barely working decoder

aaa_helpers.txt

@@ -3,12 +3,14 @@ nohup python calculate_error.py --name error_al_all_150k > nohup/error/post_al_a
 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_1" > nohup/iccv/cvae_iccv_gnn_1.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

auto_encoder_static.py

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

configs/iccv/config_abl_all.py

@@ -19,14 +19,14 @@ config['only_curvatures'] =           False
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
 
-config['gpu'] = '3'
+config['gpu'] = '4'
-config['name'] = 'iccv_gnn_3'
+config['name'] = 'iccv_gnn_decoder_1'
 config['notes'] = ''
-config['tags'] = ['iccv', 'gnn']
+config['tags'] = ['iccv', 'gnn', 'ggn_decoder']
 
 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']:
+# if config['use_skinning']:
-  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+  # assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
 
 config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
 

configs/iccv/config_ae_static_cape_m.py

@@ -0,0 +1,51 @@
+config = dict()
+
+
+config['batch_size'] = 4
+config['train_dir'] = '/home/workspace/local-data/cape_processed/train/male'
+config['valid_dir'] = '/home/workspace/local-data/cape_processed/test/male'
+config['test_dir'] = '/home/workspace/local-data/cape_processed/test/male'
+config['use_filled'] = False
+config['resolution'] = 512
+config['is_skirt'] = False
+config['dataset_in_memory'] = True
+config['is_cape'] = True
+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'] = '5'
+config['lr'] = 0.0005
+config['name'] = 'static_iccv_1_cape_m'
+config['notes'] = ' ' 
+config['tags'] = ['iccv', 'static', 'cape_m'] 
+
+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

@@ -88,7 +88,7 @@ def train_step(batch):
 
         pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=True)
 
-        mesh_pred = uv2mesh(pred)
+        mesh_pred = pred
 
         if config['use_skinning']:
             shape = batch['pose_shape'][:, 72:]
@@ -105,18 +105,18 @@ def train_step(batch):
         e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
         c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
         c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-        l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+        # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
         l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
         n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
         # conc_loss = concept_loss(pred, batch['uv_unposed'])
         kl = kl_loss(mean, logvar)
 
-        reconstr_loss = l2_loss
+        reconstr_loss = l2_mesh_loss
         if config['use_extra_losses']:
             if 'block_collision_loss' in config.keys() and config['block_collision_loss'] == True:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + (e_loss) * config['reg_loss_coeff']
             else:
-                reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+                reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
         if config['use_norm_loss'] and step > 2000:
             reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
 
@@ -132,7 +132,7 @@ def train_step(batch):
     return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/euclid_error': metric,
              'train/reconstr_loss': reconstr_loss, 'train/collision_ratio': c_ratio,
              'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl, 'train/var': np.e ** logvar,
-             'train/l2_loss': l2_loss, 'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
+              'train/l2_mesh_loss': l2_mesh_loss, 'train/normal_loss': n_loss,
              # 'train/ms_ssim': ms_ssim, 
             }
 
@@ -146,7 +146,7 @@ def valid_step(batch):
 
     pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
 
-    mesh_pred = uv2mesh(pred)
+    mesh_pred = pred
 
     if config['use_skinning']:
         shape = batch['pose_shape'][:, 72:]
@@ -163,15 +163,15 @@ def valid_step(batch):
     e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
     c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
     c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
-    l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
+    # l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
     l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
     n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
     # conc_loss = concept_loss(pred, batch['uv_unposed'])
     kl = kl_loss(mean, logvar)
 
-    reconstr_loss = l2_loss
+    reconstr_loss = l2_mesh_loss
     if config['use_extra_losses']:
-        reconstr_loss = reconstr_loss + l2_mesh_loss + (e_loss + c_loss) * config['reg_loss_coeff']
+        reconstr_loss = reconstr_loss + (e_loss + c_loss) * config['reg_loss_coeff']
     if config['use_norm_loss']:
         reconstr_loss = reconstr_loss + n_loss * config['reg_loss_coeff']
 
@@ -256,13 +256,12 @@ def plot_everything(batch: dict, type: str):
 
     pred, mean, logvar = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
     
-    mesh_pred = uv2mesh(pred)
+    mesh_pred = pred.numpy()
     
     orig_img = uv_orig
     uv_static_img = batch['uv_static']
     uv_body_posed_img = batch['uv_body_posed']
-    pred_img = pred
+    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img), axis=2)
-    img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2)
     imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
     
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")

models/gnn.py

@@ -34,29 +34,39 @@ class Smoother(tf.keras.Model):
         x = self.conv6([x, self.a], training=training)
         return x
 
+import numpy as np
+import scipy.sparse as sp
 
 class GNNDecoder(tf.keras.Model):
     def __init__(self, ):
         super().__init__()
         self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
-        self.a_dense = sio.loadmat("custom_utils/ssmpl0.mat")['L'].todense()
+        # self.a_dense = sio.loadmat("custom_utils/ssmpl0.mat")['L'].todense()
-
+        # self.a_dense = np.ones((100,100), dtype=np.float32)
+        # self.a = sp_matrix_to_sp_tensor(sp.coo_matrix(self.a_dense))
+        
         self.flatten = Flatten()
-        self.conv1 = GATConv(channels=1024, attn_heads=4, concat_heads=True, dropout_rate=0.0, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        # self.conv1 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.0, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
-        self.conv2 = GATConv(channels=512, attn_heads=4, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        # self.conv2 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
-        self.conv3 = GATConv(channels=256, attn_heads=4, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        # self.conv3 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
+        self.dense1 = Dense(512)
+        self.dense2 = Dense(256)
+        self.conv1 = GCNConv(256, use_bias=True)
+        self.conv2 = GCNConv(256, use_bias=True)
+        self.conv3 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
         self.conv4 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
         self.conv5 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='sum', activation='prelu', use_bias=True)
-        self.conv6 = GeneralConv(channels=3, batch_norm=False, dropout=0.0, aggregate='sum', activation='tanh', use_bias=True)
+        self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
 
     def call(self, inputs, training=None):
         x = inputs
         x = self.flatten(x)
         x = tf.tile(x[:,None,:], [1,14475,1])
-
+        x = self.dense1(x)
-        x = self.conv1([x, self.a_dense], training=training)
+        x = self.dense2(x)
-        x = self.conv2([x, self.a_dense], training=training)
+        x = self.conv1([x, self.a], training=training)
-        x = self.conv3([x, self.a_dense], training=training)
+        x = self.conv2([x, self.a], training=training)
+        x = self.conv3([x, self.a], training=training)
         x = self.conv4([x, self.a], training=training)
         x = self.conv5([x, self.a], training=training)
         x = self.conv6([x, self.a], training=training)