skin_gnn

aaa_helpers.txt

@@ -24,6 +24,8 @@ nohup python cvae.py --name="lhunor_abl_reg" > nohup/iccv/ablation/abl_reg.out 2
 nohup python cvae.py --name="lhunor_abl_off" > nohup/iccv/ablation/abl_off.out 2>&1 &
 nohup python cvae.py --name="lhunor_abl_skin_curv" > nohup/iccv/ablation/abl_skin_curv.out 2>&1 &
 
+nohup python cvae.py --name="lhunor_abl_skin_gnn" > nohup/iccv/ablation/abl_skin_gnn.out 2>&1 &
+
 nohup python upscale_test.py --name="iccv_32_2" > nohup/iccv/upscale/iccv_32_2.out 2>&1 &
 nohup python upscale_test.py --name="iccv_64_2" > nohup/iccv/upscale/iccv_64_2.out 2>&1 &
 nohup python upscale_test.py --name="iccv_128_2" > nohup/iccv/upscale/iccv_128_2.out 2>&1 &

configs/iccv/config_abl_skin.py

@@ -18,8 +18,8 @@ config['use_norm_loss'] =             True
 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'] = '2'
+config['gpu'] = '4'
-config['name'] = 'abl_skin'
+config['name'] = 'abl_skin_gnn'
 config['notes'] = ''
 config['tags'] = ['iccv', 'abl_skin']
 

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
@@ -34,7 +34,6 @@ else:
     from losses import edge_loss
 
 
-
 from losses import collision_ratio, kl_loss, normal_loss #, concept_loss
 # from losses import collision_penalty as collision_penalty
 from losses import collision_loss as collision_penalty
@@ -552,7 +551,7 @@ 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)

models/gnn.py

@@ -16,21 +16,20 @@ class Smoother(tf.keras.Model):
     def __init__(self, ):
         super().__init__()
         self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
-        self.conv1 = GeneralConv(channels=32, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
+        self.conv1 = GCNConv(16, activation='tanh', use_bias=False)
-        self.conv2 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
+        self.conv2 = GCNConv(3, activation='tanh', use_bias=False)
-        self.conv3 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
+        # self.conv3 = GCNConv(3, activation='tanh', use_bias=False)
-        self.conv4 = GeneralConv(channels=16, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
+        # self.conv4 = GCNConv(3, activation='tanh', use_bias=False)
-
+        # self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
-        self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
         self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
 
     def call(self, inputs, training=None):
         x = inputs
         x = self.conv1([x, self.a], training=training)
         x = self.conv2([x, self.a], training=training)
-        x = self.conv3([x, self.a], training=training)
+        # x = self.conv3([x, self.a], training=training)
-        x = self.conv4([x, self.a], training=training)
+        # x = self.conv4([x, self.a], training=training)
-        x = self.conv5([x, self.a], training=training)
+        # x = self.conv5([x, self.a], training=training)
         x = self.conv6([x, self.a], training=training)
         return x