object_detection train.py moved to legacy #4758
Description
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System information
- What is the top-level directory of the model you are using: ~/tensorflow/models/research/object_detection
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): See tf_env_collect.sh output below
- TensorFlow installed from (source or binary):
- TensorFlow version (use command below):
- Bazel version (if compiling from source):
- CUDA/cuDNN version:
- GPU model and memory:
- Exact command to reproduce: python train.py
tf_env_collect.sh output:
== cat /etc/issue ===============================================
Linux rkalliot2 4.15.0-24-generic #26~16.04.1-Ubuntu SMP Fri Jun 15 14:35:08 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
VERSION="16.04.4 LTS (Xenial Xerus)"
VERSION_ID="16.04"
VERSION_CODENAME=xenial
== are we in docker =============================================
No
== compiler =====================================================
c++ (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
== uname -a =====================================================
Linux rkalliot2 4.15.0-24-generic #26~16.04.1-Ubuntu SMP Fri Jun 15 14:35:08 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
== check pips ===================================================
numpy 1.14.5
protobuf 3.5.2
tensorflow 1.8.0
== check for virtualenv =========================================
False
== tensorflow import ============================================
tf.VERSION = 1.8.0
tf.GIT_VERSION = b'unknown'
tf.COMPILER_VERSION = b'unknown'
Sanity check: array([1], dtype=int32)
== env ==========================================================
LD_LIBRARY_PATH is unset
DYLD_LIBRARY_PATH is unset
== nvidia-smi ===================================================
Thu Jul 12 19:25:30 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.67 Driver Version: 390.67 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:81:00.0 On | N/A |
| 23% 38C P0 73W / 250W | 1046MiB / 12190MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1522 G /usr/lib/xorg/Xorg 709MiB |
| 0 2373 G compiz 197MiB |
| 0 7397 G ...-token=8E2E44D2DE2D7583A9BB9AAC21704BF0 136MiB |
+-----------------------------------------------------------------------------+
== cuda libs ===================================================
Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
it appears that train.py (and a number of other files) have been moved to object_detection/legacy. However, all documentation and tutorials and examples have the code running object_detection/train.py.
QUESTION: is there a new way to train object_detection models? If so, can you provide examples or documentation?
Source code / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.