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update test

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helinwang-patch-1
Kexin Zhao 7 years ago
parent 5e36757c37
commit 3233b2b323
1 changed files with 93 additions and 76 deletions
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python/paddle/fluid/tests/unittests/test_batch_norm_op.py
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@ -187,99 +187,116 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
class TestBatchNormOpInference(OpTest):
def setUp(self):
self.dtype = np.float32
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
def test_inference(self):
def test_with_place(place, data_layout, dtype, shape):
epsilon = 0.00001
if len(shape) == 2:
x_shape = shape
c = x_shape[1]
def check_with_place(place, data_layout, dtype, shape):
epsilon = 0.00001
if len(shape) == 2:
x_shape = shape
c = x_shape[1]
else:
n, h, w, c = shape[0], shape[1], shape[2], shape[3]
if data_layout == "NHWC":
x_shape = [n, h, w, c]
elif data_layout == "NCHW":
x_shape = [n, c, h, w]
else:
n, h, w, c = shape[0], shape[1], shape[2], shape[3]
if data_layout == "NHWC":
x_shape = [n, h, w, c]
elif data_layout == "NCHW":
x_shape = [n, c, h, w]
else:
raise ValueError("Unknown data layout.")
scale_shape = [c]
x_val = np.random.random_sample(x_shape).astype(dtype)
scale_val = np.random.random_sample(scale_shape).astype(dtype)
bias_val = np.random.random_sample(scale_shape).astype(dtype)
mean = np.zeros(scale_shape).astype(dtype)
variance = np.ones(scale_shape).astype(dtype)
y_out = _reference_testing(x_val, scale_val, bias_val, mean,
variance, epsilon,
data_layout).astype(dtype)
raise ValueError("Unknown data layout.")
scale_shape = [c]
scope = core.Scope()
x_val = np.random.random_sample(x_shape).astype(dtype)
scale_val = np.random.random_sample(scale_shape).astype(dtype)
bias_val = np.random.random_sample(scale_shape).astype(dtype)
# create input
x_tensor = create_or_get_tensor(
scope, "x_val", OpTest.np_dtype_to_fluid_dtype(x_val), place)
scale_tensor = create_or_get_tensor(
scope, "scale_val",
OpTest.np_dtype_to_fluid_dtype(scale_val), place)
bias_tensor = create_or_get_tensor(
scope, "bias_val",
OpTest.np_dtype_to_fluid_dtype(bias_val), place)
mean_tensor = create_or_get_tensor(
scope, "mean", OpTest.np_dtype_to_fluid_dtype(mean), place)
variance_tensor = create_or_get_tensor(
scope, "variance",
OpTest.np_dtype_to_fluid_dtype(variance), place)
mean = np.zeros(scale_shape).astype(dtype)
variance = np.ones(scale_shape).astype(dtype)
# create output
y_tensor = create_or_get_tensor(scope, "y_out", None, place)
saved_mean_tensor = create_or_get_tensor(scope, "saved_mean", None,
place)
saved_variance_tensor = create_or_get_tensor(
scope, "saved_variance", None, place)
mean_out_tensor = mean_tensor
variance_out_tensor = variance_tensor
y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
epsilon, data_layout).astype(dtype)
scope = core.Scope()
# create input
x_tensor = create_or_get_tensor(scope, "x_val",
OpTest.np_dtype_to_fluid_dtype(x_val),
place)
scale_tensor = create_or_get_tensor(
scope, "scale_val",
OpTest.np_dtype_to_fluid_dtype(scale_val), place)
bias_tensor = create_or_get_tensor(
scope, "bias_val", OpTest.np_dtype_to_fluid_dtype(bias_val), place)
mean_tensor = create_or_get_tensor(scope, "mean",
OpTest.np_dtype_to_fluid_dtype(mean),
place)
variance_tensor = create_or_get_tensor(
scope, "variance", OpTest.np_dtype_to_fluid_dtype(variance), place)
# create output
y_tensor = create_or_get_tensor(scope, "y_out", None, place)
saved_mean_tensor = create_or_get_tensor(scope, "saved_mean", None,
place)
saved_variance_tensor = create_or_get_tensor(scope, "saved_variance",
None, place)
mean_out_tensor = mean_tensor
variance_out_tensor = variance_tensor
batch_norm_op = Operator(
"batch_norm",
# inputs
X="x_val",
Scale="scale_val",
Bias="bias_val",
Mean="mean",
Variance="variance",
# outputs
Y="y_out",
MeanOut="mean",
VarianceOut="variance",
SavedMean="saved_mean",
SavedVariance="saved_variance",
# attrs
is_test=True,
data_layout=data_layout,
epsilon=epsilon)
batch_norm_op.run(scope, place)
# check inference result
self.__assert_close(y_tensor, y_out,
"inference output are different at " + str(place) +
", " + data_layout + ", " + str(np.dtype(dtype)))
def test_check_output(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(core.CUDAPlace(0))
batch_norm_op = Operator(
"batch_norm",
# inputs
X="x_val",
Scale="scale_val",
Bias="bias_val",
Mean="mean",
Variance="variance",
# outputs
Y="y_out",
MeanOut="mean",
VarianceOut="variance",
SavedMean="saved_mean",
SavedVariance="saved_variance",
# attrs
is_test=True,
data_layout=data_layout,
epsilon=epsilon)
for place in places:
for data_format in ["NCHW", "NHWC"]:
check_with_place(place, data_format, self.dtype, [2, 3, 4, 5])
check_with_place(place, data_format, self.dtype, [2, 3])
batch_norm_op.run(scope, place)
# check inference result
self.__assert_close(
y_tensor, y_out, "inference output are different at " +
str(place) + ", " + data_layout + ", " + str(np.dtype(dtype)))
class TestFP16BatchNormOpInference(TestBatchNormOpInference):
def setUp(self):
self.dtype = np.float16
places = [core.CPUPlace()]
def test_check_output(self):
places = []
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
place = core.CUDAPlace(0)
if self.dtype != np.float16 or core.is_float16_supported(place):
if core.is_float16_supported(place):
places.append(place)
for place in places:
for data_format in ["NCHW", "NHWC"]:
for dtype in [np.float32, np.float16]:
test_with_place(place, data_format, dtype, [2, 3, 4, 5])
test_with_place(place, data_format, dtype, [2, 3])
check_output_with_place(place, data_format, self.dtype,
[2, 3, 4, 5])
check_output_with_place(place, data_format, self.dtype, [2, 3])
class TestBatchNormOpTraining(OpTest):

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