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提交 5f13b082 authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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removed huge comment block at the end of scalar.py

上级 0663d6b3
隐藏空白字符变更
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scalar.py
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...@@ -805,644 +805,3 @@ class Composite(ScalarOp): ...@@ -805,644 +805,3 @@ class Composite(ScalarOp):
**sub) **sub)
d['name'] = name d['name'] = name
return self._c_code % d return self._c_code % d
# def as_scalar(x, name = None):
# if isinstance(x, gof.Op):
# if len(x.outputs) != 1:
# raise ValueError("It is ambiguous which output of a multi-output Op has to be fetched.", x)
# else:
# x = x.outputs[0]
# if isinstance(x, float):
# s = Scalar('float64', name = name)
# s.data = x
# return s
# if isinstance(x, int):
# s = Scalar('int32', name = name)
# s.data = x
# return s
# if isinstance(x, Scalar):
# return x
# raise TypeError("Cannot convert %s to Scalar" % x)
# def constant(x):
# res = as_scalar(x)
# res.constant = True
# return res
# class Scalar(Result):
# def __init__(self, dtype, name = None):
# Result.__init__(self, role = None, name = name)
# self.dtype = dtype
# self.dtype_specs()
# def __get_constant(self):
# if not hasattr(self, '_constant'):
# return False
# return self._constant
# def __set_constant(self, value):
# if value:
# self.indestructible = True
# self._constant = value
# constant = property(__get_constant, __set_constant)
# def desc(self):
# return (self.dtype, self.data)
# def filter(self, data):
# py_type = self.dtype_specs()[0]
# return py_type(data)
# def same_properties(self, other):
# return other.dtype == self.dtype
# def dtype_specs(self):
# try:
# return {'float32': (numpy.float32, 'npy_float32', 'PyFloat_Check', 'PyFloat_AsDouble', 'PyFloat_FromDouble'),
# 'float64': (numpy.float64, 'npy_float64', 'PyFloat_Check', 'PyFloat_AsDouble', 'PyFloat_FromDouble'),
# 'complex128': (numpy.complex128, 'theano_complex128', 'PyComplex_Check', 'PyComplex_AsCComplex', 'PyComplex_FromCComplex'),
# 'complex64': (numpy.complex64, 'theano_complex64', None, None, None),
# 'int8': (numpy.int8, 'npy_int8', 'PyInt_Check', 'PyInt_AsLong', 'PyInt_FromLong'),
# 'int16': (numpy.int16, 'npy_int16', 'PyInt_Check', 'PyInt_AsLong', 'PyInt_FromLong'),
# 'int32': (numpy.int32, 'npy_int32', 'PyInt_Check', 'PyInt_AsLong', 'PyInt_FromLong'),
# 'int64': (numpy.int64, 'npy_int64', 'PyInt_Check', 'PyInt_AsLong', 'PyInt_FromLong')
# }[self.dtype]
# except KeyError:
# raise TypeError("Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype))
# def c_literal(self):
# if 'complex' in self.dtype:
# raise NotImplementedError("No literal for complex values.")
# return str(self.data)
# def c_declare(self, name, sub):
# return """
# %(dtype)s %(name)s;
# typedef %(dtype)s %(name)s_dtype;
# """ % dict(name = name, dtype = self.dtype_specs()[1])
# def c_init(self, name, sub):
# return """
# %(name)s = 0;
# """ % locals()
# def c_extract(self, name, sub):
# specs = self.dtype_specs()
# return """
# if (!%(check)s(py_%(name)s))
# %(fail)s
# %(name)s = (%(dtype)s)%(conv)s(py_%(name)s);
# """ % dict(sub,
# name = name,
# dtype = specs[1],
# check = specs[2],
# conv = specs[3])
# def c_sync(self, name, sub):
# specs = self.dtype_specs()
# return """
# Py_XDECREF(py_%(name)s);
# py_%(name)s = %(conv)s((%(dtype)s)%(name)s);
# if (!py_%(name)s)
# py_%(name)s = Py_None;
# """ % dict(name = name,
# dtype = specs[1],
# conv = specs[4])
# def c_cleanup(self, name, sub):
# return ""
# def c_support_code(cls):
# template = """
# struct theano_complex%(nbits)s : public npy_complex%(nbits)s
# {
# typedef theano_complex%(nbits)s complex_type;
# typedef npy_float%(half_nbits)s scalar_type;
# complex_type operator +(complex_type y) {
# complex_type ret;
# ret.real = this->real + y.real;
# ret.imag = this->imag + y.imag;
# return ret;
# }
# complex_type operator -(complex_type y) {
# complex_type ret;
# ret.real = this->real - y.real;
# ret.imag = this->imag - y.imag;
# return ret;
# }
# complex_type operator *(complex_type y) {
# complex_type ret;
# ret.real = this->real * y.real - this->imag * y.imag;
# ret.imag = this->real * y.imag + this->imag * y.real;
# return ret;
# }
# complex_type operator /(complex_type y) {
# complex_type ret;
# scalar_type y_norm_square = y.real * y.real + y.imag * y.imag;
# ret.real = (this->real * y.real + this->imag * y.imag) / y_norm_square;
# ret.imag = (this->imag * y.real - this->real * y.imag) / y_norm_square;
# return ret;
# }
# };
# """
# return template % dict(nbits = 64, half_nbits = 32) + template % dict(nbits = 128, half_nbits = 64)
# def __copy__(self):
# """Return a copy of this instance (with its own attributes)"""
# cpy = self.__class__(self.dtype, self.name)
# cpy.data = self.data
# return cpy
# #UNARY
# def __abs__(self): return Abs(self).out
# def __neg__(self): return Neg(self).out
# #CASTS
# def __int__(self): return AsInt(self).out
# def __float__(self): return AsInt(self).out
# def __complex__(self): return AsComplex(self).out
# #COMPARISONS
# def __lt__(self,other): return lt(self, other)
# def __le__(self,other): return le(self, other)
# def __gt__(self,other): return gt(self, other)
# def __ge__(self,other): return ge(self, other)
# #ARITHMETIC - NORMAL
# def __add__(self,other): return add(self,other)
# def __sub__(self,other): return sub(self,other)
# def __mul__(self,other): return mul(self,other)
# def __div__(self,other): return div(self,other)
# def __pow__(self,other): return pow(self,other)
# #ARITHMETIC - RIGHT-OPERAND
# def __radd__(self,other): return add(other,self)
# def __rsub__(self,other): return sub(other,self)
# def __rmul__(self,other): return mul(other,self)
# def __rdiv__(self,other): return div(other,self)
# def __rpow__(self,other): return pow(other,self)
# # Easy constructors
# def _multi(*fns):
# def f2(f, names):
# if len(names) == 1:
# return f(names)
# else:
# return [f(name) for name in names]
# if len(fns) == 1:
# return partial(f2, fns[0])
# else:
# return [partial(f2, f) for f in fns]
# def intr(name):
# return Scalar(name = name, dtype = 'int64')
# ints = _multi(intr)
# def floatr(name):
# return Scalar(name = name, dtype = 'float64')
# floats = _multi(floatr)
# def upcast(dtype, *dtypes):
# z = numpy.zeros((), dtype = dtype)
# for dtype in dtypes:
# z = z + numpy.zeros((), dtype = dtype)
# return str(z.dtype)
# class ScalarOp(GuardedOp):
# nin = -1
# nout = 1
# def __init__(self, *inputs):
# if self.nin >= 0:
# if len(inputs) != self.nin:
# raise TypeError("Wrong number of inputs for %s (got %i, expected %i)" \
# % (self.__class__.__name__, len(inputs), self.nin))
# else:
# self.nin = len(inputs)
# inputs = [as_scalar(input) for input in inputs]
# i_dtypes = [getattr(input, 'dtype', None) for input in inputs]
# o_dtypes = self.output_dtypes(*i_dtypes)
# self.inputs = inputs
# self.outputs = [Scalar(dtype) for dtype in o_dtypes]
# def output_dtypes(self, *dtypes):
# if self.nout != 1:
# raise NotImplementedError()
# return upcast(*dtypes),
# def impl(self, *inputs):
# raise AbstractFunctionError()
# def grad(self, inputs, output_gradients):
# raise AbstractFunctionError()
# def perform(self):
# if self.nout == 1:
# self.outputs[0].data = self.impl(*[input.data for input in self.inputs])
# else:
# results = utils.from_return_values(self.impl(*[input.data for input in self.inputs]))
# for output, result in zip(self.outputs, results):
# output.data = result
# class UnaryScalarOp(ScalarOp):
# nin = 1
# class BinaryScalarOp(ScalarOp):
# nin = 2
# class FloatUnaryScalarOp(UnaryScalarOp):
# def output_dtypes(self, input_dtype):
# if 'int' in input_dtype: return 'float64',
# if 'float' in input_dtype: return input_dtype,
# raise NotImplementedError()
# class Add(ScalarOp):
# identity = 0
# commutative = True
# associative = True
# def impl(self, *inputs):
# return sum(inputs)
# def c_code(self, inputs, (z, ), sub):
# if not inputs:
# return z + " = 0;"
# else:
# return z + " = " + " + ".join(inputs) + ";"
# def grad(self, inputs, (gz, )):
# return (gz, ) * len(inputs)
# class Mul(ScalarOp):
# identity = 1
# commutative = True
# associative = True
# def impl(self, *inputs):
# return numpy.product(inputs)
# def c_code(self, inputs, (z, ), sub):
# if not inputs:
# return z + " = 1;"
# else:
# return z + " = " + " * ".join(inputs) + ";"
# def grad(self, inputs, (gz, )):
# return [mul(*([gz] + utils.difference(inputs, [input])))
# for input in inputs]
# class Sub(BinaryScalarOp):
# def impl(self, x, y):
# return x - y
# def c_code(self, (x, y), (z, ), sub):
# return "%(z)s = %(x)s - %(y)s;" % locals()
# def grad(self, (x, y), (gz, )):
# return gz, -gz
# class Div(BinaryScalarOp):
# def impl(self, x, y):
# return x / y
# def c_code(self, (x, y), (z, ), sub):
# if 'int' in self.inputs[0].dtype and 'int' in self.inputs[1].dtype:
# raise NotImplementedError("For integer arguments the behavior of division in C and in Python differ when the quotient is negative (to implement).")
# return "%(z)s = %(x)s / %(y)s;" % locals()
# def grad(self, (x, y), (gz, )):
# return gz / y, -(gz * x) / (y * y)
# class Pow(BinaryScalarOp):
# def impl(self, x, y):
# return x ** y
# def c_code(self, (x, y), (z, ), sub):
# return "%(z)s = pow(%(x)s, %(y)s);" % locals()
# def grad(self, (x, y), (gz, )):
# return gz * y * x**(y - 1), gz * log(x) * x**y
# class First(BinaryScalarOp):
# def impl(self, x, y):
# return x
# def c_code(self, (x, y), (z, ), sub):
# return "%(z)s = %(x)s;" % locals()
# def grad(self, (x, y), (gz, )):
# return gz, None
# class Second(BinaryScalarOp):
# def impl(self, x, y):
# return y
# def c_code(self, (x, y), (z, ), sub):
# return "%(z)s = %(y)s;" % locals()
# def grad(self, (x, y), (gz, )):
# return None, gz
# class Identity(UnaryScalarOp):
# def impl(self, x):
# return x
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = %(x)s;" % locals()
# def grad(self, (x, ), (gz, )):
# return gz,
# class Neg(UnaryScalarOp):
# def impl(self, x):
# return -x
# def grad(self, (x, ), (gz, )):
# return -gz,
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = -%(x)s;" % locals()
# class Abs(UnaryScalarOp):
# #TODO: for complex input, output is some flavour of float
# def impl(self, x):
# return numpy.abs(x)
# def grad(self, (x, ), (gz, )):
# return gz * sgn(x),
# def c_code(self, (x, ), (z, ), sub):
# dtype = str(self.inputs[0].dtype)
# if 'int' in dtype:
# return "%(z)s = abs(%(x)s);" % locals()
# if 'float' in dtype:
# return "%(z)s = fabs(%(x)s);" % locals()
# #complex, other?
# raise NotImplementedError('dtype not supported', dtype)
# class Sgn(UnaryScalarOp):
# def impl(self, x):
# #casting to output type is handled by filter
# return numpy.sign(x)
# def grad(self, (x, ), (gz, )):
# return None,
# def c_code(self, (x, ), (z, ), sub):
# #casting is done by compiler
# #TODO: use copysign
# return "%(z)s = (%(x)s >= 0) ? (%(x)s == 0) ? 0.0 : 1.0 : -1.0;" % locals()
# class Inv(FloatUnaryScalarOp):
# def impl(self, x):
# return 1.0 / x
# def grad(self, (x, ), (gz, )):
# return -gz / (x * x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = 1.0 / %(x)s;" % locals()
# class Log(FloatUnaryScalarOp):
# def impl(self, x):
# return math.log(x)
# def grad(self, (x, ), (gz, )):
# return gz / x,
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = log(%(x)s);" % locals()
# class Log2(FloatUnaryScalarOp):
# def impl(self, x):
# return numpy.log2(x)
# def grad(self, (x, ), (gz, )):
# return gz / (x * math.log(2.0)),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = log2(%(x)s);" % locals()
# class Exp(FloatUnaryScalarOp):
# def impl(self, x):
# return math.exp(x)
# def grad(self, (x, ), (gz, )):
# return gz * exp(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = exp(%(x)s);" % locals()
# class Sqr(UnaryScalarOp):
# def impl(self, x):
# return x*x
# def grad(self, (x, ), (gz, )):
# return gz * x * 2,
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = %(x)s * %(x)s;" % locals()
# class Sqrt(FloatUnaryScalarOp):
# def impl(self, x):
# return math.sqrt(x)
# def grad(self, (x, ), (gz, )):
# return (gz * 0.5) / sqrt(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = sqrt(%(x)s);" % locals()
# class Cos(FloatUnaryScalarOp):
# def impl(self, x):
# return math.cos(x)
# def grad(self, (x, ), (gz, )):
# return -gz * sin(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = cos(%(x)s);" % locals()
# class Sin(FloatUnaryScalarOp):
# def impl(self, x):
# return math.sin(x)
# def grad(self, (x, ), (gz, )):
# return gz * cos(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = sin(%(x)s);" % locals()
# class Tan(FloatUnaryScalarOp):
# def impl(self, x):
# return math.tan(x)
# def grad(self, (x, ), (gz, )):
# return gz / (cos(x) ** 2),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = tan(%(x)s);" % locals()
# class Cosh(FloatUnaryScalarOp):
# """
# sinh(x) = (exp(x) + exp(-x)) / 2
# """
# def impl(self, x):
# return math.cosh(x)
# def grad(self, (x, ), (gz, )):
# return gz * sinh(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = cosh(%(x)s);" % locals()
# class Sinh(FloatUnaryScalarOp):
# """
# sinh(x) = (exp(x) - exp(-x)) / 2
# """
# def impl(self, x):
# return math.sinh(x)
# def grad(self, (x, ), (gz, )):
# return gz * cosh(x),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = sinh(%(x)s);" % locals()
# class Tanh(FloatUnaryScalarOp):
# """
# tanh(x) = sinh(x) / cosh(x)
# = (exp(2*x) - 1) / (exp(2*x) + 1)
# """
# def impl(self, x):
# return math.tanh(x)
# def grad(self, (x, ), (gz, )):
# return gz * (1 - tanh(x)**2),
# def c_code(self, (x, ), (z, ), sub):
# return "%(z)s = tanh(%(x)s);" % locals()
# #NOTE WELL!!!
# # The following adds functions to this module automatically.
# # For every scalar op class, a lower-case symbol is added which is a constructor
# # for that class.
# from gof import modes
# modes.make_constructors(globals())
# def composite(inputs, outputs):
# """
# Usage: composite(inputs, outputs)
# Produces an Op class which represents the computations
# between the provided inputs and outputs as a single
# operation.
# The operations between inputs and outputs (as given by
# Env(inputs, outputs).ops()) must all be instances of
# ScalarOp.
# Examples:
# x, y = Scalar(), Scalar()
# SquareDiff = composite([x, y], [(x - y)**2])
# TimesTen = composite([x], [x * 10.0])
# Neighbors = composite([x], [x - 1, x + 1])
# """
# env = Env(inputs, outputs).clone()
# gof.opt.ConstantFinder().apply(env)
# inputs, outputs = env.inputs, env.outputs
# for op in env.ops():
# if not isinstance(op, ScalarOp):
# raise ValueError("The input env to composite must be exclusively composed of ScalarOp instances.")
# subd = dict(zip(inputs,
# ["%%(i%i)s"%i for i in range(len(inputs))]) +
# zip(outputs,
# ["%%(o%i)s"%i for i in range(len(outputs))]))
# for orphan in env.orphans():
# if orphan.constant:
# subd[orphan] = orphan.c_literal()
# else:
# raise ValueError("All orphans in the input env to composite must be constant.")
# _c_code = "{\n"
# i = 0
# j = 0
# for op in env.toposort():
# j += 1
# for output in op.outputs:
# if output not in subd:
# i += 1
# name = "V%%(id)s_tmp%i" % i
# subd[output] = name
# _c_code += "%s %s;\n" % (output.dtype_specs()[1], name)
# _c_code += op.c_code([subd[input] for input in op.inputs],
# [subd[output] for output in op.outputs],
# dict(fail = "%(fail)s",
# id = "%%(id)s_%i" % j))
# _c_code += "\n"
# _c_code += "}\n"
# def compose_impl(r):
# # this is not optimal at all eg in add(*1 -> mul(x, y), *1)
# # it will calculate *1 twice
# # it also doesn't follow env.toposort but that's (presumably)
# # still correct since we only have scalar ops
# if r in env.inputs:
# idx = env.inputs.index(r)
# return lambda inputs: inputs[idx]
# elif r in env.orphans():
# return lambda inputs: r.data
# op = r.owner
# producers = [compose_impl(input) for input in op.inputs]
# return lambda inputs: op.impl(*[p(inputs) for p in producers])
# _impls = [compose_impl(r) for r in env.outputs]
# class Composite(ScalarOp):
# nin = len(inputs)
# nout = len(outputs)
# def output_dtypes(self, *input_dtypes):
# assert input_dtypes == tuple([input.dtype for input in inputs])
# return [output.dtype for dtype in outputs]
# def perform(self):
# inputs = [input.data for input in self.inputs]
# for output, impl in zip(self.outputs, _impls):
# output.data = impl(inputs)
# def impl(self, *inputs):
# for r, input in zip(self.inputs, inputs):
# r.data = input
# self.perform()
# return utils.to_return_values([output.data for output in self.outputs])
# def grad(self, inputs, output_grads):
# raise NotImplementedError("grad is not implemented for Composite")
# def c_code(self, inames, onames, sub):
# d = dict(zip(["i%i"%i for i in range(len(inames))],
# inames) +
# zip(["o%i"%i for i in range(len(onames))],
# onames),
# **sub)
# return _c_code % d
# return Composite
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