.gitignore

@@ -13,9 +13,6 @@ ShapeNetCore.v2.PC15k*
 checkpoints
 
 *.txt
-*.vtk
-
-.ruff_cache
 
 # https://github.com/github/gitignore/blob/main/Python.gitignore
 # Basic .gitignore for a python repo.

.vscode/launch.json

@@ -13,7 +13,7 @@
       "justMyCode": true,
       "args": [
         "--model",
-        "/gpfs_new/data/users/lfainsin/PVD/output/train_generation/2023-04-14-15-02-19/epoch_499.pth",
+        "output/train_generation/2023-04-11-23-38-23/epoch_99.pth",
         "--generate",
         "True",
         "--workers",

.vscode/settings.json

@@ -33,6 +33,5 @@
     "**/.hg/store/**": true,
     "**/output/**": true,
     "**/ShapeNetCore.v2.PC15k/**": true,
-    "**/.ruff_cache/**": true,
   }
 }

compare_samples.py

@@ -1,39 +0,0 @@
-from pathlib import Path
-
-import numpy as np
-import pyvista as pv
-
-VTKFILE_NOMINAL = Path("~/data/stage-laurent-f/datasets/Rotor37/processed/nominal_blade_rotated.vtk")
-
-# load nominal blade
-nominal = pv.read(VTKFILE_NOMINAL)
-
-# for each generated/sampled blade
-gen_files = Path("./output").glob("gen*.txt")
-pc_files = Path("./output").glob("pc*.txt")
-files = list(gen_files) + list(pc_files)
-for gen_file in files:
-    # load numpy txt
-    blade = np.loadtxt(gen_file)
-
-    # get top and bottom index (filter outliers a bit)
-    top = int(blade.shape[0] * 0.001)
-    bot = int(blade.shape[0] * 0.999)
-
-    # sort blade coordinates
-    sorted_blade = np.sort(blade, axis=0)
-
-    # get center of blade
-    center = (sorted_blade[bot, :] + sorted_blade[top, :]) / 2
-
-    # translate blade to world origin
-    blade -= center
-
-    # save to txt
-    np.savetxt(f"output/test_{gen_file.stem}.txt", blade)
-
-    # swap nominal points to blade points
-    nominal.points = blade
-
-    # save altered blade to vtk
-    nominal.save(f"output/test_{gen_file.stem}.vtk")

dataset/test_rotor37_data.py

@@ -1,6 +1,5 @@
 import datasets
 import numpy as np
-from rotor37_data import MEAN, STD
 
 test_ds = datasets.load_dataset("dataset/rotor37_data.py", split="test")
 test_ds = test_ds.with_format("torch")
@@ -15,10 +14,10 @@ for idx, blade in enumerate(test_ds):
     pc = blade["positions"]
 
     # unnormalize
-    pc = pc * STD + MEAN
+    pc = pc * blade["std"] + blade["mean"]
 
     print(f"Saving point cloud {idx}...")
-    np.savetxt(f"output/pc_{idx}.txt", pc)
+    np.savetxt(f"pc_{idx}.txt", pc)
 
     if idx >= 10:
         break

environment.yml

@@ -7,12 +7,8 @@ channels:
   - nvidia
   - conda-forge
   - pyg
-  - HuggingFace
 
 dependencies:
-#---# compiler
-  - ninja
-#---# python libs
   - python
   - pytorch
   - torchvision

test_generation.py

@@ -323,9 +323,6 @@ class Model(nn.Module):
             extra_feature_channels=0,
         )
 
-        pytorch_total_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
-        print("Total params: {}".format(pytorch_total_params))
-
     def prior_kl(self, x0):
         return self.diffusion._prior_bpd(x0)
 
@@ -513,8 +510,7 @@ def generate(model, opt):
             x = data["positions"].transpose(1, 2)
             # m, s = data["mean"].float(), data["std"].float()
 
-            shape = torch.Size((*x.shape[:-1], 75000))
-            gen = model.gen_samples(shape, "cuda", clip_denoised=False).detach().cpu()
+            gen = model.gen_samples(x.shape, "cuda", clip_denoised=False).detach().cpu()
 
             gen = gen.transpose(1, 2).contiguous()
             x = x.transpose(1, 2).contiguous()
@@ -532,7 +528,7 @@ def generate(model, opt):
                 pc = pc * STD + MEAN
 
                 print(f"Saving point cloud {idx}...")
-                np.savetxt(f"output/gen_{i}_{idx}.txt", pc)
+                np.savetxt(f"gen_{i}_{idx}.txt", pc)
 
                 if idx >= 10:
                     break

train_generation.py

@@ -708,6 +708,7 @@ def train(gpu, opt, output_dir):
         lr_scheduler.step(epoch)
 
         for i, data in enumerate(dataloader):
+            # x = data["train_points"].transpose(1, 2)
             x = data["positions"].transpose(1, 2)
             noises_batch = torch.randn_like(x)