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提交 b8d5586c authored 作者: slefrancois's avatar slefrancois
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remove harcoded float32 in gpuarray testdnn

上级 c85ac411
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正在显示 包含 65 行增加 和 58 行删除
theano/gpuarray/tests/test_dnn.py
浏览文件 @ b8d5586c
......@@ -23,6 +23,12 @@ from .rnn_support import Model, GRU, LSTM, WrapperLayer
from theano.configdefaults import SUPPORTED_DNN_CONV_ALGO_FWD
# If using float16, set CUDNN precision to float32
if theano.config.floatX == "float16":
precision = "float32"
else:
precision = theano.config.floatX
def test_dnn_conv_desc_merge():
if not dnn.dnn_available(test_ctx_name):
......@@ -50,11 +56,10 @@ def test_dnn_conv_merge():
raise SkipTest(dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
kern = T.ftensor4('kern')
out = T.ftensor4('out')
desc = dnn.GpuDnnConvDesc(
border_mode='valid')(kern.shape)
img = T.tensor4('img')
kern = T.tensor4('kern')
out = T.tensor4('out')
desc = dnn.GpuDnnConvDesc(border_mode='valid')(kern.shape)
# Test forward op
o1 = dnn.dnn_conv(img, kern)
......@@ -89,9 +94,9 @@ def test_dnn_conv_inplace():
raise SkipTest(dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
kern = T.ftensor4('kern')
out = T.ftensor4('out')
img = T.tensor4('img')
kern = T.tensor4('kern')
out = T.tensor4('out')
desc1 = dnn.GpuDnnConvDesc(border_mode='valid', conv_mode='conv')(
kern.shape)
desc2 = dnn.GpuDnnConvDesc(
......@@ -142,7 +147,7 @@ def test_pooling():
else:
modes = ('max', 'average_inc_pad', 'average_exc_pad')
x = T.ftensor4()
x = T.tensor4()
for mode, pad in product(modes,
((0, 0), (1, 0), (0, 1), (2, 3), (3, 2))):
if pad != (0, 0) and mode == 'average_exc_pad':
......@@ -226,7 +231,7 @@ def test_pooling():
def test_pooling_with_tensor_vars():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
x = T.ftensor4()
x = T.tensor4()
ws = theano.shared(numpy.array([2, 2], dtype='int32'))
st = theano.shared(numpy.array([1, 1], dtype='int32'))
pad = theano.shared(numpy.array([0, 0], dtype='int32'))
......@@ -291,7 +296,7 @@ def test_pooling3d():
else:
modes = ('max', 'average_inc_pad', 'average_exc_pad')
x = T.ftensor5()
x = T.tensor5()
for mode, pad in product(modes,
((0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
(2, 3, 2), (3, 2, 2), (2, 2, 3))):
......@@ -370,7 +375,7 @@ def test_pooling_opt():
raise SkipTest(dnn.dnn_available.msg)
# 2D pooling
x = T.fmatrix()
x = T.matrix()
f = theano.function(
[x],
......@@ -409,7 +414,7 @@ def test_pooling_opt():
f(data)
# 3D pooling
x = T.ftensor3()
x = T.tensor3()
f = theano.function(
[x],
......@@ -491,7 +496,7 @@ def test_dnn_tag():
"""
Test that if cudnn isn't avail we crash and that if it is avail, we use it.
"""
x = T.ftensor4()
x = T.tensor4()
old = theano.config.on_opt_error
theano.config.on_opt_error = "raise"
......@@ -533,7 +538,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_softmax(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
t = T.ftensor4('t')
t = T.tensor4('t')
rand_tensor = numpy.asarray(
numpy.random.rand(5, 4, 3, 2),
dtype=theano.config.floatX
......@@ -576,7 +581,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
desc = dnn.GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode
conv_mode=conv_mode,
precision=precision
)(kerns.shape)
conv = dnn.GpuDnnConv(algo=algo)(img, kerns, out, desc)
self._compile_and_check(
......@@ -597,9 +603,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
if algo == 'winograd' and dnn.version(raises=False) < 5000:
raise SkipTest(dnn.dnn_available.msg)
self._test_conv(T.ftensor4('img'),
T.ftensor4('kerns'),
T.ftensor4('out'),
self._test_conv(T.tensor4('img'),
T.tensor4('kerns'),
T.tensor4('out'),
numpy.random.rand(7, 2, 8, 4),
numpy.random.rand(8, 2, 4, 3),
border_mode,
......@@ -609,9 +615,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
@parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
def test_conv3d_none(self, border_mode, conv_mode):
self._test_conv(T.ftensor5('img'),
T.ftensor5('kerns'),
T.ftensor5('out'),
self._test_conv(T.tensor5('img'),
T.tensor5('kerns'),
T.tensor5('out'),
numpy.random.rand(10, 2, 6, 4, 11),
numpy.random.rand(8, 2, 4, 3, 1),
border_mode,
......@@ -646,7 +652,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
desc = dnn.GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode
conv_mode=conv_mode,
precision=precision
)(out.shape)
conv_grad_w = dnn.GpuDnnConvGradW()(
temp_img,
......@@ -663,9 +670,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
@parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
def test_conv_gradw(self, border_mode, conv_mode):
self._test_conv_gradw(T.ftensor4('img'),
T.ftensor4('kerns'),
T.ftensor4('out'),
self._test_conv_gradw(T.tensor4('img'),
T.tensor4('kerns'),
T.tensor4('out'),
numpy.random.rand(2, 5, 6, 8),
numpy.random.rand(2, 1, 5, 6),
border_mode,
......@@ -675,9 +682,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_conv_gradi(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
kerns = T.ftensor4('kerns')
out = T.ftensor4('out')
img = T.tensor4('img')
kerns = T.tensor4('kerns')
out = T.tensor4('out')
kern_vals = numpy.asarray(
numpy.random.rand(13, 14, 15, 16),
dtype=theano.config.floatX
......@@ -701,7 +708,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
desc = dnn.GpuDnnConvDesc(
border_mode=params[0],
subsample=params[1],
conv_mode=params[2]
conv_mode=params[2],
precision=precision
)(kerns.shape)
conv_grad_i = dnn.GpuDnnConvGradI()(
kerns,
......@@ -719,7 +727,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_pool(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
img = T.tensor4('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype=theano.config.floatX
......@@ -746,7 +754,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_pool_3d(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor5('img')
img = T.tensor5('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype=theano.config.floatX
......@@ -773,9 +781,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_pool_grad(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
img_grad = T.ftensor4('img_grad')
out = T.ftensor4('out')
img = T.tensor4('img')
img_grad = T.tensor4('img_grad')
out = T.tensor4('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype=theano.config.floatX
......@@ -812,9 +820,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_pool_3d_grad(self):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor5('img')
img_grad = T.ftensor5('img_grad')
out = T.ftensor5('out')
img = T.tensor5('img')
img_grad = T.tensor5('img_grad')
out = T.tensor5('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype=theano.config.floatX
......@@ -853,8 +861,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
def test_dnn_conv_border_mode():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
img = T.tensor4()
kern = T.tensor4()
dnn.dnn_conv(img, kern, border_mode=1)
dnn.dnn_conv(img, kern, border_mode=(2, 3))
......@@ -866,9 +874,9 @@ def test_dnn_conv_border_mode():
def test_dnn_conv_alpha_output_merge():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
out = T.ftensor4()
img = T.tensor4()
kern = T.tensor4()
out = T.tensor4()
b = 1
c = 4
......@@ -943,18 +951,18 @@ def test_dnn_conv_grad():
def dconv(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(kern.shape)
conv_mode='conv', precision=precision)(kern.shape)
return dnn.GpuDnnConv()(img, kern, out, desc, alpha=0.5, beta=0.75)
def dconvi(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(kern.shape)
conv_mode='conv', precision=precision)(kern.shape)
return dnn.GpuDnnConvGradI()(kern, out, img, desc, alpha=-1.0,
beta=0.0)
def dconvw(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(kern.shape)
conv_mode='conv', precision=precision)(kern.shape)
return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
beta=-1.0)
......@@ -1146,8 +1154,8 @@ class test_SoftMax(test_nnet.test_SoftMax):
gout = numpy.asarray(f_gpu(gdata))[:, :, 0, 0]
utt.assert_allclose(out, gout)
x = T.matrix('x', theano.config.floatX)
x_gpu = T.tensor4('x_gpu', theano.config.floatX)
x = T.matrix('x')
x_gpu = T.tensor4('x_gpu')
f_z = T.nnet.softmax_op
f_gpu = dnn.GpuDnnSoftmax(
'accurate',
......@@ -1180,7 +1188,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
# Verify that the SoftmaxGrad -> Gpu[Dnn]SoftmaxGrad
# optimization is applied when cudnn is required
y = T.fvector('y')
y = T.vector('y')
f = theano.function(
[y],
T.grad(T.nnet.softmax(y).mean(), y),
......@@ -1206,7 +1214,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
# optimization is not applied when cudnn is excluded or not
# available
mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
y = T.fvector('y')
y = T.vector('y')
f = theano.function(
[y],
T.grad(T.nnet.softmax(y).mean(), y),
......@@ -1230,7 +1238,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
# Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad do not
# crash with manual graph
y = T.fvector('y')
y = T.vector('y')
o = theano.tensor.nnet.SoftmaxGrad()(y, y * 2)
f = theano.function([y], o, mode=mode_with_gpu)
sorted_f = f.maker.fgraph.toposort()
......@@ -1253,7 +1261,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
if dnn.version(raises=False) < 3000:
raise SkipTest("Log-softmax is only in cudnn v3+")
x = T.ftensor4()
x = T.tensor4()
softmax_out = dnn.GpuDnnSoftmax('accurate', 'channel')(x)
log_out = T.log(T.as_tensor_variable(softmax_out))
......@@ -1296,7 +1304,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
# Compile a reference function, on the CPU, to be used to validate the
# results of the other function.
x = T.fmatrix()
x = T.matrix()
f_ref = theano.function([x], T.nnet.LogSoftmax()(x))
# Build the first graph and ensure that the optimization is applied
......@@ -1334,7 +1342,7 @@ def test_dnn_batchnorm_train():
utt.seed_rng()
for mode in ('per-activation', 'spatial'):
for vartype in (T.ftensor5, T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
for vartype in (T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
x, scale, bias = (vartype(n) for n in ('x', 'scale', 'bias'))
ndim = x.ndim
eps = 5e-3 # some non-standard value to test if it's used
......@@ -1389,10 +1397,9 @@ def test_batchnorm_inference():
utt.seed_rng()
for mode in ('per-activation', 'spatial'):
for vartype in (T.ftensor5, T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
x, scale, bias, mean, var = (vartype(n) for n in ('x', 'scale',
'bias', 'mean',
'var'))
for vartype in (T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
x, scale, bias, mean, var = (vartype(n)
for n in ('x', 'scale', 'bias', 'mean', 'var'))
ndim = x.ndim
eps = 5e-3 # some non-standard value to test if it's used
......
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