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added license

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Zach Teed 2020-03-28 00:18:46 -04:00
parent 36d7ad338e
commit 559176d42b
2 changed files with 32 additions and 3 deletions
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29
LICENSE Normal file
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@ -0,0 +1,29 @@
BSD 3-Clause License
Copyright (c) 2020, princeton-vl
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

6
train.py
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@ -21,7 +21,7 @@ import datasets
# exclude extremly large displacements # exclude extremly large displacements
MAX_FLOW = 1000 MAX_FLOW = 1000
SUM_FREQ = 1000 SUM_FREQ = 100
VAL_FREQ = 5000 VAL_FREQ = 5000
@ -86,7 +86,7 @@ def fetch_optimizer(args, model):
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon) optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)
scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps, scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps,
pct_start=0.2, cycle_momentum=False, anneal_strategy='linear', final_div_factor=0.05) pct_start=0.2, cycle_momentum=False, anneal_strategy='linear', final_div_factor=1.0)
return optimizer, scheduler return optimizer, scheduler
@ -208,4 +208,4 @@ if __name__ == '__main__':
args.batch_size = args.batch_size * num_gpus args.batch_size = args.batch_size * num_gpus
args.lr = args.lr * num_gpus args.lr = args.lr * num_gpus
train(args) train(args)