1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
|
#include "caffe2/operators/spatial_batch_norm_op.h"
#include <string>
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
template <>
template <typename T>
void SpatialBNGradientOp<CPUContext>::
ComputeMultiBatchScaleBiasGradientsAndFusedParams(
const int N,
const int C,
const int HxW,
const T* scale,
const T* mean,
const T* rstd,
const T* dscale_sum,
const T* dbias_sum,
T* dscale,
T* dbias,
T* alpha,
T* beta,
T* gamma) {
ConstEigenVectorArrayMap<T> scale_arr(scale, C);
ConstEigenVectorArrayMap<T> mean_arr(mean, C);
ConstEigenVectorArrayMap<T> rstd_arr(rstd, C);
EigenVectorArrayMap<T> dscale_arr(dscale, C);
EigenVectorArrayMap<T> dbias_arr(dbias, C);
EigenVectorArrayMap<T> alpha_arr(alpha, C);
EigenVectorArrayMap<T> beta_arr(beta, C);
EigenVectorArrayMap<T> gamma_arr(gamma, C);
const T inv_num_batches = T(1) / static_cast<T>(num_batches_);
math::Scale<T, T, CPUContext>(
C, inv_num_batches, dscale_sum, dscale, &context_);
math::Scale<T, T, CPUContext>(
C, inv_num_batches, dbias_sum, dbias, &context_);
const T inv_nhw = T(1) / static_cast<T>(N * HxW);
alpha_arr = scale_arr * rstd_arr;
beta_arr = dscale_arr * rstd_arr;
gamma_arr = alpha_arr * (mean_arr * beta_arr - dbias_arr) * inv_nhw;
beta_arr *= -alpha_arr * inv_nhw;
}
template <>
template <typename T>
void SpatialBNGradientOp<CPUContext>::ComputeScaleBiasGradientsAndFusedParams(
const int N,
const int C,
const int HxW,
const T* dY,
const T* X,
const T* scale,
const T* mean,
const T* rstd,
T* dscale,
T* dbias,
T* alpha,
T* beta,
T* gamma,
T* /* scratch */) {
ConstEigenVectorArrayMap<T> scale_arr(scale, C);
ConstEigenVectorArrayMap<T> mean_arr(mean, C);
ConstEigenVectorArrayMap<T> rstd_arr(rstd, C);
EigenVectorArrayMap<T> dscale_arr(dscale, C);
EigenVectorArrayMap<T> dbias_arr(dbias, C);
EigenVectorArrayMap<T> alpha_arr(alpha, C);
EigenVectorArrayMap<T> beta_arr(beta, C);
EigenVectorArrayMap<T> gamma_arr(gamma, C);
math::Set<T, CPUContext>(C, T(0), dscale, &context_);
math::Set<T, CPUContext>(C, T(0), dbias, &context_);
if (order_ == StorageOrder::NCHW) {
ConstEigenArrayMap<T> dY_arr(dY, HxW, N * C);
ConstEigenArrayMap<T> X_arr(X, HxW, N * C);
for (int i = 0; i < N; ++i) {
for (int j = 0; j < C; ++j) {
const int c = i * C + j;
dscale_arr(j) +=
(dY_arr.col(c) * (X_arr.col(c) - mean_arr(j)) * rstd_arr(j)).sum();
dbias_arr(j) += dY_arr.col(c).sum();
}
}
} else {
const int outer_size = N * HxW;
ConstEigenArrayMap<T> dY_arr(dY, C, outer_size);
ConstEigenArrayMap<T> X_arr(X, C, outer_size);
for (int i = 0; i < outer_size; ++i) {
dscale_arr += dY_arr.col(i) * (X_arr.col(i) - mean_arr) * rstd_arr;
dbias_arr += dY_arr.col(i);
}
}
const T inv_nhw = T(1) / static_cast<T>(N * HxW);
alpha_arr = scale_arr * rstd_arr;
beta_arr = dscale_arr * rstd_arr;
gamma_arr = alpha_arr * (mean_arr * beta_arr - dbias_arr) * inv_nhw;
beta_arr *= -alpha_arr * inv_nhw;
}
template <>
template <typename T>
void SpatialBNGradientOp<CPUContext>::ComputeXGradient(
const int N,
const int C,
const int HxW,
const T* dY,
const T* X,
const T* alpha,
const T* beta,
const T* gamma,
T* dX) {
ConstEigenVectorArrayMap<T> alpha_arr(alpha, C);
ConstEigenVectorArrayMap<T> beta_arr(beta, C);
ConstEigenVectorArrayMap<T> gamma_arr(gamma, C);
if (order_ == NCHW) {
const int stride = C * HxW;
const T* dY_ptr = dY;
const T* X_ptr = X;
T* dX_ptr = dX;
for (int i = 0; i < N; ++i) {
EigenArrayMap<T>(dX_ptr, HxW, C) =
(ConstEigenArrayMap<T>(dY_ptr, HxW, C).rowwise() *
alpha_arr.transpose() +
ConstEigenArrayMap<T>(X_ptr, HxW, C).rowwise() *
beta_arr.transpose())
.rowwise() +
gamma_arr.transpose();
dY_ptr += stride;
X_ptr += stride;
dX_ptr += stride;
}
} else {
EigenArrayMap<T>(dX, C, N * HxW) =
(ConstEigenArrayMap<T>(dY, C, N * HxW).colwise() * alpha_arr +
ConstEigenArrayMap<T>(X, C, N * HxW).colwise() * beta_arr)
.colwise() +
gamma_arr;
}
}
REGISTER_CPU_OPERATOR(SpatialBNGradient, SpatialBNGradientOp<CPUContext>);
// Input: X, scale, dY, mean, variance, dscale, dbias
// Output: dX, dscale, dbias
OPERATOR_SCHEMA(SpatialBNGradient)
.NumInputs({5, 7})
.NumOutputs(3)
.AllowInplace({{5, 1}, {6, 2}});
namespace {
// Spatial batch normalization's gradient, depending on the various input sizes,
// is a bit more complex than usual gradient operators.
class GetSpatialBNGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
std::vector<OperatorDef> GetGradientDefs() override {
// Check if we are in training or testing mode.
const bool is_test =
ArgumentHelper::GetSingleArgument(def_, OpSchema::Arg_IsTest, 0);
const int num_batches =
ArgumentHelper::GetSingleArgument(def_, "num_batches", 1);
const std::vector<string> grad_outputs = {GI(0), GI(1), GI(2)};
std::vector<string> grad_inputs;
if (is_test) {
// This is in testing mode. The operator should have five inputs:
// X, scale, bias, estimated_mean, estimated_variance
// The gradient inputs are:
// X, scale, dY, estimated_mean, estimated_variance
CAFFE_ENFORCE_EQ(def_.input_size(), 5);
CAFFE_ENFORCE_EQ(def_.output_size(), 1);
grad_inputs = std::vector<std::string>{I(0), I(1), GO(0), I(3), I(4)};
} else if (num_batches > 1) {
CAFFE_ENFORCE_EQ(def_.input_size(), 7);
CAFFE_ENFORCE_EQ(def_.output_size(), 5);
grad_inputs =
std::vector<std::string>{I(0), I(1), GO(0), O(3), O(4), GI(1), GI(2)};
} else {
CAFFE_ENFORCE_EQ(def_.input_size(), 5);
CAFFE_ENFORCE_EQ(def_.output_size(), 5);
grad_inputs = std::vector<std::string>{I(0), I(1), GO(0), O(3), O(4)};
}
return SingleGradientDef(
"SpatialBNGradient", "", grad_inputs, grad_outputs);
}
};
} // namespace
REGISTER_GRADIENT(SpatialBN, GetSpatialBNGradient);
} // namespace caffe2
|
