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new gnn smoothing trial

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luni
2023-02-24 18:52:37 +00:00
parent 21bd18cbe2
commit 68b2d0d3af
3 changed files with 8 additions and 9 deletions
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aaa_helpers.txt
+1 -1
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@@ -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_1" > nohup/iccv/cvae_iccv_gnn_1.out 2>&1 &
nohup python cvae.py --name="lhunor_cvae_iccv_gnn_3" > nohup/iccv/cvae_iccv_gnn_3.out 2>&1 &
skirts!!!!
pyramid next week
configs/iccv/config_abl_all.py
+2 -2
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@@ -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'] = '3'
config['name'] = 'iccv_gnn_2'
config['gpu'] = '6'
config['name'] = 'iccv_gnn_3'
config['notes'] = ''
config['tags'] = ['iccv', 'gnn']
models/gnn.py
+5 -6
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@@ -16,12 +16,11 @@ 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.conv2 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
self.conv3 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
self.conv4 = GeneralConv(channels=16, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
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.conv6 = GCNConv(3, activation='tanh', use_bias=False)
def call(self, inputs, training=None):