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pytensor
提交 069a9912 authored 作者: David Warde-Farley's avatar David Warde-Farley 提交者： Arnaud Bergeron
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range fixes.

上级 34d96ebd
全部展开 隐藏空白字符变更
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正在显示 包含 220 行增加 和 182 行删除
doc/crei2013/scan_pow.py
浏览文件 @ 069a9912
......@@ -22,5 +22,5 @@ power = theano.function(inputs=[A, k],
outputs=final_result,
updates=updates)
print(power(range(10), 2))
print(power(list(range(10)), 2))
#[ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
doc/hpcs2011_tutorial/scan_pow.py
浏览文件 @ 069a9912
......@@ -18,4 +18,4 @@ final_result = result[-1]
power = theano.function(inputs=[A,k], outputs=final_result,
updates=updates)
print(power(range(10),2))
print(power(list(range(10)), 2))
doc/tutorial/loop_solution_1.py
浏览文件 @ 069a9912
......@@ -31,7 +31,7 @@ final_result = result[-1]
power = theano.function(inputs=[A, k], outputs=final_result,
updates=updates)
print(power(range(10), 2))
print(power(list(range(10)), 2))
# [ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
......
theano/compile/debugmode.py
浏览文件 @ 069a9912
......@@ -1223,8 +1223,8 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
# When the CudaNdarray is built, the underlying memory
# is c-contiguous, so we transpose it before and after.
new_buf = CudaNdarray(new_buf.T)
new_buf = cuda_dimshuffle(new_buf,
range(new_buf.ndim)[::-1])
new_buf = cuda_dimshuffle(
new_buf, reversed(list(range(new_buf.ndim))))
f_cont_outputs[r] = new_buf
......
theano/compile/function_module.py
浏览文件 @ 069a9912
......@@ -1015,7 +1015,7 @@ class FunctionMaker(object):
else:
flags = []
for output_new, output_old, i in zip(
outputs_new, outputs_old, range(len(outputs_new))):
outputs_new, outputs_old, xrange(len(outputs_new))):
print('loop through outputs node for both graphs')
graph_old.variables = set(gof.graph.variables(
graph_old.inputs, graph_old.outputs))
......
theano/gof/sched.py
浏览文件 @ 069a9912
......@@ -140,7 +140,7 @@ def posort(l, *cmps):
>>> lower_tens = lambda a, b: a/10 - b/10 # prefer lower numbers div 10
>>> prefer evens = lambda a, b: a%2 - b%2 # prefer even numbers
>>> posort(range(20), lower_tens, prefer_evens)
>>> posort(list(range(20)), lower_tens, prefer_evens)
[0, 8, 2, 4, 6, 1, 3, 5, 7, 9, 16, 18, 10, 12, 14, 17, 19, 11, 13, 15]
implemented with _toposort """
......
theano/gof/tests/test_op.py
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......@@ -248,7 +248,7 @@ class TestMakeThunk(unittest.TestCase):
def test_test_value_python_objects():
for x in (range(3), 0, 0.5, 1):
for x in (list(range(3)), 0, 0.5, 1):
assert (op.get_test_value(x) == x).all()
......
theano/gof/tests/test_sched.py
浏览文件 @ 069a9912
......@@ -60,7 +60,7 @@ def test_posort_easy():
def test_posort():
l = range(1, 20)
l = list(range(1, 20))
cmps = [lambda a, b: a % 10 - b % 10,
lambda a, b: (a / 10) % 2 - (b / 10) % 2,
lambda a, b: a - b]
......
theano/itertools.txt 0 → 100644
浏览文件 @ 069a9912
compat/python2x.py
compat/__init__.py
compile/debugmode.py
compile/function_module.py
gof/cc.py
gradient.py
ifelse.py
sandbox/cuda/tests/test_mlp.py
sandbox/gpuarray/elemwise.py
sandbox/gpuarray/tests/test_basic_ops.py
sandbox/scan.py
sandbox/scan_module/scan.py
sandbox/scan_module/scan_op.py
sandbox/scan_module/scan_utils.py
scalar/basic.py
scan_module/scan.py
scan_module/scan_op.py
scan_module/scan_utils.py
sparse/opt.py
tensor/basic.py
tensor/elemwise.py
tensor/nnet/sigm.py
tensor/nnet/tests/test_sigm.py
tensor/opt.py
tensor/subtensor.py
tensor/tests/test_basic.py
tensor/tests/test_elemwise.py
tensor/tests/test_subtensor.py
theano/sandbox/cuda/basic_ops.py
浏览文件 @ 069a9912
......@@ -275,7 +275,7 @@ class GpuElemwise(GpuOp):
# TODO: use LComplete instead
args.append(GpuDimShuffle(
input.type.broadcastable,
['x'] * difference + range(length)
['x'] * difference + list(range(length))
)(input))
_inputs = args
......@@ -813,8 +813,8 @@ class GpuCAReduce(GpuOp):
ndim = len(self.reduce_mask)
nd_out = ndim - sum(self.reduce_mask)
shapes_format = "shape=(%s)" % ",".join(["%d"] * node.inputs[0].ndim)
shapes_data = ",".join(["CudaNdarray_HOST_DIMS(%s)[%d]" % (x, i)
for i in range(node.inputs[0].ndim)])
shapes_data = ",".join("CudaNdarray_HOST_DIMS(%s)[%d]" % (x, i)
for i in xrange(node.inputs[0].ndim))
print("""
if (verbose)
......@@ -3209,7 +3209,7 @@ class GpuJoin(tensor.Join, GpuOp):
def construct_slices(curlen):
slices = [slice(None, None, None) for i in \
range(len(template_shape))]
xrange(len(template_shape))]
slices[axis] = slice(curpos, curpos + curlen, None)
return tuple(slices)
......
theano/sandbox/minimal.py
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......@@ -65,6 +65,6 @@ class T_minimal(unittest.TestCase):
print('built')
Aval = self.rng.randn(5, 5)
bval = numpy.array(range(5), dtype=float)
bval = numpy.arange(5, dtype=float)
f(Aval, bval)
print('done')
theano/sandbox/neighbourhoods.py
浏览文件 @ 069a9912
......@@ -258,9 +258,9 @@ class NeighbourhoodsFromImages(Op):
(i, i) for i in xrange(len(self.strides))])
out_idx = "".join(\
["outer_idx_%d," % (i,) for i in \
range(self.n_dims_before)] + \
xrange(self.n_dims_before)] + \
["stride_idx_%d," % (i,) for i in \
range(len(self.strides))])
xrange(len(self.strides))])
out_idx += self._py_flattened_idx()
#return_val = '\t' * (self.n_dims_before + len(self.strides)*2)
......
theano/sandbox/solve.py
浏览文件 @ 069a9912
......@@ -58,7 +58,7 @@ class T_solve(unittest.TestCase):
def test0(self):
A = self.rng.randn(5, 5)
b = numpy.array(range(5), dtype=float)
b = numpy.arange(5, dtype=float)
x = scipy.linalg.solve(A, b)
Ax = numpy.dot(A, x)
are = tensor.numeric_grad.abs_rel_err(Ax, b)
......
theano/scalar/tests/test_basic.py
浏览文件 @ 069a9912
......@@ -237,32 +237,32 @@ class test_upgrade_to_float(object):
# at least float32, not float16.
unary_ops_vals = [
(inv, range(-127, 0) + range(1, 127)),
(sqrt, range(0, 128)),
(log, range(1, 128)),
(log2, range(1, 128)),
(log10, range(1, 128)),
(log1p, range(0, 128)),
(exp, range(-127, 89)),
(exp2, range(-127, 89)),
(expm1, range(-127, 89)),
(deg2rad, range(-127, 128)),
(rad2deg, range(-127, 128)),
(cos, range(-127, 128)),
(arccos, range(-1, 2)),
(cosh, range(-89, 90)),
(arccosh, range(1, 128)),
(sin, range(-127, 128)),
(arcsin, range(-1, 2)),
(sinh, range(-89, 90)),
(arcsinh, range(-127, 128)),
(tan, range(-3, 4)),
(arctan, range(-127, 128)),
(tanh, range(-127, 128)),
(inv, list(range(-127, 0) + range(1, 127))),
(sqrt, list(range(0, 128))),
(log, list(range(1, 128))),
(log2, list(range(1, 128))),
(log10, list(range(1, 128))),
(log1p, list(range(0, 128))),
(exp, list(range(-127, 89))),
(exp2, list(range(-127, 89))),
(expm1, list(range(-127, 89))),
(deg2rad, list(range(-127, 128))),
(rad2deg, list(range(-127, 128))),
(cos, list(range(-127, 128))),
(arccos, list(range(-1, 2))),
(cosh, list(range(-89, 90))),
(arccosh, list(range(1, 128))),
(sin, list(range(-127, 128))),
(arcsin, list(range(-1, 2))),
(sinh, list(range(-89, 90))),
(arcsinh, list(range(-127, 128))),
(tan, list(range(-3, 4))),
(arctan, list(range(-127, 128))),
(tanh, list(range(-127, 128))),
(arctanh, [0])]
binary_ops_vals = [
(arctan2, range(-127, 128), range(-127, 128))]
(arctan2, list(range(-127, 128), range(-127, 128)))]
@staticmethod
def _test_unary(unary_op, x_range):
......
theano/scan_module/scan_op.py
浏览文件 @ 069a9912
......@@ -245,7 +245,7 @@ class Scan(PureOp):
# inputs and output and ensure that they have the same dtype
nb_recurr_outputs = self.n_mit_mot + self.n_mit_sot + self.n_sit_sot
for outer_oidx in range(nb_recurr_outputs):
for outer_oidx in xrange(nb_recurr_outputs):
inner_iidxs = self.var_mappings['inner_inp_from_outer_out'][outer_oidx]
inner_oidxs = self.var_mappings['inner_out_from_outer_out'][outer_oidx]
......@@ -776,8 +776,8 @@ class Scan(PureOp):
d1 = numpy.max(cython_tap_array_len)
d0 = len(self.tap_array)
cython_tap_array = numpy.zeros((d0, d1), dtype='int32')
for _d0 in range(d0):
for _d1 in range(cython_tap_array_len[_d0]):
for _d0 in xrange(d0):
for _d1 in xrange(cython_tap_array_len[_d0]):
cython_tap_array[_d0, _d1] = self.tap_array[_d0][_d1]
cython_mit_mot_out_nslices = \
numpy.asarray([len(x) for x in self.mit_mot_out_slices],
......@@ -789,8 +789,8 @@ class Scan(PureOp):
d0 = len(self.mit_mot_out_slices)
cython_mit_mot_out_slices = numpy.zeros((d0, d1),
dtype='int32')
for _d0 in range(d0):
for _d1 in range(cython_mit_mot_out_nslices[_d0]):
for _d0 in xrange(d0):
for _d1 in xrange(cython_mit_mot_out_nslices[_d0]):
cython_mit_mot_out_slices[_d0, _d1] = \
self.mit_mot_out_slices[_d0][_d1]
......@@ -1492,9 +1492,9 @@ class Scan(PureOp):
nb_inputs = len(self.inputs)
nb_outputs = len(self.outputs)
for i in range(nb_inputs):
for i in xrange(nb_inputs):
input = self.inputs[i]
inp_connection_pattern = [i == j for j in range(nb_inputs)]
inp_connection_pattern = [i == j for j in xrange(nb_inputs)]
connect_pattern_by_var[input] = inp_connection_pattern
# Iterate through the nodes used to produce the outputs from the
......@@ -1514,11 +1514,11 @@ class Scan(PureOp):
# For every output of the inner node, figure out which inputs it
# is connected to by combining the connection pattern of the inner
# node and the connection patterns of the inner node's inputs.
for out_idx in range(len(n.outputs)):
for out_idx in xrange(len(n.outputs)):
out = n.outputs[out_idx]
out_connection_pattern = [False] * nb_inputs
for inp_idx in range(len(n.inputs)):
for inp_idx in xrange(len(n.inputs)):
inp = n.inputs[inp_idx]
if inp in connect_pattern_by_var:
......@@ -1530,17 +1530,17 @@ class Scan(PureOp):
if op_connection_pattern[inp_idx][out_idx]:
out_connection_pattern = [out_connection_pattern[i] or
inp_connection_pattern[i]
for i in range(nb_inputs)]
for i in xrange(nb_inputs)]
# Store the connection pattern of the node output
connect_pattern_by_var[out] = out_connection_pattern
# Obtain the global connection pattern by combining the
# connnection patterns of the individual outputs
global_connection_pattern = [[] for o in range(len(self.inputs))]
global_connection_pattern = [[] for o in xrange(len(self.inputs))]
for out in self.outputs:
out_connection_pattern = connect_pattern_by_var[out]
for i in range(len(self.inputs)):
for i in xrange(len(self.inputs)):
global_connection_pattern[i].append(out_connection_pattern[i])
return global_connection_pattern
......@@ -1565,10 +1565,10 @@ class Scan(PureOp):
# over every possible pairing of their corresponding inner inputs
# and inner outputs and, if one such pair of inner variables is
# connected than the pair of outer variables is connected.
for outer_oidx in range(len(node.outputs)):
for outer_oidx in xrange(len(node.outputs)):
inner_oidxs = self.var_mappings['inner_out_from_outer_out'][outer_oidx]
for outer_iidx in range(len(node.inputs)):
for outer_iidx in xrange(len(node.inputs)):
inner_iidxs = self.var_mappings['inner_inp_from_outer_inp'][outer_iidx]
for inner_oidx in inner_oidxs:
......@@ -1617,7 +1617,6 @@ class Scan(PureOp):
indices because multiple inner variables can be associated with the
same state
"""
# Lists for outer variables contain individual indices, lists for
# inner variables contain sequences of indices because many inner
# variables can be associated with the same outer variable. The list
......@@ -1634,7 +1633,7 @@ class Scan(PureOp):
outer_oidx = 0
# Handle sequences inputs
for i in range(self.info['n_seqs']):
for i in xrange(self.info['n_seqs']):
outer_input_indices.append(outer_iidx)
inner_input_indices.append([inner_iidx])
inner_output_indices.append([])
......@@ -1646,7 +1645,7 @@ class Scan(PureOp):
outer_oidx += 0
# Handle mitmots, mitsots and sitsots variables
for i in range(len(self.info['tap_array'])):
for i in xrange(len(self.info['tap_array'])):
nb_input_taps = len(self.info['tap_array'][i])
if i < self.n_mit_mot:
......@@ -1655,10 +1654,10 @@ class Scan(PureOp):
nb_output_taps = 1
outer_input_indices.append(outer_iidx)
inner_input_indices.append(range(inner_iidx,
inner_iidx + nb_input_taps))
inner_output_indices.append(range(inner_oidx,
inner_oidx + nb_output_taps))
inner_input_indices.append(list(range(inner_iidx,
inner_iidx + nb_input_taps)))
inner_output_indices.append(list(range(inner_oidx,
inner_oidx + nb_output_taps)))
outer_output_indices.append(outer_oidx)
outer_iidx += 1
......@@ -1671,7 +1670,7 @@ class Scan(PureOp):
outer_iidx += self.info['n_shared_outs']
# Handle nitsots variables
for i in range(self.n_nit_sot):
for i in xrange(self.n_nit_sot):
outer_input_indices.append(outer_iidx)
inner_input_indices.append([])
inner_output_indices.append([inner_oidx])
......@@ -1687,7 +1686,7 @@ class Scan(PureOp):
outer_iidx -= (self.info['n_shared_outs'] + self.n_nit_sot)
# Handle shared states
for i in range(self.info['n_shared_outs']):
for i in xrange(self.info['n_shared_outs']):
outer_input_indices.append(outer_iidx)
inner_input_indices.append([inner_iidx])
inner_output_indices.append([inner_oidx])
......@@ -1706,7 +1705,7 @@ class Scan(PureOp):
# Note : the number of non-sequence inputs is not stored in self.info
# so it has to be inferred from the number of inner inputs that remain
# to be handled
for i in range(len(self.inputs) - inner_iidx):
for i in xrange(len(self.inputs) - inner_iidx):
outer_input_indices.append(outer_iidx)
inner_input_indices.append([inner_iidx])
inner_output_indices.append([])
......@@ -1732,10 +1731,10 @@ class Scan(PureOp):
"inner_inp_from_inner_out" : {},
"outer_out_from_inner_out" : {}}
for (oinp, iinp, iout, oout) in zip(outer_input_indices,
inner_input_indices,
inner_output_indices,
outer_output_indices):
for (oinp, iinp, iout, oout) in izip(outer_input_indices,
inner_input_indices,
inner_output_indices,
outer_output_indices):
if oout != -1:
mappings["outer_inp_from_outer_out"][oout] = oinp
......
theano/sparse/basic.py
浏览文件 @ 069a9912
......@@ -1840,8 +1840,8 @@ class SquareDiagonal(gof.op.Op):
N = len(diag)
data = diag[:N]
indices = range(N)
indptr = range(N + 1)
indices = list(range(N))
indptr = list(range(N + 1))
tup = (data, indices, indptr)
z[0] = scipy.sparse.csc_matrix(tup, copy=True)
......
theano/tensor/basic.py
浏览文件 @ 069a9912
......@@ -167,9 +167,9 @@ def as_tensor_variable(x, name=None, ndim=None):
else:
if (x.type.ndim > ndim):
# strip off leading broadcastable dimensions
first_non_broadcastable = [idx for idx in range(x.ndim)
first_non_broadcastable = [idx for idx in xrange(x.ndim)
if not x.broadcastable[idx]][0]
x = x.dimshuffle(range(x.ndim)[first_non_broadcastable:])
x = x.dimshuffle(list(range(x.ndim))[first_non_broadcastable:])
if x.ndim > ndim:
raise ValueError(
'TensorType could not be cast to have %i dimensions'
......@@ -1219,13 +1219,13 @@ class MaxAndArgmax(Op):
axis = [int(a) for a in axis]
if len(axis) != 1:
axis = list(axis)
for idx in range(len(axis)):
for idx in xrange(len(axis)):
if axis[idx] < 0:
axis[idx] += x.type.ndim
axis.sort()
if axis == range(-x.type.ndim, 0, 1):
axis = range(x.type.ndim)
assert axis == range(x.type.ndim), (
if axis == list(range(-x.type.ndim, 0, 1)):
axis = list(range(x.type.ndim))
assert axis == list(range(x.type.ndim)), (
"MaxAndArgmax does not support multiple"
" axes. the max fct supports it. Got %s" % axis)
axis = None
......@@ -1262,7 +1262,7 @@ class MaxAndArgmax(Op):
'input is: %s)' % (axis, x.type.ndim))
all_axes.add(axis)
else:
all_axes = range(x.ndim)
all_axes = list(range(x.ndim))
if axis is None:
axis = NoneConst.clone()
else:
......@@ -1408,7 +1408,7 @@ class MaxAndArgmax(Op):
if g_max_disconnected:
return [x.zeros_like(), axis_grad]
if NoneConst.equals(axis):
axis_ = range(x.ndim)
axis_ = list(range(x.ndim))
else:
axis_ = axis
xmax = max(x, axis_)
......@@ -1419,7 +1419,7 @@ class MaxAndArgmax(Op):
if NoneConst.equals(axis):
# We are taking the max/argmax over all dimensions.
axis = None
for i in range(x.ndim):
for i in xrange(x.ndim):
if axis is None or i == axis.data:
pattern.append('x')
else:
......@@ -1449,7 +1449,7 @@ def makeKeepDims(x, y, axis):
y = as_tensor_variable(y)
if axis is None:
axis = range(x.type.ndim)
axis = list(range(x.type.ndim))
elif isinstance(axis, (int, numpy.integer)):
axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
......@@ -2639,7 +2639,7 @@ class Alloc(gof.Op):
gz = grads[0]
n_axes_to_sum = gz.ndim - x.ndim
# The number of dimensions added
axis = range(n_axes_to_sum)
axis = list(range(n_axes_to_sum))
# The broadcasted dimensions
axis_broadcasted = []
axis_kept = []
......@@ -2653,9 +2653,14 @@ class Alloc(gof.Op):
axis_kept.append(i)
gx = gz.sum(axis=axis + axis_broadcasted)
if axis_broadcasted:
new_order = ['x'] * x.ndim
for idx, axis in enumerate(axis_kept):
new_order[axis] = idx
new_order = list(x.broadcastable)
idx = 0
for i in xrange(x.ndim):
if not new_order[i]:
new_order[i] = idx
idx += 1
else:
new_order[i] = 'x'
gx = gx.dimshuffle(new_order)
# Dimshuffle to add back the broadcasted dims
# The *elements* of the output are not connected to
......@@ -2905,7 +2910,7 @@ def mean(input, axis=None, dtype=None, op=False, keepdims=False,
shp = cast(shp, 'float64')
if axis is None:
axis = range(input.ndim)
axis = list(range(input.ndim))
elif isinstance(axis, (int, numpy.integer)):
axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
......@@ -2944,7 +2949,7 @@ def var(input, axis=None, keepdims=False):
input_ndim = input.type.ndim
if axis is None:
axis = range(input_ndim)
axis = list(range(input_ndim))
elif isinstance(axis, (int, numpy.integer)):
axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
......@@ -3189,9 +3194,9 @@ def transpose(x, axes=None):
"""
if axes is None:
axes = range((x.ndim - 1), -1, -1)
axes = list(range((x.ndim - 1), -1, -1))
ret = DimShuffle(x.broadcastable, axes, inplace=False)(x)
if x.name and axes == range((x.ndim - 1), -1, -1):
if x.name and axes == list(range((x.ndim - 1), -1, -1)):
ret.name = x.name + '.T'
return ret
......@@ -3232,7 +3237,7 @@ def batched_tensordot(x, y, axes=2):
the last i dimensions of the first tensor and the first
i dimensions of the second tensor (excluding the first
(batch) dimension):
axes = [range(a.ndim - i, b.ndim), range(1,i+1)]
axes = [list(range(a.ndim - i, b.ndim)), list(range(1,i+1))]
If an array, its two elements must contain compatible axes
of the two tensors. For example, [[1, 2], [2, 4]] means sum
......@@ -3372,10 +3377,10 @@ class Split(Op):
splits = node.inputs[2]
shp_x, shp_axis, shp_splits = in_shapes
out_shapes = []
for i in range(self.len_splits):
for i in xrange(self.len_splits):
temp = as_tensor_variable(shp_x)
temp = theano.tensor.subtensor.set_subtensor(temp[axis], splits[i])
temp = [temp[i] for i in range(len(shp_x))]
temp = [temp[i] for i in xrange(len(shp_x))]
out_shapes.append(temp)
return out_shapes
......@@ -3602,7 +3607,7 @@ class Join(Op):
for x in as_tensor_variable_args:
for current_axis, bflag in enumerate(x.type.broadcastable):
# Constant negative axis can no longer be negative at
# Constant negative axis can no longer be negative at
# this point. It safe to compare this way.
if current_axis == axis:
continue
......@@ -3668,7 +3673,7 @@ class Join(Op):
int axis = ((%(adtype)s *)PyArray_DATA(%(axis)s))[0];
int ndim = PyArray_NDIM(%(input_1)s);
if( axis < -ndim ){
PyErr_Format(PyExc_IndexError,
PyErr_Format(PyExc_IndexError,
"Join axis %%d out of bounds [0, %%d)", axis, ndim);
%(fail)s
}
......@@ -4411,7 +4416,7 @@ class Tile(Op):
shp = in_shapes[0]
tiled_shp = shp * reps
out_shape = []
for i in range(self.ndim):
for i in xrange(self.ndim):
out_shape.append(tiled_shp[i])
return [out_shape]
......@@ -4737,7 +4742,7 @@ class PermuteRowElements(Op):
shp_y = in_shapes[1]
assert len(shp_x) == len(shp_y)
out_shape = []
for i in range(len(shp_x)):
for i in xrange(len(shp_x)):
out_shape.append(maximum(shp_x[i], shp_y[i]))
return [out_shape]
......@@ -5088,7 +5093,7 @@ def tensordot(a, b, axes=2):
If an integer i, it is converted to an array containing
the last i dimensions of the first tensor and the first
i dimensions of the second tensor:
axes = [range(a.ndim - i, b.ndim), range(i)]
axes = [list(range(a.ndim - i, b.ndim)), list(range(i))]
If an array, its two elements must contain compatible axes
of the two tensors. For example, [[1, 2], [2, 0]] means sum
......@@ -5175,13 +5180,13 @@ def tensordot(a, b, axes=2):
outndim = a.ndim + b.ndim - (2 * axes)
a_shape_0 = b_shape_0 = a_shape_1 = b_shape_1 = 1
for s0 in range(a.ndim - axes):
for s0 in xrange(a.ndim - axes):
a_shape_0 *= a.shape[s0]
for s0 in range(axes):
for s0 in xrange(axes):
b_shape_0 *= b.shape[s0]
for s1 in range(a.ndim - axes, a.ndim):
for s1 in xrange(a.ndim - axes, a.ndim):
a_shape_1 *= a.shape[s1]
for s1 in range(axes, b.ndim):
for s1 in xrange(axes, b.ndim):
b_shape_1 *= b.shape[s1]
a_reshaped = a.reshape((a_shape_0, a_shape_1), ndim=2)
......@@ -5459,7 +5464,7 @@ def swapaxes(y, axis1, axis2):
"swap axes of inputted tensor"
y = as_tensor_variable(y)
ndim = y.ndim
li = range(0, ndim)
li = list(range(0, ndim))
li[axis1], li[axis2] = li[axis2], li[axis1]
return y.dimshuffle(li)
......@@ -5552,7 +5557,7 @@ class Choose(Op):
theano.typed_list.TypedListVariable)
raise ShapeError("Case not implemented")
shape = shapes[0]
for i in range(len(shapes[0]) - 1):
for i in xrange(len(shapes[0]) - 1):
shape[i] = shapes[1][i]
return [(shape)]
......
theano/tensor/elemwise.py
浏览文件 @ 069a9912
......@@ -411,7 +411,7 @@ class DimShufflePrinter:
if new_order != () and new_order[0] == 'x':
return "%s" % self.__p(new_order[1:], pstate, r)
# return "[%s]" % self.__p(new_order[1:], pstate, r)
if list(new_order) == range(r.type.ndim):
if list(new_order) == list(range(r.type.ndim)):
return pstate.pprinter.process(r)
if list(new_order) == list(reversed(range(r.type.ndim))):
return "%s.T" % pstate.pprinter.process(r)
......@@ -544,7 +544,7 @@ class Elemwise(OpenMPOp):
# TODO: use LComplete instead
args.append(DimShuffle(
input.type.broadcastable,
['x'] * difference + range(length),
['x'] * difference + list(range(length)),
inplace=False)(input))
inputs = args
......@@ -1006,11 +1006,11 @@ class Elemwise(OpenMPOp):
i += 1 # before this loop, i = number of inputs
sub['lv%i' % i] = oname
sub['olv'] = oname
alloc += cgen.make_declare([range(nnested)], [odtype],
alloc += cgen.make_declare([list(range(nnested))], [odtype],
dict(sub, lv0=oname))
alloc += cgen.make_alloc(orders, odtype, sub,
fortran=alloc_fortran)
alloc += cgen.make_checks([range(nnested)], [odtype],
alloc += cgen.make_checks([list(range(nnested))], [odtype],
dict(sub, lv0=oname))
olv_index = i # index of the last output
......@@ -1058,7 +1058,7 @@ class Elemwise(OpenMPOp):
}
""" % locals()
loop_orders = orders + [range(nnested)] * len(real_onames)
loop_orders = orders + [list(range(nnested))] * len(real_onames)
dtypes = (idtypes + list(real_odtypes))
if all([o.ndim <= 1 for o in node.outputs] or
# Use simpler code when output ndim == 0 or 1
......@@ -1333,8 +1333,8 @@ class CAReduce(Op):
input = as_tensor_variable(input)
axis = self.axis
if axis is None:
axis = range(len(input.type.broadcastable))
if any([a < 0 for a in axis]):
axis = list(range(len(input.type.broadcastable)))
if any(a < 0 for a in axis):
axis2 = []
for a in self.axis:
if a < 0:
......@@ -1388,7 +1388,7 @@ class CAReduce(Op):
output, = out
axis = self.axis
if axis is None:
axis = range(input.ndim)
axis = list(range(input.ndim))
variable = input
to_reduce = reversed(sorted(axis))
......@@ -1470,7 +1470,7 @@ class CAReduce(Op):
axis = self.axis
if axis is None:
axis = range(len(input.type.broadcastable))
axis = list(range(len(input.type.broadcastable)))
if len(axis) == 0:
# The acc_dtype is never a downcast compared to the input dtype
......@@ -1510,11 +1510,11 @@ class CAReduce(Op):
# Allocate output buffer
alloc += cgen.make_declare(
[range(nnested) + ['x'] * len(axis)],
[list(range(nnested)) + ['x'] * len(axis)],
[odtype], dict(sub, lv0=oname))
alloc += cgen.make_alloc([order1], odtype, sub)
alloc += cgen.make_checks(
[range(nnested) + ['x'] * len(axis)],
[list(range(nnested)) + ['x'] * len(axis)],
[odtype], dict(sub, lv0=oname))
if adtype != odtype:
......@@ -1523,11 +1523,11 @@ class CAReduce(Op):
sub['olv'] = aname
alloc += cgen.make_declare(
[range(nnested) + ['x'] * len(axis)],
[list(range(nnested)) + ['x'] * len(axis)],
[adtype], dict(sub, lv0=aname))
alloc += cgen.make_alloc([order1], adtype, sub)
alloc += cgen.make_checks(
[range(nnested) + ['x'] * len(axis)],
[list(range(nnested)) + ['x'] * len(axis)],
[adtype], dict(sub, lv0=aname))
if hasattr(self.scalar_op, 'identity'):
......@@ -1552,7 +1552,7 @@ class CAReduce(Op):
pattern = [0] * len(node.inputs[0].broadcastable)
axis = self.axis
if axis is None:
axis = range(len(pattern))
axis = list(range(len(pattern)))
for i in axis:
pattern[i] = 1
pattern_ = str(pattern)[1:-1]
......@@ -1608,7 +1608,7 @@ for(int i=0;i<PyArray_NDIM(%(iname)s);i++){
else:
all_code = [task0_decl + code1]
loop = cgen.make_loop_careduce(
[order, range(nnested) + ['x'] * len(axis)],
[order, list(range(nnested)) + ['x'] * len(axis)],
[idtype, adtype], all_code, sub)
end = ""
......@@ -1928,7 +1928,7 @@ class Sum(CAReduceDtype):
gz = as_tensor_variable(gz)
axis = self.axis
if axis is None:
axis = range(x.type.ndim)
axis = list(range(x.type.ndim))
if axis == ():
return gz,
new_dims = []
......@@ -2041,7 +2041,7 @@ class Prod(CAReduceDtype):
gz = as_tensor_variable(gz)
axis = self.axis
if axis is None:
axis = range(prod_in.type.ndim)
axis = list(range(prod_in.type.ndim))
if axis == ():
return gz,
new_dims = []
......
theano/tensor/elemwise_cgen.py
浏览文件 @ 069a9912
......@@ -373,7 +373,7 @@ def make_reordered_loop(init_loop_orders, olv_index, dtypes, inner_task, sub, op
var = sub["lv%i" % i]
declare_strides += """
%(ovar)s_loops_rit = %(ovar)s_loops.rbegin();""" % locals()
for j in reversed(range(nnested)):
for j in reversed(xrange(nnested)):
declare_strides += """
int %(var)s_stride_l%(j)i = init_strides[%(i)i][%(ovar)s_loops_rit->second];
++%(ovar)s_loops_rit;
......
theano/tensor/inplace.py
浏览文件 @ 069a9912
......@@ -356,7 +356,7 @@ pprint.assign(pow_inplace, printing.OperatorPrinter('**=', 1, 'right'))
def transpose_inplace(x, **kwargs):
"""Perform a transpose on a tensor without copying the underlying storage"""
dims = range(x.ndim-1, -1, -1)
dims = list(range(x.ndim - 1, -1, -1))
return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)
#pprint.assign(transpose_inplace, printing.MemberPrinter('T'))
......
theano/tensor/nnet/tests/speed_test_conv.py
浏览文件 @ 069a9912
......@@ -3,6 +3,7 @@ import sys, time, unittest
import numpy
import numpy as N
from six.moves import xrange
from theano.tests import unittest_tools as utt
......@@ -17,23 +18,23 @@ def flip(kern, kshp):
if len(kern.shape) == 2:
kern = kern.reshape(-1)
it = reversed(kern)
for i in range(kshp[0]):
for j in range(kshp[1]):
for i in xrange(kshp[0]):
for j in xrange(kshp[1]):
flip[i, j] = next(it)
elif len(kern.shape) == 3:
kern = kern.reshape(kern.shape[0], -1)
for k in range(kern.shape[0]):
for k in xrange(kern.shape[0]):
it = reversed(kern[k, :])
for i in range(kshp[0]):
for j in range(kshp[1]):
for i in xrange(kshp[0]):
for j in xrange(kshp[1]):
flip[k, i, j] = next(it)
elif len(kern.shape) == 4:
kern = kern.reshape(kern.shape[0], kern.shape[1], -1)
for k in range(kern.shape[0]):
for m in range(kern.shape[1]):
for k in xrange(kern.shape[0]):
for m in xrange(kern.shape[1]):
it = reversed(kern[k, m, :])
for i in range(kshp[0]):
for j in range(kshp[1]):
for i in xrange(kshp[0]):
for j in xrange(kshp[1]):
flip[k, m, i, j] = next(it)
else:
raise NotImplementedError()
......@@ -63,7 +64,8 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
tctot = 0
tpytot = 0
for kshp, kern, nkern, n_layer in zip(kshps, kerns, nkerns, range(len(nkerns))):
for kshp, kern, nkern, n_layer in zip(kshps, kerns, nkerns,
xrange(len(nkerns))):
if do_print:
print('************* layer %i ***************' % n_layer)
......@@ -92,9 +94,9 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
from scipy.signal.signaltools import _valfrommode, _bvalfromboundary
val = _valfrommode(conv_mode)
bval = _bvalfromboundary('fill')
for b in range(bsize): # loop over batches
for n in range(nkern): # loop over filters
for i in range(imshp[0]): # loop over input feature maps
for b in xrange(bsize): # loop over batches
for n in xrange(nkern): # loop over filters
for i in xrange(imshp[0]): # loop over input feature maps
outval[b, n, ...] += _convolve2d(\
imgval[b, i, ...], w_flip[n, i, ...], 1, val, bval, 0)[0::ss[0], 0::ss[1]]
ntot += time.time() - time1
......@@ -112,14 +114,14 @@ def exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp, kshps, nkerns,
propup3 = function([inputs4, kerns4], conv_op, mode=Mode(linker="py"))
time1 = time.time()
for i in range(repeat):
for i in xrange(repeat):
hidval2_ = propup2(imgval, w_flip)
hidval2 = hidval2_ # [:,:,0::ss[0],0::ss[1]]
tctot += time.time() - time1
if conv_op_py:
time1 = time.time()
for i in range(repeat):
for i in xrange(repeat):
hidval3_ = propup3(imgval, w_flip)
hidval3 = hidval3_ # [:,:,0::ss[0],0::ss[1]]
tpytot += time.time() - time1
......@@ -158,7 +160,7 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
tctot = 0
tpytot = 0
for kshp, kern, nkern, n_layer in zip(kshps, kerns, nkerns, range(len(nkerns))):
for kshp, kern, nkern, n_layer in zip(kshps, kerns, nkerns, xrange(len(nkerns))):
if do_print:
print('************* layer %i ***************' % n_layer)
......@@ -185,7 +187,7 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns,
propup2 = function([inputs4, kerns4], conv_op)
time1 = time.time()
for i in range(repeat):
for i in xrange(repeat):
hidval2_ = propup2(imgval, w_flip)
hidval2 = hidval2_ # [:,:,0::ss[0],0::ss[1]]
tctot += time.time() - time1
......@@ -231,13 +233,13 @@ def speed_multilayer_conv():
best = []
worst = []
t_ = []
for unroll_b, n_b in zip(unroll_batch, range(len(unroll_batch))):
for unroll_k, n_k in zip(unroll_kern, range(len(unroll_kern))):
for unroll_b, n_b in zip(unroll_batch, xrange(len(unroll_batch))):
for unroll_k, n_k in zip(unroll_kern, xrange(len(unroll_kern))):
t_b_k.append(str(unroll_b)+"/"+str(unroll_k))
if not t_:
tctot, tpytot, ntot = [], [], []
for conv_mode, n_mode in zip(convmodes, range(len(convmodes))):
for ss, n_ss in zip(ssizes, range(len(ssizes))):
for conv_mode, n_mode in zip(convmodes, xrange(len(convmodes))):
for ss, n_ss in zip(ssizes, xrange(len(ssizes))):
# tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=unroll_b, unroll_kern=unroll_k, validate=validate, verbose=verbose,do_print=False)
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=unroll_b, unroll_kern=unroll_k, verbose=verbose, do_print=False, repeat=repeat)
tctot += [tctot_]
......@@ -260,8 +262,8 @@ def speed_multilayer_conv():
tctot, tpytot, ntot = [], [], []
tctot_ = []
if not tctot_:
for conv_mode, n_mode in zip(convmodes, range(len(convmodes))):
for ss, n_ss in zip(ssizes, range(len(ssizes))):
for conv_mode, n_mode in zip(convmodes, xrange(len(convmodes))):
for ss, n_ss in zip(ssizes, xrange(len(ssizes))):
# tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate, verbose=verbose,do_print=False)
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, verbose=verbose, do_print=False, repeat=repeat)
tctot += [tctot_]
......@@ -273,8 +275,8 @@ def speed_multilayer_conv():
worst = N.asarray(worst)
print("timing for unrolled version")
print("unroll_batch/unroll_kern valid_mode full_mode")
for n_b in range(len(unroll_batch)):
for n_k in range(len(unroll_kern)):
for n_b in xrange(len(unroll_batch)):
for n_k in xrange(len(unroll_kern)):
print((unroll_batch[n_b], unroll_kern[n_k]) + tuple(t[n_b, n_k]), ',')
t_detail = t
t = t.sum(axis=2)
......@@ -287,8 +289,8 @@ def speed_multilayer_conv():
print('time unroll_patch')
tctot_patch = []
tctot_patch_size = []
for conv_mode, n_mode in zip(convmodes, range(len(convmodes))):
for ss, n_ss in zip(ssizes, range(len(ssizes))):
for conv_mode, n_mode in zip(convmodes, xrange(len(convmodes))):
for ss, n_ss in zip(ssizes, xrange(len(ssizes))):
#tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet_old(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate,unroll_patch=True,verbose=verbose,do_print=False)
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, unroll_patch=True, verbose=verbose, do_print=False, repeat=repeat)
tctot_patch += [tctot_]
......
theano/tensor/opt.py
浏览文件 @ 069a9912
......@@ -489,8 +489,8 @@ def local_dimshuffle_lift(node):
inplace = op.inplace and inode.op.inplace
iinput = inode.inputs[0]
# remove useless dimshuffle
if new_order == range(len(new_order)) and (len(new_order) ==
iinput.type.ndim):
if (new_order == list(range(len(new_order))) and
len(new_order) == iinput.type.ndim):
return [iinput]
else:
ret = op.__class__(iinput.type.broadcastable, new_order,
......@@ -908,7 +908,7 @@ class ShapeFeature(object):
len(s), r.ndim, sio.getvalue()))
shape_vars = []
for i in range(r.ndim):
for i in xrange(r.ndim):
if (hasattr(r.type, 'broadcastable') and
r.type.broadcastable[i]):
shape_vars.append(self.lscalar_one)
......@@ -921,7 +921,7 @@ class ShapeFeature(object):
self.lscalar_one.equals(shape_vars[i]) or
self.lscalar_one.equals(
T.extract_constant(shape_vars[i]))
for i in range(r.ndim)])
for i in xrange(r.ndim)])
self.shape_of[r] = tuple(shape_vars)
for sv in shape_vars:
self.shape_of_reverse_index.setdefault(sv, set()).add(r)
......@@ -997,7 +997,7 @@ class ShapeFeature(object):
self.lscalar_one.equals(merged_shape[i]) or
self.lscalar_one.equals(
T.extract_constant(merged_shape[i]))
for i in range(r.ndim)])
for i in xrange(r.ndim)])
self.shape_of[r] = tuple(merged_shape)
for sv in self.shape_of[r]:
self.shape_of_reverse_index.setdefault(sv, set()).add(r)
......@@ -1020,7 +1020,7 @@ class ShapeFeature(object):
# But we never timed this speed optimization!
self.lscalar_one.equals(new_shape[idx]) or
self.lscalar_one.equals(T.extract_constant(new_shape[idx]))
for idx in range(r.ndim)])
for idx in xrange(r.ndim)])
self.shape_of[r] = tuple(new_shape)
for sv in self.shape_of[r]:
self.shape_of_reverse_index.setdefault(sv, set()).add(r)
......@@ -1826,7 +1826,7 @@ def local_elemwise_alloc_op(ElemwiseOP, AllocOP, DimShuffleOP):
nb_dim_to_add = i.owner.inputs[0].ndim - alloc_input.ndim
alloc_input = alloc_input.dimshuffle(
['x'] * nb_dim_to_add +
range(alloc_input.ndim))
list(range(alloc_input.ndim)))
# We need to keep the dimshuffle. It could swap axes or
# add dimensions anywhere.
......@@ -2990,7 +2990,7 @@ def local_join_empty(node):
join_idx = get_scalar_constant_value(node.inputs[0])
except NotScalarConstantError:
return
for idx in range(1, len(node.inputs)):
for idx in xrange(1, len(node.inputs)):
inp = node.inputs[idx]
# We can not use size == 0,, as this can change shape from 3,0
# to 2,0. This trigger DebugMode error. This happen with
......@@ -3029,7 +3029,7 @@ def local_join_make_vector(node):
if not isinstance(node.op, T.Join) or node.outputs[0].ndim != 1:
return
new_inputs = [node.inputs[1]]
for idx in range(2, len(node.inputs)):
for idx in xrange(2, len(node.inputs)):
inp = node.inputs[idx]
if (inp.owner and
isinstance(inp.owner.op, MakeVector) and
......@@ -3202,7 +3202,7 @@ def local_useless_tile(node):
# implement the opt and test it.
return
x_nd = node.inputs[0].ndim
broad = ['x'] * (l - x_nd) + range(x_nd)
broad = ['x'] * (l - x_nd) + xrange(x_nd)
return [node.inputs[0].dimshuffle(broad)]
except ValueError:
return
......@@ -3948,7 +3948,7 @@ def local_sum_div_dimshuffle(node):
if isinstance(node.op, T.Sum):
axis = node.op.axis
if axis is None:
axis = range(node.inputs[0].ndim)
axis = list(range(node.inputs[0].ndim))
# print 'axis =', axis
thing_summed = node.inputs[0]
if thing_summed.owner and thing_summed.owner.op == T.true_div:
......@@ -4044,7 +4044,7 @@ def local_sum_prod_all_to_none(node):
def local_op_of_op(node):
"""
Prod(Prod()) -> single Prod()
or
or
Sum(Sum()) -> single Sum()
"""
if isinstance(node.op, T.elemwise.Prod) or isinstance(node.op, T.Sum):
......@@ -4055,13 +4055,13 @@ def local_op_of_op(node):
# doesn't affect other computations.
if len(node_inps.clients) == 1:
if (node_inps.owner and (isinstance(node_inps.owner.op, T.elemwise.Prod)
or isinstance(node_inps.owner.op, T.elemwise.Sum))):
or isinstance(node_inps.owner.op, T.elemwise.Sum))):
# check to see either the inner or outer prod is doing a
# check to see either the inner or outer prod is doing a
# product over all axis, in which case we can remove it
if node_inps.owner.op.axis is None or node.op.axis is None:
return [opt_type(None, dtype=out_dtype)(
node_inps.owner.inputs[0])]
node_inps.owner.inputs[0])]
# figure out which axes were in the original sum
newaxis = list(tuple(node_inps.owner.op.axis))
......@@ -4076,10 +4076,10 @@ def local_op_of_op(node):
assert len(newaxis) == len(list(node_inps.owner.op.axis) +
list(node.op.axis))
# The old bugged logic. We keep it there to generate a warning
# when we generated bad code.
alldims = range(node_inps.owner.inputs[0].type.ndim)
alldims = list(range(node_inps.owner.inputs[0].type.ndim))
alldims = [d for i, d in enumerate(alldims) if i
in node_inps.owner.op.axis]
alldims = [d for i, d in enumerate(alldims)
......@@ -4227,7 +4227,7 @@ def local_reduce_broadcastable(node):
new_axis = []
pattern = []
ii = 0
for p in range(reduced.ndim):
for p in xrange(reduced.ndim):
if p not in cuttable:
if p in axis:
new_axis.append(ii)
......@@ -4251,7 +4251,7 @@ def local_reduce_broadcastable(node):
@gof.local_optimizer([T.Sum, T.elemwise.Prod])
def local_opt_alloc(node):
""" sum(alloc(constant,shapes...)) => constant*prod(shapes)
or
or
prod(alloc(constant,shapes...)) => constant**prod(shapes)
"""
if isinstance(node.op, T.Sum) or isinstance(node.op, T.elemwise.Prod):
......
theano/tensor/raw_random.py
浏览文件 @ 069a9912
......@@ -418,7 +418,7 @@ def _generate_broadcasting_indices(out_shape, *shapes):
# Temporary list to generate the indices
_ret_indices = [[] for shape in all_shapes]
out_range = range(out_shape[dim])
out_range = list(range(out_shape[dim]))
# Verify the shapes are compatible along that dimension
# and generate the appropriate range: out_range, or [0, ..., 0]
......
theano/tensor/slinalg.py
浏览文件 @ 069a9912
......@@ -332,14 +332,14 @@ def kron(a, b):
o = tensor.outer(a, b)
o = o.reshape(tensor.concatenate((a.shape, b.shape)),
a.ndim + b.ndim)
shf = o.dimshuffle(0, 2, 1, * range(3, o.ndim))
shf = o.dimshuffle(0, 2, 1, * list(range(3, o.ndim)))
if shf.ndim == 3:
shf = o.dimshuffle(1, 0, 2)
o = shf.flatten()
else:
o = shf.reshape((o.shape[0] * o.shape[2],
o.shape[1] * o.shape[3]) +
tuple([o.shape[i] for i in range(4, o.ndim)]))
tuple(o.shape[i] for i in xrange(4, o.ndim)))
return o
......
theano/tensor/subtensor.py
浏览文件 @ 069a9912
......@@ -7,6 +7,7 @@ import logging
_logger = logging.getLogger("theano.tensor.subtensor")
import numpy
from six.moves import xrange
import theano
from theano.compat import izip
......@@ -1034,7 +1035,7 @@ def inc_subtensor(x, y, inplace=False, set_instead_of_inc=False,
y.ndim))
dim_offset = x.ndim - y.ndim
for dim in range(y.ndim):
for dim in xrange(y.ndim):
if (x.broadcastable[dim + dim_offset]
and not y.broadcastable[dim]):
# It is acceptable to try to increment a subtensor with a
......@@ -1132,7 +1133,7 @@ def inc_subtensor(x, y, inplace=False, set_instead_of_inc=False,
if y.ndim > 0:
# This if is needed to prevent some useless warning about
# old code bug.
expanded_y = alloc(y, *[x.shape[i] for i in range(x.ndim)])
expanded_y = alloc(y, *[x.shape[i] for i in xrange(x.ndim)])
flattened_y = expanded_y.flatten(inner_x.ndim)
else:
flattened_y = y
......@@ -1570,7 +1571,7 @@ def _sum_grad_over_bcasted_dims(x, gx):
x_broad = (True,) * x_dim_added + x.broadcastable
assert sum(gx.broadcastable) < sum(x_broad)
axis_to_sum = []
for i in range(gx.ndim):
for i in xrange(gx.ndim):
if gx.broadcastable[i] is False and x_broad[i] is True:
axis_to_sum.append(i)
elif (gx.broadcastable[i] is True and
......@@ -1584,9 +1585,9 @@ def _sum_grad_over_bcasted_dims(x, gx):
gx = gx.sum(axis=axis_to_sum, keepdims=True)
if gx.ndim != x.ndim:
assert gx.ndim > x.ndim
for i in range(x_dim_added):
for i in xrange(x_dim_added):
assert gx.broadcastable[i]
gx = gx.dimshuffle(*range(x_dim_added, gx.ndim))
gx = gx.dimshuffle(*list(range(x_dim_added, gx.ndim)))
assert gx.broadcastable == x.broadcastable
return gx
......@@ -2293,7 +2294,7 @@ def take(a, indices, axis=None, mode='raise'):
if axis < 0:
axis += a.ndim
assert axis >= 0
shuffle = range(a.ndim)
shuffle = list(range(a.ndim))
shuffle[0] = axis
shuffle[axis] = 0
return advanced_subtensor1(
......
theano/tensor/tests/test_elemwise.py
浏览文件 @ 069a9912
......@@ -7,6 +7,8 @@ from nose.plugins.skip import SkipTest
from nose.plugins.attrib import attr
from nose.tools import raises
from six.moves import xrange
import theano
from theano.compat import imap
from theano import gof, scalar, config
......@@ -348,7 +350,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
e = as_tensor_variable(tensor_op(x, axis=tosum, **d))
if tosum is None:
tosum = range(len(xsh))
tosum = list(range(len(xsh)))
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
......@@ -462,7 +464,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
else:
e = tensor_op(x, axis=tosum)
if tosum is None:
tosum = range(len(xsh))
tosum = list(range(len(xsh)))
f = copy(linker).accept(FunctionGraph([x],
[e.shape])).make_function()
if not(scalar_op in [scalar.maximum, scalar.minimum] and
......@@ -546,7 +548,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
if pre_scalar_op is not None:
x = pre_scalar_op(x)
if tosum is None:
tosum = range(len(xsh))
tosum = list(range(len(xsh)))
xv = numpy.asarray(numpy.random.rand(*xsh), dtype=dtype)
d = {}
if pre_scalar_op is not None:
......@@ -1169,7 +1171,7 @@ class TestElemwise(unittest_tools.InferShapeTester):
s = a + b + c + d + e + f
g = theano.function([a, b, c, d, e, f], s,
mode=theano.compile.Mode(linker='py'))
g(*[numpy.zeros(2 ** 11, config.floatX) for i in range(6)])
g(*[numpy.zeros(2 ** 11, config.floatX) for i in xrange(6)])
def test_gt_grad():
......
theano/tensor/tests/test_keepdims.py
浏览文件 @ 069a9912
......@@ -13,7 +13,7 @@ class TestKeepDims(unittest.TestCase):
def makeKeepDims_local(self, x, y, axis):
if axis is None:
newaxis = range(x.ndim)
newaxis = list(range(x.ndim))
elif isinstance(axis, int):
if axis < 0:
newaxis = [axis + x.type.ndim]
......
theano/tests/run_tests_in_batch.py
浏览文件 @ 069a9912
......@@ -178,7 +178,8 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
for test_id in xrange(1, n_tests + 1, batch_size):
stdout.flush()
stderr.flush()
test_range = range(test_id, min(test_id + batch_size, n_tests + 1))
test_range = list(range(test_id,
min(test_id + batch_size, n_tests + 1)))
cmd = ([python, theano_nose, '--with-id'] +
list(map(str, test_range)) +
argv)
......
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