Enable loading realistic model inputs from external images/files Currently all model inputs are arbitrarily random. We here add support for allowing loading realistic model input from external images/files. Only person_detection and mobilenet are supported for now. Change-Id: Ib189ff9735dd656a63d01d2c5b90123942ae7c60

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 201fcab..9c41392 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt

@@ -74,5 +74,6 @@
 include($ENV{ROOTDIR}/toolchain/iree/build_tools/cmake/iree_copts.cmake)
 
 include(springbok_bytecode_module)
+include(iree_model_input)
 # Add the included directory here.
 add_subdirectory(samples)

diff --git a/build_tools/gen_mlmodel_input.py b/build_tools/gen_mlmodel_input.py
new file mode 100755
index 0000000..5bd8918
--- /dev/null
+++ b/build_tools/gen_mlmodel_input.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""Generate ML model inputs from external images."""
+import argparse
+import os
+import sys
+import struct
+import urllib.request
+
+import numpy as np
+from PIL import Image
+
+
+parser = argparse.ArgumentParser(
+    description='Generate inputs for ML models.')
+parser.add_argument('--i', dest='input_name',
+                    help='Model input image name', required=True)
+parser.add_argument('--o', dest='output_file',
+                    help='Output binary name', required=True)
+parser.add_argument('--s', dest='input_shape',
+                    help='Model input shape (example: "1, 224, 224, 3")', required=True)
+parser.add_argument('--q', dest='is_quant', action='store_true',
+                    help='Indicate it is quant model (default: False)')
+parser.add_argument('--u', dest='img_url', help='Input image URL')
+args = parser.parse_args()
+
+
+def write_binary_file(file_path, input, is_quant):
+    with open(file_path, "wb+") as file:
+        for d in input:
+            if is_quant:
+                file.write(struct.pack("<B", d))
+            else:
+                file.write(struct.pack("<f", d))
+
+
+def gen_mlmodel_input(input_name, output_file, input_shape, is_quant, img_url):
+    if not os.path.exists(input_name):
+        urllib.request.urlretrieve(img_url, input_name)
+    if len(input_shape) < 3:
+        raise ValueError("Input shape < 3 dimensions")
+    resized_img = Image.open(input_name).resize(
+        (input_shape[1], input_shape[2]))
+    input = np.array(resized_img).reshape(np.prod(input_shape))
+    if not is_quant:
+        input = 2.0 / 255.0 * input - 1
+    write_binary_file(output_file, input, is_quant)
+
+
+if __name__ == '__main__':
+    # convert input shape to a list
+    input_shape = [int(x) for x in args.input_shape.split(',')]
+    # remove whitespace in image URL if any
+    img_url = args.img_url.replace(' ', '')
+    gen_mlmodel_input(args.input_name, args.output_file,
+                      input_shape, args.is_quant, img_url)

diff --git a/cmake/iree_model_input.cmake b/cmake/iree_model_input.cmake
new file mode 100644
index 0000000..9d66348
--- /dev/null
+++ b/cmake/iree_model_input.cmake

@@ -0,0 +1,66 @@
+include(CMakeParseArguments)
+
+# iree_model_input()
+#
+# CMake function to load an external model input (an image)
+# and convert to the iree_c_embed_data.
+#
+# Parameters:
+# NAME: Name of model input image.
+# SHAPE: Input shape.
+# SRC: Input image URL.
+# QUANT: When added, indicate it's a quant model.
+#
+# Examples:
+# iree_model_input(
+#   NAME
+#     person_detection_quant_input
+#   SHAPE
+#     "1, 96, 96, 1"
+#   SRC
+#     "https://github.com/tensorflow/tflite-micro/raw/aeac6f39e5c7475cea20c54e86d41e3a38312546/ \
+#     tensorflow/lite/micro/examples/person_detection/testdata/person.bmp"
+#   QUANT
+# )
+#
+function(iree_model_input)
+  cmake_parse_arguments(
+    _RULE
+    "QUANT"
+    "NAME;SHAPE;SRC"
+    ""
+    ${ARGN}
+  )
+
+  get_filename_component(EXT_STR "${_RULE_SRC}" LAST_EXT)
+  set(_GEN_INPUT_SCRIPT "${CMAKE_SOURCE_DIR}/build_tools/gen_mlmodel_input.py")
+  set(_OUTPUT_BINARY ${_RULE_NAME}.bin)
+  set(_ARGS)
+  list(APPEND _ARGS "--i=${_RULE_NAME}${EXT_STR}")
+  list(APPEND _ARGS "--o=${_OUTPUT_BINARY}")
+  list(APPEND _ARGS "--s=${_RULE_SHAPE}")
+  list(APPEND _ARGS "--u=${_RULE_SRC}")
+  if(_RULE_QUANT)
+    list(APPEND _ARGS "--q")
+  endif()
+
+  add_custom_command(
+    OUTPUT
+      ${_OUTPUT_BINARY}
+    COMMAND
+      ${_GEN_INPUT_SCRIPT} ${_ARGS}
+  )
+
+  iree_c_embed_data(
+    NAME
+      "${_RULE_NAME}_c"
+    GENERATED_SRCS
+      "${_OUTPUT_BINARY}"
+    C_FILE_OUTPUT
+      "${_RULE_NAME}_c.c"
+    H_FILE_OUTPUT
+      "${_RULE_NAME}_c.h"
+    FLATTEN
+    PUBLIC
+  )
+endfunction()

diff --git a/samples/float_model_embedding/CMakeLists.txt b/samples/float_model_embedding/CMakeLists.txt
index 8d3ef0a..fbd36b3 100644
--- a/samples/float_model_embedding/CMakeLists.txt
+++ b/samples/float_model_embedding/CMakeLists.txt

@@ -145,6 +145,34 @@
 endif(${BUILD_INTERNAL_MODELS})
 
 #-------------------------------------------------------------------------------
+# Binaries to execute the IREE model input
+#-------------------------------------------------------------------------------
+
+iree_model_input(
+  NAME
+    mobilenet_input
+  SHAPE
+    "1, 224, 224, 3"
+  SRC
+    "https://storage.googleapis.com/download.tensorflow.org/ \
+    example_images/YellowLabradorLooking_new.jpg"
+)
+
+if(${BUILD_INTERNAL_MODELS})
+
+iree_model_input(
+  NAME
+    person_detection_input
+  SHAPE
+    "1, 96, 96, 1"
+  SRC
+    "https://github.com/tensorflow/tflite-micro/raw/aeac6f39e5c7475cea20c54e86d41e3a38312546/ \
+    tensorflow/lite/micro/examples/person_detection/testdata/person.bmp"
+)
+
+endif(${BUILD_INTERNAL_MODELS})
+
+#-------------------------------------------------------------------------------
 # Binaries to execute the MLIR bytecode modules
 #-------------------------------------------------------------------------------
 
@@ -165,6 +193,7 @@
     "mobilenet_v1.c"
   DEPS
     ::mobilenet_v1_bytecode_module_dylib_c
+    ::mobilenet_input_c
     samples::util::util
   LINKOPTS
     "LINKER:--defsym=__stack_size__=100k"
@@ -241,6 +270,7 @@
     "person_detection.c"
   DEPS
     ::person_detection_bytecode_module_dylib_c
+    ::person_detection_input_c
     samples::util::util
   LINKOPTS
     "LINKER:--defsym=__stack_size__=100k"

diff --git a/samples/float_model_embedding/mobilenet_v1.c b/samples/float_model_embedding/mobilenet_v1.c
index 0472dde..7535395 100644
--- a/samples/float_model_embedding/mobilenet_v1.c
+++ b/samples/float_model_embedding/mobilenet_v1.c

@@ -9,6 +9,7 @@
 #include "samples/util/util.h"
 
 // Compiled module embedded here to avoid file IO:
+#include "samples/float_model_embedding/mobilenet_input_c.h"
 #include "samples/float_model_embedding/mobilenet_v1_bytecode_module_dylib_c.h"
 
 const MlModel kModel = {
@@ -34,14 +35,8 @@
 
 iree_status_t load_input_data(const MlModel *model, void **buffer) {
   iree_status_t result = alloc_input_buffer(model, buffer);
-  // Populate initial value
-  srand(33333333);
-  if (iree_status_is_ok(result)) {
-    for (int i = 0; i < model->input_length[0]; ++i) {
-      int x = rand();
-      ((float *)*buffer)[i] = (float)x / (float)RAND_MAX;
-    }
-  }
+  const struct iree_file_toc_t *input_file_toc = mobilenet_input_c_create();
+  memcpy(*buffer, input_file_toc->data, input_file_toc->size);
   return result;
 }
 
@@ -56,5 +51,16 @@
   }
   LOG_INFO("Output #%d data length: %d", index_output,
            mapped_memory->contents.data_length / model->output_size_bytes);
+  // find the label index with best prediction
+  float best_out = 0.0;
+  int best_idx = -1;
+  for (int i = 0; i < model->output_length[index_output]; ++i) {
+    float out = ((float *)mapped_memory->contents.data)[i];
+    if (out > best_out) {
+      best_out = out;
+      best_idx = i;
+    }
+  }
+  LOG_INFO("Image prediction result is: id: %d", best_idx + 1);
   return result;
 }

diff --git a/samples/float_model_embedding/person_detection.c b/samples/float_model_embedding/person_detection.c
index 19cfb4f..a892093 100644
--- a/samples/float_model_embedding/person_detection.c
+++ b/samples/float_model_embedding/person_detection.c

@@ -10,6 +10,7 @@
 
 // Compiled module embedded here to avoid file IO:
 #include "samples/float_model_embedding/person_detection_bytecode_module_dylib_c.h"
+#include "samples/float_model_embedding/person_detection_input_c.h"
 
 const MlModel kModel = {
     .num_input = 1,
@@ -34,14 +35,9 @@
 
 iree_status_t load_input_data(const MlModel *model, void **buffer) {
   iree_status_t result = alloc_input_buffer(model, buffer);
-  // Populate initial value
-  srand(44444444);
-  if (iree_status_is_ok(result)) {
-    for (int i = 0; i < model->input_length[0]; ++i) {
-      int x = rand();
-      ((float *)*buffer)[i] = (float)x / (float)RAND_MAX;
-    }
-  }
+  const struct iree_file_toc_t *input_file_toc =
+      person_detection_input_c_create();
+  memcpy(*buffer, input_file_toc->data, input_file_toc->size);
   return result;
 }
 
@@ -56,5 +52,15 @@
   }
   LOG_INFO("Output #%d data length: %d", index_output,
            mapped_memory->contents.data_length / model->output_size_bytes);
+
+  float *data = (float *)mapped_memory->contents.data;
+  char buffer[20];
+  int chars_needed = float_to_str(0, NULL, data[0]);
+  float_to_str(chars_needed, buffer, data[0]);
+  LOG_INFO("Output: Non-person Score: %s", buffer);
+  chars_needed = float_to_str(0, NULL, data[1]);
+  float_to_str(chars_needed, buffer, data[1]);
+  LOG_INFO("Output: Person Score: %s", buffer);
+
   return result;
 }

diff --git a/samples/quant_model_embedding/CMakeLists.txt b/samples/quant_model_embedding/CMakeLists.txt
index cca899d..f728e9e 100644
--- a/samples/quant_model_embedding/CMakeLists.txt
+++ b/samples/quant_model_embedding/CMakeLists.txt

@@ -147,6 +147,32 @@
 endif(${BUILD_INTERNAL_MODELS})
 
 #-------------------------------------------------------------------------------
+# Binaries to execute the IREE model input
+#-------------------------------------------------------------------------------
+
+iree_model_input(
+  NAME
+    mobilenet_quant_input
+  SHAPE
+    "1, 224, 224, 3"
+  SRC
+    "https://storage.googleapis.com/download.tensorflow.org/ \
+    example_images/YellowLabradorLooking_new.jpg"
+  QUANT
+)
+
+iree_model_input(
+  NAME
+    person_detection_quant_input
+  SHAPE
+    "1, 96, 96, 1"
+  SRC
+    "https://github.com/tensorflow/tflite-micro/raw/aeac6f39e5c7475cea20c54e86d41e3a38312546/ \
+    tensorflow/lite/micro/examples/person_detection/testdata/person.bmp"
+  QUANT
+)
+
+#-------------------------------------------------------------------------------
 # Binaries to execute the MLIR bytecode modules
 #-------------------------------------------------------------------------------
 
@@ -179,6 +205,7 @@
     "mobilenet_v1.c"
   DEPS
     ::mobilenet_v1_bytecode_module_dylib_c
+    ::mobilenet_quant_input_c
     samples::util::util
   LINKOPTS
     "LINKER:--defsym=__stack_size__=100k"
@@ -191,6 +218,7 @@
     "person_detection.c"
   DEPS
     ::person_detection_bytecode_module_dylib_c
+    ::person_detection_quant_input_c
     samples::util::util
   LINKOPTS
     "LINKER:--defsym=__stack_size__=128k"

diff --git a/samples/quant_model_embedding/mobilenet_v1.c b/samples/quant_model_embedding/mobilenet_v1.c
index 3570a4b..2fe8509 100644
--- a/samples/quant_model_embedding/mobilenet_v1.c
+++ b/samples/quant_model_embedding/mobilenet_v1.c

@@ -9,6 +9,7 @@
 #include "samples/util/util.h"
 
 // Compiled module embedded here to avoid file IO:
+#include "samples/quant_model_embedding/mobilenet_quant_input_c.h"
 #include "samples/quant_model_embedding/mobilenet_v1_bytecode_module_dylib_c.h"
 
 const MlModel kModel = {
@@ -34,13 +35,9 @@
 
 iree_status_t load_input_data(const MlModel *model, void **buffer) {
   iree_status_t result = alloc_input_buffer(model, buffer);
-  // Populate initial value
-  srand(33333333);
-  if (iree_status_is_ok(result)) {
-    for (int i = 0; i < model->input_length[0]; ++i) {
-      ((uint8_t *)*buffer)[i] = (uint8_t)rand();
-    }
-  }
+  const struct iree_file_toc_t *input_file_toc =
+      mobilenet_quant_input_c_create();
+  memcpy(*buffer, input_file_toc->data, input_file_toc->size);
   return result;
 }
 
@@ -55,5 +52,16 @@
   }
   LOG_INFO("Output #%d data length: %d", index_output,
            mapped_memory->contents.data_length / model->output_size_bytes);
+  // find the label index with best prediction
+  int best_out = 0;
+  int best_idx = -1;
+  for (int i = 0; i < model->output_length[index_output]; ++i) {
+    uint8_t out = ((uint8_t *)mapped_memory->contents.data)[i];
+    if (out > best_out) {
+      best_out = out;
+      best_idx = i;
+    }
+  }
+  LOG_INFO("Image prediction result is: id: %d", best_idx + 1);
   return result;
 }

diff --git a/samples/quant_model_embedding/person_detection.c b/samples/quant_model_embedding/person_detection.c
index 0cd1a09..819fc1e 100644
--- a/samples/quant_model_embedding/person_detection.c
+++ b/samples/quant_model_embedding/person_detection.c

@@ -10,6 +10,7 @@
 
 // Compiled module embedded here to avoid file IO:
 #include "samples/quant_model_embedding/person_detection_bytecode_module_dylib_c.h"
+#include "samples/quant_model_embedding/person_detection_quant_input_c.h"
 
 const MlModel kModel = {
     .num_input = 1,
@@ -34,13 +35,9 @@
 
 iree_status_t load_input_data(const MlModel *model, void **buffer) {
   iree_status_t result = alloc_input_buffer(model, buffer);
-  // Populate initial value
-  srand(44444444);
-  if (iree_status_is_ok(result)) {
-    for (int i = 0; i < model->input_length[0]; ++i) {
-      ((int8_t *)*buffer)[i] = (int8_t)rand();
-    }
-  }
+  const struct iree_file_toc_t *input_file_toc =
+      person_detection_quant_input_c_create();
+  memcpy(*buffer, input_file_toc->data, input_file_toc->size);
   return result;
 }
 
@@ -55,5 +52,7 @@
   }
   LOG_INFO("Output #%d data length: %d", index_output,
            mapped_memory->contents.data_length / model->output_size_bytes);
+  int8_t *data = (int8_t *)mapped_memory->contents.data;
+  LOG_INFO("Output: Non-person Score: %d; Person Score: %d", data[0], data[1]);
   return result;
 }