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提交 c85ac411 authored 作者: slefrancois's avatar slefrancois
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use floatX in gpuarray dnn tests

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theano/gpuarray/tests/test_dnn.py
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......@@ -60,8 +60,8 @@ def test_dnn_conv_merge():
o1 = dnn.dnn_conv(img, kern)
o2 = dnn.dnn_conv(img, kern)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
numpy.random.rand(*kern_shp).astype('float32'))
d1, d2 = f(numpy.random.rand(*img_shp).astype(theano.config.floatX),
numpy.random.rand(*kern_shp).astype(theano.config.floatX))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]) == 1
......@@ -101,8 +101,8 @@ def test_dnn_conv_inplace():
o1 = dnn.dnn_conv(img, kern, conv_mode='conv')
o2 = dnn.dnn_conv(img, kern, conv_mode='cross')
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
numpy.random.rand(*kern_shp).astype('float32'))
d1, d2 = f(numpy.random.rand(*img_shp).astype(theano.config.floatX),
numpy.random.rand(*kern_shp).astype(theano.config.floatX))
topo = f.maker.fgraph.toposort()
convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]
assert len(convs) == 2
......@@ -180,7 +180,7 @@ def test_pooling():
(1, 3, 99, 99),
(32, 1, 147, 197),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX)
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b)
......@@ -188,7 +188,7 @@ def test_pooling():
# Test the grad
for shp in [(1, 1, 2, 2),
(1, 1, 3, 3)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX) * 10
ws = 2
stride = 2
......@@ -242,7 +242,7 @@ def test_pooling_with_tensor_vars():
for shp in [(1, 1, 2, 2),
(1, 1, 3, 3)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX) * 10
theano.tests.unittest_tools.verify_grad(
fn, [data], mode=mode_with_gpu)
......@@ -266,7 +266,7 @@ def test_pooling_with_tensor_vars():
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
(32, 1, 147, 197)]:
data = numpy.random.normal(0, 1, shp).astype("float32")
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX)
# Change the window size dynamically
ws.set_value(numpy.array([i, i]).astype('int32'))
......@@ -327,11 +327,11 @@ def test_pooling3d():
(1, 3, 99, 99, 29),
(2, 1, 147, 97, 37),
]:
data = numpy.random.normal(0, 1, shp).astype("float32")
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX)
a = f_cpu(data).__array__()
b = f_gpu(data).__array__()
utt.assert_allclose(a, b,
atol=numpy.finfo(numpy.float32).eps)
atol=numpy.finfo(theano.config.floatX).eps)
# Test the grad
for shp in [(1, 1, 2, 2, 2),
......@@ -341,7 +341,7 @@ def test_pooling3d():
(1, 1, 4, 3, 3),
(1, 1, 4, 4, 4),
(1, 1, 5, 5, 5)]:
data = numpy.random.normal(0, 1, shp).astype("float32") * 10
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX) * 10
ws = 2
stride = 2
......@@ -381,7 +381,7 @@ def test_pooling_opt():
assert any([isinstance(n.op, dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
f(numpy.zeros((10, 10), dtype='float32'))
f(numpy.zeros((10, 10), dtype=theano.config.floatX))
# gradient of 2D pooling
f = theano.function(
......@@ -394,7 +394,7 @@ def test_pooling_opt():
assert any([isinstance(n.op, dnn.GpuDnnPoolGrad)
for n in f.maker.fgraph.toposort()])
f(numpy.zeros((10, 10), dtype='float32'))
f(numpy.zeros((10, 10), dtype=theano.config.floatX))
# Test sum pooling
f = theano.function(
......@@ -405,7 +405,7 @@ def test_pooling_opt():
assert any([isinstance(n.op, dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
data = numpy.random.rand(10, 10).astype('float32')
data = numpy.random.rand(10, 10).astype(theano.config.floatX)
f(data)
# 3D pooling
......@@ -420,7 +420,7 @@ def test_pooling_opt():
assert any([isinstance(n.op, dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
f(numpy.zeros((10, 10, 10), dtype='float32'))
f(numpy.zeros((10, 10, 10), dtype=theano.config.floatX))
# gradient of 3D pooling
f = theano.function(
......@@ -433,7 +433,7 @@ def test_pooling_opt():
assert any([isinstance(n.op, dnn.GpuDnnPoolGrad)
for n in f.maker.fgraph.toposort()])
f(numpy.zeros((10, 10, 10), dtype='float32'))
f(numpy.zeros((10, 10, 10), dtype=theano.config.floatX))
def test_pooling_opt_arbitrary_dimensions():
......@@ -454,7 +454,7 @@ def test_pooling_opt_arbitrary_dimensions():
# create input shape: non-pooling dimensions
# followed by 2 or 3 pooling dimensions
shp = tuple(range(2, 2 + n_non_pool_dims)) + tuple(range(5, 5 + len(ws)))
data = numpy.random.normal(0, 1, shp).astype('float32')
data = numpy.random.normal(0, 1, shp).astype(theano.config.floatX)
input = gpuarray_shared_constructor(data)
for mode in modes:
......@@ -536,7 +536,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
t = T.ftensor4('t')
rand_tensor = numpy.asarray(
numpy.random.rand(5, 4, 3, 2),
dtype='float32'
dtype=theano.config.floatX
)
self._compile_and_check(
[t],
......@@ -564,15 +564,15 @@ class TestDnnInferShapes(utt.InferShapeTester):
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
img_val = numpy.asarray(img_val, dtype='float32')
kern_vals = numpy.asarray(kern_vals, dtype='float32')
img_val = numpy.asarray(img_val, dtype=theano.config.floatX)
kern_vals = numpy.asarray(kern_vals, dtype=theano.config.floatX)
for subsample in subsamples:
out_vals = numpy.zeros(
dnn.GpuDnnConv.get_out_shape(img_val.shape, kern_vals.shape,
border_mode=border_mode,
subsample=subsample),
dtype='float32')
dtype=theano.config.floatX)
desc = dnn.GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
......@@ -625,11 +625,11 @@ class TestDnnInferShapes(utt.InferShapeTester):
img_val = numpy.asarray(
img_val,
dtype='float32'
dtype=theano.config.floatX
)
kern_vals = numpy.asarray(
kern_vals,
dtype='float32'
dtype=theano.config.floatX
)
temp_img = img.dimshuffle(1, 0, 2, 3)
......@@ -642,7 +642,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
img_val.shape[2] - kern_vals.shape[2] + 1,
img_val.shape[3] - kern_vals.shape[3] + 1
)
out_vals = numpy.zeros(shape, dtype='float32')
out_vals = numpy.zeros(shape, dtype=theano.config.floatX)
desc = dnn.GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
......@@ -680,11 +680,11 @@ class TestDnnInferShapes(utt.InferShapeTester):
out = T.ftensor4('out')
kern_vals = numpy.asarray(
numpy.random.rand(13, 14, 15, 16),
dtype='float32'
dtype=theano.config.floatX
)
out_vals = numpy.asarray(
numpy.random.rand(3, 13, 5, 6),
dtype='float32'
dtype=theano.config.floatX
)
for params in product(
......@@ -697,7 +697,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
out_vals.shape[2] + kern_vals.shape[2] - 1,
out_vals.shape[3] + kern_vals.shape[3] - 1
)
img_vals = numpy.zeros(shape, dtype='float32')
img_vals = numpy.zeros(shape, dtype=theano.config.floatX)
desc = dnn.GpuDnnConvDesc(
border_mode=params[0],
subsample=params[1],
......@@ -722,7 +722,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
img = T.ftensor4('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
dtype=theano.config.floatX
)
# 'average_exc_pad' is disabled for versions < 4004
......@@ -749,7 +749,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
img = T.ftensor5('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
dtype=theano.config.floatX
)
# 'average_exc_pad' is disabled for versions < 4004
......@@ -778,15 +778,15 @@ class TestDnnInferShapes(utt.InferShapeTester):
out = T.ftensor4('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
dtype=theano.config.floatX
)
img_grad_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
dtype=theano.config.floatX
)
out_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
dtype=theano.config.floatX
)
for params in product(
......@@ -817,15 +817,15 @@ class TestDnnInferShapes(utt.InferShapeTester):
out = T.ftensor5('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
dtype=theano.config.floatX
)
img_grad_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
dtype=theano.config.floatX
)
out_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5, 6),
dtype='float32'
dtype=theano.config.floatX
)
for params in product(
......@@ -877,16 +877,16 @@ def test_dnn_conv_alpha_output_merge():
iw = 8
kh = 2
kw = 6
img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
img_val = numpy.random.random((b, c, ih, iw)).astype(theano.config.floatX)
kern_val = numpy.random.random((f, c, kh, kw)).astype(theano.config.floatX)
out_val = numpy.random.random((b, f, ih - kh + 1,
iw - kw + 1)).astype('float32')
iw - kw + 1)).astype(theano.config.floatX)
conv = dnn.dnn_conv(img, kern)
gw = theano.grad(conv.sum(), kern)
gi = theano.grad(conv.sum(), img)
lr = numpy.asarray(0.05, dtype='float32')
lr = numpy.asarray(0.05, dtype=theano.config.floatX)
fr = lr * (conv + out)
wr = kern + lr * gw
......@@ -936,10 +936,10 @@ def test_dnn_conv_grad():
iw = 8
kh = 2
kw = 2
img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
img_val = numpy.random.random((b, c, ih, iw)).astype(theano.config.floatX)
kern_val = numpy.random.random((f, c, kh, kw)).astype(theano.config.floatX)
out_val = numpy.random.random((b, f, ih - kw + 1,
iw - kw + 1)).astype('float32')
iw - kw + 1)).astype(theano.config.floatX)
def dconv(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
......@@ -1006,8 +1006,8 @@ def test_conv3d_fwd():
def run_conv3d_fwd(inputs_shape, filters_shape, subsample,
border_mode, conv_mode):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs_val = numpy.random.random(inputs_shape).astype(theano.config.floatX)
filters_val = numpy.random.random(filters_shape).astype(theano.config.floatX)
# Scale down the input values to prevent very large absolute errors
# due to float rounding
......@@ -1055,8 +1055,8 @@ def test_conv3d_bwd():
def run_conv3d_bwd(inputs_shape, filters_shape, subsample,
border_mode, conv_mode):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs_val = numpy.random.random(inputs_shape).astype(theano.config.floatX)
filters_val = numpy.random.random(filters_shape).astype(theano.config.floatX)
inputs = theano.shared(inputs_val)
filters = theano.shared(filters_val)
......@@ -1139,15 +1139,15 @@ class test_SoftMax(test_nnet.test_SoftMax):
def test_softmax_grad(self):
def cmp(n, m, f, f_gpu):
data = numpy.arange(n * m, dtype='float32').reshape(n, m)
data = numpy.arange(n * m, dtype=theano.config.floatX).reshape(n, m)
gdata = numpy.asarray(data)[:, :, None, None]
out = f(data)
gout = numpy.asarray(f_gpu(gdata))[:, :, 0, 0]
utt.assert_allclose(out, gout)
x = T.matrix('x', 'float32')
x_gpu = T.tensor4('x_gpu', 'float32')
x = T.matrix('x', theano.config.floatX)
x_gpu = T.tensor4('x_gpu', theano.config.floatX)
f_z = T.nnet.softmax_op
f_gpu = dnn.GpuDnnSoftmax(
'accurate',
......@@ -1158,7 +1158,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
dims = (2, 3, 4, 5)
gdata = numpy.arange(
numpy.product(dims),
dtype='float32'
dtype=theano.config.floatX
).reshape(dims)
T.verify_grad(f_gpu, [gdata], rng=numpy.random,
mode=mode_with_gpu)
......@@ -1187,7 +1187,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
mode=mode_with_gpu
)
sorted_f = f.maker.fgraph.toposort()
val = numpy.random.rand(5).astype('float32')
val = numpy.random.rand(5).astype(theano.config.floatX)
out_dnn = f(val)
assert(len([i
for i in sorted_f
......@@ -1277,7 +1277,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
(2, 3, 4, 66000)]
for inp_shape in input_shapes:
input_val = numpy.random.normal(0, 1, inp_shape).astype("float32")
input_val = numpy.random.normal(0, 1, inp_shape).astype(theano.config.floatX)
out = f(input_val)
expected_out = numpy.log(numpy.exp(input_val) /
......@@ -1309,7 +1309,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
assert dnn_softmax_nodes[0].op.algo == "log"
# Compare the output of the function with the reference function
inp = numpy.random.normal(0, 1, (5, 6)).astype("float32")
inp = numpy.random.normal(0, 1, (5, 6)).astype(theano.config.floatX)
utt.assert_allclose(f(inp), f_ref(inp))
# Build the first graph and ensure that the optimization is applied
......@@ -1322,7 +1322,7 @@ class test_SoftMax(test_nnet.test_SoftMax):
assert dnn_softmax_nodes[0].op.algo == "log"
# Compare the output of the function with the reference function
inp = numpy.random.normal(0, 1, (5, 6)).astype("float32")
inp = numpy.random.normal(0, 1, (5, 6)).astype(theano.config.floatX)
utt.assert_allclose(f(inp), f_ref(inp))
......@@ -1366,10 +1366,10 @@ def test_dnn_batchnorm_train():
data_shape = data_shape[:ndim]
param_shape = tuple(1 if d in axes else s
for d, s in enumerate(data_shape))
X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
Scale = numpy.random.randn(*param_shape).astype('float32')
Bias = numpy.random.randn(*param_shape).astype('float32')
X = 4 + 3 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Scale = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Bias = numpy.random.randn(*param_shape).astype(theano.config.floatX)
outputs = f(X, Scale, Bias, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[0 + 3]) # out
......@@ -1420,12 +1420,12 @@ def test_batchnorm_inference():
data_shape = data_shape[:ndim]
param_shape = tuple(1 if d in axes else s
for d, s in enumerate(data_shape))
X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
Scale = numpy.random.randn(*param_shape).astype('float32')
Bias = numpy.random.randn(*param_shape).astype('float32')
Mean = numpy.random.randn(*param_shape).astype('float32')
Var = numpy.random.rand(*param_shape).astype('float32')
X = 4 + 3 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(theano.config.floatX)
Scale = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Bias = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Mean = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Var = numpy.random.rand(*param_shape).astype(theano.config.floatX)
outputs = f(X, Scale, Bias, Mean, Var, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[1]) # out
......
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