def init_lanenet(self):
'''
initlize the tensorflow model
'''
self.input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
phase_tensor = tf.constant('test', tf.string)
net = lanenet.LaneNet(phase=phase_tensor, net_flag='vgg')
self.binary_seg_ret, self.instance_seg_ret = net.inference(
input_tensor=self.input_tensor, name='lanenet_model')
# self.cluster = lanenet_cluster.LaneNetCluster()
self.postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
if self.use_gpu:
sess_config = tf.ConfigProto(device_count={'GPU': 1})
else:
sess_config = tf.ConfigProto(device_count={'CPU': 0})
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
self.sess = tf.Session(config=sess_config)
saver.restore(sess=self.sess, save_path=self.weight_path)
rospy.loginfo("Done Initializing")
def __init__(self, weights, config):
""" Initializes a LanePredictor which is used to register a callback
for the RGB images and predict lanes.
Args:
weights: The path of the weights to be used in the prediction.
config: The config to be used for tensorflow.
"""
self.input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
self.net = lanenet.LaneNet(phase='test', net_flag='vgg')
self.binary_seg_ret, self.instance_seg_ret = self.net.inference(
input_tensor=self.input_tensor, name='lanenet_model')
self.postprocessor = lanenet_postprocess.LaneNetPostProcessor(
ipm_remap_file_path=
'./dependencies/lanenet-lane-detection/data/tusimple_ipm_remap.yml'
)
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = \
config.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
self.sess = tf.Session(config=sess_config).__enter__()
saver = tf.train.Saver()
saver.restore(sess=self.sess, save_path=weights)
def __init__(self, camera_stream, detected_lanes_stream, flags):
camera_stream.add_callback(self.on_camera_frame,
[detected_lanes_stream])
self._flags = flags
self._logger = erdos.utils.setup_logging(self.config.name,
self.config.log_file_name)
pylot.utils.set_tf_loglevel(logging.ERROR)
self._input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test')
self._binary_seg_ret, self._instance_seg_ret = net.inference(
input_tensor=self._input_tensor, name='LaneNet')
self._gpu_options = tf.GPUOptions(
allow_growth=True,
visible_device_list=str(self._flags.lane_detection_gpu_index),
per_process_gpu_memory_fraction=flags.
lane_detection_gpu_memory_fraction,
allocator_type='BFC')
self._tf_session = tf.Session(config=tf.ConfigProto(
gpu_options=self._gpu_options, allow_soft_placement=True))
with tf.variable_scope(name_or_scope='moving_avg'):
variable_averages = tf.train.ExponentialMovingAverage(0.9995)
variables_to_restore = variable_averages.variables_to_restore()
self._postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver(variables_to_restore)
with self._tf_session.as_default():
saver.restore(sess=self._tf_session,
save_path=flags.lanenet_detection_model_path)
def _test_lanenet(image_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
log.info('Start reading image and preprocessing')
t_start = time.time()
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
log.info('Image load complete, cost time: {:.5f}s'.format(time.time() -
t_start))
input_tensor = tf.placeholder(
dtype=tf.float32,
shape=[1, CFG.TRAIN.IMG_HEIGHT, CFG.TRAIN.IMG_WIDTH, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model',
return_score=True)
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
model_file = tf.train.latest_checkpoint(weights_path)
saver.restore(sess=sess, save_path=model_file)
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
t_cost = time.time() - t_start
log.info('Single imgae inference cost time: {:.5f}s'.format(t_cost))
plt.figure('src_image')
plt.imshow(image_vis[:, :, (2, 1, 0)])
plt.axis('off')
plt.figure('binary_image')
plt.imshow(binary_seg_image[0, :, :, 1] * 255, cmap='gray')
plt.axis('off')
plt.show()
sess.close()
return
def generate_on_jiqing(src_path, pred_path, weights_path):
image_list = get_all_files(src_path)
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
saver = tf.train.Saver()
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC' # best fit with coalescing 内存管理算法
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
for index, image_path in tqdm.tqdm(enumerate(image_list),
total=len(image_list)):
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = image[540:840]
image = cv2.resize(image, (512, 256),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0 # 归一化 (只归一未改变维数)
postprocessor = lanenet_postprocess.LaneNetPostProcessor_noremap()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
postprocess_result = postprocessor.postprocess_noremap(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis,
data_source='jiqing')
# binary = lanenet_postprocess._morphological_process(binary_seg_image[0])
mask = postprocess_result['mask_image']
# binary = lanenet_postprocess._morphological_process(mask[0])
binary = cv2.resize(mask, (1920, 300),
interpolation=cv2.INTER_LINEAR)
back = np.zeros(shape=(1080, 1920, 3), dtype=np.uint8)
for i in range(1920):
for j in range(300):
# back[j+540, i] = binary[j, i]
back[j + 540, i, 0] = binary[j, i, 0]
back[j + 540, i, 1] = binary[j, i, 1]
back[j + 540, i, 2] = binary[j, i, 2]
dst_image_path = ops.join(pred_path, image_path.split('/')[-2])
if not ops.exists(dst_image_path):
os.makedirs(dst_image_path)
dst_image_path = ops.join(dst_image_path,
image_path.split('/')[-1])
cv2.imwrite(dst_image_path, back)
return
def graph_setting():
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
def plot_auc(txt_file, weights_path):
input_tensor = tf.placeholder(
dtype=tf.float32,
shape=[1, CFG.TRAIN.IMG_HEIGHT, CFG.TRAIN.IMG_WIDTH, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model',
return_score=True)
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
model_file = tf.train.latest_checkpoint(weights_path)
saver.restore(sess=sess, save_path=model_file)
gts, preds = [], []
with open(txt_file, 'r') as f:
lines = f.readlines()
for line in lines:
image_path, gt_path, _ = line.strip().split()
log.info('Processing {}.'.format(image_path))
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.resize(image,
(CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
seg_image_gt = cv2.imread(gt_path, cv2.IMREAD_GRAYSCALE)
seg_image_gt = cv2.resize(
seg_image_gt, (CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),
interpolation=cv2.INTER_LINEAR)
seg_image_score = sess.run([binary_seg_ret],
feed_dict={input_tensor: [image]})
seg_image_score = seg_image_score[0][0, :, :, 1]
gts.append((seg_image_gt > 0))
preds.append(seg_image_score)
sess.close()
gt = np.array(gts)
pred = np.array(preds)
fpr, tpr, _ = roc_curve(gt.flatten(), pred.flatten())
plt.title('ROC')
plt.plot(fpr, tpr)
plt.xlabel('fp')
plt.ylabel('tp')
plt.savefig('./tboard/roc_{}.png'.format(txt_file.split('/')[-1][:-4]))
return
def convert_ckpt_into_pb_file(ckpt_file_path, pb_file_path):
"""
:param ckpt_file_path:
:param pb_file_path:
:return:
"""
# construct compute graph
with tf.compat.v1.variable_scope('lanenet'):
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', cfg=CFG)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='LaneNet')
with tf.compat.v1.variable_scope('lanenet/'):
binary_seg_ret = tf.cast(binary_seg_ret, dtype=tf.float32)
binary_seg_ret = tf.squeeze(binary_seg_ret,
axis=0,
name='final_binary_output')
instance_seg_ret = tf.squeeze(instance_seg_ret,
axis=0,
name='final_pixel_embedding_output')
# define moving average version of the learned variables for eval
with tf.compat.v1.variable_scope(name_or_scope='moving_avg'):
variable_averages = tf.train.ExponentialMovingAverage(
CFG.SOLVER.MOVING_AVE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
# create a session
saver = tf.train.Saver(variables_to_restore)
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.85
sess_config.gpu_options.allow_growth = False
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess, ckpt_file_path)
converted_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
input_graph_def=sess.graph.as_graph_def(),
output_node_names=[
'lanenet/input_tensor', 'lanenet/final_binary_output',
'lanenet/final_pixel_embedding_output'
])
with tf.gfile.GFile(pb_file_path, "wb") as f:
f.write(converted_graph_def.SerializeToString())
def generate(src_path, pred_path, weights_path):
image_list = get_all_files(src_path)
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor_noremap()
saver = tf.train.Saver()
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC' # best fit with coalescing 内存管理算法
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
for image_path in image_list:
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
# image = image[:][300:850]
# image = cv2.resize(image, (1920, 1280), interpolation=cv2.INTER_LINEAR)
image = cv2.resize(image, (512, 256),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0 # 归一化 (只归一未改变维数)
t_strat = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
t_inference = time.time() - t_strat
postprocess_result = postprocessor.postprocess_noremap(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis,
data_source='caltech')
t_cost = time.time() - t_strat
print(t_inference, t_cost)
dst_image_path = ops.join(pred_path, image_path.split('/')[-2])
if not ops.exists(dst_image_path):
os.makedirs(dst_image_path)
dst_image_path = ops.join(dst_image_path,
image_path.split('/')[-1])
cv2.imwrite(dst_image_path,
image_vis) # postprocess_result['mask_image'])
return
def generate_tusimple_sample(scr_path, pred_path, weights_path):
listOfFiles = list()
for (dirpath, dirnames, filenames) in os.walk(scr_path):
listOfFiles += [os.path.join(dirpath, file) for file in filenames]
print(listOfFiles)
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor_noremap()
saver = tf.train.Saver()
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC' # best fit with coalescing 内存管理算法
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
for index, image_path in tqdm.tqdm(enumerate(listOfFiles),
total=len(listOfFiles)):
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0 # 归一化 (只归一未改变维数)
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
postprocess_result = postprocessor.postprocess_noremap(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis,
)
dst_image_path = ops.join(pred_path, image_path.split('/')[-2])
if not ops.exists(dst_image_path):
os.makedirs(dst_image_path)
dst_image_path = ops.join(dst_image_path,
image_path.split('/')[-1])
cv2.imwrite(dst_image_path, image_vis)
return
def convert_ckpt_into_pb_file(ckpt_file_path, pb_file_path):
"""
:param ckpt_file_path:
:param pb_file_path:
:return:
"""
# construct compute graph
with tf.variable_scope('lanenet'):
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
with tf.variable_scope('lanenet/'):
binary_seg_ret = tf.cast(binary_seg_ret, dtype=tf.float32)
binary_seg_ret = tf.squeeze(binary_seg_ret,
axis=0,
name='final_binary_output')
instance_seg_ret = tf.squeeze(instance_seg_ret,
axis=0,
name='final_pixel_embedding_output')
# create a session
saver = tf.train.Saver()
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.85
sess_config.gpu_options.allow_growth = False
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess, ckpt_file_path)
converted_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
input_graph_def=sess.graph.as_graph_def(),
output_node_names=[
'lanenet/input_tensor', 'lanenet/final_binary_output',
'lanenet/final_pixel_embedding_output'
])
with tf.gfile.GFile(pb_file_path, "wb") as f:
f.write(converted_graph_def.SerializeToString())
def optimize_model(model_path):
inputGraph = tf.GraphDef()
with tf.gfile.Open(model_path, 'rb') as model:
data2read = model.read()
inputGraph.ParseFromString(data2read)
with tf.variable_scope('lanenet'):
input_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_model')
with tf.variable_scope('lanenet/'):
binary_seg_ret = tf.cast(binary_seg_ret, dtype=tf.float32)
binary_seg_ret = tf.identity(binary_seg_ret, name= 'final_binary_output')
instance_seg_ret = tf.identity(instance_seg_ret, name='final_pixel_embedding_output')
# binary_seg_ret = tf.squeeze(binary_seg_ret, axis=0, name='final_binary_output') # 删除所有大小是 1 的维度
# instance_seg_ret = tf.squeeze(instance_seg_ret, axis=0, name='final_pixel_embedding_output')
"""outputGraph = optimize_for_inference_lib.optimize_for_inference(
inputGraph,
input_node_names=['lanenet/input_tensor'],
output_node_names=[
'lanenet/final_binary_output',
'lanenet/final_pixel_embedding_output'],
placeholder_type_enum=tf.int32.as_datatype_enum
)"""
outputGraph = TransformGraph(
inputGraph,
['lanenet/input_tensor'],
['lanenet/final_binary_output',
'lanenet/final_pixel_embedding_output'],
['remove_nodes(op=Identity, op=CheckNumerics)',
'merge_duplicate_nodes',
'strip_unused_nodes',
'fold_constants(ignore_errors=true)',
'fold_batch_norms',
'fold_old_batch_norms',
'quantize_weights',
'quantize_nodes',
'sort_by_execution_order']
)
new_name = model_path.split('/')[-2] + '/OptimizedGraph.pb'
model = tf.gfile.FastGFile(new_name, 'w')
model.write(outputGraph.SerializeToString())
def test_lanenet(image_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
log.info('Start reading image and preprocessing')
t_start = time.time()
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
log.info('Image load complete, cost time: {:.5f}s'.format(time.time() -
t_start))
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
n_images = 0
total_runtime = 0
for i in range(1000):
start_time = time.time()
binary_seg_ret, instance_seg_ret = net.inference(
input_tensor=input_tensor, name='lanenet_model')
# mask = binary_seg_ret[0]
# mask *= 255
# mask = mask.astype(np.uint8)
run_time = time.time() - start_time
total_runtime += run_time
n_images += 1
avg_time = total_runtime / n_images
print("Avg time: " + str(avg_time) + " - FPS: " + str(1 / avg_time))
return
def __init__(self, weights_path):
with tf.device('/cpu:0'):
self.input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
self.binary_seg_ret, self.instance_seg_ret = net.inference(
input_tensor=self.input_tensor, name='lanenet_model')
self.postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
self.sess = tf.Session(config=sess_config)
with self.sess.as_default():
saver.restore(sess=self.sess, save_path=weights_path)
pb_file = "model/tusimple_lanenet_mobilenet_v2_1005/culane_lanenet_mobilenet_v2_1213.pb"
#ckpt_file = cfg.YOLO.DEMO_WEIGHT
ckpt_file = 'model/tusimple_lanenet_mobilenet_v2_1005/culane_lanenet_mobilenet_v2_1005_reduce_train.ckpt'
# output_node_names = ["input/input_data", "pred_sbbox/concat_2", "pred_mbbox/concat_2", "pred_lbbox/concat_2"]
#
#output_node_names = ["input_tensor", "lanenet_loss/inference/decode/score_final/Conv2D", "lanenet_loss/pix_embedding_conv/Conv2D"]
output_node_names = [
"input_tensor", "lanenet_model/mobilenet_v2_backend/binary_seg/ArgMax",
"lanenet_model/mobilenet_v2_backend/instance_seg/pix_embedding_conv/Conv2D"
]
input_data = tf.placeholder(dtype=tf.float32,
name='input_tensor',
shape=[None, None, None, 3])
phase_tensor = tf.constant('false', tf.string)
net = lanenet.LaneNet(phase=phase_tensor, net_flag='mobilenet_v2')
model = net.inference(input_data, name='lanenet_model')
#print(model.conv_sbbox, model.conv_mbbox, model.conv_lbbox)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver = tf.train.Saver()
saver.restore(sess, ckpt_file)
converted_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
input_graph_def=sess.graph.as_graph_def(),
output_node_names=output_node_names)
with tf.gfile.GFile(pb_file, "wb") as f:
f.write(converted_graph_def.SerializeToString())
def test_lanenet(image_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
log.info('Start reading image and preprocessing')
t_start = time.time()
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
log.info('Image load complete, cost time: {:.5f}s'.format(time.time() - t_start))
input_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
writer = tf.summary.FileWriter('logs', sess.graph)
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]}
)
writer.close()
t_cost = time.time() - t_start
log.info('Single imgae inference cost time: {:.5f}s'.format(t_cost))
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis
)
mask_image = postprocess_result['mask_image']
for i in range(CFG.TRAIN.EMBEDDING_FEATS_DIMS):
instance_seg_image[0][:, :, i] = minmax_scale(instance_seg_image[0][:, :, i])
embedding_image = np.array(instance_seg_image[0], np.uint8)
print([n.name for n in sess.graph.as_graph_def().node])
#output_node_names = "lanenet_model/vgg_backend/instance_seg/pix_embedding_conv/Conv2D"
output_node_names = ["lanenet_model/vgg_backend/binary_seg/ArgMax"]
output_node_names = ["lanenet_model/vgg_backend/instance_seg/pix_embedding_conv/Conv2D", "lanenet_model/vgg_backend/binary_seg/ArgMax"]
input_graph_def = sess.graph.as_graph_def()
# output_node_names="train_IteratorGetNext"
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session
input_graph_def, # input_graph_def is useful for retrieving the nodes
output_node_names)
import tensorflow.contrib.tensorrt as trt
trt_graph = trt.create_inference_graph(
input_graph_def=output_graph_def,
outputs=output_node_names,
max_batch_size=1,
max_workspace_size_bytes=1 << 25,
precision_mode='INT8',
minimum_segment_size=50
)
with open('lanenet_trt_2outputs.pb', 'wb') as f:
f.write(trt_graph.SerializeToString())
plt.figure('mask_image')
plt.imshow(mask_image[:, :, (2, 1, 0)])
plt.figure('src_image')
plt.imshow(image_vis[:, :, (2, 1, 0)])
plt.figure('instance_image')
plt.imshow(embedding_image[:, :, (2, 1, 0)])
plt.figure('binary_image')
plt.imshow(binary_seg_image[0] * 255, cmap='gray')
plt.show()
cv2.imwrite('instance_mask_image.png', mask_image)
cv2.imwrite('source_image.png', postprocess_result['source_image'])
cv2.imwrite('binary_mask_image.png', binary_seg_image[0] * 255)
sess.close()
return
def __init__(self):
"""
initialize lanenet multi gpu trainner
"""
# define solver params and dataset
self._train_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
flags='train')
self._val_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
flags='val')
self._steps_per_epoch = len(self._train_dataset)
self._val_steps_per_epoch = len(self._val_dataset)
self._model_name = '{:s}_{:s}'.format(CFG.MODEL.FRONT_END,
CFG.MODEL.MODEL_NAME)
self._train_epoch_nums = CFG.TRAIN.EPOCH_NUMS
self._batch_size = CFG.TRAIN.BATCH_SIZE
self._val_batch_size = CFG.TRAIN.VAL_BATCH_SIZE
self._snapshot_epoch = CFG.TRAIN.SNAPSHOT_EPOCH
self._model_save_dir = ops.join(CFG.TRAIN.MODEL_SAVE_DIR,
self._model_name)
self._tboard_save_dir = ops.join(CFG.TRAIN.TBOARD_SAVE_DIR,
self._model_name)
self._enable_miou = CFG.TRAIN.COMPUTE_MIOU.ENABLE
if self._enable_miou:
self._record_miou_epoch = CFG.TRAIN.COMPUTE_MIOU.EPOCH
self._input_tensor_size = [int(tmp) for tmp in CFG.AUG.TRAIN_CROP_SIZE]
self._gpu_devices = CFG.TRAIN.MULTI_GPU.GPU_DEVICES
self._gpu_nums = len(self._gpu_devices)
self._chief_gpu_index = CFG.TRAIN.MULTI_GPU.CHIEF_DEVICE_INDEX
self._batch_size_per_gpu = int(self._batch_size / self._gpu_nums)
self._init_learning_rate = CFG.SOLVER.LR
self._moving_ave_decay = CFG.SOLVER.MOVING_AVE_DECAY
self._momentum = CFG.SOLVER.MOMENTUM
self._lr_polynimal_decay_power = CFG.SOLVER.LR_POLYNOMIAL_POWER
self._optimizer_mode = CFG.SOLVER.OPTIMIZER.lower()
if CFG.TRAIN.RESTORE_FROM_SNAPSHOT.ENABLE:
self._initial_weight = CFG.TRAIN.RESTORE_FROM_SNAPSHOT.SNAPSHOT_PATH
else:
self._initial_weight = None
if CFG.TRAIN.WARM_UP.ENABLE:
self._warmup_epoches = CFG.TRAIN.WARM_UP.EPOCH_NUMS
self._warmup_init_learning_rate = self._init_learning_rate / 1000.0
else:
self._warmup_epoches = 0
# define tensorflow session
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
self._sess = tf.Session(config=sess_config)
# define graph input tensor
with tf.variable_scope(name_or_scope='graph_input_node'):
self._input_src_image_list = []
self._input_binary_label_image_list = []
self._input_instance_label_image_list = []
for i in range(self._gpu_nums):
src_imgs, binary_label_imgs, instance_label_imgs = self._train_dataset.next_batch(
batch_size=self._batch_size_per_gpu)
self._input_src_image_list.append(src_imgs)
self._input_binary_label_image_list.append(binary_label_imgs)
self._input_instance_label_image_list.append(
instance_label_imgs)
self._val_input_src_image, self._val_input_binary_label_image, self._val_input_instance_label_image = \
self._val_dataset.next_batch(batch_size=self._val_batch_size)
# define model
self._model = lanenet.LaneNet(phase='train')
self._val_model = lanenet.LaneNet(phase='test')
# define average container
tower_grads = []
tower_total_loss = []
tower_binary_seg_loss = []
tower_instance_seg_loss = []
batchnorm_updates = None
# define learning rate
with tf.variable_scope('learning_rate'):
self._global_step = tf.Variable(1.0,
dtype=tf.float32,
trainable=False,
name='global_step')
self._val_global_step = tf.Variable(1.0,
dtype=tf.float32,
trainable=False,
name='val_global_step')
self._val_global_step_update = tf.assign_add(
self._val_global_step, 1.0)
warmup_steps = tf.constant(self._warmup_epoches *
self._steps_per_epoch,
dtype=tf.float32,
name='warmup_steps')
train_steps = tf.constant(self._train_epoch_nums *
self._steps_per_epoch,
dtype=tf.float32,
name='train_steps')
self._learn_rate = tf.cond(
pred=self._global_step < warmup_steps,
true_fn=lambda: self._compute_warmup_lr(
warmup_steps=warmup_steps, name='warmup_lr'),
false_fn=lambda: tf.train.polynomial_decay(
learning_rate=self._init_learning_rate,
global_step=self._global_step,
decay_steps=train_steps,
end_learning_rate=0.000000001,
power=self._lr_polynimal_decay_power))
self._learn_rate = tf.identity(self._learn_rate, 'lr')
# define optimizer
if self._optimizer_mode == 'sgd':
optimizer = tf.train.MomentumOptimizer(
learning_rate=self._learn_rate, momentum=self._momentum)
elif self._optimizer_mode == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate=self._learn_rate, )
else:
raise NotImplementedError(
'Not support optimizer: {:s} for now'.format(
self._optimizer_mode))
# define distributed train op
with tf.variable_scope(tf.get_variable_scope()):
is_network_initialized = False
for i in range(self._gpu_nums):
with tf.device('/gpu:{:d}'.format(i)):
with tf.name_scope('tower_{:d}'.format(i)) as _:
input_images = self._input_src_image_list[i]
input_binary_labels = self._input_binary_label_image_list[
i]
input_instance_labels = self._input_instance_label_image_list[
i]
tmp_loss, tmp_grads = self._compute_net_gradients(
input_images,
input_binary_labels,
input_instance_labels,
optimizer,
is_net_first_initialized=is_network_initialized)
is_network_initialized = True
# Only use the mean and var in the chief gpu tower to update the parameter
if i == self._chief_gpu_index:
batchnorm_updates = tf.get_collection(
tf.GraphKeys.UPDATE_OPS)
tower_grads.append(tmp_grads)
tower_total_loss.append(tmp_loss['total_loss'])
tower_binary_seg_loss.append(
tmp_loss['binary_seg_loss'])
tower_instance_seg_loss.append(
tmp_loss['discriminative_loss'])
grads = self._average_gradients(tower_grads)
self._loss = tf.reduce_mean(tower_total_loss,
name='reduce_mean_tower_total_loss')
self._binary_loss = tf.reduce_mean(
tower_binary_seg_loss, name='reduce_mean_tower_binary_loss')
self._instance_loss = tf.reduce_mean(
tower_instance_seg_loss, name='reduce_mean_tower_instance_loss')
ret = self._val_model.compute_loss(
input_tensor=self._val_input_src_image,
binary_label=self._val_input_binary_label_image,
instance_label=self._val_input_instance_label_image,
name='LaneNet',
reuse=True)
self._val_loss = ret['total_loss']
self._val_binary_loss = ret['binary_seg_loss']
self._val_instance_loss = ret['discriminative_loss']
# define moving average op
with tf.variable_scope(name_or_scope='moving_avg'):
if CFG.TRAIN.FREEZE_BN.ENABLE:
train_var_list = [
v for v in tf.trainable_variables()
if 'beta' not in v.name and 'gamma' not in v.name
]
else:
train_var_list = tf.trainable_variables()
moving_ave_op = tf.train.ExponentialMovingAverage(
self._moving_ave_decay).apply(train_var_list +
tf.moving_average_variables())
# group all the op needed for training
batchnorm_updates_op = tf.group(*batchnorm_updates)
apply_gradient_op = optimizer.apply_gradients(
grads, global_step=self._global_step)
self._train_op = tf.group(apply_gradient_op, moving_ave_op,
batchnorm_updates_op)
# define prediction
self._binary_prediciton, self._instance_prediciton = self._model.inference(
input_tensor=self._input_src_image_list[self._chief_gpu_index],
name='LaneNet',
reuse=True)
self._binary_prediciton = tf.identity(
self._binary_prediciton, name='binary_segmentation_result')
self._val_binary_prediction, self._val_instance_prediciton = self._val_model.inference(
input_tensor=self._val_input_src_image, name='LaneNet', reuse=True)
self._val_binary_prediction = tf.identity(
self._val_binary_prediction, name='val_binary_segmentation_result')
# define miou
if self._enable_miou:
with tf.variable_scope('miou'):
pred = tf.reshape(self._binary_prediciton, [
-1,
])
gt = tf.reshape(
self._input_binary_label_image_list[self._chief_gpu_index],
[
-1,
])
indices = tf.squeeze(
tf.where(tf.less_equal(gt, CFG.DATASET.NUM_CLASSES - 1)),
1)
gt = tf.gather(gt, indices)
pred = tf.gather(pred, indices)
self._miou, self._miou_update_op = tf.metrics.mean_iou(
labels=gt,
predictions=pred,
num_classes=CFG.DATASET.NUM_CLASSES)
val_pred = tf.reshape(self._val_binary_prediction, [
-1,
])
val_gt = tf.reshape(self._val_input_binary_label_image, [
-1,
])
indices = tf.squeeze(
tf.where(tf.less_equal(val_gt,
CFG.DATASET.NUM_CLASSES - 1)), 1)
val_gt = tf.gather(val_gt, indices)
val_pred = tf.gather(val_pred, indices)
self._val_miou, self._val_miou_update_op = tf.metrics.mean_iou(
labels=val_gt,
predictions=val_pred,
num_classes=CFG.DATASET.NUM_CLASSES)
# define saver and loader
with tf.variable_scope('loader_and_saver'):
self._net_var = [
vv for vv in tf.global_variables() if 'lr' not in vv.name
]
self._loader = tf.train.Saver(self._net_var)
self._saver = tf.train.Saver(max_to_keep=10)
# define summary
with tf.variable_scope('summary'):
summary_merge_list = [
tf.summary.scalar("learn_rate", self._learn_rate),
tf.summary.scalar("total_loss", self._loss),
tf.summary.scalar('binary_loss', self._binary_loss),
tf.summary.scalar('instance_loss', self._instance_loss),
]
val_summary_merge_list = [
tf.summary.scalar('val_total_loss', self._val_loss),
tf.summary.scalar('val_binary_loss', self._val_binary_loss),
tf.summary.scalar('val_instance_loss',
self._val_instance_loss),
]
if self._enable_miou:
with tf.control_dependencies([self._miou_update_op]):
summary_merge_list_with_miou = [
tf.summary.scalar("learn_rate", self._learn_rate),
tf.summary.scalar("total_loss", self._loss),
tf.summary.scalar('binary_loss', self._binary_loss),
tf.summary.scalar('instance_loss',
self._instance_loss),
tf.summary.scalar('miou', self._miou)
]
self._write_summary_op_with_miou = tf.summary.merge(
summary_merge_list_with_miou)
with tf.control_dependencies(
[self._val_miou_update_op, self._val_global_step_update]):
val_summary_merge_list_with_miou = [
tf.summary.scalar("total_loss", self._loss),
tf.summary.scalar('binary_loss', self._binary_loss),
tf.summary.scalar('instance_loss',
self._instance_loss),
tf.summary.scalar('val_miou', self._val_miou),
]
self._val_write_summary_op_with_miou = tf.summary.merge(
val_summary_merge_list_with_miou)
if ops.exists(self._tboard_save_dir):
shutil.rmtree(self._tboard_save_dir)
os.makedirs(self._tboard_save_dir, exist_ok=True)
model_params_file_save_path = ops.join(
self._tboard_save_dir, CFG.TRAIN.MODEL_PARAMS_CONFIG_FILE_NAME)
with open(model_params_file_save_path, 'w',
encoding='utf-8') as f_obj:
CFG.dump_to_json_file(f_obj)
self._write_summary_op = tf.summary.merge(summary_merge_list)
self._val_write_summary_op = tf.summary.merge(
val_summary_merge_list)
self._summary_writer = tf.summary.FileWriter(
self._tboard_save_dir, graph=self._sess.graph)
LOG.info('Initialize tusimple lanenet multi gpu trainner complete')
def evaluate_lanenet_accuracy_ckpt(data_path, weights_path):
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
gt_image_path = ops.join(data_path, 'gt_image')
gt_binary_path = ops.join(data_path, 'gt_binary_image')
accuracy_sum = 0.0
fp_sum = 0.0
fn_sum = 0.0
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
image_path_list = os.listdir(gt_image_path)
i = 0
for image_path in image_path_list:
image_path = ops.join(gt_image_path, image_path)
print(image_path)
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
# image_vis = image
image = cv2.resize(image, (512, 256),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0 # 归一化 (只归一未改变维数)
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
image_name = ops.split(image_path)[1]
gt_image = cv2.imread(ops.join(gt_binary_path, image_name),
cv2.IMREAD_COLOR)
image_vis = gt_image
postprocess_result = postprocessor.postprocess_for_test(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis)
plt.figure("result")
plt.imshow(postprocess_result['mask_image'])
plt.show()
# binary_seg_image = binary_seg_image.astype((np.float32))
# print(binary_seg_image.shape)
# binary_seg_image = binary_seg_image.reshape(1, 256, 512, 1)
# binary_seg_image = cv2.resize(postprocess_result['source_image'], (512, 256), interpolation=cv2.INTER_LINEAR)
binary_seg_image = postprocess_result['source_image'].reshape(
1, 720, 1280, 1)
# gt_image = cv2.resize(gt_image, (512, 256), interpolation=cv2.INTER_LINEAR)
gt_image = gt_image[:, :, 0].reshape(720, 1280, 1)
binary_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 720, 1280, 1],
name='binary_tensor')
gt_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 720, 1280, 1],
name='gt_tensor')
accuracy, count, idx = evaluate_model_utils.calculate_model_precision_for_test(
binary_tensor, gt_tensor)
fn = evaluate_model_utils.calculate_model_fn_for_test(
binary_tensor, gt_tensor)
fp = evaluate_model_utils.calculate_model_fp_for_test(
binary_tensor, gt_tensor)
accuracy_result, count_result, idx = sess.run(
[accuracy, count, idx],
feed_dict={
binary_tensor: binary_seg_image,
gt_tensor: [gt_image]
})
fn_result = sess.run(fn,
feed_dict={
binary_tensor: binary_seg_image,
gt_tensor: [gt_image]
})
fp_result = sess.run(fp,
feed_dict={
binary_tensor: binary_seg_image,
gt_tensor: [gt_image]
})
print("accuracy:", accuracy_result, count_result, idx)
print("fn:", fn_result)
print("fp:", fp_result)
i += 1
accuracy_sum += accuracy_result
fp_sum += fp_result
fn_sum += fn_result
if i == 100:
print("average_accuracy: ", accuracy_sum / 100.0)
print("average_fn: ", fn_sum / 100.0)
print("average_fp: ", fp_sum / 100.0)
break
return
ax4.set_title("Result Image")
plt.show()
"""
return res_img
sess.close()
if __name__ == '__main__':
weights_path = "model/tusimple_lanenet/tusimple_lanenet.ckpt"
# placeholder 자료형의 변수 생성(텐서를 placeholder에 맵핑) -> 입력텐서
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test',
cfg=CFG) # 딥러닝 네트워크 그래프 설정, 딥러닝 추론위한 텐서 생성
# 텐서플로우 그래프 빌드, 그래프는 세션 실행시 실제로 실행되는 내용 정의
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='LaneNet')
postprocessor = lanenet_postprocess.LaneNetPostProcessor(
cfg=CFG) # 이미지 후처리 하기위해 postprocessor 객체 생성
# Set sess configuration (텐서플로우 GPU메모리 할당 설정)
sess_config = tf.ConfigProto() # 세션설정정보 저장하기 위한 객체생성
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH # 상황에 맞게 자동으로 메모리 할당
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config) # 세션 설정 이용하여 세션 생성하여 저장
def test_lanenet(video_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(video_path), '{:s} not exist'.format(video_path)
obj_w = 1920
obj_h = 320
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, obj_h, obj_w, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', cfg=CFG)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='LaneNet')
postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
# define moving average version of the learned variables for eval
with tf.variable_scope(name_or_scope='moving_avg'):
variable_averages = tf.train.ExponentialMovingAverage(
CFG.SOLVER.MOVING_AVE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
# define saver
saver = tf.train.Saver(variables_to_restore)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
cap = cv2.VideoCapture(video_path)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
log.info('width: %d, height: %d', width, height)
out = cv2.VideoWriter('out.mp4',
fourcc,
15, (obj_w, obj_h),
isColor=True)
nn = 1000
#while(cap.isOpened() and nn >= 0):
while (cap.isOpened()):
nn = nn - 1
ret, image = cap.read()
if ret:
#image = cv2.imread('/workspace/lanenet-lane-detection/1410486660.jpg')
# image = cv2.resize(image, (1280, 720), interpolation=cv2.INTER_LINEAR)
image = image[442:762, :]
image_vis = image.copy()
image = image / 127.5 - 1.0
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
t_cost = time.time() - t_start
LOG.info(
'Single image inference cost time: {:.5f}s'.format(t_cost))
#log.info(instance_seg_image.shape)
#mask_image = gen_color_img(binary_seg_image[0], instance_seg_image[0], 5)
#cv2.imwrite('test.png', mask_image)
#return
postprocess_result = postprocessor.postprocess2(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis)
binary_image = postprocess_result['binary_image']
instance_image = postprocess_result['instance_image']
'''
np.set_printoptions(suppress = True, precision = 2, threshold = np.inf)
print('before: ')
print(instance_seg_image[0])
for i in range(CFG.TRAIN.EMBEDDING_FEATS_DIMS):
instance_seg_image[0][:, :, i] = minmax_scale(instance_seg_image[0][:, :, i])
embedding_image = np.array(instance_seg_image[0], np.uint8)
print('after: ')
print(embedding_image)
'''
out.write(np.uint8(instance_image))
#out.write(np.uint8(mask_image))
#cv2.imwrite('test.png', binary_image)
#return
else:
break
cap.release()
out.release()
sess.close()
return
def test_lanenet_batch(src_dir, weights_path, save_dir, net_flag):
"""
:param src_dir:
:param weights_path:
:param save_dir:
:return:
"""
assert ops.exists(src_dir), '{:s} not exist'.format(src_dir)
os.makedirs(save_dir, exist_ok=True)
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag=net_flag)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
image_list = glob.glob('{:s}/**/*.jpg'.format(src_dir), recursive=True)
avg_time_cost = []
for index, image_path in tqdm.tqdm(enumerate(image_list),
total=len(image_list)):
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256),
interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
avg_time_cost.append(time.time() - t_start)
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis)
if index % 100 == 0:
log.info(
'Mean inference time every single image: {:.5f}s'.format(
np.mean(avg_time_cost)))
avg_time_cost.clear()
input_image_dir = ops.split(image_path.split('clips')[1])[0][1:]
input_image_name = ops.split(image_path)[1]
output_image_dir = ops.join(save_dir, input_image_dir)
os.makedirs(output_image_dir, exist_ok=True)
output_image_path = ops.join(output_image_dir, input_image_name)
if ops.exists(output_image_path):
continue
cv2.imwrite(output_image_path, postprocess_result['source_image'])
return
def test_lanenet_batch(image_list, weights_path, batch_size, use_gpu, net_flag='vgg'):
"""
:param image_list:
:param weights_path:
:param batch_size:
:param use_gpu:
:param net_flag:
:return:
"""
assert ops.exists(image_list), '{:s} not exist'.format(image_list)
log.info('开始加载数据集列表...')
test_dataset = lanenet_data_processor.DataSet(image_list, traing=False)
# ==============================
gt_label_binary_list = []
with open(image_list, 'r') as file:
for _info in file:
info_tmp = _info.strip(' ').split()
gt_label_binary_list.append(info_tmp[1])
# ==============================
input_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 256, 512, 3], name='input_tensor')
binary_label_tensor = tf.placeholder(dtype=tf.int64,
shape=[None, 256, 512, 1], name='binary_input_label')
phase_tensor = tf.constant('test', tf.string)
net = lanenet.LaneNet(phase=phase_tensor, net_flag=net_flag)
binary_seg_ret, instance_seg_ret, recall_ret, false_positive, false_negative, precision_ret, accuracy_ret = \
net.compute_acc(input_tensor=input_tensor, binary_label_tensor=binary_label_tensor, name='lanenet_model')
saver = tf.train.Saver()
# ==============================
# Set sess configuration
if use_gpu:
sess_config = tf.ConfigProto(device_count={'GPU': 1})
else:
sess_config = tf.ConfigProto(device_count={'GPU': 0})
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
# ==============================
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
epoch_nums = int(math.ceil(test_dataset._dataset_size / batch_size))
mean_accuracy = 0.0
mean_recall = 0.0
mean_precision = 0.0
mean_fp = 0.0
mean_fn = 0.0
total_num = 0
t_start = time.time()
for epoch in range(epoch_nums):
gt_imgs, binary_gt_labels, instance_gt_labels = test_dataset.next_batch(batch_size)
if net_flag == 'vgg':
image_list_epoch = [tmp / 127.5 - 1.0 for tmp in gt_imgs]
elif net_flag == 'mobilenet_v2':
image_list_epoch = [tmp - [103.939, 116.779, 123.68] for tmp in gt_imgs]
binary_seg_images, instance_seg_images, recall, fp, fn, precision, accuracy = sess.run(
[binary_seg_ret, instance_seg_ret, recall_ret, false_positive, false_negative, precision_ret, accuracy_ret],
feed_dict={input_tensor: image_list_epoch, binary_label_tensor: binary_gt_labels})
# ==============================
out_dir = 'H:/Other_DataSets/TuSimple/out/'
dst_binary_image_path = ops.join(out_dir,gt_label_binary_list[epoch])
root_dir = ops.dirname(ops.abspath(dst_binary_image_path))
if not os.path.exists(root_dir):
os.makedirs(root_dir)
cv2.imwrite(dst_binary_image_path, binary_seg_images[0] * 255)
# ==============================
print(recall, fp, fn)
mean_accuracy += accuracy
mean_precision += precision
mean_recall += recall
mean_fp += fp
mean_fn += fn
total_num += len(gt_imgs)
t_cost = time.time() - t_start
mean_accuracy = mean_accuracy / epoch_nums
mean_precision = mean_precision / epoch_nums
mean_recall = mean_recall / epoch_nums
mean_fp = mean_fp / epoch_nums
mean_fn = mean_fn / epoch_nums
print('测试 {} 张图片，耗时{}，{}_recall = {}, precision = {}, accuracy = {}, fp = {}, fn = {}, '.format(
total_num, t_cost, net_flag, mean_recall, mean_precision, mean_accuracy, mean_fp, mean_fn))
sess.close()
except Exception as e:
print("Unable to show image: "+str(e))
cv2.waitKey(1)
detect_object_alive = False
"""
:param image_path:
:param weights_path:
:return:
"""
input_tensor = tf.placeholder(dtype=tf.float32, shape=[
1, 256, 512, 3], name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(
input_tensor=input_tensor, name='lanenet_model')
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
saver.restore(sess=sess, save_path=weights_path)
def inference_net():
global detect_lane_alive,image_0
detect_lane_alive = True
# preprocess
def train_lanenet(dataset_dir, weights_path=None, net_flag='vgg'):
"""
:param dataset_dir:
:param net_flag: choose which base network to use
:param weights_path:
:return:
"""
train_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
dataset_dir=dataset_dir, flags='train')
val_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
dataset_dir=dataset_dir, flags='val')
with tf.device('/gpu:1'):
# set lanenet
train_net = lanenet.LaneNet(net_flag=net_flag,
phase='train',
reuse=False)
val_net = lanenet.LaneNet(net_flag=net_flag, phase='val', reuse=True)
# set compute graph node for training
train_images, train_binary_labels, train_instance_labels = train_dataset.inputs(
CFG.TRAIN.BATCH_SIZE, 1)
train_compute_ret = train_net.compute_loss(
input_tensor=train_images,
binary_label=train_binary_labels,
instance_label=train_instance_labels,
name='lanenet_model')
train_total_loss = train_compute_ret['total_loss']
train_binary_seg_loss = train_compute_ret['binary_seg_loss']
train_disc_loss = train_compute_ret['discriminative_loss']
train_pix_embedding = train_compute_ret['instance_seg_logits']
train_prediction_logits = train_compute_ret['binary_seg_logits']
train_prediction_score = tf.nn.softmax(logits=train_prediction_logits)
train_prediction = tf.argmax(train_prediction_score, axis=-1)
train_accuracy = evaluate_model_utils.calculate_model_precision(
train_compute_ret['binary_seg_logits'], train_binary_labels)
train_fp = evaluate_model_utils.calculate_model_fp(
train_compute_ret['binary_seg_logits'], train_binary_labels)
train_fn = evaluate_model_utils.calculate_model_fn(
train_compute_ret['binary_seg_logits'], train_binary_labels)
train_binary_seg_ret_for_summary = evaluate_model_utils.get_image_summary(
img=train_prediction)
train_embedding_ret_for_summary = evaluate_model_utils.get_image_summary(
img=train_pix_embedding)
train_cost_scalar = tf.summary.scalar(name='train_cost',
tensor=train_total_loss)
train_accuracy_scalar = tf.summary.scalar(name='train_accuracy',
tensor=train_accuracy)
train_binary_seg_loss_scalar = tf.summary.scalar(
name='train_binary_seg_loss', tensor=train_binary_seg_loss)
train_instance_seg_loss_scalar = tf.summary.scalar(
name='train_instance_seg_loss', tensor=train_disc_loss)
train_fn_scalar = tf.summary.scalar(name='train_fn', tensor=train_fn)
train_fp_scalar = tf.summary.scalar(name='train_fp', tensor=train_fp)
train_binary_seg_ret_img = tf.summary.image(
name='train_binary_seg_ret',
tensor=train_binary_seg_ret_for_summary)
train_embedding_feats_ret_img = tf.summary.image(
name='train_embedding_feats_ret',
tensor=train_embedding_ret_for_summary)
train_merge_summary_op = tf.summary.merge([
train_accuracy_scalar, train_cost_scalar,
train_binary_seg_loss_scalar, train_instance_seg_loss_scalar,
train_fn_scalar, train_fp_scalar, train_binary_seg_ret_img,
train_embedding_feats_ret_img
])
# set compute graph node for validation
val_images, val_binary_labels, val_instance_labels = val_dataset.inputs(
CFG.TRAIN.VAL_BATCH_SIZE, 1)
val_compute_ret = val_net.compute_loss(
input_tensor=val_images,
binary_label=val_binary_labels,
instance_label=val_instance_labels,
name='lanenet_model')
val_total_loss = val_compute_ret['total_loss']
val_binary_seg_loss = val_compute_ret['binary_seg_loss']
val_disc_loss = val_compute_ret['discriminative_loss']
val_pix_embedding = val_compute_ret['instance_seg_logits']
val_prediction_logits = val_compute_ret['binary_seg_logits']
val_prediction_score = tf.nn.softmax(logits=val_prediction_logits)
val_prediction = tf.argmax(val_prediction_score, axis=-1)
val_accuracy = evaluate_model_utils.calculate_model_precision(
val_compute_ret['binary_seg_logits'], val_binary_labels)
val_fp = evaluate_model_utils.calculate_model_fp(
val_compute_ret['binary_seg_logits'], val_binary_labels)
val_fn = evaluate_model_utils.calculate_model_fn(
val_compute_ret['binary_seg_logits'], val_binary_labels)
val_binary_seg_ret_for_summary = evaluate_model_utils.get_image_summary(
img=val_prediction)
val_embedding_ret_for_summary = evaluate_model_utils.get_image_summary(
img=val_pix_embedding)
val_cost_scalar = tf.summary.scalar(name='val_cost',
tensor=val_total_loss)
val_accuracy_scalar = tf.summary.scalar(name='val_accuracy',
tensor=val_accuracy)
val_binary_seg_loss_scalar = tf.summary.scalar(
name='val_binary_seg_loss', tensor=val_binary_seg_loss)
val_instance_seg_loss_scalar = tf.summary.scalar(
name='val_instance_seg_loss', tensor=val_disc_loss)
val_fn_scalar = tf.summary.scalar(name='val_fn', tensor=val_fn)
val_fp_scalar = tf.summary.scalar(name='val_fp', tensor=val_fp)
val_binary_seg_ret_img = tf.summary.image(
name='val_binary_seg_ret', tensor=val_binary_seg_ret_for_summary)
val_embedding_feats_ret_img = tf.summary.image(
name='val_embedding_feats_ret',
tensor=val_embedding_ret_for_summary)
val_merge_summary_op = tf.summary.merge([
val_accuracy_scalar, val_cost_scalar, val_binary_seg_loss_scalar,
val_instance_seg_loss_scalar, val_fn_scalar, val_fp_scalar,
val_binary_seg_ret_img, val_embedding_feats_ret_img
])
# set optimizer
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.polynomial_decay(
learning_rate=CFG.TRAIN.LEARNING_RATE,
global_step=global_step,
decay_steps=CFG.TRAIN.EPOCHS,
power=0.9)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.MomentumOptimizer(
learning_rate=learning_rate,
momentum=CFG.TRAIN.MOMENTUM).minimize(
loss=train_total_loss,
var_list=tf.trainable_variables(),
global_step=global_step)
# Set tf model save path
model_save_dir = 'model/tusimple_lanenet_{:s}'.format(net_flag)
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'tusimple_lanenet_{:s}_{:s}.ckpt'.format(
net_flag, str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
saver = tf.train.Saver()
# Set tf summary save path
tboard_save_path = 'tboard/tusimple_lanenet_{:s}'.format(net_flag)
if not ops.exists(tboard_save_path):
os.makedirs(tboard_save_path)
# Set sess configuration
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = CFG.TRAIN.EPOCHS
log.info('Global configuration is as follows:')
log.info(CFG)
with sess.as_default():
if weights_path is None:
log.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
log.info('Restore model from last model checkpoint {:s}'.format(
weights_path))
saver.restore(sess=sess, save_path=weights_path)
if net_flag == 'vgg' and weights_path is None:
load_pretrained_weights(tf.trainable_variables(),
'./data/vgg16.npy', sess)
train_cost_time_mean = []
for epoch in range(train_epochs):
# training part
t_start = time.time()
_, train_c, train_accuracy_figure, train_fn_figure, train_fp_figure, lr, train_summary, train_binary_loss, \
train_instance_loss, train_embeddings, train_binary_seg_imgs, train_gt_imgs, \
train_binary_gt_labels, train_instance_gt_labels = \
sess.run([optimizer, train_total_loss, train_accuracy, train_fn, train_fp,
learning_rate, train_merge_summary_op, train_binary_seg_loss,
train_disc_loss, train_pix_embedding, train_prediction,
train_images, train_binary_labels, train_instance_labels])
if math.isnan(train_c) or math.isnan(
train_binary_loss) or math.isnan(train_instance_loss):
log.error('cost is: {:.5f}'.format(train_c))
log.error('binary cost is: {:.5f}'.format(train_binary_loss))
log.error(
'instance cost is: {:.5f}'.format(train_instance_loss))
return
if epoch % 100 == 0:
record_training_intermediate_result(
gt_images=train_gt_imgs,
gt_binary_labels=train_binary_gt_labels,
gt_instance_labels=train_instance_gt_labels,
binary_seg_images=train_binary_seg_imgs,
pix_embeddings=train_embeddings)
summary_writer.add_summary(summary=train_summary,
global_step=epoch)
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
log.info(
'Epoch: {:d} total_loss= {:6f} binary_seg_loss= {:6f} '
'instance_seg_loss= {:6f} accuracy= {:6f} fp= {:6f} fn= {:6f}'
' lr= {:6f} mean_cost_time= {:5f}s '.format(
epoch + 1, train_c, train_binary_loss,
train_instance_loss, train_accuracy_figure,
train_fp_figure, train_fn_figure, lr,
np.mean(train_cost_time_mean)))
del train_cost_time_mean[:]
# validation part
val_c, val_accuracy_figure, val_fn_figure, val_fp_figure, val_summary, val_binary_loss, \
val_instance_loss, val_embeddings, val_binary_seg_imgs, val_gt_imgs, \
val_binary_gt_labels, val_instance_gt_labels = \
sess.run([val_total_loss, val_accuracy, val_fn, val_fp,
val_merge_summary_op, val_binary_seg_loss,
val_disc_loss, val_pix_embedding, val_prediction,
val_images, val_binary_labels, val_instance_labels])
if math.isnan(val_c) or math.isnan(val_binary_loss) or math.isnan(
val_instance_loss):
log.error('cost is: {:.5f}'.format(val_c))
log.error('binary cost is: {:.5f}'.format(val_binary_loss))
log.error('instance cost is: {:.5f}'.format(val_instance_loss))
return
if epoch % 100 == 0:
record_training_intermediate_result(
gt_images=val_gt_imgs,
gt_binary_labels=val_binary_gt_labels,
gt_instance_labels=val_instance_gt_labels,
binary_seg_images=val_binary_seg_imgs,
pix_embeddings=val_embeddings,
flag='val')
cost_time = time.time() - t_start
train_cost_time_mean.append(cost_time)
summary_writer.add_summary(summary=val_summary, global_step=epoch)
if epoch % CFG.TRAIN.VAL_DISPLAY_STEP == 0:
log.info(
'Epoch_Val: {:d} total_loss= {:6f} binary_seg_loss= {:6f} '
'instance_seg_loss= {:6f} accuracy= {:6f} fp= {:6f} fn= {:6f}'
' mean_cost_time= {:5f}s '.format(
epoch + 1, val_c, val_binary_loss, val_instance_loss,
val_accuracy_figure, val_fp_figure, val_fn_figure,
np.mean(train_cost_time_mean)))
del train_cost_time_mean[:]
if epoch % 2000 == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=global_step)
return
def train_lanenet_multi_gpu(dataset_dir, weights_path=None, net_flag='vgg'):
"""
train lanenet with multi gpu
:param dataset_dir:
:param weights_path:
:param net_flag:
:return:
"""
# set lanenet dataset
train_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
dataset_dir=dataset_dir, flags='train')
val_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
dataset_dir=dataset_dir, flags='val')
# set lanenet
train_net = lanenet.LaneNet(net_flag=net_flag, phase='train', reuse=False)
val_net = lanenet.LaneNet(net_flag=net_flag, phase='val', reuse=True)
# set compute graph node
train_images, train_binary_labels, train_instance_labels = train_dataset.inputs(
CFG.TRAIN.BATCH_SIZE, 1)
val_images, val_binary_labels, val_instance_labels = val_dataset.inputs(
CFG.TRAIN.VAL_BATCH_SIZE, 1)
# set average container
tower_grads = []
train_tower_loss = []
val_tower_loss = []
batchnorm_updates = None
train_summary_op_updates = None
# set lr
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.polynomial_decay(
learning_rate=CFG.TRAIN.LEARNING_RATE,
global_step=global_step,
decay_steps=CFG.TRAIN.EPOCHS,
power=0.9)
# set optimizer
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=CFG.TRAIN.MOMENTUM)
# set distributed train op
with tf.variable_scope(tf.get_variable_scope()):
for i in range(CFG.TRAIN.GPU_NUM):
with tf.device('/gpu:{:d}'.format(i)):
with tf.name_scope('tower_{:d}'.format(i)) as _:
train_loss, grads = compute_net_gradients(
train_images, train_binary_labels,
train_instance_labels, train_net, optimizer)
# Only use the mean and var in the first gpu tower to update the parameter
if i == 0:
batchnorm_updates = tf.get_collection(
tf.GraphKeys.UPDATE_OPS)
train_summary_op_updates = tf.get_collection(
tf.GraphKeys.SUMMARIES)
tower_grads.append(grads)
train_tower_loss.append(train_loss)
with tf.name_scope('validation_{:d}'.format(i)) as _:
val_loss, _ = compute_net_gradients(
val_images, val_binary_labels, val_instance_labels,
val_net, optimizer)
val_tower_loss.append(val_loss)
grads = average_gradients(tower_grads)
avg_train_loss = tf.reduce_mean(train_tower_loss)
avg_val_loss = tf.reduce_mean(val_tower_loss)
# Track the moving averages of all trainable variables
variable_averages = tf.train.ExponentialMovingAverage(
CFG.TRAIN.MOVING_AVERAGE_DECAY, num_updates=global_step)
variables_to_average = tf.trainable_variables(
) + tf.moving_average_variables()
variables_averages_op = variable_averages.apply(variables_to_average)
# Group all the op needed for training
batchnorm_updates_op = tf.group(*batchnorm_updates)
apply_gradient_op = optimizer.apply_gradients(grads,
global_step=global_step)
train_op = tf.group(apply_gradient_op, variables_averages_op,
batchnorm_updates_op)
# Set tf summary save path
tboard_save_path = 'tboard/tusimple_lanenet_multi_gpu_{:s}'.format(
net_flag)
if not os.path.exists(tboard_save_path):
os.makedirs(tboard_save_path)
summary_writer = tf.summary.FileWriter(tboard_save_path)
avg_train_loss_scalar = tf.summary.scalar(name='average_train_loss',
tensor=avg_train_loss)
avg_val_loss_scalar = tf.summary.scalar(name='average_val_loss',
tensor=avg_val_loss)
learning_rate_scalar = tf.summary.scalar(name='learning_rate_scalar',
tensor=learning_rate)
train_merge_summary_op = tf.summary.merge(
[avg_train_loss_scalar, learning_rate_scalar] +
train_summary_op_updates)
val_merge_summary_op = tf.summary.merge([avg_val_loss_scalar])
# set tensorflow saver
saver = tf.train.Saver()
model_save_dir = 'model/tusimple_lanenet_multi_gpu_{:s}'.format(net_flag)
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'tusimple_lanenet_{:s}_{:s}.ckpt'.format(
net_flag, str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# set sess config
sess_config = tf.ConfigProto(device_count={'GPU': CFG.TRAIN.GPU_NUM},
allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
# Set the training parameters
train_epochs = CFG.TRAIN.EPOCHS
log.info('Global configuration is as follows:')
log.info(CFG)
sess = tf.Session(config=sess_config)
summary_writer.add_graph(sess.graph)
with sess.as_default():
tf.train.write_graph(
graph_or_graph_def=sess.graph,
logdir='',
name='{:s}/lanenet_model.pb'.format(model_save_dir))
if weights_path is None:
log.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
log.info('Restore model from last model checkpoint {:s}'.format(
weights_path))
saver.restore(sess=sess, save_path=weights_path)
train_cost_time_mean = []
val_cost_time_mean = []
for epoch in range(train_epochs):
# training part
t_start = time.time()
_, train_loss_value, train_summary, lr = \
sess.run(
fetches=[train_op, avg_train_loss,
train_merge_summary_op, learning_rate]
)
if math.isnan(train_loss_value):
log.error('Train loss is nan')
return
cost_time = time.time() - t_start
train_cost_time_mean.append(cost_time)
summary_writer.add_summary(summary=train_summary,
global_step=epoch)
# validation part
t_start_val = time.time()
val_loss_value, val_summary = \
sess.run(fetches=[avg_val_loss, val_merge_summary_op])
summary_writer.add_summary(val_summary, global_step=epoch)
cost_time_val = time.time() - t_start_val
val_cost_time_mean.append(cost_time_val)
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
log.info('Epoch_Train: {:d} total_loss= {:6f} '
'lr= {:6f} mean_cost_time= {:5f}s '.format(
epoch + 1, train_loss_value, lr,
np.mean(train_cost_time_mean)))
del train_cost_time_mean[:]
if epoch % CFG.TRAIN.VAL_DISPLAY_STEP == 0:
log.info('Epoch_Val: {:d} total_loss= {:6f}'
' mean_cost_time= {:5f}s '.format(
epoch + 1, val_loss_value,
np.mean(val_cost_time_mean)))
del val_cost_time_mean[:]
if epoch % 2000 == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
return
def train_lanenet(weights_path=None,
net_flag='vgg',
version_flag='',
scratch=False):
"""
:param weights_path:
:param net_flag: choose which base network to use
:param version_flag: exp flag
:return:
"""
# ========================== placeholder ========================= #
with tf.name_scope('train_input'):
train_input_tensor = tf.placeholder(dtype=tf.float32,
name='input_image',
shape=[None, None, None, 3])
train_binary_label_tensor = tf.placeholder(dtype=tf.float32,
name='binary_input_label',
shape=[None, None, None, 1])
train_instance_label_tensor = tf.placeholder(
dtype=tf.float32,
name='instance_input_label',
shape=[None, None, None, 1])
with tf.name_scope('val_input'):
val_input_tensor = tf.placeholder(dtype=tf.float32,
name='input_image',
shape=[None, None, None, 3])
val_binary_label_tensor = tf.placeholder(dtype=tf.float32,
name='binary_input_label',
shape=[None, None, None, 1])
val_instance_label_tensor = tf.placeholder(dtype=tf.float32,
name='instance_input_label',
shape=[None, None, None, 1])
# ================================================================ #
# Define Network #
# ================================================================ #
train_net = lanenet.LaneNet(net_flag=net_flag,
phase='train',
reuse=tf.AUTO_REUSE)
val_net = lanenet.LaneNet(net_flag=net_flag, phase='val', reuse=True)
# ---------------------------------------------------------------- #
# ================================================================ #
# Train Input & Output #
# ================================================================ #
trainset = DataSet('train')
# trainset = MergeDataSet('train_lane')
train_compute_ret = train_net.compute_loss(
input_tensor=train_input_tensor,
binary_label=train_binary_label_tensor,
instance_label=train_instance_label_tensor,
name='lanenet_model')
train_total_loss = train_compute_ret['total_loss']
train_binary_seg_loss = train_compute_ret['binary_seg_loss'] # 语义分割 loss
train_disc_loss = train_compute_ret[
'discriminative_loss'] # embedding loss
train_pix_embedding = train_compute_ret[
'instance_seg_logits'] # embedding feature, HxWxN
train_l2_reg_loss = train_compute_ret['l2_reg_loss']
train_prediction_logits = train_compute_ret[
'binary_seg_logits'] # 语义分割结果，HxWx2
train_prediction_score = tf.nn.softmax(logits=train_prediction_logits)
train_prediction = tf.argmax(train_prediction_score, axis=-1) # 语义分割二值图
train_accuracy = evaluate_model_utils.calculate_model_precision(
train_compute_ret['binary_seg_logits'], train_binary_label_tensor)
train_fp = evaluate_model_utils.calculate_model_fp(
train_compute_ret['binary_seg_logits'], train_binary_label_tensor)
train_fn = evaluate_model_utils.calculate_model_fn(
train_compute_ret['binary_seg_logits'], train_binary_label_tensor)
train_binary_seg_ret_for_summary = evaluate_model_utils.get_image_summary(
img=train_prediction) # (I - min) * 255 / (max -min), 归一化到0-255
train_embedding_ret_for_summary = evaluate_model_utils.get_image_summary(
img=train_pix_embedding) # (I - min) * 255 / (max -min), 归一化到0-255
# ---------------------------------------------------------------- #
# ================================================================ #
# Define Optimizer #
# ================================================================ #
# set optimizer
global_step = tf.Variable(0, trainable=False, name='global_step')
# learning_rate = tf.train.cosine_decay_restarts( # 余弦衰减
# learning_rate=cfg.TRAIN.LEARNING_RATE, # 初始学习率
# global_step=global_step, # 当前迭代次数
# first_decay_steps=cfg.TRAIN.STEPS/3, # 首次衰减周期
# t_mul=2.0, # 随后每次衰减周期倍数
# m_mul=1.0, # 随后每次初始学习率倍数
# alpha = 0.1, # 最小的学习率=alpha*learning_rate
# )
learning_rate = tf.train.polynomial_decay( # 多项式衰减
learning_rate=cfg.TRAIN.LEARNING_RATE, # 初始学习率
global_step=global_step, # 当前迭代次数
decay_steps=cfg.TRAIN.STEPS /
4, # 在迭代到该次数实际，学习率衰减为 learning_rate * dacay_rate
end_learning_rate=cfg.TRAIN.LEARNING_RATE / 10, # 最小的学习率
power=0.9,
cycle=True)
learning_rate_scalar = tf.summary.scalar(name='learning_rate',
tensor=learning_rate)
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS) # for batch normalization
with tf.control_dependencies(update_ops):
optimizer = tf.train.MomentumOptimizer(
learning_rate=learning_rate, momentum=cfg.TRAIN.MOMENTUM).minimize(
loss=train_total_loss,
var_list=tf.trainable_variables(),
global_step=global_step)
# ---------------------------------------------------------------- #
# ================================================================ #
# Train Summary #
# ================================================================ #
train_loss_scalar = tf.summary.scalar(name='train_cost',
tensor=train_total_loss)
train_accuracy_scalar = tf.summary.scalar(name='train_accuracy',
tensor=train_accuracy)
train_binary_seg_loss_scalar = tf.summary.scalar(
name='train_binary_seg_loss', tensor=train_binary_seg_loss)
train_instance_seg_loss_scalar = tf.summary.scalar(
name='train_instance_seg_loss', tensor=train_disc_loss)
train_fn_scalar = tf.summary.scalar(name='train_fn', tensor=train_fn)
train_fp_scalar = tf.summary.scalar(name='train_fp', tensor=train_fp)
train_binary_seg_ret_img = tf.summary.image(
name='train_binary_seg_ret', tensor=train_binary_seg_ret_for_summary)
train_embedding_feats_ret_img = tf.summary.image(
name='train_embedding_feats_ret',
tensor=train_embedding_ret_for_summary)
train_merge_summary_op = tf.summary.merge([
train_accuracy_scalar, train_loss_scalar, train_binary_seg_loss_scalar,
train_instance_seg_loss_scalar, train_fn_scalar, train_fp_scalar,
train_binary_seg_ret_img, train_embedding_feats_ret_img,
learning_rate_scalar
])
# ---------------------------------------------------------------- #
# ================================================================ #
# Val Input & Output #
# ================================================================ #
valset = DataSet('val', net_flag)
# valset = MergeDataSet('test_lane')
val_compute_ret = val_net.compute_loss(
input_tensor=val_input_tensor,
binary_label=val_binary_label_tensor,
instance_label=val_instance_label_tensor,
name='lanenet_model')
val_total_loss = val_compute_ret['total_loss']
val_binary_seg_loss = val_compute_ret['binary_seg_loss']
val_disc_loss = val_compute_ret['discriminative_loss']
val_pix_embedding = val_compute_ret['instance_seg_logits']
val_prediction_logits = val_compute_ret['binary_seg_logits']
val_prediction_score = tf.nn.softmax(logits=val_prediction_logits)
val_prediction = tf.argmax(val_prediction_score, axis=-1)
val_accuracy = evaluate_model_utils.calculate_model_precision(
val_compute_ret['binary_seg_logits'], val_binary_label_tensor)
val_fp = evaluate_model_utils.calculate_model_fp(
val_compute_ret['binary_seg_logits'], val_binary_label_tensor)
val_fn = evaluate_model_utils.calculate_model_fn(
val_compute_ret['binary_seg_logits'], val_binary_label_tensor)
val_binary_seg_ret_for_summary = evaluate_model_utils.get_image_summary(
img=val_prediction)
val_embedding_ret_for_summary = evaluate_model_utils.get_image_summary(
img=val_pix_embedding)
# ---------------------------------------------------------------- #
# ================================================================ #
# VAL Summary #
# ================================================================ #
val_loss_scalar = tf.summary.scalar(name='val_cost', tensor=val_total_loss)
val_accuracy_scalar = tf.summary.scalar(name='val_accuracy',
tensor=val_accuracy)
val_binary_seg_loss_scalar = tf.summary.scalar(name='val_binary_seg_loss',
tensor=val_binary_seg_loss)
val_instance_seg_loss_scalar = tf.summary.scalar(
name='val_instance_seg_loss', tensor=val_disc_loss)
val_fn_scalar = tf.summary.scalar(name='val_fn', tensor=val_fn)
val_fp_scalar = tf.summary.scalar(name='val_fp', tensor=val_fp)
val_binary_seg_ret_img = tf.summary.image(
name='val_binary_seg_ret', tensor=val_binary_seg_ret_for_summary)
val_embedding_feats_ret_img = tf.summary.image(
name='val_embedding_feats_ret', tensor=val_embedding_ret_for_summary)
val_merge_summary_op = tf.summary.merge([
val_accuracy_scalar, val_loss_scalar, val_binary_seg_loss_scalar,
val_instance_seg_loss_scalar, val_fn_scalar, val_fp_scalar,
val_binary_seg_ret_img, val_embedding_feats_ret_img
])
# ---------------------------------------------------------------- #
# ================================================================ #
# Config Saver & Session #
# ================================================================ #
# Set tf model save path
model_save_dir = 'model/tusimple_lanenet_{:s}_{:s}'.format(
net_flag, version_flag)
os.makedirs(model_save_dir, exist_ok=True)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'tusimple_lanenet_{:s}_{:s}.ckpt'.format(
net_flag, str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# ==============================
if scratch:
"""
删除 Momentum 的参数, 注意这里保存的 meta 文件也会删了
tensorflow 在 save model 的时候，如果选择了 global_step 选项，会 global_step 值也保存下来，
然后 restore 的时候也就会接着这个 global_step 继续训练下去，因此需要去掉
"""
variables = tf.contrib.framework.get_variables_to_restore()
variables_to_resotre = [
v for v in variables if 'Momentum' not in v.name.split('/')[-1]
]
variables_to_resotre = [
v for v in variables_to_resotre
if 'global_step' not in v.name.split('/')[-1]
] # remove global step
restore_saver = tf.train.Saver(variables_to_resotre)
else:
restore_saver = tf.train.Saver()
saver = tf.train.Saver(max_to_keep=10)
# ==============================
# Set tf summary save path
tboard_save_path = 'tboard/tusimple_lanenet_{:s}_{:s}'.format(
net_flag, version_flag)
os.makedirs(tboard_save_path, exist_ok=True)
# Set sess configuration
# ============================== config GPU
sess_config = tf.ConfigProto(allow_soft_placement=True)
# sess_config.gpu_options.per_process_gpu_memory_fraction = cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = cfg.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
# ==============================
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# ---------------------------------------------------------------- #
# Set the training parameters
import math
one_epoch2step = math.ceil(cfg.TRAIN.TRAIN_SIZE /
cfg.TRAIN.BATCH_SIZE) # 训练一个 epoch 需要的 batch 数量
total_epoch = math.ceil(cfg.TRAIN.STEPS / one_epoch2step) # 一共需要训练多少 epoch
log.info('Global configuration is as follows:')
log.info(cfg)
max_acc = 0.9
save_num = 0
val_step = 0
# ================================================================ #
# Train & Val #
# ================================================================ #
with sess.as_default():
# ============================== load pretrain model
# if weights_path is None:
# log.info('Training from scratch')
# sess.run(tf.global_variables_initializer())
# elif net_flag == 'vgg' and weights_path is None:
# load_pretrained_weights(tf.trainable_variables(), './data/vgg16.npy', sess)
# elif scratch: # 从头开始训练，类似 Caffe 的 --weights
# sess.run(tf.global_variables_initializer())
# log.info('Restore model from last model checkpoint {:s}, scratch'.format(weights_path))
# try:
# restore_saver.restore(sess=sess, save_path=weights_path)
# except:
# log.info('model maybe is not exist!')
# else: # 继续训练，类似 Caffe 的 --snapshot
# log.info('Restore model from last model checkpoint {:s}'.format(weights_path))
# try:
# restore_saver.restore(sess=sess, save_path=weights_path)
# except:
# log.info('model maybe is not exist!')
sess.run(tf.global_variables_initializer())
# ==============================
for epoch in range(total_epoch):
# ================================================================ #
# Train #
# ================================================================ #
train_epoch_loss = []
pbar_train = tqdm(trainset)
train_t_start = time.time()
for gt_imgs, binary_gt_labels, instance_gt_labels in pbar_train:
_, global_step_val, train_loss, train_accuracy_figure, train_fn_figure, train_fp_figure, \
lr, train_summary, train_binary_loss, train_instance_loss, \
train_embeddings, train_binary_seg_imgs, train_l2_loss = \
sess.run([optimizer, global_step, train_total_loss, train_accuracy, train_fn, train_fp,
learning_rate, train_merge_summary_op, train_binary_seg_loss,
train_disc_loss, train_pix_embedding, train_prediction, train_l2_reg_loss],
feed_dict={train_input_tensor: gt_imgs,
train_binary_label_tensor: binary_gt_labels,
train_instance_label_tensor: instance_gt_labels}
)
# ============================== 透心凉，心飞扬
if math.isnan(train_loss) or math.isnan(
train_binary_loss) or math.isnan(train_instance_loss):
log.error('cost is: {:.5f}'.format(train_loss))
log.error(
'binary cost is: {:.5f}'.format(train_binary_loss))
log.error(
'instance cost is: {:.5f}'.format(train_instance_loss))
return
# ==============================
train_epoch_loss.append(train_loss)
summary_writer.add_summary(summary=train_summary,
global_step=global_step_val)
pbar_train.set_description(
("train loss: %.4f, learn rate: %e") % (train_loss, lr))
train_cost_time = time.time() - train_t_start
mean_train_loss = np.mean(train_epoch_loss)
log.info(
'MEAN Train: total_loss= {:6f} mean_cost_time= {:5f}s'.format(
mean_train_loss, train_cost_time))
# ---------------------------------------------------------------- #
# ================================================================ #
# Val #
# ================================================================ #
# 每隔 epoch 次，测试整个验证集
pbar_val = tqdm(valset)
val_epoch_loss = []
val_epoch_binary_loss = []
val_epoch_instance_loss = []
val_epoch_accuracy_figure = []
val_epoch_fp_figure = []
val_epoch_fn_figure = []
val_t_start = time.time()
for val_images, val_binary_labels, val_instance_labels in pbar_val:
# validation part
val_step += 1
val_summary, \
val_loss, val_binary_loss, val_instance_loss, \
val_accuracy_figure, val_fn_figure, val_fp_figure = \
sess.run([val_merge_summary_op,
val_total_loss, val_binary_seg_loss, val_disc_loss,
val_accuracy, val_fn, val_fp],
feed_dict={val_input_tensor: val_images,
val_binary_label_tensor: val_binary_labels,
val_instance_label_tensor: val_instance_labels}
)
# ============================== 透心凉，心飞扬
if math.isnan(val_loss) or math.isnan(
val_binary_loss) or math.isnan(val_instance_loss):
log.error('cost is: {:.5f}'.format(val_loss))
log.error('binary cost is: {:.5f}'.format(val_binary_loss))
log.error(
'instance cost is: {:.5f}'.format(val_instance_loss))
return
# ==============================
summary_writer.add_summary(summary=val_summary,
global_step=val_step)
pbar_val.set_description(("val loss: %.4f, accuracy: %.4f") %
(val_loss, val_accuracy_figure))
val_epoch_loss.append(val_loss)
val_epoch_binary_loss.append(val_binary_loss)
val_epoch_instance_loss.append(val_instance_loss)
val_epoch_accuracy_figure.append(val_accuracy_figure)
val_epoch_fp_figure.append(val_fp_figure)
val_epoch_fn_figure.append(val_fn_figure)
val_cost_time = time.time() - val_t_start
mean_val_loss = np.mean(val_epoch_loss)
mean_val_binary_loss = np.mean(val_epoch_binary_loss)
mean_val_instance_loss = np.mean(val_epoch_instance_loss)
mean_val_accuracy_figure = np.mean(val_epoch_accuracy_figure)
mean_val_fp_figure = np.mean(val_epoch_fp_figure)
mean_val_fn_figure = np.mean(val_epoch_fn_figure)
# ==============================
if mean_val_accuracy_figure > max_acc:
max_acc = mean_val_accuracy_figure
if save_num < 3: # 前三次不算
max_acc = 0.9
log.info(
'MAX_ACC change to {}'.format(mean_val_accuracy_figure))
model_save_path_max = ops.join(
model_save_dir, 'tusimple_lanenet_{}.ckpt'.format(
mean_val_accuracy_figure))
saver.save(sess=sess,
save_path=model_save_path_max,
global_step=global_step)
save_num += 1
# ==============================
log.info(
'=> Epoch: {}, MEAN Val: total_loss= {:6f} binary_seg_loss= {:6f} '
'instance_seg_loss= {:6f} accuracy= {:6f} fp= {:6f} fn= {:6f}'
' mean_cost_time= {:5f}s '.format(
epoch, mean_val_loss, mean_val_binary_loss,
mean_val_instance_loss, mean_val_accuracy_figure,
mean_val_fp_figure, mean_val_fn_figure, val_cost_time))
# ---------------------------------------------------------------- #
return
def test_lanenet(image_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
LOG.info('Start reading image and preprocessing')
t_start = time.time()
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
LOG.info('Image load complete, cost time: {:.5f}s'.format(time.time() -
t_start))
tf.compat.v1.disable_eager_execution()
input_tensor = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', cfg=CFG)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='LaneNet')
postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG)
# Set sess configuration
sess_config = tf.compat.v1.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.compat.v1.Session(config=sess_config)
# define moving average version of the learned variables for eval
with tf.compat.v1.variable_scope(name_or_scope='moving_avg'):
variable_averages = tf.train.ExponentialMovingAverage(
CFG.SOLVER.MOVING_AVE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
# define saver
saver = tf.compat.v1.train.Saver(variables_to_restore)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
t_start = time.time()
loop_times = 500
for i in range(loop_times):
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
t_cost = time.time() - t_start
t_cost /= loop_times
LOG.info('Single imgae inference cost time: {:.5f}s'.format(t_cost))
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis)
mask_image = postprocess_result['mask_image']
for i in range(CFG.MODEL.EMBEDDING_FEATS_DIMS):
instance_seg_image[0][:, :,
i] = minmax_scale(instance_seg_image[0][:, :,
i])
embedding_image = np.array(instance_seg_image[0], np.uint8)
plt.figure('mask_image')
plt.imshow(mask_image[:, :, (2, 1, 0)])
plt.savefig(fname="results/mask.png")
plt.figure('src_image')
plt.imshow(image_vis[:, :, (2, 1, 0)])
plt.savefig(fname="results/src.png")
plt.figure('instance_image')
plt.imshow(embedding_image[:, :, (2, 1, 0)])
plt.savefig(fname="results/instance.png")
plt.figure('binary_image')
plt.imshow(binary_seg_image[0] * 255, cmap='gray')
plt.savefig(fname="results/binary.png")
sess.close()
return
def custom_test_lanenet(src_dir , weights_path, save_dir):
"""
:param src_dir:
:param weights_path:
:param save_dir:
:return:
"""
assert ops.exists(src_dir), '{:s} not exist'.format(src_dir)
os.makedirs(save_dir, exist_ok=True)
input_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor')
net = lanenet.LaneNet(phase='test', cfg=CFG)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='LaneNet')
postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG)
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
save_img_lane_dict = {}
saver.restore(sess=sess, save_path=weights_path)
img_names = os.listdir(src_dir)
image_list = [os.path.join(src_dir,i) for i in img_names]
avg_time_cost = []
for index, image_path in tqdm.tqdm(enumerate(image_list), total=len(image_list)):
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]}
)
avg_time_cost.append(time.time() - t_start)
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis
)
lanes = postprocess_result['lane_points_on_img']
if index % 100 == 0:
LOG.info('Mean inference time every single image: {:.5f}s'.format(np.mean(avg_time_cost)))
avg_time_cost.clear()
input_image_dir = src_dir
input_image_name = ops.split(image_path)[-1]
output_image_dir = save_dir
os.makedirs(output_image_dir, exist_ok=True)
output_image_path = ops.join(output_image_dir, input_image_name)
# if ops.exists(output_image_path):
# continue
save_img_lane_dict[input_image_name] = lanes
cv2.imwrite(output_image_path, postprocess_result['source_image'])
output_json_name = os.path.join(save_dir,'img_lane_points.json')
with open(output_json_name, 'w') as fp:
json.dump(save_img_lane_dict, fp)
return
def test_lanenet(image_path, weights_path):
"""
:param image_path:
:param weights_path:
:return:
"""
assert ops.exists(image_path), '{:s} not exist'.format(image_path)
log.info('Start reading image and preprocessing')
t_start = time.time()
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
log.info('Image load complete, cost time: {:.5f}s'.format(time.time() -
t_start))
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, 256, 512, 3],
name='input_tensor')
net = lanenet.LaneNet(phase='test', net_flag='vgg')
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor,
name='lanenet_model')
postprocessor = lanenet_postprocess.LaneNetPostProcessor()
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]})
t_cost = time.time() - t_start
log.info('Single imgae inference cost time: {:.5f}s'.format(t_cost))
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis)
mask_image = postprocess_result['mask_image']
for i in range(CFG.TRAIN.EMBEDDING_FEATS_DIMS):
instance_seg_image[0][:, :,
i] = minmax_scale(instance_seg_image[0][:, :,
i])
embedding_image = np.array(instance_seg_image[0], np.uint8)
plt.figure('mask_image')
plt.imshow(mask_image[:, :, (2, 1, 0)])
plt.figure('src_image')
plt.imshow(image_vis[:, :, (2, 1, 0)])
plt.figure('instance_image')
plt.imshow(embedding_image[:, :, (2, 1, 0)])
plt.figure('binary_image')
plt.imshow(binary_seg_image[0] * 255, cmap='gray')
plt.show()
cv2.imwrite('instance_mask_image.png', mask_image)
cv2.imwrite('source_image.png', postprocess_result['source_image'])
cv2.imwrite('binary_mask_image.png', binary_seg_image[0] * 255)
sess.close()
return
def custom_test_lanenet_video(src_dir , weights_path, save_dir):
"""
:param src_dir:
:param weights_path:
:param save_dir:
:return:
"""
assert ops.exists(src_dir), '{:s} not exist'.format(src_dir)
os.makedirs(save_dir, exist_ok=True)
input_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor')
net = lanenet.LaneNet(phase='test', cfg=CFG)
binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='LaneNet')
postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG)
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.GPU.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.GPU.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
# out = cv2.VideoWriter('output.mp4', fourcc, 4.0, (640,480))
out = cv2.VideoWriter(os.path.join(save_dir,'output.mp4'),0x7634706d, 4, (1280,720))
with sess.as_default():
save_img_lane_dict = {}
saver.restore(sess=sess, save_path=weights_path)
cap = cv2.VideoCapture(src_dir)
avg_time_cost = []
frame_id = 0
while(True):
ret, image = cap.read()
frame_id+=1
if not ret or frame_id>50:
break
image_vis = image
image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR)
image = image / 127.5 - 1.0
t_start = time.time()
binary_seg_image, instance_seg_image = sess.run(
[binary_seg_ret, instance_seg_ret],
feed_dict={input_tensor: [image]}
)
avg_time_cost.append(time.time() - t_start)
postprocess_result = postprocessor.postprocess(
binary_seg_result=binary_seg_image[0],
instance_seg_result=instance_seg_image[0],
source_image=image_vis
)
lanes = postprocess_result['lane_points_on_img']
if frame_id % 100 == 0:
LOG.info('Mean inference time every single image: {:.5f}s'.format(np.mean(avg_time_cost)))
avg_time_cost.clear()
output_image_dir = save_dir
os.makedirs(output_image_dir, exist_ok=True)
output_image_path = ops.join(output_image_dir, str(frame_id)+'.jpg')
# if ops.exists(output_image_path):
# continue
save_img_lane_dict[frame_id] = lanes
cv2.imwrite(output_image_path, postprocess_result['source_image'])
out.write(postprocess_result['source_image'])
out.release()
cap.release()
output_json_name = os.path.join(save_dir,'img_lane_points.json')
with open(output_json_name, 'w') as fp:
json.dump(save_img_lane_dict, fp)
return
