def train(train_loader, net, optimizer, epoch, visualizer, idx, opt):
# batch_time = AverageMeter()
# data_time = AverageMeter()
losses = AverageMeter()
pckhs = AverageMeter()
pckhs_origin_res = AverageMeter()
# switch to train mode
net.train()
# end = time.time()
for i, (img, heatmap, c, s, r, grnd_pts,
normalizer) in enumerate(train_loader):
quan_op.quantization()
# """measure data loading time"""
# data_time.update(time.time() - end)
# input and groundtruth
img_var = torch.autograd.Variable(img)
heatmap = heatmap.cuda(async=True)
target_var = torch.autograd.Variable(heatmap)
# output and loss
# output1, output2 = net(img_var)
# loss = (output1 - target_var) ** 2 + (output2 - target_var) ** 2
output = net(img_var)
# exit()
# print(type(output))
# print(len(output))
loss = 0
for per_out in output:
tmp_loss = (per_out - target_var)**2
loss = loss + tmp_loss.sum() / tmp_loss.numel()
# gradient and do SGD step
optimizer.zero_grad()
loss.backward()
quan_op.restore()
quan_op.updateQuanGradWeight()
optimizer.step()
# """measure optimization time"""
# batch_time.update(time.time() - end)
# end = time.time()
# print log
losses.update(loss.data[0])
pckh = Evaluation.accuracy(output[-1].data.cpu(),
target_var.data.cpu(), idx)
pckhs.update(pckh[0])
pckh_origin_res = Evaluation.accuracy_origin_res(
output[-1].data.cpu(), c, s, [64, 64], grnd_pts, normalizer, r)
pckhs_origin_res.update(pckh_origin_res[0])
loss_dict = OrderedDict([('loss', losses.avg), ('pckh', pckhs.avg),
('pckh_origin_res', pckhs_origin_res.avg)])
if i % opt.print_freq == 0 or i == len(train_loader) - 1:
visualizer.print_log(epoch, i, len(train_loader), value1=loss_dict)
# if i == 1:
# break
return losses.avg, pckhs_origin_res.avg
def validate(val_loader, net, epoch, visualizer, idx, joint_flip_index,
num_classes):
batch_time = AverageMeter()
losses = AverageMeter()
pckhs = AverageMeter()
pckhs_origin_res = AverageMeter()
img_batch_list = []
pts_batch_list = []
predictions = torch.Tensor(val_loader.dataset.__len__(), num_classes, 2)
# switch to evaluate mode
net.eval()
# end = time.time()
quan_op.quantization()
for i, (img, heatmap, center, scale, rot, grnd_pts, normalizer,
index) in enumerate(val_loader):
# input and groundtruth
input_var = torch.autograd.Variable(img, volatile=True)
heatmap = heatmap.cuda(async=True)
target_var = torch.autograd.Variable(heatmap)
# output and loss
# output1, output2 = net(input_var)
# loss = (output1 - target_var) ** 2 + (output2 - target_var) ** 2
output1 = net(input_var)
loss = 0
for per_out in output1:
tmp_loss = (per_out - target_var)**2
loss = loss + tmp_loss.sum() / tmp_loss.numel()
# flipping the image
img_flip = img.numpy()[:, :, :, ::-1].copy()
img_flip = torch.from_numpy(img_flip)
input_var = torch.autograd.Variable(img_flip, volatile=True)
# output11, output22 = net(input_var)
output2 = net(input_var)
output2 = HumanAug.flip_channels(output2[-1].data.cpu())
output2 = HumanAug.shuffle_channels_for_horizontal_flipping(
output2, joint_flip_index)
output = (output1[-1].data.cpu() + output2) / 2
# calculate measure
# pred_pts = HumanPts.heatmap2pts(output) # b x L x 2
# pts = HumanPts.heatmap2pts(target_var.cpu().data)
# pckh = HumanAcc.approx_PCKh(pred_pts, pts, idx, heatmap.size(3)) # b -> 1
pckh = Evaluation.accuracy(output, target_var.data.cpu(), idx)
pckhs.update(pckh[0])
pckh_origin_res = Evaluation.accuracy_origin_res(
output, center, scale, [64, 64], grnd_pts, normalizer, rot)
pckhs_origin_res.update(pckh_origin_res[0])
# """measure elapsed time"""
# batch_time.update(time.time() - end)
# end = time.time()
# print log
losses.update(loss.data[0])
loss_dict = OrderedDict([('loss', losses.avg), ('pckh', pckhs.avg),
('pckh_origin_res', pckhs_origin_res.avg)])
visualizer.print_log(epoch, i, len(val_loader), value1=loss_dict)
# img_batch_list.append(img)
# pts_batch_list.append(pred_pts*4.)
preds = Evaluation.final_preds(output, center, scale, [64, 64], rot)
for n in range(output.size(0)):
predictions[index[n], :, :] = preds[n, :, :]
# if i == 1:
# break
quan_op.restore()
return losses.avg, pckhs_origin_res.avg, predictions
