generalize Reshape.infer_shape

theano/tensor/basic.py

@@ -5355,40 +5355,33 @@ class Reshape(Op):
 
         # Here, we only simplify if the shape (node.inputs[1]) is a constant,
         # ideally it would suffice to check that it is always non-negative.
-        
-        """
-        oshape = []
-        for i in xrange(self.ndim):
-            default_os_i = theano.tensor.opt.Shape_i(i)(node.outputs[0])
-            try:
-                os_i = node.inputs[1][i]
-                if os_i == -1:
-                    os_i = default_os_i
-            except TypeError:
-                print 'erreur'
-                os_i = default_os_i
-            oshape.append(os_i)
-        return [tuple(oshape)]
-	"""
-	
-	# In contrast with the preceding block, the following will handle 
-	# an entry equal to -1 in desired shape
-	requ = node.inputs[1]
-	if isinstance(requ, theano.tensor.TensorConstant):
-	    requ = list(requ.data)
-	    requ_part = [ele for ele in requ if ele != -1]
-	    crit = len(requ) - len(requ_part)
-	    if crit == 1:
-		missing = numpy.prod(ishapes[0]) / numpy.prod(requ_part) 
-		for i, ele in enumerate(requ):
-		    if ele == -1:
-			requ[i] = missing
-	    elif crit > 1:
-		raise ValueError('shape argument to Reshape.perform'
+
+        requ = node.inputs[1]
+        if isinstance(requ, theano.tensor.TensorConstant):
+            requ = list(requ.data)
+            requ_part = [ele for ele in requ if ele != -1]
+            crit = len(requ) - len(requ_part)
+            if crit == 1:
+                missing = numpy.prod(ishapes[0]) / numpy.prod(requ_part)
+                for i, ele in enumerate(requ):
+                    if ele == -1:
+                        requ[i] = missing
+                    elif crit > 1:
+                        raise ValueError('shape argument to Reshape.perform'
                              ' must have at most one entry equal to -1')
-  	    return [requ]
-	else:
-	    return node.env.shape_feature.default_infer_shape(node, ishapes)
+            return [requ]
+        else:
+            oshape = []
+            for i in xrange(self.ndim):
+                default_os_i = theano.tensor.opt.Shape_i(i)(node.outputs[0])
+                try:
+                    os_i = get_constant_value(node.inputs[1][i]).item()
+                    if os_i == -1:
+                        os_i = default_os_i
+                except TypeError:
+                    os_i = default_os_i
+                oshape.append(os_i)
+            return [tuple(oshape)]
 
     def c_code_cache_version(self):
         return (2,)