code stringlengths 3 6.57k |
|---|
report(self) |
self.was_killed.is_set() |
get_bst_feature_api_ct(object) |
__init__(self,ip,port,params="",debug=False) |
BstRestService(ip,port) |
step1(self,jsonData) |
self.obj.postResponse(jsonData) |
str(e) |
self.obj.debugJsonPrint(self.debug,jsonData,resp) |
returnStatus(resp[0], 200) |
format(resp[0]) |
replace('Content-Type: text/json', '') |
json.loads(resp_) |
self.params.split(",") |
p.strip() |
returnStatus(sorted(plist) |
sorted(result.keys() |
getSteps(self) |
sorted([ i for i in dir(self) |
i.startswith('step') |
int(item.replace('step','') |
main(ip_address,port) |
ConfigParser.ConfigParser() |
os.path.split(__file__) |
jsonText.read(cwdir + '/testCaseJsonStrings.ini') |
dict(jsonText.items('get_bst_feature_api_ct') |
json_dict.get("paramslist","") |
get_bst_feature_api_ct(ip_address,port,params,debug=True) |
printStepHeader() |
tcObj.getSteps() |
getattr(tcObj,step) |
getattr(tcObj,step) |
printStepResult(step,desc,resp[0], resp[1]) |
getattr(tcObj,step) |
printStepResult(step,desc,resp[0], resp[1]) |
printStepFooter() |
stepResultMap.values() |
main() |
int(100) |
range(n) |
range(10) |
print("*", end="") |
print() |
__init__(self, val=0, next=None) |
swapPairs(self, head: ListNode) |
self.swapPairs(second) |
AuthenticationForm(forms.Form) |
forms.CharField() |
collections.namedtuple("DataTuple", 'dataset loader evaluator') |
logging.basicConfig(format='%(asctime) |
logging.getLogger(__name__) |
get_data_tuple(splits: str, bs:int, shuffle=False, drop_last=False) |
VQADataset(splits) |
VQATorchDataset(dset) |
VQAEvaluator(dset) |
DataTuple(dataset=dset, loader=data_loader, evaluator=evaluator) |
WarmupOptimizer(object) |
__init__(self, _lr_base, optimizer, _data_size, _batch_size) |
step(self) |
self.rate() |
self.optimizer.step() |
zero_grad(self) |
self.optimizer.zero_grad() |
rate(self, step=None) |
int(self._data_size / self._batch_size * 1) |
int(self._data_size / self._batch_size * 2) |
int(self._data_size / self._batch_size * 3) |
adjust_learning_rate(optimizer, decay_rate) |
__init__(self) |
VQAModel(self.train_tuple.dataset.num_answers) |
self.model.lxrt_encoder.load(args.load_lxmert) |
torch.load(args.patial_load) |
state_dict.copy() |
k.startswith('bert.') |
k.replace('gamma', 'weight') |
replace('beta', 'bias') |
state_dict.pop(k) |
self.model.lxrt_encoder.model.named_parameters() |
logger.info('fix param for: {}'.format(name) |
self.model.cuda() |
nn.BCEWithLogitsLoss() |
len(self.train_tuple.loader) |
int(batch_per_epoch * args.epochs) |
logger.info("BertAdam Total Iters: %d" % t_total) |
BertAdam(list(self.model.parameters() |
len(self.train_tuple.loader) |
args.optimizer(filter(lambda p: p.requires_grad, self.model.parameters() |
WarmupOptimizer(args.lr, optim, batch_per_epoch * args.batch_size, args.batch_size) |
args.optimizer(self.model.parameters() |
ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.") |
amp.initialize(self.model, self.optim, opt_level=args.amp_type) |
self.model.lxrt_encoder.multi_gpu() |
os.makedirs(self.output, exist_ok=True) |
train(self, train_tuple, eval_tuple) |
tqdm(x, total=len(loader) |
else (lambda x: x) |
range(args.epochs) |
adjust_learning_rate(self.optim, self._lr_decay_rate) |
iter_wrapper(enumerate(loader) |
self.model.train() |
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