Merge pull request #292 from delallea/minor

NEWS.txt

@@ -163,7 +163,9 @@ Others:
  * Fixed some import problems.
  * Filtering update. (James)
  * On Windows, the default compiledir changed to be local to the computer/user and not transferred with roaming profile. (Sebastian Urban)
- * New theano flag "on_shape_error". Default to "warn" (same as previous behavior): it print a warning when an error occur when infering the shape of some apply node. The other accepted value is "raise" to raise an error when this happen.
+ * New theano flag "on_shape_error". Defaults to "warn" (same as previous behavior):
+   it prints a warning when an error occurs when inferring the shape of some apply node.
+   The other accepted value is "raise" to raise an error when this happens. (Frederic)
  * The buidbot now raises optimization/shape errors instead of just printing a warning. (Frederic)
 
 Reviewers (alphabetical order):

doc/NEWS.txt

@@ -163,6 +163,9 @@ Others:
  * Fixed some import problems.
  * Filtering update. (James)
  * On Windows, the default compiledir changed to be local to the computer/user and not transferred with roaming profile. (Sebastian Urban)
+ * New theano flag "on_shape_error". Defaults to "warn" (same as previous behavior):
+   it prints a warning when an error occurs when inferring the shape of some apply node.
+   The other accepted value is "raise" to raise an error when this happens. (Frederic)
  * The buidbot now raises optimization/shape errors instead of just printing a warning. (Frederic)
 
 Reviewers (alphabetical order):

doc/library/config.txt

@@ -249,7 +249,7 @@ import theano and print the config variable, as in:
 
     Default: 'warn'
 
-    When an exception is raised when infering the shape of some apply
+    When an exception is raised when inferring the shape of some apply
     node, either warn the user and use a default value ('warn'), or
     raise the exception ('raise').
 

theano/scan_module/scan_op.py

@@ -15,6 +15,7 @@ __contact__ = "Razvan Pascanu <r.pascanu@gmail>"
 import itertools
 import logging
 import time
+from itertools import izip
 
 import numpy
 
@@ -285,7 +286,7 @@ class Scan(PureOp):
             # If everything went OK up to here, there is still one thing to
             # check. Namely, do the internal graph represent same
             # computations
-            for self_in, other_in in zip(self.inputs, other.inputs):
+            for self_in, other_in in izip(self.inputs, other.inputs):
                 if self_in.type != other_in.type:
                     return False
 
@@ -909,12 +910,12 @@ class Scan(PureOp):
         # Here, we build a list inner_ins_shape, such that inner_ins_shape[i]
         # is the shape of self.inputs[i]
 
-        for inp, inp_shp in zip(node.inputs, input_shapes):
+        for inp, inp_shp in izip(node.inputs, input_shapes):
             assert inp_shp is None or len(inp_shp) == inp.ndim
 
         # sequences
         # We skip iputs_shapes[0] as it is the total or current number
-        # of iteration
+        # of iterations.
         seqs_shape = [x[1:] for x in input_shapes[1:1 + self.n_seqs]]
 
         # mit_mot, mit_sot, sit_sot
@@ -938,7 +939,7 @@ class Scan(PureOp):
         # node.inputs
         inner_non_sequences = self.inputs[len(seqs_shape) + len(outs_shape):]
         out_equivalent = {}
-        for in_ns, out_ns in zip(inner_non_sequences, node.inputs[offset:]):
+        for in_ns, out_ns in izip(inner_non_sequences, node.inputs[offset:]):
             out_equivalent[in_ns] = out_ns
         if self.as_while:
             self_outs = self.outputs[:-1]
@@ -971,7 +972,7 @@ class Scan(PureOp):
                 r = node.outputs[n_outs + x]
                 assert r.ndim == 1 + len(out_shape_x)
                 shp = [node.inputs[offset + self.n_shared_outs + x]]
-                for i, shp_i in zip(xrange(1, r.ndim), out_shape_x):
+                for i, shp_i in izip(xrange(1, r.ndim), out_shape_x):
                     # Validate shp_i. v_shape_i is either None (if invalid),
                     # or a (variable, Boolean) tuple. The Boolean indicates
                     # whether variable is shp_i (if True), or an valid
@@ -993,7 +994,7 @@ class Scan(PureOp):
         # leading dimension so we replace it for every entry with Shape_i
         if self.as_while:
             scan_outs = [(Shape_i(0)(o),)+x[1:]
-                         for o, x in zip(node.outputs,scan_outs)]
+                         for o, x in izip(node.outputs, scan_outs)]
         return scan_outs
 
     ### GRAD FUNCTION

theano/scan_module/scan_opt.py

@@ -377,9 +377,9 @@ class ScanSaveMem(gof.Optimizer):
         if hasattr(env, 'shape_feature'):
             shape_of = node.env.shape_feature.shape_of
         else:
-            # Each call site of shape_of is in a try..except
-            # That use a default version when the variable is not
-            # in the dictionary
+            # Each access to shape_of is in a try..except block in order to
+            # use a default version when the variable is not in the shape_of
+            # dictionary.
             shape_of = {}
         # 1. Initialization of variables
         # Note 1) We do not actually care about outputs representing shared

theano/scan_module/scan_utils.py

@@ -15,6 +15,7 @@ __contact__ = "Razvan Pascanu <r.pascanu@gmail>"
 
 import copy
 import logging
+from itertools import izip
 
 import numpy
 
@@ -291,7 +292,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
     if in_ys is None:
         in_ys = []
 
-    for x, y in zip(xs, ys):
+    for x, y in izip(xs, ys):
         if x.owner and not y.owner:
             return False
         if y.owner and not x.owner:
@@ -301,7 +302,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
                 return False
     if len(in_xs) != len(in_ys):
         return False
-    for _x, _y in zip(in_xs, in_ys):
+    for _x, _y in izip(in_xs, in_ys):
         if _x.type != _y.type:
             return False
 
@@ -313,7 +314,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
     n_nodes = len(nds_x)
     cont = True
     idx = 0
-    for dx, dy in zip(xs, ys):
+    for dx, dy in izip(xs, ys):
         if not dx.owner or not dy.owner:
             if dy.owner or dx.owner:
                 return False
@@ -338,7 +339,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
         elif len(nd_x.outputs) != len(nd_y.outputs):
             cont = False
         else:
-            for dx, dy in zip(nd_x.inputs, nd_y.inputs):
+            for dx, dy in izip(nd_x.inputs, nd_y.inputs):
                 if (dx, dy) not in common:
                     if dx != dy:
                         if (isinstance(dx, tensor.Constant) and
@@ -353,7 +354,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
                             cont = False
 
         if cont:
-            for dx, dy in zip(nd_x.outputs, nd_y.outputs):
+            for dx, dy in izip(nd_x.outputs, nd_y.outputs):
                 common.add((dx, dy))
         idx += 1
 
@@ -372,7 +373,7 @@ def infer_shape(outs, inputs, input_shapes):
     # inside.  We don't use the full ShapeFeature interface, but we
     # let it initialize itself with an empty env, otherwise we will
     # need to do it manually
-    for inp, inp_shp in zip(inputs, input_shapes):
+    for inp, inp_shp in izip(inputs, input_shapes):
         if inp_shp is not None and len(inp_shp) != inp.ndim:
             assert len(inp_shp) == inp.ndim
 
@@ -380,7 +381,7 @@ def infer_shape(outs, inputs, input_shapes):
     shape_feature.on_attach(theano.gof.Env([], []))
 
     # Initialize shape_of with the input shapes
-    for inp, inp_shp in zip(inputs, input_shapes):
+    for inp, inp_shp in izip(inputs, input_shapes):
         shape_feature.set_shape(inp, inp_shp)
 
     def local_traverse(out):
@@ -689,7 +690,7 @@ def reconstruct_graph(inputs, outputs, tag=None):
         tag = ''
     nw_inputs = [safe_new(x, tag) for x in inputs]
     givens = {}
-    for nw_x, x in zip(nw_inputs, inputs):
+    for nw_x, x in izip(nw_inputs, inputs):
         givens[x] = nw_x
     allinputs = theano.gof.graph.inputs(outputs)
     for inp in allinputs:

theano/tensor/opt.py

@@ -786,9 +786,10 @@ class ShapeFeature(object):
             self.shape_of[r] = s
         else:
             if  r.ndim != len(s):
-                raise ShapeError("Something infered a shape with %d dimensions"
-                                 " for a variable with %d dimensions." % (
-                                 len(s), r.ndim))
+                raise ShapeError(
+                        "Something inferred a shape with %d dimensions "
+                        "for a variable with %d dimensions." % (
+                        len(s), r.ndim))
 
             shape_vars = [self.unpack(s_i) for s_i in s]
             self.shape_of[r] = tuple(shape_vars)
@@ -919,7 +920,7 @@ class ShapeFeature(object):
                     'supported, and one should now use tensor.ShapeError '
                     'instead. The original exception message is: %s' % e)
         except Exception, e:
-            msg = ('Failed to infer_shape from Op %s.\nInput shapes:'
+            msg = ('Failed to infer_shape from Op %s.\nInput shapes: '
                    '%s\nException encountered during infer_shape: '
                    '%s\nException message: %s\nTraceback: %s') % (
                 node.op, [self.shape_of[r] for r in node.inputs],
@@ -927,7 +928,7 @@ class ShapeFeature(object):
             if config.on_shape_error == "raise":
                 raise Exception(msg)
             else:
-                _logger.error(msg)
+                _logger.warning(msg)
             o_shapes = self.default_infer_shape(
                 node, [self.shape_of[r] for r in node.inputs])