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f153b35b9b
Summary: Adding shape inference for SpraseToDense. Proposal impl of shape inference only works when data_to_infer_dim is given, otherwise SpraseToDense output dimension depends on max value of input tensor Test Plan: buck test //caffe2/caffe2/python:sparse_to_dense_test buck test //caffe2/caffe2/python:hypothesis_test -- test_sparse_to_dense Dper3 Changes: f204594813 buck test dper3/dper3_models/ads_ranking/model_impl/sparse_nn/tests:sparse_nn_lib_test Reviewed By: zhongyx12, ChunliF Differential Revision: D22479511 fbshipit-source-id: 8983a9baea8853deec53ad6f795c874c3fb93de0
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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import numpy as np
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class TestSparseToDense(TestCase):
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def test_sparse_to_dense(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6, 7], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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print(output)
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expected = np.zeros(1000, dtype=np.int32)
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expected[2] = 1 + 7
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expected[4] = 2
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expected[999] = 6
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_shape_inference(self):
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indices = np.array([2, 4, 999, 2], dtype=np.int32)
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values = np.array([[1, 2], [2, 4], [6, 7], [7, 8]], dtype=np.int32)
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data_to_infer_dim = np.array(np.zeros(1500, ), dtype=np.int32)
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values', 'data_to_infer_dim'],
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['output'])
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workspace.FeedBlob('indices', indices)
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workspace.FeedBlob('values', values)
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workspace.FeedBlob('data_to_infer_dim', data_to_infer_dim)
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net = core.Net("sparse_to_dense")
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net.Proto().op.extend([op])
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shapes, types = workspace.InferShapesAndTypes(
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[net],
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blob_dimensions={
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"indices": indices.shape,
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"values": values.shape,
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"data_to_infer_dim": data_to_infer_dim.shape,
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},
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blob_types={
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"indices": core.DataType.INT32,
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"values": core.DataType.INT32,
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"data_to_infer_dim": core.DataType.INT32,
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},
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)
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assert (
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"output" in shapes and "output" in types
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), "Failed to infer the shape or type of output"
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self.assertEqual(shapes["output"], [1500, 2])
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self.assertEqual(types["output"], core.DataType.INT32)
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def test_sparse_to_dense_invalid_inputs(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6], dtype=np.int32))
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with self.assertRaises(RuntimeError):
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workspace.RunOperatorOnce(op)
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def test_sparse_to_dense_with_data_to_infer_dim(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values', 'data_to_infer_dim'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6, 7], dtype=np.int32))
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workspace.FeedBlob(
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'data_to_infer_dim',
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np.array(np.zeros(1500, ), dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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print(output)
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expected = np.zeros(1500, dtype=np.int32)
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expected[2] = 1 + 7
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expected[4] = 2
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expected[999] = 6
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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