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提交 2f004fac authored 作者: Harm de Vries's avatar Harm de Vries
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theano/tensor/basic.py
浏览文件 @ 2f004fac
......@@ -109,7 +109,8 @@ if 0:
# - JB 20100226
def as_cuda_or_tensor_variable(x, name=None, ndim=None):
"""
Do the same as_tensor_variable, but do not transfer the value on the gpu.
Do the same as_tensor_variable,
but do not transfer the value on the gpu.
"""
if hasattr(x, '_as_CudaNdarrayVariable'):
# TODO: pass name and ndim arguments
......@@ -516,7 +517,8 @@ def _allclose(a, b, rtol=None, atol=None):
if atol is not None:
atol_ = atol
# Work around bug in Numpy, see http://projects.scipy.org/numpy/ticket/1684
# Work around bug in Numpy, see
# http://projects.scipy.org/numpy/ticket/1684
if str(b.dtype) in int_dtypes and (numpy.absolute(b) < 0).any():
b = theano._asarray(b, dtype='float64')
......@@ -1289,18 +1291,12 @@ class MaxAndArgmax(Op):
if isinstance(axis, (tuple, list, numpy.ndarray)):
# List of axes: make them non-negative, and sort them
axis = [int(a) for a in axis]
#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)
if axis == list(range(x.type.ndim)):
axis = None
elif isinstance(axis, (int, numpy.integer)):
axis = [int(axis)]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
axis = [int(axis)]
axis = [int(axis)]
elif isinstance(axis, Variable):
if NoneConst.equals(axis):
axis = None
......@@ -1311,18 +1307,19 @@ class MaxAndArgmax(Op):
assert (axis.dtype.startswith("int") or
axis.dtype.startswith("uint"))
if isinstance(axis.data, (int, numpy.integer)) or \
(isinstance(axis.data, numpy.ndarray) and axis.data.ndim == 0):
(isinstance(axis.data, numpy.ndarray) and
axis.data.ndim == 0):
axis = [int(axis.data)]
elif isinstance(axis.data, (list, numpy.ndarray)):
axis = [int(i) for i in axis.data]
# Make axis entries non-negative, and sort them
if isinstance(axis, list):
for idx in xrange(len(axis)):
if axis[idx] < 0:
axis[idx] += x.type.ndim
axis.sort()
# Verify that axes are valid
all_axes = []
if isinstance(axis, list):
......@@ -1335,7 +1332,7 @@ class MaxAndArgmax(Op):
all_axes.append(ax)
else:
all_axes = list(range(x.ndim))
if axis is None or axis == list(range(x.type.ndim)):
axis = NoneConst.clone()
else:
......@@ -1355,8 +1352,8 @@ class MaxAndArgmax(Op):
x, axes = inp
max, max_idx = outs
if axes is None:
axes = tuple(range(x.ndim))
else:
axes = tuple(range(x.ndim))
else:
axes = tuple(axes)
max[0] = theano._asarray(numpy.max(x, axes),
dtype=node.outputs[0].dtype)
......@@ -1365,10 +1362,12 @@ class MaxAndArgmax(Op):
keep_axes = numpy.array([i for i in range(x.ndim) if i not in axes])
# Not-reduced axes in front
transposed_x = numpy.transpose(x, numpy.concatenate((keep_axes, axes)))
reshaped_x = transposed_x.reshape(transposed_x.shape[:len(keep_axes)] + (-1,))
max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1), dtype='int64')
reshaped_x = transposed_x.reshape(
transposed_x.shape[:len(keep_axes)] + (-1,))
max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1),
dtype='int64')
def c_code(self, node, name, inp, out, sub):
x, axis = inp
max, argmax = out
......@@ -1378,11 +1377,13 @@ class MaxAndArgmax(Op):
else:
assert node.inputs[1].ndim == 1
# Fall back to perform() if there are multiple axes
if len(node.inputs[1].data) > 1: raise NotImplementedError()
if len(node.inputs[1].data) > 1:
raise NotImplementedError()
axis_code = """
axis = ((dtype_%(axis)s*)PyArray_DATA(%(axis)s))[0];
if(axis > PyArray_NDIM(%(x)s)-1 || axis < -PyArray_NDIM(%(x)s)){
PyErr_SetString(PyExc_ValueError, "MaxAndArgmax, bad axis argument");
PyErr_SetString(PyExc_ValueError,
"MaxAndArgmax, bad axis argument");
%(fail)s
}
""" % locals()
......@@ -1439,7 +1440,7 @@ class MaxAndArgmax(Op):
rval = tuple([ishape[i] for (i, b) in enumerate(
node.inputs[0].type.broadcastable) if i not in axis.data])
return [rval, rval]
def R_op(self, inputs, eval_points):
if eval_points[0] is None:
return [None, None]
......
theano/tensor/tests/test_basic.py
浏览文件 @ 2f004fac
......@@ -2641,7 +2641,7 @@ def _approx_eq(a, b, eps=1.0e-4):
if _approx_eq.debug:
print(a, b)
return False
return True
return True
_approx_eq.debug = 0
......@@ -2799,10 +2799,10 @@ class T_max_and_argmax(unittest.TestCase):
def test2(self):
data = rand(2, 3)
n = as_tensor_variable(data)
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1], None), ([1, 0], None),
(NoneConst.clone(), None),
(constant(0), 0)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1], None), ([1, 0], None),
(NoneConst.clone(), None),
(constant(0), 0)]:
v, i = eval_outputs(max_and_argmax(n, axis))
assert i.dtype == 'int64'
self.assertTrue(numpy.all(v == numpy.max(data, np_axis)))
......@@ -2860,8 +2860,8 @@ class T_max_and_argmax(unittest.TestCase):
def test3(self):
data = rand(2, 3, 4)
n = as_tensor_variable(data)
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1, 2], None), ([1, 2, 0], None)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1, 2], None), ([1, 2, 0], None)]:
v, i = eval_outputs(max_and_argmax(n, axis))
assert i.dtype == 'int64'
self.assertTrue(numpy.all(v == numpy.max(data, np_axis)))
......@@ -2922,8 +2922,8 @@ class T_max_and_argmax(unittest.TestCase):
z[argmax] += 1
else:
for id, v in enumerate(argmax):
z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1])
+ id] += 1
z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1]) +
id] += 1
z = z.reshape(data.shape)
assert numpy.all(max_grad_data == z)
......@@ -2931,11 +2931,11 @@ class T_max_and_argmax(unittest.TestCase):
for axis in (-1, 0, 1, None):
for j in xrange(2):
safe_verify_grad(lambda v: max_and_argmax(v, axis=axis)[j],
[data])
[data])
if axis != 1:
safe_verify_grad(lambda v: max_and_argmax(v.flatten(),
axis=axis)[j],
[data])
axis=axis)[j],
[data])
if axis in (0, None):
check_grad_max(data, eval_outputs(grad(
max_and_argmax(n, axis=axis)[0].sum(), n)), axis=axis)
......@@ -2951,11 +2951,11 @@ class T_max_and_argmax(unittest.TestCase):
# Test 4d inner dimensions
data = rand(2, 3, 4, 5)
for i in [0, 1, 2, 3]:
safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
# Test grad with multiple axes
for i in [[0, 1], [0, 0]]:
safe_verify_grad(lambda v: max_and_argmax(v, axis=i)[0], [data])
......@@ -2968,17 +2968,17 @@ class T_max_and_argmax(unittest.TestCase):
x = tensor.matrix().dimshuffle('x', 0, 'x', 1, 'x')
y = x.max(axis=1)
assert y.type.broadcastable == (True, True, False, True)
def test_multiple_axes(self):
data = numpy.arange(24).reshape(3, 2, 4)
x = as_tensor_variable(data)
v, i = eval_outputs(max_and_argmax(x, [1, -1]))
assert numpy.all(v == numpy.array([7, 15, 23]))
assert numpy.all(i == numpy.array([7, 7, 7]))
v = eval_outputs(max_and_argmax(x, [1, -1])[0].shape)
assert tuple(v) == numpy.max(data, (1, -1)).shape
class T_argmin_argmax(unittest.TestCase):
def setUp(self):
......
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