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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | # Copyright 2020 MONAI Consortium
# 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 logging
import os
import tempfile
import unittest
import numpy as np
import torch
from parameterized import parameterized
from monai.transforms import DataStats
TEST_CASE_1 = [
{
"prefix": "test data",
"data_shape": False,
"value_range": False,
"data_value": False,
"additional_info": None,
"logger_handler": None,
},
np.array([[0, 1], [1, 2]]),
"test data statistics:",
]
TEST_CASE_2 = [
{
"prefix": "test data",
"data_shape": True,
"value_range": False,
"data_value": False,
"additional_info": None,
"logger_handler": None,
},
np.array([[0, 1], [1, 2]]),
"test data statistics:\nShape: (2, 2)",
]
TEST_CASE_3 = [
{
"prefix": "test data",
"data_shape": True,
"value_range": True,
"data_value": False,
"additional_info": None,
"logger_handler": None,
},
np.array([[0, 1], [1, 2]]),
"test data statistics:\nShape: (2, 2)\nValue range: (0, 2)",
]
TEST_CASE_4 = [
{
"prefix": "test data",
"data_shape": True,
"value_range": True,
"data_value": True,
"additional_info": None,
"logger_handler": None,
},
np.array([[0, 1], [1, 2]]),
"test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]",
]
TEST_CASE_5 = [
{
"prefix": "test data",
"data_shape": True,
"value_range": True,
"data_value": True,
"additional_info": lambda x: np.mean(x),
"logger_handler": None,
},
np.array([[0, 1], [1, 2]]),
"test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]\nAdditional info: 1.0",
]
TEST_CASE_6 = [
{
"prefix": "test data",
"data_shape": True,
"value_range": True,
"data_value": True,
"additional_info": lambda x: torch.mean(x.float()),
"logger_handler": None,
},
torch.tensor([[0, 1], [1, 2]]),
(
"test data statistics:\nShape: torch.Size([2, 2])\nValue range: (0, 2)\n"
"Value: tensor([[0, 1],\n [1, 2]])\nAdditional info: 1.0"
),
]
TEST_CASE_7 = [
np.array([[0, 1], [1, 2]]),
"test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]\nAdditional info: 1.0\n",
]
class TestDataStats(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])
def test_value(self, input_param, input_data, expected_print):
transform = DataStats(**input_param)
_ = transform(input_data)
self.assertEqual(transform.output, expected_print)
@parameterized.expand([TEST_CASE_7])
def test_file(self, input_data, expected_print):
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_data_stats.log")
handler = logging.FileHandler(filename, mode="w")
input_param = {
"prefix": "test data",
"data_shape": True,
"value_range": True,
"data_value": True,
"additional_info": lambda x: np.mean(x),
"logger_handler": handler,
}
transform = DataStats(**input_param)
_ = transform(input_data)
handler.stream.close()
transform._logger.removeHandler(handler)
with open(filename, "r") as f:
content = f.read()
self.assertEqual(content, expected_print)
if __name__ == "__main__":
unittest.main()
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