#!/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 ttools.modules import networks 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, # width canvas_height, # height samples, # num_samples_x samples, # num_samples_y 0, # seed None, # background *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 return ret # def init_validation(self): # return {"count": 0, "loss": 0} # # def update_validation(self, batch, fwd, running_data): # with th.no_grad(): # ref = batch[1].to(self.device) # loss = th.nn.functional.mse_loss(fwd, ref) # n = ref.shape[0] # # return { # "loss": running_data["loss"] + loss.item()*n, # "count": running_data["count"] + n # } # # def finalize_validation(self, running_data): # return { # "loss": running_data["loss"] / running_data["count"] # } class MNISTGenerator(th.nn.Module): def __init__(self, imsize=28): super(MNISTGenerator, self).__init__() if imsize != 28: raise NotImplementedError() mul = 2 self.convnet = th.nn.Sequential( # 4x4 th.nn.ConvTranspose2d(16 + 1, mul*32, 4, padding=1, stride=2), th.nn.LeakyReLU(inplace=True), th.nn.Conv2d(mul*32, mul*32, 3, padding=1), th.nn.LeakyReLU(inplace=True), # 8x8 th.nn.ConvTranspose2d(mul*32, mul*64, 4, padding=1, stride=2), th.nn.LeakyReLU(inplace=True), th.nn.Conv2d(mul*64, mul*64, 3, padding=1), th.nn.LeakyReLU(inplace=True), # 16x16 th.nn.ConvTranspose2d(mul*64, mul*128, 4, padding=1, stride=2), th.nn.LeakyReLU(inplace=True), th.nn.Conv2d(mul*128, mul*128, 3, padding=1), th.nn.LeakyReLU(inplace=True), # 32x32 th.nn.Conv2d(mul*128, mul*128, 3, padding=1), th.nn.LeakyReLU(inplace=True), th.nn.Conv2d(mul*128, mul*128, 3, padding=1), th.nn.LeakyReLU(inplace=True), th.nn.Conv2d(mul*128, 1, 1), # th.nn.Tanh(), ) def forward(self, im, label): bs = im.shape[0] # sample a hidden vector z = th.randn(bs, 16, 4, 4).to(im.device) # make the model conditional in_ = th.cat([z, label.float().view(bs, 1, 1, 1).repeat(1, 1, 4, 4)], 1) out = self.convnet(in_) return out, None 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): super(VectorMNISTVAE, self).__init__() # if imsize != 28: # raise NotImplementedError() self.samples = samples self.imsize = imsize self.paths = paths self.segments = segments self.zdim = zdim self.conditional = conditional self.variational = variational 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.Tanh() ) self.alpha_predictor = th.nn.Sequential( th.nn.Linear(nc, self.paths), th.nn.Tanh() ) self._reset_weights() def _reset_weights(self): for n, p in self.encoder.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.kaiming_normal_(p.data, nonlinearity="leaky_relu") th.nn.init.kaiming_normal_(self.mu_predictor.weight.data, nonlinearity="linear") if self.variational: th.nn.init.kaiming_normal_(self.logvar_predictor.weight.data, nonlinearity="linear") for n, p in self.decoder.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.kaiming_normal_(p, nonlinearity="linear") if not self.raster: for n, p in self.point_predictor.named_parameters(): pass # if 'bias' in n: # p.data.zero_() # if 'weight' in n: # th.nn.init.orthogonal_(p) for n, p in self.width_predictor.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.orthogonal_(p) for n, p in self.alpha_predictor.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.orthogonal_(p) 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: assert self.conditional, "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) * 1.5 + .25 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): bs = im.shape[0] 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 VectorMNISTGenerator(th.nn.Module): def __init__(self, imsize=28, paths=4, segments=5, samples=2, conditional=False, zdim=20, fc=False): super(VectorMNISTGenerator, self).__init__() if imsize != 28: raise NotImplementedError() self.samples = samples self.imsize = imsize self.paths = paths self.segments = segments self.conditional = conditional self.zdim = zdim self.fc = fc ncond = 0 if self.conditional: # one hot encoded input for conditional model ncond = 10 nc = 1024 self.trunk = th.nn.Sequential( th.nn.Linear(zdim + ncond, nc), # noise + one-hot th.nn.SELU(inplace=True), # th.nn.Linear(nc, nc), # th.nn.SELU(inplace=True), th.nn.Linear(nc, nc), th.nn.SELU(inplace=True), # th.nn.Linear(nc, nc), # th.nn.SELU(inplace=True), ) # 4 points bezier so 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.Linear(nc, 2*self.paths*(self.segments*1+1)), th.nn.Tanh() # bound spatial extent ) self.width_predictor = th.nn.Sequential( th.nn.Linear(nc, self.paths), th.nn.Tanh() ) self.alpha_predictor = th.nn.Sequential( th.nn.Linear(nc, self.paths), th.nn.Tanh() ) # self.postprocessor = th.nn.Sequential( # th.nn.Conv2d(1, 32, 3, padding=1), # th.nn.LeakyReLU(inplace=True), # th.nn.Conv2d(32, 1, 1), # ) self._reset_weights() def _reset_weights(self): for n, p in self.trunk.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.kaiming_normal_(p) p.data.mul_(0.7) # th.nn.init.kaiming_normal_(p, nonlinearity="leaky_relu") for n, p in self.point_predictor.named_parameters(): # if 'bias' in n: # p.data.zero_() if 'weight' in n: th.nn.init.orthogonal_(p) # th.nn.init.kaiming_normal_(p, nonlinearity="tanh") for n, p in self.width_predictor.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: # th.nn.init.orthogonal_(p) th.nn.init.kaiming_normal_(p, nonlinearity="tanh") for n, p in self.alpha_predictor.named_parameters(): if 'bias' in n: p.data.zero_() elif 'weight' in n: th.nn.init.kaiming_normal_(p, nonlinearity="tanh") # th.nn.init.orthogonal_(p) def sample_z(self, bs): return th.randn(bs, self.zdim) def gen_sample(self, z, label=None): bs = z.shape[0] if self.conditional: if label is None: raise ValueError("GAN is conditional, please provide a label") # One-hot encoding of the image label label_onehot = _onehot(label) # get some embedding in_ = th.cat([z, label_onehot.float()], 1) else: in_ = z feats = self.trunk(in_) all_points = self.point_predictor(feats) all_points = all_points.view(bs, self.paths, -1, 2) if False: all_alphas = th.ones(bs, self.paths) else: all_alphas = self.alpha_predictor(feats) # stroke size between 0.5 and 3.5 px if False: all_widths = th.ones(bs, self.paths) * 1 else: all_widths = self.width_predictor(feats) all_widths = 1.5*all_widths + 0.5 all_points = all_points*(self.imsize//2) + self.imsize//2 # Process the batch sequentially outputs = [] 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() # num_ctrl_pts = th.zeros(self.segments, dtype=th.int32)+0 num_ctrl_pts = th.zeros(self.segments, dtype=th.int32)+2 width = all_widths[k, p].cpu() alpha = all_alphas[k, p].cpu() color = th.cat([th.ones(3), alpha.view(1,)]) 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) # 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) aux_data = { "points": all_points, "raw_vector": output, } # output = self.postprocessor(output) # map to [-1, 1] output = output*2.0 - 1.0 return output, aux_data def forward(self, im, label): bs = label.shape[0] # sample a hidden vector (same dim as the raster version) z = self.sample_z(bs).to(im.device) if args.conditional: return self.gen_sample(z, label=label) else: return self.gen_sample(z) class Discriminator(th.nn.Module): def __init__(self, conditional=False, fc=False): super(Discriminator, self).__init__() self.conditional = conditional ncond = 0 if self.conditional: # one hot encoded input for conditional model ncond = 10 sn = th.nn.utils.spectral_norm # sn = lambda x: x self.fc = fc mult = 2 if self.fc: self.net = th.nn.Sequential( Flatten(), th.nn.Linear(28*28 + ncond, mult*256), th.nn.LeakyReLU(0.2, inplace=True), # th.nn.Linear(mult*256, mult*256, 4), # th.nn.LeakyReLU(0.2, inplace=True), # th.nn.Dropout(0.5), th.nn.Linear(mult*256, mult*256, 4), th.nn.LeakyReLU(0.2, inplace=True), th.nn.Linear(mult*256*1*1, 1), ) else: self.net = th.nn.Sequential( th.nn.Conv2d(1 + ncond, mult*64, 4, padding=0, stride=2), th.nn.LeakyReLU(0.2, inplace=True), # 16x16 sn(th.nn.Conv2d(mult*64, mult*128, 4, padding=0, stride=2)), th.nn.LeakyReLU(0.2, inplace=True), # 8x8 sn(th.nn.Conv2d(mult*128, mult*256, 4, padding=0, stride=2)), th.nn.LeakyReLU(0.2, inplace=True), # 4x4 Flatten(), th.nn.Linear(mult*256*1*1, 1), ) self._reset_weights() def _reset_weights(self): for n, p in self.net.named_parameters(): if 'bias' in n: p.data.zero_() if 'weight' in n: th.nn.init.kaiming_normal_(p, nonlinearity="leaky_relu") def forward(self, x): out = self.net(x) return out 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("Checkpoint's metaparams differ from CLI, aborting: %s and %s", meta, 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 = ["loss_g", "loss_d"] if args.generator == "vae": keys = ["kld", "data_loss", "loss"] elif args.generator == "ae": keys = ["data_loss", "loss"] port = 8097 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("--conditional", action="store_true", default=False) parser.add_argument("--fc", action="store_true", default=False) 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=1e-4, 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", type=int, help="max number of epochs") # Vector configs parser_train.add_argument("--paths", type=int, default=1, help="number of unique 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=2, 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)