"None Annotations" when running python download_and_convert_data.py

I extracted the coco files and annotations in data/coco in the following manner: Running from FastMaskRCNN ls data/coco

shows: annotations test2014 test2015 train2014 val2014 I merged all annotations into one folder

Running from FastMaskRCNN: python download_and_convert_data.py

delivers:

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
loading annotations into memory...
Done (t=12.21s)
creating index...
index created!
train2014 has 82783 images
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GT 730
major: 3 minor: 5 memoryClockRate (GHz) 0.9015
pciBusID 0000:01:00.0
Total memory: 1.95GiB
Free memory: 1.44GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0)
>> Converting image 1/82783 shard 0
>> Converting image 2/82783 shard 0
>> Converting image 3/82783 shard 0
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>> Converting image 14/82783 shard 0
>> Converting image 15/82783 shard 0
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>> Converting image 30/82783 shard 0
None Annotations data/coco/train2014/COCO_train2014_000000262184.jpg
Traceback (most recent call last):
  File "download_and_convert_data.py", line 35, in <module>
    tf.app.run()
  File "/home/schaal/.local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 44, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "download_and_convert_data.py", line 29, in main
    download_and_convert_coco.run(FLAGS.dataset_dir2)
  File "/home/schaal/Tensorflow/gits/FastMaskRCNN_org/libs/datasets/download_and_convert_coco.py", line 330, in run
    'train2014')
  File "/home/schaal/Tensorflow/gits/FastMaskRCNN_org/libs/datasets/download_and_convert_coco.py", line 290, in _add_to_tfrecord
    gt_boxes, masks, mask = _get_coco_masks(coco, img_id, height, width, img_name)
  File "/home/schaal/Tensorflow/gits/FastMaskRCNN_org/libs/datasets/download_and_convert_coco.py", line 221, in _get_coco_masks
    LOG('None Annotations %s' % img_name)
  File "/home/schaal/Tensorflow/gits/FastMaskRCNN_org/libs/logs/log.py", line 11, in LOG
    datefmt='%m/%d/%Y %I:%M:%S %p', format='%(asctime)s %(message)s')
  File "/usr/lib/python2.7/logging/__init__.py", line 1547, in basicConfig
    hdlr = FileHandler(filename, mode)
  File "/usr/lib/python2.7/logging/__init__.py", line 913, in __init__
    StreamHandler.__init__(self, self._open())
  File "/usr/lib/python2.7/logging/__init__.py", line 943, in _open
    stream = open(self.baseFilename, self.mode)
IOError: [Errno 2] No such file or directory: '/home/schaal/Tensorflow/gits/FastMaskRCNN_org/train/maskrcnn/maskrcnn.log'

Are you suspecting some issues with the download of the coco zip files?

[lihungchieh]

@ZheC @sebaschaal I solved this problem by creating the directory train/maskrnn in /your/path/FastMaskRCNN. The program doesn’t create this directory automatically.

[ZheC]

Both works, thank you @lihungchieh @wuzheng-sjtu