diff --git a/.gitignore b/.gitignore
index 29dc5f63fdf56545bca96f32d2f97aa75d6ab191..0f2e01bf277d36d2bc642b01704ebc3491e67b53 100644
--- a/.gitignore
+++ b/.gitignore
@@ -130,5 +130,7 @@ data/
shapenet_part_seg_hdf5_data/
ShapeNet/
maciek_data
+nibio_data
+nibio_data_no_commas
```
diff --git a/PyG_implementation/my_data_loader.py b/PyG_implementation/my_data_loader.py
index cb1c7846ce5e441fe35ff8dd8d951e49eebe11ee..37ce63dac57d8bf591e8cabaaf19a33234eb197a 100644
--- a/PyG_implementation/my_data_loader.py
+++ b/PyG_implementation/my_data_loader.py
@@ -19,12 +19,14 @@ class MyData(InMemoryDataset):
def __init__(self,
root: str,
+ label_location: int = -1,
include_normals: bool = True,
split: str = 'trainval',
split_ratio: tuple = (0.7, 0.15, 0.15),
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None):
+ self.label_location = label_location
self.split_ratio = split_ratio
super().__init__(root, transform, pre_transform, pre_filter)
@@ -87,7 +89,7 @@ class MyData(InMemoryDataset):
data = read_txt_array(osp.join(self.raw_dir, name))
pos = data[:, :3]
x = data[:, 3:6]
- y = data[:, -1].type(torch.long)
+ y = data[:, self.label_location].type(torch.long)
category = torch.tensor(0, dtype=torch.long) # there is only one category !!
data = Data(pos=pos, x=x, y=y, category=category)
if self.pre_filter is not None and not self.pre_filter(data):
diff --git a/PyG_implementation/pyg_implementaion_main_my_data_loader.py b/PyG_implementation/pyg_implementaion_main_my_data_loader.py
index 63aed1992df033ba93bc4d5e6952551919c12322..564fdccd513684c84d4057699f29f2e91a1cded1 100644
--- a/PyG_implementation/pyg_implementaion_main_my_data_loader.py
+++ b/PyG_implementation/pyg_implementaion_main_my_data_loader.py
@@ -11,11 +11,12 @@ from torch_scatter import scatter
from torchmetrics.functional import jaccard_index
import torch_geometric.transforms as T
-from torch_geometric.datasets import ShapeNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, DynamicEdgeConv
+from my_data_loader import MyData
+
wandb_project = "pyg-point-cloud" #@param {"type": "string"} , maciej-wielgosz-nibio
wandb_run_name = "train-dgcnn" #@param {"type": "string"}
@@ -35,13 +36,12 @@ random.seed(config.seed)
torch.manual_seed(config.seed)
device = torch.device(config.device)
-config.category = 'Car' #@param ["Bag", "Cap", "Car", "Chair", "Earphone", "Guitar", "Knife", "Lamp", "Laptop", "Motorbike", "Mug", "Pistol", "Rocket", "Skateboard", "Table"] {type:"raw"}
config.random_jitter_translation = 1e-2
config.random_rotation_interval_x = 15
config.random_rotation_interval_y = 15
config.random_rotation_interval_z = 15
config.validation_split = 0.2
-config.batch_size = 4
+config.batch_size = 1
config.num_workers = 6
config.num_nearest_neighbours = 30
@@ -62,23 +62,10 @@ transform = T.Compose([
])
pre_transform = T.NormalizeScale()
-
-# dataset_path = os.path.join('ShapeNet', config.category)
-
-
-# train_val_dataset = ShapeNet(
-# dataset_path, config.category, split='trainval',
-# transform=transform, pre_transform=pre_transform
-# )
-
-from my_data_loader import MyData
-
dataset_path = "/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/maciek_data/plane_maciek"
+# dataset_path = "/home/nibio/mutable-outside-world/code/nibio_graph_sem_seg/nibio_data_no_commas"
-train_val_dataset = MyData(
- dataset_path, config.category, split='trainval',
- transform=transform, pre_transform=pre_transform
-)
+train_val_dataset = MyData(dataset_path, split='trainval', transform=transform, pre_transform=pre_transform)
segmentation_class_frequency = {}
@@ -111,7 +98,6 @@ visualization_loader = DataLoader(
)
-
class DGCNN(torch.nn.Module):
def __init__(self, out_channels, k=30, aggr='max'):
super().__init__()
@@ -197,9 +183,6 @@ def train_step(epoch):
iou = torch.tensor(ious, device=device)
category = torch.cat(categories, dim=0)
- print("iou shape:", iou.shape)
- print("category shape:", category.shape)
-
mean_iou = float(scatter(iou, category, reduce='mean').mean())
@@ -236,10 +219,8 @@ def val_step(epoch):
total_nodes += data.num_nodes
sizes = (data.ptr[1:] - data.ptr[:-1]).tolist()
- for out, y, category in zip(outs.split(sizes), data.y.split(sizes),
- data.category.tolist()):
- category = list(ShapeNet.seg_classes.keys())[category]
- part = ShapeNet.seg_classes[category]
+ for out, y in zip(outs.split(sizes), data.y.split(sizes)):
+ part = MyData.seg_classes
part = torch.tensor(part, device=device)
y_map[part] = torch.arange(part.size(0), device=device)
@@ -278,12 +259,10 @@ def visualization_step(epoch, table):
y_map = torch.empty(
visualization_loader.dataset.num_classes, device=device
).long()
- for out, y, category in zip(
- outs.split(sizes), data.y.split(sizes), data.category.tolist()
- ):
- category = list(ShapeNet.seg_classes.keys())[category]
- part = ShapeNet.seg_classes[category]
+ for out, y in zip(outs.split(sizes), data.y.split(sizes)):
+ part = MyData.seg_classes
part = torch.tensor(part, device=device)
+
y_map[part] = torch.arange(part.size(0), device=device)
iou = jaccard_index(
out[:, part].argmax(dim=-1), y_map[y],
diff --git a/PyG_implementation/remove_commas.py b/PyG_implementation/remove_commas.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cafefcf250cb0cb21f18327386716e289e1890c
--- /dev/null
+++ b/PyG_implementation/remove_commas.py
@@ -0,0 +1,31 @@
+import os
+import argparse
+from tqdm import tqdm
+
+def remove_commas(source_folder, target_folder):
+ # Create target folder if it doesn't exist
+ os.makedirs(target_folder, exist_ok=True)
+
+ # Iterate over all txt files in the source folder
+ for filename in os.listdir(source_folder):
+ if filename.endswith(".txt"):
+ # Open the source file and the target file
+ with open(os.path.join(source_folder, filename), 'r') as source_file, \
+ open(os.path.join(target_folder, filename), 'w') as target_file:
+ # Read each line from the source file
+ for line in tqdm(source_file):
+ # Replace commas with nothing
+ line_without_commas = line.replace(',', ' ')
+ # Write the new line to the target file
+ target_file.write(line_without_commas)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--source_folder", help="Folder with files that contain commas")
+ parser.add_argument("--target_folder", help="Folder with files that don't contain commas")
+ args = parser.parse_args()
+
+ remove_commas(args.source_folder, args.target_folder)
+
+ print(f"Files without commas are saved in : {args.target_folder}")
+
\ No newline at end of file