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/pooling_test.py (16508B)

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

from caffe2.python import core, utils, workspace
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu

class TestPooling(hu.HypothesisTestCase):
    # CUDNN does NOT support different padding values and we skip it
    @given(stride_h=st.integers(1, 3),
           stride_w=st.integers(1, 3),
           pad_t=st.integers(0, 3),
           pad_l=st.integers(0, 3),
           pad_b=st.integers(0, 3),
           pad_r=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
                                   "MaxPool2D", "AveragePool2D"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_pooling_separate_stride_pad(self, stride_h, stride_w,
                                         pad_t, pad_l, pad_b,
                                         pad_r, kernel, size,
                                         input_channels,
                                         batch_size, order,
                                         op_type,
                                         gc, dc):
        assume(np.max([pad_t, pad_l, pad_b, pad_r]) < kernel)

op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            stride_h=stride_h,
            stride_w=stride_w,
            pad_t=pad_t,
            pad_l=pad_l,
            pad_b=pad_b,
            pad_r=pad_r,
            kernel=kernel,
            order=order,
        )
        X = np.random.rand(
            batch_size, size, size, input_channels).astype(np.float32)

if order == "NCHW":
            X = utils.NHWC2NCHW(X)
        self.assertDeviceChecks(dc, op, [X], [0])
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0])

# This test is to check if CUDNN works for bigger batch size or not
    @unittest.skipIf(not os.getenv('CAFFE2_DEBUG'),
                     "This is a test that reproduces a cudnn error. If you "
                     "want to run it, set env variable CAFFE2_DEBUG=1.")
    @given(**hu.gcs_cuda_only)
    def test_pooling_big_batch(self, gc, dc):
        op = core.CreateOperator(
            "AveragePool",
            ["X"],
            ["Y"],
            stride=1,
            kernel=7,
            pad=0,
            order="NHWC",
            engine="CUDNN",
        )
        X = np.random.rand(70000, 7, 7, 81).astype(np.float32)

self.assertDeviceChecks(dc, op, [X], [0])

@given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(1, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool",
                                    "MaxPool1D", "AveragePool1D"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_pooling_1d(self, stride, pad, kernel, size, input_channels,
                        batch_size, order, op_type, gc, dc):
        assume(pad < kernel)
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            strides=[stride],
            kernels=[kernel],
            pads=[pad, pad],
            order=order,
            engine="",
        )
        X = np.random.rand(
            batch_size, size, input_channels).astype(np.float32)
        if order == "NCHW":
            X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0])
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0])

@given(stride=st.integers(1, 3),
           pad=st.integers(0, 2),
           kernel=st.integers(1, 6),
           size=st.integers(3, 5),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool",
                                    "MaxPool3D", "AveragePool3D"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_pooling_3d(self, stride, pad, kernel, size, input_channels,
                        batch_size, order, op_type, engine, gc, dc):
        assume(pad < kernel)
        assume(size + pad + pad >= kernel)
        # Currently MIOpen Pooling only supports pooling with NCHW order.
        if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303 or  order == "NHWC"):
            assume(engine != "CUDNN")
        # some case here could be calculated with global pooling, but instead
        # calculated with general implementation, slower but should still
        # be correct.
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            strides=[stride] * 3,
            kernels=[kernel] * 3,
            pads=[pad] * 6,
            order=order,
            engine=engine,
        )
        X = np.random.rand(
            batch_size, size, size, size, input_channels).astype(np.float32)
        if order == "NCHW":
            X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)

@given(kernel=st.integers(3, 6),
           size=st.integers(3, 5),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool",
                                    "MaxPool3D", "AveragePool3D"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_global_pooling_3d(self, kernel, size, input_channels,
                               batch_size, order, op_type, engine, gc, dc):
        # Currently MIOpen Pooling only supports pooling with NCHW order.
        if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303 or  order == "NHWC"):
            assume(engine != "CUDNN")
        # pad and stride ignored because they will be inferred in global_pooling
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            kernels=[kernel] * 3,
            order=order,
            global_pooling=True,
            engine=engine,
        )
        X = np.random.rand(
            batch_size, size, size, size, input_channels).astype(np.float32)
        if order == "NCHW":
            X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)

@unittest.skipIf(not workspace.has_gpu_support, "No GPU support")
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(1, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           **hu.gcs_gpu_only)
    def test_pooling_with_index(self, stride, pad, kernel, size,
                                input_channels, batch_size, gc, dc):
        assume(pad < kernel)
        op = core.CreateOperator(
            "MaxPoolWithIndex",
            ["X"],
            ["Y", "Y_index"],
            stride=stride,
            kernel=kernel,
            pad=pad,
            order="NCHW",
            deterministic=1,
        )
        X = np.random.rand(
            batch_size, size, size, input_channels).astype(np.float32)

# transpose due to order = NCHW
        X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0])

@given(sz=st.integers(1, 20),
           batch_size=st.integers(0, 4),
           engine=st.sampled_from(["", "CUDNN"]),
           op_type=st.sampled_from(["AveragePool", "AveragePool2D"]),
           **hu.gcs)
    @settings(max_examples=3, deadline=None)
    def test_global_avg_pool_nchw(self, op_type, sz, batch_size, engine, gc, dc):
        ''' Special test to stress the fast path of NCHW average pool '''
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            stride=1,
            kernel=sz,
            pad=0,
            order="NCHW",
            engine=engine,
        )
        X = np.random.rand(
            batch_size, 3, sz, sz).astype(np.float32)

self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0])

@given(sz=st.integers(1, 20),
           batch_size=st.integers(0, 4),
           engine=st.sampled_from(["", "CUDNN"]),
           op_type=st.sampled_from(["MaxPool", "MaxPool2D"]),
           **hu.gcs)
    @settings(max_examples=3, deadline=None)
    def test_global_max_pool_nchw(self, op_type, sz,
                                  batch_size, engine, gc, dc):
        ''' Special test to stress the fast path of NCHW max pool '''
        # CuDNN 5 does not support deterministic max pooling.
        assume(workspace.GetCuDNNVersion() >= 6000 or engine != "CUDNN")
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            stride=1,
            kernel=sz,
            pad=0,
            order="NCHW",
            engine=engine,
            deterministic=1,
        )

np.random.seed(1234)
        X = np.random.rand(
            batch_size, 3, sz, sz).astype(np.float32)

self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=1e-4)

@given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(1, 5),
           size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
                                   "MaxPool2D", "AveragePool2D"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_pooling(self, stride, pad, kernel, size,
                     input_channels, batch_size,
                     order, op_type, engine, gc, dc):
        assume(pad < kernel)
        if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
            assume(order == "NCHW" and op_type != "LpPool")

op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            stride=stride,
            kernel=kernel,
            pad=pad,
            order=order,
            engine=engine,
        )
        X = np.random.rand(
            batch_size, size, size, input_channels).astype(np.float32)
        if order == "NCHW":
            X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0])
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0])

@given(size=st.integers(7, 9),
           input_channels=st.integers(1, 3),
           batch_size=st.integers(0, 3),
           order=st.sampled_from(["NCHW", "NHWC"]),
           op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_global_pooling(self, size, input_channels, batch_size,
                            order, op_type, engine, gc, dc):
        # CuDNN 5 does not support deterministic max pooling.
        assume(workspace.GetCuDNNVersion() >= 6000 or op_type != "MaxPool")

if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
            assume(order == "NCHW" and op_type != "LpPool")
        op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            order=order,
            engine=engine,
            global_pooling=True,
        )
        X = np.random.rand(
            batch_size, size, size, input_channels).astype(np.float32)
        if order == "NCHW":
            X = utils.NHWC2NCHW(X)

self.assertDeviceChecks(dc, op, [X], [0])
        if 'MaxPool' not in op_type:
            self.assertGradientChecks(gc, op, [X], 0, [0])

@given(op_type=st.sampled_from(["MaxPool", "MaxPoolND"]),
           dim=st.integers(1, 3),
           N=st.integers(1, 3),
           C=st.integers(1, 3),
           D=st.integers(3, 5),
           H=st.integers(3, 5),
           W=st.integers(3, 5),
           kernel=st.integers(1, 3),
           stride=st.integers(1, 3),
           pad=st.integers(0, 2),
           order=st.sampled_from(["NCHW", "NHWC"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=None, max_examples=50)
    def test_max_pool_grad(
            self, op_type, dim, N, C, D, H, W, kernel, stride, pad, order,
            engine, gc, dc):
        assume(pad < kernel)
        assume(dim > 1 or engine == "")
        if hiputl.run_in_hip(gc, dc):
            if dim != 2:
                assume(engine != "CUDNN")
            elif engine == "CUDNN":
                assume(order == "NCHW")

if op_type.endswith("ND"):
            op_type = op_type.replace("N", str(dim))

op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            kernels=[kernel] * dim,
            strides=[stride] * dim,
            pads=[pad] * dim * 2,
            order=order,
            engine=engine,
        )

if dim == 1:
            size = W
            dims = [N, C, W]
            axes = [0, 2, 1]
        elif dim == 2:
            size = H * W
            dims = [N, C, H, W]
            axes = [0, 2, 3, 1]
        else:
            size = D * H * W
            dims = [N, C, D, H, W]
            axes = [0, 2, 3, 4, 1]

X = np.zeros((N * C, size)).astype(np.float32)
        for i in range(N * C):
            X[i, :] = np.arange(size, dtype=np.float32) / size
            np.random.shuffle(X[i, :])
        X = X.reshape(dims)
        if order == "NHWC":
            X = np.transpose(X, axes)

self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(
            gc, op, [X], 0, [0], threshold=0.05, stepsize=0.005)

@given(op_type=st.sampled_from(["AveragePool", "AveragePoolND"]),
           dim=st.integers(1, 3),
           N=st.integers(1, 3),
           C=st.integers(1, 3),
           D=st.integers(3, 5),
           H=st.integers(3, 5),
           W=st.integers(3, 5),
           kernel=st.integers(1, 3),
           stride=st.integers(1, 3),
           pad=st.integers(0, 2),
           count_include_pad=st.booleans(),
           order=st.sampled_from(["NCHW", "NHWC"]),
           engine=st.sampled_from(["", "CUDNN"]),
           **hu.gcs)
    @settings(deadline=10000)
    def test_avg_pool_count_include_pad(
            self, op_type, dim, N, C, D, H, W, kernel, stride, pad,
            count_include_pad, order, engine, gc, dc):
        assume(pad < kernel)
        if hiputl.run_in_hip(gc, dc):
            if dim != 2:
                assume(engine != "CUDNN")
            elif engine == "CUDNN":
                assume(order == "NCHW")

if op_type.endswith("ND"):
            op_type = op_type.replace("N", str(dim))

op = core.CreateOperator(
            op_type,
            ["X"],
            ["Y"],
            kernels=[kernel] * dim,
            strides=[stride] * dim,
            pads=[pad] * dim * 2,
            count_include_pad=count_include_pad,
            order=order,
            engine=engine,
        )

if dim == 1:
            dims = [N, C, W]
            axes = [0, 2, 1]
        elif dim == 2:
            dims = [N, C, H, W]
            axes = [0, 2, 3, 1]
        else:
            dims = [N, C, D, H, W]
            axes = [0, 2, 3, 4, 1]
        X = np.random.randn(*dims).astype(np.float32)
        if order == "NHWC":
            X = np.transpose(X, axes)

self.assertDeviceChecks(dc, op, [X], [0])
        self.assertGradientChecks(gc, op, [X], 0, [0])

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

cmd: