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added test case to cifar_op 

update cifar10 dataset
fixing missing error handling code in validator
This commit is contained in:
tinazhang 2020-07-06 17:35:10 -04:00
parent 089623ad19
commit 340d98a4d1
12 changed files with 393 additions and 196 deletions
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2
mindspore/dataset/core/validator_helpers.py
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@ -271,6 +271,8 @@ def check_sampler_shuffle_shard_options(param_dict):
if sampler is not None:
if shuffle is not None:
raise RuntimeError("sampler and shuffle cannot be specified at the same time.")
if num_shards is not None:
raise RuntimeError("sampler and sharding cannot be specified at the same time.")
if num_shards is not None:
check_pos_int32(num_shards)

21
tests/ut/data/dataset/testCifar100Data/datasetSchema.json
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@ -1,21 +0,0 @@
{
"datasetType": "CIFAR100",
"numRows": 100,
"columns": {
"image": {
"type": "uint8",
"rank": 1,
"t_impl": "cvmat"
},
"coarse_label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
},
"fine_label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
}
}
}

21
tests/ut/data/dataset/testCifar100Data/datasetSchemaTestRepeat.json
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@ -1,21 +0,0 @@
{
"datasetType": "CIFAR100",
"numRows": 33,
"columns": {
"image": {
"type": "uint8",
"rank": 1,
"t_impl": "cvmat"
},
"coarse_label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
},
"fine_label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
}
}
}

BIN
tests/ut/data/dataset/testCifar10Data/data_batch_1.bin
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9
tests/ut/data/dataset/testCifar10Data/datasetDistributionAll.json
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@ -1,9 +0,0 @@
{
"deviceNum" : 3,
"deviceId" : 1,
"shardConfig" : "ALL",
"shuffle" : "ON",
"seed" : 0,
"epoch" : 2
}

9
tests/ut/data/dataset/testCifar10Data/datasetDistributionRandom.json
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@ -1,9 +0,0 @@
{
"deviceNum" : 3,
"deviceId" : 1,
"shardConfig" : "RANDOM",
"shuffle" : "ON",
"seed" : 0,
"epoch" : 1
}

9
tests/ut/data/dataset/testCifar10Data/datasetDistributionUnique.json
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@ -1,9 +0,0 @@
{
"deviceNum" : 3,
"deviceId" : 1,
"shardConfig" : "UNIQUE",
"shuffle" : "ON",
"seed" : 0,
"epoch" : 3
}

16
tests/ut/data/dataset/testCifar10Data/datasetSchema.json
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@ -1,16 +0,0 @@
{
"datasetType": "CIFAR10",
"numRows": 60000,
"columns": {
"image": {
"type": "uint8",
"rank": 1,
"t_impl": "cvmat"
},
"label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
}
}
}

16
tests/ut/data/dataset/testCifar10Data/datasetSchemaTestRepeat.json
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@ -1,16 +0,0 @@
{
"datasetType": "CIFAR10",
"numRows": 33,
"columns": {
"image": {
"type": "uint8",
"rank": 1,
"t_impl": "cvmat"
},
"label" : {
"type": "uint32",
"rank": 1,
"t_impl": "flex"
}
}
}

91
tests/ut/python/dataset/test_cifarop.py
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@ -1,91 +0,0 @@
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed 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.
# ==============================================================================
import os
import numpy as np
import mindspore.dataset as ds
from mindspore import log as logger
# Data for CIFAR and MNIST are not part of build tree
# They need to be downloaded directly
# prep_data.py can be executed or code below
# import sys
# sys.path.insert(0,"../../data")
# import prep_data
# prep_data.download_all_for_test("../../data")
DATA_DIR_10 = "../data/dataset/testCifar10Data"
DATA_DIR_100 = "../data/dataset/testCifar100Data"
def load_cifar(path):
raw = np.empty(0, dtype=np.uint8)
for file_name in os.listdir(path):
if file_name.endswith(".bin"):
with open(os.path.join(path, file_name), mode='rb') as file:
raw = np.append(raw, np.fromfile(file, dtype=np.uint8), axis=0)
raw = raw.reshape(-1, 3073)
labels = raw[:, 0]
images = raw[:, 1:]
images = images.reshape(-1, 3, 32, 32)
images = images.transpose(0, 2, 3, 1)
return images, labels
def test_case_dataset_cifar10():
"""
dataset parameter
"""
logger.info("Test dataset parameter")
# apply dataset operations
data1 = ds.Cifar10Dataset(DATA_DIR_10, 100)
num_iter = 0
for _ in data1.create_dict_iterator():
# in this example, each dictionary has keys "image" and "label"
num_iter += 1
assert num_iter == 100
def test_case_dataset_cifar100():
"""
dataset parameter
"""
logger.info("Test dataset parameter")
# apply dataset operations
data1 = ds.Cifar100Dataset(DATA_DIR_100, 100)
num_iter = 0
for _ in data1.create_dict_iterator():
# in this example, each dictionary has keys "image" and "label"
num_iter += 1
assert num_iter == 100
def test_reading_cifar10():
"""
Validate CIFAR10 image readings
"""
data1 = ds.Cifar10Dataset(DATA_DIR_10, 100, shuffle=False)
images, labels = load_cifar(DATA_DIR_10)
for i, d in enumerate(data1.create_dict_iterator()):
np.testing.assert_array_equal(d["image"], images[i])
np.testing.assert_array_equal(d["label"], labels[i])
if __name__ == '__main__':
test_case_dataset_cifar10()
test_case_dataset_cifar100()
test_reading_cifar10()

8
tests/ut/python/dataset/test_config.py
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@ -245,17 +245,17 @@ def test_deterministic_run_distribution():
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_op = c_vision.RandomHorizontalFlip(0.1)
random_horizontal_flip_op = c_vision.RandomHorizontalFlip(0.1)
decode_op = c_vision.Decode()
data1 = data1.map(input_columns=["image"], operations=decode_op)
data1 = data1.map(input_columns=["image"], operations=random_crop_op)
data1 = data1.map(input_columns=["image"], operations=random_horizontal_flip_op)
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(input_columns=["image"], operations=decode_op)
# If seed is set up on constructor, so the two ops output deterministic sequence
random_crop_op2 = c_vision.RandomHorizontalFlip(0.1)
data2 = data2.map(input_columns=["image"], operations=random_crop_op2)
random_horizontal_flip_op2 = c_vision.RandomHorizontalFlip(0.1)
data2 = data2.map(input_columns=["image"], operations=random_horizontal_flip_op2)
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
np.testing.assert_equal(item1["image"], item2["image"])

387
tests/ut/python/dataset/test_datasets_cifarop.py Normal file
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@ -0,0 +1,387 @@
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed 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.
# ==============================================================================
"""
Test Cifar10 and Cifar100 dataset operators
"""
import os
import pytest
import numpy as np
import matplotlib.pyplot as plt
import mindspore.dataset as ds
from mindspore import log as logger
DATA_DIR_10 = "../data/dataset/testCifar10Data"
DATA_DIR_100 = "../data/dataset/testCifar100Data"
def load_cifar(path, kind="cifar10"):
"""
load Cifar10/100 data
"""
raw = np.empty(0, dtype=np.uint8)
for file_name in os.listdir(path):
if file_name.endswith(".bin"):
with open(os.path.join(path, file_name), mode='rb') as file:
raw = np.append(raw, np.fromfile(file, dtype=np.uint8), axis=0)
if kind == "cifar10":
raw = raw.reshape(-1, 3073)
labels = raw[:, 0]
images = raw[:, 1:]
elif kind == "cifar100":
raw = raw.reshape(-1, 3074)
labels = raw[:, :2]
images = raw[:, 2:]
else:
raise ValueError("Invalid parameter value")
images = images.reshape(-1, 3, 32, 32)
images = images.transpose(0, 2, 3, 1)
return images, labels
def visualize_dataset(images, labels):
"""
Helper function to visualize the dataset samples
"""
num_samples = len(images)
for i in range(num_samples):
plt.subplot(1, num_samples, i + 1)
plt.imshow(images[i])
plt.title(labels[i])
plt.show()
### Testcases for Cifar10Dataset Op ###
def test_cifar10_content_check():
"""
Validate Cifar10Dataset image readings
"""
logger.info("Test Cifar10Dataset Op with content check")
data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100, shuffle=False)
images, labels = load_cifar(DATA_DIR_10)
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, d in enumerate(data1.create_dict_iterator()):
np.testing.assert_array_equal(d["image"], images[i])
np.testing.assert_array_equal(d["label"], labels[i])
num_iter += 1
assert num_iter == 100
def test_cifar10_basic():
"""
Validate CIFAR10
"""
logger.info("Test Cifar10Dataset Op")
# case 1: test num_samples
data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
num_iter1 = 0
for _ in data1.create_dict_iterator():
num_iter1 += 1
assert num_iter1 == 100
# case 2: test num_parallel_workers
data2 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=50, num_parallel_workers=1)
num_iter2 = 0
for _ in data2.create_dict_iterator():
num_iter2 += 1
assert num_iter2 == 50
# case 3: test repeat
data3 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
data3 = data3.repeat(3)
num_iter3 = 0
for _ in data3.create_dict_iterator():
num_iter3 += 1
assert num_iter3 == 300
# case 4: test batch with drop_remainder=False
data4 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
assert data4.get_dataset_size() == 100
assert data4.get_batch_size() == 1
data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
assert data4.get_dataset_size() == 15
assert data4.get_batch_size() == 7
num_iter4 = 0
for _ in data4.create_dict_iterator():
num_iter4 += 1
assert num_iter4 == 15
# case 5: test batch with drop_remainder=True
data5 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=100)
assert data5.get_dataset_size() == 100
assert data5.get_batch_size() == 1
data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
assert data5.get_dataset_size() == 14
assert data5.get_batch_size() == 7
num_iter5 = 0
for _ in data5.create_dict_iterator():
num_iter5 += 1
assert num_iter5 == 14
def test_cifar10_pk_sampler():
"""
Test Cifar10Dataset with PKSampler
"""
logger.info("Test Cifar10Dataset Op with PKSampler")
golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
sampler = ds.PKSampler(3)
data = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
num_iter = 0
label_list = []
for item in data.create_dict_iterator():
label_list.append(item["label"])
num_iter += 1
np.testing.assert_array_equal(golden, label_list)
assert num_iter == 30
def test_cifar10_sequential_sampler():
"""
Test Cifar10Dataset with SequentialSampler
"""
logger.info("Test Cifar10Dataset Op with SequentialSampler")
num_samples = 30
sampler = ds.SequentialSampler(num_samples=num_samples)
data1 = ds.Cifar10Dataset(DATA_DIR_10, sampler=sampler)
data2 = ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_samples=num_samples)
num_iter = 0
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
np.testing.assert_equal(item1["label"], item2["label"])
num_iter += 1
assert num_iter == num_samples
def test_cifar10_exception():
"""
Test error cases for Cifar10Dataset
"""
logger.info("Test error cases for Cifar10Dataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, sampler=ds.PKSampler(3))
error_msg_2 = "sampler and sharding cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_2):
ds.Cifar10Dataset(DATA_DIR_10, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
with pytest.raises(RuntimeError, match=error_msg_3):
ds.Cifar10Dataset(DATA_DIR_10, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.Cifar10Dataset(DATA_DIR_10, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar10Dataset(DATA_DIR_10, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar10Dataset(DATA_DIR_10, shuffle=False, num_parallel_workers=88)
def test_cifar10_visualize(plot=False):
"""
Visualize Cifar10Dataset results
"""
logger.info("Test Cifar10Dataset visualization")
data1 = ds.Cifar10Dataset(DATA_DIR_10, num_samples=10, shuffle=False)
num_iter = 0
image_list, label_list = [], []
for item in data1.create_dict_iterator():
image = item["image"]
label = item["label"]
image_list.append(image)
label_list.append("label {}".format(label))
assert isinstance(image, np.ndarray)
assert image.shape == (32, 32, 3)
assert image.dtype == np.uint8
assert label.dtype == np.uint32
num_iter += 1
assert num_iter == 10
if plot:
visualize_dataset(image_list, label_list)
### Testcases for Cifar100Dataset Op ###
def test_cifar100_content_check():
"""
Validate Cifar100Dataset image readings
"""
logger.info("Test Cifar100Dataset with content check")
data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, shuffle=False)
images, labels = load_cifar(DATA_DIR_100, kind="cifar100")
num_iter = 0
# in this example, each dictionary has keys "image", "coarse_label" and "fine_image"
for i, d in enumerate(data1.create_dict_iterator()):
np.testing.assert_array_equal(d["image"], images[i])
np.testing.assert_array_equal(d["coarse_label"], labels[i][0])
np.testing.assert_array_equal(d["fine_label"], labels[i][1])
num_iter += 1
assert num_iter == 100
def test_cifar100_basic():
"""
Test Cifar100Dataset
"""
logger.info("Test Cifar100Dataset")
# case 1: test num_samples
data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
num_iter1 = 0
for _ in data1.create_dict_iterator():
num_iter1 += 1
assert num_iter1 == 100
# case 2: test repeat
data1 = data1.repeat(2)
num_iter2 = 0
for _ in data1.create_dict_iterator():
num_iter2 += 1
assert num_iter2 == 200
# case 3: test num_parallel_workers
data2 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100, num_parallel_workers=1)
num_iter3 = 0
for _ in data2.create_dict_iterator():
num_iter3 += 1
assert num_iter3 == 100
# case 4: test batch with drop_remainder=False
data3 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
assert data3.get_dataset_size() == 100
assert data3.get_batch_size() == 1
data3 = data3.batch(batch_size=3)
assert data3.get_dataset_size() == 34
assert data3.get_batch_size() == 3
num_iter4 = 0
for _ in data3.create_dict_iterator():
num_iter4 += 1
assert num_iter4 == 34
# case 4: test batch with drop_remainder=True
data4 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=100)
data4 = data4.batch(batch_size=3, drop_remainder=True)
assert data4.get_dataset_size() == 33
assert data4.get_batch_size() == 3
num_iter5 = 0
for _ in data4.create_dict_iterator():
num_iter5 += 1
assert num_iter5 == 33
def test_cifar100_pk_sampler():
"""
Test Cifar100Dataset with PKSampler
"""
logger.info("Test Cifar100Dataset with PKSampler")
golden = [i for i in range(20)]
sampler = ds.PKSampler(1)
data = ds.Cifar100Dataset(DATA_DIR_100, sampler=sampler)
num_iter = 0
label_list = []
for item in data.create_dict_iterator():
label_list.append(item["coarse_label"])
num_iter += 1
np.testing.assert_array_equal(golden, label_list)
assert num_iter == 20
def test_cifar100_exception():
"""
Test error cases for Cifar100Dataset
"""
logger.info("Test error cases for Cifar100Dataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, sampler=ds.PKSampler(3))
error_msg_2 = "sampler and sharding cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_2):
ds.Cifar100Dataset(DATA_DIR_100, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
with pytest.raises(RuntimeError, match=error_msg_3):
ds.Cifar100Dataset(DATA_DIR_100, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.Cifar100Dataset(DATA_DIR_100, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar100Dataset(DATA_DIR_100, num_shards=2, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.Cifar10Dataset(DATA_DIR_100, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.Cifar100Dataset(DATA_DIR_100, shuffle=False, num_parallel_workers=88)
def test_cifar100_visualize(plot=False):
"""
Visualize Cifar100Dataset results
"""
logger.info("Test Cifar100Dataset visualization")
data1 = ds.Cifar100Dataset(DATA_DIR_100, num_samples=10, shuffle=False)
num_iter = 0
image_list, label_list = [], []
for item in data1.create_dict_iterator():
image = item["image"]
coarse_label = item["coarse_label"]
fine_label = item["fine_label"]
image_list.append(image)
label_list.append("coarse_label {}\nfine_label {}".format(coarse_label, fine_label))
assert isinstance(image, np.ndarray)
assert image.shape == (32, 32, 3)
assert image.dtype == np.uint8
assert coarse_label.dtype == np.uint32
assert fine_label.dtype == np.uint32
num_iter += 1
assert num_iter == 10
if plot:
visualize_dataset(image_list, label_list)
if __name__ == '__main__':
test_cifar10_content_check()
test_cifar10_basic()
test_cifar10_pk_sampler()
test_cifar10_sequential_sampler()
test_cifar10_exception()
test_cifar10_visualize(plot=False)
test_cifar100_content_check()
test_cifar100_basic()
test_cifar100_pk_sampler()
test_cifar100_exception()
test_cifar100_visualize(plot=False)