我正在尝试训练TF2进行对象检测。当我运行model_main_tf2.py
!python model_main_tf2.py --pipeline_config_path=training/ssd_efficientdet_d0_512x512_coco17_tpu-8.config --model_dir=training --alsologtostderr
我得到以下错误:
2021-02-21 16:46:31.616633: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
Traceback (most recent call last):
File "model_main_tf2.py", line 32, in <module>
from object_detection import model_lib_v2
ImportError: cannot import name 'model_lib_v2'
如何导入?我使用Colab，做过很多交易。我不希望它从一开始就被抹去或破坏。你的意见是什么?
COLAB代码:
from google.colab import drive
drive.mount('/content/gdrive')
%cd /content/gdrive/MyDrive/Yeni/Object_detection/models-master/research/object_detection
!python generate_tfrecord.py --csv_input=images/train_labels.csv --image_dir=images/train --output_path=train.record
!python generate_tfrecord.py --csv_input=images/test_labels.csv --image_dir=images/test --output_path=test.record
!python generate_labelmap.py
%cd /content/gdrive/MyDrive/Yeni/Object_detection/models-master/research/object_detection
!python model_main_tf2.py --pipeline_config_path=training/ssd_efficientdet_d0_512x512_coco17_tpu-8.config --model_dir=training --alsologtostderr
AND model_main_tf2.py CODES:
r"""Creates and runs TF2 object detection models.
For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
NUM_TRAIN_STEPS=10000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
python model_main_tf2.py --
--model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS
--sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES
--pipeline_config_path=$PIPELINE_CONFIG_PATH
--alsologtostderr
"""
from absl import flags
import tensorflow.compat.v2 as tf
from object_detection import model_lib_v2
flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
'file.')
flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
flags.DEFINE_bool('eval_on_train_data', False, 'Enable evaluating on train '
'data (only supported in distributed training).')
flags.DEFINE_integer('sample_1_of_n_eval_examples', None, 'Will sample one of '
'every n eval input examples, where n is provided.')
flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
'one of every n train input examples for evaluation, '
'where n is provided. This is only used if '
'`eval_training_data` is True.')
flags.DEFINE_string(
'model_dir', None, 'Path to output model directory '
'where event and checkpoint files will be written.')
flags.DEFINE_string(
'checkpoint_dir', None, 'Path to directory holding a checkpoint. If '
'`checkpoint_dir` is provided, this binary operates in eval-only mode, '
'writing resulting metrics to `model_dir`.')
flags.DEFINE_integer('eval_timeout', 3600, 'Number of seconds to wait for an'
'evaluation checkpoint before exiting.')
flags.DEFINE_bool('use_tpu', False, 'Whether the job is executing on a TPU.')
flags.DEFINE_string(
'tpu_name',
default=None,
help='Name of the Cloud TPU for Cluster Resolvers.')
flags.DEFINE_integer(
'num_workers', 1, 'When num_workers > 1, training uses '
'MultiWorkerMirroredStrategy. When num_workers = 1 it uses '
'MirroredStrategy.')
flags.DEFINE_integer(
'checkpoint_every_n', 1000, 'Integer defining how often we checkpoint.')
flags.DEFINE_boolean('record_summaries', True,
('Whether or not to record summaries during'
' training.'))
FLAGS = flags.FLAGS
def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tf.config.set_soft_device_placement(True)
if FLAGS.checkpoint_dir:
model_lib_v2.eval_continuously(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300, timeout=FLAGS.eval_timeout)
else:
if FLAGS.use_tpu:
# TPU is automatically inferred if tpu_name is None and
# we are running under cloud ai-platform.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
elif FLAGS.num_workers > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
else:
strategy = tf.compat.v2.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
use_tpu=FLAGS.use_tpu,
checkpoint_every_n=FLAGS.checkpoint_every_n,
record_summaries=FLAGS.record_summaries)
if __name__ == '__main__':
tf.compat.v1.app.run()
试试这个
import os
os.environ['PYTHONPATH']+=":/content/models"
os.environ['PYTHONPATH']+=":/content/models/research"
之前调用
!python model_main_tf2.py --pipeline_config_path=training/ssd_efficientdet_d0_512x512_coco17_tpu-8.config --model_dir=training --alsologtostderr