diff --git a/.idea/other.xml b/.idea/other.xml
new file mode 100644
index 0000000..a708ec7
--- /dev/null
+++ b/.idea/other.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="PySciProjectComponent">
+ <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/dataset/shapenet.py b/dataset/shapenet.py
index 129bd84..931237c 100644
--- a/dataset/shapenet.py
+++ b/dataset/shapenet.py
@@ -10,6 +10,12 @@ import torch
from torch_geometric.data import InMemoryDataset
from torch_geometric.data import Data
from torch.utils.data import Dataset
+import re
+
+
+def save_names(name_list, path):
+ with open(path, 'wb'):
+ pass
class CustomShapeNet(InMemoryDataset):
@@ -181,10 +187,8 @@ class ShapeNetPartSegDataset(Dataset):
class PredictionShapeNet(InMemoryDataset):
- categories = {key: val for val, key in enumerate(['Box', 'Cone', 'Cylinder', 'Sphere'])}
- def __init__(self, root, transform=None, pre_filter=None, pre_transform=None,
- headers=True, **kwargs):
+ def __init__(self, root, transform=None, pre_filter=None, pre_transform=None, headers=True):
self.has_headers = headers
super(PredictionShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0]
@@ -226,57 +230,59 @@ class PredictionShapeNet(InMemoryDataset):
def process(self, delimiter=' '):
- datasets = defaultdict(list)
- for idx, setting in enumerate(self.raw_file_names):
- path_to_clouds = os.path.join(self.raw_dir, setting)
+ datasets, filenames = defaultdict(list), []
+ path_to_clouds = os.path.join(self.raw_dir, self.raw_file_names[0])
- if '.headers' in os.listdir(path_to_clouds):
- self.has_headers = True
- elif 'no.headers' in os.listdir(path_to_clouds):
- self.has_headers = False
- else:
- pass
+ if '.headers' in os.listdir(path_to_clouds):
+ self.has_headers = True
+ elif 'no.headers' in os.listdir(path_to_clouds):
+ self.has_headers = False
+ else:
+ pass
- for pointcloud in tqdm(os.scandir(path_to_clouds)):
- if not os.path.isdir(pointcloud):
+ for pointcloud in tqdm(os.scandir(path_to_clouds)):
+ if not os.path.isdir(pointcloud):
+ continue
+ full_cloud_pattern = '\d+?_pc\.(xyz|dat)'
+ pattern = re.compile(full_cloud_pattern)
+ for file in os.scandir(pointcloud.path):
+ if not pattern.match(file.name):
continue
- for extention in ['dat', 'xyz']:
- file = os.path.join(pointcloud.path, f'pc.{extention}')
- if not os.path.exists(file):
- continue
- with open(file, 'r') as f:
- if self.has_headers:
- headers = f.__next__()
- # Check if there are no useable nodes in this file, header says 0.
- if not int(headers.rstrip().split(delimiter)[0]):
- continue
+ with open(file, 'r') as f:
+ if self.has_headers:
+ headers = f.__next__()
+ # Check if there are no useable nodes in this file, header says 0.
+ if not int(headers.rstrip().split(delimiter)[0]):
+ continue
- # Iterate over all rows
- src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
- for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
- points = torch.tensor(src, dtype=None).squeeze()
- if not len(points.shape) > 1:
- continue
- # pos = points[:, :3]
- # norm = points[:, 3:]
- y_fake_all = [-1] * points.shape[0]
- y = torch.as_tensor(y_fake_all, dtype=torch.int)
- # points = torch.as_tensor(points, dtype=torch.float)
- # norm = torch.as_tensor(norm, dtype=torch.float)
- data = Data(y=y, pos=points[:, :3])
- # , points=points, norm=points[:3], )
- # ToDo: ANy filter to apply? Then do it here.
- if self.pre_filter is not None and not self.pre_filter(data):
- data = self.pre_filter(data)
- raise NotImplementedError
- # ToDo: ANy transformation to apply? Then do it here.
- if self.pre_transform is not None:
- data = self.pre_transform(data)
- raise NotImplementedError
- datasets[setting].append(data)
+ # Iterate over all rows
+ src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
+ for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
+ points = torch.tensor(src, dtype=None).squeeze()
+ if not len(points.shape) > 1:
+ continue
+ # pos = points[:, :3]
+ # norm = points[:, 3:]
+ y_fake_all = [-1] * points.shape[0]
+ y = torch.as_tensor(y_fake_all, dtype=torch.int)
+ # points = torch.as_tensor(points, dtype=torch.float)
+ # norm = torch.as_tensor(norm, dtype=torch.float)
+ data = Data(y=y, pos=points[:, :3])
+ # , points=points, norm=points[:3], )
+ # ToDo: ANy filter to apply? Then do it here.
+ if self.pre_filter is not None and not self.pre_filter(data):
+ data = self.pre_filter(data)
+ raise NotImplementedError
+ # ToDo: ANy transformation to apply? Then do it here.
+ if self.pre_transform is not None:
+ data = self.pre_transform(data)
+ raise NotImplementedError
+ datasets[self.raw_file_names[0]].append(data)
+ filenames.append(file)
os.makedirs(self.processed_dir, exist_ok=True)
- torch.save(self.collate(datasets[setting]), self.processed_paths[idx])
+ torch.save(self.collate(datasets[self.raw_file_names[0]]), self.processed_paths[0])
+ # save_names(filenames)
def __repr__(self):
return f'{self.__class__.__name__}({len(self)})'
@@ -287,11 +293,11 @@ class PredictNetPartSegDataset(Dataset):
Resample raw point cloud to fixed number of points.
Map raw label from range [1, N] to [0, N-1].
"""
- def __init__(self, root_dir, train=False, transform=None, npoints=2048, headers=True, collate_per_segment=False):
+ def __init__(self, root_dir, num_classes, transform=None, npoints=2048, headers=True):
super(PredictNetPartSegDataset, self).__init__()
self.npoints = npoints
- self.dataset = PredictionShapeNet(root=root_dir, train=train, transform=transform,
- headers=headers, collate_per_segment=collate_per_segment)
+ self._num_classes = num_classes
+ self.dataset = PredictionShapeNet(root=root_dir, transform=transform, headers=headers)
def __getitem__(self, index):
data = self.dataset[index]
@@ -311,11 +317,10 @@ class PredictNetPartSegDataset(Dataset):
'points': points, # torch.Tensor (n, 3)
'labels': labels # torch.Tensor (n,)
}
-
return sample
def __len__(self):
return len(self.dataset)
def num_classes(self):
- return self.dataset.num_classes
+ return self._num_classes
diff --git a/predict/checkpoint/seg_model_custom_24.pth b/predict/checkpoint/seg_model_custom_24.pth
new file mode 100644
index 0000000..ef6d118
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_24.pth differ
diff --git a/predict/checkpoint/seg_model_custom_249.pth b/predict/checkpoint/seg_model_custom_249.pth
new file mode 100644
index 0000000..13a8c8f
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_249.pth differ
diff --git a/predict/checkpoint/seg_model_custom_57.pth b/predict/checkpoint/seg_model_custom_57.pth
new file mode 100644
index 0000000..32aada4
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_57.pth differ
diff --git a/predict/checkpoint/seg_model_custom_7.pth b/predict/checkpoint/seg_model_custom_7.pth
new file mode 100644
index 0000000..60f7655
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_7.pth differ
diff --git a/predict/checkpoint/seg_model_custom_8.pth b/predict/checkpoint/seg_model_custom_8.pth
new file mode 100644
index 0000000..f35d0da
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_8.pth differ
diff --git a/predict/checkpoint/seg_model_custom_all_20.pth b/predict/checkpoint/seg_model_custom_all_20.pth
new file mode 100644
index 0000000..66c7db5
Binary files /dev/null and b/predict/checkpoint/seg_model_custom_all_20.pth differ
diff --git a/predict/data/processed/predict.pt b/predict/data/processed/predict.pt
new file mode 100644
index 0000000..f7386b1
Binary files /dev/null and b/predict/data/processed/predict.pt differ
diff --git a/predict/data/raw/predict/580_9/100_pc.xyz b/predict/data/raw/predict/580_9/100_pc.xyz
new file mode 100644
index 0000000..118265b
--- /dev/null
+++ b/predict/data/raw/predict/580_9/100_pc.xyz
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diff --git a/predict/data/raw/predict/no.headers b/predict/data/raw/predict/no.headers
new file mode 100644
index 0000000..e69de29
diff --git a/predict/predict.py b/predict/predict.py
new file mode 100644
index 0000000..739c23d
--- /dev/null
+++ b/predict/predict.py
@@ -0,0 +1,79 @@
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add project root directory
+
+from dataset.shapenet import PredictNetPartSegDataset, ShapeNetPartSegDataset
+from model.pointnet2_part_seg import PointNet2PartSegmentNet
+import torch_geometric.transforms as GT
+import torch
+import numpy as np
+import argparse
+
+##
+parser = argparse.ArgumentParser()
+parser.add_argument('--dataset', type=str, default='data', help='dataset path')
+parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
+parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_249.pth', help='model path')
+parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
+opt = parser.parse_args()
+print(opt)
+
+if __name__ == '__main__':
+
+ # Load dataset
+ print('Construct dataset ..')
+ test_transform = GT.Compose([GT.NormalizeScale(),])
+
+ test_dataset = PredictNetPartSegDataset(
+ root_dir=opt.dataset,
+ num_classes=4,
+ transform=None,
+ npoints=opt.npoints
+ )
+ num_classes = test_dataset.num_classes()
+
+ print('test dataset size: ', len(test_dataset))
+
+ # Load model
+ print('Construct model ..')
+ device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+ dtype = torch.float
+
+ # net = PointNetPartSegmentNet(num_classes)
+ net = PointNet2PartSegmentNet(num_classes)
+
+ net.load_state_dict(torch.load(opt.model, map_location=device.type))
+ net = net.to(device, dtype)
+ net.eval()
+
+ ##
+ def eval_sample(net, sample):
+ '''
+ sample: { 'points': tensor(n, 3), 'labels': tensor(n,) }
+ return: (pred_label, gt_label) with labels shape (n,)
+ '''
+ net.eval()
+ with torch.no_grad():
+ # points: (n, 3)
+ points, gt_label = sample['points'], sample['labels']
+ n = points.shape[0]
+
+ points = points.view(1, n, 3) # make a batch
+ points = points.transpose(1, 2).contiguous()
+ points = points.to(device, dtype)
+
+ pred = net(points) # (batch_size, n, num_classes)
+ pred_label = pred.max(2)[1]
+ pred_label = pred_label.view(-1).cpu() # (n,)
+
+ assert pred_label.shape == gt_label.shape
+ return (pred_label, gt_label)
+
+ # Iterate over all the samples
+ for sample in test_dataset:
+ print('Eval test sample ..')
+ pred_label, gt_label = eval_sample(net, sample)
+ print('Eval done ..')
+
+ pred_labels = pred_label.numpy()
+ print(pred_labels)