def validate(data_loader, net, loss):
start_time = time.time()
net.eval()
metrics = []
for i, (data, target, coord) in enumerate(data_loader):
data = Variable(data.cuda())
target = Variable(target.cuda())
coord = Variable(coord.cuda())
output = net(data, coord)
loss_output = loss(output, target, train=False)
loss_output[0] = loss_output[0].data[0]
metrics.append(loss_output)
end_time = time.time()
metrics = np.asarray(metrics, np.float32)
print(
'Validation: tpr %3.2f, tnr %3.8f, total pos %d, total neg %d, time %3.2f'
% (100.0 * np.sum(metrics[:, 6]) / np.sum(metrics[:, 7]), 100.0 *
np.sum(metrics[:, 8]) / np.sum(metrics[:, 9]), np.sum(
metrics[:, 7]), np.sum(metrics[:, 9]), end_time - start_time))
print(
'loss %2.4f, classify loss %2.4f, regress loss %2.4f, %2.4f, %2.4f, %2.4f'
% (np.mean(metrics[:, 0]), np.mean(
metrics[:, 1]), np.mean(metrics[:, 2]), np.mean(metrics[:, 3]),
np.mean(metrics[:, 4]), np.mean(metrics[:, 5])))
def singletest(data,
net,
config,
splitfun,
combinefun,
n_per_run,
margin=64,
isfeat=False):
z, h, w = data.size(2), data.size(3), data.size(4)
print(data.size())
data = splitfun(data, config['max_stride'], margin)
data = Variable(data.cuda())
splitlist = range(0, args.split + 1, n_per_run)
outputlist = []
featurelist = []
for i in range(len(splitlist) - 1):
if isfeat:
output, feature = net(data[splitlist[i]:splitlist[i + 1]])
featurelist.append(feature)
else:
output = net(data[splitlist[i]:splitlist[i + 1]])
output = output.data.cpu().numpy()
outputlist.append(output)
output = np.concatenate(outputlist, 0)
output = combinefun(output, z / config['stride'], h / config['stride'],
w / config['stride'])
if isfeat:
feature = np.concatenate(featurelist, 0).transpose([0, 2, 3, 4, 1])
feature = combinefun(feature, z / config['stride'],
h / config['stride'], w / config['stride'])
return output, feature
else:
return output
def train(data_loader, net, loss, epoch, optimizer, get_lr, save_freq, save_dir):
start_time = time.time()
net.train()
lr = get_lr(epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
metrics = []
for i, (data, target, coord) in enumerate(data_loader):
data = Variable(data.cuda())
target = Variable(target.cuda())
coord = Variable(coord.cuda())
output = net(data, coord)
loss_output = loss(output, target)
optimizer.zero_grad()
loss_output[0].backward()
optimizer.step()
loss_output[0] = loss_output[0].data[0]
metrics.append(loss_output)
if epoch % args.save_freq == 0:
state_dict = net.module.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].cpu()
torch.save({
'epoch': epoch,
'save_dir': save_dir,
'state_dict': state_dict,
'args': args},
os.path.join(save_dir, '%03d.ckpt' % epoch))
end_time = time.time()
metrics = np.asarray(metrics, np.float32)
print('Epoch %03d (lr %.5f)' % (epoch, lr))
print('Train: tpr %3.2f, tnr %3.2f, total pos %d, total neg %d, time %3.2f' % (
100.0 * np.sum(metrics[:, 6]) / np.sum(metrics[:, 7]),
100.0 * np.sum(metrics[:, 8]) / np.sum(metrics[:, 9]),
np.sum(metrics[:, 7]),
np.sum(metrics[:, 9]),
end_time - start_time))
print('loss %2.4f, classify loss %2.4f, regress loss %2.4f, %2.4f, %2.4f, %2.4f' % (
np.mean(metrics[:, 0]),
np.mean(metrics[:, 1]),
np.mean(metrics[:, 2]),
np.mean(metrics[:, 3]),
np.mean(metrics[:, 4]),
np.mean(metrics[:, 5])))
print