forked from mindspore-Ecosystem/mindspore
317 lines
12 KiB
Python
317 lines
12 KiB
Python
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Test Compose op in Dataset
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"""
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.transforms as transforms
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import mindspore.dataset.vision.transforms as vision
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from util import visualize_list, save_and_check_md5, config_get_set_seed, config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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def test_compose():
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"""
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Test C++ and Python Compose Op
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"""
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original_seed = config_get_set_seed(0)
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def test_config(arr, op_list):
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try:
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data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
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data = data.map(input_columns=["col"], operations=op_list)
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res = []
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for i in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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res.append(i["col"].tolist())
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return res
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except (TypeError, ValueError) as e:
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return str(e)
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# Test simple compose with only 1 op, this would generate a warning
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assert test_config([[1, 0], [3, 4]], transforms.Compose([transforms.Fill(2)])) == [[2, 2], [2, 2]]
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# Test 1 column -> 2 columns -> 1 -> 2 -> 1
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assert test_config([[1, 0]],
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transforms.Compose(
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[transforms.Duplicate(), transforms.Concatenate(), transforms.Duplicate(),
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transforms.Concatenate()])) \
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== [[1, 0] * 4]
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# Test one Python transform followed by a C++ transform. Type after OneHot is a float (mixed use-case)
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assert test_config([1, 0],
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transforms.Compose([transforms.OneHot(2), transforms.TypeCast(mstype.int32)])) \
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== [[0, 1], [1, 0]]
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# Test exceptions.
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with pytest.raises(TypeError) as error_info:
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transforms.Compose([1, transforms.TypeCast(mstype.int32)])
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assert "op_list[0] is neither a c_transform op (TensorOperation) nor a callable pyfunc." in str(error_info.value)
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# Test empty op list
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with pytest.raises(ValueError) as error_info:
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test_config([1, 0], transforms.Compose([]))
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assert "op_list can not be empty." in str(error_info.value)
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# Test Python compose op
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assert test_config([1, 0], transforms.Compose([transforms.OneHot(2)])) == [[0, 1], [1, 0]]
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assert test_config([1, 0], transforms.Compose([transforms.OneHot(2), (lambda x: x + x)])) == [[0, 2],
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[2, 0]]
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# Test nested Python compose op
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assert test_config([1, 0],
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transforms.Compose([transforms.Compose([transforms.OneHot(2)]), (lambda x: x + x)])) \
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== [[0, 2], [2, 0]]
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# Test passing a list of Python implementations without Compose wrapper
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assert test_config([1, 0],
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[transforms.Compose([transforms.OneHot(2)]), (lambda x: x + x)]) \
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== [[0, 2], [2, 0]]
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assert test_config([1, 0], [transforms.OneHot(2), (lambda x: x + x)]) == [[0, 2], [2, 0]]
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# Test a non callable function
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with pytest.raises(TypeError) as error_info:
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transforms.Compose([1])
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assert "op_list[0] is neither a c_transform op (TensorOperation) nor a callable pyfunc." in str(error_info.value)
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# Test empty Python implementation list
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with pytest.raises(ValueError) as error_info:
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test_config([1, 0], transforms.Compose([]))
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assert "op_list can not be empty." in str(error_info.value)
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# Pass in extra brackets
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with pytest.raises(TypeError) as error_info:
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transforms.Compose([(lambda x: x + x)])()
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assert "Compose was called without an image. Fix invocation (avoid it being invoked as Compose([...])())." in str(
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error_info.value)
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# Restore configuration
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ds.config.set_seed(original_seed)
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def test_lambdas():
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"""
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Test Multi Column Python Compose Op
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"""
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original_seed = config_get_set_seed(0)
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def test_config(arr, input_columns, output_cols, op_list):
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data = ds.NumpySlicesDataset(arr, column_names=input_columns, shuffle=False)
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data = data.map(operations=op_list, input_columns=input_columns, output_columns=output_cols,
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column_order=output_cols)
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res = []
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for i in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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for col_name in output_cols:
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res.append(i[col_name].tolist())
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return res
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arr = ([[1]], [[3]])
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assert test_config(arr, ["col0", "col1"], ["a"], transforms.Compose([(lambda x, y: x)])) == [[1]]
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assert test_config(arr, ["col0", "col1"], ["a"], transforms.Compose([lambda x, y: x, lambda x: x])) == [[1]]
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assert test_config(arr, ["col0", "col1"], ["a", "b"],
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transforms.Compose([lambda x, y: x, lambda x: (x, x * 2)])) == \
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[[1], [2]]
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assert test_config(arr, ["col0", "col1"], ["a", "b"],
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[lambda x, y: (x, x + y), lambda x, y: (x, y * 2)]) == [[1], [8]]
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# Restore configuration
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ds.config.set_seed(original_seed)
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def test_c_py_compose_transforms_module():
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"""
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Test combining Python and C++ transforms
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"""
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original_seed = config_get_set_seed(0)
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def test_config(arr, input_columns, output_cols, op_list):
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data = ds.NumpySlicesDataset(arr, column_names=input_columns, shuffle=False)
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data = data.map(operations=op_list, input_columns=input_columns, output_columns=output_cols,
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column_order=output_cols)
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res = []
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for i in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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for col_name in output_cols:
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res.append(i[col_name].tolist())
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return res
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arr = [1, 0]
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assert test_config(arr, ["cols"], ["cols"],
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[transforms.OneHot(2), transforms.Mask(transforms.Relational.EQ, 1)]) == \
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[[False, True],
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[True, False]]
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assert test_config(arr, ["cols"], ["cols"],
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[transforms.OneHot(2), (lambda x: x + x), transforms.Fill(1)]) \
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== [[1, 1], [1, 1]]
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assert test_config(arr, ["cols"], ["cols"],
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[transforms.OneHot(2), (lambda x: x + x), transforms.Fill(1), (lambda x: x + x)]) \
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== [[2, 2], [2, 2]]
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assert test_config([[1, 3]], ["cols"], ["cols"],
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[transforms.PadEnd([3], -1), (lambda x: x + x)]) \
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== [[2, 6, -2]]
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arr = ([[1]], [[3]])
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assert test_config(arr, ["col0", "col1"], ["a"], [(lambda x, y: x + y), transforms.PadEnd([2], -1)]) == [[4, -1]]
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# Restore configuration
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ds.config.set_seed(original_seed)
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def test_c_py_compose_vision_module(plot=False, run_golden=True):
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"""
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Test combining Python and C++ vision transforms
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"""
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original_seed = config_get_set_seed(10)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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def test_config(plot, file_name, op_list):
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data_dir = "../data/dataset/testImageNetData/train/"
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data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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data1 = data1.map(operations=op_list, input_columns=["image"])
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data2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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data2 = data2.map(operations=vision.Decode(), input_columns=["image"])
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original_images = []
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transformed_images = []
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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transformed_images.append(item["image"])
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for item in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
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original_images.append(item["image"])
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if run_golden:
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# Compare with expected md5 from images
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save_and_check_md5(data1, file_name, generate_golden=GENERATE_GOLDEN)
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if plot:
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visualize_list(original_images, transformed_images)
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test_config(op_list=[vision.Decode(),
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vision.ToPIL(),
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vision.Resize((224, 224)),
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np.array],
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plot=plot, file_name="compose_c_py_1.npz")
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test_config(op_list=[vision.Decode(),
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vision.Resize((224, 244)),
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vision.ToPIL(),
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np.array,
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vision.Resize((24, 24))],
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plot=plot, file_name="compose_c_py_2.npz")
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test_config(op_list=[vision.Decode(True),
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vision.Resize((224, 224)),
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np.array,
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vision.RandomColor()],
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plot=plot, file_name="compose_c_py_3.npz")
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers((original_num_parallel_workers))
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def test_vision_with_transforms():
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"""
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Feature: Data transforms and vision ops
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Description: Test (Python implementation) vision operations with C++ implementation transforms operations
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Expectation: Valid input succeeds. Invalid input fails.
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"""
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original_seed = config_get_set_seed(0)
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def test_config(op_list):
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data_dir = "../data/dataset/testImageNetData/train/"
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data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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data1 = data1.map(operations=op_list, input_columns=["image"])
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transformed_images = []
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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transformed_images.append(item["image"])
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return transformed_images
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# Test with Mask Op
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output_arr = test_config([vision.Decode(True),
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vision.CenterCrop((2)), np.array,
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transforms.Mask(transforms.Relational.GE, 100)])
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exp_arr = [np.array([[[True, False, False],
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[True, False, False]],
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[[True, False, False],
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[True, False, False]]]),
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np.array([[[True, False, False],
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[True, False, False]],
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[[True, False, False],
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[True, False, False]]])]
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for exp_a, output in zip(exp_arr, output_arr):
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np.testing.assert_array_equal(exp_a, output)
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# Test with Fill Op
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output_arr = test_config([vision.Decode(True),
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vision.CenterCrop((4)), np.array,
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transforms.Fill(10)])
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exp_arr = [np.ones((4, 4, 3)) * 10] * 2
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for exp_a, output in zip(exp_arr, output_arr):
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np.testing.assert_array_equal(exp_a, output)
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# Test with Concatenate Op, which will raise an error since ConcatenateOp only supports rank 1 tensors.
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with pytest.raises(RuntimeError) as error_info:
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test_config([vision.Decode(True),
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vision.CenterCrop((2)), np.array,
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transforms.Concatenate(0)])
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assert "only 1D input supported" in str(error_info.value)
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# Restore configuration
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ds.config.set_seed(original_seed)
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def test_compose_with_custom_function():
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"""
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Test Python Compose with custom function
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"""
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def custom_function(x):
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return (x, x * x)
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# First dataset
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op_list = [
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lambda x: x * 3,
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custom_function,
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# convert two column output to one
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lambda *images: np.stack(images)
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]
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data = ds.NumpySlicesDataset([[1, 2]], column_names=["col0"], shuffle=False)
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data = data.map(input_columns=["col0"], operations=op_list)
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#
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res = []
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for i in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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res.append(i["col0"].tolist())
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assert res == [[[3, 6], [9, 36]]]
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if __name__ == "__main__":
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test_compose()
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test_lambdas()
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test_c_py_compose_transforms_module()
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test_c_py_compose_vision_module(plot=True)
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test_vision_with_transforms()
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test_compose_with_custom_function()
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