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/rebatching_queue_test.py (9047B)

from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase

import numpy as np
import numpy.testing as npt

from hypothesis import given, settings
import hypothesis.strategies as st

import functools

def primefac(n):
    ret = []
    divisor = 2
    while divisor * divisor <= n:
        while (n % divisor) == 0:
            ret.append(divisor)
            n = n // divisor
        divisor = divisor + 1
    if n > 1:
        ret.append(n)
    return ret

class TestReBatchingQueue(TestCase):
    def test_rebatching_queue_single_enqueue_dequeue(self):
        net = core.Net('net')

tensors = [
            net.ConstantFill([], 1, value=1.0, run_once=False)
            for times in range(3)
        ]

queue = net.CreateRebatchingQueue([], 1, capacity=10, num_blobs=1)

net.EnqueueRebatchingQueue([queue, tensors[0]], [])
        net.EnqueueRebatchingQueue([queue, tensors[1]], [])
        net.EnqueueRebatchingQueue([queue, tensors[2]], [])

results = [
            net.DequeueRebatchingQueue([queue], 1),
            net.DequeueRebatchingQueue([queue], 1),
            net.DequeueRebatchingQueue([queue], 1),
        ]

workspace.RunNetOnce(net)

for idx in range(3):
            self.assertEquals(workspace.FetchBlob(results[idx]), [1.0])

def test_rebatching_queue_multi_enqueue_dequeue(self):
        net = core.Net('net')
        workspace.FeedBlob(
            "tensors", np.array([x for x in range(10)], np.int32)
        )

queue = net.CreateRebatchingQueue([], 1, capacity=10, num_blobs=1)

net.EnqueueRebatchingQueue([queue, "tensors"], [], enqueue_batch=True)

results = [
            net.DequeueRebatchingQueue([queue], 1, num_elements=5),
            net.DequeueRebatchingQueue([queue], 1, num_elements=5),
        ]

workspace.RunNetOnce(net)

npt.assert_array_equal(
            workspace.FetchBlob(results[0]), workspace.FetchBlob("tensors")[:5]
        )
        npt.assert_array_equal(
            workspace.FetchBlob(results[1]), workspace.FetchBlob("tensors")[5:]
        )

def test_rebatching_queue_closes_properly(self):
        net = core.Net('net')
        workspace.FeedBlob(
            "tensors", np.array([x for x in range(10)], np.int32)
        )

queue = net.CreateRebatchingQueue([], 1, capacity=10, num_blobs=1)

net.EnqueueRebatchingQueue([queue, "tensors"], 0, enqueue_batch=True)

net.CloseRebatchingQueue([queue], 0)

results = [
            net.DequeueRebatchingQueue([queue], 1, num_elements=5),
            net.DequeueRebatchingQueue([queue], 1, num_elements=5),
        ]

workspace.RunNetOnce(net)

# Enqueuing more should fail now since the queue is closed
        net.EnqueueRebatchingQueue([queue, "tensors"], [], enqueue_batch=True)

with self.assertRaises(RuntimeError):
            workspace.RunNetOnce(net)

# Dequeuing more should fail now since the queue is closed
        results = [
            net.DequeueRebatchingQueue([queue], 1, num_elements=5),
        ]

with self.assertRaises(RuntimeError):
            workspace.RunNetOnce(net)

def test_rebatching_queue_multiple_components(self):
        NUM_BLOBS = 4
        NUM_ELEMENTS = 10

net = core.Net('net')

workspace.blobs['complex_tensor'] = np.array(
            [[x, x + 1] for x in range(NUM_ELEMENTS)], dtype=np.int32
        )

tensors = [
            net.GivenTensorIntFill(
                [],
                1,
                shape=[NUM_ELEMENTS],
                values=[x for x in range(NUM_ELEMENTS)]
            ),
            net.GivenTensorFill(
                [],
                1,
                shape=[NUM_ELEMENTS],
                values=[x * 1.0 for x in range(NUM_ELEMENTS)]
            ),
            net.GivenTensorBoolFill(
                [],
                1,
                shape=[NUM_ELEMENTS],
                values=[(x % 2 == 0) for x in range(NUM_ELEMENTS)]
            ),
            'complex_tensor',
        ]

queue = net.CreateRebatchingQueue(
            [], 1, capacity=10, num_blobs=NUM_BLOBS
        )

net.EnqueueRebatchingQueue([queue] + tensors, [], enqueue_batch=True)

results = net.DequeueRebatchingQueue([queue], NUM_BLOBS, num_elements=5)

workspace.RunNetOnce(net)

for idx in range(NUM_BLOBS):
            npt.assert_array_equal(
                workspace.FetchBlob(results[idx]),
                workspace.FetchBlob(tensors[idx])[:5]
            )

@given(
        num_producers=st.integers(1, 5),
        num_consumers=st.integers(1, 5),
        producer_input_size=st.integers(1, 10),
        producer_num_iterations=st.integers(1, 10),
        capacity=st.integers(1, 10)
    )
    @settings(deadline=10000)
    def test_rebatching_parallel_producer_consumer(
        self, num_producers, num_consumers, producer_input_size,
        producer_num_iterations, capacity
    ):
        ### Init ###
        total_inputs = producer_num_iterations * producer_input_size * num_producers
        inputs = []
        init_net = core.Net('init_net')
        queue = init_net.CreateRebatchingQueue(
            [], 1, capacity=capacity, num_blobs=1
        )

### Producers ###
        producer_steps = []
        for i in range(num_producers):
            name = 'producer_%d' % i
            net = core.Net(name)
            values = [
                producer_input_size * i + x for x in range(producer_input_size)
            ]
            for _ in range(producer_num_iterations):
                inputs.extend(values)
            tensors = net.GivenTensorIntFill(
                [], 1, shape=[producer_input_size], values=values
            )

net.EnqueueRebatchingQueue([queue, tensors], [], enqueue_batch=True)

step = core.execution_step(
                name, net, num_iter=producer_num_iterations
            )
            producer_steps.append(step)

producer_step = core.execution_step(
            'producer', [
                core.execution_step(
                    'producers', producer_steps, concurrent_substeps=True
                )
            ]
        )

### Consumers ###
        outputs = []

def append(ins, outs):
            # Extend is atomic
            outputs.extend(ins[0].data.tolist())

consumer_steps = []
        for i in range(num_consumers):
            # This is just a way of deterministally read all the elements.
            # We make `num_consumers` almost equal splits
            # (the reminder goes to the last consumer).
            num_elements_to_read = total_inputs // num_consumers
            if i == num_consumers - 1:
                num_elements_to_read = num_elements_to_read \
                    + total_inputs % num_consumers

# If we have nothing to read this consumer will be idle
            if (num_elements_to_read == 0):
                continue

# Now we have to make a split on number of iterations and the read
            # size for each iteration. This is again just one of many
            # deterministic  ways of doing it. We factorize the total number of
            # elements we have to read and assign half of the factors to the
            # iterations half to the read size.
            factors = list(primefac(num_elements_to_read))

num_elements_per_iteration = functools.reduce(
                lambda x, y: x * y, factors[len(factors) // 2:], 1
            )

num_iterations = functools.reduce(
                lambda x, y: x * y, factors[:len(factors) // 2], 1
            )

name = 'consumer_%d' % i
            net = core.Net(name)
            blobs = net.DequeueRebatchingQueue(
                [queue], 1, num_elements=num_elements_per_iteration
            )
            net.Python(append)([blobs], 0)
            consumer_steps.append(
                core.execution_step(name, net, num_iter=num_iterations)
            )

consumer_step = core.execution_step(
            'consumer', consumer_steps, concurrent_substeps=True
        )

init_step = core.execution_step('init', init_net)
        worker_step = core.execution_step(
            'worker', [consumer_step, producer_step], concurrent_substeps=True
        )

### Execute Plan ###
        plan = core.Plan('test')
        plan.AddStep(init_step)
        plan.AddStep(worker_step)

self.ws.run(plan)

### Check Results ###
        # We check that the outputs are a permutation of inputs
        inputs.sort()
        outputs.sort()
        self.assertEquals(inputs, outputs)

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

cmd: