InceptionV2のONNX to NNP相互変換が動作することを確認

sony · Jun 1, 2018 · 09b3d74 · 09b3d74
1 parent 4234b96
commit 09b3d74
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Showing 2 changed files with 29 additions and 14 deletions.
diff --git a/python/src/nnabla/utils/converter/onnx/exporter.py b/python/src/nnabla/utils/converter/onnx/exporter.py
@@ -486,9 +486,13 @@ def convert_to_nodes(func, variables, input_types, output_types, broadcast_targe
         }
         # separate pad values to match ONNX format
         # (S0,E0,S1,E1) => (S0,S1,E0,E1)
+        dim = len(pp.pad_width) // 2
+        zero_dim_num = 4-dim  # assuming 4D input
         it = iter(pp.pad_width)
-        starts = []
-        ends = []
+        # We need to fill empty dimensions with zero padding
+        # (at least this is what Caffe2 expects)
+        starts = [0]*zero_dim_num
+        ends = [0]*zero_dim_num
         for x in it:
             starts.append(x)
             ends.append(next(it))

diff --git a/python/test/utils/conversion/test_conversion.py b/python/test/utils/conversion/test_conversion.py
@@ -284,13 +284,11 @@ def test_onnx_nnp_conversion_maxpool_p0_0_1_1_s1_k2(tmpdir, nnp_fixture):
                                     "out_data_1", "exec_0")
 
 
-# NNP to ONNX conversion for asymmetry maxpool padding
-# currently ends in an unknown error at caffe2 backend
-#def test_nnp_onnx_conversion_maxpool_p0_0_1_1_s1_k2(tmpdir, nnp_fixture):
-#    convert_nnp_to_onnx_and_compare(tmpdir, TEST_DATA_DIR,
-#                                    "maxpool_p0_0_1_1_s1_k2.nnp",
-#                                    "maxpool_p0_0_1_1_s1_k2.onnx",
-#                                    "out_data_1", "exec_0")
+def test_nnp_onnx_conversion_maxpool_p0_0_1_1_s1_k2(tmpdir, nnp_fixture):
+    convert_nnp_to_onnx_and_compare(tmpdir, TEST_DATA_DIR,
+                                    "maxpool_p0_0_1_1_s1_k2.nnp",
+                                    "maxpool_p0_0_1_1_s1_k2.onnx",
+                                    "out_data_1", "exec_0")
 
 
 def test_onnx_nnp_conversion_conv(tmpdir, nnp_fixture):
@@ -1144,11 +1142,24 @@ def test_nnp_onnx_conversion_squeezenet(tmpdir, nnp_fixture):
                                     in_name="data_0", in_img=img)
 
 
-#def test_onnx_nnp_conversion_inception_v2(tmpdir, nnp_fixture):
-#    img = np.random.rand(1, 3, 224, 224).astype(np.float32)
-#    convert_onnx_to_nnp_and_compare(
-#        tmpdir, TEST_DATA_DIR, "inception_v2.onnx", "inception_v2.nnp", "prob_1", "exec_0",
-#        in_name="data_0", in_img=img)
+@pytest.mark.slow
+def test_onnx_nnp_conversion_inception_v2(tmpdir, nnp_fixture):
+    img = np.random.rand(1, 3, 224, 224).astype(np.float32)
+    convert_onnx_to_nnp_and_compare(tmpdir, TEST_DATA_DIR,
+                                    "inception_v2.onnx", "inception_v2.nnp",
+                                    "prob_1", "exec_0",
+                                    in_name="data_0", in_img=img,
+                                    export_nnp_path=TEST_DATA_DIR)
+
+
+@pytest.mark.slow
+def test_nnp_onnx_conversion_inception_v2(tmpdir, nnp_fixture):
+    img = np.random.rand(1, 3, 224, 224).astype(np.float32)
+    convert_nnp_to_onnx_and_compare(tmpdir, TEST_DATA_DIR,
+                                    "inception_v2.nnp", "inception_v2.onnx",
+                                    "prob_1", "exec_0",
+                                    in_name="data_0", in_img=img)
+
 
 @pytest.mark.slow
 def test_onnx_nnp_conversion_densenet121(tmpdir, nnp_fixture):