I hate to keep bugging you but
When I run this I get: no input error dimensions giving error.
nx@nx-desktop:~/cuda10.2_trt7.1_jp4.5$ ./tlt-converter -k nvidia_tlt -p input_1,1x3x160x160,1x3x160x160,2x3x160x160 -t fp16 -e /model.plan /model.etlt
Error: no input dimensions given
the arguments say that no input dimension are required when running TLT 3 models
nx@nx-desktop:~/cuda10.2_trt7.1_jp4.5$ ./tlt-converter -h
usage: ./tlt-converter [-h] [-v] [-e ENGINE_FILE_PATH]
[-k ENCODE_KEY] [-c CACHE_FILE]
[-o OUTPUTS] [-d INPUT_DIMENSIONS]
[-b BATCH_SIZE] [-m MAX_BATCH_SIZE]
[-w MAX_WORKSPACE_SIZE] [-t DATA_TYPE]
[-i INPUT_ORDER] [-s] [-u DLA_CORE]
input_file
Generate TensorRT engine from exported model
positional arguments:
input_file Input file (.etlt exported model).
required flag arguments:
-d comma separated list of input dimensions(not required for TLT 3.0 new models).
-k model encoding key.
optional flag arguments:
-b calibration batch size (default 8).
-c calibration cache file (default cal.bin).
-e file the engine is saved to (default saved.engine).
-i input dimension ordering – nchw, nhwc, nc (default nchw).
-m maximum TensorRT engine batch size (default 16). If meet with out-of-memory issue, please decrease the batch size accordingly.
-o comma separated list of output node names (default none).
-p comma separated list of optimization profile shapes in the format <input_name>,<min_shape>,<opt_shape>,<max_shape>, where each shape has the format: xxx. Can be specified multiple times if there are multiple input tensors for the model. This argument is only useful in dynamic shape case.
-s TensorRT strict_type_constraints flag for INT8 mode(default false).
-t TensorRT data type – fp32, fp16, int8 (default fp32).
-u Use DLA core N for layers that support DLA(default = -1, which means no DLA core will be utilized for inference. Note that it’ll always allow GPU fallback).
-w maximum workspace size of TensorRT engine (default 1<<30). If meet with out-of-memory issue, please increase the workspace size accordingly.