Merge pull request #2579 from nouiz/bugfix_pool_pad

theano/tensor/signal/downsample.py

@@ -220,10 +220,7 @@ class DownsampleFactorMax(Op):
         z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
                                  self.padding)
         if (z[0] is None) or (z[0].shape != z_shape):
-            z[0] = numpy.empty(
-                self.out_shape(x.shape, self.ds, self.ignore_border,
-                               self.st, self.padding),
-                dtype=x.dtype)
+            z[0] = numpy.empty(z_shape, dtype=x.dtype)
         zz = z[0]
         # number of pooling output rows
         pr = zz.shape[-2]
@@ -237,11 +234,14 @@ class DownsampleFactorMax(Op):
         img_cols = x.shape[-1] + 2 * pad_w
 
         # pad the image
-        fill = x.min()-1.
-        y = numpy.zeros(
-            (x.shape[0], x.shape[1], img_rows, img_cols),
-            dtype=x.dtype) + fill
-        y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
+        if self.padding != (0, 0):
+            fill = x.min()-1.
+            y = numpy.zeros(
+                (x.shape[0], x.shape[1], img_rows, img_cols),
+                dtype=x.dtype) + fill
+            y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
+        else:
+            y = x
         # max pooling
         for n in xrange(x.shape[0]):
             for k in xrange(x.shape[1]):
@@ -272,7 +272,7 @@ class DownsampleFactorMax(Op):
         # No implementation is currently for the case where
         # the stride size and the pooling size are different.
         # An exception is raised for such a case.
-        if self.ds != self.st:
+        if self.ds != self.st or self.padding != (0, 0):
             raise theano.gof.utils.MethodNotDefined()
         x, = inp
         z, = out
@@ -346,7 +346,7 @@ class DownsampleFactorMax(Op):
         """ % locals()
 
     def c_code_cache_version(self):
-        return (0, 1)
+        return (0, 2)
 
 
 class DownsampleFactorMaxGrad(Op):
@@ -389,10 +389,14 @@ class DownsampleFactorMaxGrad(Op):
         img_cols = x.shape[-1] + 2 * pad_w
 
         # pad the image
-        fill = x.min()-1
-        y = numpy.zeros(
-            (x.shape[0], x.shape[1], img_rows, img_cols), dtype=x.dtype) + fill
-        y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
+        if self.padding != (0, 0):
+            fill = x.min()-1
+            y = numpy.zeros(
+                (x.shape[0], x.shape[1], img_rows, img_cols),
+                dtype=x.dtype) + fill
+            y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
+        else:
+            y = x
         gx = numpy.zeros_like(y)
         for n in xrange(x.shape[0]):
             for k in xrange(x.shape[1]):
@@ -429,7 +433,7 @@ class DownsampleFactorMaxGrad(Op):
                         self, 2, gz, 'Hessian not implemented with padding')]
 
     def c_code(self, node, name, inp, out, sub):
-        if self.ds != self.st:
+        if self.ds != self.st or self.padding != (0, 0):
             raise theano.gof.utils.MethodNotDefined()
         x, z, gz = inp
         gx, = out
@@ -523,7 +527,7 @@ class DownsampleFactorMaxGrad(Op):
         """ % locals()
 
     def c_code_cache_version(self):
-        return (0, 1)
+        return (0, 2)
 
 
 class DownsampleFactorMaxGradGrad(Op):

theano/tensor/signal/tests/test_downsample.py

@@ -242,10 +242,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
     def test_DownsampleFactorMaxPaddingStride(self):
         ignore_border = True  # padding does not support ignore_border=False
         rng = numpy.random.RandomState(utt.fetch_seed())
-        maxpoolsizes = [(3, 3), (4, 4), (3, 4), (4, 3)]
-        stridesizes = [(2, 2), (2, 2), (1, 1), (1, 2)]
-        paddingsizes = [(2, 2), (1, 2), (2, 1), (0, 0)]
-        imgsizes = [(5, 5), (5, 5), (5, 6), (6, 5)]
+        maxpoolsizes = [(3, 3), (4, 4), (3, 4), (4, 3), (2, 2)]
+        stridesizes = [(2, 2), (2, 2), (1, 1), (1, 2), (2, 2)]
+        paddingsizes = [(2, 2), (1, 2), (2, 1), (0, 0), (1, 1)]
+        imgsizes = [(5, 5), (5, 5), (5, 6), (6, 5), (5, 5)]
         m = 4 # minibatch
         c = 10 # channel size
         images = tensor.dtensor4()
@@ -267,10 +267,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
 
     def test_DownsampleFactorMaxPaddingStride_grad(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
-        imgsizes = ((10, 10), (10, 5))
-        maxpoolsizes = ((5, 3),(3, 5))
-        stridesizes = ((3, 2), (2, 3))
-        paddingsizes = ((2, 2),(2, 1))
+        imgsizes = ((10, 10), (10, 5), (5, 5))
+        maxpoolsizes = ((5, 3),(3, 5), (3, 3))
+        stridesizes = ((3, 2), (2, 3), (3, 3))
+        paddingsizes = ((2, 2),(2, 1), (2, 2))
         for i in range(len(imgsizes)):
             imgsize = imgsizes[i]
             imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0