diff --git a/mindspore/_extends/graph_kernel/model/graph_split.py b/mindspore/_extends/graph_kernel/model/graph_split.py index 4899be560bf..b07648ed853 100644 --- a/mindspore/_extends/graph_kernel/model/graph_split.py +++ b/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 diff --git a/mindspore/_extends/graph_kernel/model/model_builder.py b/mindspore/_extends/graph_kernel/model/model_builder.py index b2a6b29b9ff..9ceb1bc8273 100644 --- a/mindspore/_extends/graph_kernel/model/model_builder.py +++ b/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 = { diff --git a/mindspore/_extends/parse/resources.py b/mindspore/_extends/parse/resources.py index aafe01e8a40..afd080ad957 100644 --- a/mindspore/_extends/parse/resources.py +++ b/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 = { diff --git a/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc b/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc index 2d315e1c84b..b38728805f3 100644 --- a/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc +++ b/mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc @@ -266,7 +266,6 @@ ValuePtr GenNewTensorInner(const ValuePtr &value) { std::vector value_list; if (value->isa()) { auto tensor = value->cast(); - // return std::make_shared(tensor->data_type(), tensor->shape()); auto new_tensor = std::make_shared(*tensor); new_tensor->set_device_address(nullptr); return new_tensor; diff --git a/mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc b/mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc index 6e38d72edec..1828cc21739 100644 --- a/mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc +++ b/mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc @@ -149,24 +149,6 @@ std::shared_ptr 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 attr_value_vec; \ - for (int i = 0; i < attr_tensor.type##_data_size(); ++i) { \ - auto value = static_cast(attr_tensor.type##_data(i)); \ - attr_value_vec.push_back(MakeValue(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(attr_tensor.type##_data(0)); \ @@ -212,7 +194,6 @@ bool MSANFModelParser::BuildParameterForFuncGraph(const ParameterPtr &node, cons tensor::TensorPtr tensor_info = std::make_shared(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(kDefaultValueSwitchMap[attr_tensor_type], shape); - // tensor_info->MallocData(); const std::string &tensor_buf = attr_tensor.raw_data(); auto *tensor_data_buf = reinterpret_cast(tensor_info->data_c()); auto ret = memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), tensor_buf.data(), tensor_buf.size()); diff --git a/mindspore/lite/nnacl/winograd_transform.c b/mindspore/lite/nnacl/winograd_transform.c index a298b8d6572..f59d422205e 100644 --- a/mindspore/lite/nnacl/winograd_transform.c +++ b/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++; } diff --git a/mindspore/lite/src/ops/activation_grad.cc b/mindspore/lite/src/ops/activation_grad.cc index cf52a7d996d..4e643d9d8b3 100644 --- a/mindspore/lite/src/ops/activation_grad.cc +++ b/mindspore/lite/src/ops/activation_grad.cc @@ -46,7 +46,6 @@ int ActivationGrad::UnPackAttr(const Primitive &prim, const std::vectortype = schema::ActivationType_RELU6; } - // auto alpha = GetValue(prim.GetAttr("alpha")); attr->alpha = 0; // alpha; this->primitive_->value.value = attr.release(); if (this->primitive_->value.value == nullptr) { diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.cc b/mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.cc index 6a5fb02ecee..e423f916f7f 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.cc +++ b/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]; diff --git a/mindspore/nn/graph_kernels/graph_kernels.py b/mindspore/nn/graph_kernels/graph_kernels.py index c26dcf573de..afa74f8cfb5 100644 --- a/mindspore/nn/graph_kernels/graph_kernels.py +++ b/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 diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py index ea9964ea5be..75afd977613 100644 --- a/mindspore/nn/layer/image.py +++ b/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 diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index aee67c0f9b4..ced99921cc9 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/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) diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py index 9455fcaa065..37b92ce0aa8 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py +++ b/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 diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py index c349c5705cf..f1ad55ec16a 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py +++ b/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 diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py index b43efc1b9c0..f45a02eb62f 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py +++ b/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"): diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py index 93c32837917..70e194f5920 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py +++ b/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"): """ diff --git a/mindspore/train/quant/quant.py b/mindspore/train/quant/quant.py index 9d64704d817..d02bad3b430 100644 --- a/mindspore/train/quant/quant.py +++ b/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 diff --git a/mindspore/train/quant/quant_utils.py b/mindspore/train/quant/quant_utils.py index 70a4093e441..d115cb6e82f 100644 --- a/mindspore/train/quant/quant_utils.py +++ b/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: diff --git a/model_zoo/official/cv/alexnet/src/dataset.py b/model_zoo/official/cv/alexnet/src/dataset.py index ad30007e69a..b43f2a553c6 100644 --- a/model_zoo/official/cv/alexnet/src/dataset.py +++ b/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 diff --git a/model_zoo/official/cv/densenet121/src/optimizers/__init__.py b/model_zoo/official/cv/densenet121/src/optimizers/__init__.py index 32b9242b288..d6fb6dc0321 100644 --- a/model_zoo/official/cv/densenet121/src/optimizers/__init__.py +++ b/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) diff --git a/model_zoo/official/cv/nasnet/src/nasnet_a_mobile.py b/model_zoo/official/cv/nasnet/src/nasnet_a_mobile.py index 10ef44968a4..ec92d42fcad 100755 --- a/model_zoo/official/cv/nasnet/src/nasnet_a_mobile.py +++ b/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: diff --git a/model_zoo/official/cv/psenet/src/dataset.py b/model_zoo/official/cv/psenet/src/dataset.py index 9bfe7268189..033c7f42a04 100644 --- a/model_zoo/official/cv/psenet/src/dataset.py +++ b/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: diff --git a/model_zoo/official/cv/psenet/src/generate_hccn_file.py b/model_zoo/official/cv/psenet/src/generate_hccn_file.py index ddc0b93635e..07a4a23a7ee 100644 --- a/model_zoo/official/cv/psenet/src/generate_hccn_file.py +++ b/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' diff --git a/model_zoo/official/cv/psenet/src/network_define.py b/model_zoo/official/cv/psenet/src/network_define.py index 477669e29d6..09ffe610209 100644 --- a/model_zoo/official/cv/psenet/src/network_define.py +++ b/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, diff --git a/model_zoo/official/cv/unet/src/utils.py b/model_zoo/official/cv/unet/src/utils.py index 456126661a9..8be84a16805 100644 --- a/model_zoo/official/cv/unet/src/utils.py +++ b/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)) diff --git a/model_zoo/official/lite/object_detection/app/src/main/cpp/ssd_util/ssd_util.cpp b/model_zoo/official/lite/object_detection/app/src/main/cpp/ssd_util/ssd_util.cpp index f7c53463c73..e34c7d56b1b 100644 --- a/model_zoo/official/lite/object_detection/app/src/main/cpp/ssd_util/ssd_util.cpp +++ b/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(outBuff[i][1])]; // label id diff --git a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/CameraFragment.java b/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/CameraFragment.java index 611422cd136..42f09459398 100644 --- a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/CameraFragment.java +++ b/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() { diff --git a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/DealDataActivity.java b/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/DealDataActivity.java index 38806ac3355..5b057c381b5 100644 --- a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/DealDataActivity.java +++ b/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()); } } diff --git a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/ObjectRectView.java b/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/ObjectRectView.java index e7d7248d6d4..ae30b68ea17 100644 --- a/model_zoo/official/lite/object_detection/app/src/main/java/com/mindspore/hiobject/objectdetect/ObjectRectView.java +++ b/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': 0} word_to_idx = {word: i + 1 for i, word in enumerate(vocab)} word_to_idx[''] = 0 self.__word2idx[seg] = word_to_idx diff --git a/model_zoo/official/nlp/tinybert/src/tinybert_for_gd_td.py b/model_zoo/official/nlp/tinybert/src/tinybert_for_gd_td.py index ae93d955a36..f6e816d55de 100644 --- a/model_zoo/official/nlp/tinybert/src/tinybert_for_gd_td.py +++ b/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: diff --git a/model_zoo/official/nlp/transformer/src/transformer_for_train.py b/model_zoo/official/nlp/transformer/src/transformer_for_train.py index 4eebdad4c81..99983418607 100644 --- a/model_zoo/official/nlp/transformer/src/transformer_for_train.py +++ b/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 = () diff --git a/model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py b/model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py index 0f73e7311a4..88b8b637a56 100644 --- a/model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py +++ b/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) diff --git a/model_zoo/research/cv/ssd_ghostnet/eval.py b/model_zoo/research/cv/ssd_ghostnet/eval.py index d4336c57262..f3805ba3bef 100644 --- a/model_zoo/research/cv/ssd_ghostnet/eval.py +++ b/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 diff --git a/model_zoo/research/cv/ssd_ghostnet/src/dataset.py b/model_zoo/research/cv/ssd_ghostnet/src/dataset.py index 165f4eb9c25..c363ff5d18b 100644 --- a/model_zoo/research/cv/ssd_ghostnet/src/dataset.py +++ b/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.') diff --git a/scripts/update_onnx_weight.py b/scripts/update_onnx_weight.py index 65a33539cfd..eaff46f61b3 100755 --- a/scripts/update_onnx_weight.py +++ b/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