Tensorflow lite logs do not get enabled until any LOG(FATAL) command is not encountered

Tensorflow version 1.13.0 rc0 and 1.12.0 cpu version(cuda disable) for Linux ubuntu 16.0
I am unable to generate any logs on console using toco tool of tensorflow.
_ use export TF_CPP_MIN_LOG_LEVEL=1  , the info logs do not start to appear on console.
Until I any error case while using toco is not hit in the source code i,e when LOG(FATAL)  is encountered then all debug logs appear on console.
Let me know how to enable all the logs

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):  Linux Ubuntu 18.04.1 LTS
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No , Its on Linux PC
TensorFlow installed from (source or binary): Installed from source 1.13.rc0
TensorFlow version: 1.13.rc0
Python version: Python 3.6.5 :: Anaconda, Inc
Installed using virtualenv? pip? conda?: Installed using Pip in conda virtual enviourment as well as also tried with installing using pip without conda virtual env both reproduce the behaviour
Bazel version (if compiling from source):  Build label: 0.21.0
GCC/Compiler version (if compiling from source): gcc (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CUDA/cuDNN version:  N/A compiled from source without cuda option
GPU model and memory: N/A compiled from source without cuda option

Compiled the Source code using -  bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
And installed wheel using pip ,
Provide the exact sequence of commands / steps that you executed before running into the problem
Ran the toco for quantized pb for fast style transfer model using -
FOLLOWING COMMAND IS THE SUCCESSFUL CASE IN WHICH I HAD ENABLED THE MARCO TO PRINT TENSORFLOW AND TENSORFLOW LITE LOGS BUT ONLY FEW XLA LOGS APPEAR NO TENSORFLWO LITE LOGS
toco --graph_def_file=/home/sri/Dev/test/frozen_FST_deo.pb --output_file=/home/sri/Dev/test/quantized_FST_15.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --inference_type=QUANTIZED_UINT8 --output_arrays=Tanh --input_arrays=input --mean_values=0 --std_dev_values=1 --default_ranges_min=-10 --default_ranges_max=10
/home/sri/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
2019-02-01 11:12:06.069365: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-02-01 11:12:06.069729: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x555d576a8080 executing computations on platform Host. Devices:
2019-02-01 11:12:06.069744: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
IN CASE OF ERROR CASE -
toco --graph_def_file=/home/sri/Dev/test/frozen_FST_deo.pb --output_file=/home/sri/Dev/test/quantized_FST_15.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --inference_type=QUANTIZED_UINT8 --output_arrays=Tanh --input_arrays=input --mean_values=0 --std_dev_values=1 --default_ranges_min=0 --default_ranges_max=1
/home/sri/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
2019-02-01 11:08:07.749245: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-02-01 11:08:07.749801: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55c91359c6d0 executing computations on platform Host. Devices:
2019-02-01 11:08:07.749826: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
sri@sri-MS-7A74:~$ toco --graph_def_file=/home/sri/Dev/test/frozen_FST_deo.pb --output_file=/home/sri/Dev/test/quantized_FST_15.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --inference_type=QUANTIZED_UINT8 --output_arrays=Tanh --input_arrays=input --mean_values=0 --std_dev_values=1
/home/sri/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
2019-02-01 11:08:20.645381: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-02-01 11:08:20.646016: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5629ddb2ebc0 executing computations on platform Host. Devices:
2019-02-01 11:08:20.646043: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
Traceback (most recent call last):
File "/home/sri/anaconda3/bin/toco", line 11, in
sys.exit(main())
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/tflite_convert.py", line 442, in main
app.run(main=run_main, argv=sys.argv[:1])
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/tflite_convert.py", line 438, in run_main
_convert_model(tflite_flags)
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/tflite_convert.py", line 191, in _convert_model
output_data = converter.convert()
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/lite.py", line 455, in convert
**converter_kwargs)
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 442, in toco_convert_impl
input_data.SerializeToString())
File "/home/sri/anaconda3/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 205, in toco_convert_protos
"TOCO failed. See console for info.\n%s\n%s\n" % (stdout, stderr))
tensorflow.lite.python.convert.ConverterError: TOCO failed. See console for info.
I am here ABHA ImportTensorFlowGraphDef
ABHA I am here
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END DUMP OF TENSORFLOW GRAPHDEF (AT IMPORT)
2019-02-01 11:08:21.876746: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 387 operators, 583 arrays (0 quantized)
2019-02-01 11:08:21.880104: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 387 operators, 583 arrays (0 quantized)
2019-02-01 11:08:21.893831: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] After general graph transformations pass 1: 186 operators, 318 arrays (1 quantized)
2019-02-01 11:08:21.895988: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] Before pre-quantization graph transformations: 186 operators, 318 arrays (1 quantized)
2019-02-01 11:08:21.896947: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] After pre-quantization graph transformations pass 1: 170 operators, 302 arrays (1 quantized)
2019-02-01 11:08:21.898291: F tensorflow/lite/toco/tooling_util.cc:1702] Array moments/SquaredDifference, which is an input to the Mean operator producing the output array moments/variance, is lacking min/max data, which is necessary for quantization. If accuracy matters, either target a non-quantized output format, or run quantized training with your model from a floating point checkpoint to change the input graph to contain min/max information. If you don't care about accuracy, you can pass --default_ranges_min= and --default_ranges_max= for easy experimentation.
Aborted (core dumped)
Any other info / 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.

Please attach long logs in future, as requested in the template.

This is a function of the way the TensorFlow Lite converter is wired into the Python API. At the moment, the converter runs in a separate process, and we capture the stdout/stderr output only at the end of the execution. Looks like we just need to log the output in the successful case (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/python/convert.py#L196)

@gargn should we output the TOCO output even in the successful conversion case?

TOCO is no longer the default conversion backend. Much of the logging should be dramatically improved with the new converter backend (which leverages MLIR).