Update GpuDnnConv to use new config flags

theano/sandbox/cuda/dnn.py

 import os
 import numpy
+import warnings
 
 import theano
 from theano import Apply, gof, tensor, config, Variable
@@ -405,21 +406,30 @@ class GpuDnnConv(DnnBase, COp):
     :param kernel:
     :param descr: the convolution descriptor
     """
-    __props__ = ('workmem', 'inplace')
+    __props__ = ('algo', 'inplace')
     __input_name__ = ('image', 'kernel', 'output',
                       'descriptor', 'alpha', 'beta')
 
-    def __init__(self, workmem=None, inplace=False):
+    def __init__(self, workmem=None, inplace=False, algo=None):
         """
-        :param workmem: either 'none', 'small', 'large', 'fft', 'time',
-        'time_once', 'guess' or 'guess_once'. Default is the value of
-        :attr:`config.dnn.conv.workmem`.
+        :param workmem: *deprecated*, use param algo instead
+        :param algo: either 'small', 'none', 'large', 'fft', 'guess_once',
+        'guess_on_shape_change', 'time_once' or 'time_on_shape_change'.
+        Default is the value of :attr:`config.dnn.conv.algo_fwd`.
         """
         COp.__init__(self, ["dnn_base.c", "dnn_conv_base.c", "dnn_fwd.c"],
                      "APPLY_SPECIFIC(conv_fwd)")
-        if workmem is None:
-            workmem = config.dnn.conv.workmem
-        self.workmem = workmem
+
+        if workmem is not None:
+            warnings.warn(("GpuDnnConv: parameter 'workmem' is deprecated. "
+                           "Use 'algo' instead."), stacklevel=3)
+            assert algo == None
+            self.algo = workmem
+        else:
+            if algo is None:
+                algo = config.dnn.conv.algo_fwd
+            self.algo = algo
+
         self.inplace = inplace
         if self.inplace:
             self.destroy_map = {0: [2]}
@@ -428,18 +438,21 @@ class GpuDnnConv(DnnBase, COp):
         # option to time the different implementations to get the fastest
         # are both unavailable.
         if version() < (3000, 3000):
-            if self.workmem == 'fft':
+            if self.algo == 'fft':
                 raise RuntimeError("CuDNN FFT convolution requires CuDNN v3")
-            elif self.workmem in ['time', 'time_once']:
+            elif self.algo in ['time', 'time_once']:
                 raise RuntimeError("CuDNN convolution timing requires CuDNN v3")
 
-        assert self.workmem in ['none', 'small', 'large', 'fft', 'time',
+        assert self.algo in ['none', 'small', 'large', 'fft', 'time',
                              'time_once', 'guess', 'guess_once']
 
     def __setstate__(self, d):
         self.__dict__.update(d)
-        if not hasattr(self, 'workmem'):
-            self.workmem = 'none'
+        if not hasattr(self, 'algo'):
+            if hasattr(self, 'workmem'):
+                self.algo = self.workmem
+            else:
+                self.algo = 'none'
         if not hasattr(self, 'inplace'):
             self.inplace = False
 
@@ -455,28 +468,28 @@ class GpuDnnConv(DnnBase, COp):
         if version() == -1:
             alg = "0"
         else:
-            if self.workmem == 'none':
+            if self.algo == 'none':
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM'
-            elif self.workmem == 'small':
+            elif self.algo == 'small':
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM'
-            elif self.workmem == 'large':
+            elif self.algo == 'large':
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_GEMM'
-            elif self.workmem == 'fft':
+            elif self.algo == 'fft':
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_FFT'
-            elif self.workmem in ['guess', 'guess_once']:
+            elif self.algo in ['guess', 'guess_once']:
                 # The convolution implementation should be choosen according
                 # to a heuristic
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM'
                 choose_alg = '1'
-                if self.workmem == 'guess_once':
+                if self.algo == 'guess_once':
                     choose_alg_once = '1'
-            elif self.workmem in ['time', 'time_once']:
+            elif self.algo in ['time', 'time_once']:
                 # The convolution implementation should be choosen by timing
                 # every available implementation
                 alg = 'CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM'
                 choose_alg = '1'
                 choose_alg_time = '1'
-                if self.workmem == 'time_once':
+                if self.algo == 'time_once':
                     choose_alg_once = '1'
 
         alg_def = ('CONV_ALGO', alg)