---> BasePixrayPredictor Predict Using seed: 3903845079 Running pixeldrawer with 80x45 grid 0%| | 0.00/244M [00:003.227) 0it [00:00, ?it/s]/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. warnings.warn( 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 10, loss: 2.88, losses: 0.916, 0.0816, 0.799, 0.061, 0.798, 0.0613, 0.0905, 0.0707 (-0=>2.878) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.73, losses: 0.874, 0.0823, 0.753, 0.0629, 0.74, 0.0628, 0.0843, 0.0704 (-0=>2.73) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 30, loss: 2.62, losses: 0.848, 0.0824, 0.724, 0.0635, 0.685, 0.0644, 0.0787, 0.0711 (-0=>2.617) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.53, losses: 0.818, 0.0824, 0.695, 0.0662, 0.66, 0.0663, 0.0731, 0.0716 (-3=>2.533) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 50, loss: 2.53, losses: 0.815, 0.0837, 0.7, 0.0643, 0.66, 0.0655, 0.0681, 0.0704 (-2=>2.493) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.46, losses: 0.786, 0.083, 0.676, 0.0673, 0.648, 0.0676, 0.0642, 0.0714 (-1=>2.428) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 70, loss: 2.46, losses: 0.781, 0.0844, 0.682, 0.0658, 0.648, 0.0672, 0.0608, 0.0695 (-1=>2.412) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.46, losses: 0.788, 0.0835, 0.682, 0.0654, 0.645, 0.0668, 0.0579, 0.0716 (-2=>2.376) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 90, loss: 2.41, losses: 0.771, 0.0833, 0.665, 0.067, 0.631, 0.0677, 0.0552, 0.0682 (-3=>2.364) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.35, losses: 0.746, 0.0847, 0.65, 0.067, 0.619, 0.0692, 0.053, 0.0624 (-0=>2.352) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 110, loss: 2.35, losses: 0.748, 0.0844, 0.651, 0.067, 0.616, 0.0691, 0.051, 0.0596 (-1=>2.331) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.36, losses: 0.756, 0.0844, 0.651, 0.0662, 0.618, 0.069, 0.0491, 0.0617 (-11=>2.331) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 130, loss: 2.36, losses: 0.756, 0.0839, 0.654, 0.0666, 0.622, 0.0688, 0.0472, 0.061 (-6=>2.31) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.36, losses: 0.753, 0.0854, 0.652, 0.0665, 0.623, 0.0677, 0.0458, 0.0638 (-6=>2.305) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 150, loss: 2.33, losses: 0.741, 0.0847, 0.646, 0.0665, 0.614, 0.0693, 0.0447, 0.0621 (-5=>2.297) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.31, losses: 0.734, 0.0842, 0.641, 0.0675, 0.609, 0.0699, 0.0437, 0.061 (-15=>2.297) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 170, loss: 2.3, losses: 0.736, 0.0851, 0.633, 0.0673, 0.606, 0.0703, 0.0428, 0.0597 (-3=>2.289) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.31, losses: 0.741, 0.0849, 0.637, 0.0668, 0.61, 0.0689, 0.0418, 0.0622 (-5=>2.28) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 190, loss: 2.32, losses: 0.745, 0.0844, 0.642, 0.0659, 0.613, 0.0683, 0.0409, 0.0629 (-15=>2.28) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.26, losses: 0.716, 0.0857, 0.624, 0.0677, 0.599, 0.0703, 0.0401, 0.0618 (-0=>2.265) 0it [00:01, ?it/s] 0it [00:17, ?it/s] 0it [00:00, ?it/s] iter: 210, loss: 2.33, losses: 0.746, 0.085, 0.646, 0.0659, 0.616, 0.0683, 0.0392, 0.0641 (-1=>2.255) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.27, losses: 0.724, 0.0844, 0.628, 0.0674, 0.601, 0.0704, 0.0386, 0.0578 (-5=>2.252) Dropping learning rate 0it [00:01, ?it/s] 0it [00:17, ?it/s] 0it [00:00, ?it/s] iter: 230, loss: 2.3, losses: 0.739, 0.085, 0.634, 0.0671, 0.606, 0.0693, 0.0384, 0.0602 (-1=>2.264) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.26, losses: 0.716, 0.0849, 0.627, 0.0678, 0.599, 0.07, 0.0383, 0.0565 (-5=>2.25) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 250, loss: 2.25, losses: 0.718, 0.0857, 0.619, 0.0679, 0.592, 0.0703, 0.0382, 0.056 (-0=>2.247) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.27, losses: 0.719, 0.0846, 0.628, 0.067, 0.6, 0.0704, 0.0382, 0.0594 (-5=>2.244) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 270, loss: 2.27, losses: 0.724, 0.0859, 0.63, 0.0669, 0.598, 0.0703, 0.0381, 0.0555 (-1=>2.241) 0it [00:01, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.26, losses: 0.72, 0.0853, 0.625, 0.0674, 0.597, 0.0703, 0.0381, 0.0581 (-11=>2.241) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 290, loss: 2.25, losses: 0.716, 0.0855, 0.621, 0.0681, 0.593, 0.0701, 0.0381, 0.0574 (-21=>2.241) 0it [00:00, ?it/s] 0it [00:16, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-31=>2.241) 0it [00:00, ?it/s] 0it [00:00, ?it/s]

Examples

Run time and cost