#!/usr/bin/env bash

# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.

# This script launches UNet training in FP32-AMP on 1 GPU using 16 batch size (16 per GPU)
# Usage ./UNet_FP32AMP_1GPU.sh <path to result repository> <path to dataset> <dagm classID (1-10)>

BASEDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

pip install ${BASEDIR}/../dllogger/

python ${BASEDIR}/../main.py \
    --unet_variant='tinyUNet' \
    --activation_fn='relu' \
    --exec_mode='train_and_evaluate' \
    --iter_unit='batch' \
    --num_iter=2500 \
    --batch_size=16 \
    --warmup_step=10 \
    --results_dir="${1}" \
    --data_dir="${2}" \
    --dataset_name='DAGM2007' \
    --dataset_classID="${3}" \
    --data_format='NCHW' \
    --use_auto_loss_scaling \
    --use_tf_amp \
    --nouse_xla \
    --learning_rate=1e-4 \
    --learning_rate_decay_factor=0.8 \
    --learning_rate_decay_steps=500 \
    --rmsprop_decay=0.9 \
    --rmsprop_momentum=0.8 \
    --loss_fn_name='adaptive_loss' \
    --weight_decay=1e-5 \
    --weight_init_method='he_uniform' \
    --augment_data \
    --display_every=250 \
    --debug_verbosity=0
