Datasets:
celinelee
/
python_fn_calls

Dataset card Data Studio Files
xet
 Community
1
code
stringlengths
3
6.57k
xrange(1, imdb.num_classes)
np.where(all_boxes[j][i][:, -1] >= image_thresh)
time.time()
format(i + 1, num_images, detect_time, nms_time)
sys.stdout.flush()
cv2.imwrite('result.png', im2show)
pdb.set_trace()
cv2.imshow('test', im2show)
cv2.waitKey(0)
open(det_file, 'wb')
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
time.time()
print("test time: %0.4fs" % (end - start)
click.group(cls=FlaskGroup, create_app=create_app)
main()
main()
LitClassifier(pl.LightningModule)
__init__(self, hparams, model)
super()
__init__()
self.save_hyperparameters(hparams)
get_loss(hparams.training.loss)
Accuracy()
smp.utils.losses.DiceLoss(activation='sigmoid')
forward(self, x)
self.model(x)
configure_optimizers(self)
get_optimizer(self.model.parameters()
get_scheduler(optimizer, self.hparams.training.scheduler)
training_step(self, batch, batch_idx)
torch.from_numpy(np.random.beta(alpha,alpha,int(num_batch/2)
type_as(x)
rnd.reshape(int(num_batch/2)
int(num_batch/2)
int(num_batch/2)
int(num_batch/2)
int(num_batch/2)
torch.from_numpy(np.random.beta(alpha,alpha,1)
type_as(x)
int(num_batch/2)
int(num_batch/2)
self.model(x)
self.criteria(y_hat, y[:int(num_batch/2)
self.criteria(y_hat, y[int(num_batch/2)
self.criteria(y_hat, y)
self.log('train_loss', loss, on_epoch=True)
validation_step(self, batch, batch_idx)
self.model(x)
self.criteria(y_hat, y)
self.dice(y_hat, y)
self.log('val_loss', loss)
self.log('val_dice', dice)
validation_epoch_end(self, outputs)
torch.stack([x["val_loss"] for x in outputs])
mean()
torch.stack([x["val_dice"] for x in outputs])
mean()
self.log('val_loss', avg_val_loss)
self.log('val_dice', avg_val_dice)
torch.cat([x["y"] for x in outputs])
cpu()
torch.cat([x["y_hat"] for x in outputs])
cpu()
np.argmax(y_hat, axis=1)
self.accuracy(y, preds)
self.log('avg_val_loss', avg_val_loss)
self.log('val_acc', val_accuracy)
test_step(self, batch, batch_idx)
self.model(x)
self.criteria(y_hat, y)
self.log('test_loss', loss)
Noord (1011, 'Amsterdam')
Midden (2406, 'Alphen a/d Rijn')
Zuid (4325, 'Renesse')
get_bad_scan_times()
pd.read_csv('data-ggd/ggd_bad_scans.txt', comment='#')
pd.to_datetime(df['Timestamp'])
to_list()
_mean_time(ts_list)
pd.Timedelta(0)
len(ts_list)
_delta_time_hhmm(hm)
pd.Timedelta(f'{hm}:00')
_summary_to_scores(summary)
int(pc4)
int(pc4)
score (int or float or '?')
isinstance(pc, tuple)
summary.items()
int(pc4)
print(f'{pc4} not in list...')
len(vlist)
_mean_time([v[0] for v in vlist])
qtms.append(qtm)
min(v[1] for v in vlist)
pd.Timestamp(qtm.strftime('%Y-%m-%dT00:00')
dhm('23:59')
dhm('24:00')