Preparing .npy Data of a TensorFlow Model
This version does not support the generation of NumPy (.npy) files of a TensorFlow model. You need to install the TensorFlow environment and prepare NumPy data in advance. This section provides only an example of the TensorFlow .npy file for reference.
Before generating .npy files of a TensorFlow model, a complete, executable, and standard TensorFlow model application project is required. You can use the TensorFlow debugger (tfdbg) to generate .npy files. The major steps are as follows:
- Modify the TensorFlow inference script to add the debugging configuration option by adding the following code:
- In Estimator mode:
1 2
from tensorflow.python import debug as tf_debug training_hooks = [train_helper.PrefillStagingAreaHook(), tf_debug.LocalCLIDebugHook()]
Add the tfdbg hook, as shown in Figure 1. - In session.run mode:
1 2
from tensorflow.python import debug as tf_debug sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type="readline")
Set the tfdbg wrapper before run, as shown in Figure 2.
- In Estimator mode:
- Run the inference script.
After the inference is complete, the system automatically enters the tfdbg debugging CLI interaction mode. Enter the run command.
- Collect .npy files.
After the script is executed, you can run the lt command in a new CLI to query the stored tensors, run the pt command to view the tensor content, and save it as a file in NumPy format.
The tfdbg dumps only one tensor at a time. To dump all tensors, perform the following steps:
- Run lt > tensor_name to temporarily store all tensor names to a file.
- Open a new Linux command window and run the following command to generate the commands to be executed in the tfdbg CLI:
timestamp=$[$(date +%s%N)/1000] ; cat tensor_name | awk '{print "pt",$4,$4}' | awk '{gsub("/", "_", $3);gsub(":", ".", $3);print($1,$2,"-n 0 -w "$3".""'$timestamp'"".npy")}' > tensor_name_cmd.txt
The tensor names in the example are stored in the tensor_name_cmd.txt file. The .npy file names meet the naming rules for accuracy comparison, where, tensor_name is the name of the file that stores all tensor names and timestamp is of 16 bits.
- Go back to the tfdbg CLI window, run the script, and run the command generated in the previous step for saving all .npy files.
By default, .npy files are stored using numpy.save(). Slashes (/) and colons (:) are replaced by underscores (_).
If the commands cannot be pasted, run the mouse off command in the tfdbg CLI to disable the mouse mode before pasting again.
- Check whether the names of the generated .npy files comply with the {op_name}.{output_index}.{timestamp}.npy format, as shown in Figure 3.
- If the name of an .npy file exceeds 255 characters due to the long operator name, comparison of this operator is not supported.
- The name of some .npy files may not meet the naming requirements due to the tfdbg or operating environment. You can manually rename the files based on the naming rules. If there are a large number of .npy files that do not meet the requirements, generate .npy files again. For details, see Handling Exceptions in the Generated .npy File Names in Batches.
