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提交 096c375f authored 作者: Frederic Bastien's avatar Frederic Bastien
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make the test faster in debug mode.

上级 d5c66369
隐藏空白字符变更
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正在显示 包含 30 行增加 和 22 行删除
theano/sandbox/cuda/tests/test_nnet.py
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......@@ -5,6 +5,7 @@ from theano.compile.sharedvalue import shared
from theano.compile.pfunc import pfunc
from theano import tensor
import theano.tensor.nnet
from theano import config
import theano.tensor.nnet.conv as conv
import theano.tensor.signal.downsample as downsample
......@@ -48,8 +49,10 @@ def print_diff_mode(a,b):
if a != None and isinstance(a,(theano.compile.ProfileMode,)) and isinstance(b,(theano.compile.ProfileMode,)):
a.print_diff_summary(b)
def run_nnet(use_gpu, n_batch=60, n_in=1024, n_hid=2048, n_out=10, n_iter=100):
def run_nnet(use_gpu, n_batch=60, n_in=1024, n_hid=2048, n_out=10, n_train=100):
if config.mode=='DEBUG_MODE': n_train=1
if use_gpu:
w = tcn.shared_constructor(0.01*(my_rand(n_in,n_hid)-0.5), 'w')
b = tcn.shared_constructor(my_zeros(n_hid), 'b')
......@@ -88,7 +91,7 @@ def run_nnet(use_gpu, n_batch=60, n_in=1024, n_hid=2048, n_out=10, n_iter=100):
t0 = time.time()
rval = []
for i in xrange(n_iter):
for i in xrange(n_train):
rval.append(train(xval, yval, lr))
dt = time.time() - t0
......@@ -110,11 +113,11 @@ def test_run_nnet():
def test_run_nnet_med():
numpy.random.seed(23456)
rval_cpu = run_nnet(False, 10, 128, 50, 4, n_iter=10000)
rval_cpu = run_nnet(False, 10, 128, 50, 4, n_train=10000)
def test_run_nnet_small():
numpy.random.seed(23456)
rval_cpu = run_nnet(False, 10, 10, 4, 4, n_iter=100000)
rval_cpu = run_nnet(False, 10, 10, 4, 4, n_train=100000)
def run_conv_nnet1(use_gpu):
if use_gpu:
......@@ -125,6 +128,8 @@ def run_conv_nnet1(use_gpu):
n_kern = 20
shape_img = (n_batch, 1, 32, 32)
shape_kern = (n_kern, 1, 5, 5)
n_train=10
if config.mode=='DEBUG_MODE': n_train=1
logical_hid_shape = tcn.blas.GpuConv.logical_output_shape_2d(shape_img[2:],shape_kern[2:], 'valid')
n_hid = n_kern * logical_hid_shape[0] * logical_hid_shape[1]
......@@ -163,7 +168,7 @@ def run_conv_nnet1(use_gpu):
yval = my_rand(n_batch, n_out)
lr = theano._asarray(0.01, dtype='float32')
for i in xrange(10):
for i in xrange(n_train):
rval = train(xval, yval, lr)
print 'training done'
print_mode(mode)
......@@ -205,6 +210,7 @@ def run_conv_nnet2(use_gpu): # pretend we are training LeNet for MNIST
shape_kern1 = (n_kern1, n_kern, 5, 5)
n_train=30
if config.mode=='DEBUG_MODE': n_train=1
logical_hid_shape = tcn.blas.GpuConv.logical_output_shape_2d(tuple(shape_img[2:]),tuple(shape_kern[2:]), 'valid')
logical_hid_shape1 = tcn.blas.GpuConv.logical_output_shape_2d((logical_hid_shape[0]/2, logical_hid_shape[1]/2), tuple(shape_kern1[2:]), 'valid')
......@@ -263,7 +269,7 @@ def test_conv_nnet2():
print rval_cpu[0], rval_gpu[0],rval_cpu[0]-rval_gpu[0]
assert numpy.allclose(rval_cpu, rval_gpu,rtol=1e-4,atol=1e-4)
def run_conv_nnet2_classif(use_gpu, isize, ksize, n_batch, n_iter,
def run_conv_nnet2_classif(use_gpu, isize, ksize, n_batch, n_train,
downsample_ops=True, verbose=0, version=-1):
if use_gpu:
shared_fn = tcn.shared_constructor
......@@ -341,9 +347,9 @@ def run_conv_nnet2_classif(use_gpu, isize, ksize, n_batch, n_iter,
yval = my_rand(n_batch,n_out)
lr = theano._asarray(0.01, dtype='float32')
rvals=my_zeros(n_iter)
rvals=my_zeros(n_train)
t0 = time.time()
for i in xrange(n_iter):
for i in xrange(n_train):
rvals[i] = train(xval, yval, lr)[0]
t1 = time.time()
print_mode(mode)
......@@ -351,7 +357,7 @@ def run_conv_nnet2_classif(use_gpu, isize, ksize, n_batch, n_iter,
def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
ignore_error=False,
n_iter=10,
n_train=10,
gpu_only=False,
cpu_only=False,
float_atol=1e-06,
......@@ -364,6 +370,8 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
check_isfinite: the debug mode option. We forward this value to debug mode.
For some parameter CrossentropyCategorical1Hot op generate inf when not optimized.
"""
if config.mode=='DEBUG_MODE': n_train=1
numpy.random.seed(seed)
import theano.tensor.basic
......@@ -381,7 +389,7 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
theano.tensor.basic.float32_atol=float_atol
if not cpu_only:
rval_gpu, tg, gpu_mode = run_conv_nnet2_classif(True,
isize, ksize, bsize, n_iter, verbose=verbose, version=version)
isize, ksize, bsize, n_train, verbose=verbose, version=version)
finally:
predefined_modes["DEBUG_MODE"].check_isfinite = orig_check_isfinite
theano.tensor.basic.float32_atol=orig_float32_atol
......@@ -393,7 +401,7 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
try:
predefined_modes["DEBUG_MODE"].check_isfinite = check_isfinite
numpy.random.seed(seed)
rval_cpu, tc, cpu_mode = run_conv_nnet2_classif(False, isize, ksize, bsize, n_iter,
rval_cpu, tc, cpu_mode = run_conv_nnet2_classif(False, isize, ksize, bsize, n_train,
verbose=verbose, version=version)
if pickle and isinstance(cpu_mode,(theano.compile.ProfileMode,)):
import pickle
......@@ -414,11 +422,11 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
print "gpu:", rval_gpu
print "abs diff:", numpy.absolute(rval_gpu-rval_cpu)
print "time cpu: %.3f, time gpu: %.3f, speed up %f"%(tc, tg, tc/tg)
print "estimated time for one pass through MNIST with cpu: %f" % (tc * (60000.0 / (n_iter*bsize)))
print "estimated time for one pass through MNIST with gpu: %f" % (tg * (60000.0 / (n_iter*bsize)))
print "estimated time for one pass through MNIST with cpu: %f" % (tc * (60000.0 / (n_train*bsize)))
print "estimated time for one pass through MNIST with gpu: %f" % (tg * (60000.0 / (n_train*bsize)))
else:
print "time cpu: %.3f"%(tc)
print "estimated time for one pass through MNIST with cpu: %f" % (tc * (60000.0 / (n_iter*bsize)))
print "estimated time for one pass through MNIST with cpu: %f" % (tc * (60000.0 / (n_train*bsize)))
if not ignore_error and not cpu_only and not gpu_only:
assert numpy.allclose(rval_cpu, rval_gpu,rtol=1e-3,atol=float_atol)
......@@ -430,52 +438,52 @@ verbose=0
version=-1
def test_lenet_28(): #MNIST
cmp_run_conv_nnet2_classif(23485, 28, 5, 60, n_iter=10,
cmp_run_conv_nnet2_classif(23485, 28, 5, 60, n_train=10,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose, version=version)
def test_lenet_32(): #CIFAR10 / Shapeset
cmp_run_conv_nnet2_classif(23485, 32, 5, 60, n_iter=10,
cmp_run_conv_nnet2_classif(23485, 32, 5, 60, n_train=10,
ignore_error=ignore_error, gpu_only=gpu_only,
verbose=verbose, version=version)
def test_lenet_32_long(): #CIFAR10 / Shapeset
# this tests the gradient of downsample on the GPU,
# which does not recieve specific testing
cmp_run_conv_nnet2_classif(23485, 32, 5, 30, n_iter=50,
cmp_run_conv_nnet2_classif(23485, 32, 5, 30, n_train=50,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose, version=version)
def test_lenet_64(): # ???
#float_atol need to pass in debug mode
#needed as cpu use extended precision and gpu don't
cmp_run_conv_nnet2_classif(23485, 64, 7, 10, n_iter=10,
cmp_run_conv_nnet2_classif(23485, 64, 7, 10, n_train=10,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose,
float_atol=5e-4, check_isfinite=True, version=version)
def test_lenet_108(): # NORB
cmp_run_conv_nnet2_classif(23485, 108, 7, 5, n_iter=4,
cmp_run_conv_nnet2_classif(23485, 108, 7, 5, n_train=4,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose,
check_isfinite=True, version=version, float_atol=7e-2)
def test_lenet_256(): # ImageNet
cmp_run_conv_nnet2_classif(23485, 256, 9, 2, n_iter=5,
cmp_run_conv_nnet2_classif(23485, 256, 9, 2, n_train=5,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose,
check_isfinite=True, version=version)
#I did a wanted error in the name as we don't want it to execute automatically for now as it don't work
def tes_lenet_hd(): #HD 720p: 1280(wid)x720(len)
cmp_run_conv_nnet2_classif(23485, (720,1280), 9, 2, n_iter=3,
cmp_run_conv_nnet2_classif(23485, (720,1280), 9, 2, n_train=3,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose,
check_isfinite=True, version=version)
#I did a wanted error in the name as we don't want it to execute automatically for now as it don't work
def tes_lenet_full_hd(): #HD 1080p: 1920(wid)x1080(len)
cmp_run_conv_nnet2_classif(23485, (1080,1920), 9, 2, n_iter=3,
cmp_run_conv_nnet2_classif(23485, (1080,1920), 9, 2, n_train=3,
ignore_error=ignore_error, gpu_only=gpu_only,
cpu_only=cpu_only, verbose=verbose,
check_isfinite=True, version=version)
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