fix dataset last batch check

README.md

@@ -43,7 +43,11 @@ You can type as below to see the optional arguments for training:
 If you have multiple GPUs on your machine, you can also run the multi-gpu version (similar implementation to the tensorflow cifar10 tutorial):
 
         python train_multi_gpu.py --num_gpus 2
-
+
+After training, we can evaluate the classification results (with optional multi-angle voting).
+
+        python evaluate.py --num_votes 12 
+
 <i>Side Note:</i> For the XYZ+normal experiment reported in our paper: (1) 5000 points are used and (2) a further random data dropout augmentation is used during training (see commented line after `augment_batch_data` in `train.py` and (3) the model architecture is updated such that the `nsample=128` in the first two set abstraction levels, which is suited for the larger point density in 5000-point samplings.
 
 To use normal features for classification: You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) at this <a href="https://1drv.ms/u/s!ApbTjxa06z9CgQfKl99yUDHL_wHs">OneDrive link</a>. Move the uncompressed data folder to `data/modelnet40_normal_resampled`

evaluate.py

@@ -23,11 +23,12 @@
 parser = argparse.ArgumentParser()
 parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
 parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name. [default: pointnet2_cls_ssg]')
-parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 1]')
+parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]')
 parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
 parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
 parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
 parser.add_argument('--normal', action='store_true', help='Whether to use normal information')
+parser.add_argument('--num_votes', type=int, default=1, help='Aggregate classification scores from multiple rotations [default: 1]')
 FLAGS = parser.parse_args()
 
 
@@ -73,7 +74,7 @@ def evaluate(num_votes):
         # simple model
         pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
         MODEL.get_loss(pred, labels_pl, end_points)
-        losses = tf.get_collection('losses', scope)
+        losses = tf.get_collection('losses')
         total_loss = tf.add_n(losses, name='total_loss')
 
         # Add ops to save and restore all the variables.
@@ -116,7 +117,7 @@ def eval_one_epoch(sess, ops, num_votes=1, topk=1):
     while TEST_DATASET.has_next_batch():
         batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
         bsize = batch_data.shape[0]
-        print('Batch: %d\t batch_size: %d'%(batch_idx, bsize))
+        print('Batch: %03d, batch size: %d'%(batch_idx, bsize))
         # for the last batch in the epoch, the bsize:end are from last batch
         cur_batch_data[0:bsize,...] = batch_data
         cur_batch_label[0:bsize] = batch_label
@@ -159,5 +160,5 @@ def eval_one_epoch(sess, ops, num_votes=1, topk=1):
 
 if __name__=='__main__':
     with tf.Graph().as_default():
-        evaluate(num_votes=1)
+        evaluate(num_votes=FLAGS.num_votes)
     LOG_FOUT.close()

modelnet_h5_dataset.py

@@ -90,7 +90,7 @@ def _load_data_file(self, filename):
             self.current_data, self.current_label, _ = shuffle_data(self.current_data,self.current_label)
 
     def _has_next_batch_in_file(self):
-        return (self.batch_idx+1)*self.batch_size <= self.current_data.shape[0]
+        return self.batch_idx*self.batch_size < self.current_data.shape[0]
 
     def num_channel(self):
         return 3

train.py

@@ -120,7 +120,7 @@ def train():
             # Get model and loss 
             pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
             MODEL.get_loss(pred, labels_pl, end_points)
-            losses = tf.get_collection('losses', scope)
+            losses = tf.get_collection('losses')
             total_loss = tf.add_n(losses, name='total_loss')
             tf.summary.scalar('total_loss', total_loss)
             for l in losses + [total_loss]: