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pytensor
提交 cb823769 authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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merged base_tensor with tensor

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__init__.py
浏览文件 @ cb823769
import gof import gof
import base_tensor
import tensor import tensor
import sparse import sparse
import compile import compile
import gradient import gradient
import opt import opt
from base_tensor import *
from tensor import * from tensor import *
from compile import * from compile import *
from opt import * from opt import *
......
_test_base_tensor.py deleted 100644 → 0
浏览文件 @ 55f4d322
from base_tensor import *
import unittest
from copy import copy
from compile import Function
import gof
def _tensor(data, broadcastable=None, name=None):
"""Return a BaseTensor containing given data"""
data = numpy.asarray(data)
if broadcastable is None:
broadcastable = [s==1 for s in data.shape]
elif broadcastable in [0, 1]:
broadcastable = [broadcastable] * len(data.shape)
rval = BaseTensor(data.dtype, broadcastable, name)
rval.data = data # will raise if broadcastable was mis-specified
return rval
class T_tensor(unittest.TestCase):
def test0(self): # allocate from a scalar float
t = _tensor(1.0)
self.failUnless(isinstance(t, BaseTensor))
self.failUnless(t.dtype == 'float64')
self.failUnless(t.broadcastable == ())
self.failUnless(t.role == None)
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(str(t.data.dtype) == 'float64')
self.failUnless(t.data == 1.0)
def test0_int(self): # allocate from a scalar float
t = _tensor(1)
self.failUnless(isinstance(t, BaseTensor))
self.failUnless(t.dtype == 'int64' or t.dtype == 'int32')
def test1(self): # allocate from a vector of ints, not broadcastable
t = _tensor(numpy.ones(5,dtype='int32'))
self.failUnless(isinstance(t, BaseTensor))
self.failUnless(t.dtype == 'int32')
self.failUnless(t.broadcastable == (0,))
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(str(t.data.dtype) == 'int32')
def test2(self): # allocate from a column matrix of complex with name
t = _tensor(numpy.ones((5,1),dtype='complex64'),name='bart')
self.failUnless(isinstance(t, BaseTensor))
self.failUnless(t.dtype == 'complex64')
self.failUnless(t.broadcastable == (0,1))
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(t.name == 'bart')
def test2b(self): # allocate from a column matrix, not broadcastable
t = _tensor(numpy.ones((5,1),dtype='complex64'),broadcastable=0)
self.failUnless(isinstance(t, BaseTensor))
self.failUnless(t.dtype == 'complex64')
self.failUnless(t.broadcastable == (0,0))
self.failUnless(isinstance(t.data, numpy.ndarray))
f = Function([t], [t], linker_cls=gof.CLinker)
self.failUnless(numpy.all(t.data == f(t.data)))
def test_data_normal(self): #test that assigning to .data works when it should
t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
o27 = numpy.ones((2,7), dtype='complex64')
t.data = o27
lst = t._data
self.failUnless(t.data.shape == (2,7))
self.failUnless(t.data is o27)
self.failUnless(t._data is lst)
def test_data_badrank0(self):
t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
try:
t.data = numpy.ones((2,7,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_rank)
try:
t.data = numpy.ones(1)
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_rank)
def test_data_badrank1(self):
t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
try:
t.data = numpy.ones((1,1,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_rank)
try:
t.data = numpy.ones(1)
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_rank)
def test_data_badshape0(self):
t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
try:
t.data = numpy.ones((1,2))
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_shape)
try:
t.data = numpy.ones((0,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is BaseTensor.filter.E_shape)
def test_cast0(self):
t = BaseTensor('float32', [0])
t.data = numpy.random.rand(4) > 0.5
self.failUnless(str(t.data.dtype) == t.dtype)
class T_stdlib(unittest.TestCase):
def test0(self):
t = _tensor(1.0)
tt = t.clone(False)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data is None)
self.failUnless(t.data == 1.0)
def test0b(self):
t = _tensor(1.0)
tt = t.clone()
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data is None)
self.failUnless(t.data == 1.0)
def test1(self):
t = _tensor(1.0)
tt = t.clone(True)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data == 1.0)
self.failUnless(t.data == 1.0)
self.failUnless(t.data is not tt.data)
def test1b(self):
t = _tensor(1.0)
tt = copy(t)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data == 1.0)
self.failUnless(t.data == 1.0)
self.failUnless(t.data is not tt.data)
if __name__ == '__main__':
unittest.main()
_test_tensor.py
浏览文件 @ cb823769
...@@ -1280,5 +1280,142 @@ class t_gemm(unittest.TestCase): ...@@ -1280,5 +1280,142 @@ class t_gemm(unittest.TestCase):
self.fail() self.fail()
def _tensor(data, broadcastable=None, name=None):
"""Return a Tensor containing given data"""
data = numpy.asarray(data)
if broadcastable is None:
broadcastable = [s==1 for s in data.shape]
elif broadcastable in [0, 1]:
broadcastable = [broadcastable] * len(data.shape)
rval = Tensor(data.dtype, broadcastable, name)
rval.data = data # will raise if broadcastable was mis-specified
return rval
class T_tensor(unittest.TestCase):
def test0(self): # allocate from a scalar float
t = _tensor(1.0)
self.failUnless(isinstance(t, Tensor))
self.failUnless(t.dtype == 'float64')
self.failUnless(t.broadcastable == ())
self.failUnless(t.role == None)
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(str(t.data.dtype) == 'float64')
self.failUnless(t.data == 1.0)
def test0_int(self): # allocate from a scalar float
t = _tensor(1)
self.failUnless(isinstance(t, Tensor))
self.failUnless(t.dtype == 'int64' or t.dtype == 'int32')
def test1(self): # allocate from a vector of ints, not broadcastable
t = _tensor(numpy.ones(5,dtype='int32'))
self.failUnless(isinstance(t, Tensor))
self.failUnless(t.dtype == 'int32')
self.failUnless(t.broadcastable == (0,))
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(str(t.data.dtype) == 'int32')
def test2(self): # allocate from a column matrix of complex with name
t = _tensor(numpy.ones((5,1),dtype='complex64'),name='bart')
self.failUnless(isinstance(t, Tensor))
self.failUnless(t.dtype == 'complex64')
self.failUnless(t.broadcastable == (0,1))
self.failUnless(isinstance(t.data, numpy.ndarray))
self.failUnless(t.name == 'bart')
def test2b(self): # allocate from a column matrix, not broadcastable
t = _tensor(numpy.ones((5,1),dtype='complex64'),broadcastable=0)
self.failUnless(isinstance(t, Tensor))
self.failUnless(t.dtype == 'complex64')
self.failUnless(t.broadcastable == (0,0))
self.failUnless(isinstance(t.data, numpy.ndarray))
f = Function([t], [t], linker_cls=gof.CLinker)
self.failUnless(numpy.all(t.data == f(t.data)))
def test_data_normal(self): #test that assigning to .data works when it should
t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
o27 = numpy.ones((2,7), dtype='complex64')
t.data = o27
lst = t._data
self.failUnless(t.data.shape == (2,7))
self.failUnless(t.data is o27)
self.failUnless(t._data is lst)
def test_data_badrank0(self):
t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
try:
t.data = numpy.ones((2,7,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_rank)
try:
t.data = numpy.ones(1)
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_rank)
def test_data_badrank1(self):
t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
try:
t.data = numpy.ones((1,1,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_rank)
try:
t.data = numpy.ones(1)
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_rank)
def test_data_badshape0(self):
t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
try:
t.data = numpy.ones((1,2))
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_shape)
try:
t.data = numpy.ones((0,1))
self.fail()
except ValueError, e:
self.failUnless(e[0] is Tensor.filter.E_shape)
def test_cast0(self):
t = Tensor('float32', [0])
t.data = numpy.random.rand(4) > 0.5
self.failUnless(str(t.data.dtype) == t.dtype)
class T_stdlib(unittest.TestCase):
def test0(self):
t = _tensor(1.0)
tt = t.clone(False)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data is None)
self.failUnless(t.data == 1.0)
def test0b(self):
t = _tensor(1.0)
tt = t.clone()
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data is None)
self.failUnless(t.data == 1.0)
def test1(self):
t = _tensor(1.0)
tt = t.clone(True)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data == 1.0)
self.failUnless(t.data == 1.0)
self.failUnless(t.data is not tt.data)
def test1b(self):
t = _tensor(1.0)
tt = copy(t)
self.failUnless(t.dtype == tt.dtype)
self.failUnless(t.broadcastable is tt.broadcastable)
self.failUnless(tt.data == 1.0)
self.failUnless(t.data == 1.0)
self.failUnless(t.data is not tt.data)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
base_tensor.py deleted 100644 → 0
浏览文件 @ 55f4d322
差异被折叠。
elemwise.py
浏览文件 @ cb823769
...@@ -3,7 +3,6 @@ import elemwise_cgen as cgen ...@@ -3,7 +3,6 @@ import elemwise_cgen as cgen
import numpy import numpy
from gof import Op, Viewer, Destroyer from gof import Op, Viewer, Destroyer
#from base_tensor import BaseTensor as Tensor
import scalar import scalar
from scalar import upcast, Scalar from scalar import upcast, Scalar
import gof import gof
......
sparse.py
浏览文件 @ cb823769
...@@ -11,7 +11,7 @@ import numpy ...@@ -11,7 +11,7 @@ import numpy
from scipy import sparse from scipy import sparse
import gof.op, gof.result import gof.op, gof.result
import tensor, base_tensor import tensor
...@@ -20,19 +20,19 @@ import tensor, base_tensor ...@@ -20,19 +20,19 @@ import tensor, base_tensor
def _is_sparse_result(x): def _is_sparse_result(x):
""" """
@rtype: boolean @rtype: boolean
@return: True iff x is a L{SparseResult} (and not a L{base_tensor.BaseTensor}) @return: True iff x is a L{SparseResult} (and not a L{tensor.Tensor})
""" """
if not isinstance(x, SparseResult) and not isinstance(x, base_tensor.BaseTensor): if not isinstance(x, SparseResult) and not isinstance(x, tensor.Tensor):
raise NotImplementedError("_is_sparse should only be called on sparse.SparseResult or base_tensor.BaseTensor, not,", x) raise NotImplementedError("_is_sparse should only be called on sparse.SparseResult or tensor.Tensor, not,", x)
return isinstance(x, SparseResult) return isinstance(x, SparseResult)
def _is_dense_result(x): def _is_dense_result(x):
""" """
@rtype: boolean @rtype: boolean
@return: True unless x is a L{SparseResult} (and not a L{base_tensor.BaseTensor}) @return: True unless x is a L{SparseResult} (and not a L{tensor.Tensor})
""" """
if not isinstance(x, SparseResult) and not isinstance(x, base_tensor.BaseTensor): if not isinstance(x, SparseResult) and not isinstance(x, tensor.Tensor):
raise NotImplementedError("_is_sparse should only be called on sparse.SparseResult or base_tensor.BaseTensor, not,", x) raise NotImplementedError("_is_sparse should only be called on sparse.SparseResult or tensor.Tensor, not,", x)
return isinstance(x, base_tensor.BaseTensor) return isinstance(x, tensor.Tensor)
def _is_sparse(x): def _is_sparse(x):
""" """
......
tensor.py
浏览文件 @ cb823769
差异被折叠。
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