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Add a script for inference with TPUs #5

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67 changes: 67 additions & 0 deletions run_inference_tpu.py
Original file line number Diff line number Diff line change
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# Copyright 2017 Google Inc.
#
# 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
#
# https://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.
# ==============================================================================
"""Runs FFN inference within a dense bounding box.

Inference is performed within a single process.
"""

import os
import time

from google.protobuf import text_format
from absl import app
from absl import flags
from tensorflow import gfile

from ffn.utils import bounding_box_pb2
from ffn.inference import inference
from ffn.inference import inference_flags

import tensorflow as tf
from tensorflow.contrib import tpu
from tensorflow.contrib.cluster_resolver import TPUClusterResolver

FLAGS = flags.FLAGS

flags.DEFINE_string('bounding_box', None,
'BoundingBox proto in text format defining the area '
'to segmented.')


def main(unused_argv):
tpu_grpc_url = TPUClusterResolver(tpu=[os.environ['TPU_NAME']]).get_master()

request = inference_flags.request_from_flags()

if not gfile.Exists(request.segmentation_output_dir):
gfile.MakeDirs(request.segmentation_output_dir)

bbox = bounding_box_pb2.BoundingBox()
text_format.Parse(FLAGS.bounding_box, bbox)

runner = inference.Runner()

with tf.Session(tpu_grpc_url, config=tf.ConfigProto(log_device_placement=True)) as sess:
runner.start(request, session=sess)
runner.run((bbox.start.z, bbox.start.y, bbox.start.x),
(bbox.size.z, bbox.size.y, bbox.size.x))

counter_path = os.path.join(request.segmentation_output_dir, 'counters.txt')
if not gfile.Exists(counter_path):
runner.counters.dump(counter_path)


if __name__ == '__main__':
app.run(main)