applying changes to the case where stride is used for issue #2196

theano/tensor/signal/downsample.py

@@ -104,40 +104,31 @@ class DownsampleFactorMax(Op):
 
         out_r = (r - ds[0]) // st[0] + 1
         out_c = (c - ds[1]) // st[1] + 1
-        nr = 0
-        nc = 0
+
         if isinstance(r, theano.Variable):
-            nr = tensor.switch(tensor.ge(r - ds[0], 0), out_r, 0)
-        elif r - ds[0] >= 0:
-                nr = out_r
+            nr = tensor.maximum(out_r, 0)
+        else:
+            nr = numpy.maximum(out_r, 0)
         if isinstance(c, theano.Variable):
-            nr = tensor.switch(tensor.ge(c - ds[1], 0), out_c, 0)
-        elif c - ds[1] >= 0:
-                nc = out_c
+            nc = tensor.maximum(out_c, 0)
+        else:
+            nc = numpy.maximum(out_c, 0)
 
-        out_re = 0
-        out_ce = 0
         if not ignore_border:
-            re = r - ((out_r - 1) * st[0] + ds[0])
-            rr = r - out_r * st[0]
-            ce = c - ((out_c - 1) * st[1] + ds[1])
-            cr = c - out_c * st[1]
             if isinstance(r, theano.Variable):
-                out_re = tensor.switch(tensor.gt(nr, 0), tensor.switch(tensor.gt(re, 0), tensor.switch(tensor.gt(rr, 0), 1, 0), 0), tensor.switch(tensor.gt(r, 0), 1, 0))
-            elif nr > 0:
-                if re > 0 and rr > 0:
-                    out_re = 1
-            elif r > 0:
-                    out_re = 1
+                nr = tensor.switch(tensor.ge(st[0], ds[0]), (r - 1) // st[0] + 1, tensor.maximum(0, (r - 1 - ds[0]) // st[0] + 1) + 1)
+            elif st[0] >= ds[0]: 
+                nr = (r - 1) // st[0] + 1
+            else:
+                nr = max(0, (r - 1 - ds[0]) // st[0] + 1) + 1
+
             if isinstance(c, theano.Variable):
-                out_ce = tensor.switch(tensor.gt(nc, 0), tensor.switch(tensor.gt(ce, 0), tensor.switch(tensor.gt(cr, 0), 1, 0), 0), tensor.switch(tensor.gt(c, 0), 1, 0))
-            elif nc > 0:
-                if ce > 0 and cr > 0:
-                    out_ce = 1
-            elif c > 0:
-                    out_ce = 1
-        nr += out_re
-        nc += out_ce
+                nc = tensor.switch(tensor.ge(st[1], ds[1]), (c - 1) // st[1] + 1, tensor.maximum(0, (c - 1 - ds[1]) // st[1] + 1) + 1)
+            elif st[1] >= ds[1]: 
+                nc = (c - 1) // st[1] + 1
+            else:
+                nc = max(0, (c - 1 - ds[1]) // st[1] + 1) + 1
+
         rval = list(imgshape[:-2]) + [nr, nc]
         return rval
 

theano/tensor/signal/tests/test_downsample.py

@@ -122,7 +122,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                                                  ignore_border=ignore_border)(images)
                 f = function([images], maxpool_op)
                 output_val = f(imval)
-                assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
+                utt.assert_allclose(output_val, numpy_output_val)
 
     def test_DownsampleFactorMaxStride(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
@@ -142,22 +142,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 for stride in stridesizes:
                     outputshp = outputshps[indx]
                     indx += 1
-                    print 'maxpoolshp =', maxpoolshp
-                    print 'ignore_border =', ignore_border
-                    print 'stride =', stride
-
                     #DownsampleFactorMax op
                     numpy_output_val = self.numpy_max_pool_2d_stride(imval, maxpoolshp,
                                                               ignore_border, stride)
-                    print "outshape is %s " %(outputshp, )
-                    print "calculated shape is %s " %(numpy_output_val.shape,)
-                    assert numpy_output_val.shape == outputshp
+                    assert numpy_output_val.shape == outputshp, (
+                            "outshape is %s, calculated shape is %s"
+                            %(outputshp, numpy_output_val.shape))
                     maxpool_op = DownsampleFactorMax(maxpoolshp,
                                                      ignore_border=ignore_border, st=stride)(images)
                     f = function([images], maxpool_op)
                     output_val = f(imval)
-                    print "numpy_output_val shape is %s" %(numpy_output_val.shape,) 
-                    assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
+                    utt.assert_allclose(output_val, numpy_output_val)
 
     def test_DownsampleFactorMaxStrideExtra(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
@@ -179,23 +174,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 if not ignore_border:
                     indx_out += 1
                 outputshp = outputshps[indx_out]
-                print 'maxpoolshp =', maxpoolshp
-                print 'ignore_border =', ignore_border
-                print 'stride =', stride
-
                 #DownsampleFactorMax op
                 numpy_output_val = self.numpy_max_pool_2d_stride(imval, maxpoolshp,
                                                           ignore_border, stride)
-                print "outshape is %s " %(outputshp, )
-                print "calculated shape is %s " %(numpy_output_val.shape,)
-                assert numpy_output_val.shape == outputshp
+                assert numpy_output_val.shape == outputshp, (
+                        "outshape is %s, calculated shape is %s"
+                        %(outputshp, numpy_output_val.shape))
                 maxpool_op = DownsampleFactorMax(maxpoolshp,
                                                  ignore_border=ignore_border, st=stride)(images)
                 f = function([images], maxpool_op)
                 output_val = f(imval)
-                print "numpy_output_val shape is %s" %(numpy_output_val.shape,) 
-                print "output_val shape is %s " %(output_val.shape,)
-                assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
+                utt.assert_allclose(output_val, numpy_output_val)
 
     def test_DownsampleFactorMax_grad(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
@@ -266,7 +255,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                                                           ignore_border)
                 output = max_pool_2d(images, maxpoolshp, ignore_border)
                 output_val = function([images], output)(imval)
-                assert numpy.all(output_val == numpy_output_val)
+                assert numpy.all(output_val == numpy_output_val), (
+                    "output_val is %s, numpy_output_val is %s"
+                    %(output_val, numpy_output_val))
                 def mp(input):
                     return max_pool_2d(input, maxpoolshp, ignore_border)
                 utt.verify_grad(mp, [imval], rng=rng)
@@ -285,7 +276,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                                                           ignore_border)
                 output = max_pool_2d(images, maxpoolshp, ignore_border)
                 output_val = function([images], output)(imval)
-                assert numpy.all(output_val == numpy_output_val)
+                assert numpy.all(output_val == numpy_output_val), (
+                    "output_val is %s, numpy_output_val is %s"
+                    %(output_val, numpy_output_val))
                 c = tensor.sum(output)
                 c_val = function([images], c)(imval)
                 g = tensor.grad(c, images)