aclnnUpsampleNearestExact2d

Supported Products

Product	Supported
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

API description: Applies the nearest neighbor interpolation algorithm to upsample the input signal composed of several input channels. If the input shape is (N, C, H, W), the output shape is (N, C, outputSize[0], outputSize[1]).
Formula: $h_{src} = min(floor((h_{dst} + 0.5) / scalesH), H - 1),scalesH = outputSize[0] / H$ $w_{src} = min(floor((w_{dst} + 0.5) / scalesW), W - 1),scalesW = outputSize[1] / W$ $out(N, C, h_{dst}, w_{dst}) = self(N, C, h_{src}, w_{src})$

Prototype

Each operator has two-phase API calls. First, aclnnUpsampleNearestExact2dGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnUpsampleNearestExact2d is called to perform computation.

aclnnStatus aclnnUpsampleNearestExact2dGetWorkspaceSize(
  const aclTensor   *self, 
  const aclIntArray *outputSize, 
  double             scalesH, 
  double             scalesW, 
  aclTensor         *out, 
  uint64_t          *workspaceSize, 
  aclOpExecutor    **executor)

aclnnStatus aclnnUpsampleNearestExact2d(
  void          *workspace, 
  uint64_t       workspaceSize, 
  aclOpExecutor *executor, 
  aclrtStream    stream)

aclnnUpsampleNearestExact2dGetWorkspaceSize

Parameters:

Name	Input/Output	Description	Precaution	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
self	Input	Input tensor for upsampling, corresponding to the input `self` in the formula.	Empty tensors are supported. The ND format is processed as NCHW format by default.	FLOAT32, FLOAT16, BFLOAT16	NCHW, NHWC, ND	4	√
outputSize	Input	Spatial size of `out` in the H and W dimensions, corresponding to `outputSize` in the formula.	The size is 2.	INT64	-	-	-
scalesH	Input	Scaling multiplier for spatial size in the H direction, corresponding to `scalesH` in the formula.	The value cannot be negative.	DOUBLE	-	-	-
scalesW	Input	Scaling multiplier for spatial size in the W direction, corresponding to `scalesW` in the formula.	The value cannot be negative.	DOUBLE	-	-	-
out	Output	Output tensor after sampling, corresponding to the output `out` in the formula.	Empty tensors are supported. The data type is the same as that of the input parameter `self`.	FLOAT32, FLOAT16, BFLOAT16	NCHW, NHWC, ND	4	√
workspaceSize	Output	Size of the workspace required to be allocated on the device.	-	-	-	-	-
executor	Output	Operator executor, containing the operator computation process.	-	-	-	-	-

Atlas inference products:

The data types of self and out support only FLOAT32 and FLOAT16.

Returns:

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

The first-phase API implements input parameter verification. The following errors may be thrown.

Return	Error Code	Description
ACLNN_ERR_PARAM_NULLPTR	161001	The passed self or out is a null pointer.
ACLNN_ERR_PARAM_INVALID	161002	The data type of self is not supported or is different from that of out.
		The data format of self is not supported.
		The shape of self is not 4D.
		The dimension sizes of the N or C axes of self and out are different.
		The data format of self or out is not supported.

aclnnUpsampleNearestExact2d

Parameters:

Name	Input/Output	Description
workspace	Input	Address of the workspace to be allocated on the device.
workspaceSize	Input	Size of the workspace to be allocated on the device, which is obtained by calling the first-phase API aclnnUpsampleNearestExact2dGetWorkspaceSize.
executor	Input	Operator executor, containing the operator computation process.
stream	Input	Stream for executing the task.

Returns:

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

Constraints

Either the H and W axes of the outputSize parameter or the scalesH and scalesW parameters can be used.
- If the value of scalesH or scalesW is less than or equal to 0, the value of the corresponding axis in outputSize is used.
- If the value of scalesH or scalesW is greater than 0, the value of scalesH or scalesW is used. That is, the value of the corresponding axis of outputSize is $floor(self\_H * scalesH)$ or $floor(self\_W * scalesW)$ .
Deterministic computing:
- aclnnUpsampleNearestExact2d defaults to a deterministic implementation.

Example

The following example is for reference only. For details, see Compilation and Running Sample.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_upsample_nearest_exact2d.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)
{
  // (Fixed writing) Initialize resources.
    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 allocate memory on the device.
    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 the data on the host to the memory on the device.
    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);

    // Compute the strides of the 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 aclCreateTensor 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. (Fixed writing) Initialize the device and stream. For details, see the ACL API manual.
    // Set the device ID in use.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    // Handle the check as required.
    CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

    // 2. Construct the input and output based on the API.
    int64_t outSize = 32;
    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(outSize, 0);

    double scalesH = 0.0;
    double scalesW = 0.0;

    // Create a self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create a self aclIntArray.
    auto outputSizeArray = aclCreateIntArray(outputSizeHostData.data(), 2);
    // Create an 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, which needs to be replaced with the actual API.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclnnUpsampleNearestExact2d.
    ret = aclnnUpsampleNearestExact2dGetWorkspaceSize(
        self, outputSizeArray, scalesH, scalesW, out, &workspaceSize, &executor);
    CHECK_RET(ret == ACL_SUCCESS,
        LOG_PRINT("aclnnUpsampleNearestExact2dGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Allocate device memory based on the computed workspaceSize.
    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 aclnnUpsampleNearestExact2d.
    ret = aclnnUpsampleNearestExact2d(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnUpsampleNearestExact2d failed. ERROR: %d\n", ret); return ret);
    // 4. (Fixed writing) Wait until the task execution is 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. Modify the configuration based on the 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 the configuration based on the API definition.
    aclDestroyTensor(self);
    aclDestroyIntArray(outputSizeArray);
    aclDestroyTensor(out);
    
    // 7. Release device resources. Modify the configuration based on the API definition.
    aclrtFree(selfDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}