generate overfit jumpsuit results

configs/config_abl_overfit_jumpsuit_2.py

@@ -20,7 +20,7 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
 assert ablation_count == 1 or ablation_count == 0
 
 config['gpu'] = '3'
-config['name'] = 'abl_overfit_jumpsuit_2_r4'
+config['name'] = 'abl_overfit_jumpsuit_2'
 config['notes'] = 'remove other conditions from input as well'
 
 config['avg_garment_path'] = 'custom_data/train_jumpsuit_30k_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv

curvature_analysis.py

@@ -205,19 +205,25 @@ def visualize_curvature_labels(folder, frame, neighbourhoods):
 
 if __name__ == '__main__':
 
-    is_skirt = True
+    is_skirt = False
     skirt_modifier = '_skirt' if is_skirt else ''
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     # k_neighbours(k=25)
 
-    folder = '/home/workspace/local-data/valid_skirt'
+    folder = '/home/workspace/local-data/train'
+    results_folder = ''
+
+    frames_file = 'frames_jumpsuit_30k.txt'
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines
 
-    nr_of_labels = 6
-    neighbourhood = 50
     temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
     if not is_skirt:
         dist = np.load('custom_data/dijkstra.npy')
     else:
         dist = np.load('custom_data/dijkstra_skirt.npy')
 
-    visualize_curvature_labels(folder, '02572_1', neighbourhoods=[10, 25, 50, 100])
+    visualize_curvature_labels(folder, '00005_1', neighbourhoods=[10, 25, 50, 100])

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_overfit_jumpsuit_2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
@@ -104,7 +104,7 @@ if __name__ == '__main__':
 
     iteration = ''
 
-    load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
+    load_model(model, 'checkpoints/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')
 
     output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration