From 1bd4f9085ccc912428dc468aa4f68ade19b5aacb Mon Sep 17 00:00:00 2001
From: Bin Li <libin11@xiaomi.com>
Date: Sat, 29 Dec 2018 15:38:49 +0800
Subject: [PATCH] Fix hexagon supernode without biasadd

---
 .../tools/converter_tool/hexagon_converter.py    |  8 +++++---
 tools/validate.py                                | 16 +++++++++++++++-
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/mace/python/tools/converter_tool/hexagon_converter.py b/mace/python/tools/converter_tool/hexagon_converter.py
index 121ee494..ea99e8f8 100644
--- a/mace/python/tools/converter_tool/hexagon_converter.py
+++ b/mace/python/tools/converter_tool/hexagon_converter.py
@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
+import numpy as np
+from operator import mul
 
 from mace.proto import mace_pb2
 from mace.python.tools.converter_tool import base_converter
@@ -24,9 +27,6 @@ from mace.python.tools.converter_tool.base_converter import PoolingType
 from mace.python.tools.convert_util import mace_check
 from mace.python.tools import graph_util
 
-import copy
-from operator import mul
-
 
 class HexagonOps(object):
     def __init__(self):
@@ -133,6 +133,8 @@ class HexagonConverter(base_converter.ConverterInterface):
                     print('Supernode requires biasadd, we add it.')
                     bias_data = np.zeros(channels, dtype=int)
                     bias_tensor = self._model.tensors.add()
+                    bias_tensor.data_type = mace_pb2.DT_INT32
+                    bias_tensor.dims.extend([channels])
                     bias_tensor.int32_data.extend(bias_data)
                     bias_tensor.minval = 0
                     bias_tensor.maxval = 0
diff --git a/tools/validate.py b/tools/validate.py
index 5a9a3497..65e8ba1c 100644
--- a/tools/validate.py
+++ b/tools/validate.py
@@ -66,9 +66,22 @@ def calculate_similarity(u, v, data_type=np.float64):
     return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))
 
 
+def calculate_pixel_accuracy(out_value, mace_out_value):
+    out_value = out_value.reshape((-1, out_value.shape[-1]))
+    batches = out_value.shape[0]
+    classes = out_value.shape[1]
+    mace_out_value = mace_out_value.reshape((batches, classes))
+    correct_count = 0
+    for i in range(batches):
+        if np.argmax(out_value[i]) == np.argmax(mace_out_value[i]):
+            correct_count += 1
+    return 1.0 * correct_count / batches
+
+
 def compare_output(platform, device_type, output_name, mace_out_value,
                    out_value, validation_threshold):
     if mace_out_value.size != 0:
+        pixel_accuracy = calculate_pixel_accuracy(out_value, mace_out_value)
         out_value = out_value.reshape(-1)
         mace_out_value = mace_out_value.reshape(-1)
         assert len(out_value) == len(mace_out_value)
@@ -76,7 +89,8 @@ def compare_output(platform, device_type, output_name, mace_out_value,
         similarity = calculate_similarity(out_value, mace_out_value)
         common.MaceLogger.summary(
             output_name + ' MACE VS ' + platform.upper()
-            + ' similarity: ' + str(similarity) + ' , sqnr: ' + str(sqnr))
+            + ' similarity: ' + str(similarity) + ' , sqnr: ' + str(sqnr)
+            + ' , pixel_accuracy: ' + str(pixel_accuracy))
         if similarity > validation_threshold:
             common.MaceLogger.summary(
                 common.StringFormatter.block("Similarity Test Passed"))
-- 
GitLab