try less gnn

aaa_helpers.txt

@@ -8,7 +8,7 @@ nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv
 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_3" > nohup/iccv/cvae_iccv_gnn_3.out 2>&1 &
+nohup python cvae.py --name="lhunor_cvae_iccv_gnn_5" > nohup/iccv/cvae_iccv_gnn_5.out 2>&1 &
 
 skirts!!!!
 pyramid next week

configs/iccv/config_abl_all.py

@@ -19,8 +19,8 @@ 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'] = '6'
-config['name'] = 'iccv_gnn_3'
+config['gpu'] = '0'
+config['name'] = 'iccv_gnn_5'
 config['notes'] = ''
 config['tags'] = ['iccv', 'gnn']
 

models/gnn.py

@@ -16,20 +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 = GCNConv(64, activation='tanh', use_bias=False)
-        self.conv2 = GCNConv(64, activation='tanh', use_bias=False)
-        self.conv3 = GCNConv(64, activation='tanh', use_bias=False)
-        self.conv4 = GCNConv(64, activation='tanh', use_bias=False)
-        self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
+        self.conv1 = GCNConv(3, activation='tanh', use_bias=False)
+        self.conv2 = GCNConv(3, activation='tanh', use_bias=False)
+        # self.conv3 = GCNConv(3, activation='tanh', use_bias=False)
+        # self.conv4 = GCNConv(64, activation='tanh', use_bias=False)
+        # self.conv5 = GCNConv(64, 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.conv4([x, self.a], training=training)
-        x = self.conv5([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)
         return x