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online_inference.sh
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online_inference.sh
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#!/usr/bin/env bash
#
# Copyright (c) 2020 Intel Corporation
#
# 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.
#
MODEL_DIR=${MODEL_DIR-$PWD}
if [ -z "${OUTPUT_DIR}" ]; then
echo "The required environment variable OUTPUT_DIR has not been set"
exit 1
fi
# Create the output directory in case it doesn't already exist
mkdir -p ${OUTPUT_DIR}
if [ -z "${DATASET_DIR}" ]; then
echo "The required environment variable DATASET_DIR has not been set"
exit 1
fi
if [ ! -d "${DATASET_DIR}" ]; then
echo "The DATASET_DIR '${DATASET_DIR}' does not exist"
exit 1
fi
# If precision env is not mentioned, then the workload will run with the default precision.
if [ -z "${PRECISION}"]; then
PRECISION=fp32
echo "Running with default precision ${PRECISION}"
fi
if [[ $PRECISION != "fp32" ]]; then
echo "The specified precision '${PRECISION}' is unsupported."
echo "Supported precision is fp32."
exit 1
fi
PRETRAINED_MODEL=${PRETRAINED_MODEL-${MODEL_DIR}/densenet169_fp32_pretrained_model.pb}
# If batch size env is not mentioned, then the workload will run with the default batch size.
if [ -z "${BATCH_SIZE}"]; then
BATCH_SIZE="1"
echo "Running with default batch size of ${BATCH_SIZE}"
fi
source "${MODEL_DIR}/quickstart/common/utils.sh"
_command python benchmarks/launch_benchmark.py \
--model-name=densenet169 \
--precision ${PRECISION} \
--mode=inference \
--framework tensorflow \
--in-graph ${PRETRAINED_MODEL} \
--data-location=${DATASET_DIR} \
--output-dir ${OUTPUT_DIR} \
--batch-size=${BATCH_SIZE} \
--socket-id 0 \
$@ \
-- input_height=224 input_width=224 warmup_steps=20 steps=100 \
input_layer="input" output_layer="densenet169/predictions/Reshape_1"