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mindspore2022/tests/ut/python/dataset/test_datasets_flowers102.py

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# Copyright 2021 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 Flowers102 dataset operators
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pytest
from PIL import Image
from scipy.io import loadmat
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as c_vision
from mindspore import log as logger
DATA_DIR = "../data/dataset/testFlowers102Dataset"
WRONG_DIR = "../data/dataset/testMnistData"
def load_flowers102(path, usage):
"""
load Flowers102 data
"""
assert usage in ["train", "valid", "test", "all"]
imagelabels = (loadmat(os.path.join(path, "imagelabels.mat"))["labels"][0] - 1).astype(np.uint32)
split = loadmat(os.path.join(path, "setid.mat"))
if usage == 'train':
indices = split["trnid"][0].tolist()
elif usage == 'test':
indices = split["tstid"][0].tolist()
elif usage == 'valid':
indices = split["valid"][0].tolist()
elif usage == 'all':
indices = split["trnid"][0].tolist()
indices += split["tstid"][0].tolist()
indices += split["valid"][0].tolist()
image_paths = [os.path.join(path, "jpg", "image_" + str(index).zfill(5) + ".jpg") for index in indices]
segmentation_paths = [os.path.join(path, "segmim", "segmim_" + str(index).zfill(5) + ".jpg") for index in indices]
images = [np.asarray(Image.open(path).convert("RGB")) for path in image_paths]
segmentations = [np.asarray(Image.open(path).convert("RGB")) for path in segmentation_paths]
labels = [imagelabels[index - 1] for index in indices]
return images, segmentations, 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].squeeze())
plt.title(labels[i])
plt.show()
def test_flowers102_content_check():
"""
Validate Flowers102Dataset image readings
"""
logger.info("Test Flowers102Dataset Op with content check")
all_data = ds.Flowers102Dataset(DATA_DIR, task="Segmentation", usage="all",
num_samples=6, decode=True, shuffle=False)
images, segmentations, labels = load_flowers102(DATA_DIR, "all")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(all_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(data["image"], images[i])
np.testing.assert_array_equal(data["segmentation"], segmentations[i])
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 6
train_data = ds.Flowers102Dataset(DATA_DIR, task="Segmentation", usage="train",
num_samples=2, decode=True, shuffle=False)
images, segmentations, labels = load_flowers102(DATA_DIR, "train")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(train_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(data["image"], images[i])
np.testing.assert_array_equal(data["segmentation"], segmentations[i])
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 2
test_data = ds.Flowers102Dataset(DATA_DIR, task="Segmentation", usage="test",
num_samples=2, decode=True, shuffle=False)
images, segmentations, labels = load_flowers102(DATA_DIR, "test")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(test_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(data["image"], images[i])
np.testing.assert_array_equal(data["segmentation"], segmentations[i])
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 2
val_data = ds.Flowers102Dataset(DATA_DIR, task="Segmentation", usage="valid",
num_samples=2, decode=True, shuffle=False)
images, segmentations, labels = load_flowers102(DATA_DIR, "valid")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(val_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(data["image"], images[i])
np.testing.assert_array_equal(data["segmentation"], segmentations[i])
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 2
def test_flowers102_basic():
"""
Validate Flowers102Dataset
"""
logger.info("Test Flowers102Dataset Op")
# case 1: test decode
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=False, shuffle=False)
all_data_1 = all_data.map(operations=[c_vision.Decode()], input_columns=["image"], num_parallel_workers=1)
all_data_2 = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, shuffle=False)
num_iter = 0
for item1, item2 in zip(all_data_1.create_dict_iterator(num_epochs=1, output_numpy=True),
all_data_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
np.testing.assert_array_equal(item1["label"], item2["label"])
num_iter += 1
assert num_iter == 6
# case 2: test num_samples
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_samples=4)
num_iter = 0
for _ in all_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 4
# case 3: test repeat
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_samples=4)
all_data = all_data.repeat(5)
num_iter = 0
for _ in all_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 20
# case 3: test get_dataset_size, resize and batch
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=False, num_samples=4)
all_data = all_data.map(operations=[c_vision.Decode(), c_vision.Resize((224, 224))], input_columns=["image"],
num_parallel_workers=1)
assert all_data.get_dataset_size() == 4
assert all_data.get_batch_size() == 1
all_data = all_data.batch(batch_size=3) # drop_remainder is default to be False
assert all_data.get_batch_size() == 3
assert all_data.get_dataset_size() == 2
num_iter = 0
for _ in all_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 2
# case 4: test get_class_indexing
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=False, num_samples=4)
class_indexing = all_data.get_class_indexing()
assert class_indexing["pink primrose"] == 0
assert class_indexing["blackberry lily"] == 101
def test_flowers102_sequential_sampler():
"""
Test Flowers102Dataset with SequentialSampler
"""
logger.info("Test Flowers102Dataset Op with SequentialSampler")
num_samples = 4
sampler = ds.SequentialSampler(num_samples=num_samples)
all_data_1 = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all",
decode=True, sampler=sampler)
all_data_2 = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all",
decode=True, shuffle=False, num_samples=num_samples)
label_list_1, label_list_2 = [], []
num_iter = 0
for item1, item2 in zip(all_data_1.create_dict_iterator(num_epochs=1),
all_data_2.create_dict_iterator(num_epochs=1)):
label_list_1.append(item1["label"].asnumpy())
label_list_2.append(item2["label"].asnumpy())
num_iter += 1
np.testing.assert_array_equal(label_list_1, label_list_2)
assert num_iter == num_samples
def test_flowers102_exception():
"""
Test error cases for Flowers102Dataset
"""
logger.info("Test error cases for Flowers102Dataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", shuffle=False,
decode=True, sampler=ds.SequentialSampler(1))
error_msg_2 = "sampler and sharding cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_2):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", sampler=ds.SequentialSampler(1),
decode=True, 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.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_shards=5, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_shards=5, shard_id=5)
with pytest.raises(ValueError, match=error_msg_5):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True,
shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True,
shuffle=False, num_parallel_workers=256)
with pytest.raises(ValueError, match=error_msg_6):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True,
shuffle=False, num_parallel_workers=-2)
error_msg_7 = "Argument shard_id"
with pytest.raises(TypeError, match=error_msg_7):
ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=True, num_shards=2, shard_id="0")
error_msg_8 = "does not exist or is not a directory or permission denied!"
with pytest.raises(ValueError, match=error_msg_8):
all_data = ds.Flowers102Dataset(WRONG_DIR, task="Classification", usage="all", decode=True)
for _ in all_data.create_dict_iterator(num_epochs=1):
pass
error_msg_9 = "is not of type"
with pytest.raises(TypeError, match=error_msg_9):
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", decode=123)
for _ in all_data.create_dict_iterator(num_epochs=1):
pass
def test_flowers102_visualize(plot=False):
"""
Visualize Flowers102Dataset results
"""
logger.info("Test Flowers102Dataset visualization")
all_data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage="all", num_samples=4,
decode=True, shuffle=False)
num_iter = 0
image_list, label_list = [], []
for item in all_data.create_dict_iterator(num_epochs=1, output_numpy=True):
image = item["image"]
label = item["label"]
image_list.append(image)
label_list.append("label {}".format(label))
assert isinstance(image, np.ndarray)
assert len(image.shape) == 3
assert image.shape[-1] == 3
assert image.dtype == np.uint8
assert label.dtype == np.uint32
num_iter += 1
assert num_iter == 4
if plot:
visualize_dataset(image_list, label_list)
def test_flowers102_usage():
"""
Validate Flowers102Dataset usage
"""
logger.info("Test Flowers102Dataset usage flag")
def test_config(usage):
try:
data = ds.Flowers102Dataset(DATA_DIR, task="Classification", usage=usage, decode=True, shuffle=False)
num_rows = 0
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
num_rows += 1
except (ValueError, TypeError, RuntimeError) as e:
return str(e)
return num_rows
assert test_config("all") == 6
assert test_config("train") == 2
assert test_config("test") == 2
assert test_config("valid") == 2
assert "usage is not within the valid set of ['train', 'valid', 'test', 'all']" in test_config("invalid")
assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
def test_flowers102_task():
"""
Validate Flowers102Dataset task
"""
logger.info("Test Flowers102Dataset task flag")
def test_config(task):
try:
data = ds.Flowers102Dataset(DATA_DIR, task=task, usage="all", decode=True, shuffle=False)
num_rows = 0
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
num_rows += 1
except (ValueError, TypeError, RuntimeError) as e:
return str(e)
return num_rows
assert test_config("Classification") == 6
assert test_config("Segmentation") == 6
assert "Input task is not within the valid set of ['Classification', 'Segmentation']" in test_config("invalid")
assert "Argument task with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
if __name__ == '__main__':
test_flowers102_content_check()
test_flowers102_basic()
test_flowers102_sequential_sampler()
test_flowers102_exception()
test_flowers102_visualize(plot=True)
test_flowers102_usage()
test_flowers102_task()
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