def setup_class(cls):
"""Setup method."""
cls.optimizer = Momentum(Tensor(0.12))
cls.loss_fn = SoftmaxCrossEntropyWithLogits()
cls.net = ResNet()
cls.run_context = dict()
cls.run_context['train_network'] = cls.net
cls.run_context['loss_fn'] = cls.loss_fn
cls.run_context['net_outputs'] = Tensor(np.array([0.03]))
cls.run_context['optimizer'] = cls.optimizer
cls.run_context['train_dataset'] = MindDataset(dataset_size=32)
cls.run_context['epoch_num'] = 10
cls.run_context['cur_step_num'] = 320
cls.run_context['parallel_mode'] = "stand_alone"
cls.run_context['device_number'] = 2
cls.run_context['batch_num'] = 32
cls.summary_record = SummaryRecord(SUMMARY_DIR)
callback = [
ModelCheckpoint(directory=SUMMARY_DIR),
SummaryStep(cls.summary_record),
TrainLineage(cls.summary_record)
]
cls.run_context['list_callback'] = _ListCallback(callback)
cls.user_defined_info = {"info": "info1", "version": "v1"}
def test_get_file_path(self):
"""Test get_file_path method."""
model_ckpt = ModelCheckpoint(prefix='', directory='/path/to')
summary_step = SummaryStep(
MagicMock(full_file_name='/path/to/summary.log'))
list_callback = [model_ckpt, summary_step]
ckpt_file_path, _ = AnalyzeObject.get_file_path(list_callback)
self.assertEqual(ckpt_file_path, '/path/to/test_model.ckpt')
def test_train_callback(test_with_simu):
""" test_train_callback """
dataset = get_dataset()
model = get_model()
fn = CallbackTest()
summary_recode = SummaryStep(fn, 2)
if test_with_simu:
return
model.train(2, dataset, callbacks=summary_recode)
def test_graph_summary_callback2():
dataset = get_dataset()
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optim = Momentum(net.trainable_params(), 0.1, 0.9)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH",
network=net) as test_writer:
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)
def train_lenet():
context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target="CPU")
dataset_sink_mode = False
# download mnist dataset
download_dataset()
# learning rate setting
lr = 0.01
momentum = 0.9
epoch_size = 1
mnist_path = "../MNIST_Data"
# define the loss function
net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
repeat_size = epoch_size
# create the network
network = LeNet5()
# define the optimizer
net_opt = nn.Momentum(network.trainable_params(), lr, momentum)
config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
# save the network model and parameters for subsequence fine-tuning
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
# group layers into an object with training and evaluation features
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
summary_writer = SummaryRecord(log_dir="../../summary", network=network)
summary_callback = SummaryStep(summary_writer, flush_step=10)
# Init TrainLineage to record the training information
train_callback = TrainLineage(summary_writer)
train_net(
model,
epoch_size,
mnist_path,
repeat_size,
ckpoint_cb,
dataset_sink_mode,
callbacks=[summary_callback, train_callback],
)
test_net(network, model, mnist_path)
summary_writer.close()
def test_image_summary_train():
""" test_image_summary_train """
dataset = get_dataset()
log.debug("begin test_image_summary_sample")
# step 0: create the thread
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
# step 1: create the test data for summary
# step 2: create the Event
model = get_model()
fn = ImageSummaryCallback(test_writer)
summary_recode = SummaryStep(fn, 1)
model.train(2, dataset, callbacks=summary_recode)
# step 3: send the event to mq
# step 4: accept the event and write the file
log.debug("finished test_image_summary_sample")
def test_validate():
with SummaryRecord(SUMMARY_DIR) as sr:
with pytest.raises(ValueError):
SummaryStep(sr, 0)
with pytest.raises(ValueError):
SummaryStep(sr, -1)
with pytest.raises(ValueError):
SummaryStep(sr, 1.2)
with pytest.raises(ValueError):
SummaryStep(sr, True)
with pytest.raises(ValueError):
SummaryStep(sr, "str")
sr.record(1)
with pytest.raises(ValueError):
sr.record(False)
with pytest.raises(ValueError):
sr.record(2.0)
with pytest.raises(ValueError):
sr.record((1, 3))
with pytest.raises(ValueError):
sr.record([2, 3])
with pytest.raises(ValueError):
sr.record("str")
with pytest.raises(ValueError):
sr.record(sr)
SummaryStep(sr, 1)
with pytest.raises(ValueError):
SummaryStep(sr, 1.2)
with pytest.raises(ValueError):
SummaryStep(sr, False)
with pytest.raises(ValueError):
SummaryStep(sr, "str")
with pytest.raises(ValueError):
SummaryStep(sr, (1, 2))
with pytest.raises(ValueError):
SummaryStep(sr, [3, 4])
with pytest.raises(ValueError):
SummaryStep(sr, sr)