text: #!/bin/env python
  """Train a VAE MNIST generator.
  
  Usage:
  
  * Train a model:
  
  `python mnist_vae.py train`
  
  * Generate samples from a trained model:
  
  `python mnist_vae.py sample`
  
  * Generate latent space interpolations from a trained model:
  
  `python mnist_vae.py interpolate`
  """
  import argparse
  import os
  
  import numpy as np
  import torch as th
  from torch.utils.data import DataLoader
  import torchvision.datasets as dset
  import torchvision.transforms as transforms
  
  import ttools
  import ttools.interfaces
  
  from modules import Flatten
  
  import pydiffvg
  
  LOG = ttools.get_logger(__name__)
  
  
  BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir)
  VAE_OUTPUT = os.path.join(BASE_DIR, "results", "mnist_vae")
  AE_OUTPUT = os.path.join(BASE_DIR, "results", "mnist_ae")
  
  
  def _onehot(label):
      bs = label.shape[0]
      label_onehot = label.new(bs, 10)
      label_onehot = label_onehot.zero_()
      label_onehot.scatter_(1, label.unsqueeze(1), 1)
      return label_onehot.float()
  
  
  def render(canvas_width, canvas_height, shapes, shape_groups, samples=2):
      _render = pydiffvg.RenderFunction.apply
      scene_args = pydiffvg.RenderFunction.serialize_scene(
          canvas_width, canvas_height, shapes, shape_groups)
      img = _render(canvas_width,
                    canvas_height,
                    samples,
                    samples,
                    0,
                    None,
                    *scene_args)
      return img
  
  
  class MNISTCallback(ttools.callbacks.ImageDisplayCallback):
      """Simple callback that visualize generated images during training."""
  
      def visualized_image(self, batch, step_data, is_val=False):
          im = step_data["rendering"].detach().cpu()
          im = 0.5 + 0.5*im
          ref = batch[0].cpu()
  
          vizdata = [im, ref]
  
          # tensor to visualize, concatenate images
          viz = th.clamp(th.cat(vizdata, 2), 0, 1)
          return viz
  
      def caption(self, batch, step_data, is_val=False):
          return "fake, real"
  
  
  class VAEInterface(ttools.ModelInterface):
      def __init__(self, model, lr=1e-4, cuda=True, max_grad_norm=10,
                   variational=True, w_kld=1.0):
          super(VAEInterface, self).__init__()
  
          self.max_grad_norm = max_grad_norm
  
          self.model = model
  
          self.w_kld = w_kld
  
          self.variational = variational
  
          self.device = "cpu"
          if cuda:
              self.device = "cuda"
  
          self.model.to(self.device)
  
          self.opt = th.optim.Adam(
              self.model.parameters(), lr=lr, betas=(0.5, 0.5), eps=1e-12)
  
      def training_step(self, batch):
          im, label = batch[0], batch[1]
          im = im.to(self.device)
          label = label.to(self.device)
          rendering, auxdata = self.model(im, label)
  
          im = batch[0]
          im = im.to(self.device)
  
          logvar = auxdata["logvar"]
          mu = auxdata["mu"]
  
          data_loss = th.nn.functional.mse_loss(rendering, im)
  
          ret = {}
          if self.variational:  # VAE mode
              kld = -0.5 * th.sum(1 + logvar - mu.pow(2) - logvar.exp(), 1)
              kld = kld.mean()
              loss = data_loss + kld*self.w_kld
              ret["kld"] = kld.item()
          else:  # Regular autoencoder
              loss = data_loss
  
          # optimize
          self.opt.zero_grad()
          loss.backward()
  
          # Clip large gradients if needed
          if self.max_grad_norm is not None:
              nrm = th.nn.utils.clip_grad_norm_(
                  self.model.parameters(), self.max_grad_norm)
              if nrm > self.max_grad_norm:
                  LOG.warning("Clipping generator gradients. norm = %.3f > %.3f",
                              nrm, self.max_grad_norm)
  
          self.opt.step()
  
          ret["loss"] = loss.item()
          ret["data_loss"] = data_loss.item()
          ret["auxdata"] = auxdata
          ret["rendering"] = rendering
          ret["logvar"] = logvar.abs().max().item() if self.variational else None
  
          return ret
  
  
  class VectorMNISTVAE(th.nn.Module):
      def __init__(self, imsize=28, paths=4, segments=5, samples=2, zdim=128,
                   conditional=False, variational=True, raster=False, fc=False,
                   stroke_width=None):
          super(VectorMNISTVAE, self).__init__()
  
          self.samples = samples
          self.imsize = imsize
          self.paths = paths
          self.segments = segments
          self.zdim = zdim
          self.conditional = conditional
          self.variational = variational
          print(f"Employing VectorMNISTVAE: samples:{self.samples}, imsize:{self.imsize}, paths:{self.paths}, "
                f"segments: {self.segments}, zdim:{self.zdim}, conditional: {self.conditional}, "
                f"varitional: {self.variational}")
  
          if stroke_width is None:
              self.stroke_width = (1.0, 3.0)
              LOG.warning("Setting default stroke with %s", self.stroke_width)
          else:
              self.stroke_width = stroke_width
  
          ncond = 0
          if self.conditional:  # one hot encoded input for conditional model
              ncond = 10
  
          self.fc = fc
          print(f"self.fc is {self.fc}")
          mult = 1
          nc = 1024
  
          if not self.fc:  # conv model
              self.encoder = th.nn.Sequential(
                  # 32x32
                  th.nn.Conv2d(1 + ncond, mult*64, 4, padding=0, stride=2),
                  th.nn.LeakyReLU(0.2, inplace=True),
  
                  # 16x16
                  th.nn.Conv2d(mult*64, mult*128, 4, padding=0, stride=2),
                  th.nn.LeakyReLU(0.2, inplace=True),
  
                  # 8x8
                  th.nn.Conv2d(mult*128, mult*256, 4, padding=0, stride=2),
                  th.nn.LeakyReLU(0.2, inplace=True),
                  Flatten(),
              )
          else:
              self.encoder = th.nn.Sequential(
                  # 32x32
                  Flatten(),
                  th.nn.Linear(28*28 + ncond, mult*256),
                  th.nn.LeakyReLU(0.2, inplace=True),
  
                  # 8x8
                  th.nn.Linear(mult*256, mult*256, 4),
                  th.nn.LeakyReLU(0.2, inplace=True),
              )
  
          self.mu_predictor = th.nn.Linear(256*1*1, zdim)
          if self.variational:
              self.logvar_predictor = th.nn.Linear(256*1*1, zdim)
  
          self.decoder = th.nn.Sequential(
              th.nn.Linear(zdim + ncond, nc),
              th.nn.SELU(inplace=True),
  
              th.nn.Linear(nc, nc),
              th.nn.SELU(inplace=True),
          )
  
          self.raster = raster
          print(f"self.raster is {self.raster}")
  
          if self.raster:
              self.raster_decoder = th.nn.Sequential(
                  th.nn.Linear(nc, imsize*imsize),
              )
          else:
              # 4 points bezier with n_segments -> 3*n_segments + 1 points
              self.point_predictor = th.nn.Sequential(
                  th.nn.Linear(nc, 2*self.paths*(self.segments*3+1)),
                  th.nn.Tanh()  # bound spatial extent [-1,1]
              )
  
              self.width_predictor = th.nn.Sequential(
                  th.nn.Linear(nc, self.paths),
                  th.nn.Sigmoid()
              )
  
              self.alpha_predictor = th.nn.Sequential(
                  th.nn.Linear(nc, self.paths),
                  th.nn.Sigmoid()
              )
  
      def encode(self, im, label):
          bs, _, h, w = im.shape
          if self.conditional:
              label_onehot = _onehot(label)
              if not self.fc:
                  label_onehot = label_onehot.view(
                      bs, 10, 1, 1).repeat(1, 1, h, w)
                  out = self.encoder(th.cat([im, label_onehot], 1))
              else:
                  out = self.encoder(th.cat([im.view(bs, -1), label_onehot], 1))
          else:
              out = self.encoder(im)
          mu = self.mu_predictor(out)
          if self.variational:
              logvar = self.logvar_predictor(out)
              return mu, logvar
          else:
              return mu
  
      def reparameterize(self, mu, logvar):
          std = th.exp(0.5*logvar)
          eps = th.randn_like(logvar)
          return mu + std*eps
  
      def _decode_features(self, z, label):
          if label is not None:
              if not self.conditional:
                  raise ValueError("decoding with an input label "
                                   "requires a conditional AE")
              label_onehot = _onehot(label)
              z = th.cat([z, label_onehot], 1)
  
          decoded = self.decoder(z)
          return decoded
  
      def decode(self, z, label=None):
          bs = z.shape[0]
  
          feats = self._decode_features(z, label)
  
          if self.raster:
              out = self.raster_decoder(feats).view(
                  bs, 1, self.imsize, self.imsize)
              return out, {}
  
          all_points = self.point_predictor(feats)
          all_points = all_points.view(bs, self.paths, -1, 2)
  
          all_points = all_points*(self.imsize//2-2) + self.imsize//2
  
          if False:
              all_widths = th.ones(bs, self.paths) * 0.5
          else:
              all_widths = self.width_predictor(feats)
              min_width = self.stroke_width[0]
              max_width = self.stroke_width[1]
              all_widths = (max_width - min_width) * all_widths + min_width
  
          if False:
              all_alphas = th.ones(bs, self.paths)
          else:
              all_alphas = self.alpha_predictor(feats)
  
          # Process the batch sequentially
          outputs = []
          scenes = []
          for k in range(bs):
              # Get point parameters from network
              shapes = []
              shape_groups = []
              for p in range(self.paths):
                  points = all_points[k, p].contiguous().cpu()
                  width = all_widths[k, p].cpu()
                  alpha = all_alphas[k, p].cpu()
  
                  color = th.cat([th.ones(3), alpha.view(1,)])
                  num_ctrl_pts = th.zeros(self.segments, dtype=th.int32) + 2
  
                  path = pydiffvg.Path(
                      num_control_points=num_ctrl_pts, points=points,
                      stroke_width=width, is_closed=False)
  
                  shapes.append(path)
                  path_group = pydiffvg.ShapeGroup(
                      shape_ids=th.tensor([len(shapes) - 1]),
                      fill_color=None,
                      stroke_color=color)
                  shape_groups.append(path_group)
  
              scenes.append(
                  [shapes, shape_groups, (self.imsize, self.imsize)])
  
              # Rasterize
              out = render(self.imsize, self.imsize, shapes, shape_groups,
                           samples=self.samples)
  
              # Torch format, discard alpha, make gray
              out = out.permute(2, 0, 1).view(
                  4, self.imsize, self.imsize)[:3].mean(0, keepdim=True)
  
              outputs.append(out)
  
          output = th.stack(outputs).to(z.device)
  
          auxdata = {
              "points": all_points,
              "scenes": scenes,
          }
  
          # map to [-1, 1]
          output = output*2.0 - 1.0
  
          return output, auxdata
  
      def forward(self, im, label):
          if self.variational:
              mu, logvar = self.encode(im, label)
              z = self.reparameterize(mu, logvar)
          else:
              mu = self.encode(im, label)
              z = mu
              logvar = None
  
          if self.conditional:
              output, aux = self.decode(z, label=label)
          else:
              output, aux = self.decode(z)
  
          aux["logvar"] = logvar
          aux["mu"] = mu
  
          return output, aux
  
  
  class Dataset(th.utils.data.Dataset):
      def __init__(self, data_dir, imsize):
          super(Dataset, self).__init__()
          self.mnist = dset.MNIST(root=data_dir, download=True,
                                  transform=transforms.Compose([
                                      transforms.ToTensor(),
                                  ]))
  
      def __len__(self):
          return len(self.mnist)
  
      def __getitem__(self, idx):
          im, label = self.mnist[idx]
  
          # make sure data uses [0, 1] range
          im -= im.min()
          im /= im.max() + 1e-8
          im -= 0.5
          im /= 0.5
          return im, label
  
  
  def train(args):
      th.manual_seed(0)
      np.random.seed(0)
  
      pydiffvg.set_use_gpu(args.cuda)
  
      # Initialize datasets
      imsize = 28
      dataset = Dataset(args.data_dir, imsize)
      dataloader = DataLoader(dataset, batch_size=args.bs,
                              num_workers=8, shuffle=True)
  
      if args.generator in ["vae", "ae"]:
          LOG.info("Vector config:\n  samples %d\n"
                   "  paths: %d\n  segments: %d\n"
                   "  zdim: %d\n"
                   "  conditional: %d\n"
                   "  fc: %d\n",
                   args.samples, args.paths, args.segments,
                   args.zdim, args.conditional, args.fc)
  
      model_params = dict(samples=args.samples, paths=args.paths,
                          segments=args.segments, conditional=args.conditional,
                          zdim=args.zdim, fc=args.fc)
  
      print(f"Chooose generator: {args.generator}")
      if args.generator == "vae":
          model = VectorMNISTVAE(variational=True, **model_params)
          chkpt = VAE_OUTPUT
          name = "mnist_vae"
      elif args.generator == "ae":
          model = VectorMNISTVAE(variational=False, **model_params)
          chkpt = AE_OUTPUT
          name = "mnist_ae"
      else:
          raise ValueError("unknown generator")
  
      if args.conditional:
          name += "_conditional"
          chkpt += "_conditional"
  
      if args.fc:
          name += "_fc"
          chkpt += "_fc"
  
      # Resume from checkpoint, if any
      checkpointer = ttools.Checkpointer(
          chkpt, model, meta=model_params, prefix="g_")
      extras, meta = checkpointer.load_latest()
  
      if meta is not None and meta != model_params:
          LOG.info(f"Checkpoint's metaparams differ from CLI, "
                   f"aborting: {meta} and {model_params}")
  
      # Hook interface
      if args.generator in ["vae", "ae"]:
          variational = args.generator == "vae"
          if variational:
              LOG.info("Using a VAE")
          else:
              LOG.info("Using an AE")
          interface = VAEInterface(model, lr=args.lr, cuda=args.cuda,
                                   variational=variational,
                                   w_kld=args.kld_weight)
  
      trainer = ttools.Trainer(interface)
  
      # Add callbacks
      keys = []
      if args.generator == "vae":
          keys = ["kld", "data_loss", "loss", "logvar"]
      elif args.generator == "ae":
          keys = ["data_loss", "loss"]
      trainer.add_callback(ttools.callbacks.ProgressBarCallback(
          keys=keys, val_keys=keys))
      # trainer.add_callback(ttools.callbacks.VisdomLoggingCallback(
      #     keys=keys, val_keys=keys, env=name, port=port))
      # trainer.add_callback(MNISTCallback(
      #     env=name, win="samples", port=port, frequency=args.freq))
      trainer.add_callback(ttools.callbacks.CheckpointingCallback(
          checkpointer, max_files=2, interval=600, max_epochs=50))
  
      # Start training
      trainer.train(dataloader, num_epochs=args.num_epochs)
  
  
  def generate_samples(args):
      if args.generator =="vae":
          chkpt = VAE_OUTPUT
      else:
          chkpt = AE_OUTPUT
      if args.conditional:
          chkpt += "_conditional"
      if args.fc:
          chkpt += "_fc"
  
      meta = ttools.Checkpointer.load_meta(chkpt, prefix="g_")
      if meta is None:
          LOG.info("No metadata in checkpoint (or no checkpoint), aborting.")
          return
  
      model = VectorMNISTVAE(**meta)
      checkpointer = ttools.Checkpointer(chkpt, model, prefix="g_")
      checkpointer.load_latest()
      model.eval()
  
      # Sample some latent vectors
      n = 8
      bs = n*n
      z = th.randn(bs, model.zdim)
  
      imsize = 28
      dataset = Dataset(args.data_dir, imsize)
      dataloader = DataLoader(dataset, batch_size=bs,
                              num_workers=1, shuffle=True)
  
      for batch in dataloader:
          ref, label = batch
          break
  
      autoencode = True
      if autoencode:
          LOG.info("Sampling with auto-encoder code")
          if not args.conditional:
              label = None
          mu, logvar = model.encode(ref, label)
          z = model.reparameterize(mu, logvar)
      else:
          label = None
          if args.conditional:
              label = th.clamp(th.rand(bs)*10, 0, 9).long()
              if args.digit is not None:
                  label[:] = args.digit
  
      with th.no_grad():
          images, aux = model.decode(z, label=label)
          scenes = aux["scenes"]
      images += 1.0
      images /= 2.0
  
      h = w = model.imsize
  
      images = images.view(n, n, h, w).permute(0, 2, 1, 3)
      images = images.contiguous().view(n*h, n*w)
      images = th.clamp(images, 0, 1).cpu().numpy()
      path = os.path.join(chkpt, "samples.png")
      print(f"writing sampled images to : {path}")
      pydiffvg.imwrite(images, path, gamma=2.2)
  
      if autoencode:
          ref += 1.0
          ref /= 2.0
          ref = ref.view(n, n, h, w).permute(0, 2, 1, 3)
          ref = ref.contiguous().view(n*h, n*w)
          ref = th.clamp(ref, 0, 1).cpu().numpy()
          path = os.path.join(chkpt, "ref.png")
          print(f"writing reference images to : {path}")
          pydiffvg.imwrite(ref, path, gamma=2.2)
  
      # merge scenes
      all_shapes = []
      all_shape_groups = []
      cur_id = 0
      for idx, s in enumerate(scenes):
          print(f"sampling scenes idx: {idx}")
          shapes, shape_groups, _ = s
          # width, height = sizes
  
          # Shift digit on canvas
          center_x = idx % n
          center_y = idx // n
          for shape in shapes:
              shape.points[:, 0] += center_x * model.imsize
              shape.points[:, 1] += center_y * model.imsize
              all_shapes.append(shape)
          for grp in shape_groups:
              grp.shape_ids[:] = cur_id
              cur_id += 1
              all_shape_groups.append(grp)
  
      LOG.info("Generated %d shapes", len(all_shapes))
  
      fname = os.path.join(chkpt, "digits.svg")
      print(f"writing svg images to : {fname}")
      pydiffvg.save_svg(fname, n*model.imsize, n*model.imsize, all_shapes,
                        all_shape_groups, use_gamma=False)
  
      LOG.info("Results saved to %s", chkpt)
  
  
  def interpolate(args):
      chkpt = VAE_OUTPUT
      if args.conditional:
          chkpt += "_conditional"
      if args.fc:
          chkpt += "_fc"
  
      meta = ttools.Checkpointer.load_meta(chkpt, prefix="g_")
      if meta is None:
          LOG.info("No metadata in checkpoint (or no checkpoint), aborting.")
          return
  
      model = VectorMNISTVAE(imsize=128, **meta)
      checkpointer = ttools.Checkpointer(chkpt, model, prefix="g_")
      checkpointer.load_latest()
      model.eval()
  
      # Sample some latent vectors
      bs = 10
      z = th.randn(bs, model.zdim)
  
      label = None
      label = th.arange(0, 10)
  
      animation = []
      nframes = 60
      with th.no_grad():
          for idx, _z in enumerate(z):
              if idx == 0:  # skip first
                  continue
              _z0 = z[idx-1].unsqueeze(0).repeat(nframes, 1)
              _z = _z.unsqueeze(0).repeat(nframes, 1)
              if args.conditional:
                  _label = label[idx].unsqueeze(0).repeat(nframes)
              else:
                  _label = None
  
              # interp weights
              alpha = th.linspace(0, 1, nframes).view(nframes,  1)
              batch = alpha*_z + (1.0 - alpha)*_z0
              images, aux = model.decode(batch, label=_label)
              images += 1.0
              images /= 2.0
              animation.append(images)
  
      anim_dir = os.path.join(chkpt, "interpolation")
      os.makedirs(anim_dir, exist_ok=True)
      animation = th.cat(animation, 0)
      for idx, frame in enumerate(animation):
          frame = frame.squeeze()
          frame = th.clamp(frame, 0, 1).cpu().numpy()
          path = os.path.join(anim_dir, "frame%03d.png" % idx)
          pydiffvg.imwrite(frame, path, gamma=2.2)
  
      LOG.info("Results saved to %s", anim_dir)
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      subs = parser.add_subparsers()
  
      parser.add_argument("--cpu", dest="cuda", action="store_false",
                          default=th.cuda.is_available(),
                          help="if true, use CPU instead of GPU.")
      parser.add_argument("--no-conditional", dest="conditional",
                          action="store_false", default=True)
      parser.add_argument("--no-fc", dest="fc", action="store_false",
                          default=True)
      parser.add_argument("--data_dir", default="mnist",
                          help="path to download and store the data.")
  
      # -- Train ----------------------------------------------------------------
      parser_train = subs.add_parser("train")
      parser_train.add_argument("--generator", choices=["vae", "ae"],
                                default="vae",
                                help="choice of regular or variational "
                                "autoencoder")
      parser_train.add_argument("--freq", type=int, default=100,
                                help="number of steps between visualizations")
      parser_train.add_argument("--lr", type=float, default=5e-5,
                                help="learning rate")
      parser_train.add_argument("--kld_weight", type=float, default=1.0,
                                help="scalar weight for the KL divergence term.")
      parser_train.add_argument("--bs", type=int, default=64, help="batch size")
      parser_train.add_argument("--num_epochs", default=50, type=int,
                                help="max number of epochs")
      # Vector configs
      parser_train.add_argument("--paths", type=int, default=1,
                                help="number of vector paths to generate.")
      parser_train.add_argument("--segments", type=int, default=3,
                                help="number of segments per vector path")
      parser_train.add_argument("--samples", type=int, default=4,
                                help="number of samples in the MC rasterizer")
      parser_train.add_argument("--zdim", type=int, default=20,
                                help="dimension of the latent space")
      parser_train.set_defaults(func=train)
  
      # -- Eval -----------------------------------------------------------------
      parser_sample = subs.add_parser("sample")
      parser_sample.add_argument("--digit", type=int, choices=list(range(10)),
                                 help="digits to synthesize, "
                                 "random if not specified")
      parser_sample.add_argument("--generator", choices=["vae", "ae"],
                                default="vae",
                                help="choice of regular or variational "
                                     "autoencoder")
      parser_sample.set_defaults(func=generate_samples)
  
      parser_interpolate = subs.add_parser("interpolate")
      parser_interpolate.set_defaults(func=interpolate)
  
      args = parser.parse_args()
      print(f"args: {args}")
  
      ttools.set_logger(True)
      args.func(args)
  