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/reduce_ops_test.py (17341B)

from caffe2.python import core, workspace
from hypothesis import given, settings

import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import itertools as it

class TestReduceOps(serial.SerializedTestCase):
    def run_reduce_op_test_impl(
            self, op_name, X, axes, keepdims, ref_func, gc, dc, allow_broadcast_fastpath):
        extra_args = dict(allow_broadcast_fastpath=True) if allow_broadcast_fastpath else {}
        if axes is None:
            op = core.CreateOperator(
                op_name,
                ["X"],
                ["Y"],
                keepdims=keepdims,
                **extra_args,
            )
        else:
            op = core.CreateOperator(
                op_name,
                ["X"],
                ["Y"],
                axes=axes,
                keepdims=keepdims,
                **extra_args,
            )

def ref(X):
            return [ref_func(
                X, axis=None if axes is None else tuple(axes),
                keepdims=keepdims)]

with self.set_disable_serialized_check(allow_broadcast_fastpath):
            self.assertReferenceChecks(gc, op, [X], ref)
        self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0])

def run_reduce_op_test(
            self, op_name, X, keepdims, num_axes, ref_func, gc, dc, allow_broadcast_fastpath=False):
        self.run_reduce_op_test_impl(
            op_name, X, None, keepdims, ref_func, gc, dc, allow_broadcast_fastpath)

num_dims = len(X.shape)
        if num_dims < num_axes:
            self.run_reduce_op_test_impl(
                op_name, X, range(num_dims), keepdims, ref_func, gc, dc, allow_broadcast_fastpath)
        else:
            for axes in it.combinations(range(num_dims), num_axes):
                self.run_reduce_op_test_impl(
                    op_name, X, axes, keepdims, ref_func, gc, dc, allow_broadcast_fastpath)

@serial.given(
        X=hu.tensor(max_dim=3, dtype=np.float32),
        keepdims=st.booleans(),
        allow_broadcast_fastpath=st.booleans(),
        num_axes=st.integers(1, 3), **hu.gcs)
    def test_reduce_min(self, X, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        X_dims = X.shape
        X_size = X.size
        X = np.arange(X_size, dtype=np.float32)
        np.random.shuffle(X)
        X = X.reshape(X_dims)
        self.run_reduce_op_test(
            "ReduceMin", X, keepdims, num_axes, np.min, gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

@serial.given(
        X=hu.tensor(max_dim=3, dtype=np.float32),
        keepdims=st.booleans(),
        allow_broadcast_fastpath=st.booleans(),
        num_axes=st.integers(1, 3), **hu.gcs)
    def test_reduce_max(self, X, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        X_dims = X.shape
        X_size = X.size
        X = np.arange(X_size, dtype=np.float32)
        np.random.shuffle(X)
        X = X.reshape(X_dims)
        self.run_reduce_op_test(
            "ReduceMax", X, keepdims, num_axes, np.max, gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

@given(n=st.integers(0, 5), m=st.integers(0, 5), k=st.integers(0, 5),
           t=st.integers(0, 5), keepdims=st.booleans(),
           allow_broadcast_fastpath=st.booleans(),
           num_axes=st.integers(1, 3), **hu.gcs)
    @settings(deadline=10000)
    def test_reduce_sum(self, n, m, k, t, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        X = np.random.randn(n, m, k, t).astype(np.float32)
        self.run_reduce_op_test(
            "ReduceSum", X, keepdims, num_axes, np.sum, gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

@serial.given(X=hu.tensor(dtype=np.float32), keepdims=st.booleans(),
                  allow_broadcast_fastpath=st.booleans(),
                  num_axes=st.integers(1, 4), **hu.gcs)
    def test_reduce_mean(self, X, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        self.run_reduce_op_test(
            "ReduceMean", X, keepdims, num_axes, np.mean, gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

@given(n=st.integers(1, 3), m=st.integers(1, 3), k=st.integers(1, 3),
           keepdims=st.booleans(), allow_broadcast_fastpath=st.booleans(),
           num_axes=st.integers(1, 3), **hu.gcs_cpu_only)
    @settings(deadline=10000)
    def test_reduce_l1(self, n, m, k, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        X = np.arange(n * m * k, dtype=np.float32) - 0.5
        np.random.shuffle(X)
        X = X.reshape((m, n, k))
        self.run_reduce_op_test(
            "ReduceL1", X, keepdims, num_axes, getNorm(1), gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

@serial.given(n=st.integers(1, 5), m=st.integers(1, 5), k=st.integers(1, 5),
                  keepdims=st.booleans(), allow_broadcast_fastpath=st.booleans(),
                  num_axes=st.integers(1, 3), **hu.gcs_cpu_only)
    def test_reduce_l2(self, n, m, k, keepdims, allow_broadcast_fastpath, num_axes, gc, dc):
        X = np.random.randn(n, m, k).astype(np.float32)
        self.run_reduce_op_test(
            "ReduceL2", X, keepdims, num_axes, getNorm(2), gc, dc,
            allow_broadcast_fastpath=allow_broadcast_fastpath)

def getNorm(p):
    if p == 1:
        def norm(X, axis, keepdims):
            return np.sum(np.abs(X), axis=axis, keepdims=keepdims)
    elif p == 2:
        def norm(X, axis, keepdims):
            return np.sqrt(np.sum(np.power(X, 2), axis=axis, keepdims=keepdims))
    else:
        raise RuntimeError("Only L1 and L2 norms supported")
    return norm

class TestReduceFrontReductions(serial.SerializedTestCase):
    def grad_variant_input_test(self, grad_op_name, X, ref, num_reduce_dim):
        workspace.ResetWorkspace()

Y = np.array(ref(X)[0]).astype(np.float32)
        dY = np.array(np.random.rand(*Y.shape)).astype(np.float32)
        shape = np.array(X.shape).astype(np.int64)

workspace.FeedBlob("X", X)
        workspace.FeedBlob("dY", dY)
        workspace.FeedBlob("shape", shape)

grad_op = core.CreateOperator(
            grad_op_name, ["dY", "X"], ["dX"], num_reduce_dim=num_reduce_dim)

grad_op1 = core.CreateOperator(
            grad_op_name, ["dY", "shape"], ["dX1"],
            num_reduce_dim=num_reduce_dim)

workspace.RunOperatorOnce(grad_op)
        workspace.RunOperatorOnce(grad_op1)

dX = workspace.FetchBlob("dX")
        dX1 = workspace.FetchBlob("dX1")
        np.testing.assert_array_equal(dX, dX1)

def max_op_test(
            self, op_name, num_reduce_dim, gc, dc, in_data, in_names, ref_max):

op = core.CreateOperator(
            op_name,
            in_names,
            ["outputs"],
            num_reduce_dim=num_reduce_dim
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=in_data,
            reference=ref_max,
        )

# Skip gradient check because it is too unreliable with max.
        # Just check CPU and CUDA have same results
        Y = np.array(ref_max(*in_data)[0]).astype(np.float32)
        dY = np.array(np.random.rand(*Y.shape)).astype(np.float32)
        if len(in_data) == 2:
            grad_in_names = ["dY", in_names[0], "Y", in_names[1]]
            grad_in_data = [dY, in_data[0], Y, in_data[1]]
        else:
            grad_in_names = ["dY", in_names[0], "Y"]
            grad_in_data = [dY, in_data[0], Y]

grad_op = core.CreateOperator(
            op_name + "Gradient",
            grad_in_names,
            ["dX"],
            num_reduce_dim=num_reduce_dim
        )
        self.assertDeviceChecks(dc, grad_op, grad_in_data, [0])

def reduce_op_test(self, op_name, op_ref, in_data, in_names,
                       num_reduce_dims, device):
        op = core.CreateOperator(
            op_name,
            in_names,
            ["outputs"],
            num_reduce_dim=num_reduce_dims
        )

self.assertReferenceChecks(
            device_option=device,
            op=op,
            inputs=in_data,
            reference=op_ref
        )

self.assertGradientChecks(
            device, op, in_data, 0, [0], stepsize=1e-2, threshold=1e-2)

@given(num_reduce_dim=st.integers(0, 4), **hu.gcs)
    @settings(deadline=10000)
    def test_reduce_front_sum(self, num_reduce_dim, gc, dc):
        X = np.random.rand(7, 4, 3, 5).astype(np.float32)

def ref_sum(X):
            return [np.sum(X, axis=(tuple(range(num_reduce_dim))))]

self.reduce_op_test(
            "ReduceFrontSum", ref_sum, [X], ["input"], num_reduce_dim, gc)
        self.grad_variant_input_test(
            "ReduceFrontSumGradient", X, ref_sum, num_reduce_dim)

@given(num_reduce_dim=st.integers(0, 4), seed=st.integers(0, 4), **hu.gcs)
    def test_reduce_front_sum_empty_batch(self, num_reduce_dim, seed, gc, dc):
        np.random.seed(seed)
        X = np.random.rand(0, 4, 3, 5).astype(np.float32)

def ref_sum(X):
            return [np.sum(X, axis=(tuple(range(num_reduce_dim))))]

self.reduce_op_test(
            "ReduceFrontSum", ref_sum, [X], ["input"], num_reduce_dim, gc)
        self.grad_variant_input_test(
            "ReduceFrontSumGradient", X, ref_sum, num_reduce_dim)

# test the second iteration
        not_empty_X = np.random.rand(2, 4, 3, 5).astype(np.float32)
        net = core.Net('test')
        with core.DeviceScope(gc):
            net.ReduceFrontSum(
                ['X'], ['output'],
                num_reduce_dim=num_reduce_dim
            )
            workspace.CreateNet(net)

workspace.FeedBlob('X', not_empty_X)
            workspace.RunNet(workspace.GetNetName(net))
            output = workspace.FetchBlob('output')
            np.testing.assert_allclose(
                output, ref_sum(not_empty_X)[0], atol=1e-3)

workspace.FeedBlob('X', X)
            workspace.RunNet(workspace.GetNetName(net))
            output = workspace.FetchBlob('output')
            np.testing.assert_allclose(output, ref_sum(X)[0], atol=1e-3)

@given(**hu.gcs)
    @settings(deadline=None)
    def test_reduce_front_sum_with_length(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][num_reduce_dim:]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_sum(X, lengths):
            Y = X.reshape(d, lengths.size)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.sum(Y[:lengths[ii], ii])
            return [rv.reshape((2, 3, 4, 5)[num_reduce_dim:])]

self.reduce_op_test(
            "ReduceFrontSum", ref_sum, [X, lengths], ["input", "lengths"],
            num_reduce_dim, gc)

@given(num_reduce_dim=st.integers(0, 4), **hu.gcs)
    @settings(deadline=10000)
    def test_reduce_front_mean(self, num_reduce_dim, gc, dc):
        X = np.random.rand(6, 7, 8, 2).astype(np.float32)

def ref_mean(X):
            return [np.mean(X, axis=(tuple(range(num_reduce_dim))))]

self.reduce_op_test(
            "ReduceFrontMean", ref_mean, [X], ["input"], num_reduce_dim, gc)
        self.grad_variant_input_test(
            "ReduceFrontMeanGradient", X, ref_mean, num_reduce_dim)

@given(**hu.gcs)
    @settings(deadline=10000)
    def test_reduce_front_mean_with_length(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][num_reduce_dim:]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_mean(X, lengths):
            Y = X.reshape(d, lengths.size)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.mean(Y[:lengths[ii], ii])
            return [rv.reshape((2, 3, 4, 5)[num_reduce_dim:])]

self.reduce_op_test(
            "ReduceFrontMean", ref_mean, [X, lengths], ["input", "lengths"],
            num_reduce_dim, gc)

@serial.given(num_reduce_dim=st.integers(0, 4), **hu.gcs)
    def test_reduce_front_max(self, num_reduce_dim, gc, dc):
        X = np.random.rand(6, 7, 8, 2).astype(np.float32)

def ref_frontmax(X):
            return [np.max(X, axis=(tuple(range(num_reduce_dim))))]

self.max_op_test(
            "ReduceFrontMax", num_reduce_dim, gc, dc, [X], ["X"], ref_frontmax)

@given(**hu.gcs)
    def test_reduce_front_max_with_length(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][num_reduce_dim:]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_max(X, lengths):
            Y = X.reshape(d, lengths.size)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.max(Y[:lengths[ii], ii])
            return [rv.reshape((2, 3, 4, 5)[num_reduce_dim:])]

self.max_op_test(
            "ReduceFrontMax", num_reduce_dim, gc, dc, [X, lengths],
            ["X", "lengths"], ref_max)

@serial.given(num_reduce_dim=st.integers(0, 4), **hu.gcs)
    def test_reduce_back_max(self, num_reduce_dim, gc, dc):
        X = np.random.rand(6, 7, 8, 2).astype(np.float32)

def ref_backmax(X):
            return [np.max(X, axis=(0, 1, 2, 3)[4 - num_reduce_dim:])]

self.max_op_test(
            "ReduceBackMax", num_reduce_dim, gc, dc, [X], ["X"], ref_backmax)

@given(**hu.gcs)
    def test_reduce_back_max_with_length(self, gc, dc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][:4 - num_reduce_dim]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_max(X, lengths):
            Y = X.reshape(lengths.size, d)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.max(Y[ii, :lengths[ii]])
            return [rv.reshape((2, 3, 4, 5)[:4 - num_reduce_dim])]

self.max_op_test(
            "ReduceBackMax", num_reduce_dim, gc, dc, [X, lengths],
            ["X", "lengths"], ref_max)

@given(**hu.gcs)
    @settings(deadline=10000)
    def test_reduce_back_sum(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(6, 7, 8, 2).astype(np.float32)

def ref_sum(X):
            return [np.sum(X, axis=(0, 1, 2, 3)[4 - num_reduce_dim:])]

self.reduce_op_test(
            "ReduceBackSum", ref_sum, [X], ["input"], num_reduce_dim, gc)
        self.grad_variant_input_test(
            "ReduceBackSumGradient", X, ref_sum, num_reduce_dim)

@given(**hu.gcs)
    @settings(deadline=10000)
    def test_reduce_back_sum_with_length(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][:4 - num_reduce_dim]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_sum(X, lengths):
            Y = X.reshape(lengths.size, d)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.sum(Y[ii, :lengths[ii]])
            return [rv.reshape((2, 3, 4, 5)[:4 - num_reduce_dim])]

self.reduce_op_test(
            "ReduceBackSum", ref_sum, [X, lengths], ["input", "lengths"],
            num_reduce_dim, gc)

@given(num_reduce_dim=st.integers(0, 4), **hu.gcs)
    @settings(deadline=10000)
    def test_reduce_back_mean(self, num_reduce_dim, dc, gc):
        X = np.random.rand(6, 7, 8, 2).astype(np.float32)

def ref_mean(X):
            return [np.mean(X, axis=(0, 1, 2, 3)[4 - num_reduce_dim:])]

self.reduce_op_test(
            "ReduceBackMean", ref_mean, [X], ["input"], num_reduce_dim, gc)
        self.grad_variant_input_test(
            "ReduceBackMeanGradient", X, ref_mean, num_reduce_dim)

@given(**hu.gcs)
    @settings(deadline=None)
    def test_reduce_back_mean_with_length(self, dc, gc):
        num_reduce_dim = 1
        X = np.random.rand(2, 3, 4, 5).astype(np.float32)
        batch_size = int(np.prod([2, 3, 4, 5][:4 - num_reduce_dim]))
        d = 120 // batch_size
        lengths = np.random.randint(1, d, size=batch_size).astype(np.int32)

def ref_mean(X, lengths):
            Y = X.reshape(lengths.size, d)
            rv = np.zeros((lengths.size, 1)).astype(np.float32)
            for ii in range(lengths.size):
                rv[ii] = np.mean(Y[ii, :lengths[ii]])
            return [rv.reshape((2, 3, 4, 5)[:4 - num_reduce_dim])]

self.reduce_op_test(
            "ReduceBackMean", ref_mean, [X, lengths], ["input", "lengths"],
            num_reduce_dim, gc)

cmd: