added license
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29
LICENSE
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29
LICENSE
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@ -0,0 +1,29 @@
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BSD 3-Clause License
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Copyright (c) 2020, princeton-vl
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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6
train.py
6
train.py
@ -21,7 +21,7 @@ import datasets
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# exclude extremly large displacements
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MAX_FLOW = 1000
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SUM_FREQ = 1000
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SUM_FREQ = 100
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VAL_FREQ = 5000
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@ -86,7 +86,7 @@ def fetch_optimizer(args, model):
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optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)
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scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps,
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pct_start=0.2, cycle_momentum=False, anneal_strategy='linear', final_div_factor=0.05)
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pct_start=0.2, cycle_momentum=False, anneal_strategy='linear', final_div_factor=1.0)
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return optimizer, scheduler
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@ -208,4 +208,4 @@ if __name__ == '__main__':
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args.batch_size = args.batch_size * num_gpus
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args.lr = args.lr * num_gpus
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train(args)
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train(args)
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