save

evaluate_cape.py

@@ -1,5 +1,5 @@
 import os
-from configs.config_abl_2_pyr_64_128_256_512_cape_m import config
+from configs.iccv.config_abl_pyr_64_128_cape_f import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,7 +41,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.pyramid_model_optimized import Pyramid
+from models.pyramid_model_optimized import PyramidNew as Pyramid
 
 
 def init_wandb():
@@ -324,7 +324,7 @@ def plot_everything(batch: dict, type: str):
 
 
 def test():
-    load_model(model, 'checkpoints/' + config['name']) # , iteration='60000'
+    load_model(model, 'checkpoints/iccv/' + config['name']) # , iteration='60000'
     clothing_verts_idx = np.load(os.path.join('CAPE', 'data', 'clothing_verts_idx.npy'))
     cape_mask = np.zeros((6890), bool)
     cape_mask[clothing_verts_idx] = True
@@ -386,7 +386,7 @@ def test():
     print('min: ' + str(errors.min()))
     print('max: ' + str(errors.max()))
     print('median: ' + str(np.median(errors)))
-    np.save('temp/error_'+ config['name'] +'.npy', errors)
+    np.save('temp/error_iccv_'+ config['name'] +'.npy', errors)
 
     # histograms = dict()
     # for key in test_loss.keys():