def _build_eval_network(self, metrics, eval_network, eval_indexes):
"""Build the network for evaluation."""
self._metric_fns = get_metrics(metrics)
if not self._metric_fns:
return
if eval_network is not None:
if eval_indexes is not None and not (isinstance(
eval_indexes, list) and len(eval_indexes) == 3):
raise ValueError(
"Eval_indexes must be a list or None. If eval_indexes is a list, length of it \
must be three. But got {}".format(
eval_indexes))
self._eval_network = eval_network
self._eval_indexes = eval_indexes
else:
if self._loss_fn is None:
raise ValueError("loss_fn can not be None.")
self._eval_network = nn.WithEvalCell(self._network, self._loss_fn,
self._amp_level == "O2")
self._eval_indexes = [0, 1, 2]
if self._parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL,
ParallelMode.AUTO_PARALLEL):
if self._optimizer:
self._eval_network = _VirtualDatasetCell(self._eval_network)
self._eval_network.set_auto_parallel()
def test_scalar_output_auto():
context.set_auto_parallel_context(device_num=8,
global_rank=0,
parallel_mode="auto_parallel",
full_batch=False)
net = ParallelMulNet()
loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
eval_net = nn.WithEvalCell(net, loss_fn)
x = Tensor(np.ones([4096, 1, 2, 1024]).astype(np.float32) * 0.01)
label = Tensor(np.ones([4096, 250]).astype(np.float32) * 0.01)
strategies = compile_graph_two_input(x, label, eval_net)
count = 0
for (k, v) in strategies.items():
if re.search('VirtualOutput-op', k) is not None:
assert v[0][0] == 8
count += 1
assert count == 1
def _build_eval_network(self, metrics, eval_network, eval_indexes):
"""Build the network for evaluation."""
self._metric_fns = get_metrics(metrics)
if not self._metric_fns:
return
if eval_network is not None:
if eval_indexes is not None and not (isinstance(
eval_indexes, list) and len(eval_indexes) == 3):
raise ValueError(
"Eval_indexes must be a list or None. If eval_indexes is a list, length of it \
must be three. But got {}".format(
eval_indexes))
self._eval_network = eval_network
self._eval_indexes = eval_indexes
else:
if self._loss_fn is None:
raise ValueError("loss_fn can not be None.")
self._eval_network = nn.WithEvalCell(self._network, self._loss_fn)
self._eval_indexes = [0, 1, 2]
lr_max = 0.1
lr = get_lr(lr_init=lr_init, lr_end=lr_end, lr_max=lr_max,
warmup_epochs=warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
lr_decay_mode='poly')
lr = Tensor(lr)
# define opt
loss_scale = 1024
momentum = 0.9
weight_decay = 1e-4
# define loss, model
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum)
eval_net = nn.WithEvalCell(net, loss, AMP_LEVEL in ["O2", "O3"])
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},amp_level=AMP_LEVEL, eval_network=eval_net,
eval_indexes=[0, 1, 2], keep_batchnorm_fp32=False)
# define callbacks
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
save_checkpoint = 5
if save_checkpoint:
save_checkpoint_epochs = 5
keep_checkpoint_max = 10
config_ck = CheckpointConfig(save_checkpoint_steps=save_checkpoint_epochs * step_size,
keep_checkpoint_max=keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
