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提交 acf5edee authored 作者: f0k's avatar f0k
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Add tests for cuDNN batch normalization convenience functions (replacing Op tests)

上级 e09caa8a
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正在显示 包含 97 行增加 和 89 行删除
theano/sandbox/cuda/tests/test_dnn.py
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......@@ -715,7 +715,7 @@ class test_DnnSoftMax(test_nnet.test_SoftMax):
utt.assert_allclose(f(inp), f_ref(inp))
def test_batchnorm_train_op():
def test_batchnorm_train():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if cuda.dnn.version() < (5000, 5000):
......@@ -723,52 +723,55 @@ def test_batchnorm_train_op():
utt.seed_rng()
for mode in ('per-activation', 'spatial'):
x, scale, bias = T.tensor4s('x', 'scale', 'bias')
eps = 5e-3 # some non-standard value to test if it's used
# forward pass
batchnorm_op = cuda.dnn.GpuDnnBatchNorm(mode=mode, epsilon=eps)
out, x_mean, x_invstd = batchnorm_op(x, scale, bias)
# reference forward pass
if mode == 'per-activation':
axes = (0,)
elif mode == 'spatial':
axes = (0, 2, 3)
x_mean2 = x.mean(axis=axes, keepdims=True)
x_invstd2 = T.inv(T.sqrt(x.var(axis=axes, keepdims=True) + eps))
scale2 = T.addbroadcast(scale, *axes)
bias2 = T.addbroadcast(bias, *axes)
out2 = (x - x_mean2) * (scale2 * x_invstd2) + bias2
# backward pass
dy = T.tensor4('dy')
grads = T.grad(None, wrt=[x, scale, bias], known_grads={out: dy})
# reference backward pass
grads2 = T.grad(None, wrt=[x, scale, bias], known_grads={out2: dy})
# compile
f = theano.function([x, scale, bias, dy],
[out, x_mean, x_invstd, out2, x_mean2, x_invstd2] +
grads + grads2, mode=mode_with_gpu)
# run
floatX = theano.config.floatX
for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
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(floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(floatX)
Scale = numpy.random.randn(*param_shape).astype(floatX)
Bias = numpy.random.randn(*param_shape).astype(floatX)
outputs = f(X, Scale, Bias, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[0 + 3]) # out
utt.assert_allclose(outputs[1], outputs[1 + 3]) # mean
utt.assert_allclose(outputs[2], outputs[2 + 3]) # invstd
# compare gradients
utt.assert_allclose(outputs[6], outputs[6 + 3]) # dx
utt.assert_allclose(outputs[7], outputs[7 + 3], rtol=1e-3) # dscale
utt.assert_allclose(outputs[8], outputs[8 + 3]) # dbias
def test_batchnorm_inf_op():
for vartype in (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
# forward pass
out, x_mean, x_invstd = cuda.dnn.dnn_batch_normalization_train(
x, scale, bias, mode, eps)
# reference forward pass
if mode == 'per-activation':
axes = (0,)
elif mode == 'spatial':
axes = (0,) + tuple(range(2, ndim))
x_mean2 = x.mean(axis=axes, keepdims=True)
x_invstd2 = T.inv(T.sqrt(x.var(axis=axes, keepdims=True) + eps))
scale2 = T.addbroadcast(scale, *axes)
bias2 = T.addbroadcast(bias, *axes)
out2 = (x - x_mean2) * (scale2 * x_invstd2) + bias2
# backward pass
dy = vartype('dy')
grads = T.grad(None, wrt=[x, scale, bias], known_grads={out: dy})
# reference backward pass
grads2 = T.grad(None, wrt=[x, scale, bias], known_grads={out2: dy})
# compile
f = theano.function([x, scale, bias, dy],
[out, x_mean, x_invstd, out2, x_mean2, x_invstd2] +
grads + grads2, mode=mode_with_gpu)
# run
floatX = theano.config.floatX
for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
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(floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(floatX)
Scale = numpy.random.randn(*param_shape).astype(floatX)
Bias = numpy.random.randn(*param_shape).astype(floatX)
outputs = f(X, Scale, Bias, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[0 + 3]) # out
utt.assert_allclose(outputs[1], outputs[1 + 3]) # mean
utt.assert_allclose(outputs[2], outputs[2 + 3]) # invstd
# compare gradients
utt.assert_allclose(outputs[6], outputs[6 + 3]) # dx
utt.assert_allclose(outputs[7], outputs[7 + 3], rtol=3e-3) # dscale
utt.assert_allclose(outputs[8], outputs[8 + 3]) # dbias
def test_batchnorm_inference():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if cuda.dnn.version() < (5000, 5000):
......@@ -776,48 +779,53 @@ def test_batchnorm_inf_op():
utt.seed_rng()
for mode in ('per-activation', 'spatial'):
x, scale, bias, mean, var = T.tensor4s('x', 'scale', 'bias', 'mean', 'var')
eps = 5e-3 # some non-standard value to test if it's used
# forward pass
batchnorm_op = cuda.dnn.GpuDnnBatchNormInference(mode=mode, epsilon=eps)
out = batchnorm_op(x, scale, bias, mean, var)
# reference forward pass
if mode == 'per-activation':
axes = (0,)
elif mode == 'spatial':
axes = (0, 2, 3)
scale2, bias2, mean2, var2 = (T.addbroadcast(t, *axes)
for t in (scale, bias, mean, var))
out2 = (x - mean2) * (scale2 / T.sqrt(var2 + eps)) + bias2
# backward pass
dy = T.tensor4('dy')
grads = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out: dy})
# reference backward pass
grads2 = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out2: dy})
# compile
f = theano.function([x, scale, bias, mean, var, dy],
[out, out2] + grads + grads2, mode=mode_with_gpu)
# run
floatX = theano.config.floatX
for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
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(floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(floatX)
Scale = numpy.random.randn(*param_shape).astype(floatX)
Bias = numpy.random.randn(*param_shape).astype(floatX)
Mean = numpy.random.randn(*param_shape).astype(floatX)
Var = numpy.random.rand(*param_shape).astype(floatX)
outputs = f(X, Scale, Bias, Mean, Var, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[1]) # out
# compare gradients
utt.assert_allclose(outputs[2], outputs[2 + 5]) # dx
utt.assert_allclose(outputs[3], outputs[3 + 5]) # dscale
utt.assert_allclose(outputs[4], outputs[4 + 5]) # dbias
utt.assert_allclose(outputs[5], outputs[5 + 5]) # dmean
utt.assert_allclose(outputs[6], outputs[6 + 5]) # dvar
for vartype in (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
# forward pass
out = cuda.dnn.dnn_batch_normalization_test(x, scale, bias, mean,
var, mode, eps)
# reference forward pass
if mode == 'per-activation':
axes = (0,)
elif mode == 'spatial':
axes = (0,) + tuple(range(2, ndim))
scale2, bias2, mean2, var2 = (T.addbroadcast(t, *axes)
for t in (scale, bias, mean, var))
out2 = (x - mean2) * (scale2 / T.sqrt(var2 + eps)) + bias2
# backward pass
dy = vartype('dy')
grads = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out: dy})
# reference backward pass
grads2 = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out2: dy})
# compile
f = theano.function([x, scale, bias, mean, var, dy],
[out, out2] + grads + grads2, mode=mode_with_gpu)
# run
floatX = theano.config.floatX
for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
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(floatX)
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype(floatX)
Scale = numpy.random.randn(*param_shape).astype(floatX)
Bias = numpy.random.randn(*param_shape).astype(floatX)
Mean = numpy.random.randn(*param_shape).astype(floatX)
Var = numpy.random.rand(*param_shape).astype(floatX)
outputs = f(X, Scale, Bias, Mean, Var, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[1]) # out
# compare gradients
utt.assert_allclose(outputs[2], outputs[2 + 5]) # dx
utt.assert_allclose(outputs[3], outputs[3 + 5]) # dscale
utt.assert_allclose(outputs[4], outputs[4 + 5]) # dbias
utt.assert_allclose(outputs[5], outputs[5 + 5]) # dmean
utt.assert_allclose(outputs[6], outputs[6 + 5]) # dvar
def test_dnn_tag():
......
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