#!/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() 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 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 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 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 ) 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=4, 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) 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"] port = 8080 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): 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(**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") 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") pydiffvg.imwrite(ref, path, gamma=2.2) # merge scenes all_shapes = [] all_shape_groups = [] cur_id = 0 for idx, s in enumerate(scenes): 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") 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=8, 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.set_defaults(func=generate_samples) parser_interpolate = subs.add_parser("interpolate") parser_interpolate.set_defaults(func=interpolate) args = parser.parse_args() ttools.set_logger(True) args.func(args)