aclnnUpsampleNearest2dV2

Applicable Products

Product	Whether to Support
Ascend 950PR/Ascend 950DT	√
Atlas A3 training products/Atlas A3 inference products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	×
Atlas inference products	√
Atlas training products	√

Function

Description: Applies the nearest neighbor interpolation algorithm to the input signal consisting of multiple input channels for upsampling. If the input shape is (N, C, H, W), the output shape is (N, C, outputSize[0], outputSize[1]). Compared with aclnnUpsampleNearest2d, this API adds the input parameters scalesH and scalesW. Please select the appropriate API based on the actual situation.
Corresponding formula:
$h_{src} = min(floor(h_{dst} * scalesH), H - 1), \ scalesH = outputSize[0] / self\_H$ $w_{src} = min(floor(w_{dst} * scalesW), W - 1), \ scalesW = outputSize[1] / self\_W$ $out(N, C, h_{dst}, w_{dst}) = self(N, C, h_{src}, w_{src})$

Function Prototype

Each operator is divided into a two-phase API. You must first call the "aclnnUpsampleNearest2dV2GetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnUpsampleNearest2dV2" API to perform the computation.

aclnnStatus aclnnUpsampleNearest2dV2GetWorkspaceSize(
  const aclTensor   *self,
  const aclIntArray *outputSize,
  float              scalesH,
  float              scalesW,
  aclTensor         *out,
  uint64_t          *workspaceSize,
  aclOpExecutor    **executor)

aclnnStatus aclnnUpsampleNearest2dV2(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclnnUpsampleNearest2dV2GetWorkspaceSize

Parameters

Parameter Name	Input/Output	Description	Usage Description	Data Type	Data Format	Dimension (shape)	Non-contiguous Tensor
self (aclTensor*)	Input	Represents the input data for upsampling, corresponding to `self` in the formula.	Empty tensors are supported. When the data type is DOUBLE or UINT8, both the H and W axes of self must be less than 2^24. For other data types, all dimension values of self must be less than or equal to (2^31-1).	FLOAT32, BFLOAT16, FLOAT16, DOUBLE, UINT8	NCHW, NHWC	4	√
outputSize (aclIntArray*)	Input	Indicates the spatial size of `out` in the H and W dimensions.	Each element is not equal to zero. size is 2.	INT64	-	-	-
scalesH (float)	Input	Represents the multiplier for the vertical axis of the specified spatial size, corresponding to `scalesH` in the formula.	-	-	-	-	-
scalesW (float)	Input	Indicates the multiplier for the horizontal axis of the specified spatial size, corresponding to `scalesW` in the corresponding formula.	-	-	-	-	-
out (aclTensor*)	output	Represents the output result of upsampling, corresponding to `out` in the formula.	Empty tensors are supported. The data type and data format are consistent with the input self. When the data type is FLOAT32, BFLOAT16, or FLOAT16, all dimension values of out must be less than or equal to (2^31-1). The N axis and C axis of shape are consistent with the input self.	FLOAT32, BFLOAT16, FLOAT16, DOUBLE, UINT8	NCHW, NHWC	4	√
workspaceSize (uint64_t*)	output	Returns the workspace size that the user needs to apply for on the Device side.	-	-	-	-	-
executor (aclOpExecutor**)	output	Returns the operator executor, which contains the operator computation process.	-	-	-	-	-

Atlas inference products, Atlas training products:

The data types of the input self and output out do not support FLOAT32 or BFLOAT16.

Return Value

aclnnStatus: return code. For details, see aclnn Return Codes.

The first segment of the API performs input parameter validation and reports an error in the following scenarios:

Return Code	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The input parameter is a required input, output, or required attribute, and is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of self is not within the supported range, or the data type of self differs from that of out.
		The data format of self and out is not within the supported range.
		The shape of self is not 4-dimensional.
		outputSize contains an element with a value of 0.

aclnnUpsampleNearest2dV2

Parameters

Parameter Name	Input/Output	Description
workspace	Input	The workspace memory address applied for on the Device side.
workspaceSize	Input	The workspace size to be applied on the Device side, obtained through the first-phase API aclnnUpsampleNearest2dV2GetWorkspaceSize.
executor	Input	Operator executor, which contains the operator computation flow.
stream	Input	Specifies the stream for executing the task.

Return Value

aclnnStatus: return code. For details, see aclnn Return Codes.

Constraints

The parameters self, outputSize, scalesH, and scalesW must satisfy the following constraints:
$outputSize\_H = floor(self\_H * scalesH)$ $outputSize\_W = floor(self\_W * scalesW)$
Deterministic computation:
- aclnnUpsampleNearest2dV2 is deterministic by default.

Example

The sample code is as follows, for reference only. For specific compilation and execution procedures, see Compile and Run Samples.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_upsample_nearest_2d_v2.h"

#define CHECK_RET(cond, return_expr) \
    do {                             \
        if (!(cond)) {               \
            return_expr;             \
        }                            \
    } while (0)

#define LOG_PRINT(message, ...)         \
    do {                                \
        printf(message, ##__VA_ARGS__); \
    } while (0)

int64_t GetShapeSize(const std::vector<int64_t> &shape)
{
    int64_t shape_size = 1;
    for (auto i : shape) {
        shape_size *= i;
    }
    return shape_size;
}

int Init(int32_t deviceId, aclrtStream *stream)
{
    // Boilerplate for resource initialization.
    auto ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
    ret = aclrtCreateStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
    return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T> &hostData, const std::vector<int64_t> &shape, void **deviceAddr,
    aclDataType dataType, aclTensor **tensor)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to apply for device-side memory.
    auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);

    // Call aclrtMemcpy to copy host-side data to device-side memory.
    ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

    // Calculate the strides of a contiguous tensor.
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = shape.size() - 2; i >= 0; i--) {
        strides[i] = shape[i + 1] * strides[i + 1];
    }

    // Call the aclCreateTensor API to create an aclTensor.
    *tensor = aclCreateTensor(shape.data(),
        shape.size(),
        dataType,
        strides.data(),
        0,
        aclFormat::ACL_FORMAT_NCHW,
        shape.data(),
        shape.size(),
        *deviceAddr);
    return 0;
}

int main()
{
    // 1. (Standard practice) Initialize the device/stream. See the ACL API manual.
    // Fill in the deviceId based on your actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    // Handle the check based on your own needs.
    CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. Construct the input and output. Customize the construction based on the API.
    std::vector<int64_t> selfShape = {1, 1, 4, 2};
    std::vector<int64_t> outShape = {1, 1, 8, 4};
    void *selfDeviceAddr = nullptr;
    void *outDeviceAddr = nullptr;
    aclTensor *self = nullptr;
    aclTensor *out = nullptr;
    std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7};
    std::vector<int64_t> outputSizeHostData = {8, 4};
    std::vector<float> outHostData(32, 0);
    float scalesH = 0.0;
    float scalesW = 0.0;

    // Create the self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create the self aclIntArray.
    auto outputSizeArray = aclCreateIntArray(outputSizeHostData.data(), 2);
    // Create the out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // 3. Call the CANN operator library API. Replace it with the specific API.
    uint64_t workspaceSize = 0;
    aclOpExecutor *executor;
    // Call the first-phase API of aclnnUpsampleNearest2dV2.
    ret = aclnnUpsampleNearest2dV2GetWorkspaceSize(
        self, outputSizeArray, scalesH, scalesW, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest2dV2GetWorkspaceSize failed. ERROR: %d\n", ret);
              return ret);
    // Apply for device memory based on the workspaceSize calculated by the first-phase API.
    void *workspaceAddr = nullptr;
    if (workspaceSize > 0) {
        ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
        CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret;);
    }
    // Call the second-phase API of aclnnUpsampleNearest2dV2.
    ret = aclnnUpsampleNearest2dV2(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearest2dV2 failed. ERROR: %d\n", ret); return ret);
    // 4. (Boilerplate) Synchronously wait for task execution to complete.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
    // 5. Obtain the output value and copy the result from the device memory to the host memory. Modify this based on the specific API definition.
    auto size = GetShapeSize(outShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(resultData.data(),
        resultData.size() * sizeof(resultData[0]),
        outDeviceAddr,
        size * sizeof(float),
        ACL_MEMCPY_DEVICE_TO_HOST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
    for (int64_t i = 0; i < size; i++) {
        LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
    }

    // 6. Release aclTensor. Modify based on the specific API definition.
    aclDestroyTensor(self);
    aclDestroyIntArray(outputSizeArray);
    aclDestroyTensor(out);

    // 7. Release device resources. Modify based on the specific API definition.
    aclrtFree(selfDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}