FM / python

/usr/local/lib64/python3.6/site-packages/caffe2/python/

/usr/local/lib64/python3.6/site-packages/caffe2/python

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docs/	-	0755	rm
examples/	-	0755	rm
fakelowp/	-	0755	rm
helpers/	-	0755	rm
ideep/	-	0755	rm
layers/	-	0755	rm
mint/	-	0755	rm
mkl/	-	0755	rm
modeling/	-	0755	rm
models/	-	0755	rm
onnx/	-	0755	rm
operator_test/	-	0755	rm
predictor/	-	0755	rm
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serialized_test/	-	0755	rm
test/	-	0755	rm
trt/	-	0755	rm
__pycache__/	-	0755	rm
allcompare_test.py	2255	0644	edit dl rm
attention.py	12359	0644	edit dl rm
benchmark_generator.py	4912	0644	edit dl rm
binarysize.py	5521	0644	edit dl rm
brew.py	4762	0644	edit dl rm
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build.py	153	0644	edit dl rm
cached_reader.py	4394	0644	edit dl rm
caffe2_pybind11_state.cpython-36m-x86_64-linux-gnu.so	48299712	0755	edit dl rm
caffe2_pybind11_state_gpu.cpython-36m-x86_64-linux-gnu.so	49048144	0755	edit dl rm
caffe_translator.py	35227	0644	edit dl rm
caffe_translator_test.py	3553	0644	edit dl rm
checkpoint.py	32101	0644	edit dl rm
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cnn.py	7626	0644	edit dl rm
context.py	2841	0644	edit dl rm
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convert.py	55	0644	edit dl rm
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convnet_benchmarks.py	20533	0644	edit dl rm
convnet_benchmarks_test.py	839	0644	edit dl rm
core.py	119400	0644	edit dl rm
core_gradients_test.py	38022	0644	edit dl rm
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crf.py	13250	0644	edit dl rm
crf_predict.py	1159	0644	edit dl rm
crf_viterbi_test.py	1663	0644	edit dl rm
dataio.py	23532	0644	edit dl rm
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dataset.py	12886	0644	edit dl rm
data_parallel_model.py	83100	0644	edit dl rm
data_parallel_model_test.py	56145	0644	edit dl rm
data_workers.py	15941	0644	edit dl rm
data_workers_test.py	6561	0644	edit dl rm
db_file_reader.py	6608	0644	edit dl rm
db_test.py	1110	0644	edit dl rm
device_checker.py	5157	0644	edit dl rm
dyndep.py	1533	0644	edit dl rm
embedding_generation_benchmark.py	5256	0644	edit dl rm
experiment_util.py	3625	0644	edit dl rm
extension_loader.py	744	0644	edit dl rm
fakefp16_transform_lib.py	322	0644	edit dl rm
filler_test.py	748	0644	edit dl rm
functional.py	4415	0644	edit dl rm
functional_test.py	4204	0644	edit dl rm
fused_8bit_rowwise_conversion_ops_test.py	3945	0644	edit dl rm
gradient_checker.py	15377	0644	edit dl rm
gradient_check_test.py	20729	0644	edit dl rm
gru_cell.py	5129	0644	edit dl rm
hip_test_util.py	405	0644	edit dl rm
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hypothesis_test.py	105762	0644	edit dl rm
hypothesis_test_util.py	26853	0644	edit dl rm
ideep_test_util.py	998	0644	edit dl rm
layers_test.py	92931	0644	edit dl rm
layer_model_helper.py	29340	0644	edit dl rm
layer_model_instantiator.py	3935	0644	edit dl rm
layer_parameter_sharing_test.py	9148	0644	edit dl rm
layer_test_util.py	4875	0644	edit dl rm
lazy.py	277	0644	edit dl rm
lazy_dyndep.py	2562	0644	edit dl rm
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lengths_reducer_fused_8bit_rowwise_ops_test.py	7575	0644	edit dl rm
lengths_reducer_rowwise_8bit_ops_test.py	5710	0644	edit dl rm
lstm_benchmark.py	10649	0644	edit dl rm
memonger.py	34041	0644	edit dl rm
memonger_test.py	36910	0644	edit dl rm
mkl_test_util.py	1142	0644	edit dl rm
model_device_test.py	4777	0644	edit dl rm
model_helper.py	23492	0644	edit dl rm
model_helper_test.py	2336	0644	edit dl rm
modifier_context.py	1772	0644	edit dl rm
muji.py	8131	0644	edit dl rm
muji_test.py	3058	0644	edit dl rm
net_builder.py	27679	0644	edit dl rm
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net_drawer.py	14264	0644	edit dl rm
net_printer.py	12704	0644	edit dl rm
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nomnigraph.py	4216	0644	edit dl rm
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nomnigraph_transformations_test.py	5767	0644	edit dl rm
normalizer.py	1411	0644	edit dl rm
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numa_benchmark.py	2230	0644	edit dl rm
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observer_test.py	5316	0644	edit dl rm
operator_fp_exceptions_test.py	1248	0644	edit dl rm
optimizer.py	78813	0644	edit dl rm
optimizer_context.py	1462	0644	edit dl rm
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parallelize_bmuf_distributed_test.py	9908	0644	edit dl rm
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pipeline_test.py	2542	0644	edit dl rm
predictor_constants.py	198	0644	edit dl rm
python_op_test.py	9169	0644	edit dl rm
queue_util.py	4459	0644	edit dl rm
record_queue.py	4453	0644	edit dl rm
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regularizer.py	21120	0644	edit dl rm
regularizer_context.py	1013	0644	edit dl rm
regularizer_test.py	10266	0644	edit dl rm
rnn_cell.py	68233	0644	edit dl rm
schema.py	45621	0644	edit dl rm
schema_test.py	15754	0644	edit dl rm
scope.py	3623	0644	edit dl rm
scope_test.py	5249	0644	edit dl rm
session.py	7642	0644	edit dl rm
session_test.py	2078	0644	edit dl rm
sparse_to_dense_mask_test.py	6565	0644	edit dl rm
sparse_to_dense_test.py	3556	0644	edit dl rm
task.py	24274	0644	edit dl rm
task_test.py	870	0644	edit dl rm
test_util.py	3524	0644	edit dl rm
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toy_regression_test.py	2822	0644	edit dl rm
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tt_core.py	9349	0644	edit dl rm
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utils.py	14181	0644	edit dl rm
utils_test.py	1399	0644	edit dl rm
visualize.py	6315	0644	edit dl rm
workspace.py	25263	0644	edit dl rm
workspace_test.py	34844	0644	edit dl rm
_import_c_extension.py	2250	0644	edit dl rm
__init__.py	3925	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/sparse_to_dense_mask_test.py (6565B)

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

import numpy as np

class TestSparseToDenseMask(TestCase):

def test_sparse_to_dense_mask_float(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default', 'lengths'],
            ['output'],
            mask=[999999999, 2, 6])
        workspace.FeedBlob(
            'indices',
            np.array([2, 4, 6, 1, 2, 999999999, 2], dtype=np.int32))
        workspace.FeedBlob(
            'values',
            np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.float))
        workspace.FeedBlob('default', np.array(-1, dtype=np.float))
        workspace.FeedBlob('lengths', np.array([3, 4], dtype=np.int32))
        workspace.RunOperatorOnce(op)
        output = workspace.FetchBlob('output')
        expected = np.array([[-1, 1, 3], [6, 7, -1]], dtype=np.float)
        self.assertEqual(output.shape, expected.shape)
        np.testing.assert_array_equal(output, expected)

def test_sparse_to_dense_mask_invalid_inputs(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default', 'lengths'],
            ['output'],
            mask=[999999999, 2],
            max_skipped_indices=3)
        workspace.FeedBlob(
            'indices',
            np.array([2000000000000, 999999999, 2, 3, 4, 5], dtype=np.int32))
        workspace.FeedBlob(
            'values',
            np.array([1, 2, 3, 4, 5, 6], dtype=np.float))
        workspace.FeedBlob('default', np.array(-1, dtype=np.float))
        workspace.FeedBlob('lengths', np.array([6], dtype=np.int32))
        try:
            workspace.RunOperatorOnce(op)
        except RuntimeError:
            self.fail("Exception raised with only one negative index")

# 3 invalid inputs should throw.
        workspace.FeedBlob(
            'indices',
            np.array([-1, 1, 2, 3, 4, 5], dtype=np.int32))
        with self.assertRaises(RuntimeError):
            workspace.RunOperatorMultiple(op, 3)

def test_sparse_to_dense_mask_subtensor(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default', 'lengths'],
            ['output'],
            mask=[999999999, 2, 888, 6])
        workspace.FeedBlob(
            'indices',
            np.array([2, 4, 6, 999999999, 2], dtype=np.int64))
        workspace.FeedBlob(
            'values',
            np.array([[[1, -1]], [[2, -2]], [[3, -3]], [[4, -4]], [[5, -5]]],
                     dtype=np.float))
        workspace.FeedBlob('default', np.array([[-1, 0]], dtype=np.float))
        workspace.FeedBlob('lengths', np.array([2, 3], dtype=np.int32))
        workspace.RunOperatorOnce(op)
        output = workspace.FetchBlob('output')
        expected = np.array([
            [[[-1, 0]], [[1, -1]], [[-1, 0]], [[-1, 0]]],
            [[[4, -4]], [[5, -5]], [[-1, 0]], [[3, -3]]]], dtype=np.float)
        self.assertEqual(output.shape, expected.shape)
        np.testing.assert_array_equal(output, expected)

def test_sparse_to_dense_mask_string(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default', 'lengths'],
            ['output'],
            mask=[999999999, 2, 6])
        workspace.FeedBlob(
            'indices',
            np.array([2, 4, 6, 1, 2, 999999999, 2], dtype=np.int32))
        workspace.FeedBlob(
            'values',
            np.array(['1', '2', '3', '4', '5', '6', '7'], dtype='S'))
        workspace.FeedBlob('default', np.array('-1', dtype='S'))
        workspace.FeedBlob('lengths', np.array([3, 4], dtype=np.int32))
        workspace.RunOperatorOnce(op)
        output = workspace.FetchBlob('output')
        expected =\
            np.array([['-1', '1', '3'], ['6', '7', '-1']], dtype='S')
        self.assertEqual(output.shape, expected.shape)
        np.testing.assert_array_equal(output, expected)

def test_sparse_to_dense_mask_empty_lengths(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default'],
            ['output'],
            mask=[1, 2, 6])
        workspace.FeedBlob('indices', np.array([2, 4, 6], dtype=np.int32))
        workspace.FeedBlob('values', np.array([1, 2, 3], dtype=np.float))
        workspace.FeedBlob('default', np.array(-1, dtype=np.float))
        workspace.RunOperatorOnce(op)
        output = workspace.FetchBlob('output')
        expected = np.array([-1, 1, 3], dtype=np.float)
        self.assertEqual(output.shape, expected.shape)
        np.testing.assert_array_equal(output, expected)

def test_sparse_to_dense_mask_no_lengths(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default'],
            ['output'],
            mask=[1, 2, 6])
        workspace.FeedBlob('indices', np.array([2, 4, 6], dtype=np.int32))
        workspace.FeedBlob('values', np.array([1, 2, 3], dtype=np.float))
        workspace.FeedBlob('default', np.array(-1, dtype=np.float))
        workspace.RunOperatorOnce(op)
        output = workspace.FetchBlob('output')
        expected = np.array([-1, 1, 3], dtype=np.float)
        self.assertEqual(output.shape, expected.shape)
        np.testing.assert_array_equal(output, expected)

def test_sparse_to_dense_mask_presence_mask(self):
        op = core.CreateOperator(
            'SparseToDenseMask',
            ['indices', 'values', 'default', 'lengths'],
            ['output', 'presence_mask'],
            mask=[11, 12],
            return_presence_mask=True)
        workspace.FeedBlob('indices', np.array([11, 12, 13], dtype=np.int32))
        workspace.FeedBlob('values', np.array([11, 12, 13], dtype=np.float))
        workspace.FeedBlob('default', np.array(-1, dtype=np.float))
        workspace.FeedBlob('lengths', np.array([1, 2], dtype=np.int32))

workspace.RunOperatorOnce(op)

output = workspace.FetchBlob('output')
        presence_mask = workspace.FetchBlob('presence_mask')
        expected_output = np.array([[11, -1], [-1, 12]], dtype=np.float)
        expected_presence_mask = np.array(
            [[True, False], [False, True]],
            dtype=np.bool)
        self.assertEqual(output.shape, expected_output.shape)
        np.testing.assert_array_equal(output, expected_output)
        self.assertEqual(presence_mask.shape, expected_presence_mask.shape)
        np.testing.assert_array_equal(presence_mask, expected_presence_mask)

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