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/self_binning_histogram_test.py (12915B)

import unittest

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings

class TestSelfBinningHistogramBase(object):
    def __init__(self, bin_spacing, dtype, abs=False):
        self.bin_spacing = bin_spacing
        self.dtype = dtype
        self.abs = abs

def _check_histogram(self, arrays, num_bins, expected_values=None, expected_counts=None):
        # Check that sizes match and counts add up.
        values = workspace.FetchBlob("histogram_values")
        counts = workspace.FetchBlob("histogram_counts")
        self.assertTrue(np.size(values) == num_bins)
        self.assertTrue(np.size(counts) == num_bins)
        self.assertTrue(np.sum(counts) == sum([np.size(array) for array in arrays]))

# Check counts
        if expected_counts is None:
            # Check that counts are correct for the returned values if expected_counts is not given.
            expected_counts = np.zeros(num_bins, dtype='i')
            for array in arrays:
                for input_val in array:
                    input_val = abs(input_val) if self.abs else input_val
                    found = False
                    for pos in range(np.size(values)):
                        if values[pos] > input_val:
                            found = True
                            break
                    self.assertTrue(found, f"input value must fit inside values array: "
                                           f"input={input_val}, last_value={values[-1]}")
                    if self.bin_spacing == "linear":
                        self.assertTrue(pos > 0,
                                        f"input should not be smaller than the first bin value: "
                                        f"input={input_val}, 1st bin value={values[pos]}")
                    if pos == 0:
                        self.assertEqual(self.bin_spacing, "logarithmic")
                        expected_counts[pos] += 1
                    else:
                        expected_counts[pos - 1] += 1
        self.assertTrue(np.array_equal(expected_counts, counts), f"expected:{expected_counts}\ncounts:{counts}")
        # Check values
        if expected_values is not None:
            self.assertTrue(np.allclose(expected_values, values, rtol=1e-02, atol=1e-05),
                            f"expected:{expected_values}\nvalues:{values}")
        # Ideally, the output values are sorted in a non-decreasing order.
        for idx in range(len(values) - 1):
            self.assertTrue(values[idx] <= values[idx + 1])
        if self.abs:
            self.assertTrue(values[0] >= 0)

def _run_single_op_net(self, arrays, num_bins, logspacing_start=None):
        for i in range(len(arrays)):
            workspace.FeedBlob(
                "X{}".format(i), arrays[i]
            )
        net = core.Net("test_net")
        if logspacing_start is not None:
            net.SelfBinningHistogram(
                ["X{}".format(i) for i in range(len(arrays))],
                ["histogram_values", "histogram_counts"],
                num_bins=num_bins,
                bin_spacing=self.bin_spacing,
                logspacing_start=logspacing_start,
                abs=self.abs
            )
        else:
            net.SelfBinningHistogram(
                ["X{}".format(i) for i in range(len(arrays))],
                ["histogram_values", "histogram_counts"],
                num_bins=num_bins,
                bin_spacing=self.bin_spacing,
                abs=self.abs
            )
        workspace.RunNetOnce(net)

@given(rows=st.integers(1, 1000), cols=st.integers(1, 1000), **hu.gcs_cpu_only)
    @settings(deadline=10000)
    def test_histogram_device_consistency(self, rows, cols, gc, dc):
        X = np.random.rand(rows, cols)
        op = core.CreateOperator(
            "SelfBinningHistogram",
            ["X"],
            ["histogram_values", "histogram_counts"],
            num_bins=1000,
            bin_spacing=self.bin_spacing,
        )
        self.assertDeviceChecks(dc, op, [X], [0])

def test_histogram_bin_to_fewer(self):
        X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
        if self.bin_spacing == 'linear':
            if not self.abs:
                expected_values = [-2., 0.2, 2.4, 4.6, 6.8, 9.]
                expected_counts = [5, 2, 2, 1, 1, 0]
            else:
                expected_values = [0., 1.8, 3.6, 5.4, 7.2, 9.]
                expected_counts = [4, 4, 1, 1, 1, 0]
        else:
            expected_values = [1.e-24, 9.8e-20, 9.6e-15, 9.4e-10, 9.2e-05, 9.]
            if not self.abs:
                expected_counts = [5, 0, 0, 0, 6, 0]
            else:
                expected_counts = [3, 0, 0, 0, 8, 0]
        self._run_single_op_net([X], 5)
        self._check_histogram(
            [X],
            6,
            expected_values=expected_values,
            expected_counts=expected_counts
        )

def test_histogram_bin_to_more(self):
        X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
        self._run_single_op_net([X], 100)
        self._check_histogram(
            [X],
            101,
        )

def test_histogram_bin_to_two(self):
        """This test roughly tests [min,max+EPSILON] and [N,0]"""
        X = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
        if self.bin_spacing == 'linear':
            if not self.abs:
                expected_values = [-2., 9.]
            else:
                expected_values = [0., 9.]
        else:
            expected_values = [1.e-24, 9.]
        expected_counts = [11, 0]
        self._run_single_op_net([X], 1)
        self._check_histogram(
            [X],
            2,
            expected_values=expected_values,
            expected_counts=expected_counts
        )

def test_histogram_min_max_equal(self):
        """This test uses exact value match, so is only relevant for float type."""
        X = np.array([0., 0., 0., 0., 0.], dtype='f')
        logspacing_start = np.float(1e-24)
        self._run_single_op_net([X], 3, logspacing_start)
        if self.bin_spacing == "linear":
            self._check_histogram(
                [X],
                4,
                expected_values=np.array([0., 0., 0., 0.], dtype='f'),
                expected_counts=[5, 0, 0, 0]
            )
        else:
            self.assertEqual(self.bin_spacing, "logarithmic")
            self._check_histogram(
                [X],
                4,
                expected_values=np.array([logspacing_start] * 4, dtype='f'),
                expected_counts=[5, 0, 0, 0],
            )

def test_histogram_min_max_equal_nonzero(self):
        X = np.array([1., 1., 1., 1., 1.], dtype=self.dtype)
        logspacing_start = 1e-24
        self._run_single_op_net([X], 3, logspacing_start)
        self._check_histogram(
            [X],
            4,
            expected_values=[1., 1., 1., 1.],
            expected_counts=[5, 0, 0, 0]
        )

def test_histogram_empty_input_tensor(self):
        X = np.array([], dtype=self.dtype)
        self._run_single_op_net([X], 1)
        self._check_histogram(
            [X],
            2,
            expected_values=[0., 0.],
            expected_counts=[0, 0]
        )
        self._run_single_op_net([X], 10)
        self._check_histogram(
            [X],
            11,
            expected_values=[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
            expected_counts=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        )

def test_histogram_multi_input(self):
        X1 = np.array([-2.0, -2.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
        X2 = np.array([-5.0, -3.0, 7, 7, 0.0, 1.0, 2.0, -3.0, 4.0, 6.0, 9.0], dtype=self.dtype)
        if self.bin_spacing == 'linear':
            if not self.abs:
                expected_values = [-5., -2.2, 0.6, 3.4, 6.2, 9.]
                expected_counts = [3, 6, 5, 4, 4, 0]
            else:
                expected_values = [0., 1.8, 3.6, 5.4, 7.2, 9.]
                expected_counts = [6, 7, 3, 4, 2, 0]
        else:
            expected_values = [1.e-24, 9.8e-20, 9.6e-15, 9.4e-10, 9.2e-05, 9.]
            if not self.abs:
                expected_counts = [9, 0, 0, 0, 13, 0]
            else:
                expected_counts = [4, 0, 0, 0, 18, 0]
        self._run_single_op_net([X1, X2], 5)
        self._check_histogram(
            [X1, X2],
            6,
            expected_values=expected_values,
            expected_counts=expected_counts
        )

def test_histogram_very_small_range_for_stride_underflow(self):
        """Tests a large number of bins for a very small range of values.

This test uses float type. 1-e302 is very small, and with 1M bins, it
        causes numeric underflow. This test is to show that this is handled.

Note: this test was flaky due to how compiler and OS handls floats.
        Previously, 1-e38 does not induce overflow and cuases test error for some
        combinations of compiler and OS. Now 1-e302 should be small enough.
        """
        X = np.array([0, 1e-302], dtype='f')
        large_bin_number = 1000000
        self._run_single_op_net([X], large_bin_number)
        self._check_histogram(
            [X],
            large_bin_number + 1,
            expected_counts=[2] + [0] * large_bin_number  # [2, 0, 0, ..., 0]
        )

def test_histogram_insufficient_bins(self):
        with self.assertRaisesRegex(
            RuntimeError, "Number of bins must be greater than or equal to 1."
        ):
            self._run_single_op_net([np.random.rand(111)], 0)

class TestSelfBinningHistogramLinear(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d')
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLogarithmic(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='d')
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLinearFloat(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f')
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLogarithmicFloat(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='f')
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLinearWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d', abs=True)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLogarithmicWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='d', abs=True)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLinearFloatWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f', abs=True)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLogarithmicFloatWithAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="logarithmic", dtype='f', abs=True)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLinearWithNoneAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='d', abs=None)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

class TestSelfBinningHistogramLinearFloatWithNoneAbs(TestSelfBinningHistogramBase, hu.HypothesisTestCase):
    def __init__(self, *args, **kwargs):
        TestSelfBinningHistogramBase.__init__(self, bin_spacing="linear", dtype='f', abs=None)
        hu.HypothesisTestCase.__init__(self, *args, **kwargs)

if __name__ == "__main__":
    global_options = ["caffe2"]
    core.GlobalInit(global_options)
    unittest.main()

cmd: