def _read_latest_config_files(self, run_path_pairs):
"""Reads and returns the projector config files in every run directory."""
configs = {}
config_fpaths = {}
for run_name, logdir in run_path_pairs:
config = projector_config_pb2.ProjectorConfig()
config_fpath = os.path.join(logdir, PROJECTOR_FILENAME)
if file_io.file_exists(config_fpath):
file_content = file_io.read_file_to_string(config_fpath)
text_format.Merge(file_content, config)
has_tensor_files = False
for embedding in config.embeddings:
if embedding.tensor_path:
has_tensor_files = True
break
if not config.model_checkpoint_path:
# See if you can find a checkpoint file in the logdir.
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path and not has_tensor_files:
continue
if ckpt_path:
config.model_checkpoint_path = ckpt_path
# Sanity check for the checkpoint file.
if (config.model_checkpoint_path and
not checkpoint_exists(config.model_checkpoint_path)):
logging.warning('Checkpoint file %s not found',
config.model_checkpoint_path)
continue
configs[run_name] = config
config_fpaths[run_name] = config_fpath
return configs, config_fpaths
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 _GenerateProjectorTestData(self):
config_path = os.path.join(self.log_dir, 'projector_config.pbtxt')
config = projector_config_pb2.ProjectorConfig()
embedding = config.embeddings.add()
# Add an embedding by its canonical tensor name.
embedding.tensor_name = 'var1:0'
with gfile.GFile(os.path.join(self.log_dir, 'bookmarks.json'),
'w') as f:
f.write('{"a": "b"}')
embedding.bookmarks_path = 'bookmarks.json'
config_pbtxt = text_format.MessageToString(config)
with gfile.GFile(config_path, 'w') as f:
f.write(config_pbtxt)
# Write a checkpoint with some dummy variables.
with ops.Graph().as_default():
sess = session.Session()
checkpoint_path = os.path.join(self.log_dir, 'model')
variable_scope.get_variable(
'var1', [1, 2], initializer=init_ops.constant_initializer(6.0))
variable_scope.get_variable('var2', [10, 10])
variable_scope.get_variable('var3', [100, 100])
sess.run(variables.global_variables_initializer())
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
saver.save(sess, checkpoint_path)
def testVisualizeEmbeddings(self):
# Create a dummy configuration.
config = projector_config_pb2.ProjectorConfig()
config.model_checkpoint_path = 'test'
emb1 = config.embeddings.add()
emb1.tensor_name = 'tensor1'
emb1.metadata_path = 'metadata1'
# Call the API method to save the configuration to a temporary dir.
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
writer = writer_lib.FileWriter(temp_dir)
projector.visualize_embeddings(writer, config)
# Read the configurations from disk and make sure it matches the original.
with gfile.GFile(os.path.join(temp_dir, 'projector_config.pbtxt')) as f:
config2 = projector_config_pb2.ProjectorConfig()
text_format.Parse(f.read(), config2)
self.assertEqual(config, config2)
def testRunsWithInvalidModelCheckpointPathInConfig(self):
config_path = os.path.join(self.log_dir, 'projector_config.pbtxt')
config = projector_config_pb2.ProjectorConfig()
config.model_checkpoint_path = 'does_not_exist'
embedding = config.embeddings.add()
embedding.tensor_name = 'var1'
with gfile.GFile(config_path, 'w') as f:
f.write(text_format.MessageToString(config))
self._SetupWSGIApp()
run_json = self._GetJson('/data/plugin/projector/runs')
self.assertEqual(run_json, [])
def testAddEmbeddingNoMetadata(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
manager.add_embedding('test', np.array([[1, 2, 3.1]]))
config = projector_config_pb2.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = 'test'
embedding.tensor_shape.extend([1, 3])
embedding.tensor_path = 'test_values.tsv'
expected_config_pbtxt = text_format.MessageToString(config)
self.assertEqual(manager.assets(), {
'projector_config.pbtxt': expected_config_pbtxt,
'test_values.tsv': b'1\t2\t3.1\n'
})
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 _latest_checkpoints_changed(configs, run_path_pairs):
"""Returns true if the latest checkpoint has changed in any of the runs."""
for run_name, logdir in run_path_pairs:
if run_name not in configs:
config = projector_config_pb2.ProjectorConfig()
config_fpath = os.path.join(logdir, PROJECTOR_FILENAME)
if file_io.file_exists(config_fpath):
file_content = file_io.read_file_to_string(config_fpath)
text_format.Merge(file_content, config)
else:
config = configs[run_name]
# See if you can find a checkpoint file in the logdir.
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path:
continue
if config.model_checkpoint_path != ckpt_path:
return True
return False
def _latest_checkpoints_changed(configs, run_path_pairs):
"""Returns true if the latest checkpoint has changed in any of the runs."""
for run_name, assets_dir in run_path_pairs:
if run_name not in configs:
config = projector_config_pb2.ProjectorConfig()
config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
if tf.gfile.Exists(config_fpath):
with tf.gfile.GFile(config_fpath, 'r') as f:
file_content = f.read()
text_format.Merge(file_content, config)
else:
config = configs[run_name]
# See if you can find a checkpoint file in the logdir.
logdir = _assets_dir_to_logdir(assets_dir)
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path:
continue
if config.model_checkpoint_path != ckpt_path:
return True
return False
def testAddEmbeddingWithThumbnails(self):
manager = plugin_asset.get_plugin_asset(
projector_plugin.ProjectorPluginAsset)
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('test',
np.array([[1], [2], [3]]),
thumbnails=[image1, image2],
thumbnail_dim=[2, 2])
assets = manager.assets()
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.sprite.image_path = 'test_sprite.png'
embedding.sprite.single_image_dim.extend([2, 2])
expected_config_pbtxt = text_format.MessageToString(config)
self.assertEqual(assets['projector_config.pbtxt'],
expected_config_pbtxt)
self.assertEqual(assets['test_values.tsv'], b'1\n2\n3\n')
png_bytes = assets['test_sprite.png']
with ops.Graph().as_default():
s = session.Session()
image_array = image_ops.decode_png(png_bytes).eval(
session=s).tolist()
expected_master_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_master_image)
def _read_latest_config_files(self, run_path_pairs):
"""Reads and returns the projector config files in every run directory."""
configs = {}
config_fpaths = {}
for run_name, assets_dir in run_path_pairs:
config = projector_config_pb2.ProjectorConfig()
config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME)
if tf.gfile.Exists(config_fpath):
with tf.gfile.GFile(config_fpath, 'r') as f:
file_content = f.read()
text_format.Merge(file_content, config)
has_tensor_files = False
for embedding in config.embeddings:
if embedding.tensor_path:
if not embedding.tensor_name:
embedding.tensor_name = os.path.basename(
embedding.tensor_path)
has_tensor_files = True
break
if not config.model_checkpoint_path:
# See if you can find a checkpoint file in the logdir.
logdir = _assets_dir_to_logdir(assets_dir)
ckpt_path = _find_latest_checkpoint(logdir)
if not ckpt_path and not has_tensor_files:
continue
if ckpt_path:
config.model_checkpoint_path = ckpt_path
# Sanity check for the checkpoint file.
if (config.model_checkpoint_path
and not tf.train.checkpoint_exists(
config.model_checkpoint_path)):
tf.logging.warning('Checkpoint file "%s" not found',
config.model_checkpoint_path)
continue
configs[run_name] = config
config_fpaths[run_name] = config_fpath
return configs, config_fpaths
def __init__(self):
self._config = projector_config_pb2.ProjectorConfig()
self._assets = {}
self._used_names = set()