test-16

train.py

@@ -17,8 +17,8 @@ from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_l
 from losses import collision_loss as collision_penalty
 
 
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='test_conditioned_10(test-13)', notes='same as test-12 but double discriminator feature sizes')
+os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='test_conditioned_13(test-16)', notes='learning offsets, all losses from the start')
 artifact = wandb.Artifact('code', type='code')
 artifact.add_file('train.py')
 artifact.add_file('losses.py')
@@ -206,7 +206,7 @@ class Trainer(object):
 
             mesh_pred = uv2mesh(fake_images)
             e_loss = edge_loss(mesh_pred, mesh_static, mesh_mask)
-            c_loss = 0.0 #collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
+            c_loss = collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
             m_loss = 0.0 # uv_mask_loss(fake_images, uv_mask)
 
             # scale loss
@@ -235,7 +235,7 @@ class Trainer(object):
             mesh_pred = uv2mesh(fake_images)
 
             e_loss = edge_loss(mesh_pred, mesh_static, mesh_mask)
-            c_loss = 0.0 #collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
+            c_loss = collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
             m_loss = 0.0 # uv_mask_loss(fake_images, uv_mask)
 
             # scale loss
@@ -366,7 +366,7 @@ class Trainer(object):
                 
 
             # if step > self.enable_unsupervised_losses:
-            #     self.unsupervised_coeff = (step / 40000)
+            #     self.unsupervised_coeff = 1
             
             # update g_clone
             self.g_clone.set_as_moving_average_of(self.generator)
@@ -573,7 +573,7 @@ def main():
             'train_res': args['train_res'],
         }
 
-        trainer = Trainer(training_parameters, name=f'stylegan2-test-13-{args["train_res"]}x{args["train_res"]}')
+        trainer = Trainer(training_parameters, name=f'stylegan2-test-16-{args["train_res"]}x{args["train_res"]}')
         trainer.train(dist_dataset, strategy)
     return