Undo model modifications

shuklabhay · Aug 6, 2024 · 92e6c44 · 92e6c44
1 parent 92f5bfc
commit 92e6c44
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Showing 2 changed files with 25 additions and 21 deletions.
diff --git a/.gitignore b/.gitignore
@@ -164,4 +164,5 @@ cython_debug/
 # User directories
 data/
 .vscode/
-.DS_Store
+.DS_Store
+DCGAN_generated_audio.wav
diff --git a/src/dcgan_architecture.py b/src/dcgan_architecture.py
@@ -1,46 +1,49 @@
 import torch
 import torch.nn as nn
 
-from utils.helpers import save_model
-
 # Constants Constants
 BATCH_SIZE = 16
 LATENT_DIM = 100
+N_EPOCHS = 10
 
 N_CHANNELS = 2  # Left, right
 N_FRAMES = 345
 N_FREQ_BINS = 257
 
+VALIDATION_INTERVAL = int(N_EPOCHS / 2)
+SAVE_INTERVAL = int(N_EPOCHS / 1)
+
 
+# Model Components
 class Generator(nn.Module):
     def __init__(self):
         super(Generator, self).__init__()
         self.deconv_blocks = nn.Sequential(
-            nn.ConvTranspose2d(LATENT_DIM, 512, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(LATENT_DIM, 512, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(512),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(512, 256, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(256),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(256, 128, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(128),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(128, 64, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(64),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(64, 32, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(32),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(32, 16, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(16),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(16, 8, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(16, 8, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(8),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(8, 4, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(8, 4, kernel_size=4, stride=2, padding=1),
             nn.BatchNorm2d(4),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(4, N_CHANNELS, kernel_size=6, stride=2, padding=2),
+            nn.ConvTranspose2d(4, N_CHANNELS, kernel_size=4, stride=2, padding=1),
             nn.Upsample(
                 size=(N_FRAMES, N_FREQ_BINS), mode="bilinear", align_corners=False
             ),
@@ -57,30 +60,30 @@ def __init__(self):
         super(Discriminator, self).__init__()
         self.conv_blocks = nn.Sequential(
             nn.Upsample(size=(512, 512), mode="bilinear", align_corners=False),
-            nn.Conv2d(N_CHANNELS, 4, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(N_CHANNELS, 4, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv2d(4, 8, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(4, 8, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(8),
-            nn.Conv2d(8, 16, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(8, 16, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(16),
-            nn.Conv2d(16, 32, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(16, 32, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(32),
-            nn.Conv2d(32, 64, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(64),
-            nn.Conv2d(64, 128, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(128),
-            nn.Conv2d(128, 256, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(256),
-            nn.Conv2d(256, 512, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),
             nn.LeakyReLU(0.2, inplace=True),
             nn.BatchNorm2d(512),
-            nn.Conv2d(512, 1, kernel_size=6, stride=2, padding=2),
+            nn.Conv2d(512, 1, kernel_size=4, stride=2, padding=1),
             nn.Sigmoid(),
         )