Merge pull request #4 in PROCESSOR-SDK/tensorflow-lite-examples from ~A0271529/tensorflow-lite-examples:demo-integration to masterHEADmaster

* commit '48b79a09b76f05b3417e6f67b65c84e4a465b054':
  Add demo scripts and their usage on target in README

5 files changed, 135 insertions, 0 deletions

diff --git a/README b/README
index b788fbc..90142b9 100644
--- a/README
+++ b/README
@@ -51,3 +51,17 @@ Binaries to run on target
 --input_std, -s: input standard deviation
 --profiling, -p: [0|1], profiling or not
 --threads, -t: number of threads
+
+Demos packaged with Processor SDK
+---------------------------------
+* Classification demo with Arm only, full TIDL offload, and partial TIDL offload (heterogeneous execution)
+  # cd /usr/share/tensorflow-lite/demos
+  # ./run_tidl_compiler.sh  [This is creating mobilenet_v1_1.0_224_tidl_am5.tflite, .bin files, and subgraph0.cfg for full TIDL offload]
+  # ./run_classification.sh [This runs Arm only classification first, and then with full TIDL offload]
+  # ./run_tidl_compiler1.sh [This is creating mobilenet_v1_1.0_224_tidl_am5.tflite, .bin files, and subgraph0.cfg for partial TIDL offload,]
+                            [specifying MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6 as the output tensor of the TIDL subgraph]
+  # ./run_classification.sh [This runs Arm only classification first, and then with partial TIDL offload]
+
+* Arm only segmentation demo:
+  # cd /usr/share/tensorflow-lite/demos
+  # ./run_segmentation.sh
diff --git a/scripts/run_classification.sh b/scripts/run_classification.sh
new file mode 100755
index 0000000..a92dde8
--- /dev/null
+++ b/scripts/run_classification.sh
@@ -0,0 +1,25 @@
+#!/bin/sh
+
+cd /usr/share/tensorflow-lite/demos
+
+echo -e "-----------------------------------------------------------------"
+echo -e "Running classification using mobilenet_v1_1.0_224.tflite model..."
+echo -e "-----------------------------------------------------------------"
+
+# Run tflite_classification binary with the tensorflow lite model, the input image, and the label file specified.
+# Classification label is overlayed with the input image. Exit the application after 60 seconds.
+./tflite_classification -m ./mobilenet_v1_1.0_224.tflite -i ../examples/grace_hopper.bmp -l ../examples/labels.txt -p 1 & pid=$!
+{ sleep 2; kill $pid; }
+
+echo -e
+echo -e "--------------------------------------------------------------------------------------------"
+echo -e "Running classification using the TIDL compiled model mobilenet_v1_1.0_224_tidl_am5.tflite..."
+echo -e "--------------------------------------------------------------------------------------------"
+# Run classification with the TIDL compiled tensorflow lite model
+# (e.g., mobilenet_v1_1.0_224_tidl_am5.tflite generated from run_tidl_compiler.sh)
+# TIDL API uses subgraph0.cfg created from run_tidl_compiler.sh to identify the TIDL network files,
+# as well as the boundary conversion parameters between TIDL and Arm
+# Classification label is overlayed with the input image. Right click the image display window to exit.
+cd /usr/share/tensorflow-lite/demos
+./tflite_classification -m /usr/share/ti/tidl/utils/test/testvecs/config/tflite_models/mobilenet_v1_1.0_224_tidl_am5.tflite \
+                        -i ../examples/grace_hopper.bmp -l ../examples/labels.txt -p 1
diff --git a/scripts/run_segmentation.sh b/scripts/run_segmentation.sh
new file mode 100755
index 0000000..5caea12
--- /dev/null
+++ b/scripts/run_segmentation.sh
@@ -0,0 +1,8 @@
+#!/bin/sh
+
+cd /usr/share/tensorflow-lite/demos
+
+# Run tflite_segmentation binary with the model and the input image specified.
+# The display window shows the input(resized) image, segmentation map, overlay
+# of the former two. Right click the image display window to exit.
+./tflite_segmentation -m ./deeplabv3_257_mv_gpu.tflite -i ./bird_segmentation.bmp
diff --git a/scripts/run_tidl_compiler.sh b/scripts/run_tidl_compiler.sh
new file mode 100755
index 0000000..db6d7b6
--- /dev/null
+++ b/scripts/run_tidl_compiler.sh
@@ -0,0 +1,43 @@
+#!/bin/sh
+# =======================================================================================================
+# Run tidl import tool to convert the original tensorflow lite model (e.g., mobilenet_v1_1.0_224.tflite)
+# to TIDL netowrk. The converted TIDL network are stored into tidl_net*.bin and tidl_param_*.bin files
+# (e.g., tidl_net_tflite_mobilenet_v1_1.0_224.bin, and tidl_param_tflite_mobilenet_v1_1.0_224.bin).
+# The TIDL supported subgraph are grouped as single custom operator (tidl-am5-custom-op) in tensorflow lite
+# format, and re-written into a new tensorflow lite model (e.g.,mobilenet_v1_1.0_224_tidl_am5.tflite)
+# =======================================================================================================
+# 1. Prepare the input: tensorflow lite model
+cd /usr/share/ti/tidl/utils
+mkdir -p ./test/testvecs/config/tflite_models
+mkdir -p ./test/testvecs/config/tidl_models/tflite/
+cp /usr/share/tensorflow-lite/demos/mobilenet_v1_1.0_224.tflite ./test/testvecs/config/tflite_models
+
+#2. Run tidl_model_import.out
+tidl_model_import.out ./test/testvecs/config/import/tflite/tidl_import_mobileNetv1.txt
+
+#3. Check the output and create subgraph0.cfg for TIDL offload at run-time
+
+# TIDL *.bin files are generated at /usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite
+# *tidl_am5.tflite model is generated at /usr/share/ti/tidl/utils/test/testvecs/config/tflite_models
+ls -l /usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite
+ls -l /usr/share/ti/tidl/utils/test/testvecs/config/tflite_models
+
+echo "Creating subgraph0.cfg at /usr/share/tensorflow-lite/demos..."
+echo "subgraph0.cfg will be used by TIDL API for the run-time offload"
+subgraph0_cfg_path=/usr/share/tensorflow-lite/demos/subgraph0.cfg
+# Specify TIDL network files
+echo "netBinFile      = "/usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite/tidl_net_tflite_mobilenet_v1_1.0_224.bin"" > $subgraph0_cfg_path
+echo "paramsBinFile   = "/usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite/tidl_param_tflite_mobilenet_v1_1.0_224.bin"" >> $subgraph0_cfg_path
+# Sepcify the boundary conversion parameters, see details at https://downloads.ti.com/mctools/esd/docs/tidl-api/api.html
+# The input is in NHWC format and ranges [-1,1]
+echo "inConvType = 0" >> $subgraph0_cfg_path
+echo "inIsSigned = 1" >> $subgraph0_cfg_path
+echo "inScaleF2Q = 128" >> $subgraph0_cfg_path
+echo "inIsNCHW = 0" >> $subgraph0_cfg_path
+# The output is in NHWC format and ranges [0,1]
+echo "outConvType = 0" >> $subgraph0_cfg_path
+echo "outIsSigned = 0" >> $subgraph0_cfg_path
+echo "outScaleF2Q = 255.0" >> $subgraph0_cfg_path
+echo "outIsNCHW = 0" >> $subgraph0_cfg_path
+echo "subgraph0.cfg:"
+cat $subgraph0_cfg_path
diff --git a/scripts/run_tidl_compiler1.sh b/scripts/run_tidl_compiler1.sh
new file mode 100755
index 0000000..f644cae
--- /dev/null
+++ b/scripts/run_tidl_compiler1.sh
@@ -0,0 +1,45 @@
+#!/bin/sh
+# =======================================================================================================
+# Run tidl import tool to convert the original tensorflow lite model (e.g., mobilenet_v1_1.0_224.tflite)
+# to TIDL netowrk. The converted TIDL network are stored into tidl_net*.bin and tidl_param_*.bin files
+# (e.g., tidl_net_tflite_mobilenet_v1_1.0_224.bin, and tidl_param_tflite_mobilenet_v1_1.0_224.bin).
+# The TIDL supported subgraph are grouped as single custom operator (tidl-am5-custom-op) in tensorflow lite
+# format, and re-written into a new tensorflow lite model (e.g.,mobilenet_v1_1.0_224_tidl_am5.tflite)
+# =======================================================================================================
+# 1. Prepare the input: tensorflow lite model
+cd /usr/share/ti/tidl/utils
+mkdir -p ./test/testvecs/config/tflite_models
+mkdir -p ./test/testvecs/config/tidl_models/tflite/
+cp /usr/share/tensorflow-lite/demos/mobilenet_v1_1.0_224.tflite ./test/testvecs/config/tflite_models
+
+#2. Run tidl_model_import.out with the output tensor (other than the model output) specified for partial TIDL offload
+#   The operators after the specified output tensor will remain as tensorflow lite operators (to be executed on Arm).
+#   The beginning subgraph is represented as a single tensorflow lite custom operator (tidl-am5-custom-op) (for TIDL offload).
+tidl_model_import.out ./test/testvecs/config/import/tflite/tidl_import_mobileNetv1.txt MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6
+
+#3. Check the output and create subgraph0.cfg for TIDL offload at run-time
+
+# TIDL *.bin files are generated at /usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite
+# *tidl_am5.tflite model is generated at /usr/share/ti/tidl/utils/test/testvecs/config/tflite_models
+ls -l /usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite
+ls -l /usr/share/ti/tidl/utils/test/testvecs/config/tflite_models
+
+echo "Creating subgraph0.cfg at /usr/share/tensorflow-lite/demos..."
+echo "subgraph0.cfg will be used by TIDL API for the run-time offload"
+subgraph0_cfg_path=/usr/share/tensorflow-lite/demos/subgraph0.cfg
+# Specify TIDL network files
+echo "netBinFile      = "/usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite/tidl_net_tflite_mobilenet_v1_1.0_224.bin"" > $subgraph0_cfg_path
+echo "paramsBinFile   = "/usr/share/ti/tidl/utils/test/testvecs/config/tidl_models/tflite/tidl_param_tflite_mobilenet_v1_1.0_224.bin"" >> $subgraph0_cfg_path
+# Sepcify the boundary conversion parameters, see details at https://downloads.ti.com/mctools/esd/docs/tidl-api/api.html
+# The input is in NHWC format and ranges [-1,1]
+echo "inConvType = 0" >> $subgraph0_cfg_path
+echo "inIsSigned = 1" >> $subgraph0_cfg_path
+echo "inScaleF2Q = 128" >> $subgraph0_cfg_path
+echo "inIsNCHW = 0" >> $subgraph0_cfg_path
+# The output is in NHWC format and ranges [0,6]
+echo "outConvType = 0" >> $subgraph0_cfg_path
+echo "outIsSigned = 0" >> $subgraph0_cfg_path
+echo "outScaleF2Q = 42.5" >> $subgraph0_cfg_path
+echo "outIsNCHW = 0" >> $subgraph0_cfg_path
+echo "subgraph0.cfg:"
+cat $subgraph0_cfg_path