AttributeError: module 'tensorflow.contrib.tensorrt' has no attribute 'calib_graph_to_infer_graph' when trying to Calibrate

[sehHeiden]

Hi,

I am trying TensorRT. It works fine as long as I don't try getting the INT8 to work. I got two problems:

1.: is similar to issue #56 2.: occures when trying to calibrate an Resnet v1.5 or SSD-ResNet-1.5. For reproduceability: I used the run_all.sh script from this side.

Maschine: Ubuntu 18.04, Intel(R) Core(TM) i7-6850K, NVidia 1070, also tried it with the Quadro 5000 Cuda: 10.0 CuDNN: 7 Tensorflow: compiled from source v1.13.1 with TensorRT 5.1.2

When running run_all.sh I get the error:

Traceback (most recent call last): File "tftrt_sample.py", line 306, in int8Graph=getINT8InferenceGraph(calibGraph) File "tftrt_sample.py", line 137, in getINT8InferenceGraph trt_graph=trt.calib_graph_to_infer_graph(calibGraph) AttributeError: module 'tensorflow.contrib.tensorrt' has no attribute 'calib_graph_to_infer_graph'

When I curcumvent the line 137 in my own code and don't calibrate the net, the conversion is still working. At least I don't get any errors, but as described in issue #56 the execution for INT8 is almost as slow as for nor converted FP32 and much slower than converted FP32 and FP16.

I tried it with the Quadro 5000 card for both ResNet and SSD-ResNet. With the same outcome and with a compiled Tensorflow v1.14 against TensorRT 5.1.5 same outcome (tested for ResNet) for both Problems.

With kind regards

[zhangqijun]

just get answer. TF 1.14 change method from tensorflow.python.compiler.tensorrt import trt_convert as trt converter = trt.TrtGraphConverter(input_saved_model_dir="") converter.convert() converter.save()

[pooyadavoodi]

As @zhangqijun said, TF1.14 has a new API for calibration. https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#post-train

Closing. Please reopen if the problem persists.

[mminervini]

I find myself in a similar situation as @Meresmata... I have a workflow to go from a Keras model to a TF-TRT model for deployment, that is based on freezing a graph and finally using create_inference_graph() to obtain the TRT graph. This works for FP32 or FP16. Instead, for INT8 conversion the resulting model is slower than FP32, because (as far as I understood) I am missing the calibration step, however I cannot find the function calib_graph_to_infer_graph() that is adopted in many blog posts and example codes (was it removed from v1.14?). I tried to migrate to the approach suggested by @zhangqijun, but I am stuck with converter.convert() that fails with this exception:

InvalidArgumentError: Failed to import metagraph, check error log for more info.

I tried several solutions that I found around on the Internet, however the approach that got me the furthest seems to be the one in this post on the Nvidia developer forum. When I instantiate the TrtGraphConverter I provide the path to the model directory:

converter = trt_convert.TrtGraphConverter(input_saved_model_dir=export_path)

However this results in the InvalidArgumentError when I call converter.convert(). I also tried to load the model with ParseFromString():

frozen_graph = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(os.path.join(export_path, 'model_frozen.pb'), 'rb') as f:
    frozen_graph.ParseFromString(f.read())
converter = trt_convert.TrtGraphConverter(input_graph_def=frozen_graph.as_graph_def())
trt_graph = converter.convert()

but I ended up with the same error.

1. It's not clear to me how can I check the error log mentioned in the exception.
2. How am I supposed to save/load a Keras model in a suitable way for TrtGraphConverter?

[cainiaoxy]

I find myself in a similar situation as @Meresmata... I have a workflow to go from a Keras model to a TF-TRT model for deployment, that is based on freezing a graph and finally using create_inference_graph() to obtain the TRT graph. This works for FP32 or FP16. Instead, for INT8 conversion the resulting model is slower than FP32, because (as far as I understood) I am missing the calibration step, however I cannot find the function calib_graph_to_infer_graph() that is adopted in many blog posts and example codes (was it removed from v1.14?). I tried to migrate to the approach suggested by @zhangqijun, but I am stuck with converter.convert() that fails with this exception:

InvalidArgumentError: Failed to import metagraph, check error log for more info.

I tried several solutions that I found around on the Internet, however the approach that got me the furthest seems to be the one in this post on the Nvidia developer forum. When I instantiate the TrtGraphConverter I provide the path to the model directory:

converter = trt_convert.TrtGraphConverter(input_saved_model_dir=export_path)

However this results in the InvalidArgumentError when I call converter.convert(). I also tried to load the model with ParseFromString():

frozen_graph = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(os.path.join(export_path, 'model_frozen.pb'), 'rb') as f:
    frozen_graph.ParseFromString(f.read())
converter = trt_convert.TrtGraphConverter(input_graph_def=frozen_graph.as_graph_def())
trt_graph = converter.convert()

but I ended up with the same error.

1. It's not clear to me how can I check the error log mentioned in the exception.
2. How am I supposed to save/load a Keras model in a suitable way for TrtGraphConverter?

I think i have met the same issue in tf1.14. Did you fix the problem？

[liuxingbin]

any progress? I met the same problem: ValueError: Failed to import metagraph, check error log for more info.

[DEKHTIARJonathan]

@liuxingbin please update to latest TF2. TF1 is more than two years old, even if there was a bug we couldn't release a fix.

[liuxingbin]

@liuxingbin please update to latest TF2. TF1 is more than two years old, even if there was a bug we couldn't release a fix.

Actually, I did. My tf is 2.4. Please refer to this question: https://github.com/tensorflow/tensorrt/issues/325