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mindspore2022/tests/ut/python/dataset/test_datasets_caltech101.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 Caltech101 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
DATASET_DIR = "../data/dataset/testCaltech101Data"
WRONG_DIR = "../data/dataset/notExist"
def get_index_info():
dataset_dir = os.path.realpath(DATASET_DIR)
image_dir = os.path.join(dataset_dir, "101_ObjectCategories")
classes = sorted(os.listdir(image_dir))
if "BACKGROUND_Google" in classes:
classes.remove("BACKGROUND_Google")
name_map = {"Faces": "Faces_2",
"Faces_easy": "Faces_3",
"Motorbikes": "Motorbikes_16",
"airplanes": "Airplanes_Side_2"}
annotation_classes = [name_map[class_name] if class_name in name_map else class_name for class_name in classes]
image_index = []
image_label = []
for i, c in enumerate(classes):
sub_dir = os.path.join(image_dir, c)
if not os.path.isdir(sub_dir) or not os.access(sub_dir, os.R_OK):
continue
num_images = len(os.listdir(sub_dir))
image_index.extend(range(1, num_images + 1))
image_label.extend(num_images * [i])
return image_index, image_label, classes, annotation_classes
def load_caltech101(target_type="category", decode=False):
"""
load Caltech101 data
"""
dataset_dir = os.path.realpath(DATASET_DIR)
image_dir = os.path.join(dataset_dir, "101_ObjectCategories")
annotation_dir = os.path.join(dataset_dir, "Annotations")
image_index, image_label, classes, annotation_classes = get_index_info()
images, categories, annotations = [], [], []
num_images = len(image_index)
for i in range(num_images):
image_file = os.path.join(image_dir, classes[image_label[i]], "image_{:04d}.jpg".format(image_index[i]))
if not os.path.exists(image_file):
raise ValueError("The image file {} does not exist or permission denied!".format(image_file))
if decode:
image = np.asarray(Image.open(image_file).convert("RGB"))
else:
image = np.fromfile(image_file, dtype=np.uint8)
images.append(image)
if target_type == "category":
for i in range(num_images):
categories.append(image_label[i])
return images, categories
for i in range(num_images):
annotation_file = os.path.join(annotation_dir, annotation_classes[image_label[i]],
"annotation_{:04d}.mat".format(image_index[i]))
if not os.path.exists(annotation_file):
raise ValueError("The annotation file {} does not exist or permission denied!".format(annotation_file))
annotation = loadmat(annotation_file)["obj_contour"]
annotations.append(annotation)
if target_type == "annotation":
return images, annotations
for i in range(num_images):
categories.append(image_label[i])
return images, categories, annotations
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_caltech101_content_check():
"""
Feature: Caltech101Dataset
Description: check if the image data of caltech101 dataset is read correctly
Expectation: the data is processed successfully
"""
logger.info("Test Caltech101Dataset Op with content check")
all_data = ds.Caltech101Dataset(DATASET_DIR, target_type="annotation", num_samples=4, shuffle=False, decode=True)
images, annotations = load_caltech101(target_type="annotation", decode=True)
num_iter = 0
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["annotation"], annotations[i])
num_iter += 1
assert num_iter == 4
all_data = ds.Caltech101Dataset(DATASET_DIR, target_type="all", num_samples=4, shuffle=False, decode=True)
images, categories, annotations = load_caltech101(target_type="all", decode=True)
num_iter = 0
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["category"], categories[i])
np.testing.assert_array_equal(data["annotation"], annotations[i])
num_iter += 1
assert num_iter == 4
def test_caltech101_basic():
"""
Feature: Caltech101Dataset
Description: basic test of Caltech101Dataset
Expectation: the data is processed successfully
"""
logger.info("Test Caltech101Dataset Op")
# case 1: test target_type
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, shuffle=False)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, 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["category"], item2["category"])
num_iter += 1
assert num_iter == 4
# case 2: test decode
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, decode=True, shuffle=False)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, 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["image"], item2["image"])
num_iter += 1
assert num_iter == 4
# case 3: test num_samples
all_data = ds.Caltech101Dataset(DATASET_DIR, num_samples=4)
num_iter = 0
for _ in all_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 4
# case 4: test repeat
all_data = ds.Caltech101Dataset(DATASET_DIR, num_samples=4)
all_data = all_data.repeat(2)
num_iter = 0
for _ in all_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 8
# case 5: test get_dataset_size, resize and batch
all_data = ds.Caltech101Dataset(DATASET_DIR, 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
# drop_remainder is default to be False
all_data = all_data.batch(batch_size=3)
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 6: test get_class_indexing
all_data = ds.Caltech101Dataset(DATASET_DIR, num_samples=4)
class_indexing = all_data.get_class_indexing()
assert class_indexing["Faces"] == 0
assert class_indexing["yin_yang"] == 100
def test_caltech101_target_type():
"""
Feature: Caltech101Dataset
Description: test Caltech101Dataset with target_type
Expectation: the data is processed successfully
"""
logger.info("Test Caltech101Dataset Op with target_type")
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, target_type="annotation", shuffle=False)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, target_type="annotation", 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["annotation"], item2["annotation"])
num_iter += 1
assert num_iter == 4
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, target_type="all", shuffle=False)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, target_type="all", 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["category"], item2["category"])
np.testing.assert_array_equal(item1["annotation"], item2["annotation"])
num_iter += 1
assert num_iter == 4
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, target_type="category", shuffle=False)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, target_type="category", 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["category"], item2["category"])
num_iter += 1
assert num_iter == 4
def test_caltech101_sequential_sampler():
"""
Feature: Caltech101Dataset
Description: test Caltech101Dataset with SequentialSampler
Expectation: the data is processed successfully
"""
logger.info("Test Caltech101Dataset Op with SequentialSampler")
num_samples = 4
sampler = ds.SequentialSampler(num_samples=num_samples)
all_data_1 = ds.Caltech101Dataset(DATASET_DIR, sampler=sampler)
all_data_2 = ds.Caltech101Dataset(DATASET_DIR, 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["category"].asnumpy())
label_list_2.append(item2["category"].asnumpy())
num_iter += 1
np.testing.assert_array_equal(label_list_1, label_list_2)
assert num_iter == num_samples
def test_caltech101_exception():
"""
Feature: Caltech101Dataset
Description: test error cases for Caltech101Dataset
Expectation: throw correct error and message
"""
logger.info("Test error cases for Caltech101Dataset")
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
with pytest.raises(RuntimeError, match=error_msg_1):
ds.Caltech101Dataset(DATASET_DIR, shuffle=False, 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.Caltech101Dataset(DATASET_DIR, sampler=ds.SequentialSampler(1), 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.Caltech101Dataset(DATASET_DIR, num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.Caltech101Dataset(DATASET_DIR, shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.Caltech101Dataset(DATASET_DIR, num_shards=5, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.Caltech101Dataset(DATASET_DIR, num_shards=5, shard_id=5)
with pytest.raises(ValueError, match=error_msg_5):
ds.Caltech101Dataset(DATASET_DIR, num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.Caltech101Dataset(DATASET_DIR, shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.Caltech101Dataset(DATASET_DIR, shuffle=False, num_parallel_workers=256)
with pytest.raises(ValueError, match=error_msg_6):
ds.Caltech101Dataset(DATASET_DIR, shuffle=False, num_parallel_workers=-2)
error_msg_7 = "Argument shard_id"
with pytest.raises(TypeError, match=error_msg_7):
ds.Caltech101Dataset(DATASET_DIR, 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.Caltech101Dataset(WRONG_DIR, WRONG_DIR)
for _ in all_data.create_dict_iterator(num_epochs=1):
pass
error_msg_9 = "Input target_type is not within the valid set of \\['category', 'annotation', 'all'\\]."
with pytest.raises(ValueError, match=error_msg_9):
all_data = ds.Caltech101Dataset(DATASET_DIR, target_type="cate")
for _ in all_data.create_dict_iterator(num_epochs=1):
pass
def test_caltech101_visualize(plot=False):
"""
Feature: Caltech101Dataset
Description: visualize Caltech101Dataset results
Expectation: the data is processed successfully
"""
logger.info("Test Caltech101Dataset visualization")
all_data = ds.Caltech101Dataset(DATASET_DIR, num_samples=4, decode=True, shuffle=False)
num_iter = 0
image_list, category_list = [], []
for item in all_data.create_dict_iterator(num_epochs=1, output_numpy=True):
image = item["image"]
category = item["category"]
image_list.append(image)
category_list.append("label {}".format(category))
assert isinstance(image, np.ndarray)
assert len(image.shape) == 3
assert image.shape[-1] == 3
assert image.dtype == np.uint8
assert category.dtype == np.int64
num_iter += 1
assert num_iter == 4
if plot:
visualize_dataset(image_list, category_list)
if __name__ == '__main__':
test_caltech101_content_check()
test_caltech101_basic()
test_caltech101_target_type()
test_caltech101_sequential_sampler()
test_caltech101_exception()
test_caltech101_visualize(plot=True)
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