Merge pull request #4041 from abergeron/fix_buildbot2

theano/tensor/signal/downsample.py

@@ -3,12 +3,23 @@ from . import pool
 import warnings
 
 warnings.warn("downsample module has been moved to the pool module.")
-max_pool2D = pool.max_pool2D
 max_pool_2d_same_size = pool.max_pool_2d_same_size
 max_pool_2d = pool.pool_2d
 DownsampleFactorMax = pool.Pool
 PoolGrad = pool.PoolGrad
 MaxPoolGrad = pool.MaxPoolGrad
 AveragePoolGrad = pool.AveragePoolGrad
+
+
+# This is for compatibility with pickled things.  It should go away at
+# some point.
+class DownsampleFactorMaxGrad(object):
+    def __new__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
+        if mode == 'max':
+                return MaxPoolGrad(ds=ds, ignore_border=ignore_border, st=st,
+                                   padding=padding)
+        else:
+            return AveragePoolGrad(ds=ds, ignore_border=ignore_border, st=st,
+                                   padding=padding, mode=mode)
+
 DownsampleFactorMaxGradGrad = pool.DownsampleFactorMaxGradGrad
-local_average_pool_grad = pool.local_average_pool_grad

theano/tensor/signal/pool.py

@@ -15,14 +15,6 @@ import numpy
 import theano
 from theano import gof, Op, tensor, Variable, Apply
 
-from theano.tensor.opt import register_canonicalize
-
-
-def max_pool2D(*args, **kwargs):
-    import sys
-    print("DEPRECATION: max_pool2D renamed to pool_2d", file=sys.stderr)
-    return pool_2d(*args, **kwargs)
-
 
 def max_pool_2d_same_size(input, patch_size):
     """
@@ -633,9 +625,8 @@ class PoolGrad(Op):
 
 
 class MaxPoolGrad(PoolGrad):
-
-    def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
-        PoolGrad.__init__(self, ds, ignore_border, st, padding, mode)
+    def __init__(self, ds, ignore_border, st=None, padding=(0, 0)):
+        PoolGrad.__init__(self, ds, ignore_border, st, padding, mode='max')
 
     def make_node(self, x, maxout, gz):
         # make_node should only be called by the grad function of
@@ -808,16 +799,18 @@ class MaxPoolGrad(PoolGrad):
     def c_code_cache_version(self):
         return (0, 7)
 
-DownsampleFactorMaxGrad = MaxPoolGrad
-
 
 class AveragePoolGrad(PoolGrad):
-
-    def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='average_inc_pad'):
+    def __init__(self, ds, ignore_border, st=None, padding=(0, 0),
+                 mode='average_inc_pad'):
         assert mode in ['sum', 'average_inc_pad', 'average_exc_pad']
         PoolGrad.__init__(self, ds, ignore_border, st, padding, mode)
 
-    def make_node(self, x, gz):
+    # There is an extra dummy parameter to match the parameter count
+    # of MaxPoolGrad.  They have to keep the same interface because of
+    # the DownsampleFactorMaxGrad trick to keep old scripts working
+    # (see downsample.py for details on this).
+    def make_node(self, x, gz, dummy=None):
         # make_node should only be called by the grad function of
         # Pool, so these asserts should not fail.
         assert isinstance(x, Variable) and x.ndim == 4
@@ -934,8 +927,8 @@ class DownsampleFactorMaxGradGrad(Op):
         if len(x.shape) != 4:
             raise NotImplementedError(
                 'DownsampleFactorMaxGradGrad requires 4D input for now')
-        if (z[0] is None) or (z[0].shape != x.shape):
-            z[0] = numpy.zeros(x.shape, dtype=x.dtype)
+        if (z[0] is None) or (z[0].shape != maxout.shape):
+            z[0] = numpy.zeros(maxout.shape, dtype=x.dtype)
         ggz = z[0]  # grad wrt maxout_grad has the same shape as maxout
         # number of pooling output rows
         pr = ggz.shape[-2]
@@ -1060,19 +1053,3 @@ class DownsampleFactorMaxGradGrad(Op):
 
     def c_code_cache_version(self):
         return (0, 1)
-
-
-@register_canonicalize('fast_compile')
-@gof.local_optimizer([MaxPoolGrad])
-def local_average_pool_grad(node):
-    # To assure backward compatibility with
-    # DownsampleFactorMaxGrad
-    if (not isinstance(node.op, MaxPoolGrad) or node.op.mode not in
-            ['sum', 'average_exc_pad', 'average_inc_pad']):
-        return False
-    return [AveragePoolGrad(ds=node.op.ds,
-                            ignore_border=node.op.ignore_border,
-                            st=node.op.st,
-                            padding=node.op.padding,
-                            mode=node.op.mode)(node.inputs[0],
-                                               node.inputs[2])]

theano/tensor/signal/tests/test_pool.py

@@ -10,8 +10,10 @@ import theano.tensor as tensor
 from theano.tests import unittest_tools as utt
 from theano.tensor.signal.pool import (Pool, pool_2d,
                                        MaxPoolGrad, AveragePoolGrad,
-                                       DownsampleFactorMaxGrad,
                                        max_pool_2d_same_size)
+
+from theano.tensor.signal.downsample import DownsampleFactorMaxGrad
+
 from theano import function
 
 
@@ -802,20 +804,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                   padding=(0, 0))(image)],
             [image_val], Pool)
 
-    def test_opt_max_to_average(self):
+    def test_DownsampleFactorMaxGrad(self):
         im = theano.tensor.tensor4()
         maxout = theano.tensor.tensor4()
         grad = theano.tensor.tensor4()
 
-        compilation_mode = theano.compile.get_default_mode().including(
-            'local_average_pool_grad')
-
         for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
             f = theano.function([im, maxout, grad],
                                 DownsampleFactorMaxGrad(ds=(3, 3),
                                                         ignore_border=False,
                                                         mode=mode)(im, maxout, grad),
-                                mode=compilation_mode)
+                                on_unused_input='ignore')
 
             if mode == 'max':
                 assert any(isinstance(n.op, MaxPoolGrad)

theano/tests/test_flake8.py

@@ -79,7 +79,6 @@ whitelist_flake8 = [
     "tensor/tests/test_blas_c.py",
     "tensor/tests/test_blas_scipy.py",
     "tensor/tests/test_mpi.py",
-    "tensor/signal/pool.py",
     "tensor/signal/conv.py",
     "tensor/signal/tests/test_conv.py",
     "tensor/signal/tests/test_downsample.py",