def prepare_training(pose_trainable, lr):
optimizer = RMSprop(lr=lr)
models = compile_split_models(full_model, cfg, optimizer,
pose_trainable=pose_trainable, ar_loss_weights=action_weight,
copy_replica=cfg.pose_replica)
full_model.summary()
"""Create validation callbacks."""
mpii_callback = MpiiEvalCallback(x_val, p_val, afmat_val, head_val,
eval_model=models[0], pred_per_block=1, batch_size=1, logdir=logdir)
penn_callback = PennActionEvalCallback(penn_te, eval_model=models[1],
logdir=logdir)
def end_of_epoch_callback(epoch):
save_model.on_epoch_end(epoch)
mpii_callback.on_epoch_end(epoch)
penn_callback.on_epoch_end(epoch)
if epoch in [15, 25]:
lr = float(K.get_value(optimizer.lr))
newlr = 0.1*lr
K.set_value(optimizer.lr, newlr)
printcn(WARNING, 'lr_scheduler: lr %g -> %g @ %d' \
% (lr, newlr, epoch))
return end_of_epoch_callback, models
def prepare_training(pose_trainable, lr):
optimizer = SGD(lr=lr, momentum=0.9, nesterov=True)
# optimizer = RMSprop(lr=lr)
models = compile_split_models(full_model,
cfg,
optimizer,
pose_trainable=pose_trainable,
ar_loss_weights=action_weight,
copy_replica=cfg.pose_replica)
full_model.summary()
"""Create validation callbacks."""
# mpii_callback = MpiiEvalCallback(mpii_x_val, mpii_p_val, mpii_afmat_val,
# mpii_head_val, eval_model=models[0], pred_per_block=1,
# map_to_pa16j=pa17j3d.map_to_pa16j, batch_size=1, logdir=logdir)
# h36m_callback = H36MEvalCallback(h36m_x_val, h36m_pw_val, h36m_afmat_val,
# h36m_puvd_val[:,0,2], h36m_scam_val, h36m_action,
# batch_size=1, eval_model=models[0], logdir=logdir)
ntu_callback = NtuEvalCallback(ntu_te, eval_model=models[1], logdir=logdir)
def end_of_epoch_callback(epoch):
save_model.on_epoch_end(epoch)
# if epoch == 0 or epoch >= 50:
# mpii_callback.on_epoch_end(epoch)
# h36m_callback.on_epoch_end(epoch)
ntu_callback.on_epoch_end(epoch)
if epoch in [58, 70]:
lr = float(K.get_value(optimizer.lr))
newlr = 0.1 * lr
K.set_value(optimizer.lr, newlr)
printcn(WARNING, 'lr_scheduler: lr %g -> %g @ %d' \
% (lr, newlr, epoch))
return end_of_epoch_callback, models
def prepare_training(pose_trainable, lr):
optimizer = SGD(lr=lr, momentum=0.9, nesterov=True)
# optimizer = RMSprop(lr=lr)
models = compile_split_models(full_model,
cfg,
optimizer,
pose_trainable=pose_trainable,
ar_loss_weights=action_weight,
copy_replica=cfg.pose_replica)
full_model.summary()
"""Create validation callbacks."""
mpii_callback = MpiiEvalCallback(mpii_x_val,
mpii_p_val,
mpii_afmat_val,
mpii_head_val,
eval_model=models[0],
pred_per_block=1,
map_to_pa16j=pa17j3d.map_to_pa16j,
batch_size=1,
logdir=logdir)
h36m_callback = H36MEvalCallback(h36m_x_val,
h36m_pw_val,
h36m_afmat_val,
h36m_puvd_val[:, 0, 2],
h36m_scam_val,
h36m_action,
batch_size=1,
eval_model=models[0],
logdir=logdir)
ntu_callback = NtuEvalCallback(ntu_te, eval_model=models[1], logdir=logdir)
def end_of_epoch_callback(epoch):
save_model.on_epoch_end(epoch)
y_actu = []
y_pred = []
predictions = []
printcn(OKBLUE, 'Validation on Benset')
for i in range(len(benset_dataloader.get_val_data_keys())):
#printc(OKBLUE, '%04d/%04d\t' % (i, len(val_data_keys)))
x, y = benset_val_batchloader.__next__()
prediction = full_model.predict(x)
pred_action = np.argmax(prediction[11])
annot_action = np.argmax(y[0])
y_actu.append(annot_action)
y_pred.append(pred_action)
if pred_action == annot_action:
predictions.append(1)
else:
predictions.append(0)
accuracy = 100.0 / len(predictions) * Counter(predictions)[1]
conf_mat = confusion_matrix(y_actu, y_pred)
printcn(OKBLUE, '')
printcn(OKBLUE, 'Accuracy: %d' % accuracy)
print(conf_mat)
logarray[epoch] = accuracy
with open(os.path.join(logdir, 'benset_val.json'), 'w') as f:
json.dump(logarray, f)
# if epoch == 0 or epoch >= 50:
# mpii_callback.on_epoch_end(epoch)
# h36m_callback.on_epoch_end(epoch)
#ntu_callback.on_epoch_end(epoch)
if epoch in [25, 31]:
lr = float(K.get_value(optimizer.lr))
newlr = 0.1 * lr
K.set_value(optimizer.lr, newlr)
printcn(WARNING, 'lr_scheduler: lr %g -> %g @ %d' \
% (lr, newlr, epoch))
return end_of_epoch_callback, models