Please provide the following information when requesting support.
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc) Yolo_v4_tiny
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here) 5.0
I run yolo_v4_tiny.ipynb from getting_started_v5.0.0/notebooks/tao_launcher_starter_kit/yolo_v4_tiny.
I only see evaluations for trained model and re-trained pruned model methods.
I try to run “!tao model yolo_v4_tiny evaluation -e $SPECS_DIR/yolo_v4_tiny_train_kitti.txt
-m $LOCAL_EXPERIMENT_DIR/experiment_dir_pruned/yolov4_cspdarknet_tiny_pruned.hdf5”, but I get the following error.
2023-08-11 08:30:24,570 [TAO Toolkit] [INFO] root 160: Registry: [‘nvcr.io’]
2023-08-11 08:30:24,630 [TAO Toolkit] [INFO] nvidia_tao_cli.components.instance_handler.local_instance 360: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5
2023-08-11 08:30:24,648 [TAO Toolkit] [WARNING] nvidia_tao_cli.components.docker_handler.docker_handler 262:
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the “user”:“UID:GID” in the
DockerOptions portion of the “/home/ubuntu/.tao_mounts.json” file. You can obtain your
users UID and GID by using the “id -u” and “id -g” commands on the
terminal.
2023-08-11 08:30:24,648 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 275: Printing tty value True
Using TensorFlow backend.
2023-08-11 08:30:27.648730: I tensorflow/stream_executor/platform/default/dso_loader.cc:50] Successfully opened dynamic library libcudart.so.12
2023-08-11 08:30:27,699 [TAO Toolkit] [WARNING] tensorflow 40: Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
2023-08-11 08:30:29,001 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:29,041 [TAO Toolkit] [WARNING] tensorflow 42: TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:29,046 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:30,611 [TAO Toolkit] [INFO] matplotlib.font_manager 1633: generated new fontManager
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
WARNING:tensorflow:TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:33,921 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
WARNING:tensorflow:TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:33,963 [TAO Toolkit] [WARNING] tensorflow 42: TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
WARNING:tensorflow:TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
2023-08-11 08:30:33,966 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.
2023-08-11 08:30:34,205 [TAO Toolkit] [WARNING] tensorflow 137: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
2023-08-11 08:30:34,205 [TAO Toolkit] [WARNING] tensorflow 137: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
2023-08-11 08:30:34,206 [TAO Toolkit] [WARNING] tensorflow 137: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
2023-08-11 08:30:34,207 [TAO Toolkit] [WARNING] tensorflow 137: From /usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
Traceback (most recent call last):
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/scripts/evaluate.py”, line 300, in
main()
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/common/utils.py”, line 717, in return_func
raise e
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/common/utils.py”, line 705, in return_func
return func(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/scripts/evaluate.py”, line 296, in main
raise e
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/scripts/evaluate.py”, line 284, in main
evaluate(args)
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/scripts/evaluate.py”, line 164, in evaluate
model = load_model(
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/utils/model_io.py”, line 70, in load_model
model = get_model_with_input(model_path, input_layer)
File “/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/yolo_v4/utils/model_io.py”, line 39, in get_model_with_input
return keras.models.load_model(
File “/usr/local/lib/python3.8/dist-packages/keras/engine/saving.py”, line 417, in load_model
f = h5dict(filepath, ‘r’)
File “/usr/local/lib/python3.8/dist-packages/keras/utils/io_utils.py”, line 186, in init
self.data = h5py.File(path, mode=mode)
File “/usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py”, line 312, in init
fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr)
File “/usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py”, line 142, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File “h5py/_objects.pyx”, line 54, in h5py._objects.with_phil.wrapper
File “h5py/_objects.pyx”, line 55, in h5py._objects.with_phil.wrapper
File “h5py/h5f.pyx”, line 78, in h5py.h5f.open
OSError: Unable to open file (unable to open file: name = ‘/home/ubuntu/tao-experiments/yolo_v4_tiny/experiment_dir_pruned/yolov4_cspdarknet_tiny_pruned.hdf5’, errno = 2, error message = ‘No such file or directory’, flags = 0, o_flags = 0)
Execution status: FAIL
2023-08-11 08:30:36,901 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 337: Stopping container.
I’m pretty sure the file path is correct.
Does yolov4 tiny support evaluation of pruned models?