From 5e36757c374b0e9c17dc7ab00ab87b10afc43c26 Mon Sep 17 00:00:00 2001 From: Kexin Zhao Date: Sat, 17 Mar 2018 23:14:03 -0700 Subject: [PATCH] fix test --- .../tests/unittests/test_batch_norm_op.py | 152 ++++++++++-------- 1 file changed, 88 insertions(+), 64 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_batch_norm_op.py b/python/paddle/fluid/tests/unittests/test_batch_norm_op.py index f631050e2a..2f2873c183 100644 --- a/python/paddle/fluid/tests/unittests/test_batch_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_batch_norm_op.py @@ -187,74 +187,99 @@ 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 __assert_close(self, tensor, np_array, msg, atol=1e-4): + self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg) - epsilon = 0.00001 - 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] + 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] else: - raise ValueError("Unknown data layout.") - scale_shape = [c] + 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(self.dtype) - scale_val = np.random.random_sample(scale_shape).astype(self.dtype) - bias_val = np.random.random_sample(scale_shape).astype(self.dtype) + 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(self.dtype) - variance = np.ones(scale_shape).astype(self.dtype) + mean = np.zeros(scale_shape).astype(dtype) + variance = np.ones(scale_shape).astype(dtype) - 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, + data_layout).astype(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] + 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))) + + places = [core.CPUPlace()] + 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): + 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]) class TestBatchNormOpTraining(OpTest): @@ -288,8 +313,7 @@ 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, - "inference outputs of two formats have differences") + self.__assert_close(y_out, y_out2_trans, "inference output") print 'python: NHWC, NCHW, inference checking passed' def test_python_training(self): -- GitLab