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!5473 optim pylint

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Merge pull request !5473 from jinyaohui/master
pull/5473/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent c563336541 a9972a7def
commit bb84f50407
19 changed files with 112 additions and 75 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. # limitations under the License.
# ============================================================================ # ============================================================================
import os import os
import pytest
def test_expand_loss(): 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. # limitations under the License.
# ============================================================================ # ============================================================================
import os import os
import pytest
def test_expand_loss(): def test_expand_loss():

8
tests/st/model_zoo_tests/wide_and_deep/train_and_test_multinpu_ci_data_parallel.py
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@ -14,7 +14,6 @@
# ============================================================================ # ============================================================================
"""train_multinpu.""" """train_multinpu."""
import os import os
import sys import sys
import numpy as np import numpy as np
@ -35,7 +34,6 @@ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirr
init() init()
def get_WideDeep_net(config): def get_WideDeep_net(config):
WideDeep_net = WideDeepModel(config) WideDeep_net = WideDeepModel(config)
loss_net = NetWithLossClass(WideDeep_net, config) loss_net = NetWithLossClass(WideDeep_net, config)
@ -48,6 +46,7 @@ class ModelBuilder():
""" """
ModelBuilder ModelBuilder
""" """
def __init__(self): def __init__(self):
pass pass
@ -101,14 +100,13 @@ def test_train_eval():
print("=====" * 5 + "model.eval() initialized: {}".format(out)) print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs, ds_train, model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb]) callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
expect_out0 = [0.792634,0.799862,0.803324] expect_out0 = [0.792634, 0.799862, 0.803324]
expect_out6 = [0.796580,0.803908,0.807262] expect_out6 = [0.796580, 0.803908, 0.807262]
if get_rank() == 0: if get_rank() == 0:
assert np.allclose(eval_callback.eval_values, expect_out0) assert np.allclose(eval_callback.eval_values, expect_out0)
if get_rank() == 6: if get_rank() == 6:
assert np.allclose(eval_callback.eval_values, expect_out6) assert np.allclose(eval_callback.eval_values, expect_out6)
if __name__ == "__main__": if __name__ == "__main__":
test_train_eval() 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""" """train bert network without lossscale"""
import os import os
import pytest
import numpy as np 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.common.dtype as mstype
import mindspore.dataset.engine.datasets as de 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 context
from mindspore import log as logger from mindspore import log as logger
from mindspore.common.tensor import Tensor from mindspore.common.tensor import Tensor
from mindspore.nn import learning_rate_schedule as lr_schedules
from mindspore.nn.optim import Lamb from mindspore.nn.optim import Lamb
from mindspore.train.callback import Callback from mindspore.train.callback import Callback
from mindspore.train.loss_scale_manager import DynamicLossScaleManager from mindspore.train.loss_scale_manager import DynamicLossScaleManager
from mindspore.train.model import Model 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"] DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json" 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.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.nn import TrainOneStepCell, WithLossCell from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 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 import Tensor
from mindspore.common.initializer import initializer from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter from mindspore.common.parameter import Parameter
from mindspore.nn import Dense
from mindspore.nn import TrainOneStepCell, WithLossCell from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P 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 import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.ops import operations as P from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

5
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) print("output:\n", output)
assert (output.asnumpy() == expect).all() assert (output.asnumpy() == expect).all()
class SliceGrad2(nn.Cell): class SliceGrad2(nn.Cell):
def __init__(self): def __init__(self):
super(SliceGrad2, self).__init__() super(SliceGrad2, self).__init__()
@ -62,6 +63,7 @@ class SliceGrad2(nn.Cell):
def construct(self, dy, x): def construct(self, dy, x):
return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2)) return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
@ -71,10 +73,11 @@ def test_slice_grad2():
grad = SliceGrad2() grad = SliceGrad2()
output = grad(dy, x) output = grad(dy, x)
print("output:\n", output) print("output:\n", output)
expect = [[[0., 0.], [2., 3.], [4., 5.]], expect = [[[0., 0.], [2., 3.], [4., 5.]],
[[0., 0.], [8., 9.], [10., 11.]]] [[0., 0.], [8., 9.], [10., 11.]]]
assert (output.asnumpy() == expect).all() assert (output.asnumpy() == expect).all()
if __name__ == '__main__': if __name__ == '__main__':
test_slice_grad() test_slice_grad()
test_slice_grad2() test_slice_grad2()

8
tests/st/ops/cpu/test_slice_op.py
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@ -21,10 +21,10 @@ import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore.ops import operations as P 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') context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
class Slice(nn.Cell): class Slice(nn.Cell):
def __init__(self): def __init__(self):
super(Slice, self).__init__() super(Slice, self).__init__()
@ -33,6 +33,7 @@ class Slice(nn.Cell):
def construct(self, x): def construct(self, x):
return self.slice(x, (0, 1, 0), (2, 1, 3)) return self.slice(x, (0, 1, 0), (2, 1, 3))
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
@ -47,6 +48,7 @@ def test_slice():
print("output:\n", output) print("output:\n", output)
assert (output.asnumpy() == expect).all() assert (output.asnumpy() == expect).all()
class Slice2(nn.Cell): class Slice2(nn.Cell):
def __init__(self): def __init__(self):
super(Slice2, self).__init__() super(Slice2, self).__init__()
@ -55,12 +57,13 @@ class Slice2(nn.Cell):
def construct(self, x): def construct(self, x):
return self.slice(x, (1, 0, 0), (1, 2, 3)) return self.slice(x, (1, 0, 0), (1, 2, 3))
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_slice2(): def test_slice2():
x = Tensor(np.arange(3 * 2 * 3).reshape(3, 2, 3), mstype.float32) x = Tensor(np.arange(3 * 2 * 3).reshape(3, 2, 3), mstype.float32)
expect = [[[6., 7., 8.], expect = [[[6., 7., 8.],
[9., 10., 11.]]] [9., 10., 11.]]]
slice_op = Slice2() slice_op = Slice2()
@ -68,6 +71,7 @@ def test_slice2():
print("output:\n", output) print("output:\n", output)
assert (output.asnumpy() == expect).all() assert (output.asnumpy() == expect).all()
if __name__ == '__main__': if __name__ == '__main__':
test_slice() test_slice()
test_slice2() test_slice2()

2
tests/st/ops/custom_ops_tbe/cus_add3.py
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@ -14,9 +14,11 @@
# ============================================================================ # ============================================================================
from mindspore.ops import prim_attr_register, PrimitiveWithInfer from mindspore.ops import prim_attr_register, PrimitiveWithInfer
# sum = input1 + input2 + const_bias # sum = input1 + input2 + const_bias
class CusAdd3(PrimitiveWithInfer): class CusAdd3(PrimitiveWithInfer):
"""Custom add3 definition""" """Custom add3 definition"""
@prim_attr_register @prim_attr_register
def __init__(self, const_bias=0.0): def __init__(self, const_bias=0.0):
self.init_prim_io_names(inputs=['input1', 'input2'], outputs=['sum3']) self.init_prim_io_names(inputs=['input1', 'input2'], outputs=['sum3'])

2
tests/st/summary/test_cpu_summary.py
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@ -24,8 +24,8 @@ import mindspore.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.ops import operations as P from mindspore.ops import operations as P
from tests.summary_utils import SummaryReader
from mindspore.train.summary.summary_record import SummaryRecord from mindspore.train.summary.summary_record import SummaryRecord
from tests.summary_utils import SummaryReader
context.set_context(mode=context.GRAPH_MODE, device_target='CPU') context.set_context(mode=context.GRAPH_MODE, device_target='CPU')

67
tests/ut/python/dataset/test_minddataset_padded.py
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@ -16,17 +16,15 @@
This is the test module for mindrecord This is the test module for mindrecord
""" """
import collections import collections
import json
import numpy as np
import os import os
import pytest
import re import re
import string import string
import numpy as np
import pytest
import mindspore.dataset as ds import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as vision
from mindspore import log as logger from mindspore import log as logger
from mindspore.dataset.transforms.vision import Inter
from mindspore.mindrecord import FileWriter from mindspore.mindrecord import FileWriter
FILES_NUM = 4 FILES_NUM = 4
@ -52,9 +50,9 @@ def add_and_remove_cv_file():
writer = FileWriter(CV_FILE_NAME, FILES_NUM) writer = FileWriter(CV_FILE_NAME, FILES_NUM)
data = get_data(CV_DIR_NAME) data = get_data(CV_DIR_NAME)
cv_schema_json = {"id": {"type": "int32"}, cv_schema_json = {"id": {"type": "int32"},
"file_name": {"type": "string"}, "file_name": {"type": "string"},
"label": {"type": "int32"}, "label": {"type": "int32"},
"data": {"type": "bytes"}} "data": {"type": "bytes"}}
writer.add_schema(cv_schema_json, "img_schema") writer.add_schema(cv_schema_json, "img_schema")
writer.add_index(["file_name", "label"]) writer.add_index(["file_name", "label"])
writer.write_raw_data(data) writer.write_raw_data(data)
@ -85,14 +83,14 @@ def add_and_remove_nlp_file():
writer = FileWriter(NLP_FILE_NAME, FILES_NUM) writer = FileWriter(NLP_FILE_NAME, FILES_NUM)
data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)] data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
nlp_schema_json = {"id": {"type": "string"}, "label": {"type": "int32"}, nlp_schema_json = {"id": {"type": "string"}, "label": {"type": "int32"},
"rating": {"type": "float32"}, "rating": {"type": "float32"},
"input_ids": {"type": "int64", "input_ids": {"type": "int64",
"shape": [-1]}, "shape": [-1]},
"input_mask": {"type": "int64", "input_mask": {"type": "int64",
"shape": [1, -1]}, "shape": [1, -1]},
"segment_ids": {"type": "int64", "segment_ids": {"type": "int64",
"shape": [2, -1]} "shape": [2, -1]}
} }
writer.set_header_size(1 << 14) writer.set_header_size(1 << 14)
writer.set_page_size(1 << 15) writer.set_page_size(1 << 15)
writer.add_schema(nlp_schema_json, "nlp_schema") writer.add_schema(nlp_schema_json, "nlp_schema")
@ -110,6 +108,7 @@ def add_and_remove_nlp_file():
os.remove("{}".format(x)) os.remove("{}".format(x))
os.remove("{}.db".format(x)) os.remove("{}.db".format(x))
def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file): def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
"""tutorial for cv minderdataset.""" """tutorial for cv minderdataset."""
columns_list = ["label", "file_name", "data"] columns_list = ["label", "file_name", "data"]
@ -130,7 +129,7 @@ def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
if item['label'] == -1: if item['label'] == -1:
num_padded_iter += 1 num_padded_iter += 1
assert item['file_name'] == bytes(padded_sample['file_name'], assert item['file_name'] == bytes(padded_sample['file_name'],
encoding='utf8') encoding='utf8')
assert item['label'] == padded_sample['label'] assert item['label'] == padded_sample['label']
assert (item['data'] == np.array(list(padded_sample['data']))).all() assert (item['data'] == np.array(list(padded_sample['data']))).all()
num_iter += 1 num_iter += 1
@ -177,6 +176,7 @@ def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
partitions(5, 5, 3) partitions(5, 5, 3)
partitions(9, 8, 2) partitions(9, 8, 2)
def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_file):
"""tutorial for cv minddataset.""" """tutorial for cv minddataset."""
columns_list = ["data", "file_name", "label"] 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(5, 5, 3)
partitions(9, 8, 2) partitions(9, 8, 2)
def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv_file):
"""tutorial for cv minddataset.""" """tutorial for cv minddataset."""
columns_list = ["data", "file_name", "label"] 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): with pytest.raises(RuntimeError):
partitions(4, 1) partitions(4, 1)
def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"] 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) num_padded=num_padded)
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
data_set.get_dataset_size() == 3 data_set.get_dataset_size() == 3
partitions(4, 1) partitions(4, 1)
def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"] 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("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"])) logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"])) logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample cannot match columns_list."): with pytest.raises(Exception, match="padded_sample cannot match columns_list."):
partitions(4, 2) partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_cv_file):
data = get_data(CV_DIR_NAME) data = get_data(CV_DIR_NAME)
padded_sample = data[0] 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("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"])) logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"])) logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample is specified and requires columns_list as well."): with pytest.raises(Exception, match="padded_sample is specified and requires columns_list as well."):
partitions(4, 2) partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"] columns_list = ["data", "file_name", "label"]
data = get_data(CV_DIR_NAME) 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("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"])) logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"])) logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="padded_sample is specified and requires num_padded as well."): with pytest.raises(Exception, match="padded_sample is specified and requires num_padded as well."):
partitions(4, 2) partitions(4, 2)
def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remove_cv_file): def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remove_cv_file):
columns_list = ["data", "file_name", "label"] columns_list = ["data", "file_name", "label"]
data = get_data(CV_DIR_NAME) 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("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
logger.info("-------------- item[data]: {} -----------------------------".format(item["data"])) logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"])) logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
with pytest.raises(Exception, match="num_padded is specified but padded_sample is not."): with pytest.raises(Exception, match="num_padded is specified but padded_sample is not."):
partitions(4, 2) partitions(4, 2)
def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file): def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
columns_list = ["input_ids", "id", "rating"] columns_list = ["input_ids", "id", "rating"]
data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)] data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
padded_sample = data[0] padded_sample = data[0]
padded_sample['id'] = "-1" 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 padded_sample['rating'] = 1.0
num_readers = 4 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(num_epochs=1): for item in data_set.create_dict_iterator(num_epochs=1):
logger.info("-------------- item[id]: {} ------------------------".format(item["id"])) logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
logger.info("-------------- item[rating]: {} --------------------".format(item["rating"])) 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'): if item['id'] == bytes('-1', encoding='utf-8'):
num_padded_iter += 1 num_padded_iter += 1
assert item['id'] == bytes(padded_sample['id'], encoding='utf-8') 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(5, 5, 3)
partitions(9, 8, 2) partitions(9, 8, 2)
def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_nlp_file): def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_nlp_file):
columns_list = ["input_ids", "id", "rating"] columns_list = ["input_ids", "id", "rating"]
data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)] data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
padded_sample = data[0] padded_sample = data[0]
padded_sample['id'] = "-1" 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 padded_sample['rating'] = 1.0
num_readers = 4 num_readers = 4
repeat_size = 3 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(num_epochs=1): for item in data_set.create_dict_iterator(num_epochs=1):
logger.info("-------------- item[id]: {} ------------------------".format(item["id"])) logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
logger.info("-------------- item[rating]: {} --------------------".format(item["rating"])) 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'): if item['id'] == bytes('-1', encoding='utf-8'):
num_padded_iter += 1 num_padded_iter += 1
assert item['id'] == bytes(padded_sample['id'], encoding='utf-8') 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 = {}
padded_sample['id'] = "-1" 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 padded_sample['rating'] = 1.0
num_readers = 4 num_readers = 4
repeat_size = 3 repeat_size = 3
@ -512,14 +527,15 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
logger.info("-------------- item[id]: {} ------------------------".format(item["id"])) logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
logger.info("-------------- item[rating]: {} --------------------".format(item["rating"])) logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
logger.info("-------------- item[input_ids]: {}, shape: {} -----------------" logger.info("-------------- item[input_ids]: {}, shape: {} -----------------"
.format(item["input_ids"], item["input_ids"].shape)) .format(item["input_ids"], item["input_ids"].shape))
if item['id'] == bytes('-1', encoding='utf-8'): if item['id'] == bytes('-1', encoding='utf-8'):
num_padded_iter += 1 num_padded_iter += 1
assert item['id'] == bytes(padded_sample['id'], encoding='utf-8') assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
assert (item['input_ids'] == padded_sample['input_ids']).all() assert (item['input_ids'] == padded_sample['input_ids']).all()
assert (item['rating'] == padded_sample['rating']).all() assert (item['rating'] == padded_sample['rating']).all()
# save epoch result # 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 num_iter += 1
inner_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]) 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 segment = [0] * maxlen
return input_, mask, segment return input_, mask, segment
if __name__ == '__main__': if __name__ == '__main__':
test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file) test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file)
test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file) test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file)

3
tests/ut/python/dataset/test_sampler.py
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@ -216,6 +216,7 @@ def test_sampler_chain():
assert test_config(5, 3) == [3] assert test_config(5, 3) == [3]
assert test_config(5, 4) == [4] assert test_config(5, 4) == [4]
def test_add_sampler_invalid_input(): def test_add_sampler_invalid_input():
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json" manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4} _ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
@ -231,7 +232,7 @@ def test_add_sampler_invalid_input():
sampler = ds.SequentialSampler() sampler = ds.SequentialSampler()
with pytest.raises(ValueError) as info: with pytest.raises(ValueError) as info:
data2 = ds.ManifestDataset(manifest_file, sampler=sampler, num_samples=20) data2 = ds.ManifestDataset(manifest_file, sampler=sampler, num_samples=20)
assert "Conflicting arguments during sampler assignments" in str(info.value) assert "Conflicting arguments during sampler assignments" in str(info.value)

5
tests/ut/python/dataset/test_serdes_dataset.py
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@ -19,7 +19,10 @@ import filecmp
import glob import glob
import json import json
import os import os
import numpy as np 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 as ds
import mindspore.dataset.transforms.c_transforms as c 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 import log as logger
from mindspore.dataset.transforms.vision import Inter 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): def test_imagefolder(remove_json_files=True):

47
tests/ut/python/ops/test_control_ops.py
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@ -29,7 +29,6 @@ from mindspore.common import ms_function
context.set_context(mode=context.GRAPH_MODE) context.set_context(mode=context.GRAPH_MODE)
grad_by_list = C.GradOperation(get_by_list=True) grad_by_list = C.GradOperation(get_by_list=True)
grad_all = C.GradOperation(get_all=True) grad_all = C.GradOperation(get_all=True)
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True) grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
@ -123,6 +122,7 @@ def test_if_none():
net = Net(z) net = Net(z)
assert np.all(net(x, y).asnumpy() == y.asnumpy()) assert np.all(net(x, y).asnumpy() == y.asnumpy())
def test_if_str_is_not_none_right(): def test_if_str_is_not_none_right():
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self, z: str): def __init__(self, z: str):
@ -455,8 +455,10 @@ def test_parser_switch_layer_switch_in_bprop():
super(OneInputBprop, self).__init__() super(OneInputBprop, self).__init__()
self.op = P.ReLU() self.op = P.ReLU()
self.funcs = funcs self.funcs = funcs
def construct(self, i, x): def construct(self, i, x):
return self.op(x) return self.op(x)
def bprop(self, i, x, out, dout): def bprop(self, i, x, out, dout):
return i, self.funcs[i](x, 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): def construct(self, x, y):
return self.op(x, y) return self.op(x, y)
func1 = Add() func1 = Add()
func2 = Mul() func2 = Mul()
funcs = (func1, func2) funcs = (func1, func2)
@ -572,6 +575,7 @@ def test_switch_layer_env_eliminate():
weights = self.weights weights = self.weights
grad = self.grad_op(self.net, weights)(x, index) grad = self.grad_op(self.net, weights)(x, index)
return grad return grad
net = Net() net = Net()
net2 = NetGrad(net) net2 = NetGrad(net)
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32) x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@ -601,6 +605,7 @@ def test_switch_layer_single_layer():
weights = self.weights weights = self.weights
grad = self.grad_op(self.net, weights)(x, index) grad = self.grad_op(self.net, weights)(x, index)
return grad return grad
net = Net() net = Net()
net2 = NetGrad(net) net2 = NetGrad(net)
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32) x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@ -638,6 +643,7 @@ def test_if_nested_compile():
else: else:
res = self.squre(self.value) res = self.squre(self.value)
return res return res
x = Tensor(1.0, dtype=ms.float32) x = Tensor(1.0, dtype=ms.float32)
y = Tensor(2.0, dtype=ms.float32) y = Tensor(2.0, dtype=ms.float32)
net = Net() net = Net()
@ -660,6 +666,7 @@ def test_if_inside_for():
else: else:
res = res - y res = res - y
return res return res
c1 = Tensor(1, dtype=ms.int32) c1 = Tensor(1, dtype=ms.int32)
c2 = Tensor(1, dtype=ms.int32) c2 = Tensor(1, dtype=ms.int32)
net = Net() net = Net()
@ -671,6 +678,7 @@ def test_while_in_while():
c2 = Tensor(2, dtype=ms.int32) c2 = Tensor(2, dtype=ms.int32)
c3 = Tensor(3, dtype=ms.int32) c3 = Tensor(3, dtype=ms.int32)
c4 = Tensor(4, dtype=ms.int32) c4 = Tensor(4, dtype=ms.int32)
@ms_function @ms_function
def while_in_while(x, y, z, u): def while_in_while(x, y, z, u):
out = c4 out = c4
@ -683,6 +691,7 @@ def test_while_in_while():
out = out + 3 out = out + 3
return out return out
while_in_while(c1, c2, c3, c4) while_in_while(c1, c2, c3, c4)
@ -692,6 +701,7 @@ def test_tensor_cond():
super(Net, self).__init__() super(Net, self).__init__()
self.t = Tensor(np.array(0, np.bool)) self.t = Tensor(np.array(0, np.bool))
self.t1 = Tensor(np.array([True], np.bool)) self.t1 = Tensor(np.array([True], np.bool))
def construct(self, x, y): def construct(self, x, y):
t = 0 t = 0
if self.t: if self.t:
@ -703,18 +713,19 @@ def test_tensor_cond():
else: else:
t = t + x * y t = t + x * y
return t return t
x = Tensor(np.ones([6, 8, 10], np.int32)) x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32)) y = Tensor(np.ones([6, 8, 10], np.int32))
net = Net() net = Net()
out = net(x, y) out = net(x, y)
def test_tensor_cond_exception(): def test_tensor_cond_exception():
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.t = Tensor(np.array([True, False], np.bool)) self.t = Tensor(np.array([True, False], np.bool))
def construct(self, x, y): def construct(self, x, y):
t = 0 t = 0
if self.t: if self.t:
@ -722,19 +733,20 @@ def test_tensor_cond_exception():
else: else:
t = t - x / y t = t - x / y
return t return t
x = Tensor(np.ones([6, 8, 10], np.int32)) x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32)) y = Tensor(np.ones([6, 8, 10], np.int32))
net = Net() net = Net()
with pytest.raises(ValueError): with pytest.raises(ValueError):
out = net(x, y) out = net(x, y)
def test_while_scalar(): def test_while_scalar():
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.x = 10 self.x = 10
def construct(self, x, y): def construct(self, x, y):
i = 0 i = 0
t = 0 t = 0
@ -742,17 +754,20 @@ def test_while_scalar():
t = t + x + y t = t + x + y
i = i + 1 i = i + 1
return t return t
net = Net() net = Net()
x = Tensor(np.ones([6, 8, 10], np.int32)) x = Tensor(np.ones([6, 8, 10], np.int32))
y = Tensor(np.ones([6, 8, 10], np.int32)) y = Tensor(np.ones([6, 8, 10], np.int32))
out = net(x, y) out = net(x, y)
def test_while_tensor(): def test_while_tensor():
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.t = Tensor(np.ones([6, 8, 10], np.int32)) self.t = Tensor(np.ones([6, 8, 10], np.int32))
self.count = Tensor(np.array([10], np.int32)) self.count = Tensor(np.array([10], np.int32))
def construct(self, x, y): def construct(self, x, y):
i = 0 i = 0
t = self.t t = self.t
@ -760,6 +775,7 @@ def test_while_tensor():
t = t + x + y t = t + x + y
i = i + 1 i = i + 1
return t return t
net = Net() net = Net()
x = Tensor(np.ones([6, 8, 10], np.int32)) x = Tensor(np.ones([6, 8, 10], np.int32))
y = 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): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.flatten = P.ReLU() #nn.Flatten() self.flatten = P.ReLU() # nn.Flatten()
def construct(self, x): def construct(self, x):
for elem in range(1, 1900): for elem in range(1, 1900):
@ -791,7 +807,7 @@ def test_large_for_loop_with_continue_break():
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.flatten = P.ReLU() #nn.Flatten() self.flatten = P.ReLU() # nn.Flatten()
def construct(self, x): def construct(self, x):
idx = 0 idx = 0
@ -854,7 +870,7 @@ def test_tensor_all_construct_lack_branch():
if input1.all(): if input1.all():
return self.logicaland(input1, input2) return self.logicaland(input1, input2)
while input1.any(): while input1.any():
return self.logicalor(input1, input2) return self.logicalor(input1, input2)
# NOTICE: here missing return statement, default return None # NOTICE: here missing return statement, default return None
input_np_1 = np.random.choice([True], size=(2, 3, 4, 5)) input_np_1 = np.random.choice([True], size=(2, 3, 4, 5))
@ -891,28 +907,29 @@ def test_parser_switch_layer_func_primitive():
def test_recursive_call(): def test_recursive_call():
class Net(nn.Cell): class Net(nn.Cell):
""" Net definition """ """ Net definition """
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.fc = nn.Dense(10, 10) # padding=0 self.fc = nn.Dense(10, 10) # padding=0
#self.net2 = Net2() # self.net2 = Net2()
def construct(self, x): def construct(self, x):
net2 = Net2() net2 = Net2()
x = net2(x) x = net2(x)
out = self.fc(x) out = self.fc(x)
return out return out
class Net2(nn.Cell): class Net2(nn.Cell):
def __init__(self): def __init__(self):
super(Net2, self).__init__() super(Net2, self).__init__()
self.net = Net() self.net = Net()
self.fc = nn.Dense(10, 10) self.fc = nn.Dense(10, 10)
def construct(self, x): def construct(self, x):
x = self.net(x) x = self.net(x)
out = self.fc(x) out = self.fc(x)
return out return out
context.set_context(mode=context.GRAPH_MODE, save_graphs=False) context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
old_max_call_depth = context.get_context('max_call_depth') old_max_call_depth = context.get_context('max_call_depth')
context.set_context(max_call_depth=80) context.set_context(max_call_depth=80)
@ -949,7 +966,6 @@ def test_switch_layer_shape_join_failed():
funcs = (func1, func2) funcs = (func1, func2)
net = AddFuncNet(funcs, func3) net = AddFuncNet(funcs, func3)
inp = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32)) 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) x = self.op(x)
return x return x
func1 = nn.ReLU() func1 = nn.ReLU()
func2 = Cast(mstype.int32) func2 = Cast(mstype.int32)
funcs = (func1, func2) funcs = (func1, func2)

3
tests/ut/python/ops/test_math_ops.py
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@ -14,8 +14,8 @@
# ============================================================================ # ============================================================================
""" test math ops """ """ test math ops """
import functools import functools
import numpy as np import numpy as np
import pytest
import mindspore as ms import mindspore as ms
import mindspore.context as context 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 import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \ from ....mindspore_test_framework.pipeline.forward.verify_exception \
import pipeline_for_verify_exception_for_case_by_case_config import pipeline_for_verify_exception_for_case_by_case_config
context.set_context(mode=context.GRAPH_MODE) context.set_context(mode=context.GRAPH_MODE)
# pylint: disable=W0613 # 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 import pipeline_for_compile_grad_ge_graph_for_case_by_case_config
from ....ops_common import convert from ....ops_common import convert
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True) 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) out = self.scatter_nd_sub(self.ref, indices, updates)
return out return out
class ScatterNdAdd(nn.Cell): class ScatterNdAdd(nn.Cell):
"""ScatterNdAdd net definition""" """ScatterNdAdd net definition"""
@ -311,7 +311,7 @@ class ScatterDiv(nn.Cell):
def __init__(self, ref_shape, dtype=np.float32, use_locking=False): def __init__(self, ref_shape, dtype=np.float32, use_locking=False):
super(ScatterDiv, self).__init__() super(ScatterDiv, self).__init__()
self.scatter_div = P.ScatterDiv(use_locking) 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): def construct(self, indices, updates):
out = self.scatter_div(self.ref, indices, updates) out = self.scatter_div(self.ref, indices, updates)
@ -633,7 +633,7 @@ class CTCGreedyDecoderNet(nn.Cell):
self.assert_op = P.Assert(300) self.assert_op = P.Assert(300)
def construct(self, inputs, sequence_length): 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])) self.assert_op(True, (out[0], out[1], out[2], out[3]))
return out[2] return out[2]
@ -711,12 +711,13 @@ class BasicLSTMCellNet(nn.Cell):
def construct(self, x, h, c, w, b): def construct(self, x, h, c, w, b):
return self.lstm(x, h, c, w, b) return self.lstm(x, h, c, w, b)
class EditDistance(nn.Cell): class EditDistance(nn.Cell):
def __init__(self, hypothesis_shape, truth_shape, normalize=True): def __init__(self, hypothesis_shape, truth_shape, normalize=True):
super(EditDistance, self).__init__() super(EditDistance, self).__init__()
self.edit_distance = P.EditDistance(normalize) self.edit_distance = P.EditDistance(normalize)
self.hypothesis_shape = hypothesis_shape 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): def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values):
return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape, return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape,

9
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 Tensor
from mindspore import amp from mindspore import amp
from mindspore import nn from mindspore import nn
from mindspore.train import Model from mindspore.communication.management import init
from mindspore.context import ParallelMode from mindspore.context import ParallelMode
from mindspore.common import dtype as mstype from mindspore.train import Model
from ....dataset_mock import MindData 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
def setup_module(module): def setup_module(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. # not actual run, the metrics step will fail, check if compile ok.
model.eval(dataset) model.eval(dataset)
def test_compile_model_train_O2_parallel(): def test_compile_model_train_O2_parallel():
dataset_types = (np.float32, np.float32) dataset_types = (np.float32, np.float32)
dataset_shapes = ((16, 16), (16, 16)) dataset_shapes = ((16, 16), (16, 16))

4
tests/ut/python/utils/test_initializer.py
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@ -141,6 +141,7 @@ def test_init_abnormal():
with py.raises(TypeError): with py.raises(TypeError):
init.initializer([''], [5, 4], ms.float32) init.initializer([''], [5, 4], ms.float32)
def test_initializer_reinit(): def test_initializer_reinit():
weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16) weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
assert weights.dtype == ms.float16 assert weights.dtype == ms.float16
@ -152,7 +153,8 @@ def test_initializer_reinit():
weights = init.initializer(weights, (10, 1)) weights = init.initializer(weights, (10, 1))
assert weights.dtype == ms.float16 assert weights.dtype == ms.float16
assert weights.shape == (10, 1) assert weights.shape == (10, 1)
def test_init_xavier_uniform(): def test_init_xavier_uniform():
""" test_init_xavier_uniform """ """ test_init_xavier_uniform """
gain = 1.2 gain = 1.2

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