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optim pylint

This commit is contained in:
jinyaohui 2020-08-29 10:34:52 +08:00
parent 135cfc6adf
commit a9972a7def
19 changed files with 113 additions and 76 deletions
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1
tests/st/auto_parallel/test_expand_loss.py
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@ -13,7 +13,6 @@
# limitations under the License.
# ============================================================================
import os
import pytest
def test_expand_loss():

1
tests/st/auto_parallel/test_resnet50_expand_loss.py
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@ -13,7 +13,6 @@
# limitations under the License.
# ============================================================================
import os
import pytest
def test_expand_loss():

8
tests/st/model_zoo_tests/wide_and_deep/train_and_test_multinpu_ci_data_parallel.py
View File

@ -14,7 +14,6 @@
# ============================================================================
"""train_multinpu."""
import os
import sys
import numpy as np
@ -35,7 +34,6 @@ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirr
init()
def get_WideDeep_net(config):
WideDeep_net = WideDeepModel(config)
loss_net = NetWithLossClass(WideDeep_net, config)
@ -48,6 +46,7 @@ class ModelBuilder():
"""
ModelBuilder
"""
def __init__(self):
pass
@ -101,14 +100,13 @@ def test_train_eval():
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
expect_out0 = [0.792634,0.799862,0.803324]
expect_out6 = [0.796580,0.803908,0.807262]
expect_out0 = [0.792634, 0.799862, 0.803324]
expect_out6 = [0.796580, 0.803908, 0.807262]
if get_rank() == 0:
assert np.allclose(eval_callback.eval_values, expect_out0)
if get_rank() == 6:
assert np.allclose(eval_callback.eval_values, expect_out6)
if __name__ == "__main__":
test_train_eval()

9
tests/st/networks/models/bert/test_bert_graph_kernel.py
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@ -16,8 +16,10 @@
"""train bert network without lossscale"""
import os
import pytest
import numpy as np
from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
from src.bert_model import BertConfig
import mindspore.common.dtype as mstype
import mindspore.dataset.engine.datasets as de
@ -25,14 +27,11 @@ import mindspore.dataset.transforms.c_transforms as C
from mindspore import context
from mindspore import log as logger
from mindspore.common.tensor import Tensor
from mindspore.nn import learning_rate_schedule as lr_schedules
from mindspore.nn.optim import Lamb
from mindspore.train.callback import Callback
from mindspore.train.loss_scale_manager import DynamicLossScaleManager
from mindspore.train.model import Model
from mindspore.nn import learning_rate_schedule as lr_schedules
from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
from src.bert_model import BertConfig
DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"

2
tests/st/networks/test_gpu_alexnet.py
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@ -23,10 +23,8 @@ import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

1
tests/st/networks/test_gpu_lstm.py
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@ -21,7 +21,6 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.nn import Dense
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P

1
tests/st/ops/ascend/test_aicpu_ops/test_poisson.py
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@ -18,7 +18,6 @@ import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

3
tests/st/ops/cpu/test_slice_grad_op.py
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@ -54,6 +54,7 @@ def test_slice_grad():
print("output:\n", output)
assert (output.asnumpy() == expect).all()
class SliceGrad2(nn.Cell):
def __init__(self):
super(SliceGrad2, self).__init__()
@ -62,6 +63,7 @@ class SliceGrad2(nn.Cell):
def construct(self, dy, x):
return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@ -75,6 +77,7 @@ def test_slice_grad2():
[[0., 0.], [8., 9.], [10., 11.]]]
assert (output.asnumpy() == expect).all()
if __name__ == '__main__':
test_slice_grad()
test_slice_grad2()

6
tests/st/ops/cpu/test_slice_op.py
View File

@ -21,10 +21,10 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops.operations import _grad_ops as G
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
class Slice(nn.Cell):
def __init__(self):
super(Slice, self).__init__()
@ -33,6 +33,7 @@ class Slice(nn.Cell):
def construct(self, x):
return self.slice(x, (0, 1, 0), (2, 1, 3))
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@ -47,6 +48,7 @@ def test_slice():
print("output:\n", output)
assert (output.asnumpy() == expect).all()
class Slice2(nn.Cell):
def __init__(self):
super(Slice2, self).__init__()
@ -55,6 +57,7 @@ class Slice2(nn.Cell):
def construct(self, x):
return self.slice(x, (1, 0, 0), (1, 2, 3))
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@ -68,6 +71,7 @@ def test_slice2():
print("output:\n", output)
assert (output.asnumpy() == expect).all()
if __name__ == '__main__':
test_slice()
test_slice2()

2
tests/st/ops/custom_ops_tbe/cus_add3.py
View File

@ -14,9 +14,11 @@
# ============================================================================
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
# sum = input1 + input2 + const_bias
class CusAdd3(PrimitiveWithInfer):
"""Custom add3 definition"""
@prim_attr_register
def __init__(self, const_bias=0.0):
self.init_prim_io_names(inputs=['input1', 'input2'], outputs=['sum3'])

2
tests/st/summary/test_cpu_summary.py
View File

@ -24,8 +24,8 @@ import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
from tests.summary_utils import SummaryReader
from mindspore.train.summary.summary_record import SummaryRecord
from tests.summary_utils import SummaryReader
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')

41
tests/ut/python/dataset/test_minddataset_padded.py
View File

@ -16,17 +16,15 @@
This is the test module for mindrecord
"""
import collections
import json
import numpy as np
import os
import pytest
import re
import string
import numpy as np
import pytest
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as vision
from mindspore import log as logger
from mindspore.dataset.transforms.vision import Inter
from mindspore.mindrecord import FileWriter
FILES_NUM = 4
@ -110,6 +108,7 @@ def add_and_remove_nlp_file():
os.remove("{}".format(x))
os.remove("{}.db".format(x))
def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
"""tutorial for cv minderdataset."""
columns_list = ["label", "file_name", "data"]
@ -177,6 +176,7 @@ def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
partitions(5, 5, 3)
partitions(9, 8, 2)
def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_file):
"""tutorial for cv minddataset."""
columns_list = ["data", "file_name", "label"]
@ -248,6 +248,7 @@ def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_f
partitions(5, 5, 3)
partitions(9, 8, 2)
def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv_file):
"""tutorial for cv minddataset."""
columns_list = ["data", "file_name", "label"]
@ -273,6 +274,7 @@ def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv
with pytest.raises(RuntimeError):
partitions(4, 1)
def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"]
@ -291,8 +293,10 @@ def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_a
num_padded=num_padded)
with pytest.raises(RuntimeError):
data_set.get_dataset_size() == 3
partitions(4, 1)
def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"]
@ -314,9 +318,11 @@ def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_re
logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample cannot match columns_list."):
partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_cv_file):
data = get_data(CV_DIR_NAME)
padded_sample = data[0]
@ -336,9 +342,11 @@ def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_c
logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample is specified and requires columns_list as well."):
partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"]
data = get_data(CV_DIR_NAME)
@ -357,9 +365,11 @@ def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv
logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample is specified and requires num_padded as well."):
partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"]
data = get_data(CV_DIR_NAME)
@ -378,18 +388,18 @@ def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remov
logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="num_padded is specified but padded_sample is not."):
partitions(4, 2)
def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
columns_list = ["input_ids", "id", "rating"]
data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
padded_sample = data[0]
padded_sample['id'] = "-1"
padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
padded_sample['rating'] = 1.0
num_readers = 4
@ -406,7 +416,9 @@ def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
for item in data_set.create_dict_iterator():
logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(
item["input_ids"],
item["input_ids"].shape))
if item['id'] == bytes('-1', encoding='utf-8'):
num_padded_iter += 1
assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
@ -420,13 +432,14 @@ def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
partitions(5, 5, 3)
partitions(9, 8, 2)
def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_nlp_file):
columns_list = ["input_ids", "id", "rating"]
data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
padded_sample = data[0]
padded_sample['id'] = "-1"
padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
padded_sample['rating'] = 1.0
num_readers = 4
repeat_size = 3
@ -451,7 +464,9 @@ def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_
for item in data_set.create_dict_iterator():
logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(
item["input_ids"],
item["input_ids"].shape))
if item['id'] == bytes('-1', encoding='utf-8'):
num_padded_iter += 1
assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
@ -488,7 +503,7 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
padded_sample = {}
padded_sample['id'] = "-1"
padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
padded_sample['rating'] = 1.0
num_readers = 4
repeat_size = 3
@ -519,7 +534,8 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
assert (item['input_ids'] == padded_sample['input_ids']).all()
assert (item['rating'] == padded_sample['rating']).all()
# save epoch result
epoch_result[partition_id][int(inner_num_iter / dataset_size)][inner_num_iter % dataset_size] = item["id"]
epoch_result[partition_id][int(inner_num_iter / dataset_size)][inner_num_iter % dataset_size] = item[
"id"]
num_iter += 1
inner_num_iter += 1
assert epoch_result[partition_id][0] not in (epoch_result[partition_id][1], epoch_result[partition_id][2])
@ -651,6 +667,7 @@ def inputs(vectors, maxlen=50):
segment = [0] * maxlen
return input_, mask, segment
if __name__ == '__main__':
test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file)
test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file)

1
tests/ut/python/dataset/test_sampler.py
View File

@ -216,6 +216,7 @@ def test_sampler_chain():
assert test_config(5, 3) == [3]
assert test_config(5, 4) == [4]
def test_add_sampler_invalid_input():
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}

5
tests/ut/python/dataset/test_serdes_dataset.py
View File

@ -19,7 +19,10 @@ import filecmp
import glob
import json
import os
import numpy as np
from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
from util import config_get_set_num_parallel_workers
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as c
@ -27,8 +30,6 @@ import mindspore.dataset.transforms.vision.c_transforms as vision
from mindspore import log as logger
from mindspore.dataset.transforms.vision import Inter
from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
from util import config_get_set_num_parallel_workers
def test_imagefolder(remove_json_files=True):
"""

31
tests/ut/python/ops/test_control_ops.py
View File

@ -29,7 +29,6 @@ from mindspore.common import ms_function
context.set_context(mode=context.GRAPH_MODE)
grad_by_list = C.GradOperation(get_by_list=True)
grad_all = C.GradOperation(get_all=True)
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
@ -123,6 +122,7 @@ def test_if_none():
net = Net(z)
assert np.all(net(x, y).asnumpy() == y.asnumpy())
def test_if_str_is_not_none_right():
class Net(nn.Cell):
def __init__(self, z: str):
@ -455,8 +455,10 @@ def test_parser_switch_layer_switch_in_bprop():
super(OneInputBprop, self).__init__()
self.op = P.ReLU()
self.funcs = funcs
def construct(self, i, x):
return self.op(x)
def bprop(self, i, x, out, dout):
return i, self.funcs[i](x, dout)
@ -475,6 +477,7 @@ def test_parser_switch_layer_switch_in_bprop():
def construct(self, x, y):
return self.op(x, y)
func1 = Add()
func2 = Mul()
funcs = (func1, func2)
@ -572,6 +575,7 @@ def test_switch_layer_env_eliminate():
weights = self.weights
grad = self.grad_op(self.net, weights)(x, index)
return grad
net = Net()
net2 = NetGrad(net)
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@ -601,6 +605,7 @@ def test_switch_layer_single_layer():
weights = self.weights
grad = self.grad_op(self.net, weights)(x, index)
return grad
net = Net()
net2 = NetGrad(net)
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@ -638,6 +643,7 @@ def test_if_nested_compile():
else:
res = self.squre(self.value)
return res
x = Tensor(1.0, dtype=ms.float32)
y = Tensor(2.0, dtype=ms.float32)
net = Net()
@ -660,6 +666,7 @@ def test_if_inside_for():
else:
res = res - y
return res
c1 = Tensor(1, dtype=ms.int32)
c2 = Tensor(1, dtype=ms.int32)
net = Net()
@ -671,6 +678,7 @@ def test_while_in_while():
c2 = Tensor(2, dtype=ms.int32)
c3 = Tensor(3, dtype=ms.int32)
c4 = Tensor(4, dtype=ms.int32)
@ms_function
def while_in_while(x, y, z, u):
out = c4
@ -683,6 +691,7 @@ def test_while_in_while():
out = out + 3
return out
while_in_while(c1, c2, c3, c4)
@ -692,6 +701,7 @@ def test_tensor_cond():
super(Net, self).__init__()
self.t = Tensor(np.array(0, np.bool))
self.t1 = Tensor(np.array([True], np.bool))
def construct(self, x, y):
t = 0
if self.t:
@ -704,17 +714,18 @@ def test_tensor_cond():
t = t + x * y
return t
x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32))
net = Net()
out = net(x, y)
def test_tensor_cond_exception():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.t = Tensor(np.array([True, False], np.bool))
def construct(self, x, y):
t = 0
if self.t:
@ -723,18 +734,19 @@ def test_tensor_cond_exception():
t = t - x / y
return t
x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32))
net = Net()
with pytest.raises(ValueError):
out = net(x, y)
def test_while_scalar():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.x = 10
def construct(self, x, y):
i = 0
t = 0
@ -742,17 +754,20 @@ def test_while_scalar():
t = t + x + y
i = i + 1
return t
net = Net()
x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32))
out = net(x, y)
def test_while_tensor():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.t = Tensor(np.ones([6, 8, 10], np.int32))
self.count = Tensor(np.array([10], np.int32))
def construct(self, x, y):
i = 0
t = self.t
@ -760,6 +775,7 @@ def test_while_tensor():
t = t + x + y
i = i + 1
return t
net = Net()
x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32))
@ -770,7 +786,7 @@ def test_large_for_loop():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.flatten = P.ReLU() #nn.Flatten()
self.flatten = P.ReLU() # nn.Flatten()
def construct(self, x):
for elem in range(1, 1900):
@ -791,7 +807,7 @@ def test_large_for_loop_with_continue_break():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.flatten = P.ReLU() #nn.Flatten()
self.flatten = P.ReLU() # nn.Flatten()
def construct(self, x):
idx = 0
@ -895,7 +911,7 @@ def test_recursive_call():
def __init__(self):
super(Net, self).__init__()
self.fc = nn.Dense(10, 10) # padding=0
#self.net2 = Net2()
# self.net2 = Net2()
def construct(self, x):
net2 = Net2()
@ -908,6 +924,7 @@ def test_recursive_call():
super(Net2, self).__init__()
self.net = Net()
self.fc = nn.Dense(10, 10)
def construct(self, x):
x = self.net(x)
out = self.fc(x)
@ -949,7 +966,6 @@ def test_switch_layer_shape_join_failed():
funcs = (func1, func2)
net = AddFuncNet(funcs, func3)
inp = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
@ -980,7 +996,6 @@ def test_switch_layer_dtype_join_failed():
x = self.op(x)
return x
func1 = nn.ReLU()
func2 = Cast(mstype.int32)
funcs = (func1, func2)

3
tests/ut/python/ops/test_math_ops.py
View File

@ -14,8 +14,8 @@
# ============================================================================
""" test math ops """
import functools
import numpy as np
import pytest
import mindspore as ms
import mindspore.context as context
@ -31,6 +31,7 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \
import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \
import pipeline_for_verify_exception_for_case_by_case_config
context.set_context(mode=context.GRAPH_MODE)
# pylint: disable=W0613

9
tests/ut/python/ops/test_ops.py
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@ -35,7 +35,6 @@ from ....mindspore_test_framework.pipeline.gradient.compile_gradient \
import pipeline_for_compile_grad_ge_graph_for_case_by_case_config
from ....ops_common import convert
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
@ -266,6 +265,7 @@ class ScatterNdSub(nn.Cell):
out = self.scatter_nd_sub(self.ref, indices, updates)
return out
class ScatterNdAdd(nn.Cell):
"""ScatterNdAdd net definition"""
@ -311,7 +311,7 @@ class ScatterDiv(nn.Cell):
def __init__(self, ref_shape, dtype=np.float32, use_locking=False):
super(ScatterDiv, self).__init__()
self.scatter_div = P.ScatterDiv(use_locking)
self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)*10), name="ref")
self.ref = Parameter(Tensor(np.ones(ref_shape, dtype) * 10), name="ref")
def construct(self, indices, updates):
out = self.scatter_div(self.ref, indices, updates)
@ -633,7 +633,7 @@ class CTCGreedyDecoderNet(nn.Cell):
self.assert_op = P.Assert(300)
def construct(self, inputs, sequence_length):
out = self.ctc_greedy_decoder(inputs,sequence_length)
out = self.ctc_greedy_decoder(inputs, sequence_length)
self.assert_op(True, (out[0], out[1], out[2], out[3]))
return out[2]
@ -711,12 +711,13 @@ class BasicLSTMCellNet(nn.Cell):
def construct(self, x, h, c, w, b):
return self.lstm(x, h, c, w, b)
class EditDistance(nn.Cell):
def __init__(self, hypothesis_shape, truth_shape, normalize=True):
super(EditDistance, self).__init__()
self.edit_distance = P.EditDistance(normalize)
self.hypothesis_shape = hypothesis_shape
self.truth_shape =truth_shape
self.truth_shape = truth_shape
def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values):
return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape,

11
tests/ut/python/train/test_amp.py
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@ -20,13 +20,11 @@ import mindspore.context as context
from mindspore import Tensor
from mindspore import amp
from mindspore import nn
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.common import dtype as mstype
from ....dataset_mock import MindData
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.communication.management import init
from tests.ut.python.model.resnet import resnet50
from mindspore.context import ParallelMode
from mindspore.train import Model
from ....dataset_mock import MindData
def setup_module(module):
_ = module
@ -144,6 +142,7 @@ def test_compile_model_train_O2():
# not actual run, the metrics step will fail, check if compile ok.
model.eval(dataset)
def test_compile_model_train_O2_parallel():
dataset_types = (np.float32, np.float32)
dataset_shapes = ((16, 16), (16, 16))

2
tests/ut/python/utils/test_initializer.py
View File

@ -141,6 +141,7 @@ def test_init_abnormal():
with py.raises(TypeError):
init.initializer([''], [5, 4], ms.float32)
def test_initializer_reinit():
weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
assert weights.dtype == ms.float16
@ -153,6 +154,7 @@ def test_initializer_reinit():
assert weights.dtype == ms.float16
assert weights.shape == (10, 1)
def test_init_xavier_uniform():
""" test_init_xavier_uniform """
gain = 1.2