train with fixed reg losses

configs/direct_7.py

@@ -2,9 +2,9 @@ from utils.convnext_utils import CNBlockConfig
 
 config = dict()
 
-config['gpu'] = '6'
-config['name'] = 'direct_7'
-config['notes'] = 'with normals input but only single garment sequence with msssim and l2 mesh loss only and without skinning' 
+config['gpu'] = '0'
+config['name'] = 'direct_9'
+config['notes'] = 'with normals input whole dataset with msssim and l2 mesh loss and reg losses and without skinning' 
 config['tags'] = ['torch', 'direct'] 
 config['script'] = 'scripts/direct_train.py'
 
@@ -20,12 +20,12 @@ config['batch_size'] = 4
 config['train_dir'] = '/data-local/127/train'
 config['valid_dir'] = '/data-local/127/train'
 config['test_dir'] = '/data-net/dataset/test_ready'
-config['train_frames'] = 'frames_00761.txt'
-config['valid_frames'] = 'frames_00761.txt'
+config['train_frames'] = None
+config['valid_frames'] = None
 
 config['resolution'] = 512
 config['is_skirt'] = False
-config['dataset_in_memory'] = True
+config['dataset_in_memory'] = False
 config['is_cape'] = False
 config['remove_global_orientation'] = True
 config['device'] = 'cuda'

scripts/direct_train.py

@@ -13,7 +13,7 @@ args = vars(parser.parse_args())
 config_module = importlib.import_module(args['config'])
 config = config_module.config
 if args['debug']:
-    from configs.direct_8 import config
+    from configs.direct_7 import config
     # from configs.ae_static_orig_2 import config
 os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
@@ -98,8 +98,15 @@ def train_step(batch):
 
     l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
 
+    e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
+    c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
+    c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
+    # l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
+    n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
+  
+
     ms_ssim = torch.mean(_MSSSIM(pred + 1, uv_orig + 1))
-    loss = l2_mesh_loss + (1 - ms_ssim) * 1e3
+    loss = l2_mesh_loss + (1 - ms_ssim) * 1e3 + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
 
     optimizer.zero_grad()
     loss.backward()
@@ -112,12 +119,7 @@ def train_step(batch):
     model.eval()
     with torch.inference_mode():
         metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
-        e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
-        c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
-        c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
-        # l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
-        n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
-  
+
     return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/euclid_error': metric, 'train/collision_ratio': c_ratio,
              'train/ms_ssim': ms_ssim, 'train/collision_loss': c_loss, 'train/l2_mesh_loss': l2_mesh_loss,
              'train/normal_loss': n_loss }
@@ -157,7 +159,7 @@ def valid_step(batch):
         n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
 
         ms_ssim = torch.mean(_MSSSIM(pred + 1, uv_orig + 1))
-        loss = l2_mesh_loss + (1 - ms_ssim) * 1e3
+        loss = l2_mesh_loss + (1 - ms_ssim) * 1e3 + (e_loss + c_loss + n_loss) * config['reg_loss_coeff']
 
         metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
     
@@ -225,8 +227,8 @@ def plot_everything(batch: dict, type: str):
     collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))]
     collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))]
 
-    faces_ = np.load('/data-net/dataset/00761/faces/00761.npy')
-    faces = [faces_ for i in range(len(garment_posed))]
+    # faces_ = np.load('/data-net/dataset/00761/faces/00761.npy')
+    # faces = [faces_ for i in range(len(garment_posed))]
     collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(garment_posed))]
 
     figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }