disable buggy GPU

cluster/base.py

@@ -18,7 +18,7 @@ GPUS = {'v100-p16': ('\"Tesla V100-PCIE-16GB\"', 'tesla', 16000),
         'v100-p32': ('\"Tesla V100-PCIE-32GB\"', 'tesla', 32000),
         'v100-s32': ('\"Tesla V100-SXM2-32GB\"', 'tesla', 32000),
         # 'a100-sm80': ('\"NVIDIA A100-SXM-80GB\"', 'nvidia', 80000),
-        'a100-sxm40': ('\"NVIDIA A100-SXM4-40GB\"', 'nvidia', 40000),
+        #'a100-sxm40': ('\"NVIDIA A100-SXM4-40GB\"', 'nvidia', 40000),
         'quadro6000': ('\"Quadro RTX 6000\"', 'quadro', 24000),
         'rtx2080ti': ('\"NVIDIA GeForce RTX 2080 Ti\"', 'rtx', 11000)
         }

temos/render/blender/meshes.py

@@ -64,7 +64,7 @@ class Meshes:
         # cmap = matplotlib.cm.get_cmap('Blues')
         if self.mode == 'sequence':
             cmap = self.colormap
-            begin = 0.60
+            begin = 0.70
             end = 0.90
             rgbcolor = cmap(begin + (end-begin)*frac)
             mat = colored_material(*rgbcolor)

temos/render/blender/render.py

@@ -109,6 +109,10 @@ def render(npydata, frames_folder, *, mode, faces_path,
             if not separate_actions:
                 factor=3.0 # 3.0 for 2 action sin data
                 shift_vals = factor * np.linspace(-total_num_of_rendered_frames/2, total_num_of_rendered_frames/2, total_num_of_rendered_frames)/total_num_of_rendered_frames
+                #shift_vals = factor * np.linspace(0, total_num_of_rendered_frames, total_num_of_rendered_frames)/total_num_of_rendered_frames
+
+                shift_vals[(total_num_of_rendered_frames//2):] *= 1.3
+                shift_vals[:(total_num_of_rendered_frames//2)] *= 0.8
                 for action_bodies, shift in zip(actions_bodies, [shift_vals[num*idx:num*(idx+1)] for idx in range(num_of_actions)]):
                     # put the fake translation
                     # and still 0 for gravity axis

train.py

@@ -8,7 +8,6 @@ from temos.launch.prepare import get_last_checkpoint
 from hydra.utils import to_absolute_path
 from pathlib import Path
 from typing import Optional
-from pytorch_lightning.callbacks import LearningRateMonitor
 
 logger = logging.getLogger(__name__)