Update stuff for the final release of v3.

theano/sandbox/gpuarray/dnn.py

@@ -82,18 +82,20 @@ if ((err = cudnnCreate(&_handle)) != CUDNN_STATUS_SUCCESS) {
     else:
         # If we can compile, check that we can import and run.
         v = version()
-        if v == -1:
+        if v < 2000:
             dnn_available.avail = False
             dnn_available.msg = (
-                "You have CuDNN v1 installed, upgrade to v2 or more recent.")
+                "You have an old release of CuDNN (or a release candidate) "
+                "that isn't supported.  Please update to at least v2 final "
+                "version.")
             raise RuntimeError(dnn_available.msg)
-        if v == 20:
+        if v >= 3000 and v < 3007:
             dnn_available.avail = False
             dnn_available.msg = (
-                "You have installed a release candidate of CuDNN v2."
-                " This isn't supported anymore."
-                " Update to CuDNN v2 final version.")
+                "You have installed a release candidate of CuDNN v3. This "
+                "isn't supported. Please update to v3 final version.")
             raise RuntimeError(dnn_available.msg)
+
     return dnn_available.avail
 
 dnn_available.avail = None
@@ -507,7 +509,7 @@ class GpuDnnConvGradW(DnnBase):
 
     def __init__(self, inplace=False, algo=None):
         DnnBase.__init__(self, ["dnn_conv_base.c", "dnn_gw.c"],
-                     "APPLY_SPECIFIC(conv_gw)")
+                         "APPLY_SPECIFIC(conv_gw)")
         self.inplace = inplace
         if self.inplace:
             self.destroy_map = {0: [2]}
@@ -787,7 +789,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
         # Special case: We can be faster by using GpuDnnConvGradI to compute
         # the full convolution as the backward pass of a valid convolution.
         # We just need to set up a suitable 'fake' valid convolution.
-        img = gpu_contiguous(img)  # cudnn v1 and v2 rc3 need contiguous data
+        img = gpu_contiguous(img)  # cudnn v2 rc3 need contiguous data
         kerns = gpu_contiguous(kerns.dimshuffle(1, 0, 2, 3))
         conv_mode = 'cross' if conv_mode == 'conv' else 'conv'
         shape2 = shape_i(img, 2, fgraph) + shape_i(kerns, 2, fgraph) - 1
@@ -1429,8 +1431,7 @@ gpu_seqopt.register("NoCuDNNRaise", NoCuDNNRaise(), 0, 'cudnn')
 @register_opt('cudnn')
 @op_lifter([SoftmaxGrad])
 def local_softmax_dnn_grad(node):
-    if not dnn_available() or version() != 2000:
-        # softmaxgrad (n, c, 1, 1) broken in v3 rc1
+    if not dnn_available():
         return
     ins = []
     for n in node.inputs: