wehub-resource-sync
86 lines
2.8 KiB
Bash
86 lines
2.8 KiB
Bash
#!/bin/bash
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Run models downloaded with download_models.sh with the TF benchmark tool.
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# This script must be called from its current directory.
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set -x
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source onednn_benchmark_config.sh
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date
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# Navigate to the workspace root directory and configure build configurations.
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configure_build
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pwd
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date
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for ONEDNN in 0 1; do
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date
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export CONFIG="$(benchmark_command ${ONEDNN})"
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export TF_ENABLE_ONEDNN_OPTS=${ONEDNN}
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export BENCH="${BUILDER} run ${CONFIG}"
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for BATCH in 1 16 64; do
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# Print information for parse_onednn_benchmarks.py
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echo "BATCH=${BATCH}, ONEDNN=${ONEDNN}"
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# Run each graph.
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date
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${BENCH} \
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--graph=${TF_GRAPHS}/resnet50_v1-5.pb \
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--input_layer="input_tensor:0" \
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--input_layer_shape="${BATCH},224,224,3" \
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--input_layer_type="float" \
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--output_layer="softmax_tensor:0"
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date
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${BENCH} \
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--graph=${TF_GRAPHS}/inception.pb \
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--input_layer="input:0" \
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--input_layer_shape="${BATCH},224,224,3" \
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--input_layer_type="float" \
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--output_layer="output:0"
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date
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${BENCH} \
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--graph=${TF_GRAPHS}/mobilenet-v1.pb \
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--input_layer="input:0" \
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--input_layer_shape="${BATCH},224,224,3" \
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--input_layer_type="float" \
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--output_layer="MobilenetV1/Predictions/Reshape_1:0"
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date
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${BENCH} \
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--graph=${TF_GRAPHS}/ssd-mobilenet-v1.pb \
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--input_layer="image_tensor:0" \
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--input_layer_shape="${BATCH},300,300,3" \
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--input_layer_type="uint8" \
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--output_layer="detection_classes:0"
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date
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${BENCH} \
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--graph=${TF_GRAPHS}/ssd-resnet34.pb \
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--input_layer="image:0" \
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--input_layer_shape="${BATCH},3,1200,1200" \
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--input_layer_type="float" \
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--output_layer="detection_classes:0"
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date
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# Only run BERT with batch size 1 for now.
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if [[ $BATCH == 1 ]]; then
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${BENCH} \
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--graph=${TF_GRAPHS}/bert-large.pb \
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--input_layer="input_ids:0,input_mask:0,segment_ids:0" \
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--input_layer_shape="1,384:1,384:1,384" \
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--input_layer_type="int32,int32,int32" \
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--output_layer="logits:0"
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fi
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done
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done
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