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提交 fc75dbf9 authored 作者: lamblin's avatar lamblin
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Merge pull request #642 from nouiz/scipy

Fix tests error when scipy is not there
上级 421b712f 45a97cb2
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正在显示 包含 204 行增加 和 143 行删除
theano/sandbox/test_neighbours.py
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......@@ -102,7 +102,7 @@ def test_neibs_manual():
#print images.get_value(borrow=True)
neibs = f()
print neibs
#print neibs
assert numpy.allclose(neibs,[[ 0, 1, 4, 5],
[ 2, 3, 6, 7],
[ 8, 9, 12, 13],
......@@ -410,7 +410,7 @@ def tes_neibs2images_crash_on_grad():
to_images = T.sum(neibs2images(neibs, (2, 2), (2, 3, 20, 20)))
g = T.grad(to_images, neibs)
fn = theano.function([neibs], to_images, mode=mode_without_gpu)
print "Compiled"
#print "Compiled"
fn(neibs_val)
if __name__ == '__main__':
......
theano/sandbox/test_rng_mrg.py
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import os, sys, time
import os
import sys
import time
import numpy
import theano
......@@ -22,7 +24,8 @@ from nose.plugins.skip import SkipTest
# Partly done in test_consistency_randomstreams
#TODO: test optimizer mrg_random_make_inplace
#TODO: make tests work when no flags gived. Now need: THEANO_FLAGS=device=gpu0,floatX=float32
#TODO: make tests work when no flags gived. Now need:
# THEANO_FLAGS=device=gpu0,floatX=float32
# Partly done, in test_consistency_GPU_{serial,parallel}
......@@ -36,7 +39,8 @@ utt.seed_rng()
# 7 substreams for each stream
# 5 samples drawn from each substream
java_samples = numpy.loadtxt(os.path.join(os.path.split(theano.__file__)[0],
'sandbox','samples_MRG31k3p_12_7_5.txt'))
'sandbox',
'samples_MRG31k3p_12_7_5.txt'))
def test_deterministic():
......@@ -48,7 +52,7 @@ def test_deterministic():
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
print 'use_cuda =', use_cuda
#print 'use_cuda =', use_cuda
R = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
u = R.uniform(size=sample_size)
f = theano.function([], u)
......@@ -81,9 +85,9 @@ def test_consistency_randomstreams():
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
print 'use_cuda =', use_cuda
#print 'use_cuda =', use_cuda
samples = []
rng = MRG_RandomStreams(seed = seed, use_cuda=False)
rng = MRG_RandomStreams(seed=seed, use_cuda=False)
for i in range(n_streams):
stream_samples = []
u = rng.uniform(size=(n_substreams,), nstreams=n_substreams)
......@@ -98,6 +102,7 @@ def test_consistency_randomstreams():
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
def test_consistency_cpu_serial():
'''Verify that the random numbers generated by mrg_uniform, serially,
are the same as the reference (Java) implementation by L'Ecuyer et al.
......@@ -113,9 +118,13 @@ def test_consistency_cpu_serial():
for i in range(n_streams):
stream_rstate = curr_rstate.copy()
for j in range(n_substreams):
rstate = theano.shared(numpy.array([stream_rstate.copy()], dtype='int32'))
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None, dtype=config.floatX, size=(1,))
# Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
rstate = theano.shared(numpy.array([stream_rstate.copy()],
dtype='int32'))
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
dtype=config.floatX,
size=(1,))
# Not really necessary, just mimicking
# rng_mrg.MRG_RandomStreams' behavior
sample.rstate = rstate
sample.update = (rstate, new_rstate)
......@@ -134,6 +143,7 @@ def test_consistency_cpu_serial():
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
def test_consistency_cpu_parallel():
'''Verify that the random numbers generated by mrg_uniform, in parallel,
are the same as the reference (Java) implementation by L'Ecuyer et al.
......@@ -141,10 +151,10 @@ def test_consistency_cpu_parallel():
seed = 12345
n_samples = 5
n_streams = 12
n_substreams = 7 # 7 samples will be drawn in parallel
n_substreams = 7 # 7 samples will be drawn in parallel
samples = []
curr_rstate = numpy.array([seed]*6, dtype='int32')
curr_rstate = numpy.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_samples = []
......@@ -156,7 +166,8 @@ def test_consistency_cpu_parallel():
new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
dtype=config.floatX, size=(n_substreams,))
# Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
# Not really necessary, just mimicking
# rng_mrg.MRG_RandomStreams' behavior
sample.rstate = rstate
sample.update = (rstate, new_rstate)
......@@ -175,6 +186,7 @@ def test_consistency_cpu_parallel():
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
def test_consistency_GPU_serial():
'''Verify that the random numbers generated by GPU_mrg_uniform, serially,
are the same as the reference (Java) implementation by L'Ecuyer et al.
......@@ -200,14 +212,17 @@ def test_consistency_GPU_serial():
substream_rstate = numpy.array(stream_rstate.copy(), dtype='int32')
# HACK - we transfer these int32 to the GPU memory as float32
# (reinterpret_cast)
tmp_float_buf = numpy.frombuffer(substream_rstate.data, dtype='float32')
rstate = float32_shared_constructor(tmp_float_buf) # Transfer to device
tmp_float_buf = numpy.frombuffer(substream_rstate.data,
dtype='float32')
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
dtype='float32', size=(1,))
rstate.default_update = new_rstate
# Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
# Not really necessary, just mimicking
# rng_mrg.MRG_RandomStreams' behavior
sample.rstate = rstate
sample.update = (rstate, new_rstate)
......@@ -227,9 +242,12 @@ def test_consistency_GPU_serial():
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
def test_consistency_GPU_parallel():
'''Verify that the random numbers generated by GPU_mrg_uniform, in parallel,
are the same as the reference (Java) implementation by L'Ecuyer et al.
'''Verify that the random numbers generated by GPU_mrg_uniform, in
parallel, are the same as the reference (Java) implementation by
L'Ecuyer et al.
'''
if not cuda_available:
raise SkipTest('Optional package cuda not available')
......@@ -241,10 +259,10 @@ def test_consistency_GPU_parallel():
seed = 12345
n_samples = 5
n_streams = 12
n_substreams = 7 # 7 samples will be drawn in parallel
n_substreams = 7 # 7 samples will be drawn in parallel
samples = []
curr_rstate = numpy.array([seed]*6, dtype='int32')
curr_rstate = numpy.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_samples = []
......@@ -255,13 +273,15 @@ def test_consistency_GPU_parallel():
# HACK - transfer these int32 to the GPU memory as float32
# (reinterpret_cast)
tmp_float_buf = numpy.frombuffer(rstate.data, dtype='float32')
rstate = float32_shared_constructor(tmp_float_buf) # Transfer to device
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
dtype='float32', size=(n_substreams,))
rstate.default_update = new_rstate
# Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
# Not really necessary, just mimicking
# rng_mrg.MRG_RandomStreams' behavior
sample.rstate = rstate
sample.update = (rstate, new_rstate)
......@@ -281,6 +301,7 @@ def test_consistency_GPU_parallel():
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
target_avg=0.5, target_std=None, mean_rtol=0.01):
if inputs is None:
......@@ -291,41 +312,46 @@ def basictest(f, steps, sample_size, prefix="", allow_01=False, inputs=None,
for i in xrange(steps):
t0 = time.time()
ival = f(*inputs)
assert ival.shape==sample_size
assert ival.shape == sample_size
dt += time.time() - t0
ival = numpy.asarray(ival)
if i == 0:
mean = numpy.array(ival, copy=True)
#avg_std = numpy.std(ival)
avg_std = numpy.sqrt(numpy.mean((ival - target_avg)**2))
avg_std = numpy.sqrt(numpy.mean((ival - target_avg) ** 2))
min_ = ival.min()
max_ = ival.max()
else:
alpha = 1.0 / (1+i)
mean = alpha * ival + (1-alpha)*mean
alpha = 1.0 / (1 + i)
mean = alpha * ival + (1 - alpha) * mean
#avg_std = alpha * numpy.std(ival) + (1-alpha)*avg_std
avg_std = alpha * numpy.sqrt(numpy.mean((ival - target_avg)**2)) + (1-alpha)*avg_std
min_ = min(min_,ival.min())
max_ = max(max_,ival.max())
avg_std = alpha * numpy.sqrt(numpy.mean((ival - target_avg) ** 2
)) + (1 - alpha) * avg_std
min_ = min(min_, ival.min())
max_ = max(max_, ival.max())
if not allow_01:
assert min_ > 0
assert max_ < 1
if hasattr(target_avg, 'shape'): # looks if target_avg is an array
if hasattr(target_avg, 'shape'): # looks if target_avg is an array
diff = numpy.mean(abs(mean - target_avg))
print prefix, 'mean diff with mean', diff
#print prefix, 'mean diff with mean', diff
assert diff < mean_rtol, 'bad mean? %f %f' % (mean, target_avg)
else: # if target_avg is a scalar, then we can do the mean of `mean` to get something more precise
else: # if target_avg is a scalar, then we can do the mean of
# `mean` to get something more precise
mean = numpy.mean(mean)
print prefix, 'mean', mean
assert abs(mean - target_avg) < mean_rtol, 'bad mean? %f %f'%(numpy.mean(mean), target_avg)
print prefix, 'std', avg_std
#print prefix, 'mean', mean
assert abs(mean - target_avg) < mean_rtol, 'bad mean? %f %f' % (
numpy.mean(mean), target_avg)
#print prefix, 'std', avg_std
if target_std is not None:
assert abs(avg_std - target_std) < .01, 'bad std? %f %f'%(avg_std, target_std)
print prefix, 'time', dt
print prefix, 'elements', steps*sample_size[0]*sample_size[1]
print prefix, 'samples/sec', steps*sample_size[0]*sample_size[1] / dt
print prefix, 'min',min_,'max',max_
assert abs(avg_std - target_std) < .01, 'bad std? %f %f' % (avg_std,
target_std)
#print prefix, 'time', dt
#print prefix, 'elements', steps * sample_size[0] * sample_size[1]
#print prefix, 'samples/sec', steps * sample_size[0] * sample_size[1] / dt
#print prefix, 'min', min_, 'max', max_
def test_uniform():
#TODO: test param low, high
......@@ -333,11 +359,11 @@ def test_uniform():
#TODO: test ndim!=size.ndim
#TODO: test bad seed
#TODO: test size=Var, with shape that change from call to call
if mode in ['DEBUG_MODE','DebugMode','FAST_COMPILE']:
sample_size = (10,100)
if mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE']:
sample_size = (10, 100)
steps = 50
else:
sample_size = (500,50)
sample_size = (500, 50)
steps = int(1e3)
x = tensor.matrix()
......@@ -348,8 +374,8 @@ def test_uniform():
#### TEST CPU IMPLEMENTATION ####
# The python and C implementation are tested with DebugMode
print ''
print 'ON CPU with size=(%s):'%str(size)
#print ''
#print 'ON CPU with size=(%s):' % str(size)
x = tensor.matrix()
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
......@@ -359,46 +385,51 @@ def test_uniform():
u = R.uniform(size=size,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, u, mode=mode)
assert any([isinstance(node.op,theano.sandbox.rng_mrg.mrg_uniform)
assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform)
for node in f.maker.env.toposort()])
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
cpu_out = f(*input)
print 'CPU: random?[:10], random?[-10:]'
print cpu_out[0,0:10]
print cpu_out[-1,-10:]
#print 'CPU: random?[:10], random?[-10:]'
#print cpu_out[0, 0:10]
#print cpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg cpu', inputs=input)
if mode!='FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU with size=(%s):'%str(size)
if mode != 'FAST_COMPILE' and cuda_available:
#print ''
#print 'ON GPU with size=(%s):' % str(size)
R = MRG_RandomStreams(234, use_cuda=True)
u = R.uniform(size=size, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size, warn=False))
assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
# well, it's really that this test w GPU doesn't make sense otw
assert u.dtype == 'float32'
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True), mode=mode_with_gpu)
assert any([isinstance(node.op,theano.sandbox.rng_mrg.GPU_mrg_uniform)
assert any([isinstance(node.op,
theano.sandbox.rng_mrg.GPU_mrg_uniform)
for node in f.maker.env.toposort()])
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = numpy.asarray(f(*input))
print 'GPU: random?[:10], random?[-10:]'
print gpu_out[0,0:10]
print gpu_out[-1,-10:]
#print 'GPU: random?[:10], random?[-10:]'
#print gpu_out[0, 0:10]
#print gpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg gpu', inputs=input)
numpy.testing.assert_array_almost_equal(cpu_out, gpu_out, decimal=6)
numpy.testing.assert_array_almost_equal(cpu_out, gpu_out,
decimal=6)
print ''
print 'ON CPU w Numpy with size=(%s):'%str(size)
#print ''
#print 'ON CPU w Numpy with size=(%s):' % str(size)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.uniform(size=size)
ff = theano.function(var_input, uu, mode=mode)
# It's not our problem if numpy generates 0 or 1
basictest(ff, steps, sample_size, prefix='numpy', allow_01=True, inputs=input)
basictest(ff, steps, sample_size, prefix='numpy',
allow_01=True, inputs=input)
def test_binomial():
#TODO: test size=None, ndim=X
......@@ -409,14 +440,14 @@ def test_binomial():
#we test size in a tuple of int and a tensor.shape.
#we test the param p with int.
if mode in ['DEBUG_MODE','DebugMode','FAST_COMPILE']:
sample_size = (10,50)
if mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE']:
sample_size = (10, 50)
steps = 50
rtol=0.02
rtol = 0.02
else:
sample_size = (500,50)
sample_size = (500, 50)
steps = int(1e3)
rtol=0.01
rtol = 0.01
x = tensor.matrix()
v = tensor.vector()
......@@ -426,114 +457,137 @@ def test_binomial():
(x.shape, [x], [numpy.zeros(sample_size, dtype=config.floatX)])
]:
print ''
print 'ON CPU with size=(%s) and mean(%d):'%(str(size),mean)
#print ''
#print 'ON CPU with size=(%s) and mean(%d):' % (str(size), mean)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
u = R.binomial(size=size, p=mean,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, u, mode=mode)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
out = f(*input)
print 'random?[:10]\n', out[0,0:10]
print 'random?[-1,-10:]\n', out[-1,-10:]
basictest(f, steps, sample_size, prefix='mrg cpu', inputs=input, allow_01=True, target_avg = mean, mean_rtol=rtol)
if mode!='FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU with size=(%s) and mean(%d):'%(str(size),mean)
#print 'random?[:10]\n', out[0, 0:10]
#print 'random?[-1,-10:]\n', out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg cpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
if mode != 'FAST_COMPILE' and cuda_available:
#print ''
#print 'ON GPU with size=(%s) and mean(%d):' % (str(size), mean)
R = MRG_RandomStreams(234, use_cuda=True)
u = R.binomial(size=size, p=mean, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size,
warn=False))
assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
#well, it's really that this test w GPU doesn't make sense otw
assert u.dtype == 'float32'
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True), mode=mode_with_gpu)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = numpy.asarray(f(*input))
print 'random?[:10]\n', gpu_out[0,0:10]
print 'random?[-1,-10:]\n', gpu_out[-1,-10:]
basictest(f, steps, sample_size, prefix='mrg gpu', inputs=input, allow_01=True, target_avg = mean, mean_rtol=rtol)
numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
print ''
print 'ON CPU w NUMPY with size=(%s) and mean(%d):'%(str(size),mean)
#print 'random?[:10]\n', gpu_out[0, 0:10]
#print 'random?[-1,-10:]\n', gpu_out[-1, -10:]
basictest(f, steps, sample_size, prefix='mrg gpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
numpy.testing.assert_array_almost_equal(out, gpu_out,
decimal=6)
#print ''
#print 'ON CPU w NUMPY with size=(%s) and mean(%d):' % (str(size),
# mean)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.binomial(size=size, p=mean)
ff = theano.function(var_input, uu, mode=mode)
# It's not our problem if numpy generates 0 or 1
basictest(ff, steps, sample_size, prefix='numpy', allow_01=True, inputs=input, target_avg = mean, mean_rtol=rtol)
basictest(ff, steps, sample_size, prefix='numpy', allow_01=True,
inputs=input, target_avg=mean, mean_rtol=rtol)
def test_normal0():
steps = 50
std = 2.
if mode in ['DEBUG_MODE','DebugMode','FAST_COMPILE']:
sample_size = (25,30)
default_rtol=.02
if mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE']:
sample_size = (25, 30)
default_rtol = .02
else:
sample_size = (999,50)
default_rtol=.01
sample_size_odd = (sample_size[0],sample_size[1]-1)
sample_size = (999, 50)
default_rtol = .01
sample_size_odd = (sample_size[0], sample_size[1] - 1)
x = tensor.matrix()
for size, const_size, var_input, input, avg, rtol in [
(sample_size, sample_size, [], [], -5., default_rtol),
(x.shape, sample_size, [x], [numpy.zeros(sample_size, dtype=config.floatX)], -5., default_rtol),
(sample_size_odd, sample_size_odd, [], [], -5., default_rtol),#test odd value
(x.shape, sample_size_odd, [x], [numpy.zeros(sample_size_odd, dtype=config.floatX)], -5., default_rtol),#test odd value
(sample_size, sample_size, [], [], numpy.arange(numpy.prod(sample_size), dtype='float32').reshape(sample_size), 10.*std/numpy.sqrt(steps)),
(sample_size, sample_size, [], [], -5., default_rtol),
(x.shape, sample_size, [x],
[numpy.zeros(sample_size, dtype=config.floatX)],
-5., default_rtol),
#test odd value
(sample_size_odd, sample_size_odd, [], [], -5., default_rtol),
#test odd value
(x.shape, sample_size_odd, [x],
[numpy.zeros(sample_size_odd, dtype=config.floatX)],
-5., default_rtol),
(sample_size, sample_size, [], [],
numpy.arange(numpy.prod(sample_size),
dtype='float32').reshape(sample_size),
10. * std / numpy.sqrt(steps)),
]:
print ''
print 'ON CPU:'
#print ''
#print 'ON CPU:'
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
n = R.normal(size=size, avg=avg, std=std,
nstreams=rng_mrg.guess_n_streams(size, warn=False))
f = theano.function(var_input, n, mode=mode)
theano.printing.debugprint(f)
out = f(*input)
print 'random?[:10]\n', out[0,0:10]
basictest(f, steps, const_size, target_avg=avg, target_std=std, prefix='mrg ', allow_01=True, inputs=input, mean_rtol=rtol)
#theano.printing.debugprint(f)
out = f(*input)
#print 'random?[:10]\n', out[0, 0:10]
basictest(f, steps, const_size, target_avg=avg, target_std=std,
prefix='mrg ', allow_01=True, inputs=input, mean_rtol=rtol)
sys.stdout.flush()
if mode!='FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU:'
if mode != 'FAST_COMPILE' and cuda_available:
#print ''
#print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
n = R.normal(size=size, avg=avg, std=std, dtype='float32',
nstreams=rng_mrg.guess_n_streams(size, warn=False))
assert n.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
#well, it's really that this test w GPU doesn't make sense otw
assert n.dtype == 'float32'
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
borrow=True), mode=mode_with_gpu)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
sys.stdout.flush()
gpu_out = numpy.asarray(f(*input))
print 'random?[:10]\n', gpu_out[0,0:10]
print '----'
#print 'random?[:10]\n', gpu_out[0, 0:10]
#print '----'
sys.stdout.flush()
basictest(f, steps, const_size, target_avg=avg, target_std=std, prefix='gpu mrg ', allow_01=True, inputs=input, mean_rtol=rtol)
basictest(f, steps, const_size, target_avg=avg, target_std=std,
prefix='gpu mrg ', allow_01=True, inputs=input,
mean_rtol=rtol)
# Need to allow some rounding error as their is float
# computation that are done on the gpu vs cpu
assert numpy.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)
print ''
print 'ON CPU w NUMPY:'
#print ''
#print 'ON CPU w NUMPY:'
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
nn = RR.normal(size=size, avg=avg, std=std)
ff = theano.function(var_input, nn)
basictest(ff, steps, const_size, target_avg=avg, target_std=std, prefix='numpy ', allow_01=True, inputs=input, mean_rtol=rtol)
basictest(ff, steps, const_size, target_avg=avg, target_std=std,
prefix='numpy ', allow_01=True, inputs=input, mean_rtol=rtol)
def basic_multinomialtest(f, steps, sample_size, target_pvals, prefix="", mean_rtol=0.04):
def basic_multinomialtest(f, steps, sample_size, target_pvals,
prefix="", mean_rtol=0.04):
dt = 0.0
avg_pvals = numpy.zeros(target_pvals.shape, dtype=config.floatX)
......@@ -541,18 +595,20 @@ def basic_multinomialtest(f, steps, sample_size, target_pvals, prefix="", mean_r
for i in xrange(steps):
t0 = time.time()
ival = f()
assert ival.shape==sample_size
assert ival.shape == sample_size
dt += time.time() - t0
#ival = numpy.asarray(ival)
avg_pvals += ival
avg_pvals/= steps
avg_pvals /= steps
print 'random?[:10]\n', numpy.asarray(f()[:10])
print prefix, 'mean', avg_pvals
print numpy.mean(abs(avg_pvals - target_pvals))# < mean_rtol, 'bad mean? %s %s' % (str(avg_pvals), str(target_pvals))
# < mean_rtol, 'bad mean? %s %s' % (str(avg_pvals), str(target_pvals))
print numpy.mean(abs(avg_pvals - target_pvals))
print prefix, 'time', dt
print prefix, 'elements', steps*numpy.prod(target_pvals.shape)
print prefix, 'samples/sec', steps*numpy.prod(target_pvals.shape) / dt
print prefix, 'elements', steps * numpy.prod(target_pvals.shape)
print prefix, 'samples/sec', steps * numpy.prod(target_pvals.shape) / dt
def test_multinomial():
......@@ -561,40 +617,41 @@ def test_multinomial():
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if mode in ['DEBUG_MODE','DebugMode','FAST_COMPILE']:
sample_size = (49,5)
if mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE']:
sample_size = (49, 5)
else:
sample_size = (450,6)
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
print ''
print 'ON CPU:'
#print ''
#print 'ON CPU:'
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row : row/numpy.sum(row), 1, pvals)
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
out = f()
basic_multinomialtest(f, steps, sample_size, pvals, prefix='mrg ')
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
print ''
print 'ON GPU:'
#print ''
#print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
# We give the number of streams to avoid a warning.
n = R.multinomial(pvals=pvals, dtype='float32', nstreams=30 * 256)
assert n.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
#well, it's really that this test w GPU doesn't make sense otw
assert n.dtype == 'float32'
f = theano.function(
[],
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
mode=mode_.including('gpu'))
theano.printing.debugprint(f)
#theano.printing.debugprint(f)
gpu_out = f()
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, prefix='gpu mrg ')
......
theano/sparse/tests/test_opt.py
浏览文件 @ fc75dbf9
......@@ -7,14 +7,14 @@ except ImportError:
import theano
from theano import config, tensor
from theano.sparse import (enable_sparse, CSM, CSMProperties, csm_properties,
CSC, CSR)
from theano.sparse.tests.test_basic import random_lil
from theano.sparse import enable_sparse
from theano.gof.python25 import any
if not enable_sparse:
raise SkipTest('Optional package sparse disabled')
from theano.sparse import CSM, CSMProperties, csm_properties, CSC, CSR
from theano.sparse.tests.test_basic import random_lil
def test_local_csm_properties_csm():
data = tensor.vector()
......
theano/sparse/tests/test_sp2.py
浏览文件 @ fc75dbf9
......@@ -13,13 +13,13 @@ import theano
from theano import tensor as T
from theano import sparse as S
if not S.enable_sparse:
raise SkipTest('Optional package sparse disabled')
from theano.sparse.sandbox import sp2 as S2
from theano.tests import unittest_tools as utt
if S.enable_sparse == False:
raise SkipTest('Optional package sparse disabled')
def as_sparse_format(data, format):
if format == 'csc':
return scipy.sparse.csc_matrix(data)
......
theano/sparse/tests/test_utils.py
浏览文件 @ fc75dbf9
import numpy
import theano.sparse
if not theano.sparse.enable_sparse:
raise SkipTest('Optional package sparse disabled')
from theano.sparse.utils import hash_from_sparse
from theano.sparse.tests.test_basic import as_sparse_format
......
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