Assert output size of max_pooling_2d in GPU cases

chainer/functions/pooling/max_pooling_2d.py

@@ -34,8 +34,10 @@ def forward_gpu(self, x):
         n, c, h, w = x[0].shape
         y_h = conv.get_conv_outsize(
             h, self.kh, self.sy, self.ph, self.cover_all)
+        assert y_h > 0, 'Height in the output should be positive.'
         y_w = conv.get_conv_outsize(
             w, self.kw, self.sx, self.pw, self.cover_all)
+        assert y_w > 0, 'Width in the output should be positive.'
         y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
         self.indexes = cuda.cupy.empty((n, c, y_h, y_w), dtype=numpy.int32)
 

chainer/functions/pooling/pooling_2d.py

@@ -53,8 +53,10 @@ def forward_gpu(self, x):
         n, c, h, w = x.shape
         y_h = conv.get_conv_outsize(
             h, self.kh, self.sy, self.ph, self.cover_all)
+        assert y_h > 0, 'Height in the output should be positive.'
         y_w = conv.get_conv_outsize(
             w, self.kw, self.sx, self.pw, self.cover_all)
+        assert y_w > 0, 'Width in the output should be positive.'
         y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x.dtype)
 
         handle = cudnn.get_handle()

tests/chainer_tests/functions_tests/pooling_tests/test_max_pooling_2d.py

@@ -65,6 +65,18 @@ def test_forward_cpu_wide(self):  # see #120
         x = chainer.Variable(x_data)
         functions.max_pooling_2d(x, 6, stride=6, pad=0)
 
+    def test_forward_output_size_zero_cpu(self):
+        with self.assertRaisesRegexp(
+                AssertionError, 'Height in the output should be positive.'):
+            x_data = numpy.random.rand(4, 4, 1, 4).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2)
+        with self.assertRaisesRegexp(
+                AssertionError, 'Width in the output should be positive.'):
+            x_data = numpy.random.rand(4, 4, 4, 1).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2)
+
     @attr.gpu
     @condition.retry(3)
     def test_forward_gpu(self):
@@ -75,6 +87,34 @@ def test_forward_gpu(self):
     def test_forward_gpu_no_cudnn(self):
         self.check_forward(cuda.to_gpu(self.x), False)
 
+    @attr.gpu
+    @condition.retry(3)
+    def test_forward_output_size_zero_gpu(self):
+        with self.assertRaisesRegexp(
+                AssertionError, 'Height in the output should be positive.'):
+            x_data = cuda.cupy.random.rand(4, 4, 1, 4).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2, use_cudnn=False)
+        with self.assertRaisesRegexp(
+                AssertionError, 'Width in the output should be positive.'):
+            x_data = cuda.cupy.random.rand(4, 4, 4, 1).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2, use_cudnn=False)
+
+    @attr.cudnn
+    @condition.retry(3)
+    def test_forward_output_size_zero_cudnn(self):
+        with self.assertRaisesRegexp(
+                AssertionError, 'Height in the output should be positive.'):
+            x_data = cuda.cupy.random.rand(4, 4, 1, 4).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2, use_cudnn=True)
+        with self.assertRaisesRegexp(
+                AssertionError, 'Width in the output should be positive.'):
+            x_data = cuda.cupy.random.rand(4, 4, 4, 1).astype(self.dtype)
+            x = chainer.Variable(x_data)
+            functions.max_pooling_2d(x, 3, stride=2, use_cudnn=True)
+
     def check_backward(self, x_data, y_grad, use_cudnn=True):
         gradient_check.check_backward(
             functions.MaxPooling2D(