Network ready to be trained on UniversityDataset.

code/autoencoder.py

@@ -27,10 +27,10 @@ resource.setrlimit(resource.RLIMIT_AS, (memory_limit_gb * 1024**3, hard))
 # --------
 # CONSTANTS
 # --------
-IMG_H = 256  # On better gpu use 256 and adam optimizer
+IMG_H = 224  # On better gpu use 256 and adam optimizer
 IMG_W = IMG_H
 DATASET_PATHS = [
-    "../datasets/train/google/",
+    "../datasets/train/",
 ]
 LINE = "\n----------------------------------------\n"
 
@@ -282,7 +282,7 @@ class Decoder(nn.Module):
 
     def forward(self, x):
         output = self.linear(x)
-        output = output.view(len(output), self.out_channels * 8, 8, 8)
+        output = output.view(len(output), self.out_channels * 8, 7, 7)
         # for layer in self.conv:
         #    output = layer(output)
         #    if self.debug:
@@ -495,21 +495,21 @@ def preprocess_data():
     for path in DATASET_PATHS:
         tr, val, test = GEImagePreprocess(path=path).load_images()
         training_images.extend(tr)
-        validation_images.extend(val)
         test_images.extend(test)
+        validation_images.extend(val)
 
     #  creating pytorch datasets
     training_data = GEDataset(
         training_images,
         transforms=transforms.Compose(
-            [transforms.ToTensor(), transforms.Normalize((0.5), (0.5))]
+            [transforms.ToTensor()]#, transforms.Normalize((0.5), (0.5))]
         ),
     )
 
     validation_data = GEDataset(
         validation_images,
         transforms=transforms.Compose(
-            [transforms.ToTensor(), transforms.Normalize((0.5), (0.5))]
+            [transforms.ToTensor()]#, transforms.Normalize((0.5), (0.5))]
         ),
     )
 
@@ -517,9 +517,9 @@ def preprocess_data():
         validation_images,
         transforms=transforms.Compose(
             [
-                transforms.ToTensor(),
-                transforms.Normalize((0.5), (0.5)),
-            ]
+                transforms.ToTensor()]#,
+                #transforms.Normalize((0.5), (0.5)),
+            #]
         ),
     )