Finish caffe model converter and validator.

validate_caffe.py

+import argparse
+import sys
+import os
+import os.path
+import numpy as np
+from scipy import spatial
+
+os.environ['GLOG_minloglevel'] = '1' # suprress Caffe verbose prints
+import caffe
+
+# Validation Flow:
+# 1. Generate input data
+#    python validate.py --generate_data true \
+#        --input_file input_file
+#        --input_shape 1,64,64,3
+#
+# 2. Use mace_run to run model on phone.
+# 3. adb pull the result.
+# 4. Compare output data of mace and tf
+#    python validate.py --model_file tf_model_opt.pb \
+#        --input_file input_file \
+#        --mace_out_file output_file \
+#        --input_node input_node \
+#        --output_node output_node \
+#        --input_shape 1,64,64,3 \
+#        --output_shape 1,64,64,2
+
+def generate_data(shape):
+  np.random.seed()
+  data = np.random.random(shape) * 2 - 1
+  print FLAGS.input_file
+  data.astype(np.float32).tofile(FLAGS.input_file)
+  print "Generate input file done."
+
+def load_data(file):
+  if os.path.isfile(file):
+    return np.fromfile(file=file, dtype=np.float32)
+  else:
+    return np.empty([0])
+
+def valid_output(out_shape, mace_out_file, out_value):
+  mace_out_value = load_data(mace_out_file)
+  if mace_out_value.size != 0:
+    mace_out_value = mace_out_value.reshape(out_shape)
+    out_shape[1], out_shape[2], out_shape[3] = out_shape[3], out_shape[1], out_shape[2]
+    out_value = out_value.reshape(out_shape).transpose((0, 2, 3, 1))
+    similarity = (1 - spatial.distance.cosine(out_value.flat, mace_out_value.flat))
+    print 'MACE VS Caffe similarity: ', similarity
+    if (FLAGS.mace_runtime == "cpu" and similarity > 0.999) or \
+        (FLAGS.mace_runtime == "gpu" and similarity > 0.995) or \
+        (FLAGS.mace_runtime == "dsp" and similarity > 0.930):
+      print '=======================Similarity Test Passed======================'
+    else:
+      print '=======================Similarity Test Failed======================'
+  else:
+    print '=======================Skip empty node==================='
+
+
+def run_model(input_shape):
+  if not os.path.isfile(FLAGS.model_file):
+    print("Input graph file '" + FLAGS.model_file + "' does not exist!")
+    return -1
+  if not os.path.isfile(FLAGS.weight_file):
+    print("Input weight file '" + FLAGS.weight_file + "' does not exist!")
+    return -1
+
+  caffe.set_mode_cpu()
+
+  net = caffe.Net(FLAGS.model_file, caffe.TEST, weights=FLAGS.weight_file)
+
+  input_value = load_data(FLAGS.input_file)
+  input_value = input_value.reshape(input_shape).transpose((0, 3, 1, 2))
+  net.blobs[FLAGS.input_node].data[0] = input_value
+  net.forward(start=FLAGS.input_node, end=FLAGS.output_node)
+
+  result = net.blobs[FLAGS.output_node].data[0]
+
+  return result
+
+
+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="caffe prototxt file to load.")
+  parser.add_argument(
+    "--weight_file",
+    type=str,
+    default="",
+    help="caffe model file to load.")
+  parser.add_argument(
+    "--input_file",
+    type=str,
+    default="",
+    help="input file.")
+  parser.add_argument(
+    "--mace_out_file",
+    type=str,
+    default="",
+    help="mace output file to load.")
+  parser.add_argument(
+    "--mace_runtime",
+    type=str,
+    default="gpu",
+    help="mace runtime device.")
+  parser.add_argument(
+    "--input_shape",
+    type=str,
+    default="1,64,64,3",
+    help="input shape.")
+  parser.add_argument(
+    "--output_shape",
+    type=str,
+    default="1,64,64,2",
+    help="output shape.")
+  parser.add_argument(
+    "--input_node",
+    type=str,
+    default="input_node",
+    help="input node")
+  parser.add_argument(
+    "--output_node",
+    type=str,
+    default="output_node",
+    help="output node")
+  parser.add_argument(
+    "--generate_data",
+    type='bool',
+    default="false",
+    help="Generate data or not.")
+
+  return parser.parse_known_args()
+
+
+if __name__ == '__main__':
+  FLAGS, unparsed = parse_args()
+  main(unused_args=[sys.argv[0]] + unparsed)
+

validate_tools.sh

@@ -52,6 +52,11 @@ elif [ "$PLATFORM" = "caffe" ];then
     docker run -d -it --name ${CONTAINER_NAME} ${IMAGE_NAME} /bin/bash || exit 1
   fi
 
+  if [ "$(docker inspect -f {{.State.Running}} ${CONTAINER_NAME})" == "false" ];then
+    echo "Start caffe container"
+    docker start ${CONTAINER_NAME}
+  fi
+
   rm -rf ${MODEL_OUTPUT_DIR}/${OUTPUT_FILE_NAME}
   adb </dev/null pull ${PHONE_DATA_DIR}/${OUTPUT_FILE_NAME} ${MODEL_OUTPUT_DIR}