fix
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@@ -15,6 +15,7 @@ nohup python cvae.py --name="lhunor_cvae_iccv_gnn_1" > nohup/iccv/cvae_iccv_gnn_
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nohup python cvae.py --name="lhunor_abl_all" > nohup/iccv/ablation/abl_all.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_gnn" > nohup/iccv/ablation/abl_all_gnn.out 2>&1 &
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nohup python cvae.py --name="lhunor_abl_all_gnn_3" > nohup/iccv/ablation/abl_all_gnn_3.out 2>&1 &
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nohup python upscale_test.py --name="iccv_32_2" > nohup/iccv/upscale/iccv_32_2.out 2>&1 &
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nohup python upscale_test.py --name="iccv_64_2" > nohup/iccv/upscale/iccv_64_2.out 2>&1 &
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@@ -7,7 +7,7 @@ config = dict()
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# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
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# DON'T FORGET TO CHECK AVG GARMENT USED
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config['use_skinning'] = True # and deshaping
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config['use_skinning'] = False # and deshaping
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config['remove_global_orientation'] = True
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config['use_curvature'] = True
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config['use_extra_losses'] = True # edge, collision, 3d mesh
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@@ -19,12 +19,12 @@ config['only_curvatures'] = False
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ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
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assert ablation_count == 1 or ablation_count == 0
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config['gpu'] = '0'
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config['name'] = 'abl_all_gnn_2'
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config['gpu'] = '2'
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config['name'] = 'abl_all_gnn_3'
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config['notes'] = ''
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config['tags'] = ['iccv', 'abl_all_gnn']
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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
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config['avg_garment_path'] = '' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv
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if config['use_skinning']:
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assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
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@@ -54,7 +54,7 @@ if config['is_cape']:
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config['smpl_res'] = 3638
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config['pretrained_model'] = 'checkpoints/iccv/abl_all'
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config['pretrained_model'] = 'checkpoints/iccv/abl_skin'
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###############
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config['lr'] = 0.0001# 0.0001
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@@ -568,6 +568,9 @@ if __name__ == '__main__':
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train_accumulator.reset()
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valid_accumulator.reset()
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if step % 5000 == 0:
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manager.save(checkpoint_number=step)
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print(step)
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step += 1
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+8
-8
@@ -16,20 +16,20 @@ class Smoother(tf.keras.Model):
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def __init__(self, ):
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super().__init__()
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self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
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self.conv1 = GCNConv(512, activation='tanh', use_bias=False)
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self.conv2 = GCNConv(512, activation='tanh', use_bias=False)
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self.conv3 = GCNConv(512, activation='tanh', use_bias=False)
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self.conv4 = GCNConv(512, activation='tanh', use_bias=False)
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self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv1 = GCNConv(16, activation='tanh', use_bias=False)
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self.conv2 = GCNConv(3, activation='tanh', use_bias=False)
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# self.conv3 = GCNConv(512, activation='tanh', use_bias=False)
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# self.conv4 = GCNConv(512, activation='tanh', use_bias=False)
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# self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
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def call(self, inputs, training=None):
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x = inputs
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x = self.conv1([x, self.a], training=training)
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x = self.conv2([x, self.a], training=training)
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x = self.conv3([x, self.a], training=training)
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x = self.conv4([x, self.a], training=training)
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x = self.conv5([x, self.a], training=training)
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# x = self.conv3([x, self.a], training=training)
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# x = self.conv4([x, self.a], training=training)
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# x = self.conv5([x, self.a], training=training)
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x = self.conv6([x, self.a], training=training)
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return x
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