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if isinstance(v, torch.Tensor):
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if torch.cuda.is_available():
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state[k] = v.cuda()
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else:
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state[k] = v
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if "lr_sheudler_state_dict" in checkpoint:
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self.expConfig.lr_sheudler.load_state_dict(checkpoint["lr_sheudler_state_dict"])
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#Hack lr sheudle
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#self.expConfig.lr_sheudler.milestones = [250, 400, 550]
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#load best epoch score (if available)
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if "bestMeanDice" in checkpoint:
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self.bestMeanDice = checkpoint["bestMeanDice"]
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self.bestMeanDiceEpoch = checkpoint["bestMeanDiceEpoch"]
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#load moving avg if available
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if "movingAvg" in checkpoint:
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self.movingAvg = checkpoint["movingAvg"]
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#load best moving avg epoch if available
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if "bestMovingAvgEpoch" in checkpoint:
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self.bestMovingAvgEpoch = checkpoint["bestMovingAvgEpoch"]
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if "bestMovingAvg" in checkpoint:
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self.bestMovingAvg = checkpoint["bestMovingAvg"]
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return checkpoint["epoch"]
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def _getCheckpointPathLoad(self, id, epoch):
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return self.checkpointsBasePathLoad + "{}/e_{}.pt".format(id, epoch)
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def _updateMovingAvg(self, validationMean, epoch):
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if self.movingAvg == 0:
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self.movingAvg = validationMean
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else:
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alpha = self.EXPONENTIAL_MOVING_AVG_ALPHA
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self.movingAvg = self.movingAvg * alpha + validationMean * (1 - alpha)
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if self.bestMovingAvg < self.movingAvg:
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self.bestMovingAvg = self.movingAvg
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self.bestMovingAvgEpoch = epoch
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# <FILESEP>
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#!/usr/bin/env python
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#
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# Copyright 2015, OneFold
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# All rights reserved.
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# http://www.onefold.io
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#
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# Author: Jorge Chang
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#
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# See license in LICENSE file.
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#
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# Data warehouse utility - interface to DataWarehouse + implementation for Hive.
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# basic functionaliy like create table, update table, list tables, execute queries / DMLs, etc.
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#
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import json
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import abc
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import re
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import pprint
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class DataWarehouse:
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__metaclass__ = abc.ABCMeta
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@abc.abstractmethod
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def create_dataset(self, database_name):
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return
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@abc.abstractmethod
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def delete_dataset(self, database_name):
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return
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@abc.abstractmethod
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def create_table(self, database_name, table_name, schema_fields, process_array):
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return
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@abc.abstractmethod
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def update_table(self, database_name, table_name, schema_fields):
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return
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@abc.abstractmethod
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def delete_table(self, database_name, table_name):
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return
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@abc.abstractmethod
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def get_num_rows(self, database_name, table_name):
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return
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@abc.abstractmethod
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def table_exists(self, database_name, table_name):
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return
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@abc.abstractmethod
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def get_table_schema(self, database_name, table_name):
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return
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@abc.abstractmethod
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