Python Examples of provider.loadDataFile

Source File: generate_dataset.py From pointnet-registration-framework with MIT License

6 votes

def find_models(category, model, templates, case):
	# model:		No of models to be stored for a particular category.
	# category: 	Name of the category to be stored.
	# templates:	Array having templates (BxNx3)
	# case:			Which files to be used? (test/train)

	if case == 'test':
		FILES = TEST_FILES
	if case == 'train':
		FILES = TRAIN_FILES
	print(FILES)
	count = 0														# Counter to find number of models.
	for train_idx in range(len(FILES)):						# Loop over all the training files from ModelNet40 data.
		current_data, current_label = provider.loadDataFile(FILES[train_idx])		# Load data of from a file.
		for i in range(current_data.shape[0]):
			if count<model and shapes.index(category)==current_label[i]:
				# import transforms3d.euler as t3d 
				# rot = t3d.euler2mat(0*np.pi/1	80, 0*np.pi/180, 90*np.pi/180, 'szyx')
				# templates.append((np.dot(rot, current_data[i].T).T))
				templates.append(current_data[i]/2.0)				# Append data if it belongs to the category and less than given number of models.
				count += 1
	return templates

Source File: sem_seg_util.py From deep_gcns with MIT License

6 votes

def load_data(all_files, room_filelist, test_area_idx):
  # Load all data
  data_batch_list = []
  label_batch_list = []
  for h5_filename in all_files:
    data_batch, label_batch = provider.loadDataFile(h5_filename)
    data_batch_list.append(data_batch)
    label_batch_list.append(label_batch)
  data_batches = np.concatenate(data_batch_list, 0)
  label_batches = np.concatenate(label_batch_list, 0)

  test_area = 'Area_'+test_area_idx
  train_idxs = []
  test_idxs = []
  for i,room_name in enumerate(room_filelist):
    if test_area in room_name:
      test_idxs.append(i)
    else:
      train_idxs.append(i)

  return data_batches[train_idxs,...], label_batches[train_idxs], data_batches[test_idxs,...], label_batches[test_idxs]

Source File: generate_dataset.py From pcrnet with MIT License

6 votes

def find_models(category, model, templates, case):
	# model:		No of models to be stored for a particular category.
	# category: 	Name of the category to be stored.
	# templates:	Array having templates (BxNx3)
	# case:			Which files to be used? (test/train)

	if case == 'test':
		FILES = TEST_FILES
	if case == 'train':
		FILES = TRAIN_FILES
	print(FILES)
	count = 0														# Counter to find number of models.
	for train_idx in range(len(FILES)):						# Loop over all the training files from ModelNet40 data.
		current_data, current_label = provider.loadDataFile(FILES[train_idx])		# Load data of from a file.
		for i in range(current_data.shape[0]):
			if count<model and shapes.index(category)==current_label[i]:
				# import transforms3d.euler as t3d 
				# rot = t3d.euler2mat(0*np.pi/1	80, 0*np.pi/180, 90*np.pi/180, 'szyx')
				# templates.append((np.dot(rot, current_data[i].T).T))
				templates.append(current_data[i]/2.0)				# Append data if it belongs to the category and less than given number of models.
				count += 1
	return templates

Source File: generate_dataset.py From pointnet-registration-framework with MIT License

5 votes

def loadDataFile(self, filename):
		return load_h5(filename)

Source File: ply_dataset.py From chainer-pointnet with MIT License

5 votes

def __init__(self, h5_filepath, num_point=1024, augment=False):
        print('loading ', h5_filepath)
        data, label = provider.loadDataFile(h5_filepath)
        assert len(data) == len(label)
        # data: (2048, 2048, 3) - (batchsize, point, xyz)
        # Reduce num point here.
        self.data = data[:, :num_point, :].astype(np.float32)
        # (2048,) - (batchsize,)
        self.label = np.squeeze(label).astype(np.int32)
        self.augment = augment
        self.num_point = num_point
        self.length = len(data)
        print('length ', self.length)

Source File: train.py From PointCNN.Pytorch with MIT License

5 votes

def eval_one_epoch(sess, ops, test_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = False
    total_correct = 0
    total_seen = 0
    loss_sum = 0
    total_seen_class = [0 for _ in range(NUM_CLASSES)]
    total_correct_class = [0 for _ in range(NUM_CLASSES)]
    
    for fn in range(len(TEST_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
        current_data = current_data[:,0:NUM_POINT,:]
        current_label = np.squeeze(current_label)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE

            feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: is_training}
            summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                ops['loss'], ops['pred']], feed_dict=feed_dict)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += (loss_val*BATCH_SIZE)
            for i in range(start_idx, end_idx):
                l = current_label[i]
                total_seen_class[l] += 1
                total_correct_class[l] += (pred_val[i-start_idx] == l)
            
    log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
    log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
    log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))

Source File: generate_dataset.py From pcrnet with MIT License

5 votes

def loadDataFile(self, filename):
		return self.load_h5(filename)