changes0

DisentanglementBase.py

@@ -175,8 +175,8 @@ class DisentanglementBase:
         return X
     
     def get_train_val(self, extremes=False):
-        X = self.get_encoded_latent()
         y = np.array(self.df[self.variable].values)
+        X = self.get_encoded_latent()[:y.shape[0], :]
         if self.categorical:
             bins = [(x-1) * 360 / (len(self.colors_list) - 1)  if x != 1 
                     else 1 for x in range(len(self.colors_list) + 1)]
@@ -443,14 +443,14 @@ class DisentanglementBase:
                 axs[i].imshow(image)
                 axs[i].set_title(np.round(lambd, 2))
             plt.tight_layout()
-            plt.savefig(join(self.repo_folder, 'figures', 'examples', name+'.jpg'))
+            plt.savefig(join(self.repo_folder, 'figures', 'examples_new', name+'.jpg'))
             plt.close()
             
             if save_separately:
                 for i, (image, lambd) in enumerate(zip(images, lambdas)):
                     plt.imshow(image)
                     plt.tight_layout()
-                    plt.savefig(join(self.repo_folder, 'figures', 'examples', name + '_' + str(lambd) + '.jpg'))
+                    plt.savefig(join(self.repo_folder, 'figures', 'examples_new', name + '_' + str(lambd) + '.jpg'))
                     plt.close()
             
         return images, lambdas
@@ -556,11 +556,11 @@ def continous_experiment(name, var, repo_folder, model, annotations, df, space,
             
 def main():
     repo_folder = '.'
-    annotations_file = join(repo_folder, 'data/textile_annotated_files/seeds0000-100000_S.pkl')
+    annotations_file = join(repo_folder, 'data/textile_annotated_files/seeds0000-1000000.pkl')
     with open(annotations_file, 'rb') as f:
         annotations = pickle.load(f)
 
-    df_file = join(repo_folder, 'data/textile_annotated_files/top_three_colours.csv')
+    df_file = join(repo_folder, 'data/textile_annotated_files/top_three_colours_00000-730003.csv')
     df = pd.read_csv(df_file).fillna('#000000')
 
     model_file = join(repo_folder, 'data/textile_model_files/network-snapshot-005000.pkl')
@@ -571,7 +571,7 @@ def main():
                    'Kelly Green', 'Green Blue Seafoam', 'Cyan Blue',
                    'Warm Blue', 'Indigo', 'Purple Magenta', 'Magenta Pink']
     colors_list = ['Gray', 'Red Orange', 'Yellow', 'Green', 'Light Blue',
-                   'Blue', 'Purple', 'Pink']
+                   'Blue', 'Violet', 'Pink']
     
     scores = []
     kwargs = {'CL method':['LR', 'SVM'], 'C':[0.1, 1], 'sign':[True, False], 
@@ -584,12 +584,12 @@ def main():
     if specific_examples is not None:
         disentanglemnet_exp = DisentanglementBase(repo_folder, model, annotations, df, space='w', colors_list=colors_list, compute_s=False)
 
-        separation_vectors = disentanglemnet_exp.StyleSpace_separation_vector(sign=True, num_factors=10, cutout=None)
-        # separation_vectors = disentanglemnet_exp.InterFaceGAN_separation_vector(method='LR', C=0.1)
+        # separation_vectors = disentanglemnet_exp.StyleSpace_separation_vector(sign=True, num_factors=10, cutout=None)
+        separation_vectors = disentanglemnet_exp.InterFaceGAN_separation_vector(method='LR', C=0.1)
         for specific_example in specific_examples:
             seed = specific_example
             for i, color in enumerate(colors_list):
-                disentanglemnet_exp.generate_changes(seed, separation_vectors[i], min_epsilon=-9, max_epsilon=9, savefig=True, save_separately=True, feature=color, method='StyleSpace' + '_' + str(True) + '_' + str(10) + '_' + str(None))
+                disentanglemnet_exp.generate_changes(seed, separation_vectors[i], min_epsilon=-18, max_epsilon=18, savefig=True, save_separately=True, feature=color, method='InterFaceGAN' + '_' + str('LR') + '_' + str(0.1) + '_' + str(None))
         
         return
        

test_disentanglement.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --time=1-00:00:00
+#SBATCH --time=02:00:00
 #SBATCH --mem=32GB
 #SBATCH --gres gpu:1