update

auto_encoder_curv.py

@@ -5,33 +5,25 @@ import wandb
 import random
 import numpy as np
 import tensorflow as tf
-os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
 
 from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
-from tensorboardX import SummaryWriter
 import plotly.graph_objects as go
 import plotly.express as ex
 
-from utils import str_to_bool
-from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
-from load_models import load_generator, load_discriminator
-from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss
-# from losses import collision_penalty as collision_penalty
+from tf_utils import check_tf_version, allow_memory_growth
 from losses import collision_loss as collision_penalty
 from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
-from stylegan2_ae.generator import Generator
-from custom_utils.graph_utils import Reconstructor_V4_Decoder as GNNDecoder
-from custom_utils.graph_utils import Reconstructor_v2 as SimpleReconstructor
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict
 from custom_utils.smpl import SMPL
 
-name='ae_curvature_7'
+name='ae_curvature_7_2'
 testing = False
 def init_wandb():
     if not testing:
-        run = wandb.init(project='vae', name=name, notes='convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments')
+        run = wandb.init(project='vae', name=name, notes='convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. same as 7')
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('dataset.py')
@@ -359,9 +351,11 @@ if __name__ == '__main__':
 
     dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100',
                                   batch_size=8,
-                                  signature=signature_dynamic)
+                                  signature=signature_dynamic,
+                                  use_filled=False)
     static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100',
-                                   signature=signature_static)
+                                   signature=signature_static,
+                                   use_filled=False)
     dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid',
                                   batch_size=8,
                                   signature=signature_dynamic)