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提交 897516c1 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
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Fixed #103: Import cleanup in test_basic.py

- Reordered imports in a more logical way - Uniformized notations to use tensor.* everywhere instead of T.*, basic.*, theano.tensor.* and theano.tensor.basic.*
上级 bf042b0c
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theano/tensor/tests/test_basic.py
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import itertools
import logging
import operator
import StringIO
import sys
import unittest
import warnings
from copy import copy
from nose.plugins.skip import SkipTest
import numpy
from numpy.testing import dec
from numpy.testing.noseclasses import KnownFailureTest
from theano.tensor import _shared
import theano.tensor as T
from theano.tensor import (wvector, bvector, autocast_float_as, argmin,
max_and_argmax, cscalar, Subtensor, ctensor3, join,
import theano
from theano import compile, config, function, gof, tensor
from theano.compile.mode import get_default_mode
from theano.gof.python25 import any, all, combinations
from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
argmin, max_and_argmax, cscalar, Subtensor, ctensor3, join,
horizontal_stack, vertical_stack, argmax, get_vector_length,
fscalar, zeros_like, sum, tensor3, vector, izip, add, addbroadcast,
alloc, as_tensor_variable, tensor_from_scalar, ARange, autocast_float,
basic, clip, constant, default, dot, inc_subtensor, set_subtensor,
dmatrix, dscalar, dvector, eq, eye, fill, flatten, inverse_permutation,
tensor4, permute_row_elements, Flatten, fmatrix, fscalars, grad,
inplace, iscalar, matrix, minimum, matrices, maximum, mul, neq, Reshape,
row, scalar, scalars, second, smallest, stack, sub, Tensor,
inplace, iscalar, matrix, minimum, matrices, maximum, mul, neq,
Reshape, row, scalar, scalars, second, smallest, stack, sub, Tensor,
tensor_copy, tensordot, tensordot_grad, TensorType, unbroadcast,
var, value, Join, shape, MaxAndArgmax, lscalar, zvector, exp,
get_constant_value, ivector, reshape, scalar_from_tensor, scal,
iscalars, arange, dscalars, fvector, imatrix, numeric_grad,
opt, ComplexError, TensorDot, lvector, true_div, max, min)
import warnings
from copy import copy
from theano import compile, config
from theano import gof
from theano.gof.python25 import any, all, combinations
from theano.compile.mode import get_default_mode
from theano import function
from theano.tests import unittest_tools as utt
import theano
import logging
imported_scipy_special = False
mode_no_scipy = get_default_mode()
......@@ -513,7 +507,7 @@ _good_broadcast_div_mod_normal_float = dict(empty2 = (numpy.asarray([0]), numpy.
def no_complex(d):
"""Remove pairs from dictionary d when the value contains complex data."""
return dict((k, v) for k, v in d.iteritems()
if all(str(x.dtype) not in basic.complex_dtypes for x in v))
if all(str(x.dtype) not in tensor.complex_dtypes for x in v))
# 'No-complex' versions.
......@@ -541,7 +535,7 @@ if config.floatX=='float32':
# float32.
# This is probably caused by our way of computing the gradient error.
div_grad_rtol=0.025
TrueDivTester = makeBroadcastTester(op = T.true_div,
TrueDivTester = makeBroadcastTester(op = tensor.true_div,
expected = lambda x, y: check_floatX((x, y), x / y),
good = _good_broadcast_div_mod_normal_float,
# integers = (randint(2, 3), randint_nonzero(2, 3)),
......@@ -557,7 +551,7 @@ TrueDivInplaceTester = makeBroadcastTester(op = inplace.true_div_inplace,
grad_rtol=div_grad_rtol,
inplace = True)
ModTester = makeBroadcastTester(op = T.mod,
ModTester = makeBroadcastTester(op = tensor.mod,
expected = lambda x, y: numpy.asarray(x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
good = _good_broadcast_div_mod_normal_float_no_complex,
# integers = (randint(2, 3), randint_nonzero(2, 3)),
......@@ -640,7 +634,7 @@ _grad_broadcast_unary_normal = dict(normal = (numpy.asarray(rand_ranged(-5, 5, (
AbsTester = makeBroadcastTester(op = basic.abs_,
AbsTester = makeBroadcastTester(op = tensor.abs_,
expected = lambda x: abs(x),
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -653,7 +647,7 @@ AbsInplaceTester = makeBroadcastTester(op = inplace.abs__inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
NegTester = makeBroadcastTester(op = T.neg,
NegTester = makeBroadcastTester(op = tensor.neg,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -663,7 +657,7 @@ NegInplaceTester = makeBroadcastTester(op = inplace.neg_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
SgnTester = makeBroadcastTester(op = T.sgn,
SgnTester = makeBroadcastTester(op = tensor.sgn,
expected = numpy.sign,
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal,)
......@@ -672,7 +666,7 @@ SgnInplaceTester = makeBroadcastTester(op = inplace.sgn_inplace,
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal,
inplace = True)
CeilTester = makeBroadcastTester(op = T.ceil,
CeilTester = makeBroadcastTester(op = tensor.ceil,
expected = lambda a: numpy.asarray(numpy.ceil(a),a.dtype),
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal)
......@@ -682,7 +676,7 @@ CeilInplaceTester = makeBroadcastTester(op = inplace.ceil_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
FloorTester = makeBroadcastTester(op = T.floor,
FloorTester = makeBroadcastTester(op = tensor.floor,
expected = lambda a: numpy.asarray(numpy.floor(a),a.dtype),
good = _good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal)
......@@ -692,7 +686,7 @@ FloorInplaceTester = makeBroadcastTester(op = inplace.floor_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
RoundHalfToEvenTester = makeBroadcastTester(op = T.round_half_to_even,
RoundHalfToEvenTester = makeBroadcastTester(op = tensor.round_half_to_even,
expected = numpy.round,
good = _good_broadcast_unary_normal_float_no_complex)
# TODO: Why complex are accepted in the next one?
......@@ -704,7 +698,7 @@ RoundHalfToEvenInplaceTester = makeBroadcastTester(op = inplace.round_half_to_ev
#numpy.vectorize don't handle correctly empty ndarray.
#see in their file numpy/lib/function_base.py in class vectorize.__call__
#This happen in float32 mode.
RoundHalfAwayFromZeroTester = makeBroadcastTester(op = T.round_half_away_from_zero,
RoundHalfAwayFromZeroTester = makeBroadcastTester(op = tensor.round_half_away_from_zero,
expected = theano.scalar.basic.round_half_away_from_zero_vec,
good = _good_broadcast_unary_normal_float_no_empty_no_complex)#_good_broadcast_unary_normal_float)
RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(op = inplace.round_half_away_from_zero_inplace,
......@@ -712,7 +706,7 @@ RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(op = inplace.round_half
good = _good_broadcast_unary_normal_float_no_empty_no_complex,
inplace = True)
SqrTester = makeBroadcastTester(op = T.sqr,
SqrTester = makeBroadcastTester(op = tensor.sqr,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -722,7 +716,7 @@ SqrInplaceTester = makeBroadcastTester(op = inplace.sqr_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
ExpTester = makeBroadcastTester(op = T.exp,
ExpTester = makeBroadcastTester(op = tensor.exp,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -744,7 +738,7 @@ _grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
#empty = (numpy.asarray([]),),
)
LogTester = makeBroadcastTester(op = T.log,
LogTester = makeBroadcastTester(op = tensor.log,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -754,7 +748,7 @@ LogInplaceTester = makeBroadcastTester(op = inplace.log_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log2Tester = makeBroadcastTester(op = T.log2,
Log2Tester = makeBroadcastTester(op = tensor.log2,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -764,7 +758,7 @@ Log2InplaceTester = makeBroadcastTester(op = inplace.log2_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log10Tester = makeBroadcastTester(op = T.log10,
Log10Tester = makeBroadcastTester(op = tensor.log10,
expected = numpy.log10,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -774,7 +768,7 @@ Log10InplaceTester = makeBroadcastTester(op = inplace.log10_inplace,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log1pTester = makeBroadcastTester(op = T.log1p,
Log1pTester = makeBroadcastTester(op = tensor.log1p,
expected = numpy.log1p,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -785,7 +779,7 @@ Log1pInplaceTester = makeBroadcastTester(op = inplace.log1p_inplace,
inplace = True)
SqrtTester = makeBroadcastTester(op = T.sqrt,
SqrtTester = makeBroadcastTester(op = tensor.sqrt,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
......@@ -818,7 +812,7 @@ _grad_broadcast_unary_arccos = dict(normal = (rand_ranged(-1.+1e-7, 1-1e-7, (2,
)
SinTester = makeBroadcastTester(op = T.sin,
SinTester = makeBroadcastTester(op = tensor.sin,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
......@@ -828,7 +822,7 @@ SinInplaceTester = makeBroadcastTester(op = inplace.sin_inplace,
grad = _grad_broadcast_unary_wide,
inplace = True)
CosTester = makeBroadcastTester(op = T.cos,
CosTester = makeBroadcastTester(op = tensor.cos,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
......@@ -837,7 +831,7 @@ CosInplaceTester = makeBroadcastTester(op = inplace.cos_inplace,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
ArccosTester = makeBroadcastTester(op = T.arccos,
ArccosTester = makeBroadcastTester(op = tensor.arccos,
expected = numpy.arccos,
good = _good_broadcast_unary_arccos,
grad = _grad_broadcast_unary_arccos)
......@@ -852,7 +846,7 @@ if config.floatX=='float32':
#We raise the relative tolerence for the grad as their is error in float32
#This is probably caused by our way of computing the gradient error.
tan_grad_rtol = 0.052
TanTester = makeBroadcastTester(op = T.tan,
TanTester = makeBroadcastTester(op = tensor.tan,
expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
......@@ -869,7 +863,7 @@ TanInplaceTester = makeBroadcastTester(op = inplace.tan_inplace,
inplace = True)
CoshTester = makeBroadcastTester(op = T.cosh,
CoshTester = makeBroadcastTester(op = tensor.cosh,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -879,7 +873,7 @@ CoshInplaceTester = makeBroadcastTester(op = inplace.cosh_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
SinhTester = makeBroadcastTester(op = T.sinh,
SinhTester = makeBroadcastTester(op = tensor.sinh,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -889,7 +883,7 @@ SinhInplaceTester = makeBroadcastTester(op = inplace.sinh_inplace,
grad = _grad_broadcast_unary_normal,
inplace = True)
TanhTester = makeBroadcastTester(op = T.tanh,
TanhTester = makeBroadcastTester(op = tensor.tanh,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -919,7 +913,7 @@ else:
expected_erfc = []
skip_scipy = "scipy is not present"
ErfTester = makeBroadcastTester(op = T.erf,
ErfTester = makeBroadcastTester(op = tensor.erf,
expected = expected_erf,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
......@@ -935,7 +929,7 @@ ErfInplaceTester = makeBroadcastTester(op = inplace.erf_inplace,
inplace = True,
skip = skip_scipy)
ErfcTester = makeBroadcastTester(op = T.erfc,
ErfcTester = makeBroadcastTester(op = tensor.erfc,
expected = expected_erfc,
good = _good_broadcast_unary_normal_no_int_no_complex,
grad = _grad_broadcast_unary_normal,
......@@ -951,12 +945,12 @@ ErfcInplaceTester = makeBroadcastTester(op = inplace.erfc_inplace,
inplace = True,
skip = skip_scipy)
ZerosLikeTester = makeBroadcastTester(op = T.zeros_like,
ZerosLikeTester = makeBroadcastTester(op = tensor.zeros_like,
expected = numpy.zeros_like,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
OnesLikeTester = makeBroadcastTester(op = T.ones_like,
OnesLikeTester = makeBroadcastTester(op = tensor.ones_like,
expected = numpy.ones_like,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
......@@ -1104,9 +1098,9 @@ def test_eye():
# allowed.
if M is None and theano.config.mode in ['DebugMode', 'DEBUG_MODE']:
M = N
N_symb = basic.iscalar()
M_symb = basic.iscalar()
k_symb = basic.iscalar()
N_symb = tensor.iscalar()
M_symb = tensor.iscalar()
k_symb = tensor.iscalar()
f = function([N_symb, M_symb, k_symb],
eye(N_symb, M_symb, k_symb, dtype=dtype))
result = f(N, M, k)
......@@ -1130,7 +1124,7 @@ def test_eye():
def test_identity():
def check(dtype):
obj = rand_of_dtype((2,), dtype)
sym = basic.vector(dtype=dtype)
sym = tensor.vector(dtype=dtype)
f = function([sym], tensor_copy(sym))
assert numpy.all(obj == f(obj))
assert obj.dtype == f(obj).dtype
......@@ -1152,16 +1146,16 @@ class CastTester(unittest.TestCase):
(rand_of_dtype((2,), dtype), dtype))
for dtype in ALL_DTYPES])
for testname, (obj, dtype) in good:
inp = basic.vector(dtype=obj.dtype)
out = basic.cast(inp, dtype=dtype)
inp = tensor.vector(dtype=obj.dtype)
out = tensor.cast(inp, dtype=dtype)
f = function([inp], out)
assert f(obj).dtype == numpy.dtype(dtype)
def test_cast_from_real_to_complex(self):
for real_dtype in REAL_DTYPES:
for complex_dtype in COMPLEX_DTYPES:
inp = basic.vector(dtype=real_dtype)
out = basic.cast(inp, dtype=complex_dtype)
inp = tensor.vector(dtype=real_dtype)
out = tensor.cast(inp, dtype=complex_dtype)
f = function([inp], out)
obj = rand_of_dtype((2, ), real_dtype)
assert f(obj).dtype == numpy.dtype(complex_dtype)
......@@ -1169,8 +1163,8 @@ class CastTester(unittest.TestCase):
def test_cast_from_complex_to_real_raises_error(self):
for real_dtype in REAL_DTYPES:
for complex_dtype in COMPLEX_DTYPES:
inp = basic.vector(dtype=real_dtype)
self.assertRaises(TypeError, basic.cast(inp, dtype=complex_dtype))
inp = tensor.vector(dtype=real_dtype)
self.assertRaises(TypeError, tensor.cast(inp, dtype=complex_dtype))
ClipTester = makeTester(name='ClipTester',
op=clip,
......@@ -1204,9 +1198,9 @@ ClipTester = makeTester(name='ClipTester',
class T_Clip(unittest.TestCase):
def test_complex_value(self):
for dtype in ['complex64', 'complex128']:
a = basic.vector(dtype=dtype)
b = basic.scalar()
c = basic.scalar()
a = tensor.vector(dtype=dtype)
b = tensor.scalar()
c = tensor.scalar()
self.assertRaises(TypeError, clip, a, b, c)
#TODO: consider moving this function / functionality to gradient.py
......@@ -1304,7 +1298,7 @@ def test_nan_inf_constant_signature():
assert (x.signature() == y.signature()) == (i == j)
# Also test that nan !=0 and nan != nan.
x = basic.scalar()
x = tensor.scalar()
mode = get_default_mode()
if isinstance(mode, theano.compile.debugmode.DebugMode):
# Disable the check preventing usage of NaN / Inf values.
......@@ -1833,10 +1827,10 @@ class T_subtensor(unittest.TestCase):
This is build in a way that allow to reuse it to test the equivalent gpu op.
"""
def __init__(self, name, shared=_shared,
sub=basic.Subtensor,
inc_sub=basic.IncSubtensor,
adv_sub1=basic.AdvancedSubtensor1,
adv_incsub1=basic.AdvancedIncSubtensor1,
sub=tensor.Subtensor,
inc_sub=tensor.IncSubtensor,
adv_sub1=tensor.AdvancedSubtensor1,
adv_incsub1=tensor.AdvancedIncSubtensor1,
mode=None,
dtype=theano.config.floatX,
ignore_topo=(theano.compile.function_module.DeepCopyOp)):
......@@ -2129,7 +2123,7 @@ class T_subtensor(unittest.TestCase):
t = n[idx]
# We test again AdvancedSubtensor1 as we transfer data to the cpu.
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
val = self.eval_output_and_check(t, list=True)
if isinstance(idx, list):
......@@ -2140,7 +2134,7 @@ class T_subtensor(unittest.TestCase):
self.assertTrue(numpy.allclose(val, good), (val, good))
# Test reuse of output memory
if isinstance(self.adv_sub1,basic.AdvancedSubtensor1):
if isinstance(self.adv_sub1,tensor.AdvancedSubtensor1):
op = self.adv_sub1()
# When idx is a TensorConstant.
if hasattr(idx, "data"):
......@@ -2166,7 +2160,7 @@ class T_subtensor(unittest.TestCase):
l = lvector()
t = n[l]
# We test again AdvancedSubtensor1 as we transfer data to the cpu.
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
f = function([l], t, mode=self.mode)
topo = f.maker.env.toposort()
......@@ -2179,9 +2173,9 @@ class T_subtensor(unittest.TestCase):
def test_adv_sub1_broadcast(self):
ones = numpy.ones((1,3), dtype=self.dtype)
n = self.shared(ones*5, broadcastable=(True, False))
idx = basic.lvector()
idx = tensor.lvector()
t = n[idx]
self.assertTrue(isinstance(t.owner.op, theano.tensor.basic.AdvancedSubtensor1))
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
f = function([idx], t, mode=self.mode)
topo = f.maker.env.toposort()
......@@ -2211,7 +2205,7 @@ class T_subtensor(unittest.TestCase):
t_shapes = f()
for t_shape, shape in zip(t_shapes,shapes):
assert numpy.all(t_shape == shape)
assert theano.tensor.Subtensor not in [ x.op for x in
assert tensor.Subtensor not in [ x.op for x in
f.maker.env.toposort() ]
def test_shape_i_scalar(self):
......@@ -2223,13 +2217,12 @@ class T_subtensor(unittest.TestCase):
mode_opt = compile.mode.get_mode(mode_opt)
v_data = numpy.array(numpy.arange(5), dtype=self.dtype)
t_data = self.shared(v_data)
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
f = function([start,stop,step], t_data[start:stop:step].shape, mode = mode_opt)
f2 = function([start,stop,step],t_data[start:stop:step])
assert theano.tensor.Subtensor not in [x.op for x in
f.maker.env.toposort() ]
assert tensor.Subtensor not in [x.op for x in f.maker.env.toposort()]
for start in [-8,-5,-4,-1,0,1,4,5,8]:
for stop in [-8,-5,-4,-1,0,1,4,5,8]:
for step in [-3,-1,2,5]:
......@@ -2238,17 +2231,16 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_0(self):
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,stop,step),
length)
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,stop,step), length)
f = function([start,stop,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2263,16 +2255,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_1(self):
stop = theano.tensor.iscalar('e')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,stop,step),
length)
stop = tensor.iscalar('e')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,stop,step), length)
f = function([stop,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2286,16 +2277,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_2(self):
start = theano.tensor.iscalar('b')
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,None,step),
length)
start = tensor.iscalar('b')
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,None,step), length)
f = function([start,step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2309,16 +2299,15 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_3(self):
start = theano.tensor.iscalar('b')
stop = theano.tensor.iscalar('e')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,stop,None),
length)
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,stop,None), length)
f = function([start,stop, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2331,15 +2320,14 @@ class T_subtensor(unittest.TestCase):
assert numpy.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_4(self):
step = theano.tensor.iscalar('s')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,None,step),
length)
step = tensor.iscalar('s')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,None,step), length)
f = function([step, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2352,15 +2340,14 @@ class T_subtensor(unittest.TestCase):
def test_slice_canonical_form_5(self):
start = theano.tensor.iscalar('b')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(start,None,None),
length)
start = tensor.iscalar('b')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(start,None,None), length)
f = function([start, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2372,15 +2359,14 @@ class T_subtensor(unittest.TestCase):
assert numpy.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_6(self):
stop = theano.tensor.iscalar('e')
length = theano.tensor.iscalar('l')
cnf = theano.tensor.basic.get_canonical_form_slice(slice(None,stop,None),
length)
stop = tensor.iscalar('e')
length = tensor.iscalar('l')
cnf = tensor.get_canonical_form_slice(slice(None,stop,None), length)
f = function([stop, length], [
theano.tensor.as_tensor_variable(cnf[0].start),
theano.tensor.as_tensor_variable(cnf[0].stop),
theano.tensor.as_tensor_variable(cnf[0].step),
theano.tensor.as_tensor_variable(cnf[1]) ])
tensor.as_tensor_variable(cnf[0].start),
tensor.as_tensor_variable(cnf[0].stop),
tensor.as_tensor_variable(cnf[0].step),
tensor.as_tensor_variable(cnf[1]) ])
length = 5
a = numpy.arange(length)
......@@ -2581,8 +2567,8 @@ class T_Join_and_Split(unittest.TestCase):
def test_stack_scalar_make_vector(self):
'''Test that calling stack() on scalars instantiates MakeVector,
not Join. Test that the floatX dtype stay floatX, not downcasted to int64'''
a = basic.scalar('a')
b = basic.scalar('b')
a = tensor.scalar('a')
b = tensor.scalar('b')
s = stack(a, b, a, b)
f = function([a,b], s)
val = f(1,2)
......@@ -2596,8 +2582,8 @@ class T_Join_and_Split(unittest.TestCase):
def test_stack_scalar_make_vector_dtype(self):
'''Test that calling stack() on scalars instantiates MakeVector,
event when the scalar don't have the same dtype.'''
a = basic.iscalar('a')
b = basic.lscalar('b')
a = tensor.iscalar('a')
b = tensor.lscalar('b')
s = stack(a, b, a, b)
f = function([a,b], s)
val = f(1,2)
......@@ -2610,8 +2596,8 @@ class T_Join_and_Split(unittest.TestCase):
def test_stack_scalar_make_vector_constant(self):
'''Test that calling stack() on scalars instantiates MakeVector,
event when the scalar are simple int type.'''
a = basic.iscalar('a')
b = basic.lscalar('b')
a = tensor.iscalar('a')
b = tensor.lscalar('b')
#test when the constant is the first element.
#The first element is used in a special way
s = stack(10,a,b, numpy.int8(3))
......@@ -2725,12 +2711,12 @@ class T_Join_and_Split(unittest.TestCase):
assert not c.type.broadcastable[1]
# Opt can remplace the int by a Theano constant
c = join(theano.tensor.constant(1), a, b)
c = join(tensor.constant(1), a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# In case futur opt insert other useless stuff
c = join(theano.tensor.cast(theano.tensor.constant(1), dtype="int32"),
c = join(tensor.cast(tensor.constant(1), dtype="int32"),
a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
......@@ -2875,7 +2861,7 @@ class T_Join_and_Split(unittest.TestCase):
if theano.config.mode != 'FAST_COMPILE':
for node in f.maker.env.toposort():
assert not isinstance(node.op, basic.Join)
assert not isinstance(node.op, tensor.Join)
# Test dim 1
z = join(1,x1,x2,x3)
......@@ -2885,7 +2871,7 @@ class T_Join_and_Split(unittest.TestCase):
if theano.config.mode != 'FAST_COMPILE':
for node in f.maker.env.toposort():
assert not isinstance(node.op, basic.Join)
assert not isinstance(node.op, tensor.Join)
# Test hide error
if theano.config.mode in ['DebugMode', 'DEBUG_MODE', 'FAST_COMPILE']:
......@@ -3035,7 +3021,7 @@ class T_add(unittest.TestCase):
class T_ceil(unittest.TestCase):
def test_complex(self):
self.assertRaises(TypeError, T.ceil, T.zvector())
self.assertRaises(TypeError, tensor.ceil, tensor.zvector())
class T_exp(unittest.TestCase):
def test_grad_0(self):
......@@ -3089,14 +3075,14 @@ class T_divimpl(unittest.TestCase):
class T_mean(unittest.TestCase):
def test_regression_mean_of_ndarray_failure(self):
try:
theano.tensor.mean(numpy.zeros(1))
tensor.mean(numpy.zeros(1))
except AttributeError:
self.fail()
def test0(self):
#Simple test...
x = theano.tensor.vector()
f = theano.function([x],theano.tensor.mean(x))
x = tensor.vector()
f = theano.function([x],tensor.mean(x))
data = numpy.asarray(numpy.random.rand(50), dtype=config.floatX)
assert numpy.allclose(f(data), numpy.mean(data))
......@@ -3668,7 +3654,7 @@ class test_grad(unittest.TestCase):
"""grad: Test passing a single variable param"""
o = test_grad.O()
a1 = o.make_node()
self.assertTrue(o.gval0 is T.grad(a1.outputs[0], a1.inputs[0]))
self.assertTrue(o.gval0 is tensor.grad(a1.outputs[0], a1.inputs[0]))
def test_Nparam(self):
"""grad: Test passing multiple variable params"""
......@@ -3687,14 +3673,14 @@ class test_grad(unittest.TestCase):
requires changing this test or making it fail you are almost certainly
making a common mistake, NOT fixing something. """
X = T.matrix()
X = tensor.matrix()
y = X.sum()
G = T.grad(y, [X])
G = tensor.grad(y, [X])
assert isinstance(G,list)
G = T.grad(y, X)
G = tensor.grad(y, X)
assert not isinstance(G,list)
......@@ -3855,7 +3841,7 @@ class T_reshape(unittest.TestCase):
def test_make_column_matrix_broadcastable():
# The goal of the operation made by `b` is to ensure the second dimension
# of the column matrix is broadcastable.
a = T.dmatrix()
a = tensor.dmatrix()
b = a.reshape((a.shape[0], )).dimshuffle(0, 'x')
f = function([a], b)
assert (f(numpy.zeros((3, 1))) + numpy.ones(2) == numpy.ones((3, 2))).all()
......@@ -4791,7 +4777,7 @@ def _test_autocast_numpy():
assert config.cast_policy == 'numpy'
# Go through some typical scalar values.
def ok(z):
assert basic.constant(z).dtype == numpy.asarray(z).dtype
assert tensor.constant(z).dtype == numpy.asarray(z).dtype
for x in ([2**i for i in xrange(63)] +
[0] +
[0., 1., 1.1, 1.5]):
......@@ -4813,9 +4799,9 @@ def _test_autocast_numpy_floatX():
floatX == 'float32' and
not hasattr(z, 'dtype')):
# Special case where we use 'float32' instead of 'float64'.
assert basic.constant(z).dtype == 'float32'
assert tensor.constant(z).dtype == 'float32'
else:
assert basic.constant(z).dtype == numpy.asarray(z).dtype
assert tensor.constant(z).dtype == numpy.asarray(z).dtype
try:
# Test with various values of `config.floatX`.
for floatX in ('float32', 'float64'):
......@@ -4854,9 +4840,9 @@ class test_arithmetic_cast(unittest.TestCase):
# scalar == scalar stored as a 0d array
# array == 1d array
# i_scalar == scalar type used internally by Theano
theano_scalar = lambda dtype: basic.scalar(dtype=str(dtype))
theano_scalar = lambda dtype: tensor.scalar(dtype=str(dtype))
numpy_scalar = lambda dtype: numpy.array(1, dtype=dtype)
theano_array = lambda dtype: basic.vector(dtype=str(dtype))
theano_array = lambda dtype: tensor.vector(dtype=str(dtype))
numpy_array = lambda dtype: numpy.array([1], dtype=dtype)
theano_i_scalar = lambda dtype: theano.scalar.Scalar(str(dtype))()
numpy_i_scalar = numpy_scalar
......@@ -4877,8 +4863,8 @@ class test_arithmetic_cast(unittest.TestCase):
# special way (depending on `config.int_division`).
is_int_division = (
op is operator.div and
a_type in basic.discrete_dtypes and
b_type in basic.discrete_dtypes)
a_type in tensor.discrete_dtypes and
b_type in tensor.discrete_dtypes)
# We will test all meaningful combinations of
# scalar and array operations.
for combo in (
......@@ -5093,10 +5079,10 @@ def test_mod_compile():
The c_code generated is not compiling as of 30 June 2010. I fix the compilation in the same commit.
"""
x = basic.vector()
y = basic.vector()
x = tensor.vector()
y = tensor.vector()
shape = x.shape
out = basic.switch(basic.eq(3%x.shape[0],0),y,y[:-1])
out = tensor.switch(tensor.eq(3%x.shape[0],0),y,y[:-1])
f = theano.function([x,y],out)
......@@ -5114,7 +5100,7 @@ def test_unalign():
b[:] = numpy.random.rand(len(b))
out_numpy = 2*a + 3*b
av,bv = basic.vectors('ab')
av,bv = tensor.vectors('ab')
f = theano.function([av,bv],2*av+3*bv)
f.maker.env.toposort()
# FAST_COMPILE use the python code that support unaligned data
......@@ -5132,12 +5118,12 @@ def test_unalign():
raise Exception("Theano raised an exception when none was expected")
def test_dimshuffle_duplicate():
x = theano.tensor.vector()
x = tensor.vector()
success = False
try:
y = theano.tensor.DimShuffle((False, ), (0, 0))(x)
y = tensor.DimShuffle((False, ), (0, 0))(x)
except ValueError, e:
assert str(e).find("may not appear twice") != -1
success = True
......@@ -5147,28 +5133,28 @@ def test_dimshuffle_duplicate():
class T_get_constant_value(unittest.TestCase):
def test_get_constant_value(self):
a = basic.stack(1,2,3)
a = tensor.stack(1,2,3)
assert get_constant_value(a[0])==1
assert get_constant_value(a[1])==2
assert get_constant_value(a[2])==3
b = basic.iscalar()
a = basic.stack(b,2,3)
b = tensor.iscalar()
a = tensor.stack(b,2,3)
self.assertRaises(TypeError, get_constant_value, a[0])
assert get_constant_value(a[1])==2
assert get_constant_value(a[2])==3
# For now get_constant_value goes through only MakeVector and Join of
# scalars.
v = basic.ivector()
a = basic.stack(v,2,3)
v = tensor.ivector()
a = tensor.stack(v,2,3)
self.assertRaises(TypeError, get_constant_value, a[0])
self.assertRaises(TypeError, get_constant_value, a[1])
self.assertRaises(TypeError, get_constant_value, a[2])
# Test the case SubTensor(Shape(v)) when the dimensions
# is broadcastable.
v = basic.row()
v = tensor.row()
assert get_constant_value(v.shape[0])==1
def test_subtensor_of_constant(self):
......@@ -5215,17 +5201,17 @@ class test_size(unittest.TestCase):
"""
def test_matrix(self):
x = basic.matrix()
x = tensor.matrix()
y = numpy.zeros((5, 7), dtype=config.floatX)
assert y.size == function([x], x.size)(y)
def test_vector(self):
x = basic.vector()
x = tensor.vector()
y = numpy.zeros(7, dtype=config.floatX)
assert y.size == function([x], x.size)(y)
def test_scalar(self):
x = basic.scalar()
x = tensor.scalar()
y = numpy.array(7, dtype=config.floatX)
assert y.size == function([x], x.size)(y)
......
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