d2bc3da0a1
* added UNet for medical image segmentation * added TF-AMP support for RN50 * small updates for other models (READMEs, benchmark & testing scripts)
51 lines
1.6 KiB
Bash
Executable file
51 lines
1.6 KiB
Bash
Executable file
#!/usr/bin/env bash
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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# This script launches UNet evaluation in FP32-AMP on 1 GPUs using 16 batch size
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# Usage ./UNet_FP32AMP_EVAL.sh <path to result repository> <path to dataset> <dagm classID (1-10)>
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BASEDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
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pip install ${BASEDIR}/../dllogger/
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python ${BASEDIR}/../main.py \
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--unet_variant='tinyUNet' \
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--activation_fn='relu' \
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--exec_mode='evaluate' \
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--iter_unit='epoch' \
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--num_iter=1 \
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--batch_size=16 \
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--warmup_step=10 \
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--results_dir="${1}" \
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--data_dir="${2}" \
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--dataset_name='DAGM2007' \
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--dataset_classID="${3}" \
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--data_format='NCHW' \
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--use_auto_loss_scaling \
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--use_tf_amp \
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--nouse_xla \
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--learning_rate=1e-4 \
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--learning_rate_decay_factor=0.8 \
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--learning_rate_decay_steps=500 \
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--rmsprop_decay=0.9 \
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--rmsprop_momentum=0.8 \
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--loss_fn_name='adaptive_loss' \
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--weight_decay=1e-5 \
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--weight_init_method='he_uniform' \
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--augment_data \
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--display_every=50 \
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--debug_verbosity=0
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