这是报错信息：Traceback (most recent call last):
File "main_SemanticKITTI.py", line 206, in <module>
dataset.init_input_pipeline()
File "main_SemanticKITTI.py", line 176, in init_input_pipeline
self.batch_train_data = self.batch_train_data.map(map_func=map_func)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1861, in map
return MapDataset(self, map_func, preserve_cardinality=True)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4985, in __init__
use_legacy_function=use_legacy_function)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4218, in __init__
self._function = fn_factory()
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 3151, in get_concrete_function
*args, **kwargs)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 3116, in _get_concrete_function_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 3463, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 3308, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 1007, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4195, in wrapped_fn
ret = wrapper_helper(*args)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4125, in wrapper_helper
ret = autograph.tf_convert(self._func, ag_ctx)(*nested_args)
File "/root/miniconda3/envs/randlanet/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py", line 695, in wrapper
raise e.ag_error_metadata.to_exception(e)
AttributeError: in user code:
main_SemanticKITTI.py:145 tf_map *
up_i = tf.py_func(DP.knn_search, [sub_points, batch_pc, 1], tf.int32)
AttributeError: module 'tensorflow' has no attribute 'py_func'
这是代码：from helper_tool import DataProcessing as DP
from helper_tool import ConfigSemanticKITTI as cfg
from helper_tool import Plot
from os.path import join
from RandLANet import Network
from tester_SemanticKITTI import ModelTester
import tensorflow as tf
import numpy as np
import os, argparse, pickle
class SemanticKITTI:
def __init__(self, test_id):
self.name = 'SemanticKITTI'
self.dataset_path = '/root/autodl-tmp/RandLA-Net-master/data/semantic_kitti/dataset/sequences_0.06'
self.label_to_names = {0: 'unlabeled',
1: 'car',
2: 'bicycle',
3: 'motorcycle',
4: 'truck',
5: 'other-vehicle',
6: 'person',
7: 'bicyclist',
8: 'motorcyclist',
9: 'road',
10: 'parking',
11: 'sidewalk',
12: 'other-ground',
13: 'building',
14: 'fence',
15: 'vegetation',
16: 'trunk',
17: 'terrain',
18: 'pole',
19: 'traffic-sign'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)}
self.ignored_labels = np.sort([0])
self.val_split = '08'
self.seq_list = np.sort(os.listdir(self.dataset_path))
self.test_scan_number = str(test_id)
self.train_list, self.val_list, self.test_list = DP.get_file_list(self.dataset_path,
self.test_scan_number)
self.train_list = DP.shuffle_list(self.train_list)
self.val_list = DP.shuffle_list(self.val_list)
self.possibility = []
self.min_possibility = []
# Generate the input data flow
def get_batch_gen(self, split):
if split == 'training':
num_per_epoch = int(len(self.train_list) / cfg.batch_size) * cfg.batch_size
path_list = self.train_list
elif split == 'validation':
num_per_epoch = int(len(self.val_list) / cfg.val_batch_size) * cfg.val_batch_size
cfg.val_steps = int(len(self.val_list) / cfg.batch_size)
path_list = self.val_list
elif split == 'test':
num_per_epoch = int(len(self.test_list) / cfg.val_batch_size) * cfg.val_batch_size * 4
path_list = self.test_list
for test_file_name in path_list:
points = np.load(test_file_name)
self.possibility += [np.random.rand(points.shape[0]) * 1e-3]
self.min_possibility += [float(np.min(self.possibility[-1]))]
def spatially_regular_gen():
# Generator loop
for i in range(num_per_epoch):
if split != 'test':
cloud_ind = i
pc_path = path_list[cloud_ind]
pc, tree, labels = self.get_data(pc_path)
# crop a small point cloud
pick_idx = np.random.choice(len(pc), 1)
selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
else:
cloud_ind = int(np.argmin(self.min_possibility))
pick_idx = np.argmin(self.possibility[cloud_ind])
pc_path = path_list[cloud_ind]
pc, tree, labels = self.get_data(pc_path)
selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
# update the possibility of the selected pc
dists = np.sum(np.square((selected_pc - pc[pick_idx]).astype(np.float32)), axis=1)
delta = np.square(1 - dists / np.max(dists))
self.possibility[cloud_ind][selected_idx] += delta
self.min_possibility[cloud_ind] = np.min(self.possibility[cloud_ind])
if True:
yield (selected_pc.astype(np.float32),
selected_labels.astype(np.int32),
selected_idx.astype(np.int32),
np.array([cloud_ind], dtype=np.int32))
gen_func = spatially_regular_gen
gen_types = (tf.float32, tf.int32, tf.int32, tf.int32)
gen_shapes = ([None, 3], [None], [None], [None])
return gen_func, gen_types, gen_shapes
def get_data(self, file_path):
seq_id = file_path.split('/')[-3]
frame_id = file_path.split('/')[-1][:-4]
kd_tree_path = join(self.dataset_path, seq_id, 'KDTree', frame_id + '.pkl')
# Read pkl with search tree
with open(kd_tree_path, 'rb') as f:
search_tree = pickle.load(f)
points = np.array(search_tree.data, copy=False)
# Load labels
if int(seq_id) >= 11:
labels = np.zeros(np.shape(points)[0], dtype=np.uint8)
else:
label_path = join(self.dataset_path, seq_id, 'labels', frame_id + '.npy')
labels = np.squeeze(np.load(label_path))
return points, search_tree, labels
@staticmethod
def crop_pc(points, labels, search_tree, pick_idx):
# crop a fixed size point cloud for training
center_point = points[pick_idx, :].reshape(1, -1)
select_idx = search_tree.query(center_point, k=cfg.num_points)[1][0]
select_idx = DP.shuffle_idx(select_idx)
select_points = points[select_idx]
select_labels = labels[select_idx]
return select_points, select_labels, select_idx
@staticmethod
def get_tf_mapping2():
def tf_map(batch_pc, batch_label, batch_pc_idx, batch_cloud_idx):
features = batch_pc
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
for i in range(cfg.num_layers):
neighbour_idx = tf.py_func(DP.knn_search, [batch_pc, batch_pc, cfg.k_n], tf.int32)
sub_points = batch_pc[:, :tf.shape(batch_pc)[1] // cfg.sub_sampling_ratio[i], :]
pool_i = neighbour_idx[:, :tf.shape(batch_pc)[1] // cfg.sub_sampling_ratio[i], :]
up_i = tf.py_func(DP.knn_search, [sub_points, batch_pc, 1], tf.int32)
input_points.append(batch_pc)
input_neighbors.append(neighbour_idx)
input_pools.append(pool_i)
input_up_samples.append(up_i)
batch_pc = sub_points
input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [features, batch_label, batch_pc_idx, batch_cloud_idx]
return input_list
return tf_map
def init_input_pipeline(self):
print('Initiating input pipelines')
cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
gen_function, gen_types, gen_shapes = self.get_batch_gen('training')
gen_function_val, _, _ = self.get_batch_gen('validation')
gen_function_test, _, _ = self.get_batch_gen('test')
self.train_data = tf.data.Dataset.from_generator(gen_function, gen_types, gen_shapes)
self.val_data = tf.data.Dataset.from_generator(gen_function_val, gen_types, gen_shapes)
self.test_data = tf.data.Dataset.from_generator(gen_function_test, gen_types, gen_shapes)
self.batch_train_data = self.train_data.batch(cfg.batch_size)
self.batch_val_data = self.val_data.batch(cfg.val_batch_size)
self.batch_test_data = self.test_data.batch(cfg.val_batch_size)
map_func = self.get_tf_mapping2()
self.batch_train_data = self.batch_train_data.map(map_func=map_func)
self.batch_val_data = self.batch_val_data.map(map_func=map_func)
self.batch_test_data = self.batch_test_data.map(map_func=map_func)
self.batch_train_data = self.batch_train_data.prefetch(cfg.batch_size)
self.batch_val_data = self.batch_val_data.prefetch(cfg.val_batch_size)
self.batch_test_data = self.batch_test_data.prefetch(cfg.val_batch_size)
iter = tf.data.Iterator.from_structure(self.batch_train_data.output_types, self.batch_train_data.output_shapes)
self.flat_inputs = iter.get_next()
self.train_init_op = iter.make_initializer(self.batch_train_data)
self.val_init_op = iter.make_initializer(self.batch_val_data)
self.test_init_op = iter.make_initializer(self.batch_test_data)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='the number of GPUs to use [default: 0]')
parser.add_argument('--mode', type=str, default='train', help='options: train, test, vis')
parser.add_argument('--test_area', type=str, default='14', help='options: 08, 11,12,13,14,15,16,17,18,19,20,21')
parser.add_argument('--model_path', type=str, default='None', help='pretrained model path')
FLAGS = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
Mode = FLAGS.mode
test_area = FLAGS.test_area
dataset = SemanticKITTI(test_area)
dataset.init_input_pipeline()
if Mode == 'train':
model = Network(dataset, cfg)
model.train(dataset)
elif Mode == 'test':
cfg.saving = False
model = Network(dataset, cfg)
if FLAGS.model_path is not 'None':
chosen_snap = FLAGS.model_path
else:
chosen_snapshot = -1
logs = np.sort([os.path.join('results', f) for f in os.listdir('results') if f.startswith('Log')])
chosen_folder = logs[-1]
snap_path = join(chosen_folder, 'snapshots')
snap_steps = [int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path) if f[-5:] == '.meta']
chosen_step = np.sort(snap_steps)[-1]
chosen_snap = os.path.join(snap_path, 'snap-{:d}'.format(chosen_step))
tester = ModelTester(model, dataset, restore_snap=chosen_snap)
tester.test(model, dataset)
else:
##################
# Visualize data #
##################
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(dataset.train_init_op)
while True:
flat_inputs = sess.run(dataset.flat_inputs)
pc_xyz = flat_inputs[0]
sub_pc_xyz = flat_inputs[1]
labels = flat_inputs[17]
Plot.draw_pc_sem_ins(pc_xyz[0, :, :], labels[0, :])
Plot.draw_pc_sem_ins(sub_pc_xyz[0, :, :], labels[0, 0:np.shape(sub_pc_xyz)[1]])
最新发布
该文介绍了TensorFlow中的scan和map_fn函数，它们用于处理张量序列。scan函数沿第一维度对元素应用可调用函数，而map_fn则对输入序列中的每个元素应用函数。两者都支持并行迭代和反向传播，并可以设置是否交换GPU-CPU内存来优化性能。用户需指定函数输出类型、迭代次数等参数。
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