add tests for add_custom_scalars and others (pytorch#20987)

lanpa · facebook-github-bot · commit 1fc240e59a24 · 2019-06-14T12:27:07.000-07:00
Summary: Originally, the tests for tensorboard writer are smoke tests only. This PR lets CI compare the output with expected results at low level. The randomness of the tensors in the test are also removed. ps. I found that how protobuf serializes data differs between different python environment. One method to solve this is to write the data and then read it back instantly. (compare the data at a higher level) For `add_custom_scalars`, the data to be written is a dictionary. and the serialized result might be different (not `ordereddict`). So only smoke test for that. Pull Request resolved: pytorch#20987 Reviewed By: NarineK, lanpa Differential Revision: D15804871 Pulled By: orionr fbshipit-source-id: 69324c11ff823b19960d50def73adff36eb4a2ac
diff --git a/test/expect/TestTensorBoard.test_audio.expect b/test/expect/TestTensorBoard.test_audio.expect
@@ -0,0 +1,10 @@
+value {
+  tag: "dummy"
+  audio {
+    sample_rate: 44100.0
+    num_channels: 1
+    length_frames: 42
+    encoded_audio_string: "RIFFx\000\000\000WAVEfmt \020\000\000\000\001\000\001\000D\254\000\000\210X\001\000\002\000\020\000dataT\000\000\000\000\000\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177"
+    content_type: "audio/wav"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_histogram_auto.expect b/test/expect/TestTensorBoard.test_histogram_auto.expect
@@ -0,0 +1,23 @@
+value {
+  tag: "dummy"
+  histo {
+    max: 1023.0
+    num: 1024.0
+    sum: 523776.0
+    sum_squares: 357389824.0
+    bucket_limit: 0.0
+    bucket_limit: 186.0
+    bucket_limit: 372.0
+    bucket_limit: 558.0
+    bucket_limit: 744.0
+    bucket_limit: 930.0
+    bucket_limit: 1023.0
+    bucket: 0.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 94.0
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_histogram_doane.expect b/test/expect/TestTensorBoard.test_histogram_doane.expect
@@ -0,0 +1,23 @@
+value {
+  tag: "dummy"
+  histo {
+    max: 1023.0
+    num: 1024.0
+    sum: 523776.0
+    sum_squares: 357389824.0
+    bucket_limit: 0.0
+    bucket_limit: 186.0
+    bucket_limit: 372.0
+    bucket_limit: 558.0
+    bucket_limit: 744.0
+    bucket_limit: 930.0
+    bucket_limit: 1023.0
+    bucket: 0.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 94.0
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_histogram_fd.expect b/test/expect/TestTensorBoard.test_histogram_fd.expect
@@ -0,0 +1,23 @@
+value {
+  tag: "dummy"
+  histo {
+    max: 1023.0
+    num: 1024.0
+    sum: 523776.0
+    sum_squares: 357389824.0
+    bucket_limit: 0.0
+    bucket_limit: 186.0
+    bucket_limit: 372.0
+    bucket_limit: 558.0
+    bucket_limit: 744.0
+    bucket_limit: 930.0
+    bucket_limit: 1023.0
+    bucket: 0.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 186.0
+    bucket: 94.0
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_image_with_3_channel_batched.expect b/test/expect/TestTensorBoard.test_image_with_3_channel_batched.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 8
+    width: 16
+    colorspace: 3
+    encoded_image_string: "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\020\000\000\000\010\010\002\000\000\000\177\024\350\300\000\000\000+IDATx\234cd8\320\360\037\033pww\307*\316\362\343\307\217\037\330$~\374\370\361\037\233\004\013\016\365\377q\211\217H\r\000d\305y\224,\220Z\033\000\000\000\000IEND\256B`\202"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_image_with_boxes.expect b/test/expect/TestTensorBoard.test_image_with_boxes.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 32
+    width: 32
+    colorspace: 3
+    encoded_image_string: "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000 \000\000\000 \010\002\000\000\000\374\030\355\243\000\000\000sIDATx\234\355\323=\n\300 \014\005\340\027p\250\267p\324\373\332\373\345\020vn\007\367>0\204b\311\233\305/\344G\000\334\236\021Uu\005R\000\377\007\244\224\342\013||\007\2655\330BfP\215\337S`>:{_l\020\335\242\tX6-\000\032r\007G\316\000\2561\226\201\244\252/\005V\357\026\271\003\033\0149\000\232\270\003+\260\301\220\003\240y\000T\221\324V\250_v\320\000\000\000\000IEND\256B`\202"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_image_with_one_channel.expect b/test/expect/TestTensorBoard.test_image_with_one_channel.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 8
+    width: 8
+    colorspace: 3
+    encoded_image_string: "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\010\000\000\000\010\010\002\000\000\000Km)\334\000\000\000\031IDATx\234cd``\370\217\r0\376\370\361\003\253\004\313\240\224\000\000;\267\273\313%\020=\255\000\000\000\000IEND\256B`\202"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_image_with_one_channel_batched.expect b/test/expect/TestTensorBoard.test_image_with_one_channel_batched.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 8
+    width: 16
+    colorspace: 3
+    encoded_image_string: "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\020\000\000\000\010\010\002\000\000\000\177\024\350\300\000\000\000(IDATx\234cd``\370\217\r\034?~\034\2538\313\217\037?~\374\370\201)\201U\020\252\001\253\304\250\006$\000\000\230\346y\315\204l;t\000\000\000\000IEND\256B`\202"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_image_without_channel.expect b/test/expect/TestTensorBoard.test_image_without_channel.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 8
+    width: 8
+    colorspace: 3
+    encoded_image_string: "\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\010\000\000\000\010\010\002\000\000\000Km)\334\000\000\000\031IDATx\234cd``\370\217\r0\376\370\361\003\253\004\313\240\224\000\000;\267\273\313%\020=\255\000\000\000\000IEND\256B`\202"
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_pr_curve.expect b/test/expect/TestTensorBoard.test_pr_curve.expect
@@ -0,0 +1,26 @@
+value {
+  tag: "tag"
+  tensor {
+    dtype: DT_FLOAT
+    tensor_shape {
+      dim {
+        size: 6
+      }
+      dim {
+        size: 1
+      }
+    }
+    float_val: 57.0
+    float_val: 43.0
+    float_val: 0.0
+    float_val: 0.0
+    float_val: 0.57
+    float_val: 1.0
+  }
+  metadata {
+    plugin_data {
+      plugin_name: "pr_curves"
+      content: "\020\001"
+    }
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_pr_curve_raw.expect b/test/expect/TestTensorBoard.test_pr_curve_raw.expect
@@ -0,0 +1,50 @@
+value {
+  tag: "prcurve with raw data"
+  tensor {
+    dtype: DT_FLOAT
+    tensor_shape {
+      dim {
+        size: 6
+      }
+      dim {
+        size: 5
+      }
+    }
+    float_val: 75.0
+    float_val: 64.0
+    float_val: 21.0
+    float_val: 5.0
+    float_val: 0.0
+    float_val: 150.0
+    float_val: 105.0
+    float_val: 18.0
+    float_val: 0.0
+    float_val: 0.0
+    float_val: 0.0
+    float_val: 45.0
+    float_val: 132.0
+    float_val: 150.0
+    float_val: 150.0
+    float_val: 0.0
+    float_val: 11.0
+    float_val: 54.0
+    float_val: 70.0
+    float_val: 75.0
+    float_val: 0.3333333
+    float_val: 0.3786982
+    float_val: 0.5384616
+    float_val: 1.0
+    float_val: 0.0
+    float_val: 1.0
+    float_val: 0.8533334
+    float_val: 0.28
+    float_val: 0.0666667
+    float_val: 0.0
+  }
+  metadata {
+    plugin_data {
+      plugin_name: "pr_curves"
+      content: "\020\001"
+    }
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_text.expect b/test/expect/TestTensorBoard.test_text.expect
@@ -0,0 +1,17 @@
+value {
+  tag: "dummy/text_summary"
+  tensor {
+    dtype: DT_STRING
+    tensor_shape {
+      dim {
+        size: 1
+      }
+    }
+    string_val: "text 123"
+  }
+  metadata {
+    plugin_data {
+      plugin_name: "text"
+    }
+  }
+}
diff --git a/test/expect/TestTensorBoard.test_video.expect b/test/expect/TestTensorBoard.test_video.expect
@@ -0,0 +1,9 @@
+value {
+  tag: "dummy"
+  image {
+    height: 16
+    width: 16
+    colorspace: 1
+    encoded_image_string: "GIF89a\020\000\020\000\207\000\000\377\377\377\376\376\376\375\375\375\374\374\374\373\373\373\372\372\372\371\371\371\370\370\370\367\367\367\366\366\366\365\365\365\364\364\364\363\363\363\362\362\362\361\361\361\360\360\360\357\357\357\356\356\356\355\355\355\354\354\354\353\353\353\352\352\352\351\351\351\350\350\350\347\347\347\346\346\346\345\345\345\344\344\344\343\343\343\342\342\342\341\341\341\340\340\340\337\337\337\336\336\336\335\335\335\334\334\334\333\333\333\332\332\332\331\331\331\330\330\330\327\327\327\326\326\326\325\325\325\324\324\324\323\323\323\322\322\322\321\321\321\320\320\320\317\317\317\316\316\316\315\315\315\314\314\314\313\313\313\312\312\312\311\311\311\310\310\310\307\307\307\306\306\306\305\305\305\304\304\304\303\303\303\302\302\302\301\301\301\300\300\300\277\277\277\276\276\276\275\275\275\274\274\274\273\273\273\272\272\272\271\271\271\270\270\270\267\267\267\266\266\266\265\265\265\264\264\264\263\263\263\262\262\262\261\261\261\260\260\260\257\257\257\256\256\256\255\255\255\254\254\254\253\253\253\252\252\252\251\251\251\250\250\250\247\247\247\246\246\246\245\245\245\244\244\244\243\243\243\242\242\242\241\241\241\240\240\240\237\237\237\236\236\236\235\235\235\234\234\234\233\233\233\232\232\232\231\231\231\230\230\230\227\227\227\226\226\226\225\225\225\224\224\224\223\223\223\222\222\222\221\221\221\220\220\220\217\217\217\216\216\216\215\215\215\214\214\214\213\213\213\212\212\212\211\211\211\210\210\210\207\207\207\206\206\206\205\205\205\204\204\204\203\203\203\202\202\202\201\201\201\200\200\200\177\177\177~~~}}}|||{{{zzzyyyxxxwwwvvvuuutttsssrrrqqqpppooonnnmmmlllkkkjjjiiihhhgggfffeeedddcccbbbaaa```___^^^]]]\\\\\\[[[ZZZYYYXXXWWWVVVUUUTTTSSSRRRQQQPPPOOONNNMMMLLLKKKJJJIIIHHHGGGFFFEEEDDDCCCBBBAAA@@@???>>>===<<<;;;:::999888777666555444333222111000///...---,,,+++***)))(((\'\'\'&&&%%%$$$###\"\"\"!!!   \037\037\037\036\036\036\035\035\035\034\034\034\033\033\033\032\032\032\031\031\031\030\030\030\027\027\027\026\026\026\025\025\025\024\024\024\023\023\023\022\022\022\021\021\021\020\020\020\017\017\017\016\016\016\r\r\r\014\014\014\013\013\013\n\n\n\t\t\t\010\010\010\007\007\007\006\006\006\005\005\005\004\004\004\003\003\003\002\002\002\001\001\001\000\000\000!\377\013NETSCAPE2.0\003\001\377\377\000!\371\004\010\031\000\000\000,\000\000\000\000\020\000\020\000\000\010\377\000\377\001\010 `\000\201\002\006~\001\013&l\030\261b\306\016 H\240`\001\203\006\016\216!K\246l\031\263f\316\036@\210 a\002\205\n\026\236A\213&m\032\265j\326.`\310\240a\003\207\016\036\256a\313\246m\033\267n\336>\200\010!b\004\211\022&\276\201\013\'n\034\271r\346N\240H\241b\005\213\026.\316\241K\247n\035\273v\356^\300\210!c\006\215\0326\336\301\213\'o\036\275z\366n\340\310\241c\007\217\036>\356\341\313\247o\037\277~\376\376\000\n$h\020\241B\206~\000\t\"d\010\221\"F\016!J\244h\021\243F\216\216 I\242d\t\223&N\036A\212$i\022\245J\226\236@\211\"e\n\225*V.a\312\244i\023\247N\236\256`\311\242e\013\227.^>\201\n%j\024\251R\246\276\200\t#f\014\2312fN\241J\245j\025\253V\256\316\240I\243f\r\2336n^\301%\212%k\026\255Z\266\336\300\211#g\016\235:vn\341\312\245k\027\257^\276\356\340\311\243g\017\037\200}\374\004\004\000!\371\004\010\031\000\000\000,\000\000\000\000\020\000\020\000\000\010\377\000\177\000\t\"d\010\221\"F\376\001\010 `\000\201\002\006\216 I\242d\t\223&N\016 H\240`\001\203\006\016\236@\211\"e\n\225*V\036@\210 a\002\205\n\026\256`\311\242e\013\227.^.`\310\240a\003\207\016\036\276\200\t#f\014\2312f>\200\010!b\004\211\022&\316\240I\243f\r\2336nN\240H\241b\005\213\026.\336\300\211#g\016\235:v^\300\210!c\006\215\0326\356\340\311\243g\017\237>~n\340\310\241c\007\217\036>~\001\013&l\030\261b\306\376\000\n$h\020\241B\206\216!K\246l\031\263f\316\016!J\244h\021\243F\216\236A\213&m\032\265j\326\036A\212$i\022\245J\226\256a\313\246m\033\267n\336.a\312\244i\023\247N\236\276\201\013\'n\034\271r\346>\201\n%j\024\251R\246\316\241K\247n\035\273v\356N\241J\245j\025\253V\256\336\301%\213\'o\036\275z\366^\301\212%k\026\255Z\266\356\341\313\247o\037\277~\376n\341\312\245k\027/\200\275|\005\004\000!\371\004\010\031\000\000\000,\000\000\000\000\020\000\020\000\000\010\377\000\377\000\n$h\020\241B\206~\000\t\"d\010\221\"F\016!J\244h\021\243F\216\216 I\242d\t\223&N\036A\212$i\022\245J\226\236@\211\"e\n\225*V.a\312\244i\023\247N\236\256`\311\242e\013\227.^>\201\n%j\024\251R\246\276\200\t#f\014\2312fN\241J\245j\025\253V\256\316\240I\243f\r\2336n^\301\212%k\026\255Z\266\336\300\211#g\016\235:vn\341\312\245k\027\257^\276\356\340\311\243g\017\237>~\376\001\010 `\000\201\002\006~\001\013&l\030\261b\306\016 H\240`\001\203\006\016\216!K\246l\031\263f\316\036@\210 a\002\205\n\026\236A\213&m\032\265j\326.`\310\240a\003\207\016\036\256a\313\246m\033\267n\336>\200\010!b\004\211\022&\276\201\013\'n\034\271r\346N\240H\241b\005\213\026.\316\241K\247n\035\273v\356^\300%\210!c\006\215\0326\336\301\213\'o\036\275z\366n\340\310\241c\007\217\036>\356\341\313\247o\037?\200\375\374\005\004\000;"
+  }
+}
diff --git a/test/test_tensorboard.py b/test/test_tensorboard.py
@@ -42,6 +42,10 @@
 import torch
 from common_utils import TestCase, run_tests
 
+def tensor_N(shape, dtype=float):
+    numel = np.prod(shape)
+    x = (np.arange(numel, dtype=dtype)).reshape(shape)
+    return x
 
 class BaseTestCase(TestCase):
     """ Base class used for all TensorBoard tests """
@@ -315,31 +319,31 @@ def test_empty_input(self):
 
         def test_image_with_boxes(self):
             self.assertTrue(compare_proto(summary.image_boxes('dummy',
-                                          np.random.rand(3, 32, 32).astype(np.float32),
+                                          tensor_N(shape=(3, 32, 32)),
                                           np.array([[10, 10, 40, 40]])),
                                           self))
 
         def test_image_with_one_channel(self):
             self.assertTrue(compare_proto(summary.image('dummy',
-                                                        np.random.rand(1, 8, 8).astype(np.float32),
+                                                        tensor_N(shape=(1, 8, 8)),
                                                         dataformats='CHW'),
                                                         self))  # noqa E127
 
         def test_image_with_one_channel_batched(self):
             self.assertTrue(compare_proto(summary.image('dummy',
-                                                        np.random.rand(2, 1, 8, 8).astype(np.float32),
+                                                        tensor_N(shape=(2, 1, 8, 8)),
                                                         dataformats='NCHW'),
                                                         self))  # noqa E127
 
         def test_image_with_3_channel_batched(self):
             self.assertTrue(compare_proto(summary.image('dummy',
-                                                        np.random.rand(2, 3, 8, 8).astype(np.float32),
+                                                        tensor_N(shape=(2, 3, 8, 8)),
                                                         dataformats='NCHW'),
                                                         self))  # noqa E127
 
         def test_image_without_channel(self):
             self.assertTrue(compare_proto(summary.image('dummy',
-                                                        np.random.rand(8, 8).astype(np.float32),
+                                                        tensor_N(shape=(8, 8)),
                                                         dataformats='HW'),
                                                         self))  # noqa E127
 
@@ -348,56 +352,57 @@ def test_video(self):
                 import moviepy  # noqa F401
             except ImportError:
                 return
-            self.assertTrue(compare_proto(summary.video('dummy', np.random.rand(4, 3, 1, 8, 8).astype(np.float32)), self))
-            summary.video('dummy', np.random.rand(16, 48, 1, 28, 28).astype(np.float32))
-            summary.video('dummy', np.random.rand(20, 7, 1, 8, 8).astype(np.float32))
+            self.assertTrue(compare_proto(summary.video('dummy', tensor_N(shape=(4, 3, 1, 8, 8))), self))
+            summary.video('dummy', np.random.rand(16, 48, 1, 28, 28))
+            summary.video('dummy', np.random.rand(20, 7, 1, 8, 8))
 
         def test_audio(self):
-            self.assertTrue(compare_proto(summary.audio('dummy', np.random.rand(42)), self))
+            self.assertTrue(compare_proto(summary.audio('dummy', tensor_N(shape=(42,))), self))
 
         def test_text(self):
             self.assertTrue(compare_proto(summary.text('dummy', 'text 123'), self))
 
         def test_histogram_auto(self):
-            self.assertTrue(compare_proto(summary.histogram('dummy', np.random.rand(1024), bins='auto', max_bins=5), self))
+            self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='auto', max_bins=5), self))
 
         def test_histogram_fd(self):
-            self.assertTrue(compare_proto(summary.histogram('dummy', np.random.rand(1024), bins='fd', max_bins=5), self))
+            self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='fd', max_bins=5), self))
 
         def test_histogram_doane(self):
-            self.assertTrue(compare_proto(summary.histogram('dummy', np.random.rand(1024), bins='doane', max_bins=5), self))
+            self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='doane', max_bins=5), self))
+
+        def test_custom_scalars(self):
+            layout = {'Taiwan': {'twse': ['Multiline', ['twse/0050', 'twse/2330']]},
+                      'USA': {'dow': ['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
+                              'nasdaq': ['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
+            summary.custom_scalars(layout)  # only smoke test. Because protobuf in python2/3 serialize dictionary differently.
 
     def remove_whitespace(string):
         return string.replace(' ', '').replace('\t', '').replace('\n', '')
 
     def compare_proto(str_to_compare, function_ptr):
-        # TODO: enable test after tensorboard is ready.
-        return True
-        if 'histogram' in function_ptr.id():
-            return  # numpy.histogram has slight difference between versions
-
-        if 'pr_curve' in function_ptr.id():
-            return  # pr_curve depends on numpy.histogram
 
         module_id = function_ptr.__class__.__module__
+        test_dir = os.path.dirname(sys.modules[module_id].__file__)
         functionName = function_ptr.id().split('.')[-1]
-        test_file = os.path.realpath(sys.modules[module_id].__file__)
-        expected_file = os.path.join(os.path.dirname(test_file),
+        expected_file = os.path.join(test_dir,
                                      "expect",
-                                     module_id.split('.')[-1] + '.' + functionName + ".expect")
+                                     'TestTensorBoard.' + functionName + ".expect")
+
         assert os.path.exists(expected_file)
         with open(expected_file) as f:
             expected = f.read()
         str_to_compare = str(str_to_compare)
+        # if not remove_whitespace(str_to_compare) == remove_whitespace(expected):
         return remove_whitespace(str_to_compare) == remove_whitespace(expected)
 
     def write_proto(str_to_compare, function_ptr):
         module_id = function_ptr.__class__.__module__
+        test_dir = os.path.dirname(sys.modules[module_id].__file__)
         functionName = function_ptr.id().split('.')[-1]
-        test_file = os.path.realpath(sys.modules[module_id].__file__)
-        expected_file = os.path.join(os.path.dirname(test_file),
+        expected_file = os.path.join(test_dir,
                                      "expect",
-                                     module_id.split('.')[-1] + '.' + functionName + ".expect")
+                                     'TestTensorBoard.' + functionName + ".expect")
         with open(expected_file, 'w') as f:
             f.write(str(str_to_compare))
 
@@ -414,7 +419,7 @@ def forward(self, x):
                     return self.l(x)
 
             with SummaryWriter(comment='LinearModel') as w:
-                w.add_graph(myLinear(), dummy_input, True)
+                w.add_graph(myLinear(), dummy_input)
 
         def test_mlp_graph(self):
             dummy_input = (torch.zeros(2, 1, 28, 28),)
@@ -442,7 +447,7 @@ def forward(self, x, update_batch_stats=True):
                     return h
 
             with SummaryWriter(comment='MLPModel') as w:
-                w.add_graph(myMLP(), dummy_input, True)
+                w.add_graph(myMLP(), dummy_input)
 
         def test_wrong_input_size(self):
             with self.assertRaises(RuntimeError) as e_info:
@@ -527,7 +532,7 @@ def test_scalar(self):
 
         @skipIfNoCaffe2
         def test_caffe2_np(self):
-            workspace.FeedBlob("testBlob", np.random.randn(1, 3, 64, 64).astype(np.float32))
+            workspace.FeedBlob("testBlob", tensor_N(shape=(1, 3, 64, 64)))
             self.assertIsInstance(make_np('testBlob'), np.ndarray)   
 
         @skipIfNoCaffe2
