/
projector_plugin.py
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/
projector_plugin.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The Embedding Projector plugin."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import imghdr
import os
import numpy as np
from werkzeug import wrappers
from google.protobuf import json_format
from google.protobuf import text_format
from tensorflow.contrib.tensorboard.plugins.projector import PROJECTOR_FILENAME
from tensorflow.contrib.tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
from tensorflow.python.framework import errors
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.pywrap_tensorflow import NewCheckpointReader
from tensorflow.python.training.saver import checkpoint_exists
from tensorflow.python.training.saver import latest_checkpoint
from tensorflow.tensorboard.lib.python.http_util import Respond
from tensorflow.tensorboard.plugins.base_plugin import TBPlugin
# The prefix of routes provided by this plugin.
PLUGIN_PREFIX_ROUTE = 'projector'
# HTTP routes.
CONFIG_ROUTE = '/info'
TENSOR_ROUTE = '/tensor'
METADATA_ROUTE = '/metadata'
RUNS_ROUTE = '/runs'
BOOKMARKS_ROUTE = '/bookmarks'
SPRITE_IMAGE_ROUTE = '/sprite_image'
_IMGHDR_TO_MIMETYPE = {
'bmp': 'image/bmp',
'gif': 'image/gif',
'jpeg': 'image/jpeg',
'png': 'image/png'
}
_DEFAULT_IMAGE_MIMETYPE = 'application/octet-stream'
def _read_tensor_file(fpath):
with file_io.FileIO(fpath, 'r') as f:
tensor = []
for line in f:
if line:
tensor.append(list(map(float, line.rstrip('\n').split('\t'))))
return np.array(tensor, dtype='float32')
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 = 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 _parse_positive_int_param(request, param_name):
"""Parses and asserts a positive (>0) integer query parameter.
Args:
request: The Werkzeug Request object
param_name: Name of the parameter.
Returns:
Param, or None, or -1 if parameter is not a positive integer.
"""
param = request.args.get(param_name)
if not param:
return None
try:
param = int(param)
if param <= 0:
raise ValueError()
return param
except ValueError:
return -1
class ProjectorPlugin(TBPlugin):
"""Embedding projector."""
def __init__(self):
self._handlers = None
self.readers = {}
self.run_paths = None
self.logdir = None
self._configs = None
self.old_num_run_paths = None
def get_plugin_apps(self, run_paths, logdir):
self.run_paths = run_paths
self.logdir = logdir
self._handlers = {
RUNS_ROUTE: self._serve_runs,
CONFIG_ROUTE: self._serve_config,
TENSOR_ROUTE: self._serve_tensor,
METADATA_ROUTE: self._serve_metadata,
BOOKMARKS_ROUTE: self._serve_bookmarks,
SPRITE_IMAGE_ROUTE: self._serve_sprite_image
}
return self._handlers
@property
def configs(self):
"""Returns a map of run paths to `ProjectorConfig` protos."""
run_path_pairs = list(self.run_paths.items())
# If there are no summary event files, the projector should still work,
# treating the `logdir` as the model checkpoint directory.
if not run_path_pairs:
run_path_pairs.append(('.', self.logdir))
if (self._run_paths_changed() or
_latest_checkpoints_changed(self._configs, run_path_pairs)):
self.readers = {}
self._configs, self.config_fpaths = self._read_latest_config_files(
run_path_pairs)
self._augment_configs_with_checkpoint_info()
return self._configs
def _run_paths_changed(self):
num_run_paths = len(list(self.run_paths.keys()))
if num_run_paths != self.old_num_run_paths:
self.old_num_run_paths = num_run_paths
return True
return False
def _augment_configs_with_checkpoint_info(self):
for run, config in self._configs.items():
for embedding in config.embeddings:
# Normalize the name of the embeddings.
if embedding.tensor_name.endswith(':0'):
embedding.tensor_name = embedding.tensor_name[:-2]
# Find the size of embeddings associated with a tensors file.
if embedding.tensor_path and not embedding.tensor_shape:
tensor = _read_tensor_file(embedding.tensor_path)
embedding.tensor_shape.extend([len(tensor), len(tensor[0])])
reader = self._get_reader_for_run(run)
if not reader:
continue
# Augment the configuration with the tensors in the checkpoint file.
special_embedding = None
if config.embeddings and not config.embeddings[0].tensor_name:
special_embedding = config.embeddings[0]
config.embeddings.remove(special_embedding)
var_map = reader.get_variable_to_shape_map()
for tensor_name, tensor_shape in var_map.items():
if len(tensor_shape) != 2:
continue
embedding = self._get_embedding(tensor_name, config)
if not embedding:
embedding = config.embeddings.add()
embedding.tensor_name = tensor_name
if special_embedding:
embedding.metadata_path = special_embedding.metadata_path
embedding.bookmarks_path = special_embedding.bookmarks_path
if not embedding.tensor_shape:
embedding.tensor_shape.extend(tensor_shape)
# Remove configs that do not have any valid (2D) tensors.
runs_to_remove = []
for run, config in self._configs.items():
if not config.embeddings:
runs_to_remove.append(run)
for run in runs_to_remove:
del self._configs[run]
del self.config_fpaths[run]
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 = 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 _get_reader_for_run(self, run):
if run in self.readers:
return self.readers[run]
config = self._configs[run]
reader = None
if config.model_checkpoint_path:
try:
reader = NewCheckpointReader(config.model_checkpoint_path)
except Exception: # pylint: disable=broad-except
logging.warning('Failed reading %s', config.model_checkpoint_path)
self.readers[run] = reader
return reader
def _get_metadata_file_for_tensor(self, tensor_name, config):
embedding_info = self._get_embedding(tensor_name, config)
if embedding_info:
return embedding_info.metadata_path
return None
def _get_bookmarks_file_for_tensor(self, tensor_name, config):
embedding_info = self._get_embedding(tensor_name, config)
if embedding_info:
return embedding_info.bookmarks_path
return None
def _canonical_tensor_name(self, tensor_name):
if ':' not in tensor_name:
return tensor_name + ':0'
else:
return tensor_name
def _get_embedding(self, tensor_name, config):
if not config.embeddings:
return None
for info in config.embeddings:
if (self._canonical_tensor_name(info.tensor_name) ==
self._canonical_tensor_name(tensor_name)):
return info
return None
@wrappers.Request.application
def _serve_runs(self, request):
"""Returns a list of runs that have embeddings."""
return Respond(request, list(self.configs.keys()), 'application/json')
@wrappers.Request.application
def _serve_config(self, request):
run = request.args.get('run')
if run is None:
return Respond(request, 'query parameter "run" is required', 'text/plain',
400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
config = self.configs[run]
return Respond(request,
json_format.MessageToJson(config), 'application/json')
@wrappers.Request.application
def _serve_metadata(self, request):
run = request.args.get('run')
if run is None:
return Respond(request, 'query parameter "run" is required', 'text/plain',
400)
name = request.args.get('name')
if name is None:
return Respond(request, 'query parameter "name" is required',
'text/plain', 400)
num_rows = _parse_positive_int_param(request, 'num_rows')
if num_rows == -1:
return Respond(request, 'query parameter num_rows must be integer > 0',
'text/plain', 400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
config = self.configs[run]
fpath = self._get_metadata_file_for_tensor(name, config)
if not fpath:
return Respond(
request,
'No metadata file found for tensor %s in the config file %s' %
(name, self.config_fpaths[run]), 'text/plain', 400)
if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
return Respond(request, '%s is not a file' % fpath, 'text/plain', 400)
num_header_rows = 0
with file_io.FileIO(fpath, 'r') as f:
lines = []
# Stream reading the file with early break in case the file doesn't fit in
# memory.
for line in f:
lines.append(line)
if len(lines) == 1 and '\t' in lines[0]:
num_header_rows = 1
if num_rows and len(lines) >= num_rows + num_header_rows:
break
return Respond(request, ''.join(lines), 'text/plain')
@wrappers.Request.application
def _serve_tensor(self, request):
run = request.args.get('run')
if run is None:
return Respond(request, 'query parameter "run" is required', 'text/plain',
400)
name = request.args.get('name')
if name is None:
return Respond(request, 'query parameter "name" is required',
'text/plain', 400)
num_rows = _parse_positive_int_param(request, 'num_rows')
if num_rows == -1:
return Respond(request, 'query parameter num_rows must be integer > 0',
'text/plain', 400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
reader = self._get_reader_for_run(run)
config = self.configs[run]
if reader is None:
# See if there is a tensor file in the config.
embedding = self._get_embedding(name, config)
if not embedding or not embedding.tensor_path:
return Respond(request,
'Tensor %s has no tensor_path in the config' % name,
'text/plain', 400)
if not file_io.file_exists(embedding.tensor_path):
return Respond(request,
'Tensor file %s does not exist' % embedding.tensor_path,
'text/plain', 400)
tensor = _read_tensor_file(embedding.tensor_path)
else:
if not reader.has_tensor(name):
return Respond(request, 'Tensor %s not found in checkpoint dir %s' %
(name, config.model_checkpoint_path), 'text/plain', 400)
try:
tensor = reader.get_tensor(name)
except errors.InvalidArgumentError as e:
return Respond(request, str(e), 'text/plain', 400)
if num_rows:
tensor = tensor[:num_rows]
if tensor.dtype != 'float32':
tensor = tensor.astype(dtype='float32', copy=False)
data_bytes = tensor.tobytes()
return Respond(request, data_bytes, 'application/octet-stream')
@wrappers.Request.application
def _serve_bookmarks(self, request):
run = request.args.get('run')
if not run:
return Respond(request, 'query parameter "run" is required', 'text/plain',
400)
name = request.args.get('name')
if name is None:
return Respond(request, 'query parameter "name" is required',
'text/plain', 400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
config = self.configs[run]
fpath = self._get_bookmarks_file_for_tensor(name, config)
if not fpath:
return Respond(
request,
'No bookmarks file found for tensor %s in the config file %s' %
(name, self.config_fpaths[run]), 'text/plain', 400)
if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
return Respond(request, '%s is not a file' % fpath, 'text/plain', 400)
bookmarks_json = None
with file_io.FileIO(fpath, 'rb') as f:
bookmarks_json = f.read()
return Respond(request, bookmarks_json, 'application/json')
@wrappers.Request.application
def _serve_sprite_image(self, request):
run = request.args.get('run')
if not run:
return Respond(request, 'query parameter "run" is required', 'text/plain',
400)
name = request.args.get('name')
if name is None:
return Respond(request, 'query parameter "name" is required',
'text/plain', 400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
config = self.configs[run]
embedding_info = self._get_embedding(name, config)
if not embedding_info or not embedding_info.sprite.image_path:
return Respond(
request,
'No sprite image file found for tensor %s in the config file %s' %
(name, self.config_fpaths[run]), 'text/plain', 400)
fpath = os.path.expanduser(embedding_info.sprite.image_path)
if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
return Respond(request, '%s does not exist or is directory' % fpath,
'text/plain', 400)
f = file_io.FileIO(fpath, 'rb')
encoded_image_string = f.read()
f.close()
image_type = imghdr.what(None, encoded_image_string)
mime_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
return Respond(request, encoded_image_string, mime_type)
def _find_latest_checkpoint(dir_path):
try:
ckpt_path = latest_checkpoint(dir_path)
if not ckpt_path:
# Check the parent directory.
ckpt_path = latest_checkpoint(os.path.join(dir_path, os.pardir))
return ckpt_path
except errors.NotFoundError:
return None