redundant codes clean

mindspore/_extends/graph_kernel/model/graph_split.py

@@ -111,7 +111,6 @@ class GraphSplitByPattern:
         """Split graph"""
         def _buddy(op, dom, path_ops):
             """Fuse buddy together"""
-            # pylint: disable=unused-argument
             group = self.op_group[op]
             for p in group:
                 # p is buddy
@@ -125,7 +124,6 @@ class GraphSplitByPattern:
 
         def _injective(pattern, limit):
             def _checker(op, dom, path_ops):
-                # pylint: disable=unused-argument
                 for p in op.output.to_ops:
                     if p not in self.op_group[dom]:
                         return False

mindspore/_extends/graph_kernel/model/model_builder.py

@@ -39,13 +39,11 @@ class OpInfer:
     @staticmethod
     def default_infer_dtype_func(inputs, attrs):
         """Infer dtype"""
-        # pylint: disable=unused-argument
         return inputs[0].dtype
 
     @staticmethod
     def default_infer_format_func(inputs, attrs):
         """Infer format"""
-        # pylint: disable=unused-argument
         return inputs[0].data_format
 
     infer_shape_func = {

mindspore/_extends/parse/resources.py

@@ -33,7 +33,6 @@ trope_ns = CellNamespace('mindspore._extends.parse.trope')
 NO_IMPLEMENT = None         # not implemented
 SYMBOL_UNDEFINE = 0xFF      # Undefined var and function
 
-# ops map: {op.type:(Namespace, symbol)}
 # Some space set aside for readability of code
 parse_object_map = {
     # ast grammar
@@ -75,7 +74,6 @@ parse_object_map = {
     SYMBOL_UNDEFINE: (None, 'undefine'),
 }
 
-# convert map: {obj:(Namespace, symbol)}
 # Escape an object to another object, eg: system function(len,xxx)
 # Some space set aside for readability of code
 convert_object_map = {

mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc

@@ -266,7 +266,6 @@ ValuePtr GenNewTensorInner(const ValuePtr &value) {
   std::vector<ValuePtr> value_list;
   if (value->isa<tensor::Tensor>()) {
     auto tensor = value->cast<tensor::TensorPtr>();
-    // return std::make_shared<tensor::Tensor>(tensor->data_type(), tensor->shape());
     auto new_tensor = std::make_shared<tensor::Tensor>(*tensor);
     new_tensor->set_device_address(nullptr);
     return new_tensor;

mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc

@@ -149,24 +149,6 @@ std::shared_ptr<abstract::AbstractTuple> ParserAttrShape(
   return result;
 }
 
-#if 0
-#define PARSE_ONNXATTR_IN_SCALAR_FORM(type, valuetype)                                                \
-  void ParseAttrInScalar_##type##_##valuetype(const PrimitivePtr &prim, const std::string &attr_name, \
-                                              const onnx::TensorProto &attr_tensor) {                 \
-    MS_EXCEPTION_IF_NULL(prim);                                                                       \
-    std::vector<ValuePtr> attr_value_vec;                                                             \
-    for (int i = 0; i < attr_tensor.type##_data_size(); ++i) {                                        \
-      auto value = static_cast<valuetype>(attr_tensor.type##_data(i));                                \
-      attr_value_vec.push_back(MakeValue<valuetype>(value));                                          \
-    }                                                                                                 \
-    if (attr_value_vec.size() == 1) {                                                                 \
-      prim->AddAttr(attr_name, attr_value_vec[0]);                                                    \
-    } else {                                                                                          \
-      ParserScalarAttrValue(prim, attr_name, attr_value_vec);                                         \
-    }                                                                                                 \
-  }
-#endif
-
 #define PARSE_ONNXATTR_IN_SCALAR_FORM(type, valuetype)                                    \
   ValuePtr ParseAttrInScalar_##type##_##valuetype(const onnx::TensorProto &attr_tensor) { \
     auto value = static_cast<valuetype>(attr_tensor.type##_data(0));                      \
@@ -212,7 +194,6 @@ bool MSANFModelParser::BuildParameterForFuncGraph(const ParameterPtr &node, cons
   tensor::TensorPtr tensor_info =
     std::make_shared<tensor::Tensor>(kDefaultValueSwitchMap[tensor_typeproto.elem_type()], shape);
   MS_EXCEPTION_IF_NULL(tensor_info);
-  // tensor_info->MallocData();
   auto tensor_abstract = tensor_info->ToAbstract();
   MS_EXCEPTION_IF_NULL(tensor_abstract);
   node->set_abstract(tensor_abstract);
@@ -367,7 +348,6 @@ bool MSANFModelParser::ObtainValueNodeInTensorForm(const std::string &value_node
     shape.push_back(attr_tensor.dims(i));
   }
   tensor::TensorPtr tensor_info = std::make_shared<tensor::Tensor>(kDefaultValueSwitchMap[attr_tensor_type], shape);
-  // tensor_info->MallocData();
   const std::string &tensor_buf = attr_tensor.raw_data();
   auto *tensor_data_buf = reinterpret_cast<uint8_t *>(tensor_info->data_c());
   auto ret = memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), tensor_buf.data(), tensor_buf.size());

mindspore/lite/nnacl/winograd_transform.c

@@ -76,7 +76,6 @@ void WinogradInputTransform(const float *input_data, float *trans_input, float *
       size_t dst_step = tile_num * ic4 * C4NUM;
       float *trans_input_ptr = trans_input + dst_ic4_offset;
       func(tmp_data, trans_input_ptr, C4NUM, dst_step);
-      //      GeneralInputTransformUnit(tmp_data, trans_input_ptr, matrix_b, matrix_bt, C4NUM, dst_step, input_unit);
     }
     out_tile_index++;
   }  // cal_tile_num loop
@@ -120,8 +119,6 @@ void WinogradOutputTransform(const float *gemm_out, float *out_data, const float
       const float *bias_ptr = bias_data + j * C4NUM;
       float *dst_ptr = out_data + dst_oc4_offset;
       func(src_ptr, dst_ptr, bias_ptr, C8NUM, output_w, output_channel, r_w, r_h, r_c);
-      //      GeneralOutputTransformUnit(src_ptr, dst_ptr, bias_ptr, matrix_a, matrix_at, C8NUM,
-      //                                 output_w_unit_block * output_unit, input_unit, output_unit);
     }
     out_tile_index++;
   }

mindspore/lite/src/ops/activation_grad.cc

@@ -46,7 +46,6 @@ int ActivationGrad::UnPackAttr(const Primitive &prim, const std::vector<AnfNodeP
   } else if (prim.name() == "Relu6Grad") {
     attr->type = schema::ActivationType_RELU6;
   }
-  // auto alpha = GetValue<float>(prim.GetAttr("alpha"));
   attr->alpha = 0;  // alpha;
   this->primitive_->value.value = attr.release();
   if (this->primitive_->value.value == nullptr) {

mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.cc

@@ -110,10 +110,6 @@ int SparseSoftmaxCrossEntropyWithLogitsCPUKernel::Run() {
 }
 
 int SparseSoftmaxCrossEntropyWithLogitsCPUKernel::Init() {
-  // if (context_ && context_->infer_shape_interrupt_ && !context_->running_) {
-  // set_need_reinit();
-  //  return RET_OK;
-  // }
   auto dims = in_tensors_[0]->shape();
   param->n_dim_ = 2;
   param->number_of_classes_ = dims[1];

mindspore/nn/graph_kernels/graph_kernels.py

@@ -136,7 +136,6 @@ class MinimumGrad(GraphKernel):
     def construct(self, x, y, dout):
         cmp_result = self.less_equal(x, y)
         dx = self.select(cmp_result, dout, self.zeros_like(dout))
-        # dy = self.select(cmp_result, self.zeros_like(dout), dout)
         dy = dout - dx
 
         return dx, dy

mindspore/nn/layer/image.py

@@ -380,7 +380,6 @@ class PSNR(Cell):
         img2 = _convert_img_dtype_to_float32(img2, self.max_val)
 
         mse = P.ReduceMean()(F.square(img1 - img2), (-3, -2, -1))
-        # 10*log_10(max_val^2/MSE)
         psnr = 10 * P.Log()(F.square(max_val) / mse) / F.scalar_log(10.0)
 
         return psnr

mindspore/ops/_grad/grad_math_ops.py

@@ -64,11 +64,8 @@ def binop_grad_common(x, y, dx, dy):
 
 def _sum_grad(x, axis, dout):
     """Grad definition for `Sum` operation."""
-    # input_shape = [2, 3] axis = [1]
     input_shape = shape_op(x)
-    # output_shape_kept_dims = [2, 1]
     output_shape_kept_dims = reduced_shape(input_shape, axis)
-    # tile_scaling = [1, 3]
     tile_scaling = tuple_div(input_shape, output_shape_kept_dims)
     grad = reshape(dout, output_shape_kept_dims)
     return tile(grad, tile_scaling)
@@ -76,9 +73,7 @@ def _sum_grad(x, axis, dout):
 
 def _min_or_max_grad(x, axis, out, dout):
     """Grad definition for `Min` and `Max` operations."""
-    # input_shape = [2, 3] axis = [1]
     input_shape = shape_op(x)
-    # output_shape_kept_dims = [2, 1]
     output_shape_kept_dims = reduced_shape(input_shape, axis)
     y = reshape(out, output_shape_kept_dims)
     grad = reshape(dout, output_shape_kept_dims)

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py

@@ -44,7 +44,6 @@ matmul_cube_dense_left_op_info = TBERegOp("CusMatMulCubeDenseLeft") \
     .get_op_info()
 
 
-# pylint: disable=locally-disabled,too-many-arguments,too-many-branches, too-many-statements, too-many-locals,
 def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
     """
     Check the given input if legal
@@ -244,8 +243,6 @@ 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=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,
@@ -467,3 +464,4 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F
               "tensor_list": tensor_list}
 
     te.lang.cce.cce_build_code(schedule, config)
+    return None

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py

@@ -40,7 +40,6 @@ 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"):
@@ -171,3 +170,4 @@ def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}
 
         tik_instance.BuildCCE(kernel_name=kernel_name, inputs=[input_x1, input_x2, input_x3], outputs=[resMatmul])
         return tik_instance
+    return None

mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py

@@ -41,7 +41,6 @@ matmul_cube_fracz_left_cast_op_info = TBERegOp("CusMatMulCubeFraczLeftCast") \
     .get_op_info()
 
 
-# pylint: disable=locally-disabled,too-many-arguments,too-many-branches, too-many-statements, too-many-locals,
 def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
     """
     Check the given input if legal
@@ -239,7 +238,6 @@ 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
 @op_info_register(matmul_cube_fracz_left_cast_op_info)
 def CusMatMulCubeFraczLeftCast(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,
                                kernel_name="CusMatMulCubeFraczLeftCast"):

mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py

@@ -47,7 +47,6 @@ matmul_cube_op_info = TBERegOp("CusMatMulCube") \
     .get_op_info()
 
 
-# pylint: disable=locally-disabled,too-many-arguments,too-many-branches, too-many-statements, too-many-locals,
 def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
     """
     Check the given input if legal
@@ -244,7 +243,6 @@ 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
 @op_info_register(matmul_cube_op_info)
 def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
     """

mindspore/train/quant/quant.py

@@ -317,8 +317,6 @@ class ExportToQuantInferNetwork:
 
     def __init__(self, network, mean, std_dev, *inputs, is_mindir=False):
         network = validator.check_isinstance('network', network, (nn.Cell,))
-        # quantize for inputs: q = f / scale + zero_point
-        # dequantize for outputs: f = (q - zero_point) * scale
         self.input_scale = 1 / std_dev
         self.input_zero_point = round(mean)
         self.data_type = mstype.int8

mindspore/train/quant/quant_utils.py

@@ -48,7 +48,6 @@ def cal_quantization_params(input_min,
     if (input_min > input_max).all():
         raise ValueError("input_min min should less than input max.")
     if (input_max == input_min).all():
-        # scale = 1.0, zp = 0.0
         return np.ones(input_min.shape), np.zeros(input_min.shape)
 
     if data_type == np.int8:

model_zoo/official/cv/alexnet/src/dataset.py

@@ -139,8 +139,6 @@ def _get_rank_info():
         rank_size = get_group_size()
         rank_id = get_rank()
     else:
-        # rank_size = rank_id = None
-
         rank_size = 1
         rank_id = 0
 

model_zoo/official/cv/densenet121/src/optimizers/__init__.py

@@ -25,15 +25,12 @@ def get_param_groups(network):
         parameter_name = x.name
         if parameter_name.endswith('.bias'):
             # all bias not using weight decay
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         elif parameter_name.endswith('.gamma'):
             # bn weight bias not using weight decay, be carefully for now x not include BN
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         elif parameter_name.endswith('.beta'):
             # bn weight bias not using weight decay, be carefully for now x not include BN
-            # print('no decay:{}'.format(parameter_name))
             no_decay_params.append(x)
         else:
             decay_params.append(x)

model_zoo/official/cv/nasnet/src/nasnet_a_mobile.py

@@ -33,7 +33,6 @@ GRADIENT_CLIP_VALUE = 10.0
 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):
     """
@@ -47,7 +46,7 @@ def _clip_grad(clip_type, clip_value, grad):
     Outputs:
         tuple[Tensor]: clipped gradients.
     """
-    if clip_type != 0 and clip_type != 1:
+    if clip_type not in (0, 1):
         return grad
     dt = F.dtype(grad)
     if clip_type == 0:

model_zoo/official/cv/psenet/src/dataset.py

@@ -200,10 +200,8 @@ class TrainDataset:
         img_path = self.all_img_paths[index]
         gt_path = self.all_gt_paths[index]
 
-        # start0 = time.time()
         img = get_img(img_path)
         bboxes, tags = get_bboxes(img, gt_path)
-        # end0 = time.time()
 
         # multi-scale training
         if self.is_transform:

model_zoo/official/cv/psenet/src/generate_hccn_file.py

@@ -39,7 +39,6 @@ def main():
 
     hccn_table = {}
     hccn_table['board_id'] = '0x002f'  # A+K
-    # hccn_table['board_id'] = '0x0000' # A+X
 
     hccn_table['chip_info'] = '910'
     hccn_table['deploy_mode'] = 'lab'

model_zoo/official/cv/psenet/src/network_define.py

@@ -129,7 +129,6 @@ class TrainOneStepCell(nn.Cell):
     def __init__(self, network, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
         super(TrainOneStepCell, self).__init__(auto_prefix=False)
         self.network = network
-        # self.backbone = network._backbone
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad = C.GradOperation(get_by_list=True,

model_zoo/official/cv/unet/src/utils.py

@@ -53,4 +53,3 @@ class StepLossTimeMonitor(Callback):
         if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
             # TEST
             print("step: %s, loss is %s, fps is %s" % (cur_step_in_epoch, loss, step_fps), flush=True)
-            # print("step: %s, loss is %s, fps is %s" % ( cur_step_in_epoch, loss, step_fps))

model_zoo/official/lite/object_detection/app/src/main/cpp/ssd_util/ssd_util.cpp

@@ -106,7 +106,6 @@ std::string SSDModelUtil::getDecodeResult(float *branchScores, float *branchBoxD
         std::string tmpid_str = std::to_string(outBuff[i][0]);
         result += tmpid_str;
         result += "_";
-        // tmpid_str = std::to_string(outBuff[i][1]);
         MS_PRINT("label_classes i %d, outBuff %d", i, (int) outBuff[i][1]);
         tmpid_str = label_classes[static_cast<int>(outBuff[i][1])];
         // label id

model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/CameraFragment.java

@@ -665,7 +665,6 @@ public class CameraFragment extends Fragment {
 
             mPreviewRequestBuilder = mCameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
             mPreviewRequestBuilder.addTarget(surface);
-            // mPreviewRequestBuilder.addTarget(mImageReader.getSurface());
             // Here, we create a CameraCaptureSession for ic_launcher preview.
             mCameraDevice.createCaptureSession(Arrays.asList(surface, mImageReader.getSurface()),
                     new CameraCaptureSession.StateCallback() {

model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/DealDataActivity.java

@@ -55,7 +55,6 @@ public class DealDataActivity extends AppCompatActivity {
             super.handleMessage(msg);
             if (1 == msg.what) {
                 dealData();
-              //  dealSingleData();
             }
         }
     };
@@ -121,14 +120,12 @@ public class DealDataActivity extends AppCompatActivity {
     private void dealSingleData() {
         String fileFullName = IMGPATH + "/error.jpg";
         Bitmap bitmap =  BitmapFactory.decodeResource(getResources(),R.drawable.error).copy(Bitmap.Config.ARGB_8888, true);
-//        Bitmap bitmap = BitmapFactory.decodeFile(fileFullName).copy(Bitmap.Config.ARGB_8888, true);
         if (bitmap != null) {
 
             String result = mTrackingMobile.MindSpore_runnet(bitmap);
             Log.d(TAG,  ">>result" + result);
             StringBuilder sb = new StringBuilder();
             sb.append("error.jpg").append("_").append(result);
-//            writeStringIntoSDcard(IMG_RESULT_SINGLE_PATH, sb.toString());
         }
     }
 

model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/ObjectRectView.java

@@ -89,9 +89,6 @@ public class ObjectRectView extends View {
         super.onDraw(canvas);
 
         if (mRecognitions == null || mRecognitions.size() == 0) {
-//            mPaint.setColor(Color.TRANSPARENT);
-//            mObjRectF = new RectF(0, 0, 5, 5);
-//            canvas.drawRoundRect(mObjRectF, 0, 0, mPaint);
             return;
         }
         for (int i = 0;i<mRecognitions.size();i++){

model_zoo/official/nlp/bert/src/bert_for_pre_training.py

@@ -36,7 +36,6 @@ GRADIENT_CLIP_VALUE = 1.0
 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):
     """
@@ -50,7 +49,7 @@ def _clip_grad(clip_type, clip_value, grad):
     Outputs:
         tuple[Tensor], clipped gradients.
     """
-    if clip_type != 0 and clip_type != 1:
+    if clip_type not in (0, 1):
         return grad
     dt = F.dtype(grad)
     if clip_type == 0:

model_zoo/official/nlp/bert_thor/src/bert_for_pre_training.py

@@ -42,7 +42,6 @@ GRADIENT_CLIP_VALUE = 1.0
 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):
     """
@@ -56,7 +55,7 @@ def _clip_grad(clip_type, clip_value, grad):
     Outputs:
         tuple[Tensor], clipped gradients.
     """
-    if clip_type != 0 and clip_type != 1:
+    if clip_type not in (0, 1):
         return grad
     dt = F.dtype(grad)
     if clip_type == 0:

model_zoo/official/nlp/lstm/src/imdb.py

@@ -103,7 +103,6 @@ class ImdbParser():
         vocab = set(chain(*tokenized_features))
         self.__vacab[seg] = vocab
 
-        # word_to_idx looks like {'hello': 1, 'world':111, ... '<unk>': 0}
         word_to_idx = {word: i + 1 for i, word in enumerate(vocab)}
         word_to_idx['<unk>'] = 0
         self.__word2idx[seg] = word_to_idx

model_zoo/official/nlp/tinybert/src/tinybert_for_gd_td.py

@@ -35,7 +35,6 @@ GRADIENT_CLIP_TYPE = 1
 GRADIENT_CLIP_VALUE = 1.0
 
 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):
     """
@@ -49,7 +48,7 @@ def _clip_grad(clip_type, clip_value, grad):
     Outputs:
         tuple[Tensor], clipped gradients.
     """
-    if clip_type != 0 and clip_type != 1:
+    if clip_type not in (0, 1):
         return grad
     dt = F.dtype(grad)
     if clip_type == 0:
@@ -89,7 +88,7 @@ class ClipGradients(nn.Cell):
                   clip_type,
                   clip_value):
         """clip gradients"""
-        if clip_type != 0 and clip_type != 1:
+        if clip_type not in (0, 1):
             return grads
         new_grads = ()
         for grad in grads:

model_zoo/official/nlp/transformer/src/transformer_for_train.py

@@ -32,7 +32,6 @@ GRADIENT_CLIP_TYPE = 1
 GRADIENT_CLIP_VALUE = 5.0
 
 
-# pylint: disable=consider-using-in
 class ClipGradients(nn.Cell):
     """
     Clip gradients.
@@ -56,7 +55,7 @@ class ClipGradients(nn.Cell):
                   clip_type,
                   clip_value):
         """Defines the gradients clip."""
-        if clip_type != 0 and clip_type != 1:
+        if clip_type not in (0, 1):
             return grads
 
         new_grads = ()

model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py

@@ -78,14 +78,12 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
     center_crop_p = P.CenterCrop(224)
     totensor = P.ToTensor()
     normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-    #change_op_p = P.HWC2CHW()
     composeop = P.ComposeOp(
         [decode_p, resize_p, center_crop_p, totensor, normalize_p])
     if do_train:
         trans = [resize_crop_op, horizontal_flip_op, color_op,
                  rescale_op, normalize_op, change_swap_op]
     else:
-        #trans = [decode_op, resize_op, center_crop, rescale_op, normalize_op, change_swap_op]
         trans = composeop()
     type_cast_op = C2.TypeCast(mstype.int32)
 

model_zoo/research/cv/ssd_ghostnet/eval.py

@@ -23,7 +23,6 @@ from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.ssd_ghostnet import SSD300, ssd_ghostnet
 from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
-# from src.config_ghostnet import config
 from src.config_ghostnet_13x import config
 from src.coco_eval import metrics
 

model_zoo/research/cv/ssd_ghostnet/src/dataset.py

@@ -161,7 +161,6 @@ def create_voc_label(is_training):
     voc_dir = config.voc_dir
     cls_map = {name: i for i, name in enumerate(config.coco_classes)}
     sub_dir = 'train' if is_training else 'eval'
-    #sub_dir = 'train'
     voc_dir = os.path.join(voc_dir, sub_dir)
     if not os.path.isdir(voc_dir):
         raise ValueError(f'Cannot find {sub_dir} dataset path.')

scripts/update_onnx_weight.py

@@ -36,7 +36,6 @@ def update_onnx_initializer(onnx_file, ckpt_file, output_file):
 
     for i, _ in enumerate(initializer):
         item = initializer[i]
-        #print(item.name, item.data_type, item.dims, len(item.raw_data))
         if not item.name in param_dict:
             print(f"Warning: Can not find '{item.name}' in checkpoint parameters dictionary")
             continue