def val_step(model, gpu, dataloader):
model.train(False)
apm = APMeter()
tot_loss = 0.0
error = 0.0
num_iter = 0.
num_preds = 0
full_probs = {}
# Iterate over data.
for data in dataloader:
num_iter += 1
other = data[3]
outputs, loss, probs, err = run_network(model, data, gpu)
apm.add(probs.data.cpu().numpy()[0], data[2].numpy()[0])
error += err.data[0]
tot_loss += loss.data[0]
# post-process preds
outputs = outputs.squeeze()
probs = probs.squeeze()
fps = outputs.size()[1] / other[1][0]
full_probs[other[0][0]] = (probs.data.cpu().numpy().T, fps)
epoch_loss = tot_loss / num_iter
error = error / num_iter
print('val-map:', apm.value().mean())
apm.reset()
print('val-{} Loss: {:.4f} Acc: {:.4f}'.format(dataloader.root, epoch_loss,
error))
return full_probs, epoch_loss
def val_step(model, gpu, dataloader):
model.train(False)
apm = APMeter()
tot_loss = 0.0
error = 0.0
num_iter = 0.
num_preds = 0
full_probs = {}
# Iterate over data.
for data in dataloader:
num_iter += 1
other = data[3]
outputs, loss, probs, err = run_network(model, data, gpu)
apm.add(probs.data.cpu().numpy()[0], data[2].numpy()[0])
error += err.item() #data[0]
tot_loss += loss.item() #data[0]
# post-process preds
outputs = outputs.squeeze()
probs = probs.squeeze()
fps = outputs.size()[1] / other[1][0]
full_probs[other[0][0]] = (probs.data.cpu().numpy().T, fps)
epoch_loss = tot_loss / num_iter
error = error / num_iter
#print ('val-map:', apm.value().mean())
#apm.reset()
val_map = apm.value().mean()
print('val-{} Loss: {:.4f} MAP: {:.4f}'.format(
dataloader.root.split('/')[-1], epoch_loss, val_map)) #error
#print('mu_x %f, sigma_x %f, mu_t %.10f, sigma_t %f, rho_xy %f'%(model.module.super_event.mu_x[0].item(), model.module.super_event.sigma_x[0].item(),
# model.module.super_event.mu_t[0].item(), model.module.super_event.sigma_t[0].item(), model.module.super_event.rho_xy[0].item()))
#print ('LR:%f'%lr)
#print('conv1 %f, fc1 %f'%(model.module.super_event.conv1.weight[0,0,0,0,0].item(), model.module.super_event.fc1.weight[0,0].item()))
#print('sup_mat %f, per_frame %f'%(model.module.sup_mat[0][0][0].item(), model.module.per_frame.weight[0][0][0][0][0].item()))
apm.reset()
return full_probs, epoch_loss, val_map
def train_step(model, gpu, optimizer, dataloader, reg_margin):
model.train(True)
tot_loss = 0.0
error = 0.0
num_iter = 0.
# Iterate over data.
tr_apm = APMeter()
for data in Bar(dataloader):
optimizer.zero_grad()
num_iter += 1
reg = max(0.4,
0.05 * np.exp(-1 * num_iter / 500.) + (reg_margin - 0.05))
#if num_iter<200: continue
outputs, loss, probs, err = run_network(model,
data,
gpu,
reg_margin=reg)
#del outputs
#print(err, loss)
error += err.item() #data[0]
tot_loss += loss.item() #data[0]
loss.backward()
optimizer.step()
#print(probs.shape, data[2].shape)
tr_apm.add(
probs.view(-1, probs.shape[-1]).detach().cpu().numpy(),
data[2].view(-1, data[2].shape[-1]).cpu().numpy())
epoch_loss = tot_loss / num_iter
error = error / num_iter
print('train-{} Loss: {:.4f} MAP: {:.4f}'.format(
dataloader.root.split('/')[-1], epoch_loss,
tr_apm.value().mean())) #error
tr_apm.reset()
