def testMultipleColumnsRetrieval(self):
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('Sizes', [1, 2, 3])
metadata.add_column('Labels', ['a', 'b', 'c'])
self.assertEqual(metadata.column_names, ['Sizes', 'Labels'])
self.assertEqual(metadata.name_to_values['Labels'], ['a', 'b', 'c'])
self.assertEqual(metadata.name_to_values['Sizes'], [1, 2, 3])
def testAddEmbeddingWithTwoMetadataColumns(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'друг јазик'])
metadata.add_column('sizes', [10, 20, 30])
manager.add_embedding('test', np.array([[1], [2], [3]]), metadata)
config = projector_config_pb2.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = 'test'
embedding.tensor_shape.extend([3, 1])
embedding.tensor_path = 'test_values.tsv'
embedding.metadata_path = 'test_metadata.tsv'
expected_config_pbtxt = text_format.MessageToString(config)
self.assertEqual(
manager.assets(), {
'projector_config.pbtxt':
expected_config_pbtxt,
'test_values.tsv':
b'1\n2\n3\n',
'test_metadata.tsv':
'labels\tsizes\na\t10\nb\t20\nдруг јазик\t30\n'
})
def testEndpointsMetadataForVariableAssets(self):
self._GenerateProjectorTestData()
g = ops.Graph()
with g.as_default():
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'c'])
manager.add_metadata_for_embedding_variable('test', metadata)
fw = writer.FileWriter(self.log_dir, graph=g)
fw.close()
self._SetupWSGIApp()
run_json = self._GetJson('/data/plugin/projector/runs')
self.assertTrue(run_json)
run = run_json[0]
metedata_query = '/data/plugin/projector/metadata?run=%s&name=test' % run
metadata_tsv = self._Get(metedata_query).data
self.assertEqual(metadata_tsv, b'a\nb\nc\n')
unk_tensor_query = '/data/plugin/projector/tensor?run=%s&name=test' % run
response = self._Get(unk_tensor_query)
self.assertEqual(response.status_code, 400)
expected_tensor = np.array([[6, 6]], dtype=np.float32)
tensor_query = '/data/plugin/projector/tensor?run=%s&name=var1' % run
tensor_bytes = self._Get(tensor_query).data
self._AssertTensorResponse(tensor_bytes, expected_tensor)
def testEndpointsComboTensorAssetsAndCheckpoint(self):
self._GenerateProjectorTestData()
g = ops.Graph()
with g.as_default():
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'c'])
manager.add_metadata_for_embedding_variable('var1', metadata)
new_tensor_values = np.array([[1, 2], [3, 4], [5, 6]])
manager.add_embedding('new_tensor', new_tensor_values)
fw = writer.FileWriter(self.log_dir, graph=g)
fw.close()
self._SetupWSGIApp()
run_json = self._GetJson('/data/plugin/projector/runs')
self.assertTrue(run_json)
run = run_json[0]
var1_values = np.array([[6, 6]], dtype=np.float32)
var1_tensor_query = '/data/plugin/projector/tensor?run=%s&name=var1' % run
tensor_bytes = self._Get(var1_tensor_query).data
self._AssertTensorResponse(tensor_bytes, var1_values)
metadata_query = '/data/plugin/projector/metadata?run=%s&name=var1' % run
metadata_tsv = self._Get(metadata_query).data
self.assertEqual(metadata_tsv, b'a\nb\nc\n')
tensor_query = '/data/plugin/projector/tensor?run=%s&name=new_tensor' % run
tensor_bytes = self._Get(tensor_query).data
self._AssertTensorResponse(tensor_bytes, new_tensor_values)
def testAddEmbeddingWithMetadataOfIncorrectLength(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'c'])
# values has length 2, while metadata has length 3.
values = np.array([[1], [2]])
with self.assertRaises(ValueError):
manager.add_embedding('test', values, metadata)
def testEndpointsTensorAndMetadataAssets(self):
g = ops.Graph()
with g.as_default():
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'c'])
manager.add_metadata_for_embedding_variable('test', metadata)
expected_tensor = np.array([[1, 2], [3, 4], [5, 6]])
image1 = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
image2 = np.array([[[10, 20, 30], [40, 50, 60]],
[[70, 80, 90], [100, 110, 120]]])
manager.add_embedding('emb', expected_tensor, metadata,
[image1, image2], [2, 2])
fw = writer.FileWriter(self.log_dir, graph=g)
fw.close()
self._SetupWSGIApp()
run_json = self._GetJson('/data/plugin/projector/runs')
self.assertTrue(run_json)
run = run_json[0]
metadata_query = '/data/plugin/projector/metadata?run=%s&name=emb' % run
metadata_tsv = self._Get(metadata_query).data
self.assertEqual(metadata_tsv, b'a\nb\nc\n')
unk_metadata_query = '/data/plugin/projector/metadata?run=%s&name=q' % run
response = self._Get(unk_metadata_query)
self.assertEqual(response.status_code, 400)
tensor_query = '/data/plugin/projector/tensor?run=%s&name=emb' % run
tensor_bytes = self._Get(tensor_query).data
self._AssertTensorResponse(tensor_bytes, expected_tensor)
unk_tensor_query = '/data/plugin/projector/tensor?run=%s&name=var1' % run
response = self._Get(unk_tensor_query)
self.assertEqual(response.status_code, 400)
image_query = '/data/plugin/projector/sprite_image?run=%s&name=emb' % run
image_bytes = self._Get(image_query).data
with ops.Graph().as_default():
s = session.Session()
image_array = image_ops.decode_png(image_bytes).eval(
session=s).tolist()
expected_sprite_image = [[[1, 2, 3], [4, 5, 6], [10, 20, 30],
[40, 50, 60]],
[[7, 8, 9], [10, 11, 12], [70, 80, 90],
[100, 110, 120]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]
self.assertEqual(image_array, expected_sprite_image)
def testEndpointsMetadataForVariableAssetsButNoCheckpoint(self):
g = ops.Graph()
with g.as_default():
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('labels', ['a', 'b', 'c'])
manager.add_metadata_for_embedding_variable('test', metadata)
fw = writer.FileWriter(self.log_dir, graph=g)
fw.close()
self._SetupWSGIApp()
run_json = self._GetJson('/data/plugin/projector/runs')
self.assertEqual(run_json, [])
def testAddMetadataForVariable(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('Labels', ['a', 'b', 'c'])
manager.add_metadata_for_embedding_variable('test', metadata)
config = projector_config_pb2.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = 'test'
embedding.metadata_path = 'test_metadata.tsv'
expected_config_pbtxt = text_format.MessageToString(config)
self.assertEqual(manager.assets(), {
'projector_config.pbtxt': expected_config_pbtxt,
'test_metadata.tsv': 'a\nb\nc\n'
})
def testDuplicateColumnName(self):
metadata = projector_plugin.EmbeddingMetadata(2)
metadata.add_column('Labels', np.array(['a', 'b']))
with self.assertRaises(ValueError):
metadata.add_column('Labels', np.array(['a', 'b']))
def testStringNdArrayRetrieval(self):
metadata = projector_plugin.EmbeddingMetadata(2)
metadata.add_column('Labels', np.array(['a', 'b']))
self.assertEqual(metadata.name_to_values['Labels'].tolist(),
['a', 'b'])
def testNumericNdArrayRetrieval(self):
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('Labels', np.array([1, 2, 3]))
self.assertEqual(metadata.name_to_values['Labels'].tolist(), [1, 2, 3])
def testNumericListRetrieval(self):
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('Labels', [1, 2, 3])
self.assertEqual(metadata.name_to_values['Labels'], [1, 2, 3])
def testStringListRetrieval(self):
metadata = projector_plugin.EmbeddingMetadata(3)
metadata.add_column('Labels', ['a', 'B', 'c'])
self.assertEqual(metadata.name_to_values['Labels'], ['a', 'B', 'c'])
self.assertEqual(metadata.column_names, ['Labels'])
def testValuesNot1D(self):
metadata = projector_plugin.EmbeddingMetadata(3)
values = np.array([[1, 2, 3]])
with self.assertRaises(ValueError):
metadata.add_column('Labels', values)
def testLengthDoesNotMatch(self):
metadata = projector_plugin.EmbeddingMetadata(10)
with self.assertRaises(ValueError):
metadata.add_column('Labels', [''] * 11)
def testAddMetadataForVariableButNoColumns(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
metadata = projector_plugin.EmbeddingMetadata(3)
with self.assertRaises(ValueError):
manager.add_metadata_for_embedding_variable('test', metadata)
def testValuesAreListofLists(self):
metadata = projector_plugin.EmbeddingMetadata(3)
values = [[1, 2, 3], [4, 5, 6]]
with self.assertRaises(ValueError):
metadata.add_column('Labels', values)