code
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PluginApi(ModuleApi)
namedtuple('PluginInfo', [camel_to_snake(name)
get_list(self, team_id, filters=None)
self.get_list_all_pages('plugins.list', {ApiField.TEAM_ID: team_id, ApiField.FILTER: filters or []})
get_info_by_id(self, team_id, plugin_id)
self._get_info_by_filters(team_id, filters)
Copyright (c)
ExpandingResNet(nn.Module)
__init__(self, num_init_features, args)
super()
__init__()
Linear(num_features, num_features // args.divide_channels)
nn.ModuleList([])
range(num_layers)
print('Layer ', i, kernel_size)
self.residual_blocks.append(_ResLayer(num_features, kernel_size, args)
self.reduce_channels(x)
_ResLayer(nn.Module)
x (k // 2)
__init__(self, num_features, kernel_size, args)
super()
__init__()
ValueError('Could not maintain the resolution with stride=%d' % stride)
Linear(num_features, ffn_dim)
Linear(ffn_dim, num_features)
x.permute(0, 3, 1, 2)
self.conv1(x)
F.relu(x)
self.mconv2(x, incremental_state)
x.masked_fill(encoder_mask.unsqueeze(1)
unsqueeze(1)
x.masked_fill(decoder_mask.unsqueeze(1)
unsqueeze(-1)
F.dropout(x, p=self.drop_rate, training=self.training)
x.permute(0, 2, 3, 1)
self.fc1(x)
F.relu(x)
self.fc2(x)
F.dropout(x, p=self.drop_rate, training=self.training)
Linear(in_features, out_features, bias=True)
nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0.)
TestClass(unittest.TestCase)
assertIO(self, input, output)
StringIO()
StringIO(input)
resolve()
sys.stdout.seek(0)
sys.stdout.read()
print('------------')
print(out)
print('------------')
self.assertEqual(out, output)
_1(self)
self.assertIO(input, output)
_2(self)
self.assertIO(input, output)
_3(self)
self.assertIO(input, output)
unittest.main()
Imagewang(ImageClassificationDataset)
Imagewang160(ImageClassificationDataset)
Imagewang320(ImageClassificationDataset)
Foundation (ASF)
SQLContext(object)
data (rows and columns)
__init__(self, sparkContext, sparkSession=None, jsqlContext=None)
SQLContext(sc)
Row(a=1)
datetime(2014, 8, 1, 14, 1, 5)
allTypes.toDF()
df.createOrReplaceTempView("allTypes")
collect()
Row((i + CAST(1 AS BIGINT)
CAST(1 AS DOUBLE)
datetime.datetime(2014, 8, 1, 14, 1, 5)
df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)
collect()
datetime.datetime(2014, 8, 1, 14, 1, 5)
SparkSession(sparkContext)
_monkey_patch_RDD(self.sparkSession)
install_exception_handler()
_ssql_ctx(self)
since(1.6)
getOrCreate(cls, sc)
sc._jvm.SQLContext.getOrCreate(sc._jsc.sc()
SparkSession(sc, jsqlContext.sparkSession()
cls(sc, sparkSession, jsqlContext)
since(1.6)
newSession(self)
self.__class__(self._sc, self.sparkSession.newSession()
since(1.3)
setConf(self, key, value)
self.sparkSession.conf.set(key, value)
since(1.3)
getConf(self, key, defaultValue=None)
sqlContext.getConf("spark.sql.shuffle.partitions")
sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
sqlContext.setConf("spark.sql.shuffle.partitions", u"50")