some tweaks and fixes

hyperparams.py

@@ -1,5 +1,5 @@
 FILTER_COUNT = 32
 KERNEL_SIZE = 2
 CROP_SIZE = 224
-BATCH_SIZE = 4
-EPOCHS = 5
+BATCH_SIZE = 256
+EPOCHS = 20

resnet.py

@@ -175,7 +175,7 @@ class MaiRes(nn.Module):
         )
 
         self.avgpool = nn.AvgPool2d(kernel_size=RESNET_KERNEL_SIZE)
-        self.fc = nn.Linear(in_features=8192, out_features=1)
+        self.fc = nn.Linear(in_features=2048, out_features=1)
 
     def forward(self, x):
         x = self.conv(x)

train.py

@@ -3,6 +3,7 @@ import torch
 import torch.nn
 import torch.optim
 import torch.utils.data
+from pathlib import Path
 from datetime import datetime
 from datasets import concatenate_datasets, load_dataset
 from label import label_fake, label_real
@@ -34,11 +35,11 @@ def load_data():
 
     diffusion_db_dataset = load_dataset(
         "poloclub/diffusiondb",
-        "2m_random_10k",
+        "2m_random_50k",
         trust_remote_code=True,
         split="train",
     )
-    flickr_dataset = load_dataset("nlphuji/flickr30k", split="test[:25%]")
+    flickr_dataset = load_dataset("nlphuji/flickr30k", split="test[:50%]")
     painting_dataset = load_dataset(
         "keremberke/painting-style-classification", name="full", split="train"
     )
@@ -47,7 +48,7 @@ def load_data():
     )
     movie_poaster_dataset = load_dataset("nanxstats/movie-poster-5k", split="train")
     metal_album_art_dataset = load_dataset(
-        "Alphonsce/metal_album_covers", split="train[:80%]"
+        "Alphonsce/metal_album_covers", split="train[:50%]"
     )
 
     diffusion_db_dataset = diffusion_db_dataset.select_columns("image")
@@ -58,7 +59,6 @@ def load_data():
 
     painting_dataset = painting_dataset.select_columns("image")
     painting_dataset = painting_dataset.map(label_real)
-
     anime_scene_datatset = anime_scene_datatset.select_columns("image")
     anime_scene_datatset = anime_scene_datatset.map(label_real)
 
@@ -109,14 +109,14 @@ def load_data():
         training_ds,
         batch_size=BATCH_SIZE,
         shuffle=True,
-        num_workers=4,
+        num_workers=6,
         collate_fn=collate,
     )
     validation_loader = torch.utils.data.DataLoader(
         validation_ds,
         batch_size=BATCH_SIZE,
         shuffle=True,
-        num_workers=4,
+        num_workers=6,
         collate_fn=collate,
     )
 
@@ -134,6 +134,9 @@ def train():
     optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)
     best_vloss = 1_000_000.0
 
+    Path("models").mkdir(parents=True, exist_ok=True)
+    print("models directory created")
+
     for epoch in range(EPOCHS):
         print(f"EPOCH {epoch + 1}:")
 
@@ -199,6 +202,7 @@ def train():
             best_vloss = avg_validation_loss
             model_path = f"model/mai_{timestamp}_{epoch}"
             torch.save(model.state_dict(), model_path)
+            print("current model state saved")
 
 
 train()