barely working decoder

aaa_helpers.txt

@@ -1,15 +1,16 @@
-nohup python cvae.py --name="lhunor_weights_test_6" > nohup/iccv/weights_test_6.out 2>&1 &
-nohup python evaluate_ae.py --name iccv_weights_test_6 > nohup/iccv/evaluate_iccv_weights_test_6.out 2>&1 &
-nohup python calculate_error.py --name iccv_weights_test_6 > nohup/iccv/error_iccv_weights_test_6.out 2>&1 &
-nohup python auto_encoder_static.py --name="lhunor_ae_static_iccv_resnet_2" > nohup/iccv/encoders/ae_static_iccv_resnet_2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_iccv_resnet_2" > nohup/iccv/encoders/ae_curv_iccv_resnet_2.out 2>&1 &
-nohup python auto_encoder_body.py --name="lhunor_ae_body_iccv_resnet_2" > nohup/iccv/encoders/ae_body_iccv_resnet_2.out 2>&1 &
-
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_res_skirt" > nohup/iccv/pyr/iccv_pyr_32_res_skirt.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_iccv_pyr_32_res > nohup/iccv/evaluate/iccv_pyr_32_res.out 2>&1 &
-nohup python evaluate_ae_pyramid.py --name evaluate_iccv_pyr_32_res_skirt > nohup/iccv/evaluate/iccv_pyr_32_res_skirt.out 2>&1 &
-
 one final convolutional layer for smoothing, on the final coordinates
+nohup python calculate_error.py --name error_al_all_150k > nohup/error/post_al_all_150k.out 2>&1 &
+nohup python auto_encoder_static.py --name="lhunor_static_iccv_1" > nohup/iccv/encoders/static_iccv_1.out 2>&1 &
+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_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_skirt" > nohup/iccv/encoders/body_iccv_1_skirt.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 cvae.py --name="lhunor_cvae_iccv_gnn_decoder_1" > nohup/iccv/cvae_iccv_gnn_decoder_1.out 2>&1 &
 
 skirts!!!!
 pyramid next week
@@ -80,7 +81,6 @@ nohup python cvae.py --name="lhunor_abl_skin_var_4" > nohup/ablation/abl_skin_va
 nohup python cvae_curv.py --name="lhunor_cvae_curv" > nohup/ablation/cvae_curv.out 2>&1 &
 nohup python cvae_curv.py --name="lhunor_cvae_curv_2" > nohup/ablation/cvae_curv_2.out 2>&1 &
 
-nohup python cvae_pyramid.py --name="lhunor_iccv_pyr_32_res" > nohup/iccv/pyr/iccv_pyr_32_res.out 2>&1 &
 
 abl_2_pyr_32_64_128
 abl_2_pyr_64_cape_f_03223_SS
@@ -143,7 +143,7 @@ nohup python evaluate_ae_pyramid.py --name evaluate_ae_abl_pyr_32_skirt > nohup/
 nohup python evaluate_ae.py --name abl_norm_skirt_70k > nohup/evaluate/abl_norm_skirt_70k.out 2>&1 &
 nohup python calculate_error.py --name error_abl_2_pyr_32_64_128_skirt > nohup/error_ae_abl_skin_skirt_70k.out 2>&1 &
 
-nohup python evaluate_ae_pyramid.py --name pyr_32_res_skirt > nohup/evaluate/pyr_32_res_skirt.out 2>&1 &
+nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_100k > nohup/evaluate/abl_2_pyr_32_64_100k.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_80k > nohup/evaluate/abl_2_pyr_32_64_128_80k.out 2>&1 &
 nohup python evaluate_ae_pyramid.py --name abl_2_pyr_32_64_128_256_512_skirt_50k > nohup/evaluate/abl_2_pyr_32_64_128_256_512_skirt_50k.out 2>&1 &
 

auto_encoder_body.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_body import config
+from configs.iccv.config_ae_body_cape_f import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -63,10 +63,9 @@ def train_step(batch):
 
         e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
         l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
-        # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
+        ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
 
-        loss = l1_loss
+        loss = 1e3 * (1 - ms_ssim)
 
     metric = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((mesh_orig - mesh_pred) ** 2, axis=-1)))
     gradients = tape.gradient(loss, model.trainable_variables)
@@ -84,10 +83,9 @@ def valid_step(batch):
 
     e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 3638 if config['is_cape'] else 14475))))
     l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
-    # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
+    ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-    ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=3))
 
-    loss = l1_loss
+    loss = 1e3 * (1 - ms_ssim)
 
     metric = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((mesh_orig - mesh_pred) ** 2, axis=-1)))
     return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
@@ -131,13 +129,12 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        if step % 50 == 0:
+        data_valid = valid_iterator.get_next()
-            data_valid = valid_iterator.get_next()
+        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+        valid_loss = valid_step(data_valid)
-            valid_loss = valid_step(data_valid)
+        valid_accumulator.update(valid_loss)
-            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 10 == 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')
@@ -145,11 +142,12 @@ def train():
                 to_log = { **to_log, **train_figs, **valid_figs }
             if step == 0:
                 to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+
                 
             wandb.log(to_log, step=step)
             train_accumulator.reset()
-            valid_accumulator.reset()
+            valid_accumulator.reset() 
-
+            
         if step % 10000 == 0:
             manager_enc.save(checkpoint_number=step)
             manager_dec.save(checkpoint_number=step)
@@ -161,7 +159,6 @@ def train():
 
 if __name__ == '__main__':
     init_wandb()
-    allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -207,15 +204,15 @@ if __name__ == '__main__':
 
     model = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
+    train_accumulator = Accumulator()
+    valid_accumulator = Accumulator()
 
     # load_model(model.encoder, 'checkpoints/body_1_skirt_2_enc')
     # load_model(model.decoder, 'checkpoints/body_1_skirt_2_dec')
 
     ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
     ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
-    train_accumulator = Accumulator()
-    valid_accumulator = Accumulator()
 
     train()

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_curv import config
+from configs.iccv.config_ae_curv_cape_f import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -169,13 +169,12 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        if step % 50 == 0:
+        data_valid = valid_iterator.get_next()
-            data_valid = valid_iterator.get_next()
+        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+        valid_loss = valid_step(data_valid)
-            valid_loss = valid_step(data_valid)
+        valid_accumulator.update(valid_loss)
-            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 10 == 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')
@@ -183,6 +182,7 @@ def train():
                 to_log = { **to_log, **train_figs, **valid_figs }
             if step == 0:
                 to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+
                 
             wandb.log(to_log, step=step)
             train_accumulator.reset()
@@ -214,7 +214,7 @@ def test():
 
 if __name__ == '__main__':
     init_wandb()
-    # allow_memory_growth()
+    allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -239,7 +239,7 @@ if __name__ == '__main__':
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
                                          in_memory=True, 
-                                        is_cape=config['is_cape'])
+                                         is_cape=config['is_cape'])
 
     dataset_valid = factory.get_dataset(folder=config['valid_dir'],
                                         batch_size=config['batch_size'], 
@@ -253,18 +253,18 @@ if __name__ == '__main__':
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
                                          in_memory=True, 
-                                        is_cape=config['is_cape'])
+                                         is_cape=config['is_cape'])
 
     model = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
-
-    ckpt_enc = tf.train.Checkpoint(model=model.encoder)
-    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/post/' + config['name'] + '_enc', max_to_keep=20)
-    ckpt_dec = tf.train.Checkpoint(model=model.decoder)
-    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/post/' + config['name'] + '_dec', max_to_keep=20)
     train_accumulator = Accumulator()
     valid_accumulator = Accumulator()
 
+    ckpt_enc = tf.train.Checkpoint(model=model.encoder)
+    manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
+    ckpt_dec = tf.train.Checkpoint(model=model.decoder)
+    manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
+
     if testing:
         test()
     else:

auto_encoder_static.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_ae_static import config
+from configs.iccv.config_ae_static_cape_m import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -43,7 +43,6 @@ def init_wandb():
     artifact.add_file('auto_encoder_static.py')
     artifact.add_file('losses.py')
     artifact.add_file('models/base.py')
-    artifact.add_file('models/resnet.py')
     artifact.add_file('models/cond_encoder.py')
     artifact.add_file('dataset.py')
     artifact.add_file('convnext/models/convnext_tf.py')
@@ -68,14 +67,11 @@ def train_step(batch):
 
         e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
         l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
-        # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
+        ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_static'] + 1, max_val=3))
+        loss = 1e3 * (1 - ms_ssim)
-        # loss = 1e3 * (1 - ms_ssim)
-        loss = l1_loss
 
     metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
     gradients = tape.gradient(loss, model.trainable_variables)
-    gradients = [(tf.clip_by_norm(grad, clip_norm=2.0)) for grad in gradients]
     optimizer.apply_gradients(zip(gradients, model.trainable_variables))
     return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
         
@@ -90,10 +86,8 @@ def valid_step(batch):
 
     e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
     l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
-    # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=3, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
+    ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
-    ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_static'] + 1, max_val=3))
+    loss = 1e3 * (1 - ms_ssim)
-    # loss = 1e3 * (1 - ms_ssim) 
-    loss = l1_loss
 
     metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
     return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
@@ -136,13 +130,12 @@ def train():
         train_loss = train_step(batch)
         train_accumulator.update(train_loss)
 
-        if step % 50 == 0:
+        data_valid = valid_iterator.get_next()
-            data_valid = valid_iterator.get_next()
+        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
-            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
+        valid_loss = valid_step(data_valid)
-            valid_loss = valid_step(data_valid)
+        valid_accumulator.update(valid_loss)
-            valid_accumulator.update(valid_loss)
 
-        if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
+        if step % 1000 == 0 or (step < 1000 and step % 10 == 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')
@@ -150,6 +143,7 @@ def train():
                 to_log = { **to_log, **train_figs, **valid_figs }
             if step == 0:
                 to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
+
                 
             wandb.log(to_log, step=step)
             train_accumulator.reset()
@@ -159,7 +153,7 @@ def train():
             manager_enc.save(checkpoint_number=step)
             manager_dec.save(checkpoint_number=step)
 
-        print(step)
+            print(step)
 
         step += 1
 
@@ -197,10 +191,10 @@ def test():
 
     print()
 
-  
 
 if __name__ == '__main__':
     init_wandb()
+    # allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -244,6 +238,7 @@ if __name__ == '__main__':
 
     model : AutoEncoder = AutoEncoder(config['cond_ae'])
     optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
+
     train_accumulator = Accumulator()
     valid_accumulator = Accumulator()
 

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.iccv.config_abl_pyr_32_res import config
+from configs.config_abl_all_post import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf
@@ -59,21 +59,21 @@ if __name__ == '__main__':
     folder = config['test_dir']
 
     checkpoint_name = config['name']
-    iteration = '195000'
+    iteration = ''
 
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
 
 
-    output_folder = '/home/workspace/data/results/iccv/' + checkpoint_name + '-' + iteration
+    output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration
 
 
-    # faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
+    faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
-    # ids = os.listdir(faces_folder)
+    ids = os.listdir(faces_folder)
-    # ids = [id.split('.')[0] for id in ids]
+    ids = [id.split('.')[0] for id in ids]
-    # faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
+    faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
 
-    masks_folder = '/home/workspace/local-data/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
+    masks_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/mesh_mask'
     ids = os.listdir(masks_folder)
     ids = [id.split('.')[0] for id in ids]
     masks = {id : np.load(masks_folder + '/' + id + '.npy') for id in ids }
@@ -86,7 +86,7 @@ if __name__ == '__main__':
     else:
         gt_name = 'mesh_posed'
 
-    orig_folder = '/home/workspace/local-data/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
+    orig_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
 
     errors = dict()
     if config['is_skirt']:
@@ -107,7 +107,7 @@ if __name__ == '__main__':
     for key in per_garment_errors.keys():
         print(key + ': ' + str(np.mean(per_garment_errors[key])))
 
-    with open('/home/workspace/data/results/errors/iccv/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
+    with open('/home/workspace/data/results/errors/post/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
         file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
 
         for key in sorted(errors.keys()):

compare_models_saved.py

@@ -1,188 +0,0 @@
-import os
-import sys
-from configs.config_abl_all_post import config as config_a
-
-os.environ["CUDA_VISIBLE_DEVICES"] = '5'
-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_a['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
-from plotly.subplots import make_subplots
-
-
-# uv2mesh = UV2Mesh(res=256, is_skirt=config_a['is_skirt'], is_adaptive=False)
-
-
-
-def plot(mesh_gt, pred_a, pred_b, body, mask):
-    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
-    
-    _, faces_garment = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
-    faces = np.concatenate((F, faces_garment + 14475), axis=0)
-    mesh_gt = np.concatenate((body, mesh_gt[mask, :]), axis=0)
-    pred_a = np.concatenate((body, pred_a), axis=0)
-    pred_b = np.concatenate((body, pred_b), axis=0)
-    colors = np.concatenate((np.ones_like(body, dtype=np.int32)*100, np.ones_like(mesh_gt, dtype=np.int32)*230), axis=0)
-    # colors = np.ones_like(mesh_gt, dtype=np.int32)*230
-
-    fig.add_trace(go.Mesh3d(
-            x=mesh_gt[:,0],
-            y=mesh_gt[:,2],
-            z=mesh_gt[:,1],
-            i = faces[:,0],
-            j = faces[:,1],
-            k = faces[:,2],
-            text="Ground Truth",
-            vertexcolor=colors,
-        ),
-        row=1, col=1)
-
-    fig.add_trace(go.Mesh3d(
-            x=pred_a[:,0],
-            y=pred_a[:,2],
-            z=pred_a[:,1],
-            i = faces[:,0],
-            j = faces[:,1],
-            k = faces[:,2],
-            text="Pred A",
-            vertexcolor=colors,
-        ),
-        row=1, col=2)
-
-    fig.add_trace(go.Mesh3d(
-            x=pred_b[:,0],
-            y=pred_b[:,2],
-            z=pred_b[:,1],
-            i = faces[:,0],
-            j = faces[:,1],
-            k = faces[:,2],
-            text="Pred B",
-            vertexcolor=colors,
-        ),
-        row=1, col=3)
-
-
-
-    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1), 
-                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1), 
-                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1), 
-                    )
-    helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
-                yaxis = dict(nticks=4, range=[-1,1],),
-                zaxis = dict(nticks=4, range=[-1,1],),)
-    fig.update_layout(scene = helper,
-                        scene2 = helper,
-                        scene3 = helper,
-                        )
-
-    return fig
-
-
-
-if __name__ == '__main__':
-    run = wandb.init(project='plots', name=config_a['name'], notes=config_a['notes'], dir='/wandb')
-
-    parser = argparse.ArgumentParser(description='')
-    parser.add_argument('--name', type=str)
-    args = vars(parser.parse_args())
-
-    skirt_modifier = '_skirt' if config_a['is_skirt'] else ''
-
-    folder = config_a['test_dir']
-    # smpl = SMPL(use_one_hot=False)
-
-    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
-
-    iteration = ''
-
-    # load_model(model, 'checkpoints/' + 'abl_all_120k')
-
-
-    factory = DatasetFactory()
-    dataset_a = factory.get_dataset(folder=config_a['test_dir'], 
-                                       batch_size=config_a['batch_size'], 
-                                       signature=config_a['signature_dynamic'], 
-                                       resolutions=[config_a['resolution']], 
-                                       in_memory=False, 
-                                       do_repeat=False,
-                                       use_one_hot=config_a['use_one_hot'],
-                                       use_no_orient=config_a['remove_global_orientation'],
-                                       shuffle=False,
-                                       testing=True,
-                                    #    frames_file='frames_abl.txt',
-                                       )
-    static_dataset_a = StaticDataset(folder=config_a['test_dir'], 
-                                        signature=config_a['signature_static'], 
-                                        resolutions=[config_a['resolution']], 
-                                        in_memory=True)
-
-    iterator_a = iter(dataset_a)
-    
-
-    faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config_a['is_skirt'] else '')
-    ids = os.listdir(faces_folder)
-    ids = [id.split('.')[0] for id in ids]
-    faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
-
-    results_folder = '/home/workspace/data/results/post'
-
-    count = 0
-    while True:
-        batch = iterator_a.get_next()
-
-        batch = static_dataset_a.expand_batch(batch_to_dict(batch, config_a['signature_dynamic']))
-
-        mask = batch['mesh_mask'].numpy().astype(bool)
-        gt = batch['mesh_posed'].numpy()
-        body = batch['mesh_body_posed'].numpy()
-
-        for i in range(mask.shape[0]):
-            id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
-            pred_a = np.load(results_folder + '/al_all_post-/' + id + '_' + frame_nr + '.npy')
-            pred_b = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
-            # pred_c = np.load(results_folder + '/iccv_weights_test_6-/' + id + '_' + frame_nr + '.npy')
-
-            fig = plot(gt[i], pred_a, pred_b, body[i], mask[i])
-
-            wandb.log({f'{id}_{frame_nr}': fig}, step = count)
-            count += 1
-
-            print(count)
-

configs/archive/config_abl_all_post.py

@@ -7,7 +7,7 @@ config = dict()
 # DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
 # DON'T FORGET TO CHECK AVG GARMENT USED
 
-config['use_skinning'] =              False # and deshaping
+config['use_skinning'] =              True # and deshaping
 config['remove_global_orientation'] = True
 config['use_curvature'] =             True
 config['use_extra_losses'] =          True # edge, collision, 3d mesh
@@ -20,8 +20,8 @@ ablation_count = [config['use_skinning'], config['remove_global_orientation'], c
 assert ablation_count == 1 or ablation_count == 0
 
 config['gpu'] = '4'
-config['name'] = 'iccv_weights_test_6'
+config['name'] = 'al_all_post'
-config['notes'] = 'smaller weight loss coeff, 1e-6, convolutional weight learning, continued from _5'
+config['notes'] = ''
 
 config['avg_garment_path'] = 'custom_data/train_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
 if config['use_skinning']:
@@ -53,7 +53,7 @@ if config['is_cape']:
   config['smpl_res'] = 3638
 
 
-config['pretrained_model'] = 'checkpoints/post/iccv_weights_test_5'
+config['pretrained_model'] = ''
 
 ###############    
 config['lr'] = 0.0001#                 0.0001
@@ -86,15 +86,14 @@ config['use_one_hot'] = False
 config['cvae'] = dict()
 config['cvae']['is_label'] = False
 config['cvae']['add_noise'] = True
-config['cvae']['noise_amount'] = 0.00001
+config['cvae']['noise_amount'] = 0.001
 
 config['cvae']['kernel_size'] = 7
 config['cvae']['encoder_depths'] = [3, 3, 3, 9, 3]
 config['cvae']['encoder_dims'] = [32, 64, 128, 256, 64]
 config['cvae']['decoder_depths'] = config['cvae']['encoder_depths']
 config['cvae']['decoder_dims'] = config['cvae']['encoder_dims']
-config['cvae']['decoder_weights_depths'] = [3, 3, 3, 5, 3]
+
-config['cvae']['decoder_weights_dims'] = [24, 32, 64, 128, 256]
 
 config['cond_ae_static'] = dict()
 config['cond_ae_static']['is_label'] = False

configs/archive/config_ae_static.py

@@ -31,7 +31,7 @@ config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
 config['cond_ae']['activation'] = 'linear'
 
 
-config['gpu'] = '0'
+config['gpu'] = '5'
 config['lr'] = 0.0005
 config['name'] = 'static_post_4'
 config['notes'] = 'convnext + convnext, | \