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pytensor
提交 e8d45dc8 authored 作者: Frederic's avatar Frederic
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Move numpy_scalar outside of the function that use it.

Indent what is inside. I'll use that in the next commit. This will make the change more visible.
上级 4a77221b
隐藏空白字符变更
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正在显示 包含 157 行增加 和 157 行删除
theano/tensor/basic.py
浏览文件 @ e8d45dc8
......@@ -508,6 +508,24 @@ class EmptyConstantError(NotScalarConstantError):
"""
def numpy_scalar(data):
""" Return a scalar stored in a numpy ndarray, or raise
NotScalarConstantError if the numpy ndarray is not a scalar
"""
# handle case where data is numpy.array([])
if data.ndim > 0 and (len(data.shape) == 0 or
__builtins__['max'](data.shape) == 0):
assert numpy.all(numpy.array([]) == data)
raise EmptyConstantError()
try:
numpy.complex(data) # works for all numeric scalars
return data
except Exception:
raise NotScalarConstantError(
'v.data is non-numeric, non-scalar, or has more than one'
' unique value', data)
get_scalar_constant_value_elemwises = (
scal.Cast, scal.Switch,
scal.NEQ, scal.EQ,
......@@ -526,166 +544,148 @@ def get_scalar_constant_value(v):
:note: There may be another function similar to this one in the
code, but I'm not sure where it is.
"""
if True:
if v is None:
# None is not a scalar (and many uses of this function seem to depend
# on passing it None)
raise NotScalarConstantError()
if v is None:
# None is not a scalar (and many uses of this function seem to depend
# on passing it None)
raise NotScalarConstantError()
if isinstance(v, (numpy.integer, int, float)):
return numpy.asarray(v)
if isinstance(v, (numpy.integer, int, float)):
return numpy.asarray(v)
def numpy_scalar(data):
""" Return a scalar stored in a numpy ndarray, or raise
NotScalarConstantError if the numpy ndarray is not a scalar
"""
if isinstance(v, numpy.ndarray):
return numpy_scalar(v)
# handle case where data is numpy.array([])
if data.ndim > 0 and (len(data.shape) == 0 or
__builtins__['max'](data.shape) == 0):
assert numpy.all(numpy.array([]) == data)
raise EmptyConstantError()
try:
numpy.complex(data) # works for all numeric scalars
return data
except Exception:
raise NotScalarConstantError(
'v.data is non-numeric, non-scalar, or has more than one'
' unique value', data)
if isinstance(v, numpy.ndarray):
return numpy_scalar(v)
if isinstance(v, Constant):
if getattr(v.tag, 'unique_value', None) is not None:
data = v.tag.unique_value
else:
data = v.data
return numpy_scalar(data)
if getattr(v, 'owner', None):
if isinstance(v.owner.op, (Alloc, DimShuffle, Rebroadcast,
compile.ops.OutputGuard,
compile.DeepCopyOp)):
return get_scalar_constant_value(v.owner.inputs[0])
elif (isinstance(v.owner.op, theano.compile.ops.Shape_i) and
isinstance(v.owner.inputs[0], Constant)):
return v.owner.inputs[0].data.shape[v.owner.op.i]
# Don't act as the constant_folding optimization here as this
# fct is used too early in the optimization phase. This would
# mess with the stabilization optimization and be too slow.
# We put all the scalar Ops used by get_canonical_form_slice()
# to allow it to determine the broadcast pattern correctly.
elif isinstance(v.owner.op, scal.ScalarOp):
if isinstance(v.owner.op, scal.Second):
# We don't need both input to be constant for second
shape, val = v.owner.inputs
return get_scalar_constant_value(val)
if isinstance(v.owner.op, get_scalar_constant_value_elemwises):
const = [get_scalar_constant_value(i)
for i in v.owner.inputs]
ret = [[None]]
v.owner.op.perform(v.owner, const, ret)
return ret[0][0]
elif isinstance(v.owner.op, Elemwise):
if isinstance(v.owner.op.scalar_op, scal.Second):
# We don't need both input to be constant for second
shape, val = v.owner.inputs
return get_scalar_constant_value(val)
elif isinstance(v.owner.op.scalar_op,
get_scalar_constant_value_elemwises):
const = [get_scalar_constant_value(i) for i in v.owner.inputs]
ret = [[None]]
v.owner.op.perform(v.owner, const, ret)
return ret[0][0]
elif isinstance(v.owner.op, theano.tensor.subtensor.Subtensor) and v.ndim == 0:
if isinstance(v.owner.inputs[0], TensorConstant):
cdata = tuple(v.owner.op.get_constant_idx(v.owner.inputs))
try:
return v.owner.inputs[0].data.__getitem__(cdata)
except IndexError:
raise IndexError(
str(tuple(v.owner.op.idx_list)) +
" is not a valid index into " +
str(v.owner.inputs[0].data))
# The index list 'idx_list' should have length the same
# shape as the input.
# TODO: implement the case where we take a scalar in a matrix
assert len(v.owner.op.idx_list) == v.owner.inputs[0].ndim
# Needed to make better graph in this test in theano/tensor/tests:
# test_sharedvar.py:test_shared_options.test_specify_shape_partial
if (v.owner.inputs[0].owner and
isinstance(v.owner.inputs[0].owner.op, Join) and
# Ensure the Join is joining only scalar variables (so that
# the constant value can be found at the same index as the one
# used in the sub-tensor).
python_all(var.ndim == 0 for var in
v.owner.inputs[0].owner.inputs) and
len(v.owner.op.idx_list) == 1):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(v.owner.inputs[1])
# Note the '+ 1' is because the first argument to Join is the
# axis.
ret = v.owner.inputs[0].owner.inputs[idx + 1]
ret = get_scalar_constant_value(ret)
# join can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
elif (v.owner.inputs[0].owner and
isinstance(v.owner.inputs[0].owner.op,
theano.tensor.opt.MakeVector) and
# MakeVector normally accept only scalar as input.
# We put this check in case there is change in the future
python_all(var.ndim == 0 for var in
v.owner.inputs[0].owner.inputs) and
len(v.owner.op.idx_list) == 1):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(v.owner.inputs[1])
# Python 2.4 does not support indexing with numpy.integer
# So we cast it.
idx = int(idx)
ret = v.owner.inputs[0].owner.inputs[idx]
ret = get_scalar_constant_value(ret)
# MakeVector can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
# This is needed when we take the grad as the Shape op
# are not already changed into MakeVector
owner = v.owner
leftmost_parent = owner.inputs[0]
if (leftmost_parent.owner and
isinstance(leftmost_parent.owner.op,
theano.tensor.Shape)):
op = owner.op
idx_list = op.idx_list
idx = idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(owner.inputs[1])
grandparent = leftmost_parent.owner.inputs[0]
gp_broadcastable = grandparent.type.broadcastable
ndim = grandparent.type.ndim
assert ndim == len(gp_broadcastable)
if not (idx < len(gp_broadcastable)):
msg = ("get_scalar_constant_value detected " +
"deterministic IndexError: x.shape[%d] " +
"when x.ndim=%d.") % (ndim, idx)
if config.exception_verbosity == 'high':
msg += 'x=%s' % min_informative_str(v)
else:
msg += 'x=%s' % str(v)
raise ValueError(msg)
if gp_broadcastable[idx]:
return numpy.asarray(1)
raise NotScalarConstantError(v)
if isinstance(v, Constant):
if getattr(v.tag, 'unique_value', None) is not None:
data = v.tag.unique_value
else:
data = v.data
return numpy_scalar(data)
if getattr(v, 'owner', None):
if isinstance(v.owner.op, (Alloc, DimShuffle, Rebroadcast,
compile.ops.OutputGuard,
compile.DeepCopyOp)):
return get_scalar_constant_value(v.owner.inputs[0])
elif isinstance(v.owner.op, theano.compile.ops.Shape_i):
if isinstance(v.owner.inputs[0], Constant):
return v.owner.inputs[0].data.shape[v.owner.op.i]
# Don't act as the constant_folding optimization here as this
# fct is used too early in the optimization phase. This would
# mess with the stabilization optimization and be too slow.
# We put all the scalar Ops used by get_canonical_form_slice()
# to allow it to determine the broadcast pattern correctly.
elif isinstance(v.owner.op, scal.ScalarOp):
if isinstance(v.owner.op, scal.Second):
# We don't need both input to be constant for second
shape, val = v.owner.inputs
return get_scalar_constant_value(val)
if isinstance(v.owner.op, get_scalar_constant_value_elemwises):
const = [get_scalar_constant_value(i)
for i in v.owner.inputs]
ret = [[None]]
v.owner.op.perform(v.owner, const, ret)
return ret[0][0]
elif isinstance(v.owner.op, Elemwise):
if isinstance(v.owner.op.scalar_op, scal.Second):
# We don't need both input to be constant for second
shape, val = v.owner.inputs
return get_scalar_constant_value(val)
elif isinstance(v.owner.op.scalar_op,
get_scalar_constant_value_elemwises):
const = [get_scalar_constant_value(i) for i in v.owner.inputs]
ret = [[None]]
v.owner.op.perform(v.owner, const, ret)
return ret[0][0]
elif isinstance(v.owner.op, theano.tensor.subtensor.Subtensor) and v.ndim == 0:
if isinstance(v.owner.inputs[0], TensorConstant):
cdata = tuple(v.owner.op.get_constant_idx(v.owner.inputs))
try:
return v.owner.inputs[0].data.__getitem__(cdata)
except IndexError:
raise IndexError(
str(tuple(v.owner.op.idx_list)) +
" is not a valid index into " +
str(v.owner.inputs[0].data))
# The index list 'idx_list' should have length the same
# shape as the input.
# TODO: implement the case where we take a scalar in a matrix
assert len(v.owner.op.idx_list) == v.owner.inputs[0].ndim
# Needed to make better graph in this test in theano/tensor/tests:
# test_sharedvar.py:test_shared_options.test_specify_shape_partial
if (v.owner.inputs[0].owner and
isinstance(v.owner.inputs[0].owner.op, Join) and
# Ensure the Join is joining only scalar variables (so that
# the constant value can be found at the same index as the one
# used in the sub-tensor).
python_all(var.ndim == 0 for var in
v.owner.inputs[0].owner.inputs) and
len(v.owner.op.idx_list) == 1):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(v.owner.inputs[1])
# Note the '+ 1' is because the first argument to Join is the
# axis.
ret = v.owner.inputs[0].owner.inputs[idx + 1]
ret = get_scalar_constant_value(ret)
# join can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
elif (v.owner.inputs[0].owner and
isinstance(v.owner.inputs[0].owner.op,
theano.tensor.opt.MakeVector) and
# MakeVector normally accept only scalar as input.
# We put this check in case there is change in the future
python_all(var.ndim == 0 for var in
v.owner.inputs[0].owner.inputs) and
len(v.owner.op.idx_list) == 1):
idx = v.owner.op.idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(v.owner.inputs[1])
# Python 2.4 does not support indexing with numpy.integer
# So we cast it.
idx = int(idx)
ret = v.owner.inputs[0].owner.inputs[idx]
ret = get_scalar_constant_value(ret)
# MakeVector can cast implicitly its input in some case.
return theano._asarray(ret, dtype=v.type.dtype)
# This is needed when we take the grad as the Shape op
# are not already changed into MakeVector
owner = v.owner
leftmost_parent = owner.inputs[0]
if (leftmost_parent.owner and
isinstance(leftmost_parent.owner.op,
theano.tensor.Shape)):
op = owner.op
idx_list = op.idx_list
idx = idx_list[0]
if isinstance(idx, gof.Type):
idx = get_scalar_constant_value(owner.inputs[1])
grandparent = leftmost_parent.owner.inputs[0]
gp_broadcastable = grandparent.type.broadcastable
ndim = grandparent.type.ndim
assert ndim == len(gp_broadcastable)
if not (idx < len(gp_broadcastable)):
msg = ("get_scalar_constant_value detected " +
"deterministic IndexError: x.shape[%d] " +
"when x.ndim=%d.") % (ndim, idx)
if config.exception_verbosity == 'high':
msg += 'x=%s' % min_informative_str(v)
else:
msg += 'x=%s' % str(v)
raise ValueError(msg)
if gp_broadcastable[idx]:
return numpy.asarray(1)
raise NotScalarConstantError(v)
# Easy constructors
......
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