=4, bias=True, activation='prelu', norm=None): + super(D_DownBlockPix, self).__init__() + self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, bias, activation, norm=None) + self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + self.down_conv2 = Upsampler(scale,num_filter) + self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None) + + def forward(self, x): + x = self.conv(x) + l0 = self.down_conv1(x) + h0 = self.down_conv2(l0) + l1 = self.down_conv3(h0 - x) + return l1 + l0 + +class PSBlock(nn.Module): + def __init__(self, input_size, output_size, scale_factor, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'): + super(PSBlock, self).__init__() + self.conv = nn.Conv2d(input_size, output_size * scale_factor**2, kernel_size, stride, padding, bias=bias) + self.ps = nn.PixelShuffle(scale_factor) + + self.norm = norm + if self.norm == 'batch': + self.bn = nn.BatchNorm2d(output_size) + elif norm == 'instance': + self.bn = nn.InstanceNorm2d(output_size) + + self.activation = activation + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + if self.norm is not None: + out = self.bn(self.ps(self.conv(x))) + else: + out = self.ps(self.conv(x)) + + if self.activation is not None: + out = self.act(out) + return out + + +class Upsampler(nn.Module): + def __init__(self, scale, n_feat, bn=False, act='prelu', bias=True): + super(Upsampler, self).__init__() + modules = [] + for _ in range(int(math.log(scale, 2))): + modules.append(ConvBlock(n_feat, 4 * n_feat, 3, 1, 1, bias, activation=None, norm=None)) + modules.append(nn.PixelShuffle(2)) + if bn: modules.append(nn.BatchNorm2d(n_feat)) + #modules.append(nn.PReLU()) + self.up = nn.Sequential(*modules) + + self.activation = act + if self.activation == 'relu': + self.act = nn.ReLU(True) + elif self.activation == 'prelu': + self.act = nn.PReLU() + elif self.activation == 'lrelu': + self.act = nn.LeakyReLU(0.1, True) + elif self.activation == 'tanh': + self.act = nn.Tanh() + elif self.activation == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x): + out = self.up(x) + if self.activation is not None: + out = self.act(out) + return out + + +class Upsample2xBlock(nn.Module): + def __init__(self, input_size, output_size, bias=True, upsample='deconv', activation='relu', norm='batch'): + super(Upsample2xBlock, self).__init__() + scale_factor = 2 + # 1. Deconvolution (Transposed convolution) + if upsample == 'deconv': + self.upsample = DeconvBlock(input_size, output_size, + kernel_size=4, stride=2, padding=1, + bias=bias, activation=activation, norm=norm) + + # 2. Sub-pixel convolution (Pixel shuffler) + elif upsample == 'ps': + self.upsample = PSBlock(input_size, output_size, scale_factor=scale_factor, + bias=bias, activation=activation, norm=norm) + + # 3. Resize and Convolution + elif upsample == 'rnc': + self.upsample = nn.Sequential( + nn.Upsample(scale_factor=scale_factor, mode='nearest'), + ConvBlock(input_size, output_size, + kernel_size=3, stride=1, padding=1, + bias=bias, activation=activation, norm=norm) + ) + + def forward(self, x): + out = self.upsample(x) + return out + +class PyramidModule(nn.Module): + def __init__(self, num_inchannels,activation='prelu'): + super(PyramidModule, self).__init__() + + self.l1_1 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l1_2 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l1_3 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l1_4 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l1_5 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + + self.l2_1 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l2_2 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l2_3 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l2_4 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + + self.l3_1 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l3_2 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + self.l3_3 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None) + + self.down1 = ConvBlock(num_inchannels, num_inchannels*2, 4, 2, 1, bias=True, activation=activation, norm=None) + self.down2 = ConvBlock(num_inchannels*2, num_inchannels*4, 4, 2, 1, bias=True, activation=activation, norm=None) + + self.up1 = DeconvBlock(num_inchannels*2, num_inchannels, 4, 2, 1, bias=True, activation=activation, norm=None) + self.up2 = DeconvBlock(num_inchannels*4, num_inchannels*2, 4, 2, 1, bias=True, activation=activation, norm=None) + + self.final = ConvBlock(num_inchannels, num_inchannels, 3, 1, 1, bias=True, activation=activation, norm=None) + + def forward(self, x): + out1_1 = self.l1_1(x) + out2_1 = self.l2_1(self.down1(out1_1)) + out3_1 = self.l3_1(self.down2(out2_1)) + + out1_2 = self.l1_2(out1_1 + self.up1(out2_1)) + out2_2 = self.l2_2(out2_1 + self.down1(out1_2) + self.up2(out3_1)) + out3_2 = self.l3_2(out3_1 + self.down2(out2_2)) + + out1_3 = self.l1_3(out1_2 + self.up1(out2_2)) + out2_3 = self.l2_3(out2_2 + self.down1(out1_3) + self.up2(out3_2)) + out3_3 = self.l3_3(out3_2 + self.down2(out2_3)) + + out1_4 = self.l1_4(out1_3 + self.up1(out2_3)) + out2_4 = self.l2_4(out2_3 + self.down1(out1_4) + self.up2(out3_3)) + + out1_5 = self.l1_5(out1_4 + self.up1(out2_4)) + + final = self.final(out1_5) + + return final + diff --git a/data.py b/data.py new file mode 100644 index 0000000..6e00c3a --- /dev/null +++ b/data.py @@ -0,0 +1,34 @@ +from os.path import exists, join, basename +from os import makedirs, remove +from six.moves import urllib +import tarfile +from torchvision.transforms import Compose, ToTensor, Normalize + +from dataset import * + +def transform(): + return Compose([ + ToTensor(), + Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + ]) + +##LOADER TRAINING +def get_training_set(data_dir, upscale_factor, data_augmentation, file_list, patch_size): + return DatasetFromFolder(data_dir, upscale_factor, data_augmentation, file_list, patch_size, + transform=transform()) + +def get_training_set_flow(data_dir, upscale_factor, data_augmentation, file_list, patch_size): + return DatasetFromFolderFlow(data_dir, upscale_factor, data_augmentation, file_list, patch_size, + transform=transform()) + +def get_training_set_flow_lr(data_dir, upscale_factor, data_augmentation, file_list, patch_size): + return DatasetFromFolderFlowLR(data_dir, upscale_factor, data_augmentation, file_list, patch_size, + transform=transform()) + +##LOADER EVALUATING +def get_test_set(data_dir, upscale_factor, file_list): + return DatasetFromFolderTest(data_dir, upscale_factor, file_list, transform=transform()) + +def get_test_set_interp(data_dir, file_list): + return DatasetFromFolderInterp(data_dir, file_list, transform=transform()) diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000..7d2279f --- /dev/null +++ b/dataset.py @@ -0,0 +1,304 @@ +import torch.utils.data as data +import torch +import numpy as np +import os +from os import listdir +from os.path import join +from PIL import Image, ImageOps +import random +import pyflow +from skimage import img_as_float +from skimage import color +from random import randrange +import os.path +import cv2 + +max_flow = 150.0 + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"]) + +def load_img(filepath, scale): + list=os.listdir(filepath) + list.sort() + + rate = 1 + #for vimeo90k-setuplet (multiple temporal scale) + #if random.random() < 0.5: + # rate = 2 + + index = randrange(0, len(list)-(2*rate)) + + target = [modcrop(Image.open(filepath+'/'+list[i]).convert('RGB'), scale) for i in range(index, index+3*rate, rate)] + + h,w = target[0].size + h_in,w_in = int(h//scale), int(w//scale) + + target_l = target[1].resize((h_in,w_in), Image.BICUBIC) + input = [target[j].resize((h_in,w_in), Image.BICUBIC) for j in [0,2]] + + return input, target, target_l, list + +def load_img_test(filepath, scale): + list=os.listdir(filepath) + list.sort() + + target = [modcrop(Image.open(filepath+'/'+list[i]).convert('RGB'), scale) for i in range(len(list))] + h,w = target[0].size + h_in,w_in = int(h//scale), int(w//scale) + + input = [target[j].resize((h_in,w_in), Image.BICUBIC) for j in [0,len(list)-1]] + + return input, list + +def load_img_nodown(filepath): + list=os.listdir(filepath) + list.sort() + + input = [Image.open(filepath+'/'+list[i]).convert('RGB') for i in [0,len(list)-1]] + + return input, list + +def get_flow(im1, im2): + im1 = np.array(im1) + im2 = np.array(im2) + im1 = im1.astype(float) / 255. + im2 = im2.astype(float) / 255. + + # Flow Options: + alpha = 0.012 + ratio = 0.75 #0.95 #0.75 + minWidth = 20 #50 #20 + nOuterFPIterations = 7 + nInnerFPIterations = 1 + nSORIterations = 30 + colType = 0 # 0 or default:RGB, 1:GRAY (but pass gray image with shape (h,w,1)) + + u, v, im2W = pyflow.coarse2fine_flow(im1, im2, alpha, ratio, minWidth, nOuterFPIterations, nInnerFPIterations,nSORIterations, colType) + flow = np.concatenate((u[..., None], v[..., None]), axis=2) + + #Rescale + flow = rescale_flow(flow,-1,1) + return flow + +def rescale_flow(x,max_range,min_range): + #remove noise + x[x > max_flow] = max_flow + x[x < -max_flow] = -max_flow + + max_val = max_flow + min_val = -max_flow + return (max_range-min_range)/(max_val-min_val)*(x-max_val)+max_range + +def modcrop(img, modulo): + (ih, iw) = img.size + ih = ih - (ih%modulo); + iw = iw - (iw%modulo); + img = img.crop((0, 0, ih, iw)) + return img + +def get_patch(img_in, img_tar, img_tar_l, patch_size, scale, ix=-1, iy=-1): + (ih, iw) = img_in[0].size + (th, tw) = (scale * ih, scale * iw) + + patch_mult = scale #if len(scale) > 1 else 1 + tp = patch_mult * patch_size + ip = tp // scale + + if ix == -1: + ix = random.randrange(0, iw - ip + 1) + if iy == -1: + iy = random.randrange(0, ih - ip + 1) + + (tx, ty) = (scale * ix, scale * iy) + + img_in = [j.crop((iy,ix,iy + ip, ix + ip)) for j in img_in] + img_tar = [j.crop((ty,tx,ty + tp, tx + tp)) for j in img_tar] + img_tar_l = img_tar_l.crop((iy,ix,iy + ip, ix + ip)) + + info_patch = { + 'ix': ix, 'iy': iy, 'ip': ip, 'tx': tx, 'ty': ty, 'tp': tp} + + return img_in, img_tar, img_tar_l, info_patch + +def augment(img_in, img_tar, img_tar_l, flip_h=True, rot=True): + info_aug = {'flip_h': False, 'flip_v': False, 'trans': False} + + if random.random() < 0.5 and flip_h: + img_in = [ImageOps.flip(j) for j in img_in] + img_tar = [ImageOps.flip(j) for j in img_tar] + img_tar_l = ImageOps.flip(img_tar_l) + info_aug['flip_h'] = True + + if rot: + if random.random() < 0.5: + img_in = [ImageOps.mirror(j) for j in img_in] + img_tar = [ImageOps.mirror(j) for j in img_tar] + img_tar_l = ImageOps.mirror(img_tar_l) + info_aug['flip_v'] = True + if random.random() < 0.5: + img_in = [j.rotate(180) for j in img_in] + img_tar = [j.rotate(180) for j in img_tar] + img_tar_l = img_tar_l.rotate(180) + info_aug['trans'] = True + + return img_in, img_tar, img_tar_l, info_aug + +class DatasetFromFolder(data.Dataset): + def __init__(self, image_dir, upscale_factor, data_augmentation, file_list, patch_size, transform=None): + super(DatasetFromFolder, self).__init__() + alist = [line.rstrip() for line in open(join(image_dir,file_list))] + self.image_filenames = [join(image_dir,x) for x in alist] + self.upscale_factor = upscale_factor + self.transform = transform + self.data_augmentation = data_augmentation + self.patch_size = patch_size + + def __getitem__(self, index): + input, target, target_l, file_list = load_img(self.image_filenames[index], self.upscale_factor) + + if self.patch_size != 0: + input, target, target_l, _ = get_patch(input,target,target_l,self.patch_size, self.upscale_factor) + + if self.data_augmentation: + input, target, target_l, _ = augment(input, target, target_l) + + flow_f = get_flow(input[0],input[1]) + flow_b = get_flow(input[1],input[0]) + + if self.transform: + input = [self.transform(j) for j in input] + target = [self.transform(j) for j in target] + target_l = self.transform(target_l) + flow_f = torch.from_numpy(flow_f.transpose(2,0,1)) + flow_b = torch.from_numpy(flow_b.transpose(2,0,1)) + + return input, target, target_l, flow_f, flow_b, file_list, self.image_filenames[index] + + def __len__(self): + return len(self.image_filenames) + +class DatasetFromFolderFlow(data.Dataset): + def __init__(self, image_dir, upscale_factor, data_augmentation, file_list, patch_size, transform=None): + super(DatasetFromFolderFlow, self).__init__() + alist = [line.rstrip() for line in open(join(image_dir,file_list))] + self.image_filenames = [join(image_dir,x) for x in alist] + self.upscale_factor = upscale_factor + self.transform = transform + self.data_augmentation = data_augmentation + self.patch_size = patch_size + + def __getitem__(self, index): + input, target, target_l, file_list = load_img(self.image_filenames[index], self.upscale_factor) + + if self.patch_size != 0: + input, target, target_l, _ = get_patch(input,target,target_l,self.patch_size, self.upscale_factor) + + if self.data_augmentation: + input, target, target_l, _ = augment(input, target, target_l) + + flow_f = get_flow(input[0],input[1]) + flow_b = get_flow(input[1],input[0]) + + gt_flow_f = get_flow(input[0],target_l) + get_flow(target_l,input[1]) + gt_flow_b = get_flow(input[1],target_l) + get_flow(target_l,input[0]) + + if self.transform: + input = [self.transform(j) for j in input] + target = [self.transform(j) for j in target] + target_l = self.transform(target_l) + flow_f = torch.from_numpy(flow_f.transpose(2,0,1)) + flow_b = torch.from_numpy(flow_b.transpose(2,0,1)) + gt_flow_f = torch.from_numpy(gt_flow_f.transpose(2,0,1)) + gt_flow_b = torch.from_numpy(gt_flow_b.transpose(2,0,1)) + + return input, target, target_l, flow_f, flow_b, gt_flow_f, gt_flow_b,file_list, self.image_filenames[index] + + def __len__(self): + return len(self.image_filenames) + +class DatasetFromFolderFlowLR(data.Dataset): + def __init__(self, image_dir, upscale_factor, data_augmentation, file_list, patch_size, transform=None): + super(DatasetFromFolderFlowLR, self).__init__() + alist = [line.rstrip() for line in open(join(image_dir,file_list))] + self.image_filenames = [join(image_dir,x) for x in alist] + self.upscale_factor = upscale_factor + self.transform = transform + self.data_augmentation = data_augmentation + self.patch_size = patch_size + + def __getitem__(self, index): + input, target, target_l, file_list = load_img(self.image_filenames[index], self.upscale_factor) + + if self.patch_size != 0: + input, target, target_l, _ = get_patch(input,target,target_l,self.patch_size, self.upscale_factor) + + if self.data_augmentation: + input, target, target_l, _ = augment(input, target, target_l) + + flow_f = get_flow(target[0],target[2]) + flow_b = get_flow(target[2],target[0]) + + gt_flow_f = get_flow(target[0],target[1]) + get_flow(target[1],target[2]) + gt_flow_b = get_flow(target[2],target[1]) + get_flow(target[1],target[0]) + + if self.transform: + target = [self.transform(j) for j in target] + flow_f = torch.from_numpy(flow_f.transpose(2,0,1)) + flow_b = torch.from_numpy(flow_b.transpose(2,0,1)) + gt_flow_f = torch.from_numpy(gt_flow_f.transpose(2,0,1)) + gt_flow_b = torch.from_numpy(gt_flow_b.transpose(2,0,1)) + + + return target, flow_f, flow_b, gt_flow_f, gt_flow_b, file_list, self.image_filenames[index] + + def __len__(self): + return len(self.image_filenames) + +class DatasetFromFolderTest(data.Dataset): + def __init__(self, image_dir, upscale_factor, file_list, transform=None): + super(DatasetFromFolderTest, self).__init__() + alist = [line.rstrip() for line in open(join(image_dir,file_list))] + self.image_filenames = [join(image_dir,x) for x in alist] + self.upscale_factor = upscale_factor + self.transform = transform + + def __getitem__(self, index): + input, file_list = load_img_test(self.image_filenames[index], self.upscale_factor) + + flow_f = get_flow(input[0],input[1]) + flow_b = get_flow(input[1],input[0]) + + if self.transform: + input = [self.transform(j) for j in input] + flow_f = torch.from_numpy(flow_f.transpose(2,0,1)) + flow_b = torch.from_numpy(flow_b.transpose(2,0,1)) + + return input, flow_f, flow_b, file_list, self.image_filenames[index] + + def __len__(self): + return len(self.image_filenames) + +class DatasetFromFolderInterp(data.Dataset): + def __init__(self, image_dir, file_list, transform=None): + super(DatasetFromFolderInterp, self).__init__() + alist = [line.rstrip() for line in open(join(image_dir,file_list))] + self.image_filenames = [join(image_dir,x) for x in alist] + self.transform = transform + + def __getitem__(self, index): + input, file_list = load_img_nodown(self.image_filenames[index]) + + flow_f = get_flow(input[0],input[1]) + flow_b = get_flow(input[1],input[0]) + + if self.transform: + input = [self.transform(j) for j in input] + flow_f = torch.from_numpy(flow_f.transpose(2,0,1)) + flow_b = torch.from_numpy(flow_b.transpose(2,0,1)) + + return input, flow_f, flow_b, file_list, self.image_filenames[index] + + def __len__(self): + return len(self.image_filenames) + \ No newline at end of file diff --git a/dbpns_rbpn.py b/dbpns_rbpn.py new file mode 100644 index 0000000..3752546 --- /dev/null +++ b/dbpns_rbpn.py @@ -0,0 +1,57 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, scale_factor): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + #self.feat0 = ConvBlock(num_channels, feat, 3, 1, 1, activation='prelu', norm=None) + self.feat1 = ConvBlock(base_filter, feat, 1, 1, 0, activation='prelu', norm=None) + #Back-projection stages + self.up1 = UpBlock(feat, kernel, stride, padding) + self.down1 = DownBlock(feat, kernel, stride, padding) + self.up2 = UpBlock(feat, kernel, stride, padding) + self.down2 = DownBlock(feat, kernel, stride, padding) + self.up3 = UpBlock(feat, kernel, stride, padding) + #Reconstruction + self.output = ConvBlock(num_stages*feat, feat, 1, 1, 0, activation=None, norm=None) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x): + #x = self.feat0(x) + x = self.feat1(x) + + h1 = self.up1(x) + h2 = self.up2(self.down1(h1)) + h3 = self.up3(self.down2(h2)) + + x = self.output(torch.cat((h3, h2, h1),1)) + + return x diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..de1934d --- /dev/null +++ b/eval.py @@ -0,0 +1,200 @@ +from __future__ import print_function +import argparse + +import os +import torch +import torch.nn as nn +import torch.optim as optim +from torch.autograd import Variable +from torch.utils.data import DataLoader +from fbpn_sr_rbpn_v1 import Net as FBPNSR_RBPN_V1 +from fbpn_sr_rbpn_v2 import Net as FBPNSR_RBPN_V2 +from fbpn_sr_rbpn_v3 import Net as FBPNSR_RBPN_V3 +from fbpn_sr_rbpn_v4 import Net as FBPNSR_RBPN_V4 +from fbpn_sr_rbpn_v1_ref import Net as FBPNSR_RBPN_V1_REF +from fbpn_sr_rbpn_v2_ref import Net as FBPNSR_RBPN_V2_REF +from fbpn_sr_rbpn_v3_ref import Net as FBPNSR_RBPN_V3_REF +from fbpn_sr_rbpn_v4_ref import Net as FBPNSR_RBPN_V4_REF, FeatureExtractor +from data import get_test_set +from functools import reduce +import numpy as np +import utils +from math import ceil +import time +import cv2 +import math +import pdb + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch Super Res Example') +parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor") +parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size') +parser.add_argument('--gpu_mode', type=bool, default=True) +parser.add_argument('--chop_forward', type=bool, default=False) +parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use') +parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') +parser.add_argument('--gpus', default=1, type=float, help='number of gpu') +parser.add_argument('--data_dir', type=str, default='vimeo_triplet/sequences') +parser.add_argument('--file_list', type=str, default='tri_testlist.txt') +parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4_REF') +parser.add_argument('--output', default='Results/', help='Location to save checkpoint models') +parser.add_argument('--model', default='weights/FBPNSR_RBPN_V4_REF_Lr_STAR_ST.pth', help='sr pretrained base model') + +opt = parser.parse_args() + +gpus_list=range(opt.gpus) +print(opt) + +cuda = opt.gpu_mode +if cuda and not torch.cuda.is_available(): + raise Exception("No GPU found, please run without --cuda") + +torch.manual_seed(opt.seed) +if cuda: + torch.cuda.manual_seed(opt.seed) + +print('===> Loading datasets') +test_set = get_test_set(opt.data_dir, opt.upscale_factor, opt.file_list) +testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False) + +print('===> Building model ', opt.model_type) +if opt.model_type == 'FBPNSR_RBPN_V1_REF': + model = FBPNSR_RBPN_V1_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2_REF': + model = FBPNSR_RBPN_V2_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3_REF': + model = FBPNSR_RBPN_V3_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4_REF': + model = FBPNSR_RBPN_V4_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V1': + model = FBPNSR_RBPN_V1(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2': + model = FBPNSR_RBPN_V2(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3': + model = FBPNSR_RBPN_V3(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4': + model = FBPNSR_RBPN_V4(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) + +if cuda: + model = torch.nn.DataParallel(model, device_ids=gpus_list) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + +print('---------- Networks architecture -------------') +print_network(model) +print('----------------------------------------------') + +model.load_state_dict(torch.load(opt.model, map_location=lambda storage, loc: storage)) +print('Pre-trained SR model is loaded.') + + +if cuda: + model = model.cuda(gpus_list[0]) + +def eval(): + model.eval() + avg_psnr_predicted = 0.0 + for batch in testing_data_loader: + t0 = time.time() + input, flow_f, flow_b, filename, d_dir = batch[0], batch[1], batch[2], batch[3], batch[4] + + with torch.no_grad(): + t_im1 = Variable(input[0]).cuda(gpus_list[0]) + t_im2 = Variable(input[1]).cuda(gpus_list[0]) + t_flow_f = Variable(flow_f).cuda(gpus_list[0]).float() + t_flow_b = Variable(flow_b).cuda(gpus_list[0]).float() + + if opt.chop_forward: + with torch.no_grad(): + pred_ht, pred_h1, pred_h2, pred_l = chop_forward(t_im1, t_im2, t_flow_f, t_flow_b, model) + else: + with torch.no_grad(): + pred_ht, pred_h1, pred_h2, pred_l = model(t_im1, t_im2, t_flow_f, t_flow_b, train=False) + + t1 = time.time() + + print("===> Processing: %s || Timer: %.4f sec." % (d_dir[0]+'/frame10i11.png', (t1 - t0))) + pred_ht = utils.denorm(pred_ht[0].cpu().data,vgg=True) + pred_h1 = utils.denorm(pred_h1[0].cpu().data,vgg=True) + pred_h2 = utils.denorm(pred_h2[0].cpu().data,vgg=True) + pred_l = utils.denorm(pred_l[0].cpu().data,vgg=True) + + #save_img(pred, d_dir[0],'frame10i11.png', True) + save_img(pred_ht, d_dir[0],'im2.png', False) + save_img(pred_h1, d_dir[0],'im1.png', False) + save_img(pred_h2, d_dir[0],'im3.png', False) + save_img(pred_l, d_dir[0],'im_l.png', False) + #save_img(target, str(count), False) + + #print("PSNR_predicted=", avg_psnr_predicted/count) + +def save_img(img, d_dir,img_name, pred_flag): + save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0) + filename = os.path.splitext(img_name) + + # save img + save_dir=os.path.join(opt.output, d_dir) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + if pred_flag: + save_fn = save_dir +'/'+ filename[0]+'_'+opt.model_type+filename[1] + else: + save_fn = save_dir +'/'+ img_name + cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0]) + +def chop_forward(t_im1, t_im2, t_flow_f, t_flow_b, iter, model, shave=8, min_size=160000, nGPUs=opt.gpus): + b, c, h, w = t_im1.size() + h_half, w_half = h // 2, w // 2 + h_size, w_size = h_half + shave, w_half + shave + + mod_size = 4 + if h_size%mod_size: + h_size = ceil(h_size/mod_size)*mod_size + if w_size%mod_size: + w_size = ceil(w_size/mod_size)*mod_size + + inputlist = [ + [t_im1[:, :, 0:h_size, 0:w_size], t_im2[:, :, 0:h_size, 0:w_size], t_flow_f[:, :, 0:h_size, 0:w_size], t_flow_b[:, :, 0:h_size, 0:w_size], iter], + [t_im1[:, :, 0:h_size, (w - w_size):w],t_im2[:, :, 0:h_size, (w - w_size):w],t_flow_f[:, :, 0:h_size, (w - w_size):w],t_flow_b[:, :, 0:h_size, (w - w_size):w],iter ], + [t_im1[:, :, (h - h_size):h, 0:w_size],t_im2[:, :, (h - h_size):h, 0:w_size],t_flow_f[:, :, (h - h_size):h, 0:w_size],t_flow_b[:, :, (h - h_size):h, 0:w_size],iter ], + [t_im1[:, :, (h - h_size):h, (w - w_size):w],t_im2[:, :, (h - h_size):h, (w - w_size):w],t_flow_f[:, :, (h - h_size):h, (w - w_size):w],t_flow_b[:, :, (h - h_size):h, (w - w_size):w],iter ]] + + if w_size * h_size < min_size: + outputlist = [] + for i in range(0, 4, nGPUs): + with torch.no_grad(): + input_batch = inputlist[i]#torch.cat(inputlist[i:(i + nGPUs)], dim=0) + output_batch = model(input_batch[0], input_batch[1], input_batch[2], input_batch[3], train=False) + outputlist.extend(output_batch.chunk(nGPUs, dim=0)) + else: + outputlist = [ + chop_forward(patch[0], patch[1], patch[2],patch[3],patch[4], model, shave, min_size, nGPUs) \ + for patch in inputlist] + + scale=1 + h, w = scale * h, scale * w + h_half, w_half = scale * h_half, scale * w_half + h_size, w_size = scale * h_size, scale * w_size + shave *= scale + + with torch.no_grad(): + output = Variable(t_im1.data.new(b, c, h, w)) + output[:, :, 0:h_half, 0:w_half] \ + = outputlist[0][:, :, 0:h_half, 0:w_half] + output[:, :, 0:h_half, w_half:w] \ + = outputlist[1][:, :, 0:h_half, (w_size - w + w_half):w_size] + output[:, :, h_half:h, 0:w_half] \ + = outputlist[2][:, :, (h_size - h + h_half):h_size, 0:w_half] + output[:, :, h_half:h, w_half:w] \ + = outputlist[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size] + + return output + +##Eval Start!!!! +eval() diff --git a/eval_star_t_hr.py b/eval_star_t_hr.py new file mode 100644 index 0000000..9660fe2 --- /dev/null +++ b/eval_star_t_hr.py @@ -0,0 +1,200 @@ +from __future__ import print_function +import argparse + +import os +import torch +import torch.nn as nn +import torch.optim as optim +from torch.autograd import Variable +from torch.utils.data import DataLoader +from fbpn_sr_rbpn_v1 import Net as FBPNSR_RBPN_V1 +from fbpn_sr_rbpn_v2 import Net as FBPNSR_RBPN_V2 +from fbpn_sr_rbpn_v3 import Net as FBPNSR_RBPN_V3 +from fbpn_sr_rbpn_v4 import Net as FBPNSR_RBPN_V4 +from fbpn_sr_rbpn_v1_ref import Net as FBPNSR_RBPN_V1_REF +from fbpn_sr_rbpn_v2_ref import Net as FBPNSR_RBPN_V2_REF +from fbpn_sr_rbpn_v3_ref import Net as FBPNSR_RBPN_V3_REF +from fbpn_sr_rbpn_v4_ref import Net as FBPNSR_RBPN_V4_REF, FeatureExtractor +from data import get_test_set_interp +from functools import reduce +import numpy as np +import utils +import time +import cv2 +import math +import pdb + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch Super Res Example') +parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor") +parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size') +parser.add_argument('--gpu_mode', type=bool, default=True) +parser.add_argument('--chop_forward', type=bool, default=False) +parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use') +parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') +parser.add_argument('--gpus', default=1, type=float, help='number of gpu') +parser.add_argument('--data_dir', type=str, default='vimeo_triplet/sequences') +parser.add_argument('--file_list', type=str, default='tri_testlist.txt') +parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4_REF') +parser.add_argument('--residual', type=bool, default=False) +parser.add_argument('--output', default='Results_T_SR_HR/', help='Location to save checkpoint models') +parser.add_argument('--model', default='weights/FBPNSR_RBPN_V4_REF_Lf_STAR_T_HR.pth', help='sr pretrained base model') + +opt = parser.parse_args() + +gpus_list=range(opt.gpus) +print(opt) + +cuda = opt.gpu_mode +if cuda and not torch.cuda.is_available(): + raise Exception("No GPU found, please run without --cuda") + +torch.manual_seed(opt.seed) +if cuda: + torch.cuda.manual_seed(opt.seed) + +print('===> Loading datasets') +test_set = get_test_set_interp(opt.data_dir, opt.file_list) +testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False) + +print('===> Building model ', opt.model_type) +if opt.model_type == 'FBPNSR_RBPN_V1_REF': + model = FBPNSR_RBPN_V1_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2_REF': + model = FBPNSR_RBPN_V2_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3_REF': + model = FBPNSR_RBPN_V3_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4_REF': + model = FBPNSR_RBPN_V4_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V1': + model = FBPNSR_RBPN_V1(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2': + model = FBPNSR_RBPN_V2(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3': + model = FBPNSR_RBPN_V3(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4': + model = FBPNSR_RBPN_V4(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) + + +if cuda: + model = torch.nn.DataParallel(model, device_ids=gpus_list) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + +print('---------- Networks architecture -------------') +print_network(model) +print('----------------------------------------------') + +model.load_state_dict(torch.load(opt.model, map_location=lambda storage, loc: storage)) +print('Pre-trained SR model is loaded.') + +if cuda: + model = model.cuda(gpus_list[0]) + +def eval(): + model.eval() + avg_psnr_predicted = 0.0 + for batch in testing_data_loader: + input, flow_f, flow_b, filename, d_dir = batch[0], batch[1], batch[2], batch[3], batch[4] + + with torch.no_grad(): + t_im1 = Variable(input[0]).cuda(gpus_list[0]) + t_im2 = Variable(input[1]).cuda(gpus_list[0]) + t_flow_f = Variable(flow_f).cuda(gpus_list[0]).float() + t_flow_b = Variable(flow_b).cuda(gpus_list[0]).float() + + t0 = time.time() + if opt.chop_forward: + with torch.no_grad(): + pred_l = chop_forward(t_im1, t_im2, t_flow_f, t_flow_b, model) + else: + with torch.no_grad(): + _, _, _, pred_l = model(t_im1, t_im2, t_flow_f, t_flow_b, train=False) + + t1 = time.time() + + print("===> Processing: %s || Timer: %.4f sec." % (d_dir[0]+'/frame10i11.png', (t1 - t0))) + pred_l = utils.denorm(pred_l[0].cpu().data, vgg=True) + pred_1 = utils.denorm(t_im1[0].cpu().data, vgg=True) + pred_2 = utils.denorm(t_im2[0].cpu().data, vgg=True) + + if opt.data_dir == 'ucf101_interp_ours': + save_img(pred_1, d_dir[0],'frame_00.png', False) + save_img(pred_l, d_dir[0],'frame_01_gt.png', False) + save_img(pred_2, d_dir[0],'frame_02.png', False) + else: + save_img(pred_1, d_dir[0],'im1.png', False) + save_img(pred_l, d_dir[0],'im2.png', False) + save_img(pred_2, d_dir[0],'im3.png', False) + + +def save_img(img, d_dir,img_name, pred_flag): + save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0) + filename = os.path.splitext(img_name) + + # save img + save_dir=os.path.join(opt.output, d_dir) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + if pred_flag: + save_fn = save_dir +'/'+ filename[0]+'_'+opt.model_type+filename[1] + else: + save_fn = save_dir +'/'+ img_name + cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0]) + +def chop_forward(t_im1, t_im2, t_flow_f, t_flow_b, model, shave=8, min_size=200000, nGPUs=opt.gpus): + b, c, h, w = t_im1.size() + h_half, w_half = h // 2, w // 2 + h_size, w_size = h_half + shave, w_half + shave + + if h_size%2: + h_size = h_size + 1 + + if w_size%2: + w_size = w_size + 1 + + inputlist = [ + [t_im1[:, :, 0:h_size, 0:w_size], t_im2[:, :, 0:h_size, 0:w_size], t_flow_f[:, :, 0:h_size, 0:w_size], t_flow_b[:, :, 0:h_size, 0:w_size]], + [t_im1[:, :, 0:h_size, (w - w_size):w],t_im2[:, :, 0:h_size, (w - w_size):w],t_flow_f[:, :, 0:h_size, (w - w_size):w],t_flow_b[:, :, 0:h_size, (w - w_size):w] ], + [t_im1[:, :, (h - h_size):h, 0:w_size],t_im2[:, :, (h - h_size):h, 0:w_size],t_flow_f[:, :, (h - h_size):h, 0:w_size],t_flow_b[:, :, (h - h_size):h, 0:w_size] ], + [t_im1[:, :, (h - h_size):h, (w - w_size):w],t_im2[:, :, (h - h_size):h, (w - w_size):w],t_flow_f[:, :, (h - h_size):h, (w - w_size):w],t_flow_b[:, :, (h - h_size):h, (w - w_size):w] ]] + + if w_size * h_size < min_size: + outputlist = [] + for i in range(0, 4, nGPUs): + with torch.no_grad(): + input_batch = inputlist[i] + _, _, _, output_batch = model(input_batch[0], input_batch[1], input_batch[2], input_batch[3], train=False) + outputlist.extend(output_batch.chunk(nGPUs, dim=0)) + else: + outputlist = [ + chop_forward(patch[0], patch[1], patch[2],patch[3], model, shave, min_size, nGPUs) \ + for patch in inputlist] + + scale=1 + h, w = scale * h, scale * w + h_half, w_half = scale * h_half, scale * w_half + h_size, w_size = scale * h_size, scale * w_size + shave *= scale + + with torch.no_grad(): + output = Variable(t_im1.data.new(b, c, h, w)) + output[:, :, 0:h_half, 0:w_half] \ + = outputlist[0][:, :, 0:h_half, 0:w_half] + output[:, :, 0:h_half, w_half:w] \ + = outputlist[1][:, :, 0:h_half, (w_size - w + w_half):w_size] + output[:, :, h_half:h, 0:w_half] \ + = outputlist[2][:, :, (h_size - h + h_half):h_size, 0:w_half] + output[:, :, h_half:h, w_half:w] \ + = outputlist[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size] + + return output + +##Eval Start!!!! +eval() diff --git a/evaluate.m b/evaluate.m new file mode 100644 index 0000000..6a1bcf2 --- /dev/null +++ b/evaluate.m @@ -0,0 +1,56 @@ +clc; +clear; + +addpath(genpath(fullfile(pwd,'niqe_release'))); +%% Loading model +load niqe_release/modelparameters.mat +blocksizerow = 96; +blocksizecol = 96; +blockrowoverlap = 0; +blockcoloverlap = 0; + +%% set parameters +file_list = 'tri_testlist.txt'; +target_dir = 'vimeo_triplet/sequences/'; +result_dir = 'Result_T_SR_HR_MSE/vimeo_triplet/sequences/'; + +im_file = '/im2.png'; +%im_file = '/frame_01_gt.png'; + +i=1; +fid = fopen([target_dir file_list]); +while ~feof(fid) + tline = fgetl(fid); + filelist{i} = tline; + i=i+1; +end +fclose(fid); + +psnr_drbpsr = zeros(length(filelist),1); +ssim_drbpsr = zeros(length(filelist),1); +fsim_drbpsr = zeros(length(filelist),1); +niqe_drbpsr = zeros(length(filelist),1); +ie_drbpsr = zeros(length(filelist),1); + +parfor j=1:length(filelist)-1 + im_gnd = imread([target_dir filelist{j} im_file]); + %im_gnd = imresize(imread([target_dir filelist{j} '/im2.png']), 1/4, 'bicubic'); + im_sr = imread([result_dir filelist{j} im_file]); + + %% compute PSNR + psnr_drbpsr(j) = psnr(im_gnd,im_sr); + if isinf(psnr_drbpsr(j)) + fprintf('%s;', [target_dir filelist{j} im_file]) + end + + %% compute SSIM + ssim_drbpsr(j) = ssim(im_gnd,im_sr); + + ie_drbpsr(j) = inter_error(im_gnd,im_sr); + %% compute NIQE + niqe_drbpsr(j) = computequality(im_sr,blocksizerow,blocksizecol,... + blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam); +end + +fprintf('%s, %f , %f , %f, %f\n', im_file, mean(psnr_drbpsr), mean(ssim_drbpsr), mean(niqe_drbpsr), mean(ie_drbpsr)); + diff --git a/fbpn_sr_rbpn_v1.py b/fbpn_sr_rbpn_v1.py new file mode 100644 index 0000000..6b2e2aa --- /dev/null +++ b/fbpn_sr_rbpn_v1.py @@ -0,0 +1,186 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=True, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + self.motion_feat = ConvBlock(4, base_filter, 3, 1, 1, activation='lrelu', norm=None) + + ###INTERPOLATION + #Interp_block + warping1 = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + warping1.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.warp1 = nn.Sequential(*warping1) + + warping2 = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + warping2.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.warp2 = nn.Sequential(*warping2) + + motion_net = [ + ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + motion_net.append(ConvBlock(base_filter, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.motion = nn.Sequential(*motion_net) + + interp_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + interp_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.interp_block = nn.Sequential(*interp_b) + + ###ITERATIVE REFINEMENT + #Motion Up FORWARD + modules_up_f = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_f.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_f = nn.Sequential(*modules_up_f) + + #Motion Up BACKWARD + modules_up_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_b = nn.Sequential(*modules_up_b) + + #Motion Down + modules_down = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(3)] + modules_down.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_down = nn.Sequential(*modules_down) + + self.relu_bp = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)#torch.nn.PReLU() + + #Reconstruction + self.reconstruction_l = ConvBlock(feat*2, 3, 3, 1, 1, activation=None, norm=None) + self.reconstruction_h = ConvBlock(feat, 3, 3, 1, 1, activation=None, norm=None) + + ####ALIGNMENT + ###RBPN + self.RBPN = RBPN(num_channels=3, base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=5, nFrames=2, scale_factor=scale_factor) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + if pretrained: + if scale_factor == 4: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_4x.pth", map_location=lambda storage, loc: storage)) + elif scale_factor == 2: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_2x.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.RBPN) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True): + result_l = [] + result_h1 = [] + result_ht = [] + result_h2 = [] + + ###ALIGNMENT + aux_H1, H1 = self.RBPN(t_im1,[t_im2],[t_flow_f]) + aux_H2, H2 = self.RBPN(t_im2,[t_im1],[t_flow_b]) + L1 = self.warp1(H1) + L2 = self.warp2(H2) + + ###MOTION & DEPTH + motion_feat0 = self.motion_feat(torch.cat((t_flow_f, t_flow_b),1)) + M = self.motion(motion_feat0) + + motion_feat1 = self.motion_feat(torch.cat((t_flow_f/2.0, t_flow_b/2.0),1)) + M_half = self.motion(motion_feat1) + + ###INTERPOLATION + Ht = self.interp_block(torch.cat((L1,L2,M),1)) + L = self.motion_down(Ht) + + aux_Ht = self.reconstruction_h(Ht) + aux_L = self.reconstruction_l(L) + result_l.append(aux_L) + result_h1.append(aux_H1) + result_ht.append(aux_Ht) + result_h2.append(aux_H2) + + ####Projection + backward1 = torch.cat((L1, L, M_half),1) + H_b = self.motion_up_b(backward1) + H1 = H1 + self.relu_bp(H1 - H_b) + L1 = L1 + self.relu_bp(L1 - self.motion_down(H_b)) + + forwardd2 = torch.cat((L, L2, M_half),1) + H_f = self.motion_up_f(forwardd2) + H2 = H2 + self.relu_bp(H2 - H_f) + L2 = L2 + self.relu_bp(L2 - self.motion_down(H_f)) + + forwardd = torch.cat((L1, L, M_half),1) + H_t_f = self.motion_up_f(forwardd) + Ht = Ht + self.relu_bp(Ht - H_t_f) + L = L + self.relu_bp(L - self.motion_down(H_t_f)) + + backward = torch.cat((L, L2, M_half),1) + H_t_b = self.motion_up_b(backward) + Ht = Ht + self.relu_bp(Ht - H_t_b) + L = L + self.relu_bp(L - self.motion_down(H_t_b)) + + output_ht = self.reconstruction_h(Ht) + output_h1 = self.reconstruction_h(H1) + output_h2 = self.reconstruction_h(H2) + output_l = self.reconstruction_l(L) + result_l.append(output_l) + result_h1.append(output_h1) + result_ht.append(output_ht) + result_h2.append(output_h2) + + if train: + return result_ht, result_h1, result_h2, result_l + else: + return output_ht, output_h1, output_h2, output_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v1_ref.py b/fbpn_sr_rbpn_v1_ref.py new file mode 100644 index 0000000..b0eb38b --- /dev/null +++ b/fbpn_sr_rbpn_v1_ref.py @@ -0,0 +1,69 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable +from fbpn_sr_rbpn_v1 import Net as FBPNSR_RBPN_V1 + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=False, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + self.model = FBPNSR_RBPN_V1(base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=n_resblock, scale_factor=scale_factor) + self.flow_net = UNet(8,2) + + if pretrained: + self.model.load_state_dict(torch.load("weights/pretrained/FBPNSR_RBPN_V1_STAR-T.pth", map_location=lambda storage, loc: storage)) + self.flow_net.load_state_dict(torch.load("weights/pretrained/flow_refinement.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.model) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True, flowRefine= True, noise=False): + ##flow refinement + if flowRefine: + if noise: + t_flow_f = t_flow_f + Variable(torch.randn(t_flow_f.size()).cuda() * 0.1) + t_flow_b = t_flow_b + Variable(torch.randn(t_flow_b.size()).cuda() * 0.1) + t_flow_f = t_flow_f + (self.flow_net(torch.cat((t_flow_f,t_im1, t_im2),1))) + t_flow_b = t_flow_b + (self.flow_net(torch.cat((t_flow_b,t_im2, t_im1),1))) + + pred_ht, pred_h1, pred_h2, pred_l = self.model(t_im1, t_im2, t_flow_f, t_flow_b, train=train) + + if train: + if flowRefine: + return pred_ht, pred_h1, pred_h2, pred_l, t_flow_f, t_flow_b + else: + return pred_ht, pred_h1, pred_h2, pred_l + else: + return pred_ht, pred_h1, pred_h2, pred_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v2.py b/fbpn_sr_rbpn_v2.py new file mode 100644 index 0000000..f4739de --- /dev/null +++ b/fbpn_sr_rbpn_v2.py @@ -0,0 +1,182 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=True, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + self.motion_feat = ConvBlock(4, base_filter, 3, 1, 1, activation='lrelu', norm=None) + + ###INTERPOLATION + #Interp_block + motion_net = [ + ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + motion_net.append(ConvBlock(base_filter, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.motion = nn.Sequential(*motion_net) + + t_net2 = [ConvBlock(feat*3, feat, 1, 1, 0, bias=True, activation='lrelu', norm=None)] + t_net2.append(PyramidModule(feat,activation='lrelu')) + t_net2.append(ConvBlock(feat, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.t_net_hr = nn.Sequential(*t_net2) + + self.upsample_layer = nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=True) + + interp_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + interp_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.interp_block = nn.Sequential(*interp_b) + + ###ITERATIVE REFINEMENT + #Motion Up FORWARD + modules_up_f = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_f.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_f = nn.Sequential(*modules_up_f) + + #Motion Up BACKWARD + modules_up_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_b = nn.Sequential(*modules_up_b) + + #Motion Down + modules_down = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + modules_down.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_down = nn.Sequential(*modules_down) + + self.relu_bp = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)#torch.nn.PReLU() + + #Reconstruction + self.reconstruction_l = ConvBlock(feat*2, 3, 3, 1, 1, activation=None, norm=None) + self.reconstruction_h = ConvBlock(feat, 3, 3, 1, 1, activation=None, norm=None) + + ####ALIGNMENT + ###RBPN + self.RBPN = RBPN(num_channels=3, base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=5, nFrames=2, scale_factor=scale_factor) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + if pretrained: + if scale_factor == 4: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_4x.pth", map_location=lambda storage, loc: storage)) + elif scale_factor == 2: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_2x.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.RBPN) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True): + result_l = [] + result_h1 = [] + result_ht = [] + result_h2 = [] + + ###ALIGNMENT + aux_H1, H1 = self.RBPN(t_im1,[t_im2],[t_flow_f]) + aux_H2, H2 = self.RBPN(t_im2,[t_im1],[t_flow_b]) + L1 = self.motion_down(H1) + L2 = self.motion_down(H2) + + ###MOTION & DEPTH + motion_feat0 = self.motion_feat(torch.cat((t_flow_f, t_flow_b),1)) + M = self.motion(motion_feat0) + + motion_feat1 = self.motion_feat(torch.cat((t_flow_f/2.0, t_flow_b/2.0),1)) + M_half = self.motion(motion_feat1) + + ###INTERPOLATION + Ht = self.interp_block(torch.cat((L1,L2,M),1)) + Ht = Ht + self.relu_bp(Ht - self.t_net_hr(torch.cat((H1,H2,self.upsample_layer(M)),1))) + L = self.motion_down(Ht) + + aux_Ht = self.reconstruction_h(Ht) + aux_L = self.reconstruction_l(L) + result_l.append(aux_L) + result_h1.append(aux_H1) + result_ht.append(aux_Ht) + result_h2.append(aux_H2) + + ####Projection + backward1 = torch.cat((L1, L, M_half),1) + H_b = self.motion_up_b(backward1) + H1 = H1 + self.relu_bp(H1 - H_b) + L1 = L1 + self.relu_bp(L1 - self.motion_down(H_b)) + + forwardd2 = torch.cat((L, L2, M_half),1) + H_f = self.motion_up_f(forwardd2) + H2 = H2 + self.relu_bp(H2 - H_f) + L2 = L2 + self.relu_bp(L2 - self.motion_down(H_f)) + + forwardd = torch.cat((L1, L, M_half),1) + H_t_f = self.motion_up_f(forwardd) + Ht = Ht + self.relu_bp(Ht - H_t_f) + L = L + self.relu_bp(L - self.motion_down(H_t_f)) + + backward = torch.cat((L, L2, M_half),1) + H_t_b = self.motion_up_b(backward) + Ht = Ht + self.relu_bp(Ht - H_t_b) + L = L + self.relu_bp(L - self.motion_down(H_t_b)) + + output_ht = self.reconstruction_h(Ht) + output_h1 = self.reconstruction_h(H1) + output_h2 = self.reconstruction_h(H2) + output_l = self.reconstruction_l(L) + result_l.append(output_l) + result_h1.append(output_h1) + result_ht.append(output_ht) + result_h2.append(output_h2) + + if train: + return result_ht, result_h1, result_h2, result_l + else: + return output_ht, output_h1, output_h2, output_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v2_ref.py b/fbpn_sr_rbpn_v2_ref.py new file mode 100644 index 0000000..8ae97c4 --- /dev/null +++ b/fbpn_sr_rbpn_v2_ref.py @@ -0,0 +1,69 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable +from fbpn_sr_rbpn_v2 import Net as FBPNSR_RBPN_V2 + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=False, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + self.model = FBPNSR_RBPN_V2(base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=n_resblock, scale_factor=scale_factor) + self.flow_net = UNet(8,2) + + if pretrained: + #self.model.load_state_dict(torch.load("weights/pretrained/FBPNSR_RBPN_V1_STAR-T.pth", map_location=lambda storage, loc: storage)) + self.flow_net.load_state_dict(torch.load("weights/pretrained/flow_refinement.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.model) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True, flowRefine= True, noise=False): + ##flow refinement + if flowRefine: + if noise: + t_flow_f = t_flow_f + Variable(torch.randn(t_flow_f.size()).cuda() * 0.1) + t_flow_b = t_flow_b + Variable(torch.randn(t_flow_b.size()).cuda() * 0.1) + t_flow_f = t_flow_f + (self.flow_net(torch.cat((t_flow_f,t_im1, t_im2),1))) + t_flow_b = t_flow_b + (self.flow_net(torch.cat((t_flow_b,t_im2, t_im1),1))) + + pred_ht, pred_h1, pred_h2, pred_l = self.model(t_im1, t_im2, t_flow_f, t_flow_b, train=train) + + if train: + if flowRefine: + return pred_ht, pred_h1, pred_h2, pred_l, t_flow_f, t_flow_b + else: + return pred_ht, pred_h1, pred_h2, pred_l + else: + return pred_ht, pred_h1, pred_h2, pred_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v3.py b/fbpn_sr_rbpn_v3.py new file mode 100644 index 0000000..bb005f9 --- /dev/null +++ b/fbpn_sr_rbpn_v3.py @@ -0,0 +1,181 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=True, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + self.motion_feat = ConvBlock(4, base_filter, 3, 1, 1, activation='lrelu', norm=None) + + ###INTERPOLATION + #Interp_block + motion_net = [ + ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + motion_net.append(ConvBlock(base_filter, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.motion = nn.Sequential(*motion_net) + + t_net1 = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + t_net1.append(ConvBlock(feat*5, feat*2, 3, 1, 1, activation='lrelu', norm=None)) + self.t_net_lr = nn.Sequential(*t_net1) + + interp_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + interp_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.interp_block = nn.Sequential(*interp_b) + + ###ITERATIVE REFINEMENT + #Motion Up FORWARD + modules_up_f = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_f.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_f = nn.Sequential(*modules_up_f) + + #Motion Up BACKWARD + modules_up_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_b = nn.Sequential(*modules_up_b) + + #Motion Down + modules_down = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + modules_down.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_down = nn.Sequential(*modules_down) + + self.relu_bp = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)#torch.nn.PReLU() + + #Reconstruction + self.reconstruction_l = ConvBlock(feat*2, 3, 3, 1, 1, activation=None, norm=None) + self.reconstruction_h = ConvBlock(feat, 3, 3, 1, 1, activation=None, norm=None) + + ####ALIGNMENT + ###RBPN + self.RBPN = RBPN(num_channels=3, base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=5, nFrames=2, scale_factor=scale_factor) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + if pretrained: + if scale_factor == 4: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_4x.pth", map_location=lambda storage, loc: storage)) + elif scale_factor == 2: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_2x.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.RBPN) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True): + result_l = [] + result_h1 = [] + result_ht = [] + result_h2 = [] + + ###ALIGNMENT + aux_H1, H1 = self.RBPN(t_im1,[t_im2],[t_flow_f]) + aux_H2, H2 = self.RBPN(t_im2,[t_im1],[t_flow_b]) + L1 = self.motion_down(H1) + L2 = self.motion_down(H2) + + ###MOTION & DEPTH + motion_feat0 = self.motion_feat(torch.cat((t_flow_f, t_flow_b),1)) + M = self.motion(motion_feat0) + + motion_feat1 = self.motion_feat(torch.cat((t_flow_f/2.0, t_flow_b/2.0),1)) + M_half = self.motion(motion_feat1) + + ###INTERPOLATION + Ht = self.interp_block(torch.cat((L1,L2,M),1)) + L = self.motion_down(Ht) + L = L + self.relu_bp(L - self.t_net_lr(torch.cat((L1,L2,M),1))) + + aux_Ht = self.reconstruction_h(Ht) + aux_L = self.reconstruction_l(L) + result_l.append(aux_L) + result_h1.append(aux_H1) + result_ht.append(aux_Ht) + result_h2.append(aux_H2) + + ####Projection + backward1 = torch.cat((L1, L, M_half),1) + H_b = self.motion_up_b(backward1) + H1 = H1 + self.relu_bp(H1 - H_b) + L1 = L1 + self.relu_bp(L1 - self.motion_down(H_b)) + + forwardd2 = torch.cat((L, L2, M_half),1) + H_f = self.motion_up_f(forwardd2) + H2 = H2 + self.relu_bp(H2 - H_f) + L2 = L2 + self.relu_bp(L2 - self.motion_down(H_f)) + + forwardd = torch.cat((L1, L, M_half),1) + H_t_f = self.motion_up_f(forwardd) + Ht = Ht + self.relu_bp(Ht - H_t_f) + L = L + self.relu_bp(L - self.motion_down(H_t_f)) + + backward = torch.cat((L, L2, M_half),1) + H_t_b = self.motion_up_b(backward) + Ht = Ht + self.relu_bp(Ht - H_t_b) + L = L + self.relu_bp(L - self.motion_down(H_t_b)) + + output_ht = self.reconstruction_h(Ht) + output_h1 = self.reconstruction_h(H1) + output_h2 = self.reconstruction_h(H2) + output_l = self.reconstruction_l(L) + result_l.append(output_l) + result_h1.append(output_h1) + result_ht.append(output_ht) + result_h2.append(output_h2) + + if train: + return result_ht, result_h1, result_h2, result_l + else: + return output_ht, output_h1, output_h2, output_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v3_ref.py b/fbpn_sr_rbpn_v3_ref.py new file mode 100644 index 0000000..f83ed49 --- /dev/null +++ b/fbpn_sr_rbpn_v3_ref.py @@ -0,0 +1,69 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable +from fbpn_sr_rbpn_v3 import Net as FBPNSR_RBPN_V3 + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=False, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + self.model = FBPNSR_RBPN_V3(base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=n_resblock, scale_factor=scale_factor) + self.flow_net = UNet(8,2) + + if pretrained: + #self.model.load_state_dict(torch.load("weights/pretrained/FBPNSR_RBPN_V1_STAR-T.pth", map_location=lambda storage, loc: storage)) + self.flow_net.load_state_dict(torch.load("weights/pretrained/flow_refinement.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.model) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True, flowRefine= True, noise=False): + ##flow refinement + if flowRefine: + if noise: + t_flow_f = t_flow_f + Variable(torch.randn(t_flow_f.size()).cuda() * 0.1) + t_flow_b = t_flow_b + Variable(torch.randn(t_flow_b.size()).cuda() * 0.1) + t_flow_f = t_flow_f + (self.flow_net(torch.cat((t_flow_f,t_im1, t_im2),1))) + t_flow_b = t_flow_b + (self.flow_net(torch.cat((t_flow_b,t_im2, t_im1),1))) + + pred_ht, pred_h1, pred_h2, pred_l = self.model(t_im1, t_im2, t_flow_f, t_flow_b, train=train) + + if train: + if flowRefine: + return pred_ht, pred_h1, pred_h2, pred_l, t_flow_f, t_flow_b + else: + return pred_ht, pred_h1, pred_h2, pred_l + else: + return pred_ht, pred_h1, pred_h2, pred_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v4.py b/fbpn_sr_rbpn_v4.py new file mode 100644 index 0000000..a7d34de --- /dev/null +++ b/fbpn_sr_rbpn_v4.py @@ -0,0 +1,192 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=True, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + self.motion_feat = ConvBlock(4, base_filter, 3, 1, 1, activation='lrelu', norm=None) + + ###INTERPOLATION + #Motion block + motion_net = [ + ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + motion_net.append(ConvBlock(base_filter, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.motion = nn.Sequential(*motion_net) + + #T-SR LR + t_net1 = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + t_net1.append(ConvBlock(feat*5, feat*2, 3, 1, 1, activation='lrelu', norm=None)) + self.t_net_lr = nn.Sequential(*t_net1) + + #T-SR HR + t_net2 = [ConvBlock(feat*3, feat, 1, 1, 0, bias=True, activation='lrelu', norm=None)] + t_net2.append(PyramidModule(feat,activation='lrelu')) + t_net2.append(ConvBlock(feat, feat, 3, 1, 1, activation='lrelu', norm=None)) + self.t_net_hr = nn.Sequential(*t_net2) + + self.upsample_layer = nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=True) + + #ST-SR + interp_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + interp_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.interp_block = nn.Sequential(*interp_b) + + ###ITERATIVE REFINEMENT + #Motion Up FORWARD + modules_up_f = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_f.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_f = nn.Sequential(*modules_up_f) + + #Motion Up BACKWARD + modules_up_b = [ + ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(n_resblock)] + modules_up_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_up_b = nn.Sequential(*modules_up_b) + + #Motion Down + modules_down = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \ + for _ in range(2)] + modules_down.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None)) + self.motion_down = nn.Sequential(*modules_down) + + self.relu_bp = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)#torch.nn.PReLU() + + #Reconstruction + self.reconstruction_l = ConvBlock(feat*2, 3, 3, 1, 1, activation=None, norm=None) + self.reconstruction_h = ConvBlock(feat, 3, 3, 1, 1, activation=None, norm=None) + + ####ALIGNMENT + ###RBPN + self.RBPN = RBPN(num_channels=3, base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=5, nFrames=2, scale_factor=scale_factor) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + if pretrained: + if scale_factor == 4: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_4x.pth", map_location=lambda storage, loc: storage)) + elif scale_factor == 2: + self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_2x.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.RBPN) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True): + result_l = [] + result_h1 = [] + result_ht = [] + result_h2 = [] + + ###ALIGNMENT + aux_H1, H1 = self.RBPN(t_im1,[t_im2],[t_flow_f]) + aux_H2, H2 = self.RBPN(t_im2,[t_im1],[t_flow_b]) + L1 = self.motion_down(H1) + L2 = self.motion_down(H2) + + ###MOTION & DEPTH + motion_feat0 = self.motion_feat(torch.cat((t_flow_f, t_flow_b),1)) + M = self.motion(motion_feat0) + + motion_feat1 = self.motion_feat(torch.cat((t_flow_f/2.0, t_flow_b/2.0),1)) + M_half = self.motion(motion_feat1) + + ###INTERPOLATION + Ht = self.interp_block(torch.cat((L1,L2,M),1)) + Ht = Ht + self.relu_bp(Ht - self.t_net_hr(torch.cat((H1,H2,self.upsample_layer(M)),1))) + L = self.motion_down(Ht) + L = L + self.relu_bp(L - self.t_net_lr(torch.cat((L1,L2,M),1))) + + aux_Ht = self.reconstruction_h(Ht) + aux_L = self.reconstruction_l(L) + result_l.append(aux_L) + result_h1.append(aux_H1) + result_ht.append(aux_Ht) + result_h2.append(aux_H2) + + ####Projection + backward1 = torch.cat((L1, L, M_half),1) + H_b = self.motion_up_b(backward1) + H1 = H1 + self.relu_bp(H1 - H_b) + L1 = L1 + self.relu_bp(L1 - self.motion_down(H_b)) + + forwardd2 = torch.cat((L, L2, M_half),1) + H_f = self.motion_up_f(forwardd2) + H2 = H2 + self.relu_bp(H2 - H_f) + L2 = L2 + self.relu_bp(L2 - self.motion_down(H_f)) + + forwardd = torch.cat((L1, L, M_half),1) + H_t_f = self.motion_up_f(forwardd) + Ht = Ht + self.relu_bp(Ht - H_t_f) + L = L + self.relu_bp(L - self.motion_down(H_t_f)) + + backward = torch.cat((L, L2, M_half),1) + H_t_b = self.motion_up_b(backward) + Ht = Ht + self.relu_bp(Ht - H_t_b) + L = L + self.relu_bp(L - self.motion_down(H_t_b)) + + output_ht = self.reconstruction_h(Ht) + output_h1 = self.reconstruction_h(H1) + output_h2 = self.reconstruction_h(H2) + output_l = self.reconstruction_l(L) + result_l.append(output_l) + result_h1.append(output_h1) + result_ht.append(output_ht) + result_h2.append(output_h2) + + if train: + return result_ht, result_h1, result_h2, result_l + else: + return output_ht, output_h1, output_h2, output_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/fbpn_sr_rbpn_v4_ref.py b/fbpn_sr_rbpn_v4_ref.py new file mode 100644 index 0000000..6c03882 --- /dev/null +++ b/fbpn_sr_rbpn_v4_ref.py @@ -0,0 +1,62 @@ +import os +import torch.nn as nn +import torch.optim as optim +from base_networks import * +from torchvision.transforms import * +import torch.nn.functional as F +from rbpn import Net as RBPN +from autoencoder_v4 import UNet +from torch.autograd import Variable +from fbpn_sr_rbpn_v4 import Net as FBPNSR_RBPN_V4 + +class Net(nn.Module): + def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=False, freeze=False): + super(Net, self).__init__() + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + self.model = FBPNSR_RBPN_V4(base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=n_resblock, scale_factor=scale_factor) + self.flow_net = UNet(8,2) + + if pretrained: + self.model.load_state_dict(torch.load("weights/pretrained/STAR_V4_RBPN.pth", map_location=lambda storage, loc: storage)) + self.flow_net.load_state_dict(torch.load("weights/pretrained/flow_refinement.pth", map_location=lambda storage, loc: storage)) + + if freeze: + self.freeze_model(self.model) + + def freeze_model(self, model): + for child in model.children(): + for param in child.parameters(): + param.requires_grad = False + + def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True): + ##flow refinement + t_flow_f = t_flow_f + (self.flow_net(torch.cat((t_flow_f,t_im1, t_im2),1))) + t_flow_b = t_flow_b + (self.flow_net(torch.cat((t_flow_b,t_im2, t_im1),1))) + + pred_ht, pred_h1, pred_h2, pred_l = self.model(t_im1, t_im2, t_flow_f, t_flow_b, train=train) + + if train: + return pred_ht, pred_h1, pred_h2, pred_l, t_flow_f, t_flow_b + else: + return pred_ht, pred_h1, pred_h2, pred_l + +class FeatureExtractor(nn.Module): + def __init__(self, cnn, feature_layer=35): + super(FeatureExtractor, self).__init__() + self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)]) + + def forward(self, x): + return self.features(x) \ No newline at end of file diff --git a/inter_error.m b/inter_error.m new file mode 100644 index 0000000..9740b73 --- /dev/null +++ b/inter_error.m @@ -0,0 +1,4 @@ +function ie=inter_error(im1,im2) +diff_rgb = 128.0 + im2 - im1; +ie = mean(mean(mean(abs(diff_rgb - 128.0)))); + diff --git a/main.py b/main.py new file mode 100644 index 0000000..00160f7 --- /dev/null +++ b/main.py @@ -0,0 +1,207 @@ +from __future__ import print_function +import argparse +from math import log10 + +import os +import torch +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +from torch.autograd import Variable +from torch.utils.data import DataLoader +from torchvision import models +from fbpn_sr_rbpn_v1 import Net as FBPNSR_RBPN_V1 +from fbpn_sr_rbpn_v2 import Net as FBPNSR_RBPN_V2 +from fbpn_sr_rbpn_v3 import Net as FBPNSR_RBPN_V3 +from fbpn_sr_rbpn_v4 import Net as FBPNSR_RBPN_V4, FeatureExtractor +from data import get_training_set +import utils +import pdb +import socket +import time +import cv2 + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch Super Res Example') +parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor") +parser.add_argument('--batchSize', type=int, default=10, help='training batch size') +parser.add_argument('--start_epoch', type=int, default=1, help='Starting epoch for continuing training') +parser.add_argument('--nEpochs', type=int, default=60, help='number of epochs to train for') +parser.add_argument('--snapshots', type=int, default=5, help='Snapshots') +parser.add_argument('--lr', type=float, default=1e-4, help='Learning Rate. Default=0.01') +parser.add_argument('--gpu_mode', type=bool, default=True) +parser.add_argument('--threads', type=int, default=20, help='number of threads for data loader to use') +parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') +parser.add_argument('--gpus', default=10, type=int, help='number of gpu') +parser.add_argument('--data_dir', type=str, default='./vimeo_triplet/sequences') +parser.add_argument('--file_list', type=str, default='tri_trainlist.txt') +parser.add_argument('--patch_size', type=int, default=0, help='0 to use original frame size') +parser.add_argument('--data_augmentation', type=bool, default=True) +parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4') +parser.add_argument('--pretrained_sr', default='FBPNSR_RBPN_V4_Lf_STAR.pth', help='sr pretrained base model') +parser.add_argument('--pretrained', type=bool, default=True) +parser.add_argument('--save_folder', default='weights/', help='Location to save checkpoint models') +parser.add_argument('--prefix', default='VGG_STAR_VIMEO', help='Location to save checkpoint models') + +opt = parser.parse_args() +gpus_list = range(opt.gpus) +hostname = str(socket.gethostname()) +cudnn.benchmark = True +print(opt) + +def train(epoch): + epoch_loss = 0 + model.train() + feature_extractor.eval() + for iteration, batch in enumerate(training_data_loader, 1): + input, target, target_l, flow_f, flow_b = batch[0], batch[1], batch[2], batch[3], batch[4] + + if cuda: + t_im1 = Variable(input[0]).cuda(gpus_list[0]) + t_im2 = Variable(input[1]).cuda(gpus_list[0]) + t_flow_f = Variable(flow_f).cuda(gpus_list[0]).float() + t_flow_b = Variable(flow_b).cuda(gpus_list[0]).float() + + t_target1 = Variable(target[0]).cuda(gpus_list[0]) + t_target = Variable(target[1]).cuda(gpus_list[0]) + t_target2 = Variable(target[2]).cuda(gpus_list[0]) + t_target_l = Variable(target_l).cuda(gpus_list[0]) + + optimizer.zero_grad() + t0 = time.time() + pred_ht, pred_h1, pred_h2, pred_l = model(t_im1, t_im2, t_flow_f, t_flow_b) + + l_mse_ht = mse_loss_calc(pred_ht, t_target) + l_mse_h1 = mse_loss_calc(pred_h1, t_target1) + l_mse_h2 = mse_loss_calc(pred_h2, t_target2) + l_mse_lr = mse_loss_calc(pred_l, t_target_l) + l_mse = l_mse_ht + 0.5*l_mse_h1 + 0.5*l_mse_h2 + l_mse_lr + + l_feat_ht = feat_loss_calc(pred_ht, t_target) + l_feat_h1 = feat_loss_calc(pred_h1, t_target1) + l_feat_h2 = feat_loss_calc(pred_h2, t_target2) + l_feat_lr = feat_loss_calc(pred_l, t_target_l) + l_feat = l_feat_ht + 0.5*l_feat_h1 + 0.5*l_feat_h2 + l_feat_lr + + loss = l_mse + 0.1*l_feat + t1 = time.time() + + ###show sample + predictiont = utils.denorm(pred_ht[0][0].cpu().data,vgg=True) + prediction1 = utils.denorm(pred_h1[0][0].cpu().data,vgg=True) + prediction2 = utils.denorm(pred_h2[0][0].cpu().data,vgg=True) + pred_l = utils.denorm(pred_l[0][0].cpu().data,vgg=True) + save_img(prediction1, "1") + save_img(predictiont, "2") + save_img(prediction2, "3") + save_img(pred_l, "lr") + + epoch_loss += loss.data + loss.backward() + optimizer.step() + + print("===> Epoch[{}]({}/{}): Loss: {:.4f} MSE_HR: {:.4f} Feat_HR: {:.4f} MSE_LR: {:.4f} || Timer: {:.4f} sec.".format(epoch, iteration, len(training_data_loader), loss.data, l_mse_ht, l_feat_ht, l_mse_lr, (t1 - t0))) + print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss / len(training_data_loader))) + +def mse_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_mse = 0 + for i in range(len(pred)): + l_mse_ht = criterion_l1(pred[i], target) + l_mse = l_mse + weight*l_mse_ht + weight += w_interval + return l_mse + +def feat_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_feat = 0 + hr_feature = feature_extractor(target) + for i in range(len(pred)): + sr_feature = feature_extractor(pred[i]) + l_feat_ht = criterion(sr_feature, hr_feature.detach()) + l_feat = l_feat + weight*l_feat_ht + weight += w_interval + return l_feat + +def save_img(img, img_name): + save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0) + + # save img + save_fn = 'im'+img_name+'_'+opt.model_type+opt.prefix+'.png' + cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0]) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + +def checkpoint(epoch): + model_out_path = opt.save_folder+str(opt.upscale_factor)+'x_'+hostname+opt.model_type+opt.prefix+"_epoch_{}.pth".format(epoch) + torch.save(model.state_dict(), model_out_path) + print("Checkpoint saved to {}".format(model_out_path)) + +cuda = opt.gpu_mode +if cuda and not torch.cuda.is_available(): + raise Exception("No GPU found, please run without --cuda") + +torch.manual_seed(opt.seed) +if cuda: + torch.cuda.manual_seed(opt.seed) + +print('===> Loading datasets') +train_set = get_training_set(opt.data_dir, opt.upscale_factor, opt.data_augmentation, opt.file_list, opt.patch_size) +training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True, drop_last=True) + +print('===> Building model ', opt.model_type) +if opt.model_type == 'FBPNSR_RBPN_V1': + model = FBPNSR_RBPN_V1(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2': + model = FBPNSR_RBPN_V2(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3': + model = FBPNSR_RBPN_V3(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4': + model = FBPNSR_RBPN_V4(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) + + +model = torch.nn.DataParallel(model, device_ids=gpus_list) +criterion_l1 = nn.L1Loss() +criterion = nn.MSELoss() + +###VGG +feature_extractor = FeatureExtractor(models.vgg19(pretrained=True), feature_layer=35) +feature_extractor = torch.nn.DataParallel(feature_extractor, device_ids=gpus_list) + +print('---------- Networks architecture -------------') +print_network(model) +print('----------------------------------------------') + +if opt.pretrained: + model_name = os.path.join(opt.save_folder + opt.pretrained_sr) + if os.path.exists(model_name): + model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage)) + print('Pre-trained SR model is loaded.') + +if cuda: + model = model.cuda(gpus_list[0]) + criterion = criterion.cuda(gpus_list[0]) + criterion_l1 = criterion_l1.cuda(gpus_list[0]) + feature_extractor = feature_extractor.cuda(gpus_list[0]) + +optimizer = optim.Adamax(model.parameters(), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8) + +for epoch in range(opt.start_epoch, opt.nEpochs + 1): + train(epoch) + #test() + + # learning rate is decayed by a factor of 10 every half of total epochs + if (epoch+1) % (opt.nEpochs/3) == 0: + for param_group in optimizer.param_groups: + param_group['lr'] /= 10.0 + print('Learning rate decay: lr={}'.format(optimizer.param_groups[0]['lr'])) + + if (epoch+1) % (opt.snapshots) == 0: + checkpoint(epoch) + +checkpoint(epoch) \ No newline at end of file diff --git a/main_refinement_flow.py b/main_refinement_flow.py new file mode 100644 index 0000000..076e015 --- /dev/null +++ b/main_refinement_flow.py @@ -0,0 +1,254 @@ +from __future__ import print_function +import argparse +from math import log10 + +import os +import torch +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +from torch.autograd import Variable +from torch.utils.data import DataLoader +from torchvision import models +from fbpn_sr_rbpn_v1_ref import Net as FBPNSR_RBPN_V1_REF +from fbpn_sr_rbpn_v2_ref import Net as FBPNSR_RBPN_V2_REF +from fbpn_sr_rbpn_v3_ref import Net as FBPNSR_RBPN_V3_REF +from fbpn_sr_rbpn_v4_ref import Net as FBPNSR_RBPN_V4_REF, FeatureExtractor +from data import get_training_set_flow +import utils +import pdb +import socket +import time +import cv2 + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch Super Res Example') +parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor") +parser.add_argument('--batchSize', type=int, default=10, help='training batch size') +parser.add_argument('--start_epoch', type=int, default=1, help='Starting epoch for continuing training') +parser.add_argument('--nEpochs', type=int, default=60, help='number of epochs to train for') +parser.add_argument('--snapshots', type=int, default=2, help='Snapshots') +parser.add_argument('--lr', type=float, default=1e-4, help='Learning Rate. Default=0.01') +parser.add_argument('--gpu_mode', type=bool, default=True) +parser.add_argument('--threads', type=int, default=20, help='number of threads for data loader to use') +parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') +parser.add_argument('--gpus', default=10, type=int, help='number of gpu') +parser.add_argument('--data_dir', type=str, default='./vimeo_triplet/sequences') +parser.add_argument('--file_list', type=str, default='tri_trainlist.txt') +parser.add_argument('--patch_size', type=int, default=0, help='0 to use original frame size') +parser.add_argument('--data_augmentation', type=bool, default=True) +parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4_REF') +parser.add_argument('--pretrained_sr', default='FBPNSR_RBPN_V4_REF_Lf_STAR.pth', help='sr pretrained base model') +parser.add_argument('--pretrained', type=bool, default=True) +parser.add_argument('--save_folder', default='weights/', help='Location to save checkpoint models') +parser.add_argument('--train_obj', default='STAR', help='[STAR,STAR-ST,STAR-S,STAR-T-LR]') +parser.add_argument('--prefix', default='VGG_FR_VIMEO', help='Location to save checkpoint models') + +opt = parser.parse_args() +gpus_list = range(opt.gpus) +hostname = str(socket.gethostname()) +cudnn.benchmark = False +cudnn.deterministic = True +print(opt) + +def train(epoch): + epoch_loss = 0 + model.train() + feature_extractor.eval() + for iteration, batch in enumerate(training_data_loader, 1): + input, target, target_l, flow_f, flow_b ,gt_flow_f, gt_flow_b = batch[0], batch[1], batch[2], batch[3], batch[4], batch[5], batch[6] + + if cuda: + t_lr1 = Variable(input[0]).cuda(gpus_list[0]) + t_lr2 = Variable(input[1]).cuda(gpus_list[0]) + t_flow_f = Variable(flow_f).cuda(gpus_list[0]).float() + t_flow_b = Variable(flow_b).cuda(gpus_list[0]).float() + + gt_flow_f = Variable(gt_flow_f).cuda(gpus_list[0]).float() + gt_flow_b = Variable(gt_flow_b).cuda(gpus_list[0]).float() + + t_hr1 = Variable(target[0]).cuda(gpus_list[0]) + t_hr = Variable(target[1]).cuda(gpus_list[0]) + t_hr2 = Variable(target[2]).cuda(gpus_list[0]) + t_lr = Variable(target_l).cuda(gpus_list[0]) + + optimizer.zero_grad() + t0 = time.time() + + pred_ht, pred_h1, pred_h2, pred_l, pred_flow_f, pred_flow_b = model(t_lr1, t_lr2, t_flow_f, t_flow_b) + + if opt.train_obj == 'STAR': + l_mse_ht = mse_loss_calc(pred_ht, t_hr) + l_mse_h1 = mse_loss_calc(pred_h1, t_hr1) + l_mse_h2 = mse_loss_calc(pred_h2, t_hr2) + l_mse_lr = mse_loss_calc(pred_l, t_lr) + l_mse = l_mse_ht + 0.5*l_mse_h1 + 0.5*l_mse_h2 + l_mse_lr + + l_feat_ht = feat_loss_calc(pred_ht, t_hr) + l_feat_h1 = feat_loss_calc(pred_h1, t_hr1) + l_feat_h2 = feat_loss_calc(pred_h2, t_hr2) + l_feat_lr = feat_loss_calc(pred_l, t_lr) + l_feat = l_feat_ht + 0.5*l_feat_h1 + 0.5*l_feat_h2 + l_feat_lr + + l_flow = (criterion(pred_flow_f, gt_flow_f) + criterion(pred_flow_b, gt_flow_b)) + + #total loss + loss = l_mse + 0.1*l_feat + 0.1*l_flow + t1 = time.time() + elif opt.train_obj == 'STAR-ST': + l_mse_ht = mse_loss_calc(pred_ht, t_hr) + l_mse_h1 = mse_loss_calc(pred_h1, t_hr1) + l_mse_h2 = mse_loss_calc(pred_h2, t_hr2) + l_mse = l_mse_ht + 0.5*l_mse_h1 + 0.5*l_mse_h2 + + l_feat_ht = feat_loss_calc(pred_ht, t_hr) + l_feat_h1 = feat_loss_calc(pred_h1, t_hr1) + l_feat_h2 = feat_loss_calc(pred_h2, t_hr2) + l_feat = l_feat_ht + 0.5*l_feat_h1 + 0.5*l_feat_h2 + + l_flow = (criterion(pred_flow_f, gt_flow_f) + criterion(pred_flow_b, gt_flow_b)) + + #total loss + loss = l_mse + 0.1*l_feat + 0.1*l_flow + t1 = time.time() + elif opt.train_obj == 'STAR-S': + l_mse_h1 = mse_loss_calc(pred_h1, t_hr1) + l_mse_h2 = mse_loss_calc(pred_h2, t_hr2) + l_mse = l_mse_h1 + l_mse_h2 + + l_feat_h1 = feat_loss_calc(pred_h1, t_hr1) + l_feat_h2 = feat_loss_calc(pred_h2, t_hr2) + l_feat = l_feat_h1 + l_feat_h2 + + l_flow = (criterion(pred_flow_f, gt_flow_f) + criterion(pred_flow_b, gt_flow_b)) + + #total loss + loss = l_mse + 0.1*l_feat + 0.1*l_flow + t1 = time.time() + elif opt.train_obj == 'STAR-T-LR': + l_mse = mse_loss_calc(pred_l, t_lr) + l_feat = feat_loss_calc(pred_l, t_lr) + l_flow = (criterion(pred_flow_f, gt_flow_f) + criterion(pred_flow_b, gt_flow_b)) + + #total loss + loss = l_mse + 0.1*l_feat + 0.1*l_flow + t1 = time.time() + + ###show sample + predictiont = utils.denorm(pred_ht[0][0].cpu().data,vgg=True) + prediction1 = utils.denorm(pred_h1[0][0].cpu().data,vgg=True) + prediction2 = utils.denorm(pred_h2[0][0].cpu().data,vgg=True) + pred_l = utils.denorm(pred_l[0][0].cpu().data,vgg=True) + save_img(prediction1, "1") + save_img(predictiont, "2") + save_img(prediction2, "3") + save_img(pred_l, "lr") + + epoch_loss += loss.data + loss.backward() + optimizer.step() + print("===> Epoch[{}]({}/{}): Loss: {:.4f} MSE: {:.4f} Feat: {:.4f} Flow: {:.8f} || Timer: {:.4f} sec.".format(epoch, iteration, len(training_data_loader), loss.data, l_mse, l_feat, l_flow, (t1 - t0))) + print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss / len(training_data_loader))) + +def mse_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_mse = 0 + for i in range(len(pred)): + l_mse_ht = criterion_l1(pred[i], target) + l_mse = l_mse + weight*l_mse_ht + weight += w_interval + return l_mse + +def feat_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_feat = 0 + hr_feature = feature_extractor(target) + for i in range(len(pred)): + sr_feature = feature_extractor(pred[i]) + l_feat_ht = criterion(sr_feature, hr_feature.detach()) + l_feat = l_feat + weight*l_feat_ht + weight += w_interval + return l_feat + +def save_img(img, img_name): + save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0) + + # save img + save_fn = 'im'+img_name+'_'+opt.model_type+opt.prefix+'_'+opt.train_obj+'.png' + cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0]) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + +def checkpoint(epoch): + model_out_path = opt.save_folder+str(opt.upscale_factor)+'x_'+hostname+opt.model_type+opt.prefix+"_epoch_{}.pth".format(epoch) + torch.save(model.state_dict(), model_out_path) + print("Checkpoint saved to {}".format(model_out_path)) + +cuda = opt.gpu_mode +if cuda and not torch.cuda.is_available(): + raise Exception("No GPU found, please run without --cuda") + +torch.manual_seed(opt.seed) +if cuda: + torch.cuda.manual_seed(opt.seed) + +print('===> Loading datasets') +train_set = get_training_set_flow(opt.data_dir, opt.upscale_factor, opt.data_augmentation, opt.file_list, opt.patch_size) +training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True, drop_last=True) + +print('===> Building model ', opt.model_type) +if opt.model_type == 'FBPNSR_RBPN_V1_REF': + model = FBPNSR_RBPN_V1_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2_REF': + model = FBPNSR_RBPN_V2_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3_REF': + model = FBPNSR_RBPN_V3_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4_REF': + model = FBPNSR_RBPN_V4_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) + +model = torch.nn.DataParallel(model, device_ids=gpus_list) +criterion_l1 = nn.L1Loss() +criterion = nn.MSELoss() + +###VGG +feature_extractor = FeatureExtractor(models.vgg19(pretrained=True), feature_layer=35) +feature_extractor = torch.nn.DataParallel(feature_extractor, device_ids=gpus_list) + +print('---------- Networks architecture -------------') +print_network(model) +print('----------------------------------------------') + +if opt.pretrained: + model_name = os.path.join(opt.save_folder + opt.pretrained_sr) + if os.path.exists(model_name): + #model= torch.load(model_name, map_location=lambda storage, loc: storage) + model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage)) + print('Pre-trained SR model is loaded.') + +if cuda: + model = model.cuda(gpus_list[0]) + criterion = criterion.cuda(gpus_list[0]) + criterion_l1 = criterion_l1.cuda(gpus_list[0]) + feature_extractor = feature_extractor.cuda(gpus_list[0]) + +optimizer = optim.Adamax(model.parameters(), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8) + +for epoch in range(opt.start_epoch, opt.nEpochs + 1): + train(epoch) + #test() + + # learning rate is decayed by a factor of 10 every half of total epochs + if (epoch+1) % (opt.nEpochs/3) == 0: + for param_group in optimizer.param_groups: + param_group['lr'] /= 10.0 + print('Learning rate decay: lr={}'.format(optimizer.param_groups[0]['lr'])) + + if (epoch+1) % (opt.snapshots) == 0: + checkpoint(epoch) + +checkpoint(epoch) \ No newline at end of file diff --git a/main_refinement_t_sr_hr.py b/main_refinement_t_sr_hr.py new file mode 100644 index 0000000..296b9ea --- /dev/null +++ b/main_refinement_t_sr_hr.py @@ -0,0 +1,203 @@ +from __future__ import print_function +import argparse +from math import log10 + +import os +import torch +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +from torch.autograd import Variable +from torch.utils.data import DataLoader +from torchvision import models +from fbpn_sr_rbpn_v1_ref import Net as FBPNSR_RBPN_V1_REF +from fbpn_sr_rbpn_v2_ref import Net as FBPNSR_RBPN_V2_REF +from fbpn_sr_rbpn_v3_ref import Net as FBPNSR_RBPN_V3_REF +from fbpn_sr_rbpn_v4_ref import Net as FBPNSR_RBPN_V4_REF, FeatureExtractor +from data import get_training_set_flow_lr +import utils +import pdb +import socket +import time +import cv2 + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch Super Res Example') +parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor") +parser.add_argument('--batchSize', type=int, default=4, help='training batch size') +parser.add_argument('--start_epoch', type=int, default=1, help='Starting epoch for continuing training') +parser.add_argument('--nEpochs', type=int, default=20, help='number of epochs to train for') +parser.add_argument('--snapshots', type=int, default=1, help='Snapshots') +parser.add_argument('--lr', type=float, default=1e-4, help='Learning Rate. Default=0.01') +parser.add_argument('--gpu_mode', type=bool, default=True) +parser.add_argument('--threads', type=int, default=8, help='number of threads for data loader to use') +parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123') +parser.add_argument('--gpus', default=4, type=int, help='number of gpu') +parser.add_argument('--data_dir', type=str, default='./vimeo_triplet/sequences/') +parser.add_argument('--file_list', type=str, default='tri_trainlist.txt') +parser.add_argument('--patch_size', type=int, default=48, help='0 to use original frame size') +parser.add_argument('--data_augmentation', type=bool, default=True) +parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4_REF') +parser.add_argument('--pretrained_sr', default='FBPNSR_RBPN_V4_REF_Lf_STAR_T_HR.pth', help='sr pretrained base model') +parser.add_argument('--pretrained', type=bool, default=True) +parser.add_argument('--save_folder', default='weights/', help='Location to save checkpoint models') +parser.add_argument('--prefix', default='VGG_FR_VIMEO_STAR_T_HR', help='Location to save checkpoint models') + +opt = parser.parse_args() +gpus_list = range(opt.gpus) +hostname = str(socket.gethostname()) +cudnn.benchmark = True +print(opt) + +def train(epoch): + epoch_loss = 0 + model.train() + feature_extractor.eval() + for iteration, batch in enumerate(training_data_loader, 1): + target, flow_f, flow_b,gt_flow_f, gt_flow_b = batch[0], batch[1], batch[2],batch[3], batch[4] + + if cuda: + t_flow_f = Variable(flow_f).cuda(gpus_list[0]).float() + t_flow_b = Variable(flow_b).cuda(gpus_list[0]).float() + gt_flow_f = Variable(gt_flow_f).cuda(gpus_list[0]).float() + gt_flow_b = Variable(gt_flow_b).cuda(gpus_list[0]).float() + + t_hr1 = Variable(target[0]).cuda(gpus_list[0]) + t_hr = Variable(target[1]).cuda(gpus_list[0]) + t_hr2 = Variable(target[2]).cuda(gpus_list[0]) + + optimizer.zero_grad() + t0 = time.time() + + _, _, _, pred_l,pred_flow_f, pred_flow_b = model(t_hr1, t_hr2, t_flow_f, t_flow_b) + + l_mse_lr = mse_loss_calc(pred_l, t_hr) + l_mse = l_mse_lr + + l_feat_lr = feat_loss_calc(pred_l, t_hr) + l_feat = l_feat_lr + + l_flow = criterion(pred_flow_f, gt_flow_f) + criterion(pred_flow_b, gt_flow_b) + + #loss = l_flow + loss = l_mse + 0.1*l_feat + 0.1*l_flow + t1 = time.time() + + ###show sample + pred_l = utils.denorm(pred_l[0][0].cpu().data,vgg=True) + pred_l1 = utils.denorm(t_hr1[0].cpu().data,vgg=True) + pred_l2 = utils.denorm(t_hr2[0].cpu().data,vgg=True) + save_img(pred_l, "lr2") + save_img(pred_l1, "lr1") + save_img(pred_l2, "lr3") + + epoch_loss += loss.data + loss.backward() + optimizer.step() + #print(loss_mse_hr.data , loss_mse_lr.data, loss_feat_hr.data, loss_feat_l.data) + print("===> Epoch[{}]({}/{}): Loss: {:.4f} MSE_LR: {:.4f} FEAT_LR: {:.4f} Flow: {:.8f} || Timer: {:.4f} sec.".format(epoch, iteration, len(training_data_loader), loss.data, l_mse_lr,l_feat_lr, l_flow, (t1 - t0))) + print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss / len(training_data_loader))) + +def mse_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_mse = 0 + for i in range(len(pred)): + l_mse_ht = criterion_l1(pred[i], target) + l_mse = l_mse + weight*l_mse_ht + weight += w_interval + return l_mse + +def feat_loss_calc(pred, target, w_begin=0.4 , w_interval=0.6): + weight = w_begin + l_feat = 0 + hr_feature = feature_extractor(target) + for i in range(len(pred)): + sr_feature = feature_extractor(pred[i]) + l_feat_ht = criterion(sr_feature, hr_feature.detach()) + l_feat = l_feat + weight*l_feat_ht + weight += w_interval + return l_feat + +def save_img(img, img_name): + save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0) + + # save img + save_fn = 'im'+img_name+'_'+opt.model_type+opt.prefix+'.png' + cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0]) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + +def checkpoint(epoch): + model_out_path = opt.save_folder+str(opt.upscale_factor)+'x_'+hostname+opt.model_type+opt.prefix+"_epoch_{}.pth".format(epoch) + torch.save(model.state_dict(), model_out_path) + print("Checkpoint saved to {}".format(model_out_path)) + +cuda = opt.gpu_mode +if cuda and not torch.cuda.is_available(): + raise Exception("No GPU found, please run without --cuda") + +torch.manual_seed(opt.seed) +if cuda: + torch.cuda.manual_seed(opt.seed) + +print('===> Loading datasets') +train_set = get_training_set_flow_lr(opt.data_dir, opt.upscale_factor, opt.data_augmentation, opt.file_list, opt.patch_size) +training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True, drop_last=True) + +print('===> Building model ', opt.model_type) +if opt.model_type == 'FBPNSR_RBPN_V1_REF': + model = FBPNSR_RBPN_V1_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V2_REF': + model = FBPNSR_RBPN_V2_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V3_REF': + model = FBPNSR_RBPN_V3_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) +elif opt.model_type == 'FBPNSR_RBPN_V4_REF': + model = FBPNSR_RBPN_V4_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor) + + +model = torch.nn.DataParallel(model, device_ids=gpus_list) +criterion_l1 = nn.L1Loss() +criterion = nn.MSELoss() + +###VGG +feature_extractor = FeatureExtractor(models.vgg19(pretrained=True), feature_layer=35) +feature_extractor = torch.nn.DataParallel(feature_extractor, device_ids=gpus_list) + +print('---------- Networks architecture -------------') +print_network(model) +print('----------------------------------------------') + +if opt.pretrained: + model_name = os.path.join(opt.save_folder + opt.pretrained_sr) + if os.path.exists(model_name): + #model= torch.load(model_name, map_location=lambda storage, loc: storage) + model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage)) + print('Pre-trained SR model is loaded.') + +if cuda: + model = model.cuda(gpus_list[0]) + criterion = criterion.cuda(gpus_list[0]) + criterion_l1 = criterion_l1.cuda(gpus_list[0]) + feature_extractor = feature_extractor.cuda(gpus_list[0]) + +optimizer = optim.Adamax(model.parameters(), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8) +#optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8) + +for epoch in range(opt.start_epoch, opt.nEpochs + 1): + train(epoch) + #test() + + # learning rate is decayed by a factor of 10 every half of total epochs + if (epoch+1) % (opt.nEpochs/2) == 0: + for param_group in optimizer.param_groups: + param_group['lr'] /= 10.0 + print('Learning rate decay: lr={}'.format(optimizer.param_groups[0]['lr'])) + + if (epoch+1) % (opt.snapshots) == 0: + checkpoint(epoch) +checkpoint(epoch) diff --git a/middlebury/eval-data/test.txt b/middlebury/eval-data/test.txt new file mode 100644 index 0000000..0a9f286 --- /dev/null +++ b/middlebury/eval-data/test.txt @@ -0,0 +1,12 @@ +Yosemite +Wooden +Urban +Teddy +Schefflera +Mequon +Grove +Evergreen +Dumptruck +Basketball +Backyard +Army diff --git a/middlebury/other-data/test.txt b/middlebury/other-data/test.txt new file mode 100644 index 0000000..3d3acf7 --- /dev/null +++ b/middlebury/other-data/test.txt @@ -0,0 +1,12 @@ +Walking +Venus +Urban3 +Urban2 +RubberWhale +MiniCooper +Hydrangea +Grove3 +Grove2 +DogDance +Dimetrodon +Beanbags diff --git a/niqe_release/computefeature.m b/niqe_release/computefeature.m new file mode 100644 index 0000000..b879bc7 --- /dev/null +++ b/niqe_release/computefeature.m @@ -0,0 +1,24 @@ +function feat = computefeature(structdis) + +% Input - MSCn coefficients +% Output - Compute the 18 dimensional feature vector + +feat = []; + + + +[alpha betal betar] = estimateaggdparam(structdis(:)); + +feat = [feat;alpha;(betal+betar)/2]; + +shifts = [ 0 1;1 0 ;1 1;1 -1]; + +for itr_shift =1:4 + +shifted_structdis = circshift(structdis,shifts(itr_shift,:)); +pair = structdis(:).*shifted_structdis(:); +[alpha betal betar] = estimateaggdparam(pair); +meanparam = (betar-betal)*(gamma(2/alpha)/gamma(1/alpha)); +feat = [feat;alpha;meanparam;betal;betar]; + +end diff --git a/niqe_release/computemean.m b/niqe_release/computemean.m new file mode 100644 index 0000000..be9e7f2 --- /dev/null +++ b/niqe_release/computemean.m @@ -0,0 +1,3 @@ +function val = computemean(patch) + +val = mean2(patch); \ No newline at end of file diff --git a/niqe_release/computequality.m b/niqe_release/computequality.m new file mode 100644 index 0000000..432e982 --- /dev/null +++ b/niqe_release/computequality.m @@ -0,0 +1,91 @@ +function quality = computequality(im,blocksizerow,blocksizecol,... + blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam) + +% Input +% im - Image whose quality needs to be computed +% blocksizerow - Height of the blocks in to which image is divided +% blocksizecol - Width of the blocks in to which image is divided +% blockrowoverlap - Amount of vertical overlap between blocks +% blockcoloverlap - Amount of horizontal overlap between blocks +% mu_prisparam - mean of multivariate Gaussian model +% cov_prisparam - covariance of multivariate Gaussian model + +% For good performance, it is advisable to use make the multivariate Gaussian model +% using same size patches as the distorted image is divided in to + +% Output +%quality - Quality of the input distorted image + +% Example call +%quality = computequality(im,96,96,0,0,mu_prisparam,cov_prisparam) + +% --------------------------------------------------------------- +%Number of features +% 18 features at each scale +featnum = 18; +%---------------------------------------------------------------- +%Compute features +if(size(im,3)==3) +im = rgb2gray(im); +end +im = double(im); +[row col] = size(im); +block_rownum = floor(row/blocksizerow); +block_colnum = floor(col/blocksizecol); + +im = im(1:block_rownum*blocksizerow,1:block_colnum*blocksizecol); +[row col] = size(im); +block_rownum = floor(row/blocksizerow); +block_colnum = floor(col/blocksizecol); +im = im(1:block_rownum*blocksizerow, ... + 1:block_colnum*blocksizecol); +window = fspecial('gaussian',7,7/6); +window = window/sum(sum(window)); +scalenum = 2; +warning('off') + +feat = []; + + +for itr_scale = 1:scalenum + + +mu = imfilter(im,window,'replicate'); +mu_sq = mu.*mu; +sigma = sqrt(abs(imfilter(im.*im,window,'replicate') - mu_sq)); +structdis = (im-mu)./(sigma+1); + + + +feat_scale = blkproc(structdis,[blocksizerow/itr_scale blocksizecol/itr_scale], ... + [blockrowoverlap/itr_scale blockcoloverlap/itr_scale], ... + @computefeature); +feat_scale = reshape(feat_scale,[featnum .... + size(feat_scale,1)*size(feat_scale,2)/featnum]); +feat_scale = feat_scale'; + + +if(itr_scale == 1) +sharpness = blkproc(sigma,[blocksizerow blocksizecol], ... + [blockrowoverlap blockcoloverlap],@computemean); +sharpness = sharpness(:); +end + + +feat = [feat feat_scale]; + +im =imresize(im,0.5); + +end + + +% Fit a MVG model to distorted patch features +distparam = feat; +mu_distparam = nanmean(distparam); +cov_distparam = nancov(distparam); + +% Compute quality +invcov_param = pinv((cov_prisparam+cov_distparam)/2); +quality = sqrt((mu_prisparam-mu_distparam)* ... + invcov_param*(mu_prisparam-mu_distparam)'); + diff --git a/niqe_release/estimateaggdparam.m b/niqe_release/estimateaggdparam.m new file mode 100644 index 0000000..880367c --- /dev/null +++ b/niqe_release/estimateaggdparam.m @@ -0,0 +1,20 @@ +function [alpha betal betar] = estimateaggdparam(vec) + + +gam = 0.2:0.001:10; +r_gam = ((gamma(2./gam)).^2)./(gamma(1./gam).*gamma(3./gam)); + + +leftstd = sqrt(mean((vec(vec<0)).^2)); +rightstd = sqrt(mean((vec(vec>0)).^2)); + +gammahat = leftstd/rightstd; +rhat = (mean(abs(vec)))^2/mean((vec).^2); +rhatnorm = (rhat*(gammahat^3 +1)*(gammahat+1))/((gammahat^2 +1)^2); +[min_difference, array_position] = min((r_gam - rhatnorm).^2); +alpha = gam(array_position); + +betal = leftstd *sqrt(gamma(1/alpha)/gamma(3/alpha)); +betar = rightstd*sqrt(gamma(1/alpha)/gamma(3/alpha)); + + diff --git a/niqe_release/estimatemodelparam.m b/niqe_release/estimatemodelparam.m new file mode 100644 index 0000000..ca988c7 --- /dev/null +++ b/niqe_release/estimatemodelparam.m @@ -0,0 +1,116 @@ +function [mu_prisparam cov_prisparam] = estimatemodelparam(folderpath,... + blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,sh_th) + +% Input +% folderpath - Folder containing the pristine images +% blocksizerow - Height of the blocks in to which image is divided +% blocksizecol - Width of the blocks in to which image is divided +% blockrowoverlap - Amount of vertical overlap between blocks +% blockcoloverlap - Amount of horizontal overlap between blocks +% sh_th - The sharpness threshold level +%Output +%mu_prisparam - mean of multivariate Gaussian model +%cov_prisparam - covariance of multivariate Gaussian model + +% Example call + +%[mu_prisparam cov_prisparam] = estimatemodelparam('pristine',96,96,0,0,0.75); + + +%---------------------------------------------------------------- +% Find the names of images in the folder +current = pwd; +cd(sprintf('%s',folderpath)) +names = ls; +names = names(3:end,:); +cd(current) +% --------------------------------------------------------------- +%Number of features +% 18 features at each scale +featnum = 18; +% --------------------------------------------------------------- +% Make the directory for storing the features +mkdir(sprintf('local_risquee_prisfeatures')) +% --------------------------------------------------------------- +% Compute pristine image features +for itr = 1:size(names,1) +itr +im = imread(sprintf('%s\\%s',folderpath,names(itr,:))); +if(size(im,3)==3) +im = rgb2gray(im); +end +im = double(im); +[row col] = size(im); +block_rownum = floor(row/blocksizerow); +block_colnum = floor(col/blocksizecol); +im = im(1:block_rownum*blocksizerow, ... + 1:block_colnum*blocksizecol); +window = fspecial('gaussian',7,7/6); +window = window/sum(sum(window)); +scalenum = 2; +warning('off') + +feat = []; + + +for itr_scale = 1:scalenum + + +mu = imfilter(im,window,'replicate'); +mu_sq = mu.*mu; +sigma = sqrt(abs(imfilter(im.*im,window,'replicate') - mu_sq)); +structdis = (im-mu)./(sigma+1); + + + +feat_scale = blkproc(structdis,[blocksizerow/itr_scale blocksizecol/itr_scale], ... + [blockrowoverlap/itr_scale blockcoloverlap/itr_scale], ... + @computefeature); +feat_scale = reshape(feat_scale,[featnum .... + size(feat_scale,1)*size(feat_scale,2)/featnum]); +feat_scale = feat_scale'; + + +if(itr_scale == 1) +sharpness = blkproc(sigma,[blocksizerow blocksizecol], ... + [blockrowoverlap blockcoloverlap],@computemean); +sharpness = sharpness(:); +end + + +feat = [feat feat_scale]; + +im =imresize(im,0.5); + +end + +save(sprintf('local_risquee_prisfeatures\\prisfeatures_local%d.mat',... + itr),'feat','sharpness'); +end + + + +%---------------------------------------------- +% Load pristine image features +prisparam = []; +current = pwd; +cd(sprintf('%s','local_risquee_prisfeatures')) +names = ls; +names = names(3:end,:); +cd(current) +for itr = 1:size(names,1) +% Load the features and select the only features +load(sprintf('local_risquee_prisfeatures\\%s',strtrim(names(itr,:)))); +IX = find(sharpness(:) >sh_th*max(sharpness(:))); +feat = feat(IX,:); +prisparam = [prisparam; feat]; + +end +%---------------------------------------------- +% Compute model parameters +mu_prisparam = 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(LIVE, http://live.ece.utexas.edu) +and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, +http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research +is to be cited in the bibliography as: + +1) A. Mittal, R. Soundararajan and A. C. Bovik, "NIQE Software Release", +URL: http://live.ece.utexas.edu/research/quality/niqe.zip, 2012. + +2) A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", submitted to IEEE Signal Processing Letters, 2012. + +IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, +OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS +AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, +AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. + +-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------% + +Author : Anish Mittal +Version : 1.0 + +The authors are with the Laboratory for Image and Video Engineering +(LIVE), Department of Electrical and Computer Engineering, The +University of Texas at Austin, Austin, TX. + +Kindly report any suggestions or corrections to mittal.anish@gmail.com + +======================================================================= + +This is a demonstration of the Naturalness Image Quality Evaluator(NIQE) index. The algorithm is described in: + +A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer", submitted to IEEE Signal Processing Letters, 2012. + +You can change this program as you like and use it anywhere, but please +refer to its original source (cite our paper and our web page at +http://live.ece.utexas.edu/research/quality/niqe_release.zip). + +======================================================================= +Running on Matlab + +Input : A test image loaded in an array + +Output: A quality score of the image. Higher value represents a lower quality. + +Usage: + +1. Load the image, for example + + image = imread('testimage1.bmp'); + +2. Load the parameters of pristine multivariate Gaussian model. + + + load modelparameters.mat; + + +The images used for making the current model may be viewed at http://live.ece.utexas.edu/research/quality/pristinedata.zip + + +3. Initialize different parameters + + Height of the block + blocksizerow = 96; + Width of the block + blocksizecol = 96; + Verical overlap between blocks + blocksizerow = 0; + Horizontal overlap between blocks + blocksizecol = 0; + + For good performance, it is advisable to divide the distorted image in to same size patched as used for the construction of multivariate Gaussian model. + +3. Call this function to calculate the quality score: + + + qualityscore = computequality(im,blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam) + +Sample execution is also shown through example.m + + +======================================================================= + +MATLAB files: (provided with release): example.m, computefeature.m, computemean.m, computequality.m, estimateaggdparam.m and estimatemodelparam.m + +Image Files: image1.bmp, image2.bmp, image3.bmp and image4.bmp + +Dependencies: Mat file: modelparameters.mat provided with release + +======================================================================= + +Note on training: +This release version of NIQE was trained on 125 pristine images with patch size set to 96X96 and sharpness threshold of 0.75. + +Training the model + +If the user wants to retrain the model using different set of pristine image or set the patch sizes to different values, he/she can do so +use the following function. The images used for making the current model may be viewed at http://live.ece.utexas.edu/research/quality/pristinedata.zip + +Folder containing the pristine images +folderpath = 'pristine' +Height of the block +blocksizerow = 96; +Width of the block +blocksizecol = 96; +Verical overlap between blocks +blocksizerow = 0; +Horizontal overlap between blocks +blocksizecol = 0; +The sharpness threshold level +sh_th = 0.75; + + +[mu_prisparam cov_prisparam] = estimatemodelparam(folderpath,blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,sh_th) +======================================================================= + \ No newline at end of file diff --git a/pyflow.cpython-35m-x86_64-linux-gnu.so b/pyflow.cpython-35m-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..55215657ecad7a61119fc75d51603cc942a5b0cd GIT binary patch literal 759480 zcmeFadq9-c_CNffj-qLU8AWzOqr%D#qhg>^jDS9)L!x*)-2efFLLnFw%L-*E$7zc0 zb~|yp+U?}&lxa8dirUH0tn5x@^*oYBH#NKPe%9Li88(m1Ip_QP`<>JH?6vmVYp=cb zT5GSppFNzLvb+;|L`7-Vr>AzYMo@f_z@*EHl+baqq-p6|iq;4Jo}-<`w2pAiv4v;G z^kAg*1ZRe+kKXw1mGyha3}w9aeB%TLwZ>TSwAv9jYF9B%@U0jp+F4Icpf&3mEt~hk zlfK*JQ0>nINoPHYmOeRO`--R>f1+S)fA*4u;U~A!w5e!Ea{jNM7)dxy_7_PniBj3t zQx1p6b|QH6#CPJQGqkfp_s%voL2ox1n|-&^se{`%po_m1ur zoxVY9>=9pbV&gQewrOenutKfYt)=VB{JpfMct>pS_&BY9ZNI2NlZw(WsBK6;zE}Fv zkTyA2>v8v!Nz1nnO|M-QlhC)JOzS^Hi|H}izij2wfzcV~ZXO!nm~(SfZI77r0Z{|A z=$II7S$e!yTN_o|t8c$?G5t566UW*kc|!P-fDQQGf$v@T-h=PG_|j)3zW3w%00G^6 z9+J4f;rR%@f0yv1cs?e}^mtq;KY{X-5`GHLRZ88{O8Hq?eoj80$MYXb{R=3s#`mB2 zz9{i8$>+;>u90Q+@rnxA%4-3?itlUqzHX~~!v+)hro^qsvq_dW$md2pH{rWk!reW# zNaR+fvRRh5DLAXR9fh|npquC061TIPIzqjJZ;Mj58|8QL-GlG@3jPD7OvSzUerUse z4EPg#KgIVmTU{jZb5ykA`-P3*E5Kjl`whMZz9zo=@wI=xlZXR&9>n*1I|1PKZg7Mm z|D(^3;?d>bpC$AQo`;m0!%CToMe$0_ChC|@(};uW7|&C%=nuGH4Ob||TBjc`l<-3c2fAHxP_lTdKC@O2X_0V9~ zSA(m!Uw!iMSL%lk82H)n5sxkYFuPCFXCHiibol+bPh>yv!e{yw(XZWnMaHieOuFaB zTYr3a=!3qmoa0vws?W&!@Y>m@J@I(MyU7I|@1JzSg#$(|JU#Sx-

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super(Net, self).__init__() + #base_filter=256 + #feat=64 + self.nFrames = nFrames + + if scale_factor == 2: + kernel = 6 + stride = 2 + padding = 2 + elif scale_factor == 4: + kernel = 8 + stride = 4 + padding = 2 + elif scale_factor == 8: + kernel = 12 + stride = 8 + padding = 2 + + #Initial Feature Extraction + self.feat0 = ConvBlock(num_channels, base_filter, 3, 1, 1, activation='prelu', norm=None) + self.feat1 = ConvBlock(8, base_filter, 3, 1, 1, activation='prelu', norm=None) + + ###DBPNS + self.DBPN = DBPNS(base_filter, feat, num_stages, scale_factor) + + #Res-Block1 + modules_body1 = [ + ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None) \ + for _ in range(n_resblock)] + modules_body1.append(DeconvBlock(base_filter, feat, kernel, stride, padding, activation='prelu', norm=None)) + self.res_feat1 = nn.Sequential(*modules_body1) + + #Res-Block2 + modules_body2 = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None) \ + for _ in range(n_resblock)] + modules_body2.append(ConvBlock(feat, feat, 3, 1, 1, activation='prelu', norm=None)) + self.res_feat2 = nn.Sequential(*modules_body2) + + #Res-Block3 + modules_body3 = [ + ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None) \ + for _ in range(n_resblock)] + modules_body3.append(ConvBlock(feat, base_filter, kernel, stride, padding, activation='prelu', norm=None)) + self.res_feat3 = nn.Sequential(*modules_body3) + + #Reconstruction + self.output = ConvBlock((nFrames-1)*feat, num_channels, 3, 1, 1, activation=None, norm=None) + + for m in self.modules(): + classname = m.__class__.__name__ + if classname.find('Conv2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + elif classname.find('ConvTranspose2d') != -1: + torch.nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x, neigbor, flow): + ### initial feature extraction + feat_input = self.feat0(x) + feat_frame=[] + for j in range(len(neigbor)): + feat_frame.append(self.feat1(torch.cat((x, neigbor[j], flow[j]),1))) + + ####Projection + Ht = [] + for j in range(len(neigbor)): + h0 = self.DBPN(feat_input) + h1 = self.res_feat1(feat_frame[j]) + + e = h0-h1 + e = self.res_feat2(e) + h = h0+e + Ht.append(h) + feat_input = self.res_feat3(h) + + ####Reconstruction + out = torch.cat(Ht,1) + #out=Ht[0] + #for j in range(1,len(neigbor)): + # out=torch.cat((out,Ht[j]),1) + + output = self.output(out) + return output, out diff --git a/ucf101_interp_ours/test.txt b/ucf101_interp_ours/test.txt new file mode 100644 index 0000000..9bb5734 --- /dev/null +++ b/ucf101_interp_ours/test.txt @@ -0,0 +1,379 @@ +1 +1001 +101 +1011 +1021 +1031 +1041 +1051 +1061 +1071 +1081 +1091 +11 +1101 +111 +1111 +1121 +1131 +1141 +1151 +1161 +1171 +1181 +1191 +1201 +121 +1211 +1221 +1231 +1241 +1251 +1261 +1271 +1281 +1291 +1301 +131 +1311 +1321 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0000000..17416a9 --- /dev/null +++ b/utils.py @@ -0,0 +1,46 @@ +import torch +from torch.autograd import Variable +import torchvision.transforms as transforms +from torch import mm + +def norm(img, vgg=False): + if vgg: + # normalize for pre-trained vgg model + # https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101 + transform = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + else: + # normalize [-1, 1] + transform = transforms.Normalize(mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5]) + return transform(img) + +def gram_matrix(input): + a, b, c, d = input.size() # a=batch size(=1) + # b=number of feature maps + # (c,d)=dimensions of a f. map (N=c*d) + + features = input.view(a * b, c * d) # resise F_XL into \hat F_XL + + G = mm(features, features.t()) # compute the gram product + + # we 'normalize' the values of the gram matrix + # by dividing by the number of element in each feature maps. + return G.div(a * b * c * d) + +def denorm(img, vgg=False): + if vgg: + transform = transforms.Normalize(mean=[-2.118, -2.036, -1.804], + std=[4.367, 4.464, 4.444]) + return transform(img) + else: + out = (img + 1) / 2 + return out.clamp(0, 1) + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + diff --git a/vimeo_triplet/sequences/tri_testlist.txt b/vimeo_triplet/sequences/tri_testlist.txt new file mode 100644 index 0000000..718c59f --- /dev/null +++ b/vimeo_triplet/sequences/tri_testlist.txt @@ -0,0 +1,3782 @@ +00001/0389 +00001/0402 +00001/0411 +00001/0412 +00001/0413 +00001/0415 +00001/0437 +00001/0438 +00001/0459 +00001/0461 +00001/0495 +00001/0505 +00001/0581 +00001/0600 +00001/0602 +00001/0603 +00001/0604 +00001/0605 +00001/0608 +00001/0621 +00001/0622 +00001/0624 +00001/0626 +00001/0628 +00001/0776 +00001/0778 +00001/0781 +00001/0782 +00001/0785 +00001/0786 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