FM / operator

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation_ops_test.py	9691	0644	edit dl rm
adadelta_test.py	7932	0644	edit dl rm
adagrad_test.py	7586	0644	edit dl rm
adagrad_test_helper.py	5181	0644	edit dl rm
adam_test.py	21559	0644	edit dl rm
affine_channel_op_test.py	3784	0644	edit dl rm
alias_with_name_test.py	938	0644	edit dl rm
apmeter_test.py	2738	0644	edit dl rm
arg_ops_test.py	1917	0644	edit dl rm
assert_test.py	797	0644	edit dl rm
async_net_barrier_test.py	946	0644	edit dl rm
atomic_ops_test.py	4104	0644	edit dl rm
basic_rnn_test.py	4720	0644	edit dl rm
batch_box_cox_test.py	5080	0644	edit dl rm
batch_bucketize_op_test.py	3730	0644	edit dl rm
batch_moments_op_test.py	2795	0644	edit dl rm
batch_sparse_to_dense_op_test.py	4199	0644	edit dl rm
bbox_transform_test.py	12258	0644	edit dl rm
bisect_percentile_op_test.py	6227	0644	edit dl rm
blobs_queue_db_test.py	3240	0644	edit dl rm
boolean_mask_test.py	16389	0644	edit dl rm
boolean_unmask_test.py	1711	0644	edit dl rm
box_with_nms_limit_op_test.py	8750	0644	edit dl rm
bucketize_op_test.py	930	0644	edit dl rm
cast_op_test.py	1600	0644	edit dl rm
ceil_op_test.py	888	0644	edit dl rm
channel_backprop_stats_op_test.py	2131	0644	edit dl rm
channel_shuffle_test.py	1794	0644	edit dl rm
channel_stats_op_test.py	2639	0644	edit dl rm
checkpoint_test.py	1500	0644	edit dl rm
clip_op_test.py	1984	0644	edit dl rm
clip_tensor_op_test.py	2076	0644	edit dl rm
collect_and_distribute_fpn_rpn_proposals_op_test.py	11269	0644	edit dl rm
concat_op_cost_test.py	2858	0644	edit dl rm
concat_split_op_test.py	7266	0644	edit dl rm
conditional_test.py	995	0644	edit dl rm
conftest.py	1446	0644	edit dl rm
conv_test.py	32473	0644	edit dl rm
conv_transpose_test.py	15945	0644	edit dl rm
copy_ops_test.py	7374	0644	edit dl rm
copy_rows_to_tensor_op_test.py	2526	0644	edit dl rm
cosine_embedding_criterion_op_test.py	1953	0644	edit dl rm
counter_ops_test.py	3348	0644	edit dl rm
crf_test.py	5315	0644	edit dl rm
cross_entropy_ops_test.py	10085	0644	edit dl rm
ctc_beam_search_decoder_op_test.py	5197	0644	edit dl rm
ctc_greedy_decoder_op_test.py	4743	0644	edit dl rm
cudnn_recurrent_test.py	5817	0644	edit dl rm
dataset_ops_test.py	23847	0644	edit dl rm
data_couple_op_test.py	858	0644	edit dl rm
decay_adagrad_test.py	2694	0644	edit dl rm
deform_conv_test.py	19276	0644	edit dl rm
dense_vector_to_id_list_op_test.py	2044	0644	edit dl rm
depthwise_3x3_conv_test.py	1863	0644	edit dl rm
detectron_keypoints.py	7973	0644	edit dl rm
distance_op_test.py	4351	0644	edit dl rm
dropout_op_test.py	2971	0644	edit dl rm
duplicate_operands_test.py	734	0644	edit dl rm
elementwise_linear_op_test.py	1382	0644	edit dl rm
elementwise_logical_ops_test.py	4617	0644	edit dl rm
elementwise_ops_test.py	33354	0644	edit dl rm
elementwise_op_broadcast_test.py	17466	0644	edit dl rm
emptysample_ops_test.py	1977	0644	edit dl rm
enforce_finite_op_test.py	1286	0644	edit dl rm
ensure_clipped_test.py	1505	0644	edit dl rm
ensure_cpu_output_op_test.py	1244	0644	edit dl rm
erf_op_test.py	749	0644	edit dl rm
expand_op_test.py	2109	0644	edit dl rm
fc_operator_test.py	3720	0644	edit dl rm
feature_maps_ops_test.py	21492	0644	edit dl rm
filler_ops_test.py	8476	0644	edit dl rm
find_op_test.py	1316	0644	edit dl rm
flatten_op_test.py	922	0644	edit dl rm
flexible_top_k_test.py	2609	0644	edit dl rm
floor_op_test.py	894	0644	edit dl rm
fused_nbit_rowwise_conversion_ops_test.py	14077	0644	edit dl rm
fused_nbit_rowwise_test_helper.py	2693	0644	edit dl rm
gather_ops_test.py	9216	0644	edit dl rm
gather_ranges_op_test.py	9125	0644	edit dl rm
given_tensor_byte_string_to_uint8_fill_op_test.py	1392	0644	edit dl rm
given_tensor_fill_op_test.py	1503	0644	edit dl rm
glu_op_test.py	1212	0644	edit dl rm
group_conv_test.py	2870	0644	edit dl rm
group_norm_op_test.py	5252	0644	edit dl rm
gru_test.py	12932	0644	edit dl rm
heatmap_max_keypoint_op_test.py	4770	0644	edit dl rm
histogram_test.py	3097	0644	edit dl rm
hsm_test.py	9456	0644	edit dl rm
hyperbolic_ops_test.py	1472	0644	edit dl rm
im2col_col2im_test.py	4311	0644	edit dl rm
image_input_op_test.py	17345	0644	edit dl rm
index_hash_ops_test.py	2885	0644	edit dl rm
index_ops_test.py	4597	0644	edit dl rm
instance_norm_test.py	9917	0644	edit dl rm
integral_image_ops_test.py	3419	0644	edit dl rm
jsd_ops_test.py	1044	0644	edit dl rm
key_split_ops_test.py	1289	0644	edit dl rm
lars_test.py	1354	0644	edit dl rm
layer_norm_op_test.py	14983	0644	edit dl rm
leaky_relu_test.py	5639	0644	edit dl rm
learning_rate_adaption_op_test.py	2837	0644	edit dl rm
learning_rate_op_test.py	8652	0644	edit dl rm
lengths_pad_op_test.py	1625	0644	edit dl rm
lengths_reducer_fused_nbit_rowwise_ops_test.py	15495	0644	edit dl rm
lengths_tile_op_test.py	1332	0644	edit dl rm
lengths_top_k_ops_test.py	2371	0644	edit dl rm
length_split_op_test.py	4868	0644	edit dl rm
listwise_l2r_operator_test.py	8740	0644	edit dl rm
load_save_test.py	33241	0644	edit dl rm
locally_connected_op_test.py	7761	0644	edit dl rm
loss_ops_test.py	902	0644	edit dl rm
lpnorm_op_test.py	2725	0644	edit dl rm
map_ops_test.py	2249	0644	edit dl rm
margin_ranking_criterion_op_test.py	1816	0644	edit dl rm
math_ops_test.py	1603	0644	edit dl rm
matmul_op_test.py	10096	0644	edit dl rm
mean_op_test.py	1469	0644	edit dl rm
merge_id_lists_op_test.py	2989	0644	edit dl rm
mkl_conv_op_test.py	1547	0644	edit dl rm
mkl_packed_fc_op_test.py	2647	0644	edit dl rm
mod_op_test.py	1459	0644	edit dl rm
moments_op_test.py	1722	0644	edit dl rm
momentum_sgd_test.py	6480	0644	edit dl rm
mpi_test.py	8154	0644	edit dl rm
mul_gradient_benchmark.py	1509	0644	edit dl rm
negate_gradient_op_test.py	1518	0644	edit dl rm
ngram_ops_test.py	2327	0644	edit dl rm
normalize_op_test.py	1679	0644	edit dl rm
numpy_tile_op_test.py	1924	0644	edit dl rm
one_hot_ops_test.py	7478	0644	edit dl rm
onnx_while_test.py	3070	0644	edit dl rm
order_switch_test.py	1306	0644	edit dl rm
pack_ops_test.py	12634	0644	edit dl rm
pack_rnn_sequence_op_test.py	2891	0644	edit dl rm
pad_test.py	1377	0644	edit dl rm
partition_ops_test.py	6838	0644	edit dl rm
percentile_op_test.py	4427	0644	edit dl rm
piecewise_linear_transform_test.py	6187	0644	edit dl rm
pooling_test.py	16508	0644	edit dl rm
prepend_dim_test.py	1505	0644	edit dl rm
python_op_test.py	1312	0644	edit dl rm
quantile_test.py	3276	0644	edit dl rm
rand_quantization_op_speed_test.py	3128	0644	edit dl rm
rank_loss_operator_test.py	5752	0644	edit dl rm
rebatching_queue_test.py	9047	0644	edit dl rm
record_queue_test.py	3125	0644	edit dl rm
recurrent_network_test.py	14048	0644	edit dl rm
recurrent_net_executor_test.py	10922	0644	edit dl rm
reduce_ops_test.py	17341	0644	edit dl rm
reduction_ops_test.py	4664	0644	edit dl rm
reshape_ops_test.py	8211	0644	edit dl rm
resize_op_test.py	9417	0644	edit dl rm
rmac_regions_op_test.py	3178	0644	edit dl rm
rms_norm_op_test.py	1325	0644	edit dl rm
rnn_cell_test.py	59707	0644	edit dl rm
roi_align_rotated_op_test.py	7567	0644	edit dl rm
rowwise_counter_test.py	2205	0644	edit dl rm
scale_op_test.py	2177	0644	edit dl rm
segment_ops_test.py	25745	0644	edit dl rm
self_binning_histogram_test.py	12915	0644	edit dl rm
selu_op_test.py	3232	0644	edit dl rm
sequence_ops_test.py	16000	0644	edit dl rm
shape_inference_test.py	25708	0644	edit dl rm
sinusoid_position_encoding_op_test.py	2308	0644	edit dl rm
softmax_ops_test.py	23685	0644	edit dl rm
softplus_op_test.py	516	0644	edit dl rm
sparse_dropout_with_replacement_op_test.py	2885	0644	edit dl rm
sparse_gradient_checker_test.py	1294	0644	edit dl rm
sparse_itemwise_dropout_with_replacement_op_test.py	2913	0644	edit dl rm
sparse_lengths_sum_benchmark.py	4159	0644	edit dl rm
sparse_lp_regularizer_test.py	2553	0644	edit dl rm
sparse_normalize_test.py	3136	0644	edit dl rm
sparse_ops_test.py	3469	0644	edit dl rm
sparse_to_dense_mask_op_test.py	3693	0644	edit dl rm
spatial_bn_op_test.py	20182	0644	edit dl rm
specialized_segment_ops_test.py	11775	0644	edit dl rm
split_op_cost_test.py	8645	0644	edit dl rm
square_root_divide_op_test.py	2179	0644	edit dl rm
stats_ops_test.py	1789	0644	edit dl rm
stats_put_ops_test.py	6596	0644	edit dl rm
storm_test.py	6507	0644	edit dl rm
string_ops_test.py	4154	0644	edit dl rm
text_file_reader_test.py	2517	0644	edit dl rm
thresholded_relu_op_test.py	2323	0644	edit dl rm
tile_op_test.py	3887	0644	edit dl rm
top_k_test.py	9113	0644	edit dl rm
torch_integration_test.py	39941	0644	edit dl rm
transpose_op_test.py	2722	0644	edit dl rm
trigonometric_op_test.py	1715	0644	edit dl rm
unique_ops_test.py	2255	0644	edit dl rm
unique_uniform_fill_op_test.py	1335	0644	edit dl rm
unsafe_coalesce_test.py	2940	0644	edit dl rm
upsample_op_test.py	7308	0644	edit dl rm
utility_ops_test.py	15054	0644	edit dl rm
video_input_op_test.py	10503	0644	edit dl rm
weighted_multi_sample_test.py	1997	0644	edit dl rm
weighted_sample_test.py	2739	0644	edit dl rm
weighted_sum_test.py	3052	0644	edit dl rm
weight_scale_test.py	2057	0644	edit dl rm
wngrad_test.py	8279	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/spatial_bn_op_test.py (20182B)

from caffe2.python import brew, core, utils, workspace
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu
from caffe2.python.model_helper import ModelHelper
import caffe2.python.serialized_test.serialized_test_util as serial

from hypothesis import given, assume, settings
import hypothesis.strategies as st
import numpy as np
import unittest

class TestSpatialBN(serial.SerializedTestCase):

@serial.given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           order=st.sampled_from(["NCHW", "NHWC"]),
           epsilon=st.floats(min_value=1e-5, max_value=1e-2),
           inplace=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_spatialbn_test_mode_3d(
            self, size, input_channels, batch_size, seed, order, epsilon,
            inplace, engine, gc, dc):
        op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["X" if inplace else "Y"],
            order=order,
            is_test=True,
            epsilon=epsilon,
            engine=engine,
        )

def reference_spatialbn_test(X, scale, bias, mean, var):
            if order == "NCHW":
                scale = scale[np.newaxis, :,
                              np.newaxis, np.newaxis, np.newaxis]
                bias = bias[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]
                mean = mean[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]
                var = var[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]

return ((X - mean) / np.sqrt(var + epsilon) * scale + bias,)

np.random.seed(1701)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(batch_size, input_channels, size, size, size)\
            .astype(np.float32) - 0.5

if order == "NHWC":
            X = utils.NCHW2NHWC(X)
        self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
                                   reference_spatialbn_test)
        self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])

@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
    @given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           order=st.sampled_from(["NCHW", "NHWC"]),
           epsilon=st.floats(min_value=1e-5, max_value=1e-2),
           inplace=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_spatialbn_test_mode_1d(
            self, size, input_channels, batch_size, seed, order, epsilon,
            inplace, engine, gc, dc):
        # Currently MIOPEN SpatialBN only supports 2D
        if hiputl.run_in_hip(gc, dc):
            assume(engine != "CUDNN")
        op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["X" if inplace else "Y"],
            order=order,
            is_test=True,
            epsilon=epsilon,
            engine=engine,
        )

def reference_spatialbn_test(X, scale, bias, mean, var):
            if order == "NCHW":
                scale = scale[np.newaxis, :, np.newaxis]
                bias = bias[np.newaxis, :, np.newaxis]
                mean = mean[np.newaxis, :, np.newaxis]
                var = var[np.newaxis, :, np.newaxis]
            return ((X - mean) / np.sqrt(var + epsilon) * scale + bias,)

np.random.seed(1701)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size).astype(np.float32) - 0.5

if order == "NHWC":
            X = X.swapaxes(1, 2)
        self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
                                   reference_spatialbn_test)
        self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])

@given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           order=st.sampled_from(["NCHW", "NHWC"]),
           epsilon=st.floats(min_value=1e-5, max_value=1e-2),
           engine=st.sampled_from(["", "CUDNN"]),
           inplace=st.booleans(),
           **hu.gcs)
    def test_spatialbn_test_mode(
            self, size, input_channels, batch_size, seed, order, epsilon,
            inplace, engine, gc, dc):
        # Currently HIP SpatialBN only supports NCHW
        if hiputl.run_in_hip(gc, dc):
            assume(order == "NCHW")

op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["X" if inplace else "Y"],
            order=order,
            is_test=True,
            epsilon=epsilon,
            engine=engine
        )

def reference_spatialbn_test(X, scale, bias, mean, var):
            if order == "NCHW":
                scale = scale[np.newaxis, :, np.newaxis, np.newaxis]
                bias = bias[np.newaxis, :, np.newaxis, np.newaxis]
                mean = mean[np.newaxis, :, np.newaxis, np.newaxis]
                var = var[np.newaxis, :, np.newaxis, np.newaxis]
            return ((X - mean) / np.sqrt(var + epsilon) * scale + bias,)

np.random.seed(1701)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5

if order == "NHWC":
            X = X.swapaxes(1, 2).swapaxes(2, 3)

self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
                                   reference_spatialbn_test)
        self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])

@given(size=st.integers(1, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           order=st.sampled_from(["NCHW", "NHWC"]),
           epsilon=st.floats(1e-5, 1e-2),
           momentum=st.floats(0.5, 0.9),
           engine=st.sampled_from(["", "CUDNN"]),
           inplace=st.sampled_from([True, False]),
           **hu.gcs)
    def test_spatialbn_train_mode(
            self, size, input_channels, batch_size, seed, order, epsilon,
            momentum, inplace, engine, gc, dc):
        # Currently HIP SpatialBN only supports NCHW
        if hiputl.run_in_hip(gc, dc):
            assume(order == "NCHW")

assume(batch_size == 0 or batch_size * size * size > 1)

op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "running_mean", "running_var"],
            ["X" if inplace else "Y",
             "running_mean", "running_var", "saved_mean", "saved_var"],
            order=order,
            is_test=False,
            epsilon=epsilon,
            momentum=momentum,
            engine=engine,
        )
        np.random.seed(1701)
        scale = np.random.randn(input_channels).astype(np.float32)
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.randn(
            batch_size, input_channels, size, size).astype(np.float32)

if order == "NHWC":
            X = np.transpose(X, (0, 2, 3, 1))

def batch_norm_ref(X, scale, bias, running_mean, running_var):
            if batch_size == 0:
                Y = np.zeros(X.shape)
                saved_mean = np.zeros(running_mean.shape)
                saved_var = np.zeros(running_var.shape)
                return (Y, running_mean, running_var, saved_mean, saved_var)

if order == "NHWC":
                X = np.transpose(X, (0, 3, 1, 2))

C = X.shape[1]
            reduce_size = batch_size * size * size
            saved_mean = np.mean(X, (0, 2, 3))
            saved_var = np.var(X, (0, 2, 3))
            if reduce_size == 1:
                unbias_scale = float('inf')
            else:
                unbias_scale = reduce_size / (reduce_size - 1)
            running_mean = momentum * running_mean + (
                1.0 - momentum) * saved_mean
            running_var = momentum * running_var + (
                1.0 - momentum) * unbias_scale * saved_var
            std = np.sqrt(saved_var + epsilon)
            broadcast_shape = (1, C, 1, 1)
            Y = (X - np.reshape(saved_mean, broadcast_shape)) / np.reshape(
                std, broadcast_shape) * np.reshape(
                    scale, broadcast_shape) + np.reshape(bias, broadcast_shape)

if order == "NHWC":
                Y = np.transpose(Y, (0, 2, 3, 1))

return (Y, running_mean, running_var, saved_mean, 1.0 / std)

self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
                                   batch_norm_ref)
        self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var],
                                [0, 1, 2, 3, 4])

op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["Y", "mean", "var", "saved_mean", "saved_var"],
            order=order,
            is_test=False,
            epsilon=epsilon,
            momentum=momentum,
            engine=engine
        )
        np.random.seed(seed)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5
        if order == "NHWC":
            X = X.swapaxes(1, 2).swapaxes(2, 3)

for input_to_check in [0, 1, 2]:  # dX, dScale, dBias
            self.assertGradientChecks(gc, op, [X, scale, bias, mean, var],
                                      input_to_check, [0])

@given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           order=st.sampled_from(["NCHW", "NHWC"]),
           epsilon=st.floats(min_value=1e-5, max_value=1e-2),
           momentum=st.floats(min_value=0.5, max_value=0.9),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_spatialbn_train_mode_gradient_check_1d(
            self, size, input_channels, batch_size, seed, order, epsilon,
            momentum, engine, gc, dc):
        # Currently MIOPEN SpatialBN only supports 2D
        if hiputl.run_in_hip(gc, dc):
            assume(engine != "CUDNN")
        op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["Y", "mean", "var", "saved_mean", "saved_var"],
            order=order,
            is_test=False,
            epsilon=epsilon,
            momentum=momentum,
            engine=engine,
        )
        np.random.seed(seed)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size).astype(np.float32) - 0.5
        if order == "NHWC":
            X = X.swapaxes(1, 2)

for input_to_check in [0, 1, 2]:  # dX, dScale, dBias
            self.assertGradientChecks(gc, op, [X, scale, bias, mean, var],
                                      input_to_check, [0], stepsize=0.01)

@given(N=st.integers(0, 5),
           C=st.integers(1, 10),
           H=st.integers(1, 5),
           W=st.integers(1, 5),
           epsilon=st.floats(1e-5, 1e-2),
           momentum=st.floats(0.5, 0.9),
           order=st.sampled_from(["NCHW", "NHWC"]),
           num_batches=st.integers(2, 5),
           in_place=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_spatial_bn_multi_batch(
            self, N, C, H, W, epsilon, momentum, order, num_batches, in_place,
            engine, gc, dc):
        if in_place:
            outputs = ["Y", "mean", "var", "batch_mean", "batch_var"]
        else:
            outputs = ["Y", "mean", "var", "saved_mean", "saved_var"]
        op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var", "batch_mean", "batch_var"],
            outputs,
            order=order,
            is_test=False,
            epsilon=epsilon,
            momentum=momentum,
            num_batches=num_batches,
            engine=engine,
        )
        if order == "NCHW":
            X = np.random.randn(N, C, H, W).astype(np.float32)
        else:
            X = np.random.randn(N, H, W, C).astype(np.float32)
        scale = np.random.randn(C).astype(np.float32)
        bias = np.random.randn(C).astype(np.float32)
        mean = np.random.randn(C).astype(np.float32)
        var = np.random.rand(C).astype(np.float32)
        batch_mean = np.random.rand(C).astype(np.float32) - 0.5
        batch_var = np.random.rand(C).astype(np.float32) + 1.0
        inputs = [X, scale, bias, mean, var, batch_mean, batch_var]

def spatial_bn_multi_batch_ref(
                X, scale, bias, mean, var, batch_mean, batch_var):
            if N == 0:
                batch_mean = np.zeros(C).astype(np.float32)
                batch_var = np.zeros(C).astype(np.float32)
            else:
                size = num_batches * N * H * W
                batch_mean /= size
                batch_var = batch_var / size - np.square(batch_mean)
                mean = momentum * mean + (1.0 - momentum) * batch_mean
                var = momentum * var + (1.0 - momentum) * (
                    size / (size - 1)) * batch_var
                batch_var = 1.0 / np.sqrt(batch_var + epsilon)
            if order == "NCHW":
                scale = np.reshape(scale, (C, 1, 1))
                bias = np.reshape(bias, (C, 1, 1))
                batch_mean = np.reshape(batch_mean, (C, 1, 1))
                batch_var = np.reshape(batch_var, (C, 1, 1))
            Y = (X - batch_mean) * batch_var * scale + bias
            if order == "NCHW":
                batch_mean = np.reshape(batch_mean, (C))
                batch_var = np.reshape(batch_var, (C))
            return (Y, mean, var, batch_mean, batch_var)

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=inputs,
            reference=spatial_bn_multi_batch_ref,
        )
        self.assertDeviceChecks(dc, op, inputs, [0, 1, 2, 3, 4])

@given(N=st.integers(0, 5),
           C=st.integers(1, 10),
           H=st.integers(1, 5),
           W=st.integers(1, 5),
           epsilon=st.floats(1e-5, 1e-2),
           order=st.sampled_from(["NCHW", "NHWC"]),
           num_batches=st.integers(2, 5),
           in_place=st.booleans(),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=None)
    def test_spatial_bn_multi_batch_grad(
            self, N, C, H, W, epsilon, order, num_batches, in_place, engine,
            gc, dc):
        if in_place:
            outputs = ["dX", "dscale_sum", "dbias_sum"]
        else:
            outputs = ["dX", "dscale", "dbias"]
        op = core.CreateOperator(
            "SpatialBNGradient",
            ["X", "scale", "dY", "mean", "rstd", "dscale_sum", "dbias_sum"],
            outputs,
            order=order,
            epsilon=epsilon,
            num_batches=num_batches,
            engine=engine,
        )
        if order == "NCHW":
            dY = np.random.randn(N, C, H, W).astype(np.float32)
            X = np.random.randn(N, C, H, W).astype(np.float32)
        else:
            dY = np.random.randn(N, H, W, C).astype(np.float32)
            X = np.random.randn(N, H, W, C).astype(np.float32)
        scale = np.random.randn(C).astype(np.float32)
        mean = np.random.randn(C).astype(np.float32)
        rstd = np.random.rand(C).astype(np.float32)
        dscale_sum = np.random.randn(C).astype(np.float32)
        dbias_sum = np.random.randn(C).astype(np.float32)
        inputs = [X, scale, dY, mean, rstd, dscale_sum, dbias_sum]

def spatial_bn_multi_batch_grad_ref(
                X, scale, dY, mean, rstd, dscale_sum, dbias_sum):
            if N == 0:
                dscale = np.zeros(C).astype(np.float32)
                dbias = np.zeros(C).astype(np.float32)
                alpha = np.zeros(C).astype(np.float32)
                beta = np.zeros(C).astype(np.float32)
                gamma = np.zeros(C).astype(np.float32)
            else:
                dscale = dscale_sum / num_batches
                dbias = dbias_sum / num_batches
                alpha = scale * rstd
                beta = -alpha * dscale * rstd / (N * H * W)
                gamma = alpha * (mean * dscale * rstd - dbias) / (N * H * W)
            if order == "NCHW":
                alpha = np.reshape(alpha, (C, 1, 1))
                beta = np.reshape(beta, (C, 1, 1))
                gamma = np.reshape(gamma, (C, 1, 1))
            dX = alpha * dY + beta * X + gamma
            return (dX, dscale, dbias)

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=inputs,
            reference=spatial_bn_multi_batch_grad_ref,
        )
        self.assertDeviceChecks(dc, op, inputs, [0, 1, 2])

@given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(0, 3),
           seed=st.integers(0, 65535),
           epsilon=st.floats(1e-5, 1e-2),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    def test_spatialbn_brew_wrapper(
            self, size, input_channels, batch_size, seed, epsilon,
            engine, gc, dc):
        np.random.seed(seed)
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32)

workspace.FeedBlob('X', X)

model = ModelHelper(name='test_spatialbn_brew_wrapper')

brew.spatial_bn(
            model,
            'X',
            'Y',
            input_channels,
            epsilon=epsilon,
            is_test=False,
        )

workspace.RunNetOnce(model.param_init_net)
        workspace.RunNetOnce(model.net)

if __name__ == "__main__":
    unittest.main()

cmd: