Define __setstate__ on _ConvNd to handle pre-padding_mode pickles. (#21687)

ezyang · facebook-github-bot · commit 029a968212b0 · 2019-06-14T11:00:21.000-07:00
Summary: Pull Request resolved: #21687 ghimport-source-id: df49530 Differential Revision: D15807402 Pulled By: ezyang fbshipit-source-id: f51b221444afc4e017db7544642a9c0a7d2a3efb
diff --git a/.gitignore b/.gitignore
@@ -31,10 +31,8 @@ test/.coverage
 test/.hypothesis/
 test/cpp/api/mnist
 test/custom_operator/model.pt
-test/data/gpu_tensors.pt
 test/data/legacy_modules.t7
-test/data/legacy_serialized.pt
-test/data/linear.pt
+test/data/*.pt
 dropout_model.pt
 test/generated_type_hints_smoketest.py
 test/htmlcov
diff --git a/test/test_nn.py b/test/test_nn.py
@@ -1,4 +1,5 @@
 import math
+import sys
 import random
 import string
 import unittest
@@ -579,6 +580,26 @@ def test_module_backcompat(self):
         input = torch.randn(2, 3, dtype=torch.float)
         self.assertEqual(m(input).size(), (2, 5))
 
+    def test_conv_backcompat(self):
+        from torch.serialization import SourceChangeWarning
+        # This file was generated by running on PyTorch 1.0.1 on Python 2:
+        #
+        #     import torch
+        #     from torch import nn
+        #     m = nn.Conv2d(1, 1, 1)
+        #     torch.save(m, 'legacy_conv2d.pt')
+        #
+        # NB: This Pickle also contains some Unicode data!
+        path = download_file('https://download.pytorch.org/test_data/legacy_conv2d.pt')
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', SourceChangeWarning)
+            if sys.version_info[0] == 2:
+                m = torch.load(path)
+            else:
+                m = torch.load(path, encoding='utf-8')
+        input = torch.randn((1, 1, 1, 1), dtype=torch.float)
+        self.assertEqual(m(input).size(), (1, 1, 1, 1))
+
     def test_share_memory(self):
         class Net(nn.Module):
             def __init__(self):
diff --git a/torch/nn/modules/conv.py b/torch/nn/modules/conv.py
@@ -68,6 +68,11 @@ def extra_repr(self):
             s += ', bias=False'
         return s.format(**self.__dict__)
 
+    def __setstate__(self, state):
+        super(_ConvNd, self).__setstate__(state)
+        if not hasattr(self, 'padding_mode'):
+            self.padding_mode = 'zeros'
+
 
 @weak_module
 class Conv1d(_ConvNd):
