piiswrong closed pull request #9151: removes python path insert of tests folder
for examples
URL: https://github.com/apache/incubator-mxnet/pull/9151
This is a PR merged from a forked repository.
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diff --git a/example/adversary/adversary_generation.ipynb
b/example/adversary/adversary_generation.ipynb
index 8adae3d3b7..b8804bd813 100644
--- a/example/adversary/adversary_generation.ipynb
+++ b/example/adversary/adversary_generation.ipynb
@@ -28,10 +28,7 @@
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
- "import os\n",
- "import sys\n",
- "sys.path.append(os.path.join(os.getcwd(),
\"../../tests/python/common\"))\n",
- "from get_data import MNISTIterator"
+ "from mxnet.test_utils import get_mnist_iterator"
]
},
{
@@ -53,7 +50,7 @@
"source": [
"dev = mx.cpu()\n",
"batch_size = 100\n",
- "train_iter, val_iter = mnist_iterator(batch_size=batch_size, input_shape
= (1,28,28))"
+ "train_iter, val_iter = get_mnist_iterator(batch_size=batch_size,
input_shape = (1,28,28))"
]
},
{
diff --git a/example/caffe/data.py b/example/caffe/data.py
index fac8e11989..15276c4236 100644
--- a/example/caffe/data.py
+++ b/example/caffe/data.py
@@ -15,19 +15,14 @@
# specific language governing permissions and limitations
# under the License.
-import sys
-import os
-# code to automatically download dataset
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-import get_data
import mxnet as mx
+from mxnet.test_utils import get_mnist_ubyte
def get_iterator(data_shape, use_caffe_data):
def get_iterator_impl_mnist(args, kv):
"""return train and val iterators for mnist"""
# download data
- get_data.GetMNIST_ubyte()
+ get_mnist_ubyte()
flat = False if len(data_shape) != 1 else True
train = mx.io.MNISTIter(
diff --git a/example/gluon/data.py b/example/gluon/data.py
index 67519e6a20..dc8f12e81f 100644
--- a/example/gluon/data.py
+++ b/example/gluon/data.py
@@ -19,39 +19,11 @@
""" data iterator for mnist """
import os
import random
-import sys
-# code to automatically download dataset
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-import get_data
import mxnet as mx
+from mxnet.test_utils import get_cifar10
-def mnist_iterator(batch_size, input_shape):
- """return train and val iterators for mnist"""
- # download data
- get_data.GetMNIST_ubyte()
- flat = False if len(input_shape) == 3 else True
-
- train_dataiter = mx.io.MNISTIter(
- image="data/train-images-idx3-ubyte",
- label="data/train-labels-idx1-ubyte",
- input_shape=input_shape,
- batch_size=batch_size,
- shuffle=True,
- flat=flat)
-
- val_dataiter = mx.io.MNISTIter(
- image="data/t10k-images-idx3-ubyte",
- label="data/t10k-labels-idx1-ubyte",
- input_shape=input_shape,
- batch_size=batch_size,
- flat=flat)
-
- return (train_dataiter, val_dataiter)
-
-
-def cifar10_iterator(batch_size, data_shape, resize=-1):
- get_data.GetCifar10()
+def get_cifar10_iterator(batch_size, data_shape, resize=-1, num_parts=1,
part_index=0):
+ get_cifar10()
train = mx.io.ImageRecordIter(
path_imgrec = "data/cifar/train.rec",
@@ -60,7 +32,9 @@ def cifar10_iterator(batch_size, data_shape, resize=-1):
data_shape = data_shape,
batch_size = batch_size,
rand_crop = True,
- rand_mirror = True)
+ rand_mirror = True,
+ num_parts=num_parts,
+ part_index=part_index)
val = mx.io.ImageRecordIter(
path_imgrec = "data/cifar/test.rec",
@@ -69,11 +43,14 @@ def cifar10_iterator(batch_size, data_shape, resize=-1):
rand_crop = False,
rand_mirror = False,
data_shape = data_shape,
- batch_size = batch_size)
+ batch_size = batch_size,
+ num_parts=num_parts,
+ part_index=part_index)
return train, val
-def imagenet_iterator(train_data, val_data, batch_size, data_shape, resize=-1):
+
+def get_imagenet_iterator(train_data, val_data, batch_size, data_shape,
resize=-1, num_parts=1, part_index=0):
train = mx.io.ImageRecordIter(
path_imgrec = train_data,
data_shape = data_shape,
@@ -96,7 +73,9 @@ def imagenet_iterator(train_data, val_data, batch_size,
data_shape, resize=-1):
max_random_shear_ratio = 0.1,
max_random_aspect_ratio = 0.25,
fill_value = 127,
- min_random_scale = 0.533)
+ min_random_scale = 0.533,
+ num_parts = num_parts,
+ part_index = part_index)
val = mx.io.ImageRecordIter(
path_imgrec = val_data,
@@ -109,7 +88,9 @@ def imagenet_iterator(train_data, val_data, batch_size,
data_shape, resize=-1):
std_b = 57.375,
preprocess_threads = 32,
batch_size = batch_size,
- resize = resize)
+ resize = resize,
+ num_parts = num_parts,
+ part_index = part_index)
return train, val
diff --git a/example/gluon/image_classification.py
b/example/gluon/image_classification.py
index a2fb757683..529b977a79 100644
--- a/example/gluon/image_classification.py
+++ b/example/gluon/image_classification.py
@@ -26,12 +26,13 @@
from mxnet.gluon import nn
from mxnet.gluon.model_zoo import vision as models
from mxnet import autograd as ag
+from mxnet.test_utils import get_mnist_iterator
from data import *
# CLI
parser = argparse.ArgumentParser(description='Train a model for image
classification.')
-parser.add_argument('--dataset', type=str, default='mnist',
+parser.add_argument('--dataset', type=str, default='cifar10',
help='dataset to use. options are mnist, cifar10, and
dummy.')
parser.add_argument('--train-data', type=str, default='',
help='training record file to use, required for imagenet.')
@@ -92,25 +93,31 @@
net = models.get_model(opt.model, **kwargs)
-# get dataset iterators
-if dataset == 'mnist':
- train_data, val_data = mnist_iterator(batch_size, (1, 32, 32))
-elif dataset == 'cifar10':
- train_data, val_data = cifar10_iterator(batch_size, (3, 32, 32))
-elif dataset == 'imagenet':
- if model_name == 'inceptionv3':
- train_data, val_data = imagenet_iterator(opt.train_data, opt.val_data,
- batch_size, (3, 299, 299))
- else:
- train_data, val_data = imagenet_iterator(opt.train_data, opt.val_data,
- batch_size, (3, 224, 224))
-elif dataset == 'dummy':
- if model_name == 'inceptionv3':
- train_data, val_data = dummy_iterator(batch_size, (3, 299, 299))
- else:
- train_data, val_data = dummy_iterator(batch_size, (3, 224, 224))
-
-def test(ctx):
+def get_data_iters(dataset, batch_size, num_workers=1, rank=0):
+ # get dataset iterators
+ if dataset == 'mnist':
+ train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28),
+ num_parts=num_workers,
part_index=rank)
+ elif dataset == 'cifar10':
+ train_data, val_data = get_cifar10_iterator(batch_size, (3, 32, 32),
+ num_parts=num_workers,
part_index=rank)
+ elif dataset == 'imagenet':
+ if model_name == 'inceptionv3':
+ train_data, val_data = get_imagenet_iterator(opt.train_data,
opt.val_data,
+ batch_size, (3, 299,
299),
+
num_parts=num_workers, part_index=rank)
+ else:
+ train_data, val_data = get_imagenet_iterator(opt.train_data,
opt.val_data,
+ batch_size, (3, 224,
224),
+
num_parts=num_workers, part_index=rank)
+ elif dataset == 'dummy':
+ if model_name == 'inceptionv3':
+ train_data, val_data = dummy_iterator(batch_size, (3, 299, 299))
+ else:
+ train_data, val_data = dummy_iterator(batch_size, (3, 224, 224))
+ return train_data, val_data
+
+def test(ctx, val_data):
metric = mx.metric.Accuracy()
val_data.reset()
for batch in val_data:
@@ -127,9 +134,11 @@ def train(epochs, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
+ kv = mx.kv.create(opt.kvstore)
+ train_data, val_data = get_data_iters(dataset, batch_size, kv.num_workers,
kv.rank)
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': opt.lr, 'wd': opt.wd,
'momentum': opt.momentum},
- kvstore = opt.kvstore)
+ kvstore = kv)
metric = mx.metric.Accuracy()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
@@ -164,7 +173,7 @@ def train(epochs, ctx):
name, acc = metric.get()
logging.info('[Epoch %d] training: %s=%f'%(epoch, name, acc))
logging.info('[Epoch %d] time cost: %f'%(epoch, time.time()-tic))
- name, val_acc = test(ctx)
+ name, val_acc = test(ctx, val_data)
logging.info('[Epoch %d] validation: %s=%f'%(epoch, name, val_acc))
net.save_params('image-classifier-%s-%d.params'%(opt.model, epochs))
@@ -175,10 +184,12 @@ def main():
out = net(data)
softmax = mx.sym.SoftmaxOutput(out, name='softmax')
mod = mx.mod.Module(softmax, context=[mx.gpu(i) for i in
range(num_gpus)] if num_gpus > 0 else [mx.cpu()])
+ kv = mx.kv.create(opt.kvstore)
+ train_data, val_data = get_data_iters(dataset, batch_size,
kv.num_workers, kv.rank)
mod.fit(train_data,
eval_data = val_data,
num_epoch=opt.epochs,
- kvstore=opt.kvstore,
+ kvstore=kv,
batch_end_callback = mx.callback.Speedometer(batch_size,
max(1, opt.log_interval)),
epoch_end_callback =
mx.callback.do_checkpoint('image-classifier-%s'% opt.model),
optimizer = 'sgd',
diff --git a/example/multi-task/example_multi_task.py
b/example/multi-task/example_multi_task.py
index 9ea9ad0173..9e898494a1 100644
--- a/example/multi-task/example_multi_task.py
+++ b/example/multi-task/example_multi_task.py
@@ -16,12 +16,8 @@
# under the License.
# pylint: skip-file
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-from get_data import MNISTIterator
import mxnet as mx
+from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
import time
@@ -142,7 +138,7 @@ def get_name_value(self):
lr = 0.01
network = build_network()
-train, val = MNISTIterator(batch_size=batch_size, input_shape = (784,))
+train, val = get_mnist_iterator(batch_size=batch_size, input_shape = (784,))
train = Multi_mnist_iterator(train)
val = Multi_mnist_iterator(val)
diff --git a/example/numpy-ops/custom_softmax.py
b/example/numpy-ops/custom_softmax.py
index a2ec5d54b7..ab94401185 100644
--- a/example/numpy-ops/custom_softmax.py
+++ b/example/numpy-ops/custom_softmax.py
@@ -16,12 +16,8 @@
# under the License.
# pylint: skip-file
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-from get_data import MNISTIterator
import mxnet as mx
+from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
@@ -75,7 +71,7 @@ def create_operator(self, ctx, shapes, dtypes):
# data
-train, val = MNISTIterator(batch_size=100, input_shape = (784,))
+train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
# train
diff --git a/example/numpy-ops/ndarray_softmax.py
b/example/numpy-ops/ndarray_softmax.py
index 4ced2c5cd8..58eab3d538 100644
--- a/example/numpy-ops/ndarray_softmax.py
+++ b/example/numpy-ops/ndarray_softmax.py
@@ -16,16 +16,11 @@
# under the License.
# pylint: skip-file
-import os
-import sys
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-from get_data import MNISTIterator
import mxnet as mx
+from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
-
class NDArraySoftmax(mx.operator.NDArrayOp):
def __init__(self):
super(NDArraySoftmax, self).__init__(False)
@@ -97,7 +92,7 @@ def backward(self, out_grad, in_data, out_data, in_grad):
# data
-train, val = MNISTIterator(batch_size=100, input_shape = (784,))
+train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
# train
diff --git a/example/numpy-ops/numpy_softmax.py
b/example/numpy-ops/numpy_softmax.py
index c10dfe3779..88d2473492 100644
--- a/example/numpy-ops/numpy_softmax.py
+++ b/example/numpy-ops/numpy_softmax.py
@@ -16,12 +16,8 @@
# under the License.
# pylint: skip-file
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-from get_data import MNISTIterator
import mxnet as mx
+from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
@@ -70,7 +66,7 @@ def backward(self, out_grad, in_data, out_data, in_grad):
# data
-train, val = MNISTIterator(batch_size=100, input_shape = (784,))
+train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
# train
diff --git a/example/python-howto/monitor_weights.py
b/example/python-howto/monitor_weights.py
index ab77b4908b..929b0e7bf7 100644
--- a/example/python-howto/monitor_weights.py
+++ b/example/python-howto/monitor_weights.py
@@ -16,12 +16,8 @@
# under the License.
# pylint: skip-file
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-sys.path.append(os.path.join(curr_path, "../../tests/python/common"))
-from get_data import MNISTIterator
import mxnet as mx
+from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
@@ -35,7 +31,7 @@
mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
# data
-train, val = MNISTIterator(batch_size=100, input_shape = (784,))
+train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
# monitor
def norm_stat(d):
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 53814b766f..1b41011fd9 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1441,6 +1441,74 @@ def read_data(label_url, image_url):
return {'train_data':train_img, 'train_label':train_lbl,
'test_data':test_img, 'test_label':test_lbl}
+def get_mnist_pkl():
+ """Downloads MNIST dataset as a pkl.gz into a directory in the current
directory
+ with the name `data`
+ """
+ if not os.path.isdir("data"):
+ os.makedirs('data')
+ if not os.path.exists('data/mnist.pkl.gz'):
+ download('http://deeplearning.net/data/mnist/mnist.pkl.gz',
+ dirname='data')
+
+def get_mnist_ubyte():
+ """Downloads ubyte version of the MNIST dataset into a directory in the
current directory
+ with the name `data` and extracts all files in the zip archive to this
directory.
+ """
+ if not os.path.isdir("data"):
+ os.makedirs('data')
+ if (not os.path.exists('data/train-images-idx3-ubyte')) or \
+ (not os.path.exists('data/train-labels-idx1-ubyte')) or \
+ (not os.path.exists('data/t10k-images-idx3-ubyte')) or \
+ (not os.path.exists('data/t10k-labels-idx1-ubyte')):
+ zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
+ dirname='data')
+ with zipfile.ZipFile(zip_file_path) as zf:
+ zf.extractall('data')
+
+def get_cifar10():
+ """Downloads CIFAR10 dataset into a directory in the current directory
with the name `data`,
+ and then extracts all files into the directory `data/cifar`.
+ """
+ if not os.path.isdir("data"):
+ os.makedirs('data')
+ if (not os.path.exists('data/cifar/train.rec')) or \
+ (not os.path.exists('data/cifar/test.rec')) or \
+ (not os.path.exists('data/cifar/train.lst')) or \
+ (not os.path.exists('data/cifar/test.lst')):
+ zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
+ dirname='data')
+ with zipfile.ZipFile(zip_file_path) as zf:
+ zf.extractall('data')
+
+def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
+ """Returns training and validation iterators for MNIST dataset
+ """
+
+ get_mnist_ubyte()
+ flat = False if len(input_shape) == 3 else True
+
+ train_dataiter = mx.io.MNISTIter(
+ image="data/train-images-idx3-ubyte",
+ label="data/train-labels-idx1-ubyte",
+ input_shape=input_shape,
+ batch_size=batch_size,
+ shuffle=True,
+ flat=flat,
+ num_parts=num_parts,
+ part_index=part_index)
+
+ val_dataiter = mx.io.MNISTIter(
+ image="data/t10k-images-idx3-ubyte",
+ label="data/t10k-labels-idx1-ubyte",
+ input_shape=input_shape,
+ batch_size=batch_size,
+ flat=flat,
+ num_parts=num_parts,
+ part_index=part_index)
+
+ return (train_dataiter, val_dataiter)
+
def get_zip_data(data_dir, url, data_origin_name):
"""Download and extract zip data.
diff --git a/tests/python/common/get_data.py b/tests/python/common/get_data.py
deleted file mode 100644
index 5802a06919..0000000000
--- a/tests/python/common/get_data.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# pylint: skip-file
-import os, gzip
-import pickle as pickle
-import sys
-from mxnet.test_utils import download
-import zipfile
-import mxnet as mx
-
-# download mnist.pkl.gz
-def GetMNIST_pkl():
- if not os.path.isdir("data"):
- os.makedirs('data')
- if not os.path.exists('data/mnist.pkl.gz'):
- download('http://deeplearning.net/data/mnist/mnist.pkl.gz',
- dirname='data')
-
-# download ubyte version of mnist and untar
-def GetMNIST_ubyte():
- if not os.path.isdir("data"):
- os.makedirs('data')
- if (not os.path.exists('data/train-images-idx3-ubyte')) or \
- (not os.path.exists('data/train-labels-idx1-ubyte')) or \
- (not os.path.exists('data/t10k-images-idx3-ubyte')) or \
- (not os.path.exists('data/t10k-labels-idx1-ubyte')):
- zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
- dirname='data')
- with zipfile.ZipFile(zip_file_path) as zf:
- zf.extractall('data')
-
-# download cifar
-def GetCifar10():
- if not os.path.isdir("data"):
- os.makedirs('data')
- if (not os.path.exists('data/cifar/train.rec')) or \
- (not os.path.exists('data/cifar/test.rec')) or \
- (not os.path.exists('data/cifar/train.lst')) or \
- (not os.path.exists('data/cifar/test.lst')):
- zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
- dirname='data')
- with zipfile.ZipFile(zip_file_path) as zf:
- zf.extractall('data')
-
-def MNISTIterator(batch_size, input_shape):
- """return train and val iterators for mnist"""
- # download data
- GetMNIST_ubyte()
- flat = False if len(input_shape) == 3 else True
-
- train_dataiter = mx.io.MNISTIter(
- image="data/train-images-idx3-ubyte",
- label="data/train-labels-idx1-ubyte",
- input_shape=input_shape,
- batch_size=batch_size,
- shuffle=True,
- flat=flat)
-
- val_dataiter = mx.io.MNISTIter(
- image="data/t10k-images-idx3-ubyte",
- label="data/t10k-labels-idx1-ubyte",
- input_shape=input_shape,
- batch_size=batch_size,
- flat=flat)
-
- return (train_dataiter, val_dataiter)
diff --git a/tests/python/train/test_autograd.py
b/tests/python/train/test_autograd.py
index c9921ecf4f..712672cd0a 100644
--- a/tests/python/train/test_autograd.py
+++ b/tests/python/train/test_autograd.py
@@ -21,9 +21,9 @@
import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
+from mxnet.test_utils import get_mnist_ubyte
import numpy as np
import logging
-from common import get_data
from mxnet import autograd
logging.basicConfig(level=logging.DEBUG)
@@ -36,7 +36,7 @@ def get_net():
net.add(nn.Dense(10, prefix='fc3_'))
return net
-get_data.GetMNIST_ubyte()
+get_mnist_ubyte()
batch_size = 100
train_data = mx.io.MNISTIter(
diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
index 46e06848f8..adceb3ebc1 100644
--- a/tests/python/train/test_conv.py
+++ b/tests/python/train/test_conv.py
@@ -19,10 +19,10 @@
import sys
sys.path.insert(0, '../../python')
import mxnet as mx
+from mxnet.test_utils import get_mnist_ubyte
import numpy as np
import os, pickle, gzip, argparse
import logging
-from common import get_data
def get_model(use_gpu):
# symbol net
@@ -52,7 +52,7 @@ def get_model(use_gpu):
def get_iters():
# check data
- get_data.GetMNIST_ubyte()
+ get_mnist_ubyte()
batch_size = 100
train_dataiter = mx.io.MNISTIter(
diff --git a/tests/python/train/test_dtype.py b/tests/python/train/test_dtype.py
index 52f04bf9a1..2e3ff06d2e 100644
--- a/tests/python/train/test_dtype.py
+++ b/tests/python/train/test_dtype.py
@@ -22,7 +22,7 @@
import numpy as np
import os, pickle, gzip
import logging
-from common import get_data
+from mxnet.test_utils import get_cifar10
batch_size = 128
@@ -39,7 +39,7 @@ def get_net():
return softmax
# check data
-get_data.GetCifar10()
+get_cifar10()
def get_iterator_uint8(kv):
data_shape = (3, 28, 28)
diff --git a/tests/python/train/test_mlp.py b/tests/python/train/test_mlp.py
index a0a45b41e1..1b8e06f530 100644
--- a/tests/python/train/test_mlp.py
+++ b/tests/python/train/test_mlp.py
@@ -21,7 +21,7 @@
import os, sys
import pickle as pickle
import logging
-from common import get_data
+from mxnet.test_utils import get_mnist_ubyte
# symbol net
batch_size = 100
@@ -41,7 +41,7 @@ def accuracy(label, pred):
prefix = './mlp'
#check data
-get_data.GetMNIST_ubyte()
+get_mnist_ubyte()
train_dataiter = mx.io.MNISTIter(
image="data/train-images-idx3-ubyte",
diff --git a/tests/python/unittest/common.py b/tests/python/unittest/common.py
index 12ed60d2bc..782534bf80 100644
--- a/tests/python/unittest/common.py
+++ b/tests/python/unittest/common.py
@@ -21,8 +21,6 @@
sys.path.insert(0, os.path.join(curr_path, '../../../python'))
import models
-import get_data
-
def assertRaises(expected_exception, func, *args, **kwargs):
try:
diff --git a/tests/python/unittest/test_io.py b/tests/python/unittest/test_io.py
index fa314e0f8b..03d829efd7 100644
--- a/tests/python/unittest/test_io.py
+++ b/tests/python/unittest/test_io.py
@@ -27,13 +27,12 @@
except ImportError:
h5py = None
import sys
-from common import get_data, assertRaises
+from common import assertRaises
import unittest
-
def test_MNISTIter():
# prepare data
- get_data.GetMNIST_ubyte()
+ get_mnist_ubyte()
batch_size = 100
train_dataiter = mx.io.MNISTIter(
@@ -61,7 +60,7 @@ def test_MNISTIter():
assert(sum(label_0 - label_1) == 0)
def test_Cifar10Rec():
- get_data.GetCifar10()
+ get_cifar10()
dataiter = mx.io.ImageRecordIter(
path_imgrec="data/cifar/train.rec",
mean_img="data/cifar/cifar10_mean.bin",
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