initial changes for issue #2196

theano/tensor/signal/downsample.py

@@ -68,7 +68,7 @@ class DownsampleFactorMax(Op):
     """
 
     @staticmethod
-    def out_shape(imgshape, ds, ignore_border=False):
+    def out_shape(imgshape, ds, st, ignore_border=False):
         """Return the shape of the output from this op, for input of given
         shape and flags.
 
@@ -78,8 +78,12 @@ class DownsampleFactorMax(Op):
             scalar Theano variable.
 
         :param ds: downsample factor over rows and columns
+                   this parameter indicates the pooling region
         :type ds: list or tuple of two ints
 
+        :param st: the stride size 
+        :type st: list or tuple of two ints
+
         :param ignore_border: if ds doesn't divide imgshape, do we include an
             extra row/col of partial downsampling (False) or ignore it (True).
         :type ignore_border: bool
@@ -93,24 +97,30 @@ class DownsampleFactorMax(Op):
             raise TypeError('imgshape must have at least two elements '
                             '(rows, cols)')
         r, c = imgshape[-2:]
-        rval = list(imgshape[:-2]) + [r // ds[0], c // ds[1]]
+        rval = list(imgshape[:-2]) + [(r - ds[0]) // st[0] + 1, (c - ds[1]) // st[1] + 1]
 
         if not ignore_border:
             if isinstance(r, theano.Variable):
-                rval[-2] = tensor.switch(r % ds[0], rval[-2] + 1, rval[-2])
-            elif r % ds[0]:
+                rval[-2] = tensor.switch((r - ds[0]) % st[0], rval[-2] + 1, rval[-2])
+            elif (r - ds[0]) % st[0]:
                 rval[-2] += 1
             if isinstance(c, theano.Variable):
-                rval[-1] = tensor.switch(c % ds[1], rval[-1] + 1, rval[-1])
-            elif c % ds[1]:
+                rval[-1] = tensor.switch((c - ds[1]) % st[1], rval[-1] + 1, rval[-1])
+            elif (c - ds[1]) % st[1]:
                 rval[-1] += 1
         return rval
 
-    def __init__(self, ds, ignore_border=False):
+    def __init__(self, ds, ignore_border=False, st=None):
         """
-        :param ds: downsample factor over rows and columns
+        :param ds: downsample factor over rows and column. ds indicates the pool region size
         :type ds: list or tuple of two ints
 
+        : param st: stride size, which is the number of shifts 
+            over rows/cols to get the the next pool region.
+            if st is None, it is considered equal to ds 
+            (no overlap on pooling regions)
+        : type st: list or tuple of two ints        
+
         :param ignore_border: if ds doesn't divide imgshape, do we include
             an extra row/col of partial downsampling (False) or
             ignore it (True).
@@ -123,19 +133,23 @@ class DownsampleFactorMax(Op):
             raise ValueError(
                 "DownsampleFactorMax downsample parameters must be ints."
                 " Got %s" % str(ds))
+        if st == None:
+            st = ds
+        self.st = tuple(st)
         self.ignore_border = ignore_border
 
     def __eq__(self, other):
         return (type(self) == type(other) and
                 self.ds == other.ds and
+                self.st == other.st and
                 self.ignore_border == other.ignore_border)
 
     def __hash__(self):
-        return hash(type(self)) ^ hash(self.ds) ^ hash(self.ignore_border)
+        return hash(type(self)) ^ hash(self.ds) ^ hash(self.st) ^ hash(self.ignore_border)
 
     def __str__(self):
         return '%s{%s,%s}' % (self.__class__.__name__,
-                              self.ds, self.ignore_border)
+                              self.ds, self.st, self.ignore_border)
 
     def make_node(self, x):
         if x.type.ndim != 4:
@@ -151,35 +165,49 @@ class DownsampleFactorMax(Op):
         if len(x.shape) != 4:
             raise NotImplementedError(
                 'DownsampleFactorMax requires 4D input for now')
-        z_shape = self.out_shape(x.shape, self.ds, self.ignore_border)
+        z_shape = self.out_shape(x.shape, self.ds, self.st, self.ignore_border)
         if (z[0] is None) or (z[0].shape != z_shape):
-            z[0] = numpy.zeros(self.out_shape(x.shape, self.ds,
+            z[0] = numpy.zeros(self.out_shape(x.shape, self.ds, self.st,
                                               self.ignore_border))
             z[0] = theano._asarray(z[0], dtype=x.dtype)
         zz = z[0]
 
         ## zz needs to be initialized with -inf for the following to work
         zz -= numpy.inf
+        pr = zz.shape[-2] # number of pooling output rows
+        pc = zz.shape[-1] # number of pooling output cols
         ds0, ds1 = self.ds
+        st0, st1 = self.st
+        img_rows = x.shape[-2]
+        img_cols = x.shape[-1]
+
         if self.ignore_border:
-            x_usable2 = (x.shape[2] // ds0 * ds0)
+            x_usable2 = (x.shape[2] - ds0) // st0 * st0 + ds0
         else:
             x_usable2 = x.shape[2]
         if self.ignore_border:
-            x_usable3 = (x.shape[3] // ds1 * ds1)
+            x_usable3 = (x.shape[3] - ds1) // st1 * st1 + ds1
         else:
             x_usable3 = x.shape[3]
         for n in xrange(x.shape[0]):
             for k in xrange(x.shape[1]):
-                for i in xrange(x_usable2):
-                    zi = i / ds0
-                    for j in xrange(x_usable3):
-                        zj = j / ds1
-                        zz[n, k, zi, zj] = __builtin__.max(zz[n, k, zi, zj],
-                                                           x[n, k, i, j])
+                for r in xrange(pr):
+                    row_st = r * st0
+                    for c in xrange(pc):
+                        col_st = c * st1
+                        for i in xrange(ds0):
+                            row_ind = row_st + i
+                            if row_ind >= img_rows:
+                                continue
+                            for j in xrange(ds1):
+                                col_ind = col_st + j
+                                if col_ind >= img_cols:
+                                    continue
+                                zz[n, k, r, c] = __builtin__.max(zz[n, k, r, c],
+                                                           x[n, k, row_ind, col_ind])
 
     def infer_shape(self, node, in_shapes):
-        shp = self.out_shape(in_shapes[0], self.ds, self.ignore_border)
+        shp = self.out_shape(in_shapes[0], self.ds, self.st, self.ignore_border)
         return [shp]
 
     def grad(self, inp, grads):
@@ -190,7 +218,7 @@ class DownsampleFactorMax(Op):
                                         ignore_border=self.ignore_border)(
                                             x, maxout, gz)]
 
-    def c_code(self, node, name, inp, out, sub):
+    def c_code_tmp(self, node, name, inp, out, sub):
         x, = inp
         z, = out
         fail = sub['fail']
@@ -262,7 +290,7 @@ class DownsampleFactorMax(Op):
         }
         """ % locals()
 
-    def c_code_cache_version(self):
+    def c_code_cache_version_tmp(self):
         return (0, 1)