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#include "caffe2/operators/instance_norm_op.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
namespace {
template <typename T>
void ComputeFusedParams(
const int64_t N,
const int64_t C,
const T* mean,
const T* rstd,
const T* gamma,
const T* beta,
T* scale,
T* bias) {
ConstEigenArrayMap<T> mean_arr(mean, C, N);
ConstEigenArrayMap<T> rstd_arr(rstd, C, N);
ConstEigenVectorArrayMap<T> gamma_arr(gamma, C);
ConstEigenVectorArrayMap<T> beta_arr(beta, C);
EigenArrayMap<T> scale_arr(scale, C, N);
EigenArrayMap<T> bias_arr(bias, C, N);
scale_arr = rstd_arr.colwise() * gamma_arr;
bias_arr = (-scale_arr * mean_arr).colwise() + beta_arr;
}
template <typename T>
void InstanceNormForwardNHWC(
const int64_t N,
const int64_t C,
const int64_t HxW,
const T* X,
const T* scale,
const T* bias,
T* Y) {
ConstEigenArrayMap<T> scale_arr(scale, C, N);
ConstEigenArrayMap<T> bias_arr(bias, C, N);
for (int64_t i = 0; i < N; ++i) {
ConstEigenArrayMap<T> X_arr(X + i * HxW * C, C, HxW);
EigenArrayMap<T> Y_arr(Y + i * HxW * C, C, HxW);
Y_arr = (X_arr.colwise() * scale_arr.col(i)).colwise() + bias_arr.col(i);
}
}
} // namespace
template <>
bool InstanceNormOp<float, CPUContext>::RunOnDeviceWithOrderNCHW(
const int64_t N,
const int64_t C,
const int64_t HxW,
const float* X,
const float* gamma,
const float* beta,
float* Y,
float* mean,
float* rstd) {
ConstEigenArrayMap<float> X_arr(X, HxW, N * C);
for (int64_t i = 0; i < N * C; ++i) {
const float mean_val = X_arr.col(i).mean();
float rstd_val =
std::max(X_arr.col(i).square().mean() - mean_val * mean_val, 0.0f);
rstd_val = 1.0f / std::sqrt(rstd_val + epsilon_);
const int64_t c = i % C;
const float scale = gamma[c] * rstd_val;
const float bias = beta[c] - scale * mean_val;
for (int64_t j = 0; j < HxW; ++j) {
Y[i * HxW + j] = scale * X[i * HxW + j] + bias;
}
mean[i] = mean_val;
rstd[i] = rstd_val;
}
return true;
}
template <>
bool InstanceNormOp<float, CPUContext>::RunOnDeviceWithOrderNHWC(
const int64_t N,
const int64_t C,
const int64_t HxW,
const float* X,
const float* gamma,
const float* beta,
float* Y,
float* mean,
float* rstd) {
ReinitializeTensor(&scale_, {N, C}, at::dtype<float>().device(CPU));
ReinitializeTensor(&bias_, {N, C}, at::dtype<float>().device(CPU));
float* scale_data = scale_.template mutable_data<float>();
float* bias_data = bias_.template mutable_data<float>();
const float c = 1.0f / static_cast<float>(HxW);
EigenArrayMap<float> mean_arr(mean, C, N);
EigenArrayMap<float> rstd_arr(rstd, C, N);
for (int64_t n = 0; n < N; ++n) {
ConstEigenArrayMap<float> X_arr(X + n * HxW * C, C, HxW);
mean_arr.col(n) = X_arr.col(0);
rstd_arr.col(n) = X_arr.col(0).square();
for (int64_t i = 1; i < HxW; ++i) {
mean_arr.col(n) += X_arr.col(i);
rstd_arr.col(n) += X_arr.col(i).square();
}
}
mean_arr *= c;
rstd_arr = ((rstd_arr * c - mean_arr.square()).max(0.0f) + epsilon_).rsqrt();
ComputeFusedParams<float>(
N, C, mean, rstd, gamma, beta, scale_data, bias_data);
InstanceNormForwardNHWC<float>(N, C, HxW, X, scale_data, bias_data, Y);
return true;
}
REGISTER_CPU_OPERATOR(InstanceNorm, InstanceNormOp<float, CPUContext>);
OPERATOR_SCHEMA(InstanceNorm)
.NumInputs(3)
.NumOutputs(1, 3)
.AllowInplace({{0, 0}})
.SetDoc(R"DOC(
The *InstanceNorm* op applies Instance Normalization over a 4D input as described in [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
$$output = \frac{input-\mu_{input}}{\sqrt{\sigma_{input}^2} + \epsilon}*scale + bias$$
Notice, two of the outputs are optional so there are three output cases for this op. Case 1: output; Case 2: output, saved_mean; Case 3: output, saved_mean, saved_inv_stdev.
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/instance_norm_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/instance_norm_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"InstanceNorm",
["input", "scale", "bias"],
["output"],
epsilon=1e-5,
)
workspace.FeedBlob("input", np.random.randn(2, 1, 3, 3).astype(np.float32))
print("input:\n", workspace.FetchBlob("input"), "\n")
workspace.FeedBlob("scale", np.array([1.5]).astype(np.float32))
print("scale: ", workspace.FetchBlob("scale"))
workspace.FeedBlob("bias", np.array([1.]).astype(np.float32))
print("bias: ", workspace.FetchBlob("bias"))
workspace.RunOperatorOnce(op)
print("output:\n", workspace.FetchBlob("output"))
```
**Result**
```
input:
[[[[ 0.97856593 -1.1832817 -0.2540021 ]
[-1.3315694 -0.7485018 0.3787225 ]
[-0.6826597 -1.4637762 0.57116514]]]
[[[-0.44948956 0.85544354 -0.9315333 ]
[-0.37202677 -0.22266895 -0.27194235]
[ 0.4948163 -0.7296504 1.3393803 ]]]]
scale: [1.5]
bias: [1.]
output:
[[[[ 3.5017493 -0.3791256 1.2890853 ]
[-0.6453266 0.40137637 2.4249308 ]
[ 0.5195738 -0.8826599 2.7703972 ]]]
[[[ 0.12639964 2.856744 -0.8821926 ]
[ 0.28847694 0.60098207 0.49788612]
[ 2.1021945 -0.45978796 3.869297 ]]]]
```
</details>
)DOC")
.Arg(
"epsilon",
"*(type: float; default: 1e-5)* The epsilon value to use to avoid division by zero.")
.Arg(
"order",
// NOLINTNEXTLINE(modernize-raw-string-literal)
"*(type: string; default: \"NCHW\")* Specifies the order of the input data blob, where $N$ is batch size, $C$ is number of channels, $H$ is spatial height, and $W$ is spatial width. The only other valid option is \"NHWC\".")
.Input(0, "input", "The input 4-dimensional NCHW tensor to be operated on.")
.Input(1, "scale", "The input 1-dimensional scale tensor of size *C*.")
.Input(2, "bias", "The input 1-dimensional bias tensor of size *C*.")
.Output(
0,
"output",
"The output 4-dimensional tensor of the same shape as input.")
.Output(
1,
"saved_mean",
"(Optional) Saved mean used during training to speed up gradient computation. Should not be used for testing.")
.Output(
2,
"saved_inv_stdev",
"(Optional) Saved inverse stdev used during training to speed up gradient computation. Should not be used for testing.");
} // namespace caffe2
|
