Skip to content

P
pytensor
提交 7d09193b authored 作者: Frederic Bastien's avatar Frederic Bastien
浏览文件
操作

In the old back-end, for float32 for tests

上级 6e45042c
隐藏空白字符变更
内嵌 并排
正在显示 包含 34 行增加 和 34 行删除
theano/sandbox/cuda/tests/test_dnn.py
浏览文件 @ 7d09193b
......@@ -732,7 +732,7 @@ def test_batchnorm_train():
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
tensor6 = T.TensorType(theano.config.floatX, (False,) * 6)
tensor6 = T.TensorType('float32', (False,) * 6)
for mode in ('per-activation', 'spatial'):
for vartype in (tensor6, T.ftensor5, T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
......@@ -766,7 +766,7 @@ def test_batchnorm_train():
x_invstd_ref = T.inv(T.sqrt(x_var_ref + eps))
scale_ref = T.addbroadcast(scale, *axes)
bias_ref = T.addbroadcast(bias, *axes)
m = T.cast(T.prod(x.shape) / T.prod(scale.shape), theano.config.floatX)
m = T.cast(T.prod(x.shape) / T.prod(scale.shape), 'float32')
out_ref = (x - x_mean_ref) * (scale_ref * x_invstd_ref) + bias_ref
out_running_mean_ref = running_mean * (1 - running_average_factor) + \
x_mean_ref * running_average_factor
......@@ -805,12 +805,12 @@ def test_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(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)
Running_mean = numpy.random.randn(*param_shape).astype(theano.config.floatX)
Running_var = numpy.random.randn(*param_shape).astype(theano.config.floatX)
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')
Running_mean = numpy.random.randn(*param_shape).astype('float32')
Running_var = numpy.random.randn(*param_shape).astype('float32')
outputs_gpu = f_gpu(X, Scale, Bias, Running_mean, Running_var, Dy)
outputs_abstract = f_abstract(X, Scale, Bias, Running_mean, Running_var, Dy)
outputs_ref = f_ref(X, Scale, Bias, Running_mean, Running_var, Dy)
......@@ -844,7 +844,7 @@ def test_dnn_batchnorm_train_without_running_averages():
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
x, scale, bias, dy = T.tensor4('x'), T.tensor4('scale'), T.tensor4('bias'), T.tensor4('dy')
x, scale, bias, dy = T.ftensor4('x'), T.ftensor4('scale'), T.ftensor4('bias'), T.ftensor4('dy')
data_shape = (5, 10, 30, 25)
param_shape = (1, 10, 30, 25)
......@@ -875,10 +875,10 @@ def test_dnn_batchnorm_train_without_running_averages():
bn.AbstractBatchNormTrainGrad))
for n in f_abstract.maker.fgraph.toposort()])
# run
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)
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')
f_gpu(X, Scale, Bias, Dy)
f_abstract(X, Scale, Bias, Dy)
......@@ -891,14 +891,14 @@ def test_dnn_batchnorm_train_inplace():
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
x, scale, bias = T.tensor4('x'), T.tensor4('scale'), T.tensor4('bias')
x, scale, bias = T.ftensor4('x'), T.ftensor4('scale'), T.ftensor4('bias')
data_shape = (5, 10, 30, 25)
param_shape = (1, 10, 30, 25)
running_mean = shared(
numpy.random.randn(*param_shape).astype(theano.config.floatX),
numpy.random.randn(*param_shape).astype('float32'),
broadcastable=(True, False, False, False))
running_var = shared(
numpy.random.randn(*param_shape).astype(theano.config.floatX),
numpy.random.randn(*param_shape).astype('float32'),
broadcastable=(True, False, False, False))
# forward pass
......@@ -923,9 +923,9 @@ def test_dnn_batchnorm_train_inplace():
assert nodes[0].op.inplace_running_var
assert nodes[0].op.inplace_output
# run
X = 4 + 3 * 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)
X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
Scale = numpy.random.randn(*param_shape).astype('float32')
Bias = numpy.random.randn(*param_shape).astype('float32')
f(X, Scale, Bias)
......@@ -936,10 +936,10 @@ def test_batchnorm_inference():
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
tensor6 = T.TensorType(theano.config.floatX, (False,) * 6)
tensor6 = T.TensorType('float32', (False,) * 6)
for mode in ('per-activation', 'spatial'):
for vartype in (tensor6, T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
for vartype in (tensor6, 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'))
ndim = x.ndim
......@@ -984,12 +984,12 @@ 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(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)
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')
outputs_gpu = f_gpu(X, Scale, Bias, Mean, Var, Dy)
outputs_abstract = f_abstract(X, Scale, Bias, Mean, Var, Dy)
outputs_ref = f_ref(X, Scale, Bias, Mean, Var, Dy)
......@@ -1017,7 +1017,7 @@ def test_batchnorm_inference_inplace():
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
x, scale, bias, mean, var = (T.tensor4(n) for n in ('x', 'scale', 'bias', 'mean', 'var'))
x, scale, bias, mean, var = (T.ftensor4(n) for n in ('x', 'scale', 'bias', 'mean', 'var'))
data_shape = (5, 10, 30, 25)
param_shape = (1, 10, 30, 25)
......@@ -1031,11 +1031,11 @@ def test_batchnorm_inference_inplace():
assert nodes[0].op.inplace
# run
X = 4 + 3 * 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)
X = 4 + 3 * 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')
f(X, Scale, Bias, Mean, Var)
......@@ -1045,7 +1045,7 @@ def test_dnn_batchnorm_valid_and_invalid_axes():
if cuda.dnn.version() < (5000, 5000):
raise SkipTest("batch normalization requires cudnn v5+")
for vartype in (T.tensor5, T.tensor4, T.tensor3, T.matrix):
for vartype in (T.ftensor5, T.ftensor4, T.ftensor3, T.fmatrix):
x, scale, bias, mean, var, dy = (vartype(n)
for n in ('x', 'scale', 'bias', 'mean', 'var', 'dy'))
ndim = x.ndim
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论