You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
143 lines
4.5 KiB
143 lines
4.5 KiB
# Copyright 2019 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 mindspore.dataset as ds
|
|
|
|
DATA_DIR = ["./data.data"]
|
|
SCHEMA_DIR = "./schema.json"
|
|
|
|
|
|
def test_case_0():
|
|
"""
|
|
Test PyFunc
|
|
"""
|
|
print("Test 1-1 PyFunc : lambda x : x + x")
|
|
|
|
col = "col0"
|
|
|
|
# apply dataset operations
|
|
ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
|
|
|
|
ds1 = ds1.map(operations=(lambda x: x + x), input_columns=col, output_columns="out")
|
|
|
|
print("************** Output Tensor *****************")
|
|
for data in ds1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
print(data["out"])
|
|
print("************** Output Tensor *****************")
|
|
|
|
|
|
def test_case_1():
|
|
"""
|
|
Test PyFunc
|
|
"""
|
|
print("Test 1-n PyFunc : (lambda x : (x , x + x)) ")
|
|
|
|
col = "col0"
|
|
|
|
# apply dataset operations
|
|
ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
|
|
|
|
ds1 = ds1.map(operations=(lambda x: (x, x + x)), input_columns=col, output_columns=["out0", "out1"])
|
|
|
|
print("************** Output Tensor *****************")
|
|
for data in ds1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
print("out0")
|
|
print(data["out0"])
|
|
print("out1")
|
|
print(data["out1"])
|
|
print("************** Output Tensor *****************")
|
|
|
|
|
|
def test_case_2():
|
|
"""
|
|
Test PyFunc
|
|
"""
|
|
print("Test n-1 PyFunc : (lambda x, y : x + y) ")
|
|
|
|
col = ["col0", "col1"]
|
|
|
|
# apply dataset operations
|
|
ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
|
|
|
|
ds1 = ds1.map(operations=(lambda x, y: x + y), input_columns=col, output_columns="out")
|
|
|
|
print("************** Output Tensor *****************")
|
|
for data in ds1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
print(data["out"])
|
|
|
|
print("************** Output Tensor *****************")
|
|
|
|
|
|
def test_case_3():
|
|
"""
|
|
Test PyFunc
|
|
"""
|
|
print("Test n-m PyFunc : (lambda x, y : (x , x + 1, x + y)")
|
|
|
|
col = ["col0", "col1"]
|
|
|
|
# apply dataset operations
|
|
ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
|
|
|
|
ds1 = ds1.map(operations=(lambda x, y: (x, x + y, x + x + y)), input_columns=col,
|
|
output_columns=["out0", "out1", "out2"])
|
|
|
|
print("************** Output Tensor *****************")
|
|
for data in ds1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
print("out0")
|
|
print(data["out0"])
|
|
print("out1")
|
|
print(data["out1"])
|
|
print("out2")
|
|
print(data["out2"])
|
|
print("************** Output Tensor *****************")
|
|
|
|
|
|
def test_case_4():
|
|
"""
|
|
Test PyFunc
|
|
"""
|
|
print("Test Parallel n-m PyFunc : (lambda x, y : (x , x + 1, x + y)")
|
|
|
|
col = ["col0", "col1"]
|
|
|
|
# apply dataset operations
|
|
ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
|
|
|
|
ds1 = ds1.map(operations=(lambda x, y: (x, x + y, x + x + y)), input_columns=col,
|
|
output_columns=["out0", "out1", "out2"], num_parallel_workers=4)
|
|
|
|
print("************** Output Tensor *****************")
|
|
for data in ds1.create_dict_iterator(num_epochs=1, output_numpy=True): # each data is a dictionary
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
print("out0")
|
|
print(data["out0"])
|
|
print("out1")
|
|
print(data["out1"])
|
|
print("out2")
|
|
print(data["out2"])
|
|
print("************** Output Tensor *****************")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_case_0()
|
|
# test_case_1()
|
|
# test_case_2()
|
|
# test_case_3()
|
|
# test_case_4()
|