more training

aaa_helpers.txt

@@ -4,13 +4,19 @@ nohup python auto_encoder_static.py --name="lhunor_static_iccv_1" > nohup/iccv/e
 nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_skirt" > nohup/iccv/encoders/static_iccv_1_skirt.out 2>&1 &
 nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_f" > nohup/iccv/encoders/static_iccv_1_cape_f.out 2>&1 &
 nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_cape_m" > nohup/iccv/encoders/static_iccv_1_cape_m.out 2>&1 &
+nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_10p" > nohup/iccv/encoders/static_iccv_1_10p.out 2>&1 &
+nohup python auto_encoder_static.py --name="lhunor_static_iccv_1_25p" > nohup/iccv/encoders/static_iccv_1_25p.out 2>&1 &
 nohup python auto_encoder_body.py --name="lhunor_body_iccv_1" > nohup/iccv/encoders/body_iccv_1.out 2>&1 &
 nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv/encoders/body_iccv_1_skirt.out 2>&1 &
 nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_cape_m" > nohup/iccv/encoders/body_iccv_1_cape_m.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_10p" > nohup/iccv/encoders/body_iccv_1_10p.out 2>&1 &
+nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_25p" > nohup/iccv/encoders/body_iccv_1_25p.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1" > nohup/iccv/encoders/curv_iccv_1.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_skirt" > nohup/iccv/encoders/curv_iccv_1_skirt.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_f" > nohup/iccv/encoders/curv_iccv_1_cape_f.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_m" > nohup/iccv/encoders/curv_iccv_1_cape_m.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_10p" > nohup/iccv/encoders/curv_iccv_1_10p.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_25p" > nohup/iccv/encoders/curv_iccv_1_25p.out 2>&1 &
 nohup python cvae.py --name="lhunor_cvae_iccv_gnn_1" > nohup/iccv/cvae_iccv_gnn_1.out 2>&1 &
 
 nohup python cvae.py --name="lhunor_abl_all" > nohup/iccv/ablation/abl_all_2.out 2>&1 &

auto_encoder_body.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_body_cape_m import config
+from configs.iccv.config_ae_body_25p import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -129,12 +129,13 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        data_valid = valid_iterator.get_next()
+        if step % 25 == 0:
-        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+            data_valid = valid_iterator.get_next()
-        valid_loss = valid_step(data_valid)
+            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-        valid_accumulator.update(valid_loss)
+            valid_loss = valid_step(data_valid)
+            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 10 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
             to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
             if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
                 train_figs = plot_everything(batch, 'train')

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_curv_cape_m import config
+from configs.iccv.config_ae_curv_25p import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -169,12 +169,13 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        data_valid = valid_iterator.get_next()
+        if step % 25 == 0:
-        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+            data_valid = valid_iterator.get_next()
-        valid_loss = valid_step(data_valid)
+            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-        valid_accumulator.update(valid_loss)
+            valid_loss = valid_step(data_valid)
+            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 10 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
             to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
             if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
                 train_figs = plot_everything(batch, 'train')

auto_encoder_static.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_static_cape_m import config
+from configs.iccv.config_ae_static_25p import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -130,12 +130,13 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        data_valid = valid_iterator.get_next()
+        if step % 25 == 0:
-        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+            data_valid = valid_iterator.get_next()
-        valid_loss = valid_step(data_valid)
+            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-        valid_accumulator.update(valid_loss)
+            valid_loss = valid_step(data_valid)
+            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 10 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
             to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
             if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
                 train_figs = plot_everything(batch, 'train')

configs/iccv/config_abl_pyr_32.py

@@ -1,6 +1,6 @@
 config = dict()
 
-config['gpu'] = '0'
+config['gpu'] = '3'
 config['name'] = 'iccv_pyr_32'
 config['notes'] = ''
 config['tags'] = ['pyr', 'iccv']

configs/iccv/config_ae_body_10p.py

@@ -0,0 +1,53 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_10p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '3'
+config['lr'] = 0.0001
+config['name'] = 'body_iccv_1_10p'
+config['notes'] = '' 
+config['tags'] = ['iccv', 'body', '10p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (3, 512, 256,),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                        }
+config['signature_static'] = { 
+                         'mesh_mask': (config['smpl_res'],),
+                        }

configs/iccv/config_ae_body_25p.py

@@ -0,0 +1,53 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_25p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 16 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '5'
+config['lr'] = 0.0001
+config['name'] = 'body_iccv_1_25p'
+config['notes'] = '' 
+config['tags'] = ['iccv', 'body', '25p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (3, 512, 256,),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                        }
+config['signature_static'] = { 
+                         'mesh_mask': (config['smpl_res'],),
+                        }

configs/iccv/config_ae_curv_10p.py

@@ -0,0 +1,52 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_10p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = True
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '2'
+config['lr'] = 0.001
+config['name'] = 'curv_iccv_1_10p'
+config['notes'] = '' 
+config['tags'] = ['iccv', 'curv', '10p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }

configs/iccv/config_ae_curv_25p.py

@@ -0,0 +1,52 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_25p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = True
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 40 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [16, 32, 64, 128, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '4'
+config['lr'] = 0.001
+config['name'] = 'curv_iccv_1_25p'
+config['notes'] = '' 
+config['tags'] = ['iccv', 'curv', '25p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }

configs/iccv/config_ae_static_10p.py

@@ -0,0 +1,52 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_10p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 8 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
+config['cond_ae']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '1'
+config['lr'] = 0.0005
+config['name'] = 'static_iccv_1_10p'
+config['notes'] = ' ' 
+config['tags'] = ['iccv', 'static', '10p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (), }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                         'mesh_static': (config['smpl_res'], 3,),
+                         'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }

configs/iccv/config_ae_static_25p.py

@@ -0,0 +1,52 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/train'
+config['valid_dir'] = '/home/workspace/local-data/valid'
+config['test_dir'] = '/home/workspace/data/dataset/test_ready'
+config['frames_file'] = 'frames_25p.txt'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+config['remove_global_orientation'] = True
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+config['latent_dim'] = 128
+
+config['bottleneck_scale'] = 2
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 8 * config['bottleneck_scale']
+config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
+config['cond_ae']['encoder_dims'] = [8, 16, 32, 64, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
+config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['activation'] = 'tanh'
+
+
+config['gpu'] = '6'
+config['lr'] = 0.0005
+config['name'] = 'static_iccv_1_25p'
+config['notes'] = ' ' 
+config['tags'] = ['iccv', 'static', '25p'] 
+
+config['cond_ae']['upscale_output'] = False
+config['pretrained_model'] = ''
+
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (), }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                         'mesh_static': (config['smpl_res'], 3,),
+                         'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_skin_curv import config
+from configs.iccv.config_abl_skin import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf

cvae_pyramid.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_skin import config
+from configs.iccv.config_abl_pyr_32 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -794,7 +794,7 @@ if __name__ == '__main__':
     # generate_results()
 
     ckpt = tf.train.Checkpoint(model=model)
-    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/post/' + config['name'], max_to_keep=20)
+    manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + config['name'], max_to_keep=20)
     train_accumulator = Accumulator()
     valid_accumulator = Accumulator()
 
@@ -854,7 +854,7 @@ if __name__ == '__main__':
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        if step % 50 == 0:
+        if step % 25 == 0:
             data_valid = valid_iterator.get_next()
             data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
             valid_loss = valid_step(data_valid)
@@ -882,7 +882,7 @@ if __name__ == '__main__':
 
         if step % 5000 == 0:
             manager.save(checkpoint_number=step)
-            
+
         print(step)
 
         step += 1