提交 151cfff9 编写于 作者: K Kexin Zhao

add more tests

上级 0a95a44b
......@@ -188,14 +188,27 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
class TestBatchNormOpInference(OpTest):
def setUp(self):
self.op_type = "conv2d"
self.is_test = True
self.dtype = np.float32
self.data_layout = "NCHW"
init_dtype()
init_data_layout()
init_test_case()
def test_python(self):
data_format = "NHWC"
epsilon = 0.00001
n, h, w, c = 2, 3, 4, 5
shape = self.shape
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 self.data_layout == "NHWC":
x_shape = [n, h, w, c]
elif self.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(self.dtype)
......@@ -205,7 +218,64 @@ class TestBatchNormOpInference(OpTest):
mean = np.zeros(scale_shape).astype(self.dtype)
variance = np.ones(scale_shape).astype(self.dtype)
# run forward
saved_mean = np.zeros(scale_shape).astype(self.dtype)
saved_variance = np.ones(scale_shape).astype(self.dtype)
y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
epsilon, self.data_layout).astype(self.dtype)
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(x_val),
'Scale': OpTest.np_dtype_to_fluid_dtype(scale_val),
'Bias': OpTest.np_dtype_to_fluid_dtype(bias_val),
'Mean': OpTest.np_dtype_to_fluid_dtype(mean),
'Variance': OpTest.np_dtype_to_fluid_dtype(variance)
}
self.attrs = {
'is_test': self.is_test,
'epsilon': epsilon,
'data_layout': self.data_layout
}
self.outputs = {
'Y': y_out,
'MeanOut': mean,
'VarianceOut': variance,
'SavedMean': saved_mean,
'SavedVariance': saved_variance
}
def test_check_output(self):
self.check_output()
def init_dtype(self):
pass
def init_data_layout(self):
pass
def init_test_case(self):
self.shape = [2, 3, 4, 5]
class TestBatchNormOpTraining(OpTest):
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_python_testing(self):
data_format = "NHWC"
epsilon = 0.00001
n, h, w, c = 2, 3, 4, 5
x_shape = [n, h, w, c]
scale_shape = [c]
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
bias_val = np.random.random_sample(scale_shape).astype(np.float32)
mean = np.zeros(scale_shape).astype(np.float32)
variance = np.ones(scale_shape).astype(np.float32)
y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
epsilon, "NHWC")
......@@ -218,15 +288,11 @@ class TestBatchNormOpInference(OpTest):
# transfer (N, C, H, W) back to (N, H, W, C)
y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
self.__assert_close(y_out, y_out2_trans, "inference output")
self.__assert_close(y_out, y_out2_trans,
"inference outputs of two formats have differences")
print 'python: NHWC, NCHW, inference checking passed'
class TestBatchNormOpTraining(OpTest):
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_python(self):
def test_python_training(self):
data_format = "NHWC"
epsilon = 0.00001
momentum = 0.9
......@@ -264,7 +330,7 @@ class TestBatchNormOpTraining(OpTest):
# transfer (N, C, H, W) back to (N, H, W, C)
y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
self.__assert_close(y_out, y_out2_trans, "batch variance")
self.__assert_close(y_out, y_out2_trans, "batch output")
print 'python: NHWC, NCHW, forward checking passed'
# test backward now
......
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