Fixed confusing names. #38, #35

Fixed memory leaking related to matplotlib #17

Hope did not break anything. If you have an issues, try a revision before this commit.

README.md

@@ -38,7 +38,7 @@
 > **Adversarial Latent Autoencoders**<br>
 > Stanislav Pidhorskyi, Donald Adjeroh, Gianfranco Doretto<br>
 >
-> **Abstract:** *Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond, the capabilities of a generator-only type of architecture.*
+> **Abstract:** *Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.*
 
 ## Citation
 * Stanislav Pidhorskyi, Donald A. Adjeroh, and Gianfranco Doretto. Adversarial Latent Autoencoders. In *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)*, 2020. [to appear] 

checkpointer.py

@@ -108,7 +108,7 @@ def load(self, ignore_last_checkpoint=False, file_name=None):
                         self.auxiliary[name].load_state_dict(checkpoint["optimizers"].pop(name))
                     if name in checkpoint:
                         self.auxiliary[name].load_state_dict(checkpoint.pop(name))
-                except IndexError:
+                except (IndexError, ValueError):
                     self.logger.warning('%s\nFailed to load: %s\n%s' % ('!' * 160, name, '!' * 160))
             checkpoint.pop('auxiliary')
 

configs/mnist_fc.yaml

@@ -18,7 +18,7 @@ MODEL:
   CHANNELS: 1
   GENERATOR: "GeneratorFC"
   ENCODER: "EncoderFC"
-  MAPPING_TO_LATENT: "MappingToLatentNoStyle"
+  MAPPING_D: "MappingDNoStyle"
 
 OUTPUT_DIR: mnist_results_fc2z_2
 TRAIN:

defaults.py

@@ -51,8 +51,8 @@
 _C.MODEL.CHANNELS = 3
 _C.MODEL.GENERATOR = "GeneratorDefault"
 _C.MODEL.ENCODER = "EncoderDefault"
-_C.MODEL.MAPPING_TO_LATENT = "MappingToLatent"
-_C.MODEL.MAPPING_FROM_LATENT = "MappingFromLatent"
+_C.MODEL.MAPPING_D = "MappingD"
+_C.MODEL.MAPPING_F = "MappingF"
 _C.MODEL.Z_REGRESSION = False
 
 _C.TRAIN = CN()

interactive_demo.py

@@ -62,8 +62,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -97,7 +97,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):
@@ -186,9 +186,9 @@ def loadRandom():
     def update_image(w, latents_original):
         with torch.no_grad():
             w = w + model.dlatent_avg.buff.data[0]
-            w = w[None, None, ...].repeat(1, model.mapping_fl.num_layers, 1)
+            w = w[None, None, ...].repeat(1, model.mapping_f.num_layers, 1)
 
-            layer_idx = torch.arange(model.mapping_fl.num_layers)[np.newaxis, :, np.newaxis]
+            layer_idx = torch.arange(model.mapping_f.num_layers)[np.newaxis, :, np.newaxis]
             cur_layers = (7 + 1) * 2
             mixing_cutoff = cur_layers
             styles = torch.where(layer_idx < mixing_cutoff, w, latents_original)

make_figures/make_generation_figure.py

@@ -65,8 +65,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
 
     dlatent_avg = model.dlatent_avg
 

make_figures/make_recon_figure_celeba_pioneer.py

@@ -73,8 +73,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -108,7 +108,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):

make_figures/make_recon_figure_ffhq_real.py

@@ -69,8 +69,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -104,7 +104,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):

make_figures/make_recon_figure_interpolation.py

@@ -70,8 +70,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -105,7 +105,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):
@@ -144,7 +144,7 @@ def open_image(filename):
 
     def make(w):
         with torch.no_grad():
-            w = w[None, None, ...].repeat(1, model.mapping_fl.num_layers, 1)
+            w = w[None, None, ...].repeat(1, model.mapping_f.num_layers, 1)
             x_rec = decode(w)
             return x_rec
 

make_figures/make_recon_figure_multires.py

@@ -71,8 +71,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -106,7 +106,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):

make_figures/make_recon_figure_paged.py

@@ -72,8 +72,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -107,7 +107,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):

make_figures/make_traversarls.py

@@ -48,8 +48,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -83,7 +83,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):
@@ -122,9 +122,9 @@ def do_attribute_traversal(path, attrib_idx, start, end):
         def update_image(w):
             with torch.no_grad():
                 w = w + model.dlatent_avg.buff.data[0]
-                w = w[None, None, ...].repeat(1, model.mapping_fl.num_layers, 1)
+                w = w[None, None, ...].repeat(1, model.mapping_f.num_layers, 1)
 
-                layer_idx = torch.arange(model.mapping_fl.num_layers)[np.newaxis, :, np.newaxis]
+                layer_idx = torch.arange(model.mapping_f.num_layers)[np.newaxis, :, np.newaxis]
                 cur_layers = (7 + 1) * 2
                 mixing_cutoff = cur_layers
                 styles = torch.where(layer_idx < mixing_cutoff, w, _latents[0])

make_figures/old/make_recon_figure_bed.py

@@ -74,8 +74,8 @@ def sample(cfg, logger):
 
     decoder = model.decoder
     encoder = model.encoder
-    mapping_tl = model.mapping_tl
-    mapping_fl = model.mapping_fl
+    mapping_tl = model.mapping_d
+    mapping_fl = model.mapping_f
     dlatent_avg = model.dlatent_avg
 
     logger.info("Trainable parameters generator:")
@@ -109,7 +109,7 @@ def sample(cfg, logger):
 
     def encode(x):
         Z, _ = model.encode(x, layer_count - 1, 1)
-        Z = Z.repeat(1, model.mapping_fl.num_layers, 1)
+        Z = Z.repeat(1, model.mapping_f.num_layers, 1)
         return Z
 
     def decode(x):