add show classification results

Author: fxia22

show_cls.py

@@ -0,0 +1,51 @@
+from __future__ import print_function
+import argparse
+import os
+import random
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.backends.cudnn as cudnn
+import torch.optim as optim
+import torch.utils.data
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from torch.autograd import Variable
+from datasets import PartDataset
+from pointnet import PointNetCls
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+
+
+#showpoints(np.random.randn(2500,3), c1 = np.random.uniform(0,1,size = (2500)))
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('--model', type=str, default = '',  help='model path')
+
+
+opt = parser.parse_args()
+print (opt)
+
+test_dataset = PartDataset(root = 'shapenetcore_partanno_segmentation_benchmark_v0' , train = False, classification = True)
+
+testdataloader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle = False)
+
+
+classifier = PointNetCls(k = len(test_dataset.classes))
+classifier.cuda()
+classifier.load_state_dict(torch.load(opt.model))
+classifier.eval()
+
+for i, data in enumerate(testdataloader, 0):
+    points, target = data
+    points, target = Variable(points), Variable(target[:,0])
+    points = points.transpose(2,1)
+    points, target = points.cuda(), target.cuda()
+    pred, _ = classifier(points)
+    loss = F.nll_loss(pred, target)
+    pred_choice = pred.data.max(1)[1]
+    correct = pred_choice.eq(target.data).cpu().sum()
+    print('i:%d  loss: %f accuracy: %f' %(i, loss.data[0], correct/float(32)))