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')
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')
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)
config = self.configs[run]
tensor = self.tensor_cache.get(name)
if tensor is None:
# See if there is a tensor file in the config.
embedding = self._get_embedding(name, config)
if embedding and embedding.tensor_path:
fpath = _rel_to_abs_asset_path(embedding.tensor_path,
self.config_fpaths[run])
if not tf.gfile.Exists(fpath):
return Respond(request,
'Tensor file "%s" does not exist' % fpath,
'text/plain', 400)
tensor = _read_tensor_tsv_file(fpath)
else:
reader = self._get_reader_for_run(run)
if not reader or 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 tf.errors.InvalidArgumentError as e:
return Respond(request, str(e), 'text/plain', 400)
self.tensor_cache.set(name, tensor)
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')
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 _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')
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)
fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
if not tf.gfile.Exists(fpath) or tf.gfile.IsDirectory(fpath):
return Respond(request, '"%s" not found, or is not a file' % fpath,
'text/plain', 400)
bookmarks_json = None
with tf.gfile.GFile(fpath, 'rb') as f:
bookmarks_json = f.read()
return Respond(request, bookmarks_json, 'application/json')
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)
fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
return Respond(request, '"%s" not found, or 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')
def _serve_runs(self, request): """Returns a list of runs that have embeddings.""" return Respond(request, list(self.configs.keys()), 'application/json')
