for pylint

example/resnet101_imagenet2012_THOR/crossentropy.py → example/resnet50_imagenet2012_THOR/crossentropy.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CrossEntropy"""
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
@@ -22,6 +22,7 @@ from mindspore.ops import operations as P
 
 
 class CrossEntropy(_Loss):
+    """CrossEntropy"""
     def __init__(self, smooth_factor=0., num_classes=1000):
         super(CrossEntropy, self).__init__()
         self.onehot = P.OneHot()

example/resnet101_imagenet2012_THOR/cus_ops/batch_matmul_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/batch_matmul_impl.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 from mindspore.ops.op_info_register import op_info_register
-
+"""batch_matmul_impl"""
 
 @op_info_register("""{
     "op_name": "CusBatchMatMul",
@@ -71,4 +71,5 @@ from mindspore.ops.op_info_register import op_info_register
     ]
 }""")
 def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=True, kernel_name="batchmatmul"):
+"""CusBatchMatMul"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/cholesky_trsm.py → example/resnet50_imagenet2012_THOR/cus_ops/cholesky_trsm.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 from mindspore.ops.op_info_register import op_info_register
-
+"""CusCholeskyTrsm"""
 
 @op_info_register("""{
     "op_name": "CusCholeskyTrsm",
@@ -59,4 +59,5 @@ from mindspore.ops.op_info_register import op_info_register
    ]
 }""")
 def CusCholeskyTrsm(input_x, output, kernel_name):
+"""CusCholeskyTrsm"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/cus_batch_matmul.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_batch_matmul.py

@@ -12,11 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusBatchMatMul"""
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C
 
-
-# y = x^2
 class CusBatchMatMul(PrimitiveWithInfer):
     """CusMatMulCube definition"""
 
@@ -35,4 +34,5 @@ class CusBatchMatMul(PrimitiveWithInfer):
         return data1_shape
 
     def infer_dtype(self, data1_dtype, data2_dtype):
-        return data1_dtype
+        return data1_dtype
+

example/resnet101_imagenet2012_THOR/cus_ops/cus_cholesky_trsm.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_cholesky_trsm.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusCholeskyTrsm"""
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 
 
@@ -24,11 +25,14 @@ class CusCholeskyTrsm(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['x1'], outputs=['y'])
 
     def infer_shape(self, data1_shape):
-        m, n = data1_shape
+        ll = []
+        m, _ = data1_shape
         if m >= 128:
-            return [m // 128, 128, 128]
+            ll = [m // 128, 128, 128]
         else:
-            return [1, 64, 64]
+            ll = [1, 64, 64]
+        return ll
 
     def infer_dtype(self, data1_dtype):
-        return data1_dtype
+        return data1_dtype
+

example/resnet101_imagenet2012_THOR/cus_ops/cus_fused_abs_max1.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_fused_abs_max1.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusFusedAbsMax1"""
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C
 
@@ -32,10 +33,13 @@ class CusFusedAbsMax1(PrimitiveWithInfer):
         return bprop
 
     def infer_shape(self, data1_shape):
+        ll = []
         if len(data1_shape) == 2:
-            return [1, ]
+            ll = [1,]
         else:
-            return [32, 64]
+            ll = [32, 64]
+        return ll
 
     def infer_dtype(self, data1_dtype):
         return data1_dtype
+

example/resnet101_imagenet2012_THOR/cus_ops/cus_img2col.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_img2col.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusImg2Col"""
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C
 
@@ -49,3 +49,4 @@ class CusImg2Col(PrimitiveWithInfer):
 
     def infer_dtype(self, data1_dtype):
         return data1_dtype
+

example/resnet101_imagenet2012_THOR/cus_ops/cus_matmul_cube.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusMatMulCube"""
 import mindspore as ms
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C
@@ -37,11 +38,11 @@ class CusMatMulCube(PrimitiveWithInfer):
     def infer_shape(self, data1_shape, data2_shape):
         # shape = [1, data1_shape[1], data2_shape[2], 16, 16]
         # return shape
-        if self.transpose_a == True:
+        if self.transpose_a:
             k1, m = data1_shape
         else:
             m, k1 = data1_shape
-        if self.transpose_b == True:
+        if self.transpose_b:
             n, k2 = data2_shape
         else:
             k2, n = data2_shape
@@ -51,3 +52,4 @@ class CusMatMulCube(PrimitiveWithInfer):
 
     def infer_dtype(self, data1_dtype, data2_dtype):
         return ms.common.dtype.tensor_type(getattr(ms, "float32"))
+

example/resnet101_imagenet2012_THOR/cus_ops/cus_matmul_cube_dense_left.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube_dense_left.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusMatMulCubeDenseLeft"""
 import mindspore as ms
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C

example/resnet101_imagenet2012_THOR/cus_ops/cus_matmul_cube_fracz_right_mul.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube_fracz_right_mul.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusMatMulCubeFraczRightMul"""
 import mindspore as ms
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C

example/resnet101_imagenet2012_THOR/cus_ops/cus_matrix_combine.py → example/resnet50_imagenet2012_THOR/cus_ops/cus_matrix_combine.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""CusMatrixCombine"""
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore.ops.composite import multitype_ops as C
 

example/resnet101_imagenet2012_THOR/cus_ops/fused_abs_max1.py → example/resnet50_imagenet2012_THOR/cus_ops/fused_abs_max1.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusFusedAbsMax1"""
 from mindspore.ops.op_info_register import op_info_register
 
 
@@ -65,4 +65,5 @@ from mindspore.ops.op_info_register import op_info_register
     ]
 }""")
 def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_max1"):
+    """CusFusedAbsMax1"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/img2col_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/img2col_impl.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusImg2ColNC1HWC0"""
 from mindspore.ops.op_info_register import op_info_register
 
 
@@ -83,4 +83,5 @@ from mindspore.ops.op_info_register import op_info_register
     ]
 }""")
 def CusImg2ColNC1HWC0(input_x, output, ksizes, strides, dilates, padding, kernel_name="img2col"):
+    """CusImg2ColNC1HWC0"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/matmul_cube_dense_left.py → example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_dense_left.py

@@ -2,11 +2,11 @@
 # -*- coding:utf-8 -*-
 """
 copyright 2020 Huawei Technologies Co., Ltd
- 
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
- 
+
 http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
@@ -14,7 +14,7 @@ distributed under the License == distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
- 
+
 matmul
 """
 from __future__ import absolute_import
@@ -98,4 +98,5 @@ NoneType = type(None)
 @util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
 def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,
                            kernel_name="matmulcube"):
+    """CusMatMulCubeDenseLeft"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/matmul_cube_fracz_left_cast_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_fracz_left_cast_impl.py

@@ -2,19 +2,19 @@
 # -*- coding:utf-8 -*-
 """
 copyright 2020 Huawei Technologies Co., Ltd
- 
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
- 
+
 http://www.apache.org/licenses/LICENSE-2.0
- 
+
 Unless required by applicable law or agreed to in writing, software
 distributed under the License == distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
- 
+
 matmul
 """
 from __future__ import absolute_import
@@ -99,4 +99,5 @@ NoneType = type(None)
 @util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
 def CusMatMulCubeFraczLeftCast(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,
                                kernel_name="CusMatMulCubeFraczLeftCast"):
+    """CusMatMulCubeFraczLeftCast"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/matmul_cube_fracz_right_mul_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_fracz_right_mul_impl.py

@@ -2,19 +2,19 @@
 # -*- coding:utf-8 -*-
 """
 copyright 2020 Huawei Technologies Co., Ltd
- 
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
- 
+
 http://www.apache.org/licenses/LICENSE-2.0
- 
+
 Unless required by applicable law or agreed to in writing, software
 distributed under the License == distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
- 
+
 matmul
 """
 from __future__ import absolute_import

example/resnet101_imagenet2012_THOR/cus_ops/matmul_cube_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_impl.py

@@ -2,19 +2,19 @@
 # -*- coding:utf-8 -*-
 """
 copyright 2020 Huawei Technologies Co., Ltd
- 
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
- 
+
 http://www.apache.org/licenses/LICENSE-2.0
- 
+
 Unless required by applicable law or agreed to in writing, software
 distributed under the License == distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
- 
+
 matmul
 """
 from __future__ import absolute_import
@@ -110,4 +110,5 @@ NoneType = type(None)
 # pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements
 @util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
 def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
+    """CusMatMulCube"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/matrix_combine_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/matrix_combine_impl.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusMatrixCombine"""
 from mindspore.ops.op_info_register import op_info_register
 
 
@@ -59,4 +59,5 @@ from mindspore.ops.op_info_register import op_info_register
     ]
 }""")
 def CusMatrixCombine(input_x, output, kernel_name="matrix_combine"):
+    """CusMatrixCombine"""
     return

example/resnet101_imagenet2012_THOR/cus_ops/transpose02314_impl.py → example/resnet50_imagenet2012_THOR/cus_ops/transpose02314_impl.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""CusTranspose02314"""
 from mindspore.ops.op_info_register import op_info_register
 
 
@@ -59,4 +59,5 @@ from mindspore.ops.op_info_register import op_info_register
     ]
 }""")
 def CusTranspose02314(input_x, output, kernel_name="transpose021354"):
+    """CusTranspose02314"""
     return

example/resnet101_imagenet2012_THOR/lr_generator.py → example/resnet50_imagenet2012_THOR/lr_generator.py

@@ -19,12 +19,14 @@ import numpy as np
 
 
 def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
+    """linear_warmup_lr"""
     lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
     lr = float(init_lr) + lr_inc * current_step
     return lr
 
 
 def cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0, num_periods=0.5):
+    """linear_warmup_lr"""
     base_lr = lr
     warmup_init_lr = 0
     total_steps = int(max_epoch * steps_per_epoch)
@@ -44,6 +46,7 @@ def cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, et
 
 
 def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0, num_periods=0.5):
+    """warmup_cosine_annealing_lr"""
     base_lr = lr
     warmup_init_lr = 0
     total_steps = int(max_epoch * steps_per_epoch * 0.99)

example/resnet101_imagenet2012_THOR/model/dataset_helper.py → example/resnet50_imagenet2012_THOR/model/dataset_helper.py

@@ -25,17 +25,17 @@ from mindspore.train.parallel_utils import ParallelMode
 class DatasetHelper:
     """
     Help function to use the Minddata dataset.
- 
+
     According to different context, change the iter of dataset, to use the same for loop in different context.
- 
+
     Note:
         The iter of DatasetHelper will give one epoch data.
- 
+
     Args:
         dataset (DataSet): The dataset.
         dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host.
             Default: True.
- 
+
     Examples:
         >>> dataset_helper = DatasetHelper(dataset)
         >>> for inputs in dataset_helper:

example/resnet101_imagenet2012_THOR/model/grad_reducer_thor.py → example/resnet50_imagenet2012_THOR/model/grad_reducer_thor.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""grad_reducer_thor"""
 import mindspore.common.dtype as mstype
 from mindspore.communication.management import GlobalComm, get_group_size
 from mindspore.nn.cell import Cell

example/resnet101_imagenet2012_THOR/model/model_thor.py → example/resnet50_imagenet2012_THOR/model/model_thor.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """Model."""
-import mindspore.nn as nn
-import numpy as np
 from mindspore import context
 from mindspore import log as logger
 from mindspore._c_expression import init_exec_dataset
@@ -30,16 +28,17 @@ from mindspore.parallel._utils import _get_parallel_mode, _get_device_num, _get_
 from mindspore.train import amp
 from mindspore.train.callback import _InternalCallbackParam, RunContext, _build_callbacks
 from mindspore.train.parallel_utils import ParallelMode
+import mindspore.nn as nn
 from second_order.dataset_helper import DatasetHelper
-
+import numpy as np
 
 def _convert_type(types):
     """
     Convert from numpy type to tensor type.
- 
+
     Args:
         types (list): Numpy type list of element in dataset.
- 
+
     Returns:
         list, list of element in dataset.
     """
@@ -76,9 +75,9 @@ def _exec_datagraph(exec_dataset, dataset_size, phase='dataset'):
 class Model:
     """
     High-Level API for Training or Testing.
- 
+
     `Model` groups layers into an object with training and inference features.
- 
+
     Args:
         network (Cell): The training or testing network.
         loss_fn (Cell): Objective function, if loss_fn is None, the
@@ -96,15 +95,15 @@ class Model:
                              metric. Default: None.
         amp_level (str): Option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
             precision training. Supports [O0, O2]. Default: "O0".
- 
+
             - O0: Do not change.
             - O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.
- 
+
         loss_scale_manager (Union[None, LossScaleManager]): If None, not scale the loss, or else
             scale the loss by LossScaleManager. If it is set, overwrite the level setting. It's a eyword argument.
             e.g. Use `loss_scale_manager=None` to set the value.
         keep_batchnorm_fp32 (bool): Keep Batchnorm run in `float32`. If set, overwrite the level setting. Default: True.
- 
+
     Examples:
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
@@ -250,7 +249,7 @@ class Model:
     def _train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True):
         """
         Training.
- 
+
         Args:
             epoch (int): Total number of iterations on the data.
             train_dataset (Dataset): A training dataset iterator. If there is no
@@ -296,7 +295,7 @@ class Model:
     def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
         """
         Training process. The data would be passed to network through dataset channel.
- 
+
         Args:
             epoch (int): Total number of iterations on the data.
             train_dataset (Dataset): A training dataset iterator. If there is no
@@ -366,7 +365,7 @@ class Model:
     def _train_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
         """
         Training process. The data would be passed to network directly.
- 
+
         Args:
             epoch (int): Total number of iterations on the data.
             train_dataset (Dataset): A training dataset iterator. If there is no
@@ -426,9 +425,9 @@ class Model:
     def train(self, epoch, train_dataset, callbacks=None, dataset_sink_mode=True):
         """
         Training API where the iteration is controlled by python front-end.
- 
+
         When setting pynative mode, the training process will be performed with dataset not sink.
- 
+
         Note:
             CPU is not supported when dataset_sink_mode is true.
             If dataset_sink_mode is True, epoch of training should be equal to the count of repeat
@@ -436,7 +435,7 @@ class Model:
             is not the amount training requires.
             If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
             of data will be transferred one by one. The limitation of data transmission per time is 256M.
- 
+
         Args:
             epoch (int): Total number of iterations on the data.
             train_dataset (Dataset): A training dataset iterator. If there is no
@@ -448,8 +447,8 @@ class Model:
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
                                       Configure pynative mode, the training process will be performed with
                                       dataset not sink.
- 
- 
+
+
         Examples:
             >>> dataset = get_dataset()
             >>> net = Net()
@@ -477,12 +476,12 @@ class Model:
     def _eval_dataset_sink_process(self, valid_dataset, list_callback=None, cb_params=None):
         """
         Evaluation. The data would be passed to network through dataset channel.
- 
+
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.
             list_callback (ListCallback): Executor of callback list. Default: None.
             cb_params (_InternalCallbackParam): Callback parameters. Default: None.
- 
+
         Returns:
             Dict, returns the loss value & metrics values for the model in test mode.
         """
@@ -526,7 +525,7 @@ class Model:
     def _eval_process(self, valid_dataset, list_callback=None, cb_params=None):
         """
         Evaluation. The data would be passed to network directly.
- 
+
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.
             list_callback (ListCallback): Executor of callback list. Default: None.
@@ -555,23 +554,23 @@ class Model:
     def eval(self, valid_dataset, callbacks=None, dataset_sink_mode=True):
         """
         Evaluation API where the iteration is controlled by python front-end.
- 
+
         Configure to pynative mode, the evaluation will be performed with dataset non-sink mode.
- 
+
         Note:
             CPU is not supported when dataset_sink_mode is true.
             If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
             of data will be transferred one by one. The limitation of data transmission per time is 256M.
- 
+
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.
             callbacks (list): List of callback object. Callbacks which should be excuted
                               while training. Default: None.
             dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
- 
+
         Returns:
             Dict, returns the loss value & metrics values for the model in test mode.
- 
+
         Examples:
             >>> dataset = get_dataset()
             >>> net = Net()
@@ -603,18 +602,18 @@ class Model:
     def predict(self, *predict_data):
         """
         Generates output predictions for the input samples.
- 
+
         Data could be single tensor, or list of tensor, tuple of tensor.
- 
+
         Note:
             Batch data should be put together in one tensor.
- 
+
         Args:
            predict_data (Tensor): Tensor of predict data. can be array, list or tuple.
- 
+
         Returns:
             Tensor, array(s) of predictions.
- 
+
         Examples:
             >>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mindspore.float32)
             >>> model = Model(Net())

example/resnet101_imagenet2012_THOR/model/resnet.py → example/resnet50_imagenet2012_THOR/model/resnet.py

@@ -126,8 +126,8 @@ def _bn_last(channel):
 def _fc(in_channel, out_channel, damping, loss_scale, frequency):
     weight_shape = (out_channel, in_channel)
     weight = Tensor(kaiming_uniform(weight_shape, a=math.sqrt(5))
-    return Dense_Thor(in_channel, out_channel, has_bias=False, weight_init=weight, bias_init=0,
-                      damping=damping, loss_scale=loss_scale, frequency=frequency)
+    return Dense_Thor(in_channel, out_channel, has_bias=False, weight_init=weight,
+                      bias_init=0, damping=damping, loss_scale=loss_scale, frequency=frequency)
 
 
 class ResidualBlock(nn.Cell):

example/resnet101_imagenet2012_THOR/model/thor.py → example/resnet50_imagenet2012_THOR/model/thor.py

@@ -14,8 +14,6 @@
 # ============================================================================
 """momentum"""
 import mindspore.common.dtype as mstype
-from cus_ops.cus_matmul_cube_dense_right import CusMatMulCubeDenseRight
-from cus_ops.cus_matmul_cube_fracz_left_cast import CusMatMulCubeFraczLeftCast
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.common.parameter import ParameterTuple
@@ -24,6 +22,8 @@ from mindspore.nn.optim.optimizer import Optimizer
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.parallel._utils import _get_device_num, _get_mirror_mean
 
+from cus_ops.cus_matmul_cube_dense_right import CusMatMulCubeDenseRight
+from cus_ops.cus_matmul_cube_fracz_left_cast import CusMatMulCubeFraczLeftCast
 from cus_ops.cus_matmul_cube_dense_left import CusMatMulCubeDenseLeft
 from cus_ops.cus_matmul_cube_fracz_right_mul import CusMatMulCubeFraczRightMul
 from model.grad_reducer_thor import DistributedGradReducerThor
@@ -52,6 +52,7 @@ def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
 
 
 class THOR(Optimizer):
+    """THOR"""
     def __init__(self, params, learning_rate, momentum, matrix_A, matrix_G, A_inv_max, G_inv_max, weight_decay=0.0,
                  loss_scale=1.0,
                  decay_filter=lambda x: x.name not in []):

example/resnet101_imagenet2012_THOR/model/thor_layer.py → example/resnet50_imagenet2012_THOR/model/thor_layer.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
+"""thor_layer"""
 import mindspore as ms
 import mindspore.common.dtype as mstype
-import numpy as np
 from mindspore._checkparam import check_bool, twice, check_int_positive
 from mindspore._extends import cell_attr_register
 from mindspore.common.initializer import initializer
@@ -33,6 +32,7 @@ from cus_ops.cus_matmul_cube import CusMatMulCube
 from cus_ops.cus_matrix_combine import CusMatrixCombine
 from cus_ops.cus_transpose02314 import CusTranspose02314
 
+import numpy as np
 C0 = 16
 
 
@@ -91,8 +91,7 @@ class _Conv(Cell):
                              'attr \'group\' of \'Conv2D\' Op.')
 
         self.weight = Parameter(initializer(
-            weight_init, [out_channels, in_channels // group, *kernel_size]),
-            name='weight')
+                                weight_init, [out_channels, in_channels // group, *kernel_size]), name='weight')
 
         if check_bool(has_bias):
             self.bias = Parameter(_initializer(
@@ -107,6 +106,7 @@ class _Conv(Cell):
 
 
 class Conv2d_Thor(_Conv):
+    """Conv2d_Thor"""
     def __init__(self,
                  in_channels,
                  out_channels,
@@ -180,7 +180,7 @@ class Conv2d_Thor(_Conv):
         self.G_inv_max = Parameter(initializer(0, [1], mstype.float32), name="G_inv_max", requires_grad=False)
         self.fake_G = Tensor(
             np.reshape(np.identity(self.matrix_G_device_dim).astype(np.float16), self.matrix_G_device_shape))
-        self.fake_G_inv_max = Tensor(np.zeros([1, ]).astype(np.float32))
+        self.fake_G_inv_max = Tensor(np.zeros([1,]).astype(np.float32))
 
         self.shape = P.Shape()
         self.reshape = P.Reshape()
@@ -196,8 +196,8 @@ class Conv2d_Thor(_Conv):
             self.channels_slice_flag = True
 
         self.padA_flag = False
-        if (
-                self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim and self.matrix_A_dim > self.diag_block_dim:
+        if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim
+            and self.matrix_A_dim > self.diag_block_dim:
             self.padA_flag = True
             pad_dim = self.diag_block_dim - self.matrix_A_dim % self.diag_block_dim
             self.padA = P.Pad(((0, pad_dim), (0, pad_dim)))
@@ -228,6 +228,7 @@ class Conv2d_Thor(_Conv):
         self.getG = P.InsertGradientOf(self.save_gradient)
 
     def save_gradient(self, dout):
+        """save_gradient"""
         out = dout
         dout = self.mul(dout, self.loss_scale)
         dout = self.mul(dout, 32.0)
@@ -252,7 +253,6 @@ class Conv2d_Thor(_Conv):
         matrix_G_inv_max = self.fused_abs_max2(matrix_G_inv_max)
         self.G_inv_max = matrix_G_inv_max
         matrix_G_inv = self.matrix_combine(matrix_G_inv)
-        matrix_G_inv_shape = self.shape(matrix_G_inv)
         matrix_G_inv = self.reshape(matrix_G_inv, self.matrix_G_device_temp_shape)
         matrix_G_inv = self.transpose(matrix_G_inv, (2, 0, 1, 3))
         matrix_G = self.cast(matrix_G_inv, mstype.float16)
@@ -287,7 +287,6 @@ class Conv2d_Thor(_Conv):
             self.A_inv_max = matrix_A_inv_max
             matrix_A_inv = self.matrix_combine(matrix_A_inv)
             matrix_A_inv = self.cast(matrix_A_inv, mstype.float16)
-            in_channels = self.in_channels
             if self.padA_flag:
                 matrix_A_inv = self.slice(matrix_A_inv, (0, 0), (self.matrix_A_dim, self.matrix_A_dim))
 
@@ -307,22 +306,23 @@ class Conv2d_Thor(_Conv):
         return out
 
     def extra_repr(self):
+        """extra_repr"""
         s = 'input_channels={}, output_channels={}, kernel_size={},' \
             'stride={},  pad_mode={}, padding={}, dilation={}, ' \
             'group={}, data_format={}, has_bias={},' \
             'weight_init={}, bias_init={}'.format(
-            self.in_channels,
-            self.out_channels,
-            self.kernel_size,
-            self.stride,
-            self.pad_mode,
-            self.padding,
-            self.dilation,
-            self.group,
-            self.data_format,
-            self.has_bias,
-            self.weight,
-            self.bias)
+                                                  self.in_channels,
+                                                  self.out_channels,
+                                                  self.kernel_size,
+                                                  self.stride,
+                                                  self.pad_mode,
+                                                  self.padding,
+                                                  self.dilation,
+                                                  self.group,
+                                                  self.data_format,
+                                                  self.has_bias,
+                                                  self.weight,
+                                                  self.bias)
 
         if self.has_bias:
             s += ', bias={}'.format(self.bias)
@@ -330,6 +330,7 @@ class Conv2d_Thor(_Conv):
 
 
 class Dense_Thor(Cell):
+    """Dense_Thor"""
     @cell_attr_register(attrs=['has_bias', 'activation'])
     def __init__(self,
                  in_channels,
@@ -405,6 +406,7 @@ class Dense_Thor(Cell):
         self.getG = P.InsertGradientOf(self.save_gradient)
 
     def save_gradient(self, dout):
+        """save_gradient"""
         out = dout
         dout = self.mul(dout, self.loss_scale)
         dout = self.mul(dout, 32.0)
@@ -435,6 +437,7 @@ class Dense_Thor(Cell):
         return out
 
     def construct(self, x):
+        """construct"""
         if self.thor:
             inputs = self.cube_matmul(x, x)
             normalizer = 32
@@ -472,6 +475,7 @@ class Dense_Thor(Cell):
         return output
 
     def extend_repr(self):
+        """extend_repr"""
         str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
             .format(self.in_channels, self.out_channels, self.weight, self.has_bias)
         if self.has_bias:

example/resnet101_imagenet2012_THOR/train.py → example/resnet50_imagenet2012_THOR/train.py

@@ -18,7 +18,6 @@ import os
 import random
 
 import mindspore.dataset.engine as de
-import numpy as np
 from mindspore import Tensor
 from mindspore import context
 from mindspore.communication.management import init
@@ -30,6 +29,7 @@ from second_order.model_second_order import Model
 from second_order.resnet import resnet50
 from second_order.thor import THOR
 
+import numpy as np
 from config_imagenet import config
 from crossentropy import CrossEntropy
 from dataset_imagenet import create_dataset
@@ -56,13 +56,14 @@ context.set_context(enable_mem_reuse=True)
 
 
 def get_second_order_lr(global_step, lr_init, decay, total_epochs, steps_per_epoch):
+    """get_second_order_lr"""
     lr_each_step = []
     total_steps = steps_per_epoch * total_epochs
     for i in range(total_steps):
         epoch = (i + 1) / steps_per_epoch
         base = (1.0 - float(epoch) / total_epochs) ** decay
-        lr = lr_init * base
-        lr_each_step.append(lr)
+        lr_local = lr_init * base
+        lr_each_step.append(lr_local)
     current_step = global_step
     lr_each_step = np.array(lr_each_step).astype(np.float32)
     print("learning_rate_is=====", lr_each_step)
@@ -71,12 +72,13 @@ def get_second_order_lr(global_step, lr_init, decay, total_epochs, steps_per_epo
 
 
 def get_second_order_damping(global_step, damping_init, decay_rate, total_epochs, steps_per_epoch):
+    """get_second_order_damping"""
     damping_each_step = []
     total_steps = steps_per_epoch * total_epochs
     for step in range(total_steps):
         epoch = (step + 1) / steps_per_epoch
-        damping = damping_init * (decay_rate ** (epoch / 10))
-        damping_each_step.append(damping)
+        damping_here = damping_init * (decay_rate ** (epoch / 10))
+        damping_each_step.append(damping_here)
 
     current_step = global_step
     damping_each_step = np.array(damping_each_step).astype(np.float32)