Add icnet validation script for auto test.

mace/kernels/opencl/cl/conv_2d_1x1.cl

@@ -49,13 +49,11 @@ __kernel void conv_2d_1x1_v2(__global const float *input, /* n, c, h, w */
   int out_chan_len = out_chan_end - out_chan_begin;
   int pixel_len = out_pixel_end - out_pixel_begin;
 
-  if (bias != NULL) {
-    for (int out_chan = out_chan_begin; out_chan < out_chan_end; ++out_chan) {
-      float *output_ptr = output_base + out_chan * pixel_num;
-      float bias_value = bias[out_chan];
-      for (int p = 0; p < pixel_len; ++p) {
-        output_ptr[p] = bias_value;
-      }
+  for (int out_chan = out_chan_begin; out_chan < out_chan_end; ++out_chan) {
+    float *output_ptr = output_base + out_chan * pixel_num;
+    float bias_value = bias == NULL ? 0 : bias[out_chan];
+    for (int p = 0; p < pixel_len; ++p) {
+      output_ptr[p] = bias_value;
     }
   }
 

tools/validate_icnet.py

+import argparse
+import sys
+import tensorflow as tf
+import numpy as np
+
+from tensorflow import gfile
+
+# Validation Flow:
+# 1. Generate input data
+#    python validate_icnet.py --generate_data 1 \
+#          --random_seed 1
+# 2. Use mace_run to run icnet on phone.
+# 3. adb pull the result.
+# 4. Compare output data of mace and tf
+#    python validate_icnet.py --model_file opt_icnet.pb \
+#        --tf_input_file input_file \
+#        --mace_out_file icnet.out
+
+
+def generate_data(shape):
+  np.random.seed(FLAGS.random_seed)
+  data = np.random.random(shape)
+  print FLAGS.tf_input_file
+  data.astype(np.float32).tofile(FLAGS.tf_input_file)
+  mace_data = np.transpose(data, axes=(2, 0, 1))
+  mace_data.astype(np.float32).tofile(FLAGS.mace_input_file)
+  print "Generate input file done."
+
+def load_data(file):
+  return np.fromfile(file=file, dtype=np.float32)
+
+def valid_output(out_shape, mace_out_file, tf_out_value):
+  mace_out_value = load_data(mace_out_file)
+  mace_out_value = mace_out_value.reshape(out_shape)
+  tf_out_data_t = np.transpose(tf_out_value, axes=(0, 3, 1, 2))
+  res = np.allclose(mace_out_value, tf_out_data_t, rtol=0, atol=1e-5)
+  print 'Passed! Haha' if res else 'Failed! Oops'
+
+
+def run_model(input_shape):
+  if not gfile.Exists(FLAGS.model_file):
+    print("Input graph file '" + FLAGS.model_file + "' does not exist!")
+    return -1
+
+  input_graph_def = tf.GraphDef()
+  with gfile.Open(FLAGS.model_file, "rb") as f:
+    data = f.read()
+    input_graph_def.ParseFromString(data)
+    tf.import_graph_def(input_graph_def, name="")
+
+    with tf.Session() as session:
+      with session.graph.as_default() as graph:
+        tf.import_graph_def(input_graph_def, name="")
+        input_node = graph.get_tensor_by_name('input_node:0')
+        output_node = graph.get_tensor_by_name('output_node:0')
+
+        input_value = load_data(FLAGS.tf_input_file)
+        input_value = input_value.reshape(input_shape)
+        
+        output_value = session.run(output_node, feed_dict={input_node: [input_value]})
+        return output_value
+
+def main(unused_args):
+  input_shape = [int(x) for x in FLAGS.input_shape.split(',')]
+  output_shape = [int(x) for x in FLAGS.output_shape.split(',')]
+  if FLAGS.generate_data:
+    generate_data(input_shape)
+  else:
+    output_value = run_model(input_shape)
+    valid_output(output_shape, FLAGS.mace_out_file, output_value)
+
+
+def parse_args():
+  """Parses command line arguments."""
+  parser = argparse.ArgumentParser()
+  parser.register("type", "bool", lambda v: v.lower() == "true")
+  parser.add_argument(
+    "--model_file",
+    type=str,
+    default="",
+    help="TensorFlow \'GraphDef\' file to load.")
+  parser.add_argument(
+    "--tf_input_file",
+    type=str,
+    default="",
+    help="tensorflow input data to load.")
+  parser.add_argument(
+    "--mace_input_file",
+    type=str,
+    default="",
+    help="mace input data to load.")
+  parser.add_argument(
+    "--mace_out_file",
+    type=str,
+    default="",
+    help="mace output file to load.")
+  parser.add_argument(
+    "--input_shape",
+    type=str,
+    default="480,480,3",
+    help="input shape.")
+  parser.add_argument(
+    "--output_shape",
+    type=str,
+    default="1,2,480,480",
+    help="output shape.")
+  parser.add_argument(
+    "--generate_data",
+    type='bool',
+    default="false",
+    help="Random seed for generate test case.")
+  parser.add_argument(
+    "--random_seed",
+    type=int,
+    default="0",
+    help="Random seed for generate test case.")
+
+  return parser.parse_known_args()
+
+
+if __name__ == '__main__':
+  FLAGS, unparsed = parse_args()
+  main(unused_args=[sys.argv[0]] + unparsed)
+

tools/validate_icnet.sh

+#!/bin/bash
+# Must run at root dir of mace project.
+set -e
+
+Usage() {
+  echo 'Usage: bash tools/validate_icnet.sh tf_model_file'
+}
+
+if [ $# != 1 ];then
+  Usage
+  exit -1
+fi
+
+TF_MODEL_FILE_PATH=$1
+MODEL_DIR=$(dirname ${TF_MODEL_FILE_PATH})
+MACE_MODEL_NAME='mace_model.pb'
+TF_INPUT_FILE_NAME='tf_model_input'
+MACE_INPUT_FILE_NAME='mace_model_input'
+OUTPUT_FILE_NAME='icnet.out'
+PHONE_DATA_DIR="/data/local/tmp/${MACE_MODEL_NAME}"
+KERNEL_DIR="${PHONE_DATA_DIR}/cl/"
+
+# Step 1: convert tf model to mace model
+echo "Step 1: convert tf model to mace model"
+bazel build //mace/python/tools:tf_converter
+bazel-bin/mace/python/tools/tf_converter --input=${TF_MODEL_FILE_PATH} --output=${MODEL_DIR}/${MACE_MODEL_NAME}
+
+# Step 2: Generate input data
+echo "Step 2: Generate input data"
+python tools/validate_icnet.py --generate_data true --random_seed 1 \
+ --mace_input_file ${MODEL_DIR}/${MACE_INPUT_FILE_NAME} --tf_input_file ${MODEL_DIR}/${TF_INPUT_FILE_NAME}
+
+# Step 3: Run model on the phone
+echo "Step 3: Run model on the phone"
+bazel build -c opt --strip always mace/examples:mace_run  \
+    --crosstool_top=//external:android/crosstool \
+    --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+    --cpu=arm64-v8a
+
+adb shell "mkdir -p ${PHONE_DATA_DIR}"
+adb shell "mkdir -p ${KERNEL_DIR}"
+adb push mace/kernels/opencl/cl/* ${KERNEL_DIR}
+adb push ${MODEL_DIR}/${MACE_MODEL_NAME} ${PHONE_DATA_DIR}
+adb push ${MODEL_DIR}/${MACE_INPUT_FILE_NAME} ${PHONE_DATA_DIR}
+adb push bazel-bin/mace/examples/mace_run ${PHONE_DATA_DIR}
+
+num_threads=${1:-1}
+
+adb shell MACE_RUN_PARAMETER_PATH=${PHONE_DATA_DIR}/mace_run.config \
+          MACE_KERNEL_PATH=$KERNEL_DIR \
+          OMP_NUM_THREADS=$num_threads \
+          ${PHONE_DATA_DIR}/mace_run \
+            --model=${PHONE_DATA_DIR}/${MACE_MODEL_NAME} \
+            --input=input_node \
+            --output=icnet/Conv_11/BatchNorm/batchnorm/add_1 \
+            --input_shape=1,3,480,480\
+            --input_file=${PHONE_DATA_DIR}/${MACE_INPUT_FILE_NAME} \
+            --output_file=${PHONE_DATA_DIR}/${OUTPUT_FILE_NAME} \
+            --device=OPENCL
+
+# Step 4: pull the mace run result.
+echo "Step 4: pull the mace run result."
+adb pull ${PHONE_DATA_DIR}/${OUTPUT_FILE_NAME} ${MODEL_DIR}
+
+# Step 5: validate the result
+echo "Step 5: validate the result"
+python tools/validate_icnet.py --model_file ${TF_MODEL_FILE_PATH} \
+  --tf_input_file ${MODEL_DIR}/${TF_INPUT_FILE_NAME} \
+  --mace_out_file ${MODEL_DIR}/${OUTPUT_FILE_NAME}
+
+