def test_Cifar10Rec():
# skip-this test for saving time
return
get_data.GetCifar10()
dataiter = mx.io.ImageRecordIter(
path_imgrec="data/cifar/train.rec",
mean_img="data/cifar/cifar10_mean.bin",
rand_crop=False,
and_mirror=False,
shuffle=False,
data_shape=(3,28,28),
batch_size=100,
preprocess_threads=4,
prefetch_buffer=1)
labelcount = [0 for i in range(10)]
batchcount = 0
for batch in dataiter:
npdata = batch.data[0].asnumpy().flatten().sum()
sys.stdout.flush()
batchcount += 1
nplabel = batch.label[0].asnumpy()
for i in range(nplabel.shape[0]):
labelcount[int(nplabel[i])] += 1
for i in range(10):
assert(labelcount[i] == 5000)
# small mlp network
def get_net():
data = mx.symbol.Variable('data')
float_data = mx.symbol.Cast(data=data, dtype="float32")
fc1 = mx.symbol.FullyConnected(float_data, name='fc1', num_hidden=128)
act1 = mx.symbol.Activation(fc1, name='relu1', act_type="relu")
fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=64)
act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")
fc3 = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=10)
softmax = mx.symbol.SoftmaxOutput(fc3, name="softmax")
return softmax
# check data
get_data.GetCifar10()
def get_iterator_uint8(kv):
data_shape = (3, 28, 28)
train = mx.io.ImageRecordUInt8Iter(path_imgrec="data/cifar/train.rec",
data_shape=data_shape,
batch_size=batch_size,
rand_crop=True,
rand_mirror=True,
num_parts=kv.num_workers,
part_index=kv.rank)
train = mx.io.PrefetchingIter(train)
val = mx.io.ImageRecordUInt8Iter(path_imgrec="data/cifar/test.rec",