code stringlengths 3 6.57k |
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self.optim.zero_grad() |
feats.cuda() |
boxes.cuda() |
target.cuda() |
self.model(feats, boxes, sent) |
logit.dim() |
target.dim() |
self.bce_loss(logit, target) |
logit.size(1) |
loss.mean() |
mean() |
amp.scale_loss(loss, self.optim) |
scaled_loss.backward() |
loss.backward() |
nn.utils.clip_grad_norm_(self.model.parameters() |
self.optim.step() |
logit.max(1) |
zip(ques_id, label.cpu() |
numpy() |
qid.item() |
evaluator.evaluate(quesid2ans) |
self.evaluate(eval_tuple) |
self.save("BEST") |
logger.info(log_str) |
open(self.output + "/log.log", 'a') |
f.write(log_str) |
f.flush() |
self.save("LAST") |
predict(self, eval_tuple: DataTuple, dump=None) |
self.model.eval() |
enumerate(loader) |
torch.no_grad() |
feats.cuda() |
boxes.cuda() |
self.model(feats, boxes, sent) |
nn.Softmax(dim=1) |
logit.max(1) |
zip(ques_id, label.cpu() |
numpy() |
score.cpu() |
numpy() |
qid.item() |
str(s) |
zip(ques_id, label.cpu() |
numpy() |
qid.item() |
evaluator.dump_result(quesid2ans, dump) |
evaluate(self, eval_tuple: DataTuple, dump=None) |
self.predict(eval_tuple, dump) |
eval_tuple.evaluator.evaluate(quesid2ans) |
oracle_score(data_tuple) |
enumerate(loader) |
target.max(1) |
zip(ques_id, label.cpu() |
numpy() |
qid.item() |
evaluator.evaluate(quesid2ans) |
save(self, name) |
torch.save(self.model.state_dict() |
os.path.join(self.output, "%s.pth" % name) |
load(self, path) |
logger.info("Load model from %s" % path) |
torch.load("%s.pth" % path) |
self.model.load_state_dict(state_dict) |
VQA() |
vqa.load(args.load) |
os.path.join(args.output, 'test_predict.json') |
os.path.join(args.output, 'minival_predict.json') |
logger.info(result) |
print('Splits in Train data:', vqa.train_tuple.dataset.splits) |
logger.info('Splits in Train data: {}'.format(vqa.train_tuple.dataset.splits) |
logger.info('Splits in Valid data: {}'.format(vqa.valid_tuple.dataset.splits) |
logger.info("Valid Oracle: %0.2f" % (vqa.oracle_score(vqa.valid_tuple) |
logger.info("DO NOT USE VALIDATION") |
vqa.train(vqa.train_tuple, vqa.valid_tuple) |
compress2(s1) |
range(len(s1) |
newStr.append(s1[i-1] + str(count) |
newStr.append(s1[-1] + str(count) |
min(s1, ''.join(newStr) |
compress(s1) |
range(len(s1) |
str(count) |
str(count) |
len(newStr) |
len(s1) |
Test(unittest.TestCase) |
test(self) |
print(input,' vs ',expected) |
compress(input) |
self.assertEqual(result, expected) |
unittest.main() |
find_subsets(nums) |
subsets.append([]) |
range(len(nums) |
len(subsets) |
range(0,storeLen) |
list(subsets[j]) |
currSet.append(nums[i]) |
subsets.append(currSet) |
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