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clean pylint

This commit is contained in:
jinyaohui 2020-05-29 20:23:05 +08:00
parent 85e686e0b3
commit 86d197dfeb
48 changed files with 211 additions and 185 deletions
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4
mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py
View File

@ -244,8 +244,8 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t
return True
# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements
# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements,
# pylint: disable=inconsistent-return-statements
# @util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
@op_info_register(matmul_cube_dense_left_op_info)
def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,

1
mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py
View File

@ -40,6 +40,7 @@ matmul_cube_dense_right_op_info = TBERegOp("CusMatMulCubeDenseRight") \
.get_op_info()
# pylint: disable=inconsistent-return-statements
@op_info_register(matmul_cube_dense_right_op_info)
def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
kernel_name="matmulcube"):

13
model_zoo/Transformer/src/transformer_for_train.py
View File

@ -31,6 +31,8 @@ from .transformer_model import TransformerModel
GRADIENT_CLIP_TYPE = 1
GRADIENT_CLIP_VALUE = 5.0
# pylint: disable=consider-using-in
class ClipGradients(nn.Cell):
"""
Clip gradients.
@ -48,11 +50,12 @@ class ClipGradients(nn.Cell):
self.clip_by_norm = nn.ClipByNorm()
self.cast = P.Cast()
self.dtype = P.DType()
def construct(self,
grads,
clip_type,
clip_value):
#return grads
# return grads
if clip_type != 0 and clip_type != 1:
return grads
@ -83,8 +86,8 @@ class TransformerTrainingLoss(nn.Cell):
super(TransformerTrainingLoss, self).__init__(auto_prefix=False)
self.vocab_size = config.vocab_size
self.onehot = P.OneHot()
self.on_value = Tensor(float(1-config.label_smoothing), mstype.float32)
self.off_value = Tensor(config.label_smoothing/float(self.vocab_size-1), mstype.float32)
self.on_value = Tensor(float(1 - config.label_smoothing), mstype.float32)
self.off_value = Tensor(config.label_smoothing / float(self.vocab_size - 1), mstype.float32)
self.reduce_sum = P.ReduceSum()
self.reduce_mean = P.ReduceMean()
self.reshape = P.Reshape()
@ -92,7 +95,7 @@ class TransformerTrainingLoss(nn.Cell):
self.flatten = P.Flatten()
self.neg = P.Neg()
self.cast = P.Cast()
self.flat_shape = (config.batch_size*config.seq_length,)
self.flat_shape = (config.batch_size * config.seq_length,)
def construct(self, prediction_scores, label_ids, label_weights):
"""Defines the computation performed."""
@ -217,10 +220,12 @@ class TransformerTrainOneStepCell(nn.Cell):
grad_scale = C.MultitypeFuncGraph("grad_scale")
reciprocal = P.Reciprocal()
@grad_scale.register("Tensor", "Tensor")
def tensor_grad_scale(scale, grad):
return grad * F.cast(reciprocal(scale), F.dtype(grad))
class TransformerTrainOneStepWithLossScaleCell(nn.Cell):
"""
Encapsulation class of Transformer network training.

5
model_zoo/bert/src/bert_for_pre_training.py
View File

@ -34,6 +34,9 @@ GRADIENT_CLIP_VALUE = 1.0
_nn_clip_by_norm = nn.ClipByNorm()
clip_grad = C.MultitypeFuncGraph("clip_grad")
# pylint: disable=consider-using-in
@clip_grad.register("Number", "Number", "Tensor")
def _clip_grad(clip_type, clip_value, grad):
"""
@ -57,6 +60,7 @@ def _clip_grad(clip_type, clip_value, grad):
new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
return new_grad
class GetMaskedLMOutput(nn.Cell):
"""
Get masked lm output.
@ -377,6 +381,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
self.add_flags(has_effect=True)
def construct(self,
input_ids,
input_mask,

7
tests/mindspore_test_framework/apps/test_bert_parts.py
View File

@ -15,14 +15,15 @@
"""Test bert submodules."""
import numpy as np
import os
from mindspore import Tensor
from mindspore import nn, context
import numpy as np
from mindspore.model_zoo.Bert_NEZHA import EmbeddingLookup, GetMaskedLMOutput, \
BertConfig, BertPreTraining, BertNetworkWithLoss
from mindspore.model_zoo.Bert_NEZHA.bert_model import BertModel
from mindspore import Tensor
from mindspore import nn, context
from ..mindspore_test import mindspore_test
from ..pipeline.forward.compile_forward import pipeline_for_compile_forward_anf_graph_for_case_by_case_config, \
pipeline_for_compile_forward_ge_graph_for_case_by_case_config

3
tests/mindspore_test_framework/components/executor/check_exceptions.py
View File

@ -15,9 +15,10 @@
"""Component that Check if the function raises the expected Exception."""
import pytest
import sys
import pytest
from ...components.icomponent import IExectorComponent
from ...utils import keyword

3
tests/mindspore_test_framework/utils/check_gradient.py
View File

@ -16,9 +16,10 @@
"""Implementation of Numerical gradients checking."""
# pylint: disable=missing-docstring
from typing import Callable, List, Any
import mindspore._c_expression as _c_expression
import numpy as np
from typing import Callable, List, Any
from mindspore import ParameterTuple
from mindspore import Tensor

3
tests/mindspore_test_framework/utils/dataset_util.py
View File

@ -15,9 +15,10 @@
"""Dataset utils."""
import numpy as np
import random
import numpy as np
from mindspore import Tensor

3
tests/mindspore_test_framework/utils/debug_util.py
View File

@ -24,8 +24,7 @@ from mindspore.ops import operations as P
from mindspore.ops._grad.grad_base import bprop_getters
from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
logging.basicConfig(level=logging.DEBUG, format=
'[%(levelname)s] %(asctime)s %(pathname)s:%(lineno)d %(message)s')
logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] %(asctime)s %(pathname)s:%(lineno)d %(message)s')
logger = logging.getLogger(__name__)

3
tests/mindspore_test_framework/utils/other_util.py
View File

@ -14,9 +14,8 @@
# ============================================================================
"""Other utils."""
import mindspore._c_expression as _c_expression
import numpy as np
import mindspore._c_expression as _c_expression
from mindspore.common.tensor import Tensor

5
tests/st/networks/models/bert/src/bert_for_pre_training.py
View File

@ -34,6 +34,9 @@ GRADIENT_CLIP_VALUE = 1.0
_nn_clip_by_norm = nn.ClipByNorm()
clip_grad = C.MultitypeFuncGraph("clip_grad")
# pylint: disable=consider-using-in
@clip_grad.register("Number", "Number", "Tensor")
def _clip_grad(clip_type, clip_value, grad):
"""
@ -57,6 +60,7 @@ def _clip_grad(clip_type, clip_value, grad):
new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
return new_grad
class GetMaskedLMOutput(nn.Cell):
"""
Get masked lm output.
@ -377,6 +381,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
self.add_flags(has_effect=True)
def construct(self,
input_ids,
input_mask,

19
tests/st/ops/ascend/test_autocast.py
View File

@ -23,35 +23,41 @@ from mindspore.ops import functional as F, composite as C
import mindspore.context as context
import pytest
class TensorIntAutoCast(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(TensorIntAutoCast, self).__init__()
self.i = 2
def construct(self, t):
z = F.tensor_mul(t, self.i)
return z
class TensorFPAutoCast(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(TensorFPAutoCast, self).__init__()
self.f = 1.2
def construct(self, t):
z = F.tensor_mul(t, self.f)
return z
class TensorBoolAutoCast(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(TensorBoolAutoCast, self).__init__()
self.f = True
def construct(self, t):
z = F.tensor_mul(t, self.f)
return z
class TensorAutoCast(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(TensorAutoCast, self).__init__()
def construct(self, t1, t2):
z = F.tensor_mul(t1, t2)
return z
@ -68,7 +74,7 @@ def test_tensor_auto_cast():
t_fp16 = Tensor(np.ones([2, 1, 2, 2]), mstype.float16)
t_fp32 = Tensor(np.ones([2, 1, 2, 2]), mstype.float32)
t_fp64 = Tensor(np.ones([2, 1, 2, 2]), mstype.float64)
net = TensorAutoCast()
net = TensorAutoCast()
rs = net(t_uint8, t_int8)
assert rs.dtype() == mstype.int16
rs = net(t_uint8, t_int16)
@ -96,7 +102,7 @@ def test_tensor_auto_cast():
assert rs.dtype() == mstype.float64
rs = net(t_fp32, t_fp64)
assert rs.dtype() == mstype.float64
rs = net(t_uint8, t_fp16)
assert rs.dtype() == mstype.float16
rs = net(t_uint8, t_fp32)
@ -210,7 +216,6 @@ def test_tensor_auto_cast():
with pytest.raises(TypeError):
net(t_uint64, t_fp64)
with pytest.raises(TypeError):
tfp(t_uint16)
with pytest.raises(TypeError):

6
tests/st/ops/ascend/test_ops_infer.py
View File

@ -21,6 +21,7 @@ import mindspore.common.dtype as mstype
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore import context
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
@ -29,14 +30,16 @@ def test_cast_op_attr():
def __init__(self):
super(CastNet, self).__init__()
self.cast = P.Cast()
def construct(self, x, t):
return self.cast(x, t)
class CastTypeTest(nn.Cell):
def __init__(self, net):
super(CastTypeTest, self).__init__()
self.net = net
self.cast = P.Cast()
def construct(self, x, y, z):
cast_op = self.cast
t1 = cast_op(x, mstype.float32)
@ -46,6 +49,7 @@ def test_cast_op_attr():
t4 = cast_net(y, mstype.int32)
t5 = cast_net(z, mstype.float16)
return (t1, t2, t3, t4, t5)
net = CastTypeTest(CastNet())
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))

4
tests/st/ops/cpu/test_transpose_op.py
View File

@ -142,4 +142,6 @@ def test_transpose():
assert (output[1].asnumpy() == expect1).all()
assert (output[2].asnumpy() == expect2).all()
assert (output[3].asnumpy() == expect3).all()
test_transpose()
test_transpose()

1
tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py
View File

@ -1043,6 +1043,7 @@ def test_print_tuple_wrapper(tag):
return fns[tag]
# pylint: disable=unnecessary-semicolon
def test_constant_duplicate_mul(tag):
fns = FnDict()
Mul = Primitive('Mul');

4
tests/ut/python/dtype/test_dictionary.py
View File

@ -152,7 +152,7 @@ def test_dict_set_item():
x = Tensor(np.ones([2, 2, 3], np.float32))
net = DictSetNet()
out = net(x)
_ = net(x)
# if the dictionary item does not exist, create a new one
@ -168,4 +168,4 @@ def test_dict_set_item_create_new():
return my_dict
x = Tensor(np.ones([2, 2, 3], np.float32))
net = DictSetNet()
out = net(x)
_ = net(x)

28
tests/ut/python/dtype/test_hypermap.py
View File

@ -81,31 +81,3 @@ def test_hypermap_func_const():
net = NetMap()
assert net() == (8, 12, 16)
"""
def test_hypermap_func_variable():
class NetMap(Cell):
def __init__(self):
super(NetMap, self).__init__()
def double(self, x):
return 2 * x
def triple(self, x):
return 3 * x
def square(self, x):
return x * x
def construct(self, x):
_list = [self.double, self.triple, self.square]
return map(lambda f: f(x), _list)
x = Tensor(np.ones([3, 2, 3], np.float32))
net = NetMap()
with pytest.raises(RuntimeError) as ex:
net(x)
assert "HyperMap don't support Closure with free variable yet" in str(ex.value)
"""

4
tests/ut/python/dtype/test_list.py
View File

@ -133,7 +133,7 @@ def test_list_append_2():
class ListOperate(nn.Cell):
def __init__(self, ):
def __init__(self,):
super(ListOperate, self).__init__()
def construct(self, t, l):
@ -153,7 +153,7 @@ class ListOperate(nn.Cell):
class InListNet(nn.Cell):
def __init__(self, ):
def __init__(self,):
super(InListNet, self).__init__()
self.list_ = [1, 2, 3, 4, 5, "ok"]

2
tests/ut/python/dtype/test_tuple.py
View File

@ -53,7 +53,7 @@ class NestTupleGraphNet(nn.Cell):
class InTupleNet(nn.Cell):
def __init__(self, ):
def __init__(self,):
super(InTupleNet, self).__init__()
self.tuple_ = (1, 2, 3, 4, 5, "ok")

2
tests/ut/python/exec/test_AssignAdd.py
View File

@ -99,4 +99,4 @@ def test_assignadd_scalar_cast():
net = AssignAddNet()
x = Tensor(np.ones([1]).astype(np.int64) * 102)
# _executor.compile(net, 1)
result = net(x)
_ = net(x)

6
tests/ut/python/ir/test_tensor.py
View File

@ -429,9 +429,9 @@ def test_tensor_dtype_np_int64():
def test_tensor_dtype_fp32_to_bool():
with pytest.raises(RuntimeError):
input = np.random.randn(2, 3, 4, 5).astype(np.float32)
input = ms.Tensor(input)
input_me = ms.Tensor(input, dtype=ms.bool_)
input_ = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_ = ms.Tensor(input_)
_ = ms.Tensor(input_, dtype=ms.bool_)
def test_tensor_operation():

8
tests/ut/python/keep_order/test_keep_order.py
View File

@ -41,10 +41,10 @@ class Func(nn.Cell):
def construct(self, x, y):
init = self.alloc_status()
sum = add(x, y)
sum_ = add(x, y)
product = mul1(x, y)
flag = self.get_status(init)
out = add2(sum, product)
out = add2(sum_, product)
clear = self.clear_status(flag)
out = F.depend(out, clear)
return out
@ -88,7 +88,7 @@ def test_sens():
sens = Tensor(np.ones([3, 3]).astype(np.float32))
net = Net()
net.add_flags(has_effect=True)
out = net(x, y, sens)
_ = net(x, y, sens)
class Net_hyper(nn.Cell):
@ -119,7 +119,7 @@ def test_hyper_add():
sens = Tensor(np.ones([3, 3]).astype(np.float32))
net = Net_hyper()
net.add_flags(has_effect=True)
out = net(x, y, sens)
_ = net(x, y, sens)
def test_keep_order_io_effect_exception_return_dtype():

3
tests/ut/python/model/test_mix_precision.py
View File

@ -148,9 +148,6 @@ def test_cast():
_executor.compile(net, x)
"""test grad of PReLU, which cause AddN(generated by grad) fail"""
class IRBlockZ(nn.Cell):
def __init__(self, inplanes, planes):
super(IRBlockZ, self).__init__()

5
tests/ut/python/nn/test_pooling.py
View File

@ -46,6 +46,7 @@ class MaxNet(nn.Cell):
kernel_size,
stride=None,
padding=0):
_ = padding
super(MaxNet, self).__init__()
self.maxpool = nn.MaxPool2d(kernel_size,
stride)
@ -73,5 +74,5 @@ class Avg1dNet(nn.Cell):
def test_avg1d():
net = Avg1dNet(6, 1)
input = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
_executor.compile(net, input)
input_ = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
_executor.compile(net, input_)

6
tests/ut/python/nn/test_psnr.py
View File

@ -52,19 +52,19 @@ def test_compile_psnr_grayscale():
def test_psnr_max_val_negative():
max_val = -1
with pytest.raises(ValueError):
net = PSNRNet(max_val)
_ = PSNRNet(max_val)
def test_psnr_max_val_bool():
max_val = True
with pytest.raises(TypeError):
net = PSNRNet(max_val)
_ = PSNRNet(max_val)
def test_psnr_max_val_zero():
max_val = 0
with pytest.raises(ValueError):
net = PSNRNet(max_val)
_ = PSNRNet(max_val)
def test_psnr_different_shape():

30
tests/ut/python/nn/test_ssim.py
View File

@ -51,59 +51,59 @@ def test_compile_grayscale():
def test_ssim_max_val_negative():
max_val = -1
with pytest.raises(ValueError):
net = SSIMNet(max_val)
_ = SSIMNet(max_val)
def test_ssim_max_val_bool():
max_val = True
with pytest.raises(TypeError):
net = SSIMNet(max_val)
_ = SSIMNet(max_val)
def test_ssim_max_val_zero():
max_val = 0
with pytest.raises(ValueError):
net = SSIMNet(max_val)
_ = SSIMNet(max_val)
def test_ssim_filter_size_float():
with pytest.raises(TypeError):
net = SSIMNet(filter_size=1.1)
_ = SSIMNet(filter_size=1.1)
def test_ssim_filter_size_zero():
with pytest.raises(ValueError):
net = SSIMNet(filter_size=0)
_ = SSIMNet(filter_size=0)
def test_ssim_filter_sigma_zero():
with pytest.raises(ValueError):
net = SSIMNet(filter_sigma=0.0)
_ = SSIMNet(filter_sigma=0.0)
def test_ssim_filter_sigma_negative():
with pytest.raises(ValueError):
net = SSIMNet(filter_sigma=-0.1)
_ = SSIMNet(filter_sigma=-0.1)
def test_ssim_k1_k2_wrong_value():
with pytest.raises(ValueError):
net = SSIMNet(k1=1.1)
_ = SSIMNet(k1=1.1)
with pytest.raises(ValueError):
net = SSIMNet(k1=1.0)
_ = SSIMNet(k1=1.0)
with pytest.raises(ValueError):
net = SSIMNet(k1=0.0)
_ = SSIMNet(k1=0.0)
with pytest.raises(ValueError):
net = SSIMNet(k1=-1.0)
_ = SSIMNet(k1=-1.0)
with pytest.raises(ValueError):
net = SSIMNet(k2=1.1)
_ = SSIMNet(k2=1.1)
with pytest.raises(ValueError):
net = SSIMNet(k2=1.0)
_ = SSIMNet(k2=1.0)
with pytest.raises(ValueError):
net = SSIMNet(k2=0.0)
_ = SSIMNet(k2=0.0)
with pytest.raises(ValueError):
net = SSIMNet(k2=-1.0)
_ = SSIMNet(k2=-1.0)
def test_ssim_different_shape():

5
tests/ut/python/onnx/test_onnx.py
View File

@ -64,13 +64,13 @@ class BatchNormTester(nn.Cell):
def test_batchnorm_train_onnx_export():
"test onnx export interface does not modify trainable flag of a network"
input = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
input_ = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
net = BatchNormTester(3)
net.set_train()
if not net.training:
raise ValueError('netowrk is not in training mode')
onnx_file = 'batch_norm.onnx'
export(net, input, file_name=onnx_file, file_format='ONNX')
export(net, input_, file_name=onnx_file, file_format='ONNX')
if not net.training:
raise ValueError('netowrk is not in training mode')
@ -172,6 +172,7 @@ net_cfgs = [
def get_id(cfg):
_ = cfg
return list(map(lambda x: x[0], net_cfgs))

7
tests/ut/python/ops/test_math_ops_check.py
View File

@ -28,7 +28,7 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \
class AssignAddNet(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(AssignAddNet, self).__init__()
self.op = P.AssignAdd()
self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_add1")
@ -39,7 +39,7 @@ class AssignAddNet(nn.Cell):
class AssignSubNet(nn.Cell):
def __init__(self,):
def __init__(self, ):
super(AssignSubNet, self).__init__()
self.op = P.AssignSub()
self.inputdata = Parameter(Tensor(np.zeros([1]).astype(np.bool_), mstype.bool_), name="assign_sub1")
@ -635,7 +635,7 @@ test_case_math_ops = [
'skip': ['backward']}),
# type of x and y not match
('Greater1', {
'block': P.Greater(),
'block': P.Greater(),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
@ -660,6 +660,7 @@ test_case_math_ops = [
'skip': ['backward']}),
]
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
def test_check_exception():
return raise_set

51
tests/ut/python/ops/test_ops_attr_infer.py
View File

@ -21,21 +21,25 @@ import mindspore.context as context
from mindspore import Tensor
from mindspore.ops import functional as F
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
class FakeOp(PrimitiveWithInfer):
@prim_attr_register
def __init__(self):
""""""
def infer_shape(self, x, y):
self.second_shape = y
self.add_prim_attr("second_shape", y)
return x
def infer_dtype(self, x, y):
return x
# test the normal case that should generate independent primitive because of different
# test the normal case that should generate independent primitive because of different
# generated attributes after inference
def test_conv2d_same_primitive():
class Conv2DSameNet(nn.Cell):
@ -43,15 +47,18 @@ def test_conv2d_same_primitive():
super(Conv2DSameNet, self).__init__()
self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
def construct(self, x, y):
r1 = self.conv1(x)
r2 = self.conv2(y)
return (r1, r2)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
net = Conv2DSameNet()
net(t1, t2)
# test cell as high order argument
# The graph with free variables used as argument is not supported yet
# because of the limit of inference specialize system
@ -59,18 +66,22 @@ def Xtest_conv2d_op_with_arg():
class Conv2dNet(nn.Cell):
def __init__(self):
super(Conv2dNet, self).__init__()
def construct(self, op, x):
return op(x)
class OpsNet(nn.Cell):
def __init__(self, net):
super(OpsNet, self).__init__()
self.opnet = net
self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
def construct(self, x, y):
conv_op = self.conv2
a = self.opnet(conv_op, x)
b = self.opnet(conv_op, y)
return (a, b)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
net = OpsNet(Conv2dNet())
@ -82,23 +93,29 @@ def test_conv2d_op_with_arg():
def __init__(self):
super(FackOpNet, self).__init__()
self.op = FakeOp()
def construct(self, x, y):
return self.op(x, y)
class OpNet(nn.Cell):
def __init__(self):
super(OpNet, self).__init__()
def construct(self, op, x, y):
return op(x, y)
class OpsNet(nn.Cell):
def __init__(self, net):
super(OpsNet, self).__init__()
self.opnet = net
self.op = FackOpNet()
def construct(self, x, y):
op = self.op
a = self.opnet(op, x, y)
b = self.opnet(op, y, x)
return (a, b)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
net = OpsNet(OpNet())
@ -110,63 +127,77 @@ def test_conv2d_op_with_arg_same_input():
def __init__(self):
super(FackOpNet, self).__init__()
self.op = FakeOp()
def construct(self, x, y):
return self.op(x, y)
class OpNet(nn.Cell):
def __init__(self):
super(OpNet, self).__init__()
def construct(self, op, x, y):
return op(x, y)
class OpsNet(nn.Cell):
def __init__(self, net):
super(OpsNet, self).__init__()
self.opnet = net
self.op = FackOpNet()
def construct(self, x, y):
op = self.op
a = self.opnet(op, x, x)
b = self.opnet(op, y, x)
return (a, b)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
net = OpsNet(OpNet())
net(t1, t2)
# test op with partial
def test_op_as_partial():
class OpAsPartial(nn.Cell):
def __init__(self):
super(OpAsPartial, self).__init__()
self.op = FakeOp()
def construct(self, x, y, z):
partial_op = F.partial(self.op, x)
a = partial_op(y)
b = partial_op(z)
return a, b
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
net = OpAsPartial()
net(t1, t2, t3)
# test op with partial
def test_op_as_partial_inside():
class OpAsPartial(nn.Cell):
def __init__(self):
super(OpAsPartial, self).__init__()
self.op = FakeOp()
def construct(self, x, y, z):
partial_op = F.partial(self.op, x)
a = partial_op(y)
b = partial_op(z)
return a, b
class OuterNet(nn.Cell):
def __init__(self):
super(OuterNet, self).__init__()
self.net = OpAsPartial()
def construct(self, x, y, z):
a, b = self.net(x, y, z)
return a, b
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
@ -180,12 +211,14 @@ def test_op_as_partial_independent():
def __init__(self):
super(OpAsPartial, self).__init__()
self.op = FakeOp()
def construct(self, x, y, z):
partial_op1 = F.partial(self.op, x)
a = partial_op1(y)
partial_op2 = F.partial(self.op, x)
b = partial_op2(z)
return a, b
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
@ -198,6 +231,7 @@ def test_nest_partial():
def __init__(self):
super(NestPartial, self).__init__()
self.op = FakeOp()
def construct(self, x, y, z):
partial_op1 = F.partial(self.op)
partial_op2 = F.partial(partial_op1, x)
@ -206,54 +240,65 @@ def test_nest_partial():
partial_op4 = F.partial(partial_op3, x)
b = partial_op4(z)
return a, b
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
net = NestPartial()
net(t1, t2, t3)
# high order argument
# op and op args as network arguments
def test_op_with_arg_as_input():
class WithOpArgNet(nn.Cell):
def __init__(self):
super(WithOpArgNet, self).__init__()
def construct(self, op, x, y):
return op(x, y)
class OpsNet(nn.Cell):
def __init__(self, net):
super(OpsNet, self).__init__()
self.opnet = net
self.op = FakeOp()
def construct(self, x, y, z):
op = self.op
a = self.opnet(op, x, z)
b = self.opnet(op, x, y)
return (a, b)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
net = OpsNet(WithOpArgNet())
net(t1, t2, t3)
# The partial application used as argument is not supported yet
# because of the limit of inference specialize system
def Xtest_partial_as_arg():
class PartialArgNet(nn.Cell):
def __init__(self):
super(PartialArgNet, self).__init__()
def construct(self, partial_op, y):
return partial_op(y)
class OpsNet(nn.Cell):
def __init__(self, net):
super(OpsNet, self).__init__()
self.partial_net = net
self.op = FakeOp()
def construct(self, x, y, z):
partial_op = F.partial(self.op, x)
a = self.partial_net(partial_op, z)
b = self.partial_net(partial_op, y)
return (a, b)
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))

2
tests/ut/python/ops/test_ops_check.py
View File

@ -32,6 +32,7 @@ from ....mindspore_test_framework.pipeline.forward.verify_exception \
logging.basicConfig(level=logging.WARNING)
# pylint: disable=abstract-method
class NetMissConstruct(nn.Cell):
""" NetMissConstruct definition """
@ -46,7 +47,6 @@ class NetMissConstruct(nn.Cell):
self.max_pool2d = nn.MaxPool2d(kernel_size=2)
self.flatten = P.Flatten()
# pylint: disable=abstract-method
# TestCase: Mis-spelled 'construct' to 'construtc'
def construtc(self, x):
x = self.max_pool2d(self.relu(self.conv1(x)))

10
tests/ut/python/optimizer/test_debug_location.py
View File

@ -44,7 +44,7 @@ class MockNeg(PrimitiveWithInfer):
def infer_dtype(self, input_x):
raise TypeError("InferError")
return input_x
# return input_x
class MockSub(PrimitiveWithInfer):
@ -79,8 +79,8 @@ class Net(nn.Cell):
self.matmul = P.MatMul()
self.add = P.TensorAdd()
def construct(self, input):
output = self.add(self.matmul(input, self.weight), self.bias)
def construct(self, input_):
output = self.add(self.matmul(input_, self.weight), self.bias)
return output
@ -93,9 +93,9 @@ class NetFP16(nn.Cell):
self.add = P.TensorAdd()
self.cast = P.Cast()
def construct(self, input):
def construct(self, input_):
output = self.cast(
self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
self.add(self.matmul(self.cast(input_, mstype.float16), self.cast(self.weight, mstype.float16)),
self.cast(self.bias, mstype.float16)), mstype.float32)
return output

16
tests/ut/python/optimizer/test_optimize_with_loss_scale.py
View File

@ -42,10 +42,10 @@ class MindDataSet(MindData):
if self._size < self._iter_num:
raise StopIteration
self._iter_num += 1
next = []
for shape, type in zip(self._output_shapes, self._np_types):
next.append(Tensor(np.ones(shape).astype(type)))
return tuple(next)
lst = []
for shape_, type_ in zip(self._output_shapes, self._np_types):
lst.append(Tensor(np.ones(shape_).astype(type_)))
return tuple(lst)
class Net(nn.Cell):
@ -56,8 +56,8 @@ class Net(nn.Cell):
self.matmul = P.MatMul()
self.add = P.TensorAdd()
def construct(self, input):
output = self.add(self.matmul(input, self.weight), self.bias)
def construct(self, input_):
output = self.add(self.matmul(input_, self.weight), self.bias)
return output
@ -70,9 +70,9 @@ class NetFP16(nn.Cell):
self.add = P.TensorAdd()
self.cast = P.Cast()
def construct(self, input):
def construct(self, input_):
output = self.cast(
self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
self.add(self.matmul(self.cast(input_, mstype.float16), self.cast(self.weight, mstype.float16)),
self.cast(self.bias, mstype.float16)), mstype.float32)
return output

4
tests/ut/python/parallel/test_alltoall.py
View File

@ -97,8 +97,8 @@ def test_all_to_all():
print(strategys)
expect_dict = {'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits'
'/SoftmaxCrossEntropyWithLogits-op3': [[8, 1], [8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/OneHot-op4': [
[8, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/'
'OneHot-op4': [[8, 1], [], []],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/Transpose-op1': [
[8, 1]],
'Default/network-_VirtualDatasetCell/_backbone-WithLossCell/_backbone-AllToAllNet/MatMul-op0': [

2
tests/ut/python/parallel/test_two_matmul.py
View File

@ -170,4 +170,4 @@ def test_matmul_forward_reduce_scatter_transpose():
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
y = Tensor(np.ones([64, 32]), dtype=ms.float32)
b = Tensor(np.ones([128, 64]), dtype=ms.float32)
compile_net(net, x, y, b)
compile_net(net, x, y, b)

2
tests/ut/python/parameter_feature/test_parameter.py
View File

@ -280,4 +280,4 @@ def test_mixed_precision_const_parameter():
x = Tensor(np.ones((1, 3, 28, 28), np.float32))
y = Tensor(np.ones((1, 3, 14, 14), np.float32))
z = Tensor(np.ones((1, 3, 28, 28), np.float32))
out = net(x, y, z)
_ = net(x, y, z)

22
tests/ut/python/parameter_feature/test_var_grad.py
View File

@ -39,7 +39,7 @@ def test_net_vargs_expand():
y = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
net = AddNet()
out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
_ = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
class VarNet(Cell):
@ -111,7 +111,7 @@ def test_all_var_args_grad_with_sens():
sens = Tensor(1.0, dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y, sens)
_ = grad_net(x, y, sens)
def test_grad_list_var_args():
@ -128,7 +128,7 @@ def test_grad_list_var_args():
y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y)
_ = grad_net(x, y)
def test_grad_all_var_args():
@ -145,7 +145,7 @@ def test_grad_all_var_args():
y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y)
_ = grad_net(x, y)
def test_grad_all_var_args_with_sens():
@ -163,7 +163,7 @@ def test_grad_all_var_args_with_sens():
sens = Tensor(1.0, dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y, sens)
_ = grad_net(x, y, sens)
def test_grad_var_args_with_sens():
@ -181,7 +181,7 @@ def test_grad_var_args_with_sens():
sens = Tensor(1.0, dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y, sens)
_ = grad_net(x, y, sens)
def test_var_args_grad():
@ -219,7 +219,7 @@ def test_var_args_grad():
sens = Tensor(1.0, dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y, sens)
_ = grad_net(x, y, sens)
def test_var_args_positional():
@ -253,7 +253,7 @@ def test_var_args_positional():
y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y)
_ = grad_net(x, y)
def test_grad_within_if_else():
@ -271,7 +271,7 @@ def test_grad_within_if_else():
x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
sens = Tensor(1.0, dtype=mstype.float32)
_ = Tensor(1.0, dtype=mstype.float32)
net = VarNet(SecondNet())
grad_net = GradNet(net)
out = grad_net(x, y)
@ -316,10 +316,10 @@ def test_grad_for_concat():
net = Concat(axis=self.axis)
grad_net = GradNet(net)
grad_net.set_train()
input_grad = grad_net(*inputs, Tensor(self.out_grad_np))
_ = grad_net(*inputs, Tensor(self.out_grad_np))
def grad_cmp(self):
input_grad_mindspore = self.grad_mindspore_impl()
self.grad_mindspore_impl()
fact = ConcatFactory(input_shape=(
(2, 184320, 1), (2, 46080, 1), (2, 11520, 1), (2, 2880, 1), (2, 720, 1)), axis=1)

2
tests/ut/python/pipeline/parse/test_cont_break.py
View File

@ -84,7 +84,7 @@ class for_loop_with_cont_break(Cell):
if i > 5:
x *= 3
break
x *= 2
# x *= 2
x = x * 2
pass
return x

2
tests/ut/python/pynative_mode/test_framstruct.py
View File

@ -123,6 +123,7 @@ def sub(x, y):
return x - y
# pylint: disable=using-constant-test
@ms_function
def if_always_true(x):
""" if_always_true """
@ -870,6 +871,7 @@ def test_grad_refactor_14():
assert C.grad_all(grad_refactor_14)(2, 3) == (3, 9)
# pylint: disable=using-constant-test
class IfDeferInline(nn.Cell):
def __init__(self, mul_size):
super().__init__()

29
tests/ut/python/pynative_mode/test_hook.py
View File

@ -1,12 +1,10 @@
import numpy as np
import mindspore.nn as nn
import mindspore.ops.operations as P
from mindspore import context
from mindspore.ops import composite as C
from mindspore.common import dtype as mstype
from mindspore import context, Tensor, ParameterTuple
from mindspore.common.initializer import TruncatedNormal
from mindspore.nn import Dense, WithLossCell, SoftmaxCrossEntropyWithLogits, Momentum
from mindspore.nn import WithLossCell, Momentum
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
@ -18,25 +16,28 @@ def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels):
"""weight initial for fc layer"""
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
"""weight initial"""
return TruncatedNormal(0.02)
def cell_hook_function(cell_id, grad_input, grad_output):
print(cell_id)
assert(grad_output[0].asnumpy().shape == (32, 6, 14, 14))
assert(grad_input[0].asnumpy().shape == (32, 16, 10, 10))
assert (grad_output[0].asnumpy().shape == (32, 6, 14, 14))
assert (grad_input[0].asnumpy().shape == (32, 16, 10, 10))
def var_hook_function(grad_out):
print("grad:", grad_out)
assert(grad_out[0].asnumpy().shape == (32, 120))
assert (grad_out[0].asnumpy().shape == (32, 120))
class LeNet5(nn.Cell):
@ -82,7 +83,7 @@ class LeNet5(nn.Cell):
x = self.fc3(x)
return x
class GradWrap(nn.Cell):
""" GradWrap definition """
def __init__(self, network):
@ -94,6 +95,7 @@ class GradWrap(nn.Cell):
weights = self.weights
return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)
def test_hook():
net = LeNet5()
optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
@ -101,7 +103,7 @@ def test_hook():
net_with_criterion = WithLossCell(net, criterion)
train_network = GradWrap(net_with_criterion)
train_network.set_train()
input_data = Tensor(np.ones([net.batch_size, 1, 32, 32]).astype(np.float32) * 0.01)
label = Tensor(np.ones([net.batch_size, net.num_class]).astype(np.float32))
output = net(Tensor(input_data))
@ -111,8 +113,6 @@ def test_hook():
print(loss_output.asnumpy().shape)
class MulAdd(nn.Cell):
def __init__(self):
super(MulAdd, self).__init__()
@ -121,12 +121,13 @@ class MulAdd(nn.Cell):
return 2 * x + y
def bprop(self, x, y, out, dout):
assert(x == 1)
assert(y == 2)
assert(out == 4)
assert(dout == 1)
assert (x == 1)
assert (y == 2)
assert (out == 4)
assert (dout == 1)
return 3 * dout, 2 * y
def test_custom_bprop():
mul_add = MulAdd()
mul_add.bprop_debug = True

3
tests/ut/python/pynative_mode/test_stop_gradient.py
View File

@ -18,10 +18,9 @@ import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Parameter, ParameterTuple, Tensor
from mindspore import Parameter, ParameterTuple
from mindspore import Tensor
from mindspore import context
from mindspore import context
from mindspore.common.api import ms_function
from mindspore.ops import composite as C
from mindspore.ops import operations as P

1
tests/ut/python/train/quant/mobilenetv2.py
View File

@ -60,6 +60,7 @@ class InvertedResidual(nn.Cell):
class MobileNetV2(nn.Cell):
def __init__(self, num_class=1000, input_size=224, width_mul=1.):
super(MobileNetV2, self).__init__()
_ = input_size
block = InvertedResidual
input_channel = 32
last_channel = 1280

1
tests/ut/python/train/quant/mobilenetv2_combined.py
View File

@ -68,6 +68,7 @@ class InvertedResidual(nn.Cell):
class MobileNetV2(nn.Cell):
def __init__(self, num_class=1000, input_size=224, width_mul=1.):
super(MobileNetV2, self).__init__()
_ = input_size
block = InvertedResidual
input_channel = 32
last_channel = 1280

30
tests/ut/python/train/quant/test_quant.py
View File

@ -63,33 +63,3 @@ class LeNet5(nn.Cell):
x = self.fc2(x)
x = self.fc3(x)
return x
"""
def test_qat_lenet():
net = LeNet5()
net = qat.convert_quant_network(
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
def test_qat_mobile():
net = MobileNetV2()
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
net = qat.convert_quant_network(
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
net(img)
def test_qat_mobile_train():
net = MobileNetV2(num_class=10)
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
label = Tensor(np.ones((1, 10)).astype(np.float32))
net = qat.convert_quant_network(
net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
optimizer = nn.Momentum(net.trainable_params(),
learning_rate=0.1, momentum=0.9)
net = nn.WithLossCell(net, loss)
net = nn.TrainOneStepCell(net, optimizer)
net(img, label)
"""

3
tests/ut/python/train/summary/summary_reader.py
View File

@ -13,9 +13,10 @@
# limitations under the License.
# ============================================================================
"""Summary reader."""
import mindspore.train.summary_pb2 as summary_pb2
import struct
import mindspore.train.summary_pb2 as summary_pb2
_HEADER_SIZE = 8
_HEADER_CRC_SIZE = 4
_DATA_CRC_SIZE = 4

17
tests/ut/python/train/test_amp.py
View File

@ -25,6 +25,7 @@ from ....dataset_mock import MindData
def setup_module(module):
_ = module
context.set_context(mode=context.GRAPH_MODE)
@ -56,7 +57,7 @@ def test_amp_o0():
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_network = amp.build_train_network(net, optimizer, level="O0")
output = train_network(inputs, label)
_ = train_network(inputs, label)
def test_amp_o2():
@ -66,7 +67,7 @@ def test_amp_o2():
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_network = amp.build_train_network(net, optimizer, level="O2")
output = train_network(inputs, label)
_ = train_network(inputs, label)
def test_amp_o2_loss():
@ -76,7 +77,7 @@ def test_amp_o2_loss():
loss = nn.MSELoss()
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_network = amp.build_train_network(net, optimizer, loss, level="O2")
output = train_network(inputs, label)
_ = train_network(inputs, label)
def test_amp_o0_loss():
@ -86,7 +87,7 @@ def test_amp_o0_loss():
loss = nn.MSELoss()
optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
train_network = amp.build_train_network(net, optimizer, loss)
output = train_network(inputs, label)
_ = train_network(inputs, label)
class MindDataSet(MindData):
@ -100,10 +101,10 @@ class MindDataSet(MindData):
if self._size < self._iter_num:
raise StopIteration
self._iter_num += 1
next = []
for shape, type in zip(self._output_shapes, self._np_types):
next.append(Tensor(np.ones(shape).astype(type)))
return tuple(next)
lst = []
for shape_, type_ in zip(self._output_shapes, self._np_types):
lst.append(Tensor(np.ones(shape_).astype(type_)))
return tuple(lst)
def test_compile_model_train_O0():

2
tests/ut/python/train/test_training.py
View File

@ -151,7 +151,7 @@ def test_eval():
with pytest.raises(ValueError):
model2.eval(dataset)
net3 = LossNet()
_ = LossNet()
model3 = Model(net2, eval_network=net2, metrics={"loss"})
with pytest.raises(ValueError):
model3.eval(dataset)

5
tests/ut/python/utils/test_callback.py
View File

@ -15,6 +15,7 @@
"""test callback function."""
import os
import stat
import numpy as np
import pytest
@ -283,14 +284,14 @@ def test_build_callbacks():
callbacks = [ck_obj, loss_cb_1, 'Error', None]
with pytest.raises(TypeError):
callback_list = _build_callbacks(callbacks)
_ = _build_callbacks(callbacks)
def test_RunContext():
"""Test RunContext."""
context_err = 666
with pytest.raises(TypeError):
context = RunContext(context_err)
_ = RunContext(context_err)
cb_params = _InternalCallbackParam()
cb_params.member1 = 1

2
tests/vm_impl/nn_ops_vm_impl.py
View File

@ -223,6 +223,7 @@ def vm_impl_avg_pool_grad(self):
return vm_impl
# pylint: disable=function-redefined
@vm_impl_getters.register(G.FusedBatchNormGrad)
def vm_impl_fused_batch_norm_grad(self):
"""Generate vm_impl function for FusedBatchNormGrad"""
@ -239,6 +240,7 @@ def vm_impl_fused_batch_norm_grad(self):
return vm_impl
# pylint: disable=function-redefined
@vm_impl_getters.register(G.BatchNormGrad)
def vm_impl_fused_batch_norm_grad(self):
"""Generate vm_impl function for BatchNormGrad"""