Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
scr[i] += output.cpu().numpy()
gts = np.concatenate(gts, axis=0).T
scr = np.concatenate(scr, axis=0).T
aps = []
for i in range(20):
# Subtract eps from score to make AP work for tied scores
ap = metrics.average_precision_score(gts[i][gts[i]<=1], scr[i][gts[i]<=1]-1e-5*gts[i][gts[i]<=1])
aps.append( ap )
print(np.mean(aps), ' ', ' '.join(['%0.2f'%a for a in aps]))
def train(loader, model, optimizer, criterion, fc6_8, losses, it=0, total_iterations=None, stepsize=None, verbose=True):
# to log
batch_time = AverageMeter()
data_time = AverageMeter()
top1 = AverageMeter()
end = time.time()
current_iteration = it
# use dropout for the MLP
model.train()
# in the batch norms always use global statistics
model.features.eval()
for (input, target) in loader:
# measure data loading time
data_time.update(time.time() - end)
# adjust learning rate
if current_iteration != 0 and current_iteration % stepsize == 0:
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr'] * 0.5
print('iter {0} learning rate is {1}'.format(current_iteration, param_group['lr']))
# move input to gpu
input = input.cuda(non_blocking=True)
# forward pass with or without grad computation
output = model(input)
target = target.float().cuda()
mask = (target == 255)
loss = torch.sum(criterion(output, target).masked_fill_(mask, 0)) / target.size(0)
# backward
optimizer.zero_grad()
loss.backward()
# clip gradients
torch.nn.utils.clip_grad_norm_(model.parameters(), 10)
# and weights update
optimizer.step()
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if verbose is True and current_iteration % 25 == 0:
print('Iteration[{0}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
current_iteration, batch_time=batch_time,
data_time=data_time, loss=losses))
current_iteration = current_iteration + 1
if total_iterations is not None and current_iteration == total_iterations:
break
return current_iteration
class VOC2007_dataset(torch.utils.data.Dataset):
def __init__(self, voc_dir, split='train', transform=None):
# Find the image sets
image_set_dir = os.path.join(voc_dir, 'ImageSets', 'Main')
image_sets = glob.glob(os.path.join(image_set_dir, '*_' + split + '.txt'))
assert len(image_sets) == 20
# Read the labels
self.n_labels = len(image_sets)
images = defaultdict(lambda:-np.ones(self.n_labels, dtype=np.uint8))
for k, s in enumerate(sorted(image_sets)):
for l in open(s, 'r'):
name, lbl = l.strip().split()
lbl = int(lbl)
# Switch the ignore label and 0 label (in VOC -1: not present, 0: ignore)
if lbl < 0:
lbl = 0
elif lbl == 0:
lbl = 255
images[os.path.join(voc_dir, 'JPEGImages', name + '.jpg')][k] = lbl
self.images = [(k, images[k]) for k in images.keys()]
np.random.shuffle(self.images)
self.transform = transform
def __len__(self):
return len(self.images)
def __getitem__(self, i):
img = Image.open(self.images[i][0])