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/boolean_mask_test.py (16389B)

from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import assume, given, settings
import hypothesis.strategies as st
import numpy as np

class TestBooleanMaskOp(serial.SerializedTestCase):
    @given(x=hu.tensor1d(min_len=1,
                         max_len=100,
                         elements=hu.floats(min_value=0.5, max_value=1.0)),
           **hu.gcs_cpu_only)
    @settings(deadline=10000)
    def test_boolean_mask_gradient(self, x, gc, dc):
        op = core.CreateOperator("BooleanMask",
                                 ["data", "mask"],
                                 "masked_data")
        mask = np.random.choice(a=[True, False], size=x.shape[0])
        expected_gradient = np.copy(mask).astype(int)
        self.assertDeviceChecks(dc, op, [x, mask], [0])
        self.assertGradientChecks(gc, op, [x, mask], 0, [0])

@given(x=hu.tensor1d(min_len=1,
                         max_len=5,
                         elements=hu.floats(min_value=0.5, max_value=1.0)),
           **hu.gcs)
    @settings(deadline=10000)
    def test_boolean_mask(self, x, gc, dc):
        op = core.CreateOperator("BooleanMask",
                                 ["data", "mask"],
                                 "masked_data")
        mask = np.random.choice(a=[True, False], size=x.shape[0])

def ref(x, mask):
            return (x[mask],)
        self.assertReferenceChecks(gc, op, [x, mask], ref)
        self.assertDeviceChecks(dc, op, [x, mask], [0])

@given(x=hu.tensor1d(min_len=1,
                         max_len=5,
                         elements=hu.floats(min_value=0.5, max_value=1.0)),
           **hu.gcs)
    def test_boolean_mask_indices(self, x, gc, dc):
        op = core.CreateOperator("BooleanMask",
                                 ["data", "mask"],
                                 ["masked_data", "masked_indices"])
        mask = np.random.choice(a=[True, False], size=x.shape[0])

def ref(x, mask):
            return (x[mask], np.where(mask)[0])

self.assertReferenceChecks(gc, op, [x, mask], ref)
        self.assertDeviceChecks(dc, op, [x, mask], [0])

@staticmethod
    def _dtype_conversion(x, dtype, gc, dc):
        """SequenceMask only supports fp16 with CUDA/ROCm."""
        if dtype == np.float16:
            assume(core.IsGPUDeviceType(gc.device_type))
            dc = [d for d in dc if core.IsGPUDeviceType(d.device_type)]
            x = x.astype(dtype)
        return x, dc

@given(x=hu.tensor(min_dim=2,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    def test_sequence_mask_with_lengths(self, x, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        op = core.CreateOperator("SequenceMask",
                                 ["data", "lengths"],
                                 ["masked_data"],
                                 mode="sequence",
                                 axis=len(x.shape) - 1,
                                 fill_val=fill_val)
        elem_dim = x.shape[-1]
        leading_dim = 1
        for dim in x.shape[:-1]:
            leading_dim *= dim
        lengths = np.random.randint(0, elem_dim, [leading_dim])\
            .astype(np.int32)

def ref(x, lengths):
            ref = np.reshape(x, [leading_dim, elem_dim])
            for i in range(leading_dim):
                for j in range(elem_dim):
                    if j >= lengths[i]:
                        ref[i, j] = fill_val
            return [ref.reshape(x.shape)]

self.assertReferenceChecks(gc, op, [x, lengths], ref)
        self.assertDeviceChecks(dc, op, [x, lengths], [0])

@given(x=hu.tensor(min_dim=2,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_sequence_mask_with_window(self, x, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        radius = 2
        op = core.CreateOperator("SequenceMask",
                                 ["data", "centers"],
                                 ["masked_data"],
                                 mode="window",
                                 radius=radius,
                                 axis=len(x.shape) - 1,
                                 fill_val=fill_val)
        elem_dim = x.shape[-1]
        leading_dim = 1
        for dim in x.shape[:-1]:
            leading_dim *= dim
        centers = np.random.randint(0, elem_dim, [leading_dim])\
            .astype(np.int32)

def ref(x, centers):
            ref = np.reshape(x, [leading_dim, elem_dim])
            for i in range(leading_dim):
                for j in range(elem_dim):
                    if j > centers[i] + radius or j < centers[i] - radius:
                        ref[i, j] = fill_val
            return [ref.reshape(x.shape)]

self.assertReferenceChecks(gc, op, [x, centers], ref)
        self.assertDeviceChecks(dc, op, [x, centers], [0])

# Gradient check with np.float16 is found to be flakey, disable for now
        # with high threshold (to repro, set threshold to 0.4).
        threshold = 1.0 if dtype == np.float16 else 0.005
        self.assertGradientChecks(gc, op, [x, centers], 0, [0],
                                  threshold=threshold)

@given(x=hu.tensor(min_dim=2,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           mode=st.sampled_from(['upper', 'lower', 'upperdiag', 'lowerdiag']),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_sequence_mask_triangle(self, x, mode, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        op = core.CreateOperator("SequenceMask",
                                 ["data"],
                                 ["masked_data"],
                                 mode=mode,
                                 axis=len(x.shape) - 1,
                                 fill_val=fill_val)
        elem_dim = x.shape[-1]
        leading_dim = 1
        for dim in x.shape[:-1]:
            leading_dim *= dim

if mode == 'upper':
            def compare(i, j):
                return j > i
        elif mode == 'lower':
            def compare(i, j):
                return j < i
        elif mode == 'upperdiag':
            def compare(i, j):
                return j >= i
        elif mode == 'lowerdiag':
            def compare(i, j):
                return j <= i

def ref(x):
            ref = np.reshape(x, [leading_dim, elem_dim])
            for i in range(leading_dim):
                for j in range(elem_dim):
                    if compare(i, j):
                        ref[i, j] = fill_val
            return [ref.reshape(x.shape)]

self.assertReferenceChecks(gc, op, [x], ref)
        self.assertDeviceChecks(dc, op, [x], [0])

# Gradient check with np.float16 is found to be flakey, disable for now
        # with high threshold (to repro, set threshold to 0.4).
        threshold = 1.0 if dtype == np.float16 else 0.005
        stepsize = 0.1 if dtype == np.float16 else 0.05
        self.assertGradientChecks(gc, op, [x], 0, [0],
                                  threshold=threshold, stepsize=stepsize)

@given(x=hu.tensor(min_dim=2,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_sequence_mask_batching_lengths(self, x, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        # choose _different_ batch and axis dimensions, w/ axis != 0.
        axis = 0
        batch = 0
        while axis == 0 or axis < batch:
            inds = np.arange(len(x.shape))
            np.random.shuffle(inds)
            batch = inds[0]
            axis = inds[1]
        op = core.CreateOperator("SequenceMask",
                                 ["data", "lengths"],
                                 ["masked_data"],
                                 mode='sequence',
                                 axis=axis,
                                 fill_val=fill_val,
                                 batch=batch)

before = int(np.prod(x.shape[:batch + 1]))
        between = int(np.prod(x.shape[batch + 1:axis]))
        after = int(np.prod(x.shape[axis:]))

lengths = np.random.randint(0, after, [between])\
            .astype(np.int32)

def ref(z, l):
            w = np.reshape(z, [before, between, after])

for b in range(before):
                r = w[b, :, :]
                for i in range(between):
                    for j in range(after):
                        if j >= l[i]:
                            r[i, j] = fill_val
            return [w.reshape(z.shape)]

self.assertReferenceChecks(gc, op, [x, lengths], ref)
        self.assertDeviceChecks(dc, op, [x, lengths], [0])

# Gradient check with np.float16 is found to be flakey, disable for now
        # with high threshold (to repro, set threshold to 0.4).
        threshold = 1.0 if dtype == np.float16 else 0.005
        self.assertGradientChecks(gc, op, [x, lengths], 0, [0],
                                  threshold=threshold)

@given(x=hu.tensor(min_dim=4,
                       max_dim=4,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_sequence_mask_batching_window(self, x, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        radius = 1
        # choose _different_ batch and axis dimensions, w/ axis != 0.
        axis = 0
        batch = 0
        while axis == 0 or axis < batch:
            inds = np.arange(len(x.shape))
            np.random.shuffle(inds)
            batch = inds[0]
            axis = inds[1]
        op = core.CreateOperator("SequenceMask",
                                 ["data", "centers"],
                                 ["masked_data"],
                                 mode='window',
                                 radius=radius,
                                 axis=axis,
                                 fill_val=fill_val,
                                 batch=batch)

before = int(np.prod(x.shape[:batch + 1]))
        between = int(np.prod(x.shape[batch + 1:axis]))
        after = int(np.prod(x.shape[axis:]))

centers = np.random.randint(0, after, [between])\
            .astype(np.int32)

def ref(z, c):
            w = np.reshape(z, [before, between, after])

for b in range(before):
                r = w[b, :, :]
                for i in range(between):
                    for j in range(after):
                        if j > c[i] + radius or j < c[i] - radius:
                            r[i, j] = fill_val
            return [w.reshape(z.shape)]

self.assertReferenceChecks(gc, op, [x, centers], ref)
        self.assertDeviceChecks(dc, op, [x, centers], [0])

@given(x=hu.tensor(min_dim=3,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           mode=st.sampled_from(['upper', 'lower', 'upperdiag', 'lowerdiag']),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_sequence_mask_batching_triangle(self, x, mode, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        # choose _different_ batch and axis dimensions, w/ axis != 0.
        axis = 0
        batch = 0
        while axis == 0 or axis < batch:
            inds = np.arange(len(x.shape))
            np.random.shuffle(inds)
            batch = inds[0]
            axis = inds[1]
        op = core.CreateOperator("SequenceMask",
                                 ["data"],
                                 ["masked_data"],
                                 mode=mode,
                                 axis=axis,
                                 fill_val=fill_val,
                                 batch=batch)

def ref(z):
            before = int(np.prod(z.shape[:batch + 1]))
            between = int(np.prod(z.shape[batch + 1:axis]))
            after = int(np.prod(z.shape[axis:]))

w = np.reshape(z, [before, between, after])

for b in range(before):
                r = w[b, :, :]
                for i in range(between):
                    for j in range(after):
                        if compare(i, j):
                            r[i, j] = fill_val
            return [w.reshape(z.shape)]

self.assertReferenceChecks(gc, op, [x], ref)
        self.assertDeviceChecks(dc, op, [x], [0])

@given(x=hu.tensor(min_dim=3,
                       max_dim=5,
                       elements=hu.floats(min_value=0.5, max_value=1.0)),
           dtype=st.sampled_from([np.float32, np.float16]),
           **hu.gcs)
    def test_sequence_mask_repeated(self, x, dtype, gc, dc):
        x, dc = self._dtype_conversion(x, dtype, gc, dc)
        # finite fill value needed for gradient check
        fill_val = 1e-3 if dtype == np.float16 else 1e-9
        op = core.CreateOperator("SequenceMask",
                                 ["data", "lengths"],
                                 ["masked_data"],
                                 mode="sequence",
                                 axis=len(x.shape) - 2,
                                 repeat_from_axis=-1,
                                 fill_val=fill_val)

elem_dim = x.shape[-2]
        leading_dim = 1
        for dim in x.shape[:-2]:
            leading_dim *= dim
        lengths = np.random.randint(0, elem_dim, [leading_dim])\
            .astype(np.int32)

def ref(x, lengths):
            ref = np.reshape(x, [leading_dim, elem_dim, -1])
            for i in range(leading_dim):
                for j in range(elem_dim):
                    if j >= lengths[i]:
                        ref[i, j, :] = fill_val
            return [ref.reshape(x.shape)]

self.assertReferenceChecks(gc, op, [x, lengths], ref)
        self.assertDeviceChecks(dc, op, [x, lengths], [0])

cmd: