update image saving

.gitignore

@@ -0,0 +1,2 @@
+datasets/*
+data_scrape/*

code/autoencoder.py

@@ -2,8 +2,6 @@
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
 import torchvision.transforms as transforms
 from torch.utils.data import Dataset, DataLoader
 import numpy as np
@@ -17,6 +15,7 @@ import argparse
 import pickle
 from multiprocessing import Pool
 from functools import partial
+import torchvision.transforms.functional as TF
 
 # -------------
 # MEMORY SAFETY
@@ -31,7 +30,7 @@ resource.setrlimit(resource.RLIMIT_AS, (memory_limit_gb * 1024**3, hard))
 IMG_H = 160  # On better gpu use 256 and adam optimizer
 IMG_W = IMG_H * 2
 DATASET_PATHS = [
-    "../datasets/train/",
+    "../datasets/train",
 ]
 LINE="\n----------------------------------------\n"
 
@@ -396,24 +395,17 @@ class ConvolutionalAutoencoder:
                         [test_images.view(-1, 1, IMG_H, IMG_W), reconstructed_imgs],
                         dim=1,
                     ).flatten(0, 1)
-                    grid = make_grid(imgs, nrow=10, normalize=True, padding=1)
-                    grid = grid.permute(1, 2, 0)
-                    plt.figure(dpi=170)
-                    plt.title(
-                        f"Original/Reconstructed, training loss: {round(loss.item(), 4)} validation loss: {round(val_loss.item(), 4)}"
-                    )
-                    plt.imshow(grid)
-                    plt.axis("off")
 
                     # Check if directory exists, if not create it
                     if not os.path.exists("visualizations"):
-                        os.makedirs("visualizations")
-                    if not os.path.exists(f"visualizations/epoch_{epoch+1}"):
-                        os.makedirs(f"visualizations/epoch_{epoch+1}")
+                        os.makedirs("visualizations/")
+                    if not os.path.exists(f"visualizations/epoch_{epoch+1}/"):
+                        os.makedirs(f"visualizations/epoch_{epoch+1}/")
 
-                    plt.savefig(f"visualizations/epoch_{epoch+1}/img_{plt_ix}.png")
-                    plt.clf()
-                    plt.close()
+                    for i, img in enumerate(imgs):
+                        pil_img = TF.to_pil_image(img)
+                        pil_img.save(f"visualizations/epoch_{epoch+1}/img_{plt_ix}_{i}.png")
+    
                     plt_ix += 1
 
     def test(self, loss_function, test_set):
@@ -526,6 +518,13 @@ def preprocess_data():
 
     return training_data, validation_data, test_data
 
+def print_dataset_info(training_set, validation_set, test_set):
+    print(LINE)
+    print("Training set size: ", len(training_set))
+    print("Validation set size: ", len(validation_set))
+    print("Test set size: ", len(test_set))
+    print(LINE)
+
 
 def main():
     global device
@@ -554,6 +553,7 @@ def main():
 
     if args.train:
         training_data, validation_data, test_data = preprocess_data()
+        print_dataset_info(training_data, validation_data, test_data)
         model = ConvolutionalAutoencoder(Autoencoder(Encoder(), Decoder()))
         model.train(
             nn.MSELoss(reduction="sum"),
@@ -567,11 +567,13 @@ def main():
 
     elif args.test:
         _, _, td = preprocess_data()
+        print_dataset_info(td, td, td)
         model = ConvolutionalAutoencoder(Autoencoder(Encoder(), Decoder()))
         model.test(nn.MSELoss(reduction="sum"), td)
 
     elif args.encode:
         _, _, td = preprocess_data()
+        print_dataset_info(td, td, td)
         model = ConvolutionalAutoencoder(Autoencoder(Encoder(), Decoder()))
         model.encode_images(td)