rerun curv encoder training with new curvs
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@@ -1,6 +1,7 @@
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nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
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nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2_2" > nohup/review/curv_1_orig_curv_100_10p_r2_2.out 2>&1 &
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one final convolutional layer for smoothing, on the final coordinates
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@@ -1,6 +1,6 @@
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import os
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import sys
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from configs.config_ae_curv_1_orig_curv_100_10p_r2 import config
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from configs.config_ae_curv_1_orig_curv_10_10p_r2_2 import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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import tensorflow as tf
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@@ -0,0 +1,30 @@
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from configs.config_base import config
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config['gpu'] = '1'
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config['lr'] = 0.001
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config['name'] = 'curv_1_orig_curv_100_10p_r2_2' #'ae_curv_9'
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config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
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| \
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'
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config['tags'] = ['review_2']
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config['remove_global_orientation'] = True
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config['dataset_in_memory'] = True
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config['cond_ae']['is_label'] = True
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config['is_skirt'] = True
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config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
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config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
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config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
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config['frames_file'] = 'frames_skirt_10p.txt'
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config['frames_file_valid'] = 'frames_skirt_10p.txt'
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config['curv_neighbourhood'] = 100
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config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
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'uv_curvature': (512, 256, 7),
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'mesh_curvature': (config['smpl_res'])
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}
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config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
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'uv_mask': (512, 256,),
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}
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@@ -0,0 +1,30 @@
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from configs.config_base import config
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config['gpu'] = '4'
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config['lr'] = 0.001
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config['name'] = 'curv_1_orig_curv_10_10p_r2_2' #'ae_curv_9'
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config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
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| \
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'
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config['tags'] = ['review_2']
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config['remove_global_orientation'] = True
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config['dataset_in_memory'] = True
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config['cond_ae']['is_label'] = True
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config['is_skirt'] = True
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config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
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config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
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config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
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config['frames_file'] = 'frames_skirt_10p.txt'
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config['frames_file_valid'] = 'frames_skirt_10p.txt'
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config['curv_neighbourhood'] = 10
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config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
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'uv_curvature': (512, 256, 7),
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'mesh_curvature': (config['smpl_res'])
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}
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config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
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'uv_mask': (512, 256,),
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}
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+3
-3
@@ -261,7 +261,7 @@ class DatasetFactory():
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if 'mesh_curvature' in signature.keys():
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mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
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mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
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item['mesh_curvature'] = mesh_curvature.astype(np.int32)
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@@ -271,8 +271,8 @@ class DatasetFactory():
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res_string = '_' + str(res)
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if 'uv_curvature' + res_string in signature.keys():
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print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
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uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'));
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# print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
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uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'));
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if res_string != '':
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uv_curvature = uv_curvature['uv_curvature']
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else:
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