Merge remote-tracking branch 'origin/main' into publication

Author: Azure Pipelines

course_UvA-DL/requirements.txt

@@ -1,4 +1,4 @@
-numpy <2.0 # needed for older Torch
+numpy <3.0 # needed for older Torch
 torch >=1.8.1,<2.5
 pytorch-lightning >=2.0,<2.4
 torchmetrics >=1.0,<1.5

lightning_examples/basic-gan/gan.py

@@ -88,7 +88,7 @@ def block(in_feat, out_feat, normalize=True):
             layers = [nn.Linear(in_feat, out_feat)]
             if normalize:
                 layers.append(nn.BatchNorm1d(out_feat, 0.8))
-            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            layers.append(nn.LeakyReLU(0.01, inplace=True))
             return layers
 
         self.model = nn.Sequential(
@@ -193,15 +193,15 @@ def training_step(self, batch):
         # log sampled images
         sample_imgs = self.generated_imgs[:6]
         grid = torchvision.utils.make_grid(sample_imgs)
-        self.logger.experiment.add_image("generated_images", grid, 0)
+        self.logger.experiment.add_image("train/generated_images", grid, self.current_epoch)
 
         # ground truth result (ie: all fake)
         # put on GPU because we created this tensor inside training_loop
         valid = torch.ones(imgs.size(0), 1)
         valid = valid.type_as(imgs)
 
         # adversarial loss is binary cross-entropy
-        g_loss = self.adversarial_loss(self.discriminator(self(z)), valid)
+        g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)
         self.log("g_loss", g_loss, prog_bar=True)
         self.manual_backward(g_loss)
         optimizer_g.step()
@@ -222,7 +222,7 @@ def training_step(self, batch):
         fake = torch.zeros(imgs.size(0), 1)
         fake = fake.type_as(imgs)
 
-        fake_loss = self.adversarial_loss(self.discriminator(self(z).detach()), fake)
+        fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs.detach()), fake)
 
         # discriminator loss is the average of these
         d_loss = (real_loss + fake_loss) / 2
@@ -232,6 +232,9 @@ def training_step(self, batch):
         optimizer_d.zero_grad()
         self.untoggle_optimizer(optimizer_d)
 
+    def validation_step(self, batch, batch_idx):
+        pass
+
     def configure_optimizers(self):
         lr = self.hparams.lr
         b1 = self.hparams.b1
@@ -247,7 +250,7 @@ def on_validation_epoch_end(self):
         # log sampled images
         sample_imgs = self(z)
         grid = torchvision.utils.make_grid(sample_imgs)
-        self.logger.experiment.add_image("generated_images", grid, self.current_epoch)
+        self.logger.experiment.add_image("validation/generated_images", grid, self.current_epoch)
 
 
 # %%
@@ -263,4 +266,4 @@ def on_validation_epoch_end(self):
 # %%
 # Start tensorboard.
 # %load_ext tensorboard
-# %tensorboard --logdir lightning_logs/
+# %tensorboard --logdir lightning_logs/ --samples_per_plugin=images=60

lightning_examples/requirements.txt

@@ -1,4 +1,4 @@
-numpy <2.0 # needed for older Torch
+numpy <3.0 # needed for older Torch
 torch>=1.8.1, <2.5
 pytorch-lightning >=2.0,<2.4
 torchmetrics>=1.0, <1.5