Final *hopefully* adjustments

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)