backup

evaluate_ae_support.py

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+import os
+import sys
+from configs.config_abl_all import config
+
+os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
+import tensorflow as tf
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    try:
+        # Currently, memory growth needs to be the same across GPUs
+        for gpu in gpus:
+            tf.config.experimental.set_memory_growth(gpu, True)
+        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
+        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+    except RuntimeError as e:
+        # Memory growth must be set before GPUs have been initialized
+        print(e)
+
+import glob
+import time
+import argparse
+import wandb
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+from PIL import Image
+from postprocessing import fix_collision
+
+if config['is_skirt']:
+    from losses import edge_loss_skirt as edge_loss
+else:
+    from losses import edge_loss
+
+
+
+from losses import collision_ratio, kl_loss #, concept_loss
+# from losses import collision_penalty as collision_penalty
+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 CVAE
+from custom_utils.supersmpl_io import writeOBJ
+
+
+
+
+if __name__ == '__main__':
+    # init_wandb()
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('--name', type=str)
+    args = vars(parser.parse_args())
+
+    smpl = SMPL(use_one_hot=config['use_one_hot'])
+    skirt_modifier = '_skirt' if config['is_skirt'] else ''
+
+    folder = config['test_dir']
+
+
+    # str_split = config['name'].split('_')
+    # test_idx = str_split.index('test')
+    # checkpoint_name = '_'.join(str_split[:test_idx])
+    checkpoint_name = config['name']
+
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+
+
+    output_folder = '/home/workspace/data/results/support'
+
+
+    os.makedirs(output_folder + '/bodies', exist_ok=True)
+    os.makedirs(output_folder + '/faces', exist_ok=True)
+
+    config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                            'mesh_body_posed': (14475, 3,),
+                            }
+    config['signature_static'] = { 
+                                'mesh_mask': (14475,),
+                                }
+
+    factory = DatasetFactory()
+    dataset_test = factory.get_dataset(folder=config['test_dir'], 
+                                       batch_size=config['batch_size'], 
+                                       signature=config['signature_dynamic'], 
+                                       resolutions=[config['resolution']], 
+                                       in_memory=False, 
+                                       do_repeat=False,
+                                       use_one_hot=config['use_one_hot'],
+                                       use_no_orient=config['remove_global_orientation'])
+    static_dataset_test = StaticDataset(folder=config['test_dir'], 
+                                        signature=config['signature_static'], 
+                                        resolutions=[config['resolution']], 
+                                        in_memory=False)
+   
+    for batch in tqdm.tqdm(dataset_test):
+        batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
+
+        # mask = batch['mesh_mask'].numpy().astype(bool)
+        body = batch['mesh_body_posed'].numpy()
+        # _, faces = maskFacesBatch(np.zeros((body.shape[0],14475,3)), F, mask)
+
+        for i in range(body.shape[0]):
+            id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
+            writeOBJ(os.path.join(output_folder, 'bodies', id + '_' + frame_nr + '_body_no_orient.obj'), body[i], F)
+            # np.save(os.path.join(output_folder, 'faces', id + '_' + frame_nr + '.npy'), faces[i].astype(np.int32))
+
+