From 2dd059d8e876ed8aee1fe03ccf4533d603a6d591 Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Tue, 14 Jun 2022 11:06:14 +0000 Subject: [PATCH] test-13 --- train.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/train.py b/train.py index 296b8bb..3b6ec92 100644 --- a/train.py +++ b/train.py @@ -17,13 +17,13 @@ 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"] = "1" -# run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='test_conditioned_10(test-13)') -# artifact = wandb.Artifact('code', type='code') -# artifact.add_file('train.py') -# artifact.add_file('losses.py') -# artifact.add_file('stylegan2/layers/mapping_block.py') -# run.log_artifact(artifact) +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') +artifact = wandb.Artifact('code', type='code') +artifact.add_file('train.py') +artifact.add_file('losses.py') +artifact.add_file('stylegan2/layers/mapping_block.py') +run.log_artifact(artifact) def initiate_models(g_params, d_params, use_custom_cuda): discriminator = load_discriminator(d_params, ckpt_dir=None, custom_cuda=use_custom_cuda) @@ -206,7 +206,7 @@ class Trainer(object): mesh_pred = uv2mesh(fake_images) e_loss = edge_loss(mesh_pred, mesh_static, mesh_mask) - c_loss = collision_penalty(mesh_pred, mesh_body_posed, mesh_mask) + c_loss = 0.0 #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 = collision_penalty(mesh_pred, mesh_body_posed, mesh_mask) + c_loss = 0.0 #collision_penalty(mesh_pred, mesh_body_posed, mesh_mask) m_loss = 0.0 # uv_mask_loss(fake_images, uv_mask) # scale loss @@ -515,22 +515,24 @@ def main(): # network params resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024] - featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16] + g_featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16] + d_featuremaps = [512, 512, 512, 512, 512, 256, 128, 64, 32] # featuremaps = [512, 512, 512, 512, 512, 256, 128, 64, 32] # featuremaps = [64, 64, 64, 64, 64, 32, 16, 8, 4] - train_resolutions, train_featuremaps = filter_resolutions_featuremaps(resolutions, featuremaps, args['train_res']) + train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, args['train_res']) + _, train_d_featuremaps = filter_resolutions_featuremaps(resolutions, d_featuremaps, args['train_res']) g_params = { 'z_dim': 256, 'w_dim': 256, 'labels_dim': -1, 'n_mapping': 8, 'resolutions': train_resolutions, - 'featuremaps': train_featuremaps, + 'featuremaps': train_g_featuremaps, } d_params = { 'labels_dim': -1, 'resolutions': train_resolutions, - 'featuremaps': train_featuremaps, + 'featuremaps': train_d_featuremaps, } # prepare distribute strategy