def validate(val_loader, model_path, epoch, restore):
net = CrowdCounter(ce_weights=train_set.wts)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
print '='*50
val_loss_mse = []
val_loss_cls = []
val_loss_seg = []
val_loss = []
mae = 0.0
mse = 0.0
for vi, data in enumerate(val_loader, 0):
img, gt_map, gt_cnt, roi, gt_roi, gt_seg = data
# pdb.set_trace()
img = Variable(img, volatile=True).cuda()
gt_map = Variable(gt_map, volatile=True).cuda()
gt_seg = Variable(gt_seg, volatile=True).cuda()
roi = Variable(roi[0], volatile=True).cuda().float()
gt_roi = Variable(gt_roi[0], volatile=True).cuda()
pred_map,pred_cls,pred_seg = net(img, gt_map, roi, gt_roi, gt_seg)
loss1,loss2,loss3 = net.f_loss()
val_loss_mse.append(loss1.data)
val_loss_cls.append(loss2.data)
val_loss_seg.append(loss3.data)
val_loss.append(net.loss.data)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
pred_seg = pred_seg.cpu().max(1)[1].squeeze_(1).data.numpy()
gt_seg = gt_seg.data.cpu().numpy()
gt_count = np.sum(gt_map)
pred_cnt = np.sum(pred_map)
mae += abs(gt_count-pred_cnt)
mse += ((gt_count-pred_cnt)*(gt_count-pred_cnt))
x = []
if vi==0:
for idx, tensor in enumerate(zip(img.cpu().data, pred_map, gt_map, pred_seg, gt_seg)):
if idx>cfg.VIS.VISIBLE_NUM_IMGS:
break
# pdb.set_trace()
pil_input = restore(tensor[0]/255.)
pil_label = torch.from_numpy(tensor[2]/(tensor[2].max()+1e-10)).repeat(3,1,1)
pil_output = torch.from_numpy(tensor[1]/(tensor[1].max()+1e-10)).repeat(3,1,1)
pil_gt_seg = torch.from_numpy(tensor[4]).repeat(3,1,1).float()
pil_pred_seg = torch.from_numpy(tensor[3]).repeat(3,1,1).float()
# pdb.set_trace()
x.extend([pil_to_tensor(pil_input.convert('RGB')), pil_label, pil_output, pil_gt_seg, pil_pred_seg])
x = torch.stack(x, 0)
x = vutils.make_grid(x, nrow=5, padding=5)
writer.add_image(exp_name + '_epoch_' + str(epoch+1), (x.numpy()*255).astype(np.uint8))
mae = mae/val_set.get_num_samples()
mse = np.sqrt(mse/val_set.get_num_samples())
'''
loss1 = float(np.mean(np.array(val_loss_mse)))
loss2 = float(np.mean(np.array(val_loss_cls)))
loss3 = float(np.mean(np.array(val_loss_seg)))
loss = float(np.mean(np.array(val_loss)))'''
loss1 = np.mean(np.array(val_loss_mse))[0]
loss2 = np.mean(np.array(val_loss_cls))[0]
loss3 = np.mean(np.array(val_loss_seg))[0]
loss = np.mean(np.array(val_loss))[0]
writer.add_scalar('val_loss_mse', loss1, epoch + 1)
writer.add_scalar('val_loss_cls', loss2, epoch + 1)
writer.add_scalar('val_loss_seg', loss3, epoch + 1)
writer.add_scalar('val_loss', loss, epoch + 1)
writer.add_scalar('mae', mae, epoch + 1)
writer.add_scalar('mse', mse, epoch + 1)
if mae < train_record['best_mae']:
train_record['best_mae'] = mae
train_record['mse'] = mse
train_record['corr_epoch'] = epoch + 1
train_record['corr_loss'] = loss
print '='*50
print exp_name
print ' '+ '-'*20
print ' [mae %.1f mse %.1f], [val loss %.8f %.8f %.4f %.4f]' % (mae, mse, loss, loss1, loss2, loss3)
print ' '+ '-'*20
# pdb.set_trace()
print '[best] [mae %.1f mse %.1f], [loss %.8f], [epoch %d]' % (train_record['best_mae'], train_record['mse'], train_record['corr_loss'], train_record['corr_epoch'])
print '='*50
def validate(val_loader, model_path, epoch, restore):
net = CrowdCounter(ce_weights=train_set.wts)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
print '=' * 50
val_loss_mse = []
val_loss_cls = []
val_loss_seg = []
val_loss = []
mae = 0.0
mse = 0.0
for vi, data in enumerate(val_loader, 0):
img, gt_map, gt_cnt, roi, gt_roi, gt_seg = data
# pdb.set_trace()
img = Variable(img, volatile=True).cuda()
gt_map = Variable(gt_map, volatile=True).cuda()
gt_seg = Variable(gt_seg, volatile=True).cuda()
roi = Variable(roi[0], volatile=True).cuda().float()
gt_roi = Variable(gt_roi[0], volatile=True).cuda()
pred_map, pred_cls, pred_seg = net(img, gt_map, roi, gt_roi, gt_seg)
loss1, loss2, loss3 = net.f_loss()
val_loss_mse.append(loss1.data)
val_loss_cls.append(loss2.data)
val_loss_seg.append(loss3.data)
val_loss.append(net.loss.data)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
pred_seg = pred_seg.cpu().max(1)[1].squeeze_(1).data.numpy()
gt_seg = gt_seg.data.cpu().numpy()
# pdb.set_trace()
# pred_map = pred_map*pred_seg
gt_count = np.sum(gt_map)
pred_cnt = np.sum(pred_map)
mae += abs(gt_count - pred_cnt)
mse += ((gt_count - pred_cnt) * (gt_count - pred_cnt))
# pdb.set_trace()
mae = mae / val_set.get_num_samples()
mse = np.sqrt(mse / val_set.get_num_samples())
loss1 = np.mean(np.array(val_loss_mse))[0]
loss2 = np.mean(np.array(val_loss_cls))[0]
loss3 = np.mean(np.array(val_loss_seg))[0]
loss = np.mean(np.array(val_loss))[0]
print '=' * 50
print exp_name
print ' ' + '-' * 20
print ' [mae %.1f mse %.1f], [val loss %.8f %.8f %.4f %.4f]' % (
mae, mse, loss, loss1, loss2, loss3)
print ' ' + '-' * 20
print '=' * 50