bringing back subtensor, fixing add in c

tensor.py

@@ -4,6 +4,7 @@ import numpy
 from copy import copy
 import inspect
 from gof import ResultBase, Op, utils, Destroyer, Viewer, AbstractFunctionError
+import gof.result
 
 from base_tensor import BaseTensor, BaseTensorOp
 from elemwise import Elemwise
@@ -61,8 +62,8 @@ class Tensor(BaseTensor):
     T = property(__get_T)
 
     #SLICING
-    def __getitem__(self, key): raise NotImplementedError()
-    def __getslice__(self, key): raise NotImplementedError()
+    def __getitem__(self, item): return subtensor(self, item)
+    def __getslice__(self, *args): return subtensor(self, slice(*args))
 
 # alternate Tensor constructor
 def tinit(data, broadcastable=None, role=None, name=None):
@@ -113,6 +114,11 @@ def _assert_tensor_scalar(x, a):
     if numpy.product(a.shape) != 1:
         raise ValueError("The second argument must be a scalar.")
 
+def _as_tensor(obj):
+    if isinstance(obj, Tensor):
+        return obj
+    else:
+        return tinit(obj)
 
 class _Op(BaseTensorOp):
     """A convenient base for the ops in this file"""
@@ -121,13 +127,7 @@ class _Op(BaseTensorOp):
 
     @classmethod
     def input_wrapper(cls, obj):
-        if isinstance(obj, Tensor):
-            return obj
-        else:
-            return tinit(obj)
-    
-#    nin = -1
-#    nout = 1
+        return _as_tensor(obj)
     
 #         def upcast(dtype, *dtypes):
 #             z = numpy.zeros((), dtype = dtype)
@@ -344,22 +344,21 @@ class TensorCopy(_Elemwise):
         return "%(z)s_i = %(x)s_i;"
 tensor_copy = _constructor(TensorCopy)
 
-if 0:
-    ##########################
-    # View Operations
-    ##########################
+##########################
+# View Operations
+##########################
 
-    class transpose(_Op, Viewer):
+class Transpose(_Op, Viewer):
     def view_map(self):
         return {self.out: [self.inputs[0]]}
-        def impl(self, x):
-            return x.T
-        def grad(self, x, gz):
-            return transpose_copy(gz)
     def propagate_broadcastable(self, x):
         rval = list(x)
         rval.reverse()
         return [rval]
+    def impl(self, x):
+        return x.T #numpy's transpose
+    def grad(self, x, gz):
+        return transpose_copy(gz)
     
     def c_impl(self, x, z):
         return """
@@ -369,21 +368,52 @@ if 0:
         }
         %(z)s = transposed;
         """
+transpose = _constructor(Transpose)
 
-    class Subtensor(_Op, Viewer):
+class Subtensor(Op, Viewer):
+    nin = 2
+    nout = 1
+    e_invalid = 'invalid index'
+    def __init__(self, *args,**kwargs):
+        def as_tuple_result(obj):
+            if isinstance(obj, ResultBase):
+                return obj
+            r = gof.result.PythonResult(None)
+            if isinstance(obj, tuple):
+                r.data = obj
+            else:
+                r.data = (obj,)
+            return r
+
+        print 'Subtensor.__init__', args, kwargs
+        #Olivier says not to call this
+        #Op.__init__(self,  *args,**kwargs) 
+        #Viewer.__init__(self, *args,**kwargs)
+        t, coord = args
+        t = _as_tensor(t)
+        coord = as_tuple_result(coord)
+        if len(coord.data) != len(t.broadcastable):
+            raise ValueError(Subtensor.e_invalid)
+        broadcastable = [0 for c in coord.data if isinstance(c, slice)]
+        self.inputs = [t, coord]
+        self.outputs = [Tensor(t.dtype, broadcastable)]
     def view_map(self): 
         return {self.out: [self.inputs[0]]}
-        def impl(x, item): 
-            rval = x.__getitem__(item)
-            #print 'get_slice running', rval
-            return rval
+    def perform(self):
+        x = self.inputs[0].data
+        c = self.inputs[1].data
+        if len(c) == 1:
+            self.outputs[0].data = x.__getitem__(c[0])
+        else:
+            self.outputs[0].data = x.__getitem__(c)
     def grad(x, gz):
         # - option: allocate a potentially large matrix of zeros, and fill in
         # the appropriate elements from gz
         # - option: return a sparse matrix
         # - option: return gz, but think about how to include a special addition
-            # function that uses a matching view over the original data
-            raise NotImplemented 
+        # function that works on a corresponding view of the original data
+        raise NotImplementedError() 
+subtensor = _constructor(Subtensor)
 
 
 ##########################
@@ -398,7 +428,7 @@ class AddElemwise(_Elemwise):
     def grad(self, (x, y), gz):
         return gz, gz
     def c_foreach(self, (x_i, y_i), (z_i, )):
-        return "z_i = x_i + y_i;"
+        return "%(z)s_i = %(x)s_i + %(y)s_i;"
 add_elemwise = _constructor(AddElemwise)
 
 class AddElemwiseInplace(AddElemwise.inplace_version()):