def test_resume_checkpoint():
with TemporaryDirectory() as tmpdir:
model_prefix = 'test_net'
file_path = os.path.join(tmpdir, model_prefix)
test_data = _get_test_data()
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.gluon.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
checkpoint_handler = event_handler.CheckpointHandler(model_dir=tmpdir,
model_prefix=model_prefix,
monitor=acc,
max_checkpoints=1)
est.fit(test_data, event_handlers=[checkpoint_handler], epochs=2)
assert os.path.isfile(file_path + '-epoch1batch8.params')
assert os.path.isfile(file_path + '-epoch1batch8.states')
checkpoint_handler = event_handler.CheckpointHandler(model_dir=tmpdir,
model_prefix=model_prefix,
monitor=acc,
max_checkpoints=1,
resume_from_checkpoint=True)
est.fit(test_data, event_handlers=[checkpoint_handler], epochs=5)
# should only continue to train 3 epochs and last checkpoint file is epoch4
assert est.max_epoch == 3
assert os.path.isfile(file_path + '-epoch4batch20.states')
def test_np_save_load_ndarrays():
shapes = [(2, 0, 1), (0, ), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1),
(0, 5, 0), (4, 5, 6), (0, 0, 0)]
array_list = [_np.random.randint(0, 10, size=shape) for shape in shapes]
array_list = [np.array(arr, dtype=arr.dtype) for arr in array_list]
# test save/load single ndarray
for i, arr in enumerate(array_list):
with TemporaryDirectory() as work_dir:
fname = os.path.join(work_dir, 'dataset.npy')
npx.save(fname, arr)
arr_loaded = npx.load(fname)
assert isinstance(arr_loaded, list)
assert len(arr_loaded) == 1
assert _np.array_equal(arr_loaded[0].asnumpy(),
array_list[i].asnumpy())
# test save/load a list of ndarrays
with TemporaryDirectory() as work_dir:
fname = os.path.join(work_dir, 'dataset.npy')
npx.save(fname, array_list)
array_list_loaded = mx.nd.load(fname)
assert isinstance(arr_loaded, list)
assert len(array_list) == len(array_list_loaded)
assert all(isinstance(arr, np.ndarray) for arr in arr_loaded)
for a1, a2 in zip(array_list, array_list_loaded):
assert _np.array_equal(a1.asnumpy(), a2.asnumpy())
# test save/load a dict of str->ndarray
arr_dict = {}
keys = [str(i) for i in range(len(array_list))]
for k, v in zip(keys, array_list):
arr_dict[k] = v
with TemporaryDirectory() as work_dir:
fname = os.path.join(work_dir, 'dataset.npy')
npx.save(fname, arr_dict)
arr_dict_loaded = npx.load(fname)
assert isinstance(arr_dict_loaded, dict)
assert len(arr_dict_loaded) == len(arr_dict)
for k, v in arr_dict_loaded.items():
assert k in arr_dict
assert _np.array_equal(v.asnumpy(), arr_dict[k].asnumpy())
def test_np_ndarray_pickle():
a = np.random.uniform(size=(4, 5))
a_copy = a.copy()
import pickle
with TemporaryDirectory() as work_dir:
fname = os.path.join(work_dir, 'np_ndarray_pickle_test_file')
with open(fname, 'wb') as f:
pickle.dump(a_copy, f)
with open(fname, 'rb') as f:
a_load = pickle.load(f)
same(a.asnumpy(), a_load.asnumpy())
def test_buffer_load():
nrepeat = 10
with TemporaryDirectory(prefix='test_buffer_load_') as tmpdir:
for repeat in range(nrepeat):
# test load_buffer as list
data = []
for i in range(10):
data.append(random_ndarray(np.random.randint(1, 5)))
fname = os.path.join(tmpdir, 'list_{0}.param'.format(repeat))
mx.nd.save(fname, data)
with open(fname, 'rb') as dfile:
buf_data = dfile.read()
data2 = mx.nd.load_frombuffer(buf_data)
assert len(data) == len(data2)
for x, y in zip(data, data2):
assert np.sum(x.asnumpy() != y.asnumpy()) == 0
# test garbage values
assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer,
buf_data[:-10])
# test load_buffer as dict
dmap = {'ndarray xx %s' % i: x for i, x in enumerate(data)}
fname = os.path.join(tmpdir, 'dict_{0}.param'.format(repeat))
mx.nd.save(fname, dmap)
with open(fname, 'rb') as dfile:
buf_dmap = dfile.read()
dmap2 = mx.nd.load_frombuffer(buf_dmap)
assert len(dmap2) == len(dmap)
for k, x in dmap.items():
y = dmap2[k]
assert np.sum(x.asnumpy() != y.asnumpy()) == 0
# test garbage values
assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer,
buf_dmap[:-10])
# we expect the single ndarray to be converted into a list containing the ndarray
single_ndarray = data[0]
fname = os.path.join(tmpdir, 'single_{0}.param'.format(repeat))
mx.nd.save(fname, single_ndarray)
with open(fname, 'rb') as dfile:
buf_single_ndarray = dfile.read()
single_ndarray_loaded = mx.nd.load_frombuffer(
buf_single_ndarray)
assert len(single_ndarray_loaded) == 1
single_ndarray_loaded = single_ndarray_loaded[0]
assert np.sum(single_ndarray.asnumpy() !=
single_ndarray_loaded.asnumpy()) == 0
# test garbage values
assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer,
buf_single_ndarray[:-10])
def test_checkpoint_handler():
with TemporaryDirectory() as tmpdir:
model_prefix = 'test_epoch'
file_path = os.path.join(tmpdir, model_prefix)
test_data = _get_test_data()
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.gluon.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
checkpoint_handler = event_handler.CheckpointHandler(
model_dir=tmpdir,
model_prefix=model_prefix,
monitor=acc,
save_best=True,
epoch_period=1)
est.fit(test_data, event_handlers=[checkpoint_handler], epochs=1)
assert checkpoint_handler.current_epoch == 1
assert checkpoint_handler.current_batch == 4
assert os.path.isfile(file_path + '-best.params')
assert os.path.isfile(file_path + '-best.states')
assert os.path.isfile(file_path + '-epoch0batch4.params')
assert os.path.isfile(file_path + '-epoch0batch4.states')
model_prefix = 'test_batch'
file_path = os.path.join(tmpdir, model_prefix)
net = _get_test_network(nn.HybridSequential())
net.hybridize()
est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
checkpoint_handler = event_handler.CheckpointHandler(
model_dir=tmpdir,
model_prefix=model_prefix,
epoch_period=None,
batch_period=2,
max_checkpoints=2)
est.fit(test_data, event_handlers=[checkpoint_handler], batches=10)
assert checkpoint_handler.current_batch == 10
assert checkpoint_handler.current_epoch == 3
assert not os.path.isfile(file_path + 'best.params')
assert not os.path.isfile(file_path + 'best.states')
assert not os.path.isfile(file_path + '-epoch0batch0.params')
assert not os.path.isfile(file_path + '-epoch0batch0.states')
assert os.path.isfile(file_path + '-symbol.json')
assert os.path.isfile(file_path + '-epoch1batch7.params')
assert os.path.isfile(file_path + '-epoch1batch7.states')
assert os.path.isfile(file_path + '-epoch2batch9.params')
assert os.path.isfile(file_path + '-epoch2batch9.states')
def test_logging():
with TemporaryDirectory() as tmpdir:
test_data = _get_test_data()
file_name = 'test_log'
output_dir = os.path.join(tmpdir, file_name)
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, metrics=acc)
train_metrics, val_metrics = est.prepare_loss_and_metrics()
logging_handler = event_handler.LoggingHandler(
file_name=file_name,
file_location=tmpdir,
train_metrics=train_metrics,
val_metrics=val_metrics)
est.fit(test_data, event_handlers=[logging_handler], epochs=3)
assert logging_handler.batch_index == 0
assert logging_handler.current_epoch == 3
assert os.path.isfile(output_dir)
def test_logging():
with TemporaryDirectory() as tmpdir:
test_data = _get_test_data()
file_name = 'test_log'
output_dir = os.path.join(tmpdir, file_name)
net = _get_test_network()
ce_loss = loss.SoftmaxCrossEntropyLoss()
acc = mx.gluon.metric.Accuracy()
est = estimator.Estimator(net, loss=ce_loss, train_metrics=acc)
est.logger.addHandler(logging.FileHandler(output_dir))
train_metrics = est.train_metrics
val_metrics = est.val_metrics
logging_handler = event_handler.LoggingHandler(metrics=train_metrics)
est.fit(test_data, event_handlers=[logging_handler], epochs=3)
assert logging_handler.batch_index == 0
assert logging_handler.current_epoch == 3
assert os.path.isfile(output_dir)
del est # Clean up estimator and logger before deleting tmpdir
def test_load_save_symbol():
batch_size = 10
num_hdidden = 128
num_features = 784
def get_net():
data = mx.sym.var('data')
weight = mx.sym.var('weight', shape=(num_hdidden, 0))
return mx.sym.FullyConnected(data, weight, num_hidden=num_hdidden)
for flag1 in [False, True]:
with np_shape(flag1):
net_json_str = get_net().tojson()
net_data = json.loads(net_json_str)
assert "attrs" in net_data
if flag1:
assert "is_np_shape" in net_data["attrs"]
else:
assert "is_np_shape" not in net_data["attrs"]
with TemporaryDirectory() as work_dir:
fname = os.path.join(work_dir, 'test_sym.json')
with open(fname, 'w') as fp:
json.dump(net_data, fp)
# test loading 1.5.0 symbol file since 1.6.0
# w/ or w/o np_shape semantics
for flag2 in [False, True]:
if flag1: # Do not need to test this case since 0 indicates zero-size dim
continue
with np_shape(flag2):
net = mx.sym.load(fname)
arg_shapes, out_shapes, aux_shapes = net.infer_shape(
data=(batch_size, num_features))
assert arg_shapes[0] == (batch_size, num_features) # data
assert arg_shapes[1] == (num_hdidden, num_features
) # weight
assert arg_shapes[2] == (num_hdidden, ) # bias
assert out_shapes[0] == (batch_size, num_hdidden) # output
assert len(aux_shapes) == 0