d2bc3da0a1
* added UNet for medical image segmentation * added TF-AMP support for RN50 * small updates for other models (READMEs, benchmark & testing scripts)
19 lines
848 B
Bash
19 lines
848 B
Bash
CKPT_DIR=${1:-"/results/SSD320_FP32_1GPU"}
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PIPELINE_CONFIG_PATH=${2:-"/workdir/models/research/configs"}"/ssd320_bench.config"
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GPUS=1
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TENSOR_OPS=0
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export TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
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export TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
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export TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=${TENSOR_OPS}
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TRAIN_LOG=$(python -u ./object_detection/model_main.py \
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--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
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--model_dir=${CKPT_DIR} \
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--alsologtostder \
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"${@:3}" 2>&1)
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PERF=$(echo "$TRAIN_LOG" | awk -v GPUS=$GPUS '/global_step\/sec/{ array[num++]=$2 } END { for (x = 3*num/4; x < num; ++x) { sum += array[x] }; print GPUS*32*4*sum/num " img/s"}')
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mkdir -p $CKPT_DIR
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echo "Single GPU single precision training performance: $PERF" | tee $CKPT_DIR/train_log
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echo "$TRAIN_LOG" >> $CKPT_DIR/train_log
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