First commit OCR_earsing and Synthetics Handwritten Recognition awesome repo

OCR_earsing/latent_diffusion/taming/modules/losses/__init__.py

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+from taming.modules.losses.vqperceptual import DummyLoss
+

OCR_earsing/latent_diffusion/taming/modules/losses/lpips.py

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+"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
+
+import torch
+import torch.nn as nn
+from torchvision import models
+from collections import namedtuple
+
+from taming.util import get_ckpt_path
+
+
+class LPIPS(nn.Module):
+    # Learned perceptual metric
+    def __init__(self, use_dropout=True):
+        super().__init__()
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]  # vg16 features
+        self.net = vgg16(pretrained=True, requires_grad=False)
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.load_from_pretrained()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def load_from_pretrained(self, name="vgg_lpips"):
+        ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips")
+        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+        print("loaded pretrained LPIPS loss from {}".format(ckpt))
+
+    @classmethod
+    def from_pretrained(cls, name="vgg_lpips"):
+        if name != "vgg_lpips":
+            raise NotImplementedError
+        model = cls()
+        ckpt = get_ckpt_path(name)
+        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+        return model
+
+    def forward(self, input, target):
+        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+        outs0, outs1 = self.net(in0_input), self.net(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+        for kk in range(len(self.chns)):
+            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
+        val = res[0]
+        for l in range(1, len(self.chns)):
+            val += res[l]
+        return val
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
+        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    """ A single linear layer which does a 1x1 conv """
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = [nn.Dropout(), ] if (use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
+        self.model = nn.Sequential(*layers)
+
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+
+def normalize_tensor(x,eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
+    return x/(norm_factor+eps)
+
+
+def spatial_average(x, keepdim=True):
+    return x.mean([2,3],keepdim=keepdim)
+

OCR_earsing/latent_diffusion/taming/modules/losses/segmentation.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BCELoss(nn.Module):
+    def forward(self, prediction, target):
+        loss = F.binary_cross_entropy_with_logits(prediction,target)
+        return loss, {}
+
+
+class BCELossWithQuant(nn.Module):
+    def __init__(self, codebook_weight=1.):
+        super().__init__()
+        self.codebook_weight = codebook_weight
+
+    def forward(self, qloss, target, prediction, split):
+        bce_loss = F.binary_cross_entropy_with_logits(prediction,target)
+        loss = bce_loss + self.codebook_weight*qloss
+        return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(),
+                      "{}/bce_loss".format(split): bce_loss.detach().mean(),
+                      "{}/quant_loss".format(split): qloss.detach().mean()
+                      }

OCR_earsing/latent_diffusion/taming/modules/losses/vqperceptual.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from taming.modules.losses.lpips import LPIPS
+from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
+
+
+class DummyLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+
+def adopt_weight(weight, global_step, threshold=0, value=0.):
+    if global_step < threshold:
+        weight = value
+    return weight
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1. - logits_real))
+    loss_fake = torch.mean(F.relu(1. + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real)) +
+        torch.mean(torch.nn.functional.softplus(logits_fake)))
+    return d_loss
+
+
+class VQLPIPSWithDiscriminator(nn.Module):
+    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_ndf=64, disc_loss="hinge"):
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.codebook_weight = codebook_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+
+        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
+                                                 n_layers=disc_num_layers,
+                                                 use_actnorm=use_actnorm,
+                                                 ndf=disc_ndf
+                                                 ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        if disc_loss == "hinge":
+            self.disc_loss = hinge_d_loss
+        elif disc_loss == "vanilla":
+            self.disc_loss = vanilla_d_loss
+        else:
+            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
+        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        if last_layer is not None:
+            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+        else:
+            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
+                global_step, last_layer=None, cond=None, split="train"):
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+        else:
+            p_loss = torch.tensor([0.0])
+
+        nll_loss = rec_loss
+        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        nll_loss = torch.mean(nll_loss)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if cond is None:
+                assert not self.disc_conditional
+                logits_fake = self.discriminator(reconstructions.contiguous())
+            else:
+                assert self.disc_conditional
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+            g_loss = -torch.mean(logits_fake)
+
+            try:
+                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+            except RuntimeError:
+                assert not self.training
+                d_weight = torch.tensor(0.0)
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
+
+            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
+                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
+                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                   "{}/p_loss".format(split): p_loss.detach().mean(),
+                   "{}/d_weight".format(split): d_weight.detach(),
+                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                   "{}/g_loss".format(split): g_loss.detach().mean(),
+                   }
+            return loss, log
+
+        if optimizer_idx == 1:
+            # second pass for discriminator update
+            if cond is None:
+                logits_real = self.discriminator(inputs.contiguous().detach())
+                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+            else:
+                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                   "{}/logits_real".format(split): logits_real.detach().mean(),
+                   "{}/logits_fake".format(split): logits_fake.detach().mean()
+                   }
+            return d_loss, log