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提交 ccfd8fc6 authored 作者: Frederic Bastien's avatar Frederic Bastien
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make import respect more the convention.

上级 6cba56c4
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正在显示 包含 39 行增加 和 37 行删除
theano/tensor/tests/test_opt.py
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......@@ -10,11 +10,17 @@ from numpy.testing import dec
from numpy.testing.noseclasses import KnownFailureTest
import theano
import theano.scalar as scal
from theano import config
from theano import gof
from theano.tensor.opt import *
import theano.tensor.opt as opt
from theano.tensor.opt import local_dimshuffle_lift, out2in, local_greedy_distributor, mul_canonizer, local_add_specialize
from theano.tensor.opt import Shape_i
from theano.tensor import scalar, iscalar, dscalar, lscalar, vectors, lvector, fvector, dvector, fmatrix, dmatrix, matrices, fmatrices, dmatrices, Subtensor, as_tensor_variable, Join, join
from theano import tensor #do not use, there is an import * below that hides it
from theano import tensor as TT #ugly but works for now...
from theano import tensor as T #ugly but works for now...
from theano.tensor import TensorType, inplace
from theano.gof import Env
from theano.tensor.elemwise import DimShuffle
......@@ -86,9 +92,6 @@ def test_add_canonizer_problem0():
r = segment_labels * 5
f = function([label], r)
from theano.tensor import *
# Why is there TWO 'import *' in this file???
class test_greedy_distribute(unittest.TestCase):
def test_main(self):
a, b, c, d, x, y, z = matrices('abcdxyz')
......@@ -106,7 +109,7 @@ class test_greedy_distribute(unittest.TestCase):
#r = theano.tensor.mul(theano.tensor.fill(x, 2.*a), x/a , (y+z) , a)
#r = theano.tensor.mul((x/a+y) , a, z)
r = mul(
r = tensor.mul(
s - 1
, eps + x/s
, eps + y/s
......@@ -317,7 +320,6 @@ class test_canonize(unittest.TestCase):
with and without DimShuffle
TODO: with DimShuffle
"""
import theano.tensor, theano.compile
shp=(3,3)
fx, fy, fz, fw = fmatrices('xyzw')
......@@ -558,7 +560,6 @@ class test_canonize(unittest.TestCase):
def test_multiple_case_that_fail(self):
import theano.tensor, theano.compile
raise SkipTest("Current implementation of Canonizer don't implement all case. Skip the corresponding test")
shp=(4,4)
......@@ -660,12 +661,12 @@ def test_const_type_in_mul_canonizer():
betas = dvector()
a = dvector()
def sigm(x): return 1./(1+exp(-x))
def sigm(x): return 1./(1+tensor.exp(-x))
hid = sigm( (dot(w,input) + hidb) * betas )
hid = sigm( (tensor.dot(w,input) + hidb) * betas )
vis_gauss1 = (dot(w.T, hid) + visb) * betas / (2 * a * a)
vis_gauss2 = (dot(w.T, hid) + visb) * betas / (2. * a * a)
vis_gauss1 = (tensor.dot(w.T, hid) + visb) * betas / (2 * a * a)
vis_gauss2 = (tensor.dot(w.T, hid) + visb) * betas / (2. * a * a)
f1 = function([input,w,visb,hidb,betas,a],vis_gauss1)
f2 = function([input,w,visb,hidb,betas,a],vis_gauss2)
......@@ -757,8 +758,8 @@ class test_fusion(unittest.TestCase):
#, nout=1, env=[add(add(<float32>, <float32>), add(<float32>, <float32>))]}}(InplaceDimShuffle{x,x}.0, Elemwise{add,no_inplace}.0, y, z)]
((fwx.sum())+(fwx)+(fy+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),4,(fwv+fxv).sum()+fwv+fxv+fyv+fzv,'float32'),
#test other elemwise op
(fx+fy+cos(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cos(fzv),'float32'),
(fx+fy+cosh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cosh(fzv),'float32'),
(fx+fy+tensor.cos(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cos(fzv),'float32'),
(fx+fy+tensor.cosh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cosh(fzv),'float32'),
(fx+fy+abs(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.absolute(fzv),'float32'),
(ix+iy+abs(iz),(ix,iy,iz),(ixv,iyv,izv),1,ixv+iyv+numpy.absolute(izv),'int32'),#30
(fx+fy+theano.tensor.log(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.log(fzv),'float32'),
......@@ -779,10 +780,10 @@ class test_fusion(unittest.TestCase):
(fx-(fy<=fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv<=fzv),'float32'),
(fx-T.eq(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv==fzv),'float32'),
(fx-T.neq(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv!=fzv),'float32'),
(fx-fy+tan(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tan(fzv),'float32'),
(fx-fy+tanh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tanh(fzv),'float32'),#50
(fx-fy+sin(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sin(fzv),'float32'),
(fx-fy+sinh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sinh(fzv),'float32'),
(fx-fy+tensor.tan(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tan(fzv),'float32'),
(fx-fy+tensor.tanh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tanh(fzv),'float32'),#50
(fx-fy+tensor.sin(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sin(fzv),'float32'),
(fx-fy+tensor.sinh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sinh(fzv),'float32'),
(fx-fy+theano.tensor.sqr(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+(fzv*fzv),'float32'),
(fx-fy+theano.tensor.sqrt(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sqrt(fzv),'float32'),
(fx-fy+theano.tensor.inv(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+(1/fzv),'float32'),#55
......@@ -794,7 +795,7 @@ class test_fusion(unittest.TestCase):
# (fx-theano.tensor.xor(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fy^fz),'float32'),
(theano.tensor.pow(fx*fy+fz,fx*fy),(fx,fy,fz),(fxv,fyv,fzv),1,numpy.power(fxv*fyv+fzv,fxv*fyv),'float32'),
(fv+fy**fz,(fv,fy,fz),(fvv,fyv,fzv),2,fvv+fyv**fzv,'float32'),#fused with a dimshuffle
(fv-fy+tanh(fz),(fv,fy,fz),(fvv,fyv,fzv),2,fvv-fyv+numpy.tanh(fzv),'float32'),#fused with a dimshuffle
(fv-fy+tensor.tanh(fz),(fv,fy,fz),(fvv,fyv,fzv),2,fvv-fyv+numpy.tanh(fzv),'float32'),#fused with a dimshuffle
# Cases where the same input is reused many times.
(theano.tensor.mul(fx,fx,fx,fx),(fx,),(fxv,),1,fxv*fxv*fxv*fxv,'float32'),
......@@ -954,7 +955,7 @@ class test_fusion(unittest.TestCase):
x, y, z = dmatrices('xyz')
f=theano.function([x,y,z],dot(x,y)+x+y+z,mode=mode)
f=theano.function([x,y,z],tensor.dot(x,y)+x+y+z,mode=mode)
topo = f.maker.env.toposort()
assert len(topo) == 2
assert f.maker.env.toposort()[-1].op.inplace_pattern
......@@ -962,7 +963,7 @@ class test_fusion(unittest.TestCase):
def speed_fusion_gpu(self):
import theano.sandbox.cuda as cuda
self.speed_fusion(shared_fn=tcn.float32_shared_constructor, gpu=True, s=slice(0,15))
self.speed_fusion(shared_fn=cuda.float32_shared_constructor, gpu=True, s=slice(0,15))
def speed_log_exp(self):
s=slice(31,36)
......@@ -1070,20 +1071,20 @@ def test_log1p():
# check trickier cases (and use different dtype)
y = fmatrix()
f = function([x,y], T.log(fill(y,1)+(x)), mode=m)
f = function([x,y], T.log(tensor.fill(y,1)+(x)), mode=m)
print f.maker.env.toposort()
# the first three ops are Shape_i, Shape_i, and Dimshuffle
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()][3:] \
== [T.log1p, alloc]
f = function([x,y], T.log(0+(x) + fill(y,1.0)), mode=m)
== [T.log1p, tensor.alloc]
f = function([x,y], T.log(0+(x) + tensor.fill(y,1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()][3:] \
== [T.log1p, alloc]
f = function([x,y], T.log(2+(x) - fill(y,1.0)), mode=m)
== [T.log1p, tensor.alloc]
f = function([x,y], T.log(2+(x) - tensor.fill(y,1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()][3:] \
== [T.log1p, alloc]
== [T.log1p, tensor.alloc]
f([1e-7, 10], [[0, 0], [0, 0]]) #debugmode will verify values
......@@ -1091,14 +1092,14 @@ def test_log1p():
# at one point this worked, but it has been broken since
# the constant up-casting made 1 -> 1.0+0.0j
# I was never sure if this optimization should work on complex numbers or not.
z = zmatrix()
z = tensor.zmatrix()
f = function([z], T.log(1+(z)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [T.log1p]
if 1:
# should work for int
z = imatrix()
z = tensor.imatrix()
f = function([z], T.log(1+(z)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [T.log1p]
......@@ -1159,7 +1160,7 @@ def test_local_useless_subtensor():
assert len(prog)==1
f([[0,1,2],[3,4,5]]) # let debugmode test something
x_c = specify_shape(x, (2,3))
x_c = tensor.specify_shape(x, (2,3))
# Test constant
for dims, res in [((slice(0,2),), True),
((slice(0,2),slice(0,None)), True),
......@@ -1618,11 +1619,11 @@ class test_shapeoptimizer(unittest.TestCase):
assert [] == f.maker.env.toposort()
def test_local_track_shape_i(self):
class IdentityNoShape(Op):
class IdentityNoShape(gof.Op):
'''Op that does not infer the output shape from the input one'''
def make_node(self, x):
x = as_tensor_variable(x)
return Apply(self, [x], [x.type()])
return gof.Apply(self, [x], [x.type()])
def perform(self, node, inp, out_):
x, = inp
out, = out_
......@@ -1631,11 +1632,11 @@ class test_shapeoptimizer(unittest.TestCase):
#return [tuple([self.shape_i(i)(r) for i in xrange(r.ndim)])]
identity_noshape = IdentityNoShape()
class IdentityShape(Op):
class IdentityShape(gof.Op):
'''Op that does infer the output shape from the input one'''
def make_node(self, x):
x = as_tensor_variable(x)
return Apply(self, [x], [x.type()])
return gof.Apply(self, [x], [x.type()])
def perform(self, node, inp, out_):
x, = inp
out, = out_
......@@ -1667,7 +1668,7 @@ class test_shapeoptimizer(unittest.TestCase):
assert identity_shape not in f_ops
# Register the optimization
register_specialize(local_identity_noshape_to_identity_shape)
opt.register_specialize(local_identity_noshape_to_identity_shape)
# With the optimization
# The identity_shape op should not be needed anymore to compute
......@@ -2512,8 +2513,8 @@ class T_local_sum(unittest.TestCase):
input=numpy.asarray(numpy.arange(2*3*4).reshape(2,3,4),dtype='float64')
mode = self.mode.including('specialize').excluding('fusion')
for t_like,n_like,nb_nodes in [(zeros_like,numpy.zeros_like,(0,3,3,2)),
(ones_like,numpy.ones_like,(5,5,5,6))]:
for t_like,n_like,nb_nodes in [(tensor.zeros_like,numpy.zeros_like,(0,3,3,2)),
(tensor.ones_like,numpy.ones_like,(5,5,5,6))]:
f = theano.function([a],t_like(a).sum(None),mode=mode)
assert numpy.allclose(f(input),n_like(input).sum())
......@@ -2551,6 +2552,7 @@ class T_local_sum_dimshuffle(unittest.TestCase):
b = T.vector('b')
c = T.tensor3('c')
d = T.scalar('d')
sum = tensor.sum
sums = [
sum(a/d),
sum(a/d.dimshuffle('x','x')),
......@@ -2692,7 +2694,7 @@ def test_make_vector():
def test_local_join_1():
#test for vector
a = TT.vector('a')
s = stack(a)
s = tensor.stack(a)
f = function([a], s, mode=mode_opt)
val = f([1])
assert numpy.all(val == [1])
......
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