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)
fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
if not tf.io.gfile.exists(fpath) or tf.io.gfile.isdir(fpath):
return Respond(
request,
'"%s" does not exist or is directory' % fpath,
"text/plain",
400,
)
f = tf.io.gfile.GFile(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,
)
fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
if not tf.io.gfile.exists(fpath) or tf.io.gfile.isdir(fpath):
return Respond(
request,
'"%s" not found, or is not a file' % fpath,
"text/plain",
400,
)
bookmarks_json = None
with tf.io.gfile.GFile(fpath, "rb") as f:
bookmarks_json = f.read()
return Respond(request, bookmarks_json, "application/json")
def _serve_runs(self, request): """Returns a list of runs that have embeddings.""" return Respond(request, list(self.configs.keys()), 'application/json')
def _serve_file(self, file_path, request):
"""Returns a resource file."""
res_path = os.path.join(os.path.dirname(__file__), file_path)
with open(res_path, 'rb') as read_file:
mimetype = mimetypes.guess_type(file_path)[0]
return Respond(request, read_file.read(), content_type=mimetype)
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((run, 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.io.gfile.exists(fpath):
return Respond(
request,
'Tensor file "%s" does not exist' % fpath,
"text/plain",
400,
)
try:
tensor = _read_tensor_tsv_file(fpath)
except UnicodeDecodeError:
tensor = _read_tensor_binary_file(fpath,
embedding.tensor_shape)
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((run, 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_runs(self, request):
"""Returns a list of runs that have embeddings."""
self._update_configs()
return Respond(request, list(self._configs.keys()), "application/json")
