text: # Copyright 2019-2020 Stanislav Pidhorskyi
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #  http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  import torch.utils.data
  from torchvision.utils import save_image
  import random
  from net import *
  from model import Model
  from launcher import run
  from checkpointer import Checkpointer
  from dlutils.pytorch import count_parameters
  from defaults import get_cfg_defaults
  import lreq
  import tqdm
  from PIL import Image
  
  
  lreq.use_implicit_lreq.set(True)
  
  
  def place(canvas, image, x, y):
      im_size = image.shape[2]
      if len(image.shape) == 4:
          image = image[0]
      canvas[:, y: y + im_size, x: x + im_size] = image * 0.5 + 0.5
  
  
  def save_sample(model, sample, i):
      os.makedirs('results', exist_ok=True)
  
      with torch.no_grad():
          model.eval()
          x_rec = model.generate(model.generator.layer_count - 1, 1, z=sample)
  
          def save_pic(x_rec):
              resultsample = x_rec * 0.5 + 0.5
              resultsample = resultsample.cpu()
              save_image(resultsample,
                         'sample_%i_lr.png' % i, nrow=16)
  
          save_pic(x_rec)
  
  
  def sample(cfg, logger):
      torch.cuda.set_device(0)
      model = Model(
          startf=cfg.MODEL.START_CHANNEL_COUNT,
          layer_count=cfg.MODEL.LAYER_COUNT,
          maxf=cfg.MODEL.MAX_CHANNEL_COUNT,
          latent_size=cfg.MODEL.LATENT_SPACE_SIZE,
          truncation_psi=cfg.MODEL.TRUNCATIOM_PSI,
          truncation_cutoff=cfg.MODEL.TRUNCATIOM_CUTOFF,
          mapping_layers=cfg.MODEL.MAPPING_LAYERS,
          channels=cfg.MODEL.CHANNELS,
          generator=cfg.MODEL.GENERATOR,
          encoder=cfg.MODEL.ENCODER)
      model.cuda(0)
      model.eval()
      model.requires_grad_(False)
  
      decoder = model.decoder
      encoder = model.encoder
      mapping_tl = model.mapping_tl
      mapping_fl = model.mapping_fl
      dlatent_avg = model.dlatent_avg
  
      logger.info("Trainable parameters generator:")
      count_parameters(decoder)
  
      logger.info("Trainable parameters discriminator:")
      count_parameters(encoder)
  
      arguments = dict()
      arguments["iteration"] = 0
  
      model_dict = {
          'discriminator_s': encoder,
          'generator_s': decoder,
          'mapping_tl_s': mapping_tl,
          'mapping_fl_s': mapping_fl,
          'dlatent_avg': dlatent_avg
      }
  
      checkpointer = Checkpointer(cfg,
                                  model_dict,
                                  {},
                                  logger=logger,
                                  save=False)
  
      extra_checkpoint_data = checkpointer.load()
  
      model.eval()
  
      layer_count = cfg.MODEL.LAYER_COUNT
  
      def encode(x):
          Z, _ = model.encode(x, layer_count - 1, 1)
          Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
          return Z
  
      def decode(x):
          layer_idx = torch.arange(2 * cfg.MODEL.LAYER_COUNT)[np.newaxis, :, np.newaxis]
          ones = torch.ones(layer_idx.shape, dtype=torch.float32)
          coefs = torch.where(layer_idx < model.truncation_cutoff, ones, ones)
          # x = torch.lerp(model.dlatent_avg.buff.data, x, coefs)
          return model.decoder(x, layer_count - 1, 1, noise=True)
  
      path = cfg.DATASET.SAMPLES_PATH
      im_size = 2 ** (cfg.MODEL.LAYER_COUNT + 1)
  
      paths = list(os.listdir(path))
  
      paths = sorted(paths)
      random.seed(1)
      random.shuffle(paths)
  
      def make(paths):
          canvas = []
          with torch.no_grad():
              for filename in paths:
                  img = np.asarray(Image.open(path + '/' + filename))
                  if img.shape[2] == 4:
                      img = img[:, :, :3]
                  im = img.transpose((2, 0, 1))
                  x = torch.tensor(np.asarray(im, dtype=np.float32), device='cpu', requires_grad=True).cuda() / 127.5 - 1.
                  if x.shape[0] == 4:
                      x = x[:3]
                  factor = x.shape[2] // im_size
                  if factor != 1:
                      x = torch.nn.functional.avg_pool2d(x[None, ...], factor, factor)[0]
                  assert x.shape[2] == im_size
                  latents = encode(x[None, ...].cuda())
                  f = decode(latents)
                  r = torch.cat([x[None, ...].detach().cpu(), f.detach().cpu()], dim=3)
                  canvas.append(r)
          return canvas
  
      def chunker_list(seq, n):
          return [seq[i * n:(i + 1) * n] for i in range((len(seq) + n - 1) // n)]
  
      paths = chunker_list(paths, 8 * 3)
  
      for i, chunk in enumerate(paths):
          canvas = make(chunk)
          canvas = torch.cat(canvas, dim=0)
  
          save_path = 'make_figures/output/%s/reconstructions_%d.png' % (cfg.NAME, i)
          os.makedirs(os.path.dirname(save_path), exist_ok=True)
          save_image(canvas * 0.5 + 0.5, save_path,
                     nrow=3,
                     pad_value=1.0)
  
  
  if __name__ == "__main__":
      gpu_count = 1
      run(sample, get_cfg_defaults(), description='ALAE-figure-reconstructions-paged', default_config='configs/ffhq.yaml',
          world_size=gpu_count, write_log=False)
  