forked from huawei/mindspore2022
304 lines
11 KiB
Python
304 lines
11 KiB
Python
# 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 USPS dataset operators
|
|
"""
|
|
import os
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pytest
|
|
from PIL import Image
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.vision.c_transforms as vision
|
|
from mindspore import log as logger
|
|
|
|
DATA_DIR = "../data/dataset/testSBUDataset"
|
|
WRONG_DIR = "../data/dataset/testMnistData"
|
|
|
|
|
|
def load_sbu(path):
|
|
"""
|
|
load SBU data
|
|
"""
|
|
images = []
|
|
captions = []
|
|
|
|
file1 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_urls.txt'))
|
|
file2 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_captions.txt'))
|
|
|
|
for line1, line2 in zip(open(file1), open(file2)):
|
|
url = line1.rstrip()
|
|
image = url[23:].replace("/", "_")
|
|
filename = os.path.join(path, 'sbu_images', image)
|
|
if os.path.exists(filename):
|
|
caption = line2.rstrip()
|
|
images.append(np.asarray(Image.open(filename).convert('RGB')).astype(np.uint8))
|
|
captions.append(caption)
|
|
return images, captions
|
|
|
|
|
|
def visualize_dataset(images, captions):
|
|
"""
|
|
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(captions[i])
|
|
plt.show()
|
|
|
|
|
|
def test_sbu_content_check():
|
|
"""
|
|
Validate SBUDataset image readings
|
|
"""
|
|
logger.info("Test SBUDataset Op with content check")
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=50, shuffle=False)
|
|
images, captions = load_sbu(DATA_DIR)
|
|
num_iter = 0
|
|
# in this example, each dictionary has keys "image" and "caption"
|
|
for i, data in enumerate(dataset.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
assert data["image"].shape == images[i].shape
|
|
assert data["caption"].item().decode("utf8") == captions[i]
|
|
num_iter += 1
|
|
assert num_iter == 5
|
|
|
|
|
|
def test_sbu_case():
|
|
"""
|
|
Validate SBUDataset cases
|
|
"""
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True)
|
|
|
|
dataset = dataset.map(operations=[vision.Resize((224, 224))], input_columns=["image"])
|
|
repeat_num = 4
|
|
dataset = dataset.repeat(repeat_num)
|
|
batch_size = 2
|
|
dataset = dataset.batch(batch_size, drop_remainder=True, pad_info={})
|
|
|
|
num = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num += 1
|
|
# 4 x 5 / 2
|
|
assert num == 10
|
|
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=False)
|
|
|
|
dataset = dataset.map(operations=[vision.Decode(rgb=True), vision.Resize((224, 224))], input_columns=["image"])
|
|
repeat_num = 4
|
|
dataset = dataset.repeat(repeat_num)
|
|
batch_size = 2
|
|
dataset = dataset.batch(batch_size, drop_remainder=True, pad_info={})
|
|
|
|
num = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num += 1
|
|
# 4 x 5 / 2
|
|
assert num == 10
|
|
|
|
|
|
def test_sbu_basic():
|
|
"""
|
|
Validate SBUDataset
|
|
"""
|
|
logger.info("Test SBUDataset Op")
|
|
|
|
# case 1: test loading whole dataset
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True)
|
|
num_iter = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter += 1
|
|
assert num_iter == 5
|
|
|
|
|
|
# case 2: test num_samples
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
|
|
num_iter = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter += 1
|
|
assert num_iter == 5
|
|
|
|
# case 3: test repeat
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
|
|
dataset = dataset.repeat(5)
|
|
num_iter = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter += 1
|
|
assert num_iter == 25
|
|
|
|
# case 4: test batch
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
|
|
assert dataset.get_dataset_size() == 5
|
|
assert dataset.get_batch_size() == 1
|
|
|
|
num_iter = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter += 1
|
|
assert num_iter == 5
|
|
|
|
# case 5: test get_class_indexing
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
|
|
assert dataset.get_class_indexing() == {}
|
|
|
|
# case 6: test get_col_names
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
|
|
assert dataset.get_col_names() == ["image", "caption"]
|
|
|
|
|
|
def test_sbu_sequential_sampler():
|
|
"""
|
|
Test SBUDataset with SequentialSampler
|
|
"""
|
|
logger.info("Test SBUDataset Op with SequentialSampler")
|
|
num_samples = 5
|
|
sampler = ds.SequentialSampler(num_samples=num_samples)
|
|
dataset_1 = ds.SBUDataset(DATA_DIR, decode=True, sampler=sampler)
|
|
dataset_2 = ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_samples=num_samples)
|
|
|
|
num_iter = 0
|
|
for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
|
|
dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
np.testing.assert_array_equal(item1["caption"], item2["caption"])
|
|
num_iter += 1
|
|
assert num_iter == num_samples
|
|
|
|
|
|
def test_sbu_exception():
|
|
"""
|
|
Test error cases for SBUDataset
|
|
"""
|
|
logger.info("Test error cases for SBUDataset")
|
|
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
|
|
with pytest.raises(RuntimeError, match=error_msg_1):
|
|
ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, sampler=ds.SequentialSampler())
|
|
|
|
error_msg_2 = "sampler and sharding cannot be specified at the same time"
|
|
with pytest.raises(RuntimeError, match=error_msg_2):
|
|
ds.SBUDataset(DATA_DIR, decode=True, sampler=ds.SequentialSampler(), 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.SBUDataset(DATA_DIR, 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.SBUDataset(DATA_DIR, 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.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=-1)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=5)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.SBUDataset(DATA_DIR, decode=True, num_shards=2, shard_id=5)
|
|
|
|
error_msg_6 = "num_parallel_workers exceeds"
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=0)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=256)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=-2)
|
|
|
|
error_msg_7 = "Argument shard_id"
|
|
with pytest.raises(TypeError, match=error_msg_7):
|
|
ds.SBUDataset(DATA_DIR, decode=True, num_shards=2, shard_id="0")
|
|
|
|
def exception_func(item):
|
|
raise Exception("Error occur!")
|
|
|
|
error_msg_8 = "The corresponding data files"
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True)
|
|
dataset = dataset.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
|
for _ in dataset.__iter__():
|
|
pass
|
|
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True)
|
|
dataset = dataset.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
|
|
for _ in dataset.__iter__():
|
|
pass
|
|
|
|
error_msg_9 = "does not exist or permission denied"
|
|
with pytest.raises(ValueError, match=error_msg_9):
|
|
dataset = ds.SBUDataset(WRONG_DIR, decode=True)
|
|
for _ in dataset.__iter__():
|
|
pass
|
|
|
|
error_msg_10 = "Argument decode with value"
|
|
with pytest.raises(TypeError, match=error_msg_10):
|
|
dataset = ds.SBUDataset(DATA_DIR, decode="not_bool")
|
|
for _ in dataset.__iter__():
|
|
pass
|
|
|
|
|
|
def test_sbu_visualize(plot=False):
|
|
"""
|
|
Visualize SBUDataset results
|
|
"""
|
|
logger.info("Test SBUDataset visualization")
|
|
|
|
dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=10, shuffle=False)
|
|
num_iter = 0
|
|
image_list, caption_list = [], []
|
|
for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
image = item["image"]
|
|
caption = item["caption"].item().decode("utf8")
|
|
image_list.append(image)
|
|
caption_list.append("caption {}".format(caption))
|
|
assert isinstance(image, np.ndarray)
|
|
|
|
assert image.dtype == np.uint8
|
|
assert isinstance(caption, str)
|
|
num_iter += 1
|
|
assert num_iter == 5
|
|
if plot:
|
|
visualize_dataset(image_list, caption_list)
|
|
|
|
|
|
def test_sbu_decode():
|
|
"""
|
|
Validate SBUDataset image readings
|
|
"""
|
|
logger.info("Test SBUDataset decode flag")
|
|
|
|
sampler = ds.SequentialSampler(num_samples=50)
|
|
dataset = ds.SBUDataset(dataset_dir=DATA_DIR, decode=False, sampler=sampler)
|
|
dataset_1 = dataset.map(operations=[vision.Decode(rgb=True)], input_columns=["image"])
|
|
|
|
dataset_2 = ds.SBUDataset(dataset_dir=DATA_DIR, decode=True, sampler=sampler)
|
|
|
|
num_iter = 0
|
|
for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
|
|
dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
np.testing.assert_array_equal(item1["caption"], item2["caption"])
|
|
num_iter += 1
|
|
|
|
assert num_iter == 5
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_sbu_content_check()
|
|
test_sbu_basic()
|
|
test_sbu_case()
|
|
test_sbu_sequential_sampler()
|
|
test_sbu_exception()
|
|
test_sbu_visualize(plot=True)
|
|
test_sbu_decode()
|