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pjyazdian · Feb 7, 2024 · 8e2f45d · 8e2f45d
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diff --git a/scripts/model/Autoencoder_VQVAE_model.py b/scripts/model/Autoencoder_VQVAE_model.py
diff --git a/scripts/model/Autoencoder_model.py b/scripts/model/Autoencoder_model.py
diff --git a/scripts/model/DAE_model.py b/scripts/model/DAE_model.py
diff --git a/scripts/model/Helper_models.py b/scripts/model/Helper_models.py
diff --git a/scripts/model/__pycache__/Autoencoder_VQVAE_model.cpython-38.pyc b/scripts/model/__pycache__/Autoencoder_VQVAE_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/Autoencoder_model.cpython-38.pyc b/scripts/model/__pycache__/Autoencoder_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/DAE_model.cpython-38.pyc b/scripts/model/__pycache__/DAE_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/Helper_models.cpython-38.pyc b/scripts/model/__pycache__/Helper_models.cpython-38.pyc
diff --git a/scripts/model/__pycache__/seq2seq_net.cpython-38.pyc b/scripts/model/__pycache__/seq2seq_net.cpython-38.pyc
diff --git a/scripts/model/__pycache__/seq2seq_with_cluster_model.cpython-38.pyc b/scripts/model/__pycache__/seq2seq_with_cluster_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/tcn.cpython-38.pyc b/scripts/model/__pycache__/tcn.cpython-38.pyc
diff --git a/scripts/model/__pycache__/text2embedding_GAN_model.cpython-38.pyc b/scripts/model/__pycache__/text2embedding_GAN_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/text2embedding_model.cpython-38.pyc b/scripts/model/__pycache__/text2embedding_model.cpython-38.pyc
diff --git a/scripts/model/__pycache__/vocab.cpython-38.pyc b/scripts/model/__pycache__/vocab.cpython-38.pyc
diff --git a/scripts/model/__pycache__/vocab.cpython-39.pyc b/scripts/model/__pycache__/vocab.cpython-39.pyc
diff --git a/scripts/model/autoencoder_backup.py b/scripts/model/autoencoder_backup.py
@@ -1,38 +1,38 @@
-import torch
-from torch.autograd import Variable
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-
-
-class DAE_Network(nn.Module):
-    def __init__(self, motion_dim, latent_dim):
-        super(DAE_Network, self).__init__()
-        print("init");
-        self.encoder = nn.Sequential(
-            nn.Linear(motion_dim, latent_dim),
-            nn.Tanh(),
-            # nn.Linear(motion_dim-10, motion_dim-30),
-            # nn.Tanh(),
-        )
-
-        self.decoder = nn.Sequential(
-            # nn.Linear(motion_dim-30, motion_dim-10),
-            # nn.Tanh(),
-            nn.Linear(latent_dim, motion_dim),
-
-        )
-
-    def forward(self, x):
-        # print("_________________")
-        # print(self.encoder)
-        # print(x.shape)
-        # print("_________________")
-        x = torch.squeeze(x)
-        # print(x.shape)
-        x = self.encoder(x)
-        # print("Encoded", x.shape)
-        x = self.decoder(x)
-        x = torch.unsqueeze(x, 2)
-        # print("Decoder", x.shape)
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+class DAE_Network(nn.Module):
+    def __init__(self, motion_dim, latent_dim):
+        super(DAE_Network, self).__init__()
+        print("init");
+        self.encoder = nn.Sequential(
+            nn.Linear(motion_dim, latent_dim),
+            nn.Tanh(),
+            # nn.Linear(motion_dim-10, motion_dim-30),
+            # nn.Tanh(),
+        )
+
+        self.decoder = nn.Sequential(
+            # nn.Linear(motion_dim-30, motion_dim-10),
+            # nn.Tanh(),
+            nn.Linear(latent_dim, motion_dim),
+
+        )
+
+    def forward(self, x):
+        # print("_________________")
+        # print(self.encoder)
+        # print(x.shape)
+        # print("_________________")
+        x = torch.squeeze(x)
+        # print(x.shape)
+        x = self.encoder(x)
+        # print("Encoded", x.shape)
+        x = self.decoder(x)
+        x = torch.unsqueeze(x, 2)
+        # print("Decoder", x.shape)
         return x