| /third_party/mindspore/mindspore-src/source/tests/st/ops/gpu/ |
| D | test_ps_roi_pooling_v2_op.py | 36 def __init__(self, spatial_scale, group_size, output_dim): argument 38 self.ps_roi_pooling = G.PSROIPooling(spatial_scale, group_size, output_dim) 62 output_dim = 4 65 x_size = [batch_size, output_dim * group_size * group_size, x_height, x_width] 74 net = NetPSROIPooling(spatial_scale, group_size, output_dim) 136 output_dim = 4 139 x_size = [batch_size, output_dim * group_size * group_size, x_height, x_width] 146 net = NetPSROIPooling(spatial_scale, group_size, output_dim) 200 output_dim = 4 203 x_size = [batch_size, output_dim * group_size * group_size, x_height, x_width] [all …]
|
| D | test_ps_roi_pooling_grad_v2_op.py | 30 def __init__(self, input_size, spatial_scale, group_size, output_dim, dynamic_shape=False): argument 34 group_size, output_dim) 62 output_dim = 2 64 y_size = (batch_size * rois_number, output_dim, group_size, group_size) 75 group_size, output_dim,
|
| /third_party/mindspore/mindspore-src/source/mindspore/core/ops/ |
| D | splice.h | 52 /// \brief Method to set output_dim attributes. 54 /// \param[in] output_dim Define the output_dim. 55 void set_output_dim(int64_t output_dim); 67 /// \brief Method to set output_dim attributes. 69 /// \param[in] output_dim Define the output_dim.
|
| D | affine.h | 28 constexpr auto kAffineOutputDim = "output_dim"; 37 void Init(const std::vector<int64_t> &contexts, int64_t output_dim, bool transpose_a = false, 43 /// \brief Method to set output_dim attributes. 45 /// \param[in] output_dim Define the output dim. 46 void set_output_dim(int64_t output_dim); 66 /// \brief Method to get output_dim attributes.
|
| D | ps_roi_pooling.cc | 63 auto output_dim_ptr = primitive->GetAttr("output_dim"); in PSROIPoolingInferShape() 65 auto output_dim = GetValue<int64_t>(output_dim_ptr); in PSROIPoolingInferShape() local 82 // the first dimension of the input data should be equal group_size * group_size * output_dim in PSROIPoolingInferShape() 83 if (x_shape[1] / (group_size * group_size) != output_dim) { in PSROIPoolingInferShape() 86 … << ") * group_size(" << group_size << ") * output_dim(" << output_dim << ")."; in PSROIPoolingInferShape() 93 ret_shape = {-1, output_dim, group_size, group_size}; in PSROIPoolingInferShape() 102 ret_shape = {-1, output_dim, group_size, group_size}; in PSROIPoolingInferShape() 104 ret_shape = {rois_shape[0] * rois_shape[dim2], output_dim, group_size, group_size}; in PSROIPoolingInferShape()
|
| D | affine.cc | 31 void Affine::Init(const std::vector<int64_t> &contexts, int64_t output_dim, bool transpose_a, bool … in Init() argument 33 this->set_output_dim(output_dim); in Init() 42 void Affine::set_output_dim(int64_t output_dim) { (void)this->AddAttr(kAffineOutputDim, api::MakeVa… in set_output_dim() argument
|
| D | splice.cc | 46 void Splice::set_output_dim(int64_t output_dim) { (void)this->AddAttr(kSpliceOutputDims, api::MakeV… in set_output_dim() argument
|
| /third_party/mindspore/mindspore-src/source/tests/st/ops/cpu/ |
| D | test_ps_roi_pooling_v2_op.py | 38 def __init__(self, spatial_scale, group_size, output_dim, dynamic_shape=False): argument 43 self.ps_roi_pooling = G.PSROIPooling(spatial_scale, group_size, output_dim) 72 output_dim = 4 75 x_size = [batch_size * rois_num, output_dim * group_size * group_size, x_height, x_width] 84 net = NetPSROIPooling(spatial_scale, group_size, output_dim, dynamic_shape=dynamic_shape) 158 output_dim = 4 161 x_size = [batch_size * rois_num, output_dim * group_size * group_size, x_height, x_width] 165 net = NetPSROIPooling(spatial_scale, group_size, output_dim, dynamic_shape=False) 281 _check_attr_validation(arg_name="output_dim", arg_value=7.1) 293 _check_attr_validation(arg_name="output_dim", arg_value=-1) [all …]
|
| D | test_ps_roi_pooling_grad_op.py | 35 def __init__(self, input_size, spatial_scale, group_size, output_dim, dynamic_shape=False): argument 41 group_size, output_dim) 68 output_dim = 2 70 y_size = [batch_size * rois_number, output_dim, group_size, group_size] 82 input_size, spatial_scale, group_size, output_dim, 196 output_dim = 2 198 y_size = [batch_size * rois_number, output_dim, group_size, group_size] 204 net = NetPSROIPoolingGrad(input_size, spatial_scale, group_size, output_dim) 464 _check_attr_validation(arg_name="output_dim", arg_value=7.1) 476 _check_attr_validation(arg_name="output_dim", arg_value=-1) [all …]
|
| /third_party/mindspore/mindspore-src/source/tests/st/ops/ascend/test_tbe_ops/ |
| D | test_p_s_r_o_i_pooling_grad.py | 28 def __init__(self, input_size, spatial_scale, group_size, output_dim): argument 30 self.roi_pooling = G.PSROIPoolingGrad(input_size, spatial_scale, group_size, output_dim) 37 def test_net(x_shape, rois_shape, input_size, spatial_scale, group_size, output_dim): argument 49 output_dim: (output_dim + C0 - 1) // C0 == c, where c0 is 16 in davinci. 63 roi_grad = Net(input_size, spatial_scale, group_size, output_dim)
|
| /third_party/mindspore/mindspore-src/source/tests/st/mindscience/mindsponge/mindsponge/cell/ |
| D | basic.py | 33 def __init__(self, num_head, hidden_size, gating, q_data_dim, m_data_dim, output_dim, argument 38 self.output_dim = output_dim 130 self.output_dim, 134 self.o_biases = Parameter(Tensor(np.zeros([self.batch_size, self.output_dim]), 160 Tensor(np.zeros([self.output_dim, self.num_head * self.dim_per_head]), 162 self.o_biases = Parameter(Tensor(np.zeros([self.output_dim]), mstype.float32)) 180 def __init__(self, num_head, gating, input_dim, output_dim, batch_size=None): argument 186 self.output_dim = output_dim 271 (b, -1, self.output_dim)) 299 self.output_dim, [all …]
|
| /third_party/mindspore/mindspore-src/source/mindspore/core/ops/grad/ |
| D | p_s_r_o_i_pooling_grad.cc | 55 auto dim_ptr = primitive->GetAttr("output_dim"); in PSROIPoolingGradInferShape() 57 auto output_dim = GetValue<int64_t>(dim_ptr); in PSROIPoolingGradInferShape() local 63 if (output_dim != x_shape[1]) { in PSROIPoolingGradInferShape() 65 << ", must be equal to output_dim: " << output_dim << "."; in PSROIPoolingGradInferShape() 71 int64_t output_c = group_size * group_size * output_dim; in PSROIPoolingGradInferShape()
|
| /third_party/mindspore/mindspore-src/source/mindspore/lite/tools/converter/parser/onnx/ |
| D | onnx_splice_parser.cc | 32 int64_t output_dim = 0; in Parse() local 47 } else if (attribute_name == "output_dim") { in Parse() 48 output_dim = static_cast<int>(onnx_node_attr.i()); in Parse() 54 primitive->Init(context, forward_indexes, output_dim); in Parse()
|
| D | onnx_einsum_adjust.cc | 64 auto output_dim = output_dims.substr(output_dims.length() - DIMENSION_2D); in CheckCanConvertToMatmul() local 74 auto dim_out = *trans_out ? get_reversed_string(output_dim) : output_dim; in CheckCanConvertToMatmul()
|
| /third_party/mindspore/mindspore-src/source/mindspore/ccsrc/transform/graph_ir/op_declare/ |
| D | nn_detect_ops_declare.cc | 93 ATTR_MAP(PSROIPooling) = {{"output_dim", ATTR_DESC(output_dim, AnyTraits<int32_t>())}, 101 ATTR_MAP(PSROIPoolingV2) = {{"output_dim", ATTR_DESC(output_dim, AnyTraits<int64_t>(), AnyTraits<in… 109 ATTR_MAP(PSROIPoolingGradV2D) = {{"output_dim", ATTR_DESC(output_dim, AnyTraits<int64_t>(), AnyTrai…
|
| /third_party/mindspore/mindspore-src/source/mindspore/lite/tools/converter/adapter/dpico/parser/caffe/ |
| D | caffe_psroi_pooling_parser.cc | 64 int32_t output_dim = psroi_pooling_param.output_dim(); in Parse() local 65 (void)prim->AddAttr(dpico::kOutputDim, api::MakeValue<int64_t>(output_dim)); in Parse() 68 …if (memcpy_s(output_dim_attr.data(), output_dim_attr.size() * sizeof(uint8_t), &output_dim, sizeof… in Parse()
|
| /third_party/mindspore/mindspore-src/source/docs/api/api_python/ops/ |
| D | mindspore.ops.PSROIPooling.rst | 4 .. py:class:: mindspore.ops.PSROIPooling(spatial_scale, group_size, output_dim) 11 - **output_dim** (int) - 执行池化后输出的维度。 22 - **TypeError** - `group_size` 或者 `output_dim` 不是 int类型。
|
| /third_party/mindspore/mindspore-src/source/mindspore/lite/tools/converter/micro/coder/opcoders/nnacl/int8/ |
| D | concat_int8_coder.cc | 82 int output_dim = static_cast<int>(output_tensor_->shape().size()); in Prepare() local 83 micro_concat_.output_shapes_ = reinterpret_cast<int *>(malloc(output_dim * sizeof(int))); in Prepare() 85 …MS_CHECK_RET_CODE(memcpy_s(reinterpret_cast<void *>(micro_concat_.output_shapes_), output_dim * si… in Prepare() 86 output_tensor_->shape().data(), sizeof(int) * output_dim), in Prepare() 88 for (int i = micro_concat_.axis_ + 1; i < output_dim; i++) { in Prepare()
|
| /third_party/mindspore/mindspore-src/source/mindspore/lite/tools/converter/adapter/dpico/infer/ |
| D | dpico_psroi_pool_infer.cc | 59 int32_t output_dim = 0; in Infer() local 83 if (memcpy_s(&output_dim, sizeof(int32_t), custom_attrs[dpico::kOutputDim]->data(), in Infer() 89 MS_LOG(ERROR) << "output_dim attr doesn't exist."; in Infer() 108 output_shape[dpico::kAxis1] = output_dim; in Infer()
|
| /third_party/mindspore/mindspore-src/source/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_ops/customize/op_proto/ |
| D | concat_proto.cc | 128 size_t output_dim = output_shape.GetDimNum(); in ConcatInferShapeCommonStatic() local 129 if ((axis < -static_cast<int64_t>(output_dim)) || (axis >= static_cast<int64_t>(output_dim))) { in ConcatInferShapeCommonStatic() 134 axis += static_cast<int64_t>(output_dim); in ConcatInferShapeCommonStatic() 141 if (IsScalar(input_i_shape) && output_dim == 1) { in ConcatInferShapeCommonStatic() 149 if (input_i_shape.GetDimNum() != output_dim) { in ConcatInferShapeCommonStatic() 154 for (int64_t check_dim = 0; check_dim < static_cast<int64_t>(output_dim); check_dim++) { in ConcatInferShapeCommonStatic()
|
| D | array_ops_proto.cc | 334 std::vector<int64_t> output_dim; in IMPLEMT_COMMON_INFERFUNC() local 340 output_dim.push_back(length_data); in IMPLEMT_COMMON_INFERFUNC() 342 ge::Shape output_shape = ge::Shape(output_dim); in IMPLEMT_COMMON_INFERFUNC() 433 std::vector<int64_t> output_dim; in CUST_IMPLEMT_INFERFUNC() local 435 output_dim.push_back(batch_dim); in CUST_IMPLEMT_INFERFUNC() 436 output_dim.push_back(expert_num_data); in CUST_IMPLEMT_INFERFUNC() 437 output_dim.push_back(capacity_data); in CUST_IMPLEMT_INFERFUNC() 438 ge::Shape dispatch_shape = ge::Shape(output_dim); in CUST_IMPLEMT_INFERFUNC()
|
| /third_party/mindspore/mindspore-src/source/mindspore/lite/src/litert/kernel/cpu/fp32/ |
| D | sparse_to_dense_fp32.cc | 70 int output_dim = static_cast<int>(output->shape().size()); in ReSize() local 71 MS_CHECK_TRUE_MSG(output_dim <= DIMENSION_4D, RET_ERROR, "output_dim should <= 4"); in ReSize() 75 int pad_dims = DIMENSION_4D - output_dim; in ReSize()
|
| /third_party/mindspore/mindspore-src/source/mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_ops/cpu_kernel/ms_kernel/ |
| D | scale_and_translate.h | 60 // int32 tensor of size [output_dim]. 62 // float tensor of size [output_dim, span_size].
|
| /third_party/mindspore/mindspore-src/source/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/cuda_ops/ |
| D | lp_norm_impl.cu | 43 int output_dim = static_cast<int>(output_shape_length - 1); in LpCalKernel() local 49 for (int j = output_dim; j >= 0; --j) { in LpCalKernel() 52 output_dim = j - 1; in LpCalKernel()
|
| D | hypot_impl.cu | 102 size_t output_dim[5]; in BroadcastHypot() local 105 CalShapeData(y_shape, output_dim); in BroadcastHypot() 108 cudaMemcpyToSymbol(output_cal, output_dim, sizeof(size_t) * 5); in BroadcastHypot()
|