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Remove batch_map multiprocess test case

tags/v1.1.0
hesham 5 years ago
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      tests/ut/python/dataset/test_var_batch_map_multi.py

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tests/ut/python/dataset/test_var_batch_map_multi.py View File

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# 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 os
import time
import numpy as np

import mindspore.dataset as ds
from mindspore import log as logger

from mindspore.dataset.transforms.py_transforms import Compose
import mindspore.dataset.vision.py_transforms as py_vision


def test_batch_corner_cases():
def gen(num):
for i in range(num):
yield (np.array([i]),)

def test_repeat_batch(gen_num, repeats, batch_size, drop, res):
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).repeat(repeats).batch(batch_size, drop)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])

def test_batch_repeat(gen_num, repeats, batch_size, drop, res):
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).batch(batch_size, drop).repeat(repeats)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])

tst1, tst2, tst3, tst4 = [], [], [], []
# case 1 & 2, where batch_size is greater than the entire epoch, with drop equals to both val
test_repeat_batch(gen_num=2, repeats=4, batch_size=7, drop=False, res=tst1)
np.testing.assert_array_equal(np.array([[0], [1], [0], [1], [0], [1], [0]]), tst1[0], "\nATTENTION BATCH FAILED\n")
np.testing.assert_array_equal(np.array([[1]]), tst1[1], "\nATTENTION TEST BATCH FAILED\n")
assert len(tst1) == 2, "\nATTENTION TEST BATCH FAILED\n"
test_repeat_batch(gen_num=2, repeats=4, batch_size=5, drop=True, res=tst2)
np.testing.assert_array_equal(np.array([[0], [1], [0], [1], [0]]), tst2[0], "\nATTENTION BATCH FAILED\n")
assert len(tst2) == 1, "\nATTENTION TEST BATCH FAILED\n"
# case 3 & 4, batch before repeat with different drop
test_batch_repeat(gen_num=5, repeats=2, batch_size=4, drop=True, res=tst3)
np.testing.assert_array_equal(np.array([[0], [1], [2], [3]]), tst3[0], "\nATTENTION BATCH FAILED\n")
np.testing.assert_array_equal(tst3[0], tst3[1], "\nATTENTION BATCH FAILED\n")
assert len(tst3) == 2, "\nATTENTION BATCH FAILED\n"
test_batch_repeat(gen_num=5, repeats=2, batch_size=4, drop=False, res=tst4)
np.testing.assert_array_equal(np.array([[0], [1], [2], [3]]), tst4[0], "\nATTENTION BATCH FAILED\n")
np.testing.assert_array_equal(tst4[0], tst4[2], "\nATTENTION BATCH FAILED\n")
np.testing.assert_array_equal(tst4[1], np.array([[4]]), "\nATTENTION BATCH FAILED\n")
np.testing.assert_array_equal(tst4[1], tst4[3], "\nATTENTION BATCH FAILED\n")
assert len(tst4) == 4, "\nATTENTION BATCH FAILED\n"


# each sub-test in this function is tested twice with exact parameter except that the second test passes each row
# to a pyfunc which makes a deep copy of the row
def test_variable_size_batch():
def check_res(arr1, arr2):
for ind, _ in enumerate(arr1):
if not np.array_equal(arr1[ind], np.array(arr2[ind])):
return False
return len(arr1) == len(arr2)

def gen(num):
for i in range(num):
yield (np.array([i]),)

def add_one_by_batch_num(batchInfo):
return batchInfo.get_batch_num() + 1

def add_one_by_epoch(batchInfo):
return batchInfo.get_epoch_num() + 1

def simple_copy(colList, batchInfo):
_ = batchInfo
return ([np.copy(arr) for arr in colList],)

def test_repeat_batch(gen_num, r, drop, func, res):
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).repeat(r).batch(batch_size=func,
drop_remainder=drop)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])

# same as test_repeat_batch except each row is passed through via a map which makes a copy of each element
def test_repeat_batch_with_copy_map(gen_num, r, drop, func):
res = []
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).repeat(r) \
.batch(batch_size=func, drop_remainder=drop, input_columns=["num"], per_batch_map=simple_copy,
python_multiprocessing=True)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])
return res

def test_batch_repeat(gen_num, r, drop, func, res):
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).batch(batch_size=func, drop_remainder=drop).repeat(
r)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])

# same as test_batch_repeat except each row is passed through via a map which makes a copy of each element
def test_batch_repeat_with_copy_map(gen_num, r, drop, func):
res = []
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]) \
.batch(batch_size=func, drop_remainder=drop, input_columns=["num"], per_batch_map=simple_copy,
python_multiprocessing=True).repeat(r)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])
return res

tst1, tst2, tst3, tst4, tst5, tst6, tst7 = [], [], [], [], [], [], []

# no repeat, simple var size, based on batch_num
test_repeat_batch(7, 1, True, add_one_by_batch_num, tst1)
assert check_res(tst1, [[[0]], [[1], [2]], [[3], [4], [5]]]), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst1, test_repeat_batch_with_copy_map(7, 1, True, add_one_by_batch_num)), "\nMAP FAILED\n"
test_repeat_batch(9, 1, False, add_one_by_batch_num, tst2)
assert check_res(tst2, [[[0]], [[1], [2]], [[3], [4], [5]], [[6], [7], [8]]]), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst2, test_repeat_batch_with_copy_map(9, 1, False, add_one_by_batch_num)), "\nMAP FAILED\n"
# batch after repeat, cross epoch batch
test_repeat_batch(7, 2, False, add_one_by_batch_num, tst3)
assert check_res(tst3, [[[0]], [[1], [2]], [[3], [4], [5]], [[6], [0], [1], [2]],
[[3], [4], [5], [6]]]), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst3, test_repeat_batch_with_copy_map(7, 2, False, add_one_by_batch_num)), "\nMAP FAILED\n"
# repeat after batch, no cross epoch batch, remainder dropped
test_batch_repeat(9, 7, True, add_one_by_batch_num, tst4)
assert check_res(tst4, [[[0]], [[1], [2]], [[3], [4], [5]]] * 7), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst4, test_batch_repeat_with_copy_map(9, 7, True, add_one_by_batch_num)), "\nAMAP FAILED\n"
# repeat after batch, no cross epoch batch, remainder kept
test_batch_repeat(9, 3, False, add_one_by_batch_num, tst5)
assert check_res(tst5, [[[0]], [[1], [2]], [[3], [4], [5]], [[6], [7], [8]]] * 3), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst5, test_batch_repeat_with_copy_map(9, 3, False, add_one_by_batch_num)), "\nMAP FAILED\n"
# batch_size based on epoch number, drop
test_batch_repeat(4, 4, True, add_one_by_epoch, tst6)
assert check_res(tst6, [[[0]], [[1]], [[2]], [[3]], [[0], [1]], [[2], [3]], [[0], [1], [2]],
[[0], [1], [2], [3]]]), "\nATTENTION VAR BATCH FAILED\n"
assert check_res(tst6, test_batch_repeat_with_copy_map(4, 4, True, add_one_by_epoch)), "\nMAP FAILED\n"
# batch_size based on epoch number, no drop
test_batch_repeat(4, 4, False, add_one_by_epoch, tst7)
assert check_res(tst7, [[[0]], [[1]], [[2]], [[3]], [[0], [1]], [[2], [3]], [[0], [1], [2]], [[3]],
[[0], [1], [2], [3]]]), "\nATTENTION VAR BATCH FAILED\n" + str(tst7)
assert check_res(tst7, test_batch_repeat_with_copy_map(4, 4, False, add_one_by_epoch)), "\nMAP FAILED\n"


def test_basic_batch_map():
def check_res(arr1, arr2):
for ind, _ in enumerate(arr1):
if not np.array_equal(arr1[ind], np.array(arr2[ind])):
return False
return len(arr1) == len(arr2)

def gen(num):
for i in range(num):
yield (np.array([i]),)

def invert_sign_per_epoch(colList, batchInfo):
return ([np.copy(((-1) ** batchInfo.get_epoch_num()) * arr) for arr in colList],)

def invert_sign_per_batch(colList, batchInfo):
return ([np.copy(((-1) ** batchInfo.get_batch_num()) * arr) for arr in colList],)

def batch_map_config(num, r, batch_size, func, res):
data1 = ds.GeneratorDataset((lambda: gen(num)), ["num"]) \
.batch(batch_size=batch_size, input_columns=["num"], per_batch_map=func,
python_multiprocessing=True).repeat(r)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(item["num"])

tst1, tst2, = [], []
batch_map_config(4, 2, 2, invert_sign_per_epoch, tst1)
assert check_res(tst1, [[[0], [1]], [[2], [3]], [[0], [-1]], [[-2], [-3]]]), "\nATTENTION MAP BATCH FAILED\n" + str(
tst1)
# each batch, the sign of a row is changed, test map is corrected performed according to its batch_num
batch_map_config(4, 2, 2, invert_sign_per_batch, tst2)
assert check_res(tst2,
[[[0], [1]], [[-2], [-3]], [[0], [1]], [[-2], [-3]]]), "\nATTENTION MAP BATCH FAILED\n" + str(tst2)


def test_batch_multi_col_map():
def check_res(arr1, arr2):
for ind, _ in enumerate(arr1):
if not np.array_equal(arr1[ind], np.array(arr2[ind])):
return False
return len(arr1) == len(arr2)

def gen(num):
for i in range(num):
yield (np.array([i]), np.array([i ** 2]))

def col1_col2_add_num(col1, col2, batchInfo):
_ = batchInfo
return ([[np.copy(arr + 100) for arr in col1],
[np.copy(arr + 300) for arr in col2]])

def invert_sign_per_batch(colList, batchInfo):
return ([np.copy(((-1) ** batchInfo.get_batch_num()) * arr) for arr in colList],)

def invert_sign_per_batch_multi_col(col1, col2, batchInfo):
return ([np.copy(((-1) ** batchInfo.get_batch_num()) * arr) for arr in col1],
[np.copy(((-1) ** batchInfo.get_batch_num()) * arr) for arr in col2])

def batch_map_config(num, r, batch_size, func, col_names, res):
data1 = ds.GeneratorDataset((lambda: gen(num)), ["num", "num_square"]) \
.batch(batch_size=batch_size, input_columns=col_names, per_batch_map=func,
python_multiprocessing=True).repeat(r)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(np.array([item["num"], item["num_square"]]))

tst1, tst2, tst3, tst4 = [], [], [], []
batch_map_config(4, 2, 2, invert_sign_per_batch, ["num_square"], tst1)
assert check_res(tst1, [[[[0], [1]], [[0], [1]]], [[[2], [3]], [[-4], [-9]]], [[[0], [1]], [[0], [1]]],
[[[2], [3]], [[-4], [-9]]]]), "\nATTENTION MAP BATCH FAILED\n" + str(tst1)

batch_map_config(4, 2, 2, invert_sign_per_batch_multi_col, ["num", "num_square"], tst2)
assert check_res(tst2, [[[[0], [1]], [[0], [1]]], [[[-2], [-3]], [[-4], [-9]]], [[[0], [1]], [[0], [1]]],
[[[-2], [-3]], [[-4], [-9]]]]), "\nATTENTION MAP BATCH FAILED\n" + str(tst2)

# the two tests below verify the order of the map.
# num_square column adds 100, num column adds 300.
batch_map_config(4, 3, 2, col1_col2_add_num, ["num_square", "num"], tst3)
assert check_res(tst3, [[[[300], [301]], [[100], [101]]],
[[[302], [303]], [[104], [109]]]] * 3), "\nATTENTION MAP BATCH FAILED\n" + str(tst3)
# num column adds 100, num_square column adds 300.
batch_map_config(4, 3, 2, col1_col2_add_num, ["num", "num_square"], tst4)
assert check_res(tst4, [[[[100], [101]], [[300], [301]]],
[[[102], [103]], [[304], [309]]]] * 3), "\nATTENTION MAP BATCH FAILED\n" + str(tst4)


def test_var_batch_multi_col_map():
def check_res(arr1, arr2):
for ind, _ in enumerate(arr1):
if not np.array_equal(arr1[ind], np.array(arr2[ind])):
return False
return len(arr1) == len(arr2)

# gen 3 columns
# first column: 0, 3, 6, 9 ... ...
# second column:1, 4, 7, 10 ... ...
# third column: 2, 5, 8, 11 ... ...
def gen_3_cols(num):
for i in range(num):
yield (np.array([i * 3]), np.array([i * 3 + 1]), np.array([i * 3 + 2]))

# first epoch batch_size per batch: 1, 2 ,3 ... ...
# second epoch batch_size per batch: 2, 4, 6 ... ...
# third epoch batch_size per batch: 3, 6 ,9 ... ...
def batch_func(batchInfo):
return (batchInfo.get_batch_num() + 1) * (batchInfo.get_epoch_num() + 1)

# multiply first col by batch_num, multiply second col by -batch_num
def map_func(col1, col2, batchInfo):
return ([np.copy((1 + batchInfo.get_batch_num()) * arr) for arr in col1],
[np.copy(-(1 + batchInfo.get_batch_num()) * arr) for arr in col2])

def batch_map_config(num, r, fbatch, fmap, col_names, res):
data1 = ds.GeneratorDataset((lambda: gen_3_cols(num)), ["col1", "col2", "col3"]) \
.batch(batch_size=fbatch, input_columns=col_names, per_batch_map=fmap, python_multiprocessing=True) \
.repeat(r)
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(np.array([item["col1"], item["col2"], item["col3"]]))

tst1 = []
tst1_res = [[[[0]], [[-1]], [[2]]], [[[6], [12]], [[-8], [-14]], [[5], [8]]],
[[[27], [36], [45]], [[-30], [-39], [-48]], [[11], [14], [17]]],
[[[72], [84], [96], [108]], [[-76], [-88], [-100], [-112]], [[20], [23], [26], [29]]]]
batch_map_config(10, 1, batch_func, map_func, ["col1", "col2"], tst1)
assert check_res(tst1, tst1_res), "test_var_batch_multi_col_map FAILED"


def test_var_batch_var_resize():
# fake resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
def np_psedo_resize(col, batchInfo):
s = (batchInfo.get_batch_num() + 1) ** 2
return ([np.copy(c[0:s, 0:s, :]) for c in col],)

def add_one(batchInfo):
return batchInfo.get_batch_num() + 1

data1 = ds.ImageFolderDataset("../data/dataset/testPK/data/", num_parallel_workers=4, decode=True)
data1 = data1.batch(batch_size=add_one, drop_remainder=True, input_columns=["image"], per_batch_map=np_psedo_resize,
python_multiprocessing=True)
# i-th batch has shape [i, i^2, i^2, 3]
i = 1
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
assert item["image"].shape == (i, i ** 2, i ** 2, 3), "\ntest_var_batch_var_resize FAILED\n"
i += 1


def test_exception():
def gen(num):
for i in range(num):
yield (np.array([i]),)

def bad_batch_size(batchInfo):
raise StopIteration
# return batchInfo.get_batch_num()

def bad_map_func(col, batchInfo):
raise StopIteration
# return (col,)

data1 = ds.GeneratorDataset((lambda: gen(100)), ["num"]).batch(bad_batch_size)
try:
for _ in data1.create_dict_iterator(num_epochs=1):
pass
assert False
except RuntimeError:
pass

data2 = ds.GeneratorDataset((lambda: gen(100)), ["num"]).batch(4, input_columns=["num"], per_batch_map=bad_map_func,
python_multiprocessing=True)
try:
for _ in data2.create_dict_iterator(num_epochs=1):
pass
assert False
except RuntimeError:
pass


def test_multi_col_map():
def gen_2_cols(num):
for i in range(1, 1 + num):
yield (np.array([i]), np.array([i ** 2]))

def split_col(col, batchInfo):
return ([np.copy(arr) for arr in col], [np.copy(-arr) for arr in col])

def merge_col(col1, col2, batchInfo):
merged = []
for k, v in enumerate(col1):
merged.append(np.array(v + col2[k]))
return (merged,)

def swap_col(col1, col2, batchInfo):
return ([np.copy(a) for a in col2], [np.copy(b) for b in col1])

def batch_map_config(num, s, f, in_nms, out_nms, col_order=None):
try:
dst = ds.GeneratorDataset((lambda: gen_2_cols(num)), ["col1", "col2"])
dst = dst.batch(batch_size=s, input_columns=in_nms, output_columns=out_nms, per_batch_map=f,
column_order=col_order, python_multiprocessing=True)
res = []
for row in dst.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(row)
return res
except (ValueError, RuntimeError, TypeError) as e:
return str(e)

# split 1 col into 2 cols
res = batch_map_config(2, 2, split_col, ["col2"], ["col_x", "col_y"])[0]
assert np.array_equal(res["col1"], [[1], [2]])
assert np.array_equal(res["col_x"], [[1], [4]]) and np.array_equal(res["col_y"], [[-1], [-4]])

# merge 2 cols into 1 col
res = batch_map_config(4, 4, merge_col, ["col1", "col2"], ["merged"])[0]
assert np.array_equal(res["merged"], [[2], [6], [12], [20]])

# swap once
res = batch_map_config(3, 3, swap_col, ["col1", "col2"], ["col1", "col2"])[0]
assert np.array_equal(res["col1"], [[1], [4], [9]]) and np.array_equal(res["col2"], [[1], [2], [3]])

# swap twice
res = batch_map_config(3, 3, swap_col, ["col1", "col2"], ["col2", "col1"])[0]
assert np.array_equal(res["col2"], [[1], [4], [9]]) and np.array_equal(res["col1"], [[1], [2], [3]])

# test project after map
res = batch_map_config(2, 2, split_col, ["col2"], ["col_x", "col_y"], ["col_x", "col_y", "col1"])[0]
assert list(res.keys()) == ["col_x", "col_y", "col1"]

# test the insertion order is maintained
res = batch_map_config(2, 2, split_col, ["col2"], ["col_x", "col_y"], ["col1", "col_x", "col_y"])[0]
assert list(res.keys()) == ["col1", "col_x", "col_y"]

# test exceptions
assert "output_columns with value 233 is not of type" in batch_map_config(2, 2, split_col, ["col2"], 233)
assert "column_order with value 233 is not of type" in batch_map_config(2, 2, split_col, ["col2"], ["col1"], 233)
assert "output_columns is NOT set correctly" in batch_map_config(2, 2, split_col, ["col2"], ["col1"])
assert "Incorrect number of columns" in batch_map_config(2, 2, split_col, ["col2"], ["col3", "col4", "col5"])
assert "col-1 doesn't exist" in batch_map_config(2, 2, split_col, ["col-1"], ["col_x", "col_y"])


def test_exceptions_2():
def gen(num):
for i in range(num):
yield (np.array([i]),)

def simple_copy(colList, batchInfo):
return ([np.copy(arr) for arr in colList],)

def test_wrong_col_name(gen_num, batch_size):
data1 = ds.GeneratorDataset((lambda: gen(gen_num)), ["num"]).batch(batch_size, input_columns=["num1"],
per_batch_map=simple_copy,
python_multiprocessing=True)
try:
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
pass
return "success"
except RuntimeError as e:
return str(e)

# test exception where column name is incorrect
assert "error. col:num1 doesn't exist" in test_wrong_col_name(4, 2)


IMAGENET_RAWDATA_DIR = "../data/dataset/testImageNetData2/train"


def skip_test_performance():
def trans(images, batchInfo):
start_time = time.time()
print(os.getppid(), batchInfo.get_batch_num(), time.strftime("%H:%M:%S", time.localtime()))
for _ in range(50):
op = Compose([py_vision.Decode(), py_vision.Resize(20), py_vision.ToTensor()])
images2 = [op(img) for img in images]
end_time = time.time()
print(os.getppid(), time.strftime("%H:%M:%S", time.localtime()), end_time - start_time)
return (images2,)

def trans2(img):
start_time = time.time()
img2 = None
print(os.getppid(), time.strftime("%H:%M:%S", time.localtime()))
for _ in range(50):
op = Compose([py_vision.Decode(), py_vision.Resize(20), py_vision.ToTensor()])
img2 = op(img)
end_time = time.time()
print(os.getppid(), time.strftime("%H:%M:%S", time.localtime()), end_time - start_time)
return img2

print(os.getppid())
data = ds.ImageFolderDataset(IMAGENET_RAWDATA_DIR, shuffle=False).repeat(10)
print(data.get_dataset_size())
data = data.batch(1, per_batch_map=trans, input_columns=["image"], num_parallel_workers=12,
python_multiprocessing=True)
data = data.map(operations=trans2, num_parallel_workers=8, python_multiprocessing=False)
start = time.time()
for _ in data:
pass
end = time.time()

print("Taken= ", end - start)


if __name__ == '__main__':
logger.info("Running test_var_batch_map.py test_batch_corner_cases() function")
test_batch_corner_cases()

logger.info("Running test_var_batch_map.py test_variable_size_batch() function")
test_variable_size_batch()

logger.info("Running test_var_batch_map.py test_basic_batch_map() function")
test_basic_batch_map()

logger.info("Running test_var_batch_map.py test_batch_multi_col_map() function")
test_batch_multi_col_map()

logger.info("Running test_var_batch_map.py tesgit t_var_batch_multi_col_map() function")
test_var_batch_multi_col_map()

logger.info("Running test_var_batch_map.py test_var_batch_var_resize() function")
test_var_batch_var_resize()

logger.info("Running test_var_batch_map.py test_exception() function")
test_exception()

logger.info("Running test_var_batch_map.py test_multi_col_map() function")
test_multi_col_map()

logger.info("Running test_var_batch_map.py test_exceptions_2() function")
test_exceptions_2()

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