Please scroll to the FINAL for Ultimate Solution!
Follow the procedure to install the libs and dependencies.
In the last step, testing the installation:
Running tests under Python 3.6.10: /users/b145872/project-dir/Anaconda3_2020/envs/tf1.14/bin/python
[ RUN ] ModelBuilderTest.test_create_experimental_model
[ OK ] ModelBuilderTest.test_create_experimental_model
[ RUN ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner
[ OK ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner
[ RUN ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
[ OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
[ RUN ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
[ OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
[ RUN ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
[ OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
[ RUN ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ RUN ] ModelBuilderTest.test_create_rfcn_model_from_config
[ OK ] ModelBuilderTest.test_create_rfcn_model_from_config
[ RUN ] ModelBuilderTest.test_create_ssd_fpn_model_from_config
[ OK ] ModelBuilderTest.test_create_ssd_fpn_model_from_config
[ RUN ] ModelBuilderTest.test_create_ssd_models_from_config
[ OK ] ModelBuilderTest.test_create_ssd_models_from_config
[ RUN ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update
[ OK ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update
[ RUN ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold
[ OK ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold
[ RUN ] ModelBuilderTest.test_invalid_model_config_proto
[ OK ] ModelBuilderTest.test_invalid_model_config_proto
[ RUN ] ModelBuilderTest.test_invalid_second_stage_batch_size
[ OK ] ModelBuilderTest.test_invalid_second_stage_batch_size
[ RUN ] ModelBuilderTest.test_session
[ SKIPPED ] ModelBuilderTest.test_session
[ RUN ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor
[ OK ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor
[ RUN ] ModelBuilderTest.test_unknown_meta_architecture
[ OK ] ModelBuilderTest.test_unknown_meta_architecture
[ RUN ] ModelBuilderTest.test_unknown_ssd_feature_extractor
[ OK ] ModelBuilderTest.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 17 tests in 0.427s
Now we test the object detection script. As the model need to run on GPU cluster, we cannot simply use jupyter notebook. So convert to plain python code.
jupyter nbconvert --to python object_detection_tutorial.ipynb
Excecute the python code, got:
ModuleNotFoundError: No module named 'object_detection'
Compile protobufs and install the object_detection package:
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
pip install .
Execute again, got:
tensorflow.python.framework.errors_impl.NotFoundError: models/research/object_detection/data/mscoco_label_map.pbtxt; No such file or directory
This is the relative path problem caused by changing directory from ipython notebook.
Execute again, got:
File "object_detection_tutorial.py", line 98, in <module>
detection_model = load_model(model_name)
File "object_detection_tutorial.py", line 62, in load_model
model = tf.saved_model.load(str(model_dir), None)
File "/users/b145872/project-dir/Anaconda3_2020/envs/tf1.14/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func
return func(*args, **kwargs)
TypeError: load() missing 1 required positional argument: 'export_dir'
Version Change:
model = tf.saved_model.load(export_dir=str(model_dir))
# change to:
model = tf.saved_model.load_v2(export_dir=str(model_dir),tags=None)
New error:
Traceback (most recent call last):
File "object_detection_tutorial.py", line 191, in <module>
show_inference(detection_model, image_path)
File "object_detection_tutorial.py", line 172, in show_inference
output_dict = run_inference_for_single_image(model, image_np)
File "object_detection_tutorial.py", line 141, in run_inference_for_single_image
num_detections = int(output_dict.pop('num_detections'))
TypeError: int() argument must be a string, a bytes-like object or a number, not 'Tensor'
…….
Ultimate Solution
After multiple tests, we still cannot run the script on gpu smoothly, tf1.9 1.14 and 2.1 all failed. When I come back to the github page. I found the updated ipynb…
And this time, the tf2.1-based env can run it with GPU! Although there are still errors, we reinstalled the tf2.1 by conda --force-reinstall, everything goes nice!
Updated 2020-05-10