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mindspore/tests/ut/python/dataset/test_compose.py

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# Copyright 2020-2021 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.
# ==============================================================================
import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as c_transforms
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.dataset.vision.c_transforms as c_vision
import mindspore.dataset.vision.py_transforms as py_vision
from util import visualize_list, save_and_check_md5, config_get_set_seed, config_get_set_num_parallel_workers
GENERATE_GOLDEN = False
def test_compose():
"""
Test C++ and Python Compose Op
"""
ds.config.set_seed(0)
def test_config(arr, op_list):
try:
data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
data = data.map(input_columns=["col"], operations=op_list)
res = []
for i in data.create_dict_iterator(output_numpy=True):
res.append(i["col"].tolist())
return res
except (TypeError, ValueError) as e:
return str(e)
# Test simple compose with only 1 op, this would generate a warning
assert test_config([[1, 0], [3, 4]], c_transforms.Compose([c_transforms.Fill(2)])) == [[2, 2], [2, 2]]
# Test 1 column -> 2 columns -> 1 -> 2 -> 1
assert test_config([[1, 0]],
c_transforms.Compose(
[c_transforms.Duplicate(), c_transforms.Concatenate(), c_transforms.Duplicate(),
c_transforms.Concatenate()])) \
== [[1, 0] * 4]
# Test one Python transform followed by a C++ transform. Type after OneHot is a float (mixed use-case)
assert test_config([1, 0],
c_transforms.Compose([py_transforms.OneHotOp(2), c_transforms.TypeCast(mstype.int32)])) \
== [[[0, 1]], [[1, 0]]]
# Test exceptions.
with pytest.raises(TypeError) as error_info:
c_transforms.Compose([1, c_transforms.TypeCast(mstype.int32)])
assert "op_list[0] is neither a c_transform op (TensorOperation) nor a callable pyfunc." in str(error_info.value)
# Test empty op list
with pytest.raises(ValueError) as error_info:
test_config([1, 0], c_transforms.Compose([]))
assert "op_list can not be empty." in str(error_info.value)
# Test Python compose op
assert test_config([1, 0], py_transforms.Compose([py_transforms.OneHotOp(2)])) == [[[0, 1]], [[1, 0]]]
assert test_config([1, 0], py_transforms.Compose([py_transforms.OneHotOp(2), (lambda x: x + x)])) == [[[0, 2]],
[[2, 0]]]
# Test nested Python compose op
assert test_config([1, 0],
py_transforms.Compose([py_transforms.Compose([py_transforms.OneHotOp(2)]), (lambda x: x + x)])) \
== [[[0, 2]], [[2, 0]]]
# Test passing a list of Python ops without Compose wrapper
assert test_config([1, 0],
[py_transforms.Compose([py_transforms.OneHotOp(2)]), (lambda x: x + x)]) \
== [[[0, 2]], [[2, 0]]]
assert test_config([1, 0], [py_transforms.OneHotOp(2), (lambda x: x + x)]) == [[[0, 2]], [[2, 0]]]
# Test a non callable function
with pytest.raises(ValueError) as error_info:
py_transforms.Compose([1])
assert "transforms[0] is not callable." in str(error_info.value)
# Test empty Python op list
with pytest.raises(ValueError) as error_info:
test_config([1, 0], py_transforms.Compose([]))
assert "transforms list is empty." in str(error_info.value)
# Pass in extra brackets
with pytest.raises(TypeError) as error_info:
py_transforms.Compose([(lambda x: x + x)])()
assert "Compose was called without an image. Fix invocation (avoid it being invoked as Compose([...])())." in str(
error_info.value)
def test_lambdas():
"""
Test Multi Column Python Compose Op
"""
ds.config.set_seed(0)
def test_config(arr, input_columns, output_cols, op_list):
data = ds.NumpySlicesDataset(arr, column_names=input_columns, shuffle=False)
data = data.map(operations=op_list, input_columns=input_columns, output_columns=output_cols,
column_order=output_cols)
res = []
for i in data.create_dict_iterator(output_numpy=True):
for col_name in output_cols:
res.append(i[col_name].tolist())
return res
arr = ([[1]], [[3]])
assert test_config(arr, ["col0", "col1"], ["a"], py_transforms.Compose([(lambda x, y: x)])) == [[1]]
assert test_config(arr, ["col0", "col1"], ["a"], py_transforms.Compose([lambda x, y: x, lambda x: x])) == [[1]]
assert test_config(arr, ["col0", "col1"], ["a", "b"],
py_transforms.Compose([lambda x, y: x, lambda x: (x, x * 2)])) == \
[[1], [2]]
assert test_config(arr, ["col0", "col1"], ["a", "b"],
[lambda x, y: (x, x + y), lambda x, y: (x, y * 2)]) == [[1], [8]]
def test_c_py_compose_transforms_module():
"""
Test combining Python and C++ transforms
"""
ds.config.set_seed(0)
def test_config(arr, input_columns, output_cols, op_list):
data = ds.NumpySlicesDataset(arr, column_names=input_columns, shuffle=False)
data = data.map(operations=op_list, input_columns=input_columns, output_columns=output_cols,
column_order=output_cols)
res = []
for i in data.create_dict_iterator(output_numpy=True):
for col_name in output_cols:
res.append(i[col_name].tolist())
return res
arr = [1, 0]
assert test_config(arr, ["cols"], ["cols"],
[py_transforms.OneHotOp(2), c_transforms.Mask(c_transforms.Relational.EQ, 1)]) == \
[[[False, True]],
[[True, False]]]
assert test_config(arr, ["cols"], ["cols"],
[py_transforms.OneHotOp(2), (lambda x: x + x), c_transforms.Fill(1)]) \
== [[[1, 1]], [[1, 1]]]
assert test_config(arr, ["cols"], ["cols"],
[py_transforms.OneHotOp(2), (lambda x: x + x), c_transforms.Fill(1), (lambda x: x + x)]) \
== [[[2, 2]], [[2, 2]]]
assert test_config([[1, 3]], ["cols"], ["cols"],
[c_transforms.PadEnd([3], -1), (lambda x: x + x)]) \
== [[2, 6, -2]]
arr = ([[1]], [[3]])
assert test_config(arr, ["col0", "col1"], ["a"], [(lambda x, y: x + y), c_transforms.PadEnd([2], -1)]) == [[4, -1]]
def test_c_py_compose_vision_module(plot=False, run_golden=True):
"""
Test combining Python and C++ vision transforms
"""
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
def test_config(plot, file_name, op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data1 = data1.map(operations=op_list, input_columns=["image"])
data2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data2 = data2.map(operations=c_vision.Decode(), input_columns=["image"])
original_images = []
transformed_images = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
transformed_images.append(item["image"])
for item in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
original_images.append(item["image"])
if run_golden:
# Compare with expected md5 from images
save_and_check_md5(data1, file_name, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(original_images, transformed_images)
test_config(op_list=[c_vision.Decode(),
py_vision.ToPIL(),
py_vision.Resize((224, 224)),
np.array],
plot=plot, file_name="compose_c_py_1.npz")
test_config(op_list=[c_vision.Decode(),
c_vision.Resize((224, 244)),
py_vision.ToPIL(),
np.array,
c_vision.Resize((24, 24))],
plot=plot, file_name="compose_c_py_2.npz")
test_config(op_list=[py_vision.Decode(),
py_vision.Resize((224, 224)),
np.array,
c_vision.RandomColor()],
plot=plot, file_name="compose_c_py_3.npz")
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_py_transforms_with_c_vision():
"""
These examples will fail, as c_transform should not be used in py_transforms.Random(Apply/Choice/Order)
"""
ds.config.set_seed(0)
def test_config(op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data = data.map(operations=op_list)
res = []
for i in data.create_dict_iterator(output_numpy=True):
for col_name in output_cols:
res.append(i[col_name].tolist())
return res
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomApply([c_vision.RandomResizedCrop(200)]))
assert "transforms[0] is not a py transforms." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomChoice([c_vision.RandomResizedCrop(200)]))
assert "transforms[0] is not a py transforms." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomOrder([np.array, c_vision.RandomResizedCrop(200)]))
assert "transforms[1] is not a py transforms." in str(error_info.value)
with pytest.raises(RuntimeError) as error_info:
test_config([py_transforms.OneHotOp(20, 0.1)])
assert "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" in str(
error_info.value)
def test_py_vision_with_c_transforms():
"""
Test combining Python vision operations with C++ transforms operations
"""
ds.config.set_seed(0)
def test_config(op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data1 = data1.map(operations=op_list, input_columns=["image"])
transformed_images = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
transformed_images.append(item["image"])
return transformed_images
# Test with Mask Op
output_arr = test_config([py_vision.Decode(),
py_vision.CenterCrop((2)), np.array,
c_transforms.Mask(c_transforms.Relational.GE, 100)])
exp_arr = [np.array([[[True, False, False],
[True, False, False]],
[[True, False, False],
[True, False, False]]]),
np.array([[[True, False, False],
[True, False, False]],
[[True, False, False],
[True, False, False]]])]
for exp_a, output in zip(exp_arr, output_arr):
np.testing.assert_array_equal(exp_a, output)
# Test with Fill Op
output_arr = test_config([py_vision.Decode(),
py_vision.CenterCrop((4)), np.array,
c_transforms.Fill(10)])
exp_arr = [np.ones((4, 4, 3)) * 10] * 2
for exp_a, output in zip(exp_arr, output_arr):
np.testing.assert_array_equal(exp_a, output)
# Test with Concatenate Op, which will raise an error since ConcatenateOp only supports rank 1 tensors.
with pytest.raises(RuntimeError) as error_info:
test_config([py_vision.Decode(),
py_vision.CenterCrop((2)), np.array,
c_transforms.Concatenate(0)])
assert "only 1D input supported" in str(error_info.value)
def test_compose_with_custom_function():
"""
Test Python Compose with custom function
"""
def custom_function(x):
return (x, x * x)
# First dataset
op_list = [
lambda x: x * 3,
custom_function,
# convert two column output to one
lambda *images: np.stack(images)
]
data = ds.NumpySlicesDataset([[1, 2]], column_names=["col0"], shuffle=False)
data = data.map(input_columns=["col0"], operations=op_list)
#
res = []
for i in data.create_dict_iterator(output_numpy=True):
res.append(i["col0"].tolist())
assert res == [[[3, 6], [9, 36]]]
if __name__ == "__main__":
test_compose()
test_lambdas()
test_c_py_compose_transforms_module()
test_c_py_compose_vision_module(plot=True)
test_py_transforms_with_c_vision()
test_py_vision_with_c_transforms()
test_compose_with_custom_function()
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