91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
import sys
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import os
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add project root directory
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from dataset.shapenet import ShapeNetPartSegDataset
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from model.pointnet2_part_seg import PointNet2PartSegmentNet
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import torch_geometric.transforms as GT
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import torch
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from distutils.util import strtobool
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import numpy as np
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import argparse
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##
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parser = argparse.ArgumentParser()
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parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
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parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_3.pth', help='model path')
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parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
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parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers')
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parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub')
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parser.add_argument('--has_variations', type=strtobool, default=False,
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help='whether a single pointcloud has variations '
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'named int(id)_pc.(xyz|dat) look at pointclouds or sub')
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opt = parser.parse_args()
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print(opt)
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if __name__ == '__main__':
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# Load dataset
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print('Construct dataset ..')
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test_transform = GT.Compose([GT.NormalizeScale(),])
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test_dataset = ShapeNetPartSegDataset(
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mode='predict',
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root_dir='data',
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npoints=opt.npoints,
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refresh=True,
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collate_per_segment=opt.collate_per_segment,
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has_variations=opt.has_variations,
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headers=opt.headers
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)
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num_classes = test_dataset.num_classes()
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print('test dataset size: ', len(test_dataset))
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# Load model
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print('Construct model ..')
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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dtype = torch.float
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# net = PointNetPartSegmentNet(num_classes)
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net = PointNet2PartSegmentNet(num_classes)
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net.load_state_dict(torch.load(opt.model, map_location=device.type))
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net = net.to(device, dtype)
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net.eval()
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##
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def eval_sample(net, sample):
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'''
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sample: { 'points': tensor(n, 3), 'labels': tensor(n,) }
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return: (pred_label, gt_label) with labels shape (n,)
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'''
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net.eval()
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with torch.no_grad():
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# points: (n, 3)
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points, gt_label = sample['points'], sample['labels']
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n = points.shape[0]
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points = points.view(1, n, 3) # make a batch
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points = points.transpose(1, 2).contiguous()
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points = points.to(device, dtype)
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pred = net(points) # (batch_size, n, num_classes)
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pred_label = pred.max(2)[1]
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pred_label = pred_label.view(-1).cpu() # (n,)
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assert pred_label.shape == gt_label.shape
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return (pred_label, gt_label)
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# Iterate over all the samples
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for sample in test_dataset:
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print('Eval test sample ..')
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pred_label, gt_label = eval_sample(net, sample)
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print('Eval done ..')
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pred_labels = pred_label.numpy()
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print(pred_labels)
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