1 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2
3 Licensed under the Apache License, Version 2.0 (the "License");
4 you may not use this file except in compliance with the License.
5 You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9 Unless required by applicable law or agreed to in writing, software
10 distributed under the License is distributed on an "AS IS" BASIS,
11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 See the License for the specific language governing permissions and
13 limitations under the License.
14 ==============================================================================*/
15
16 // NEON implementations of Image methods for compatible devices. Control
17 // should never enter this compilation unit on incompatible devices.
18
19 #ifdef __ARM_NEON
20
21 #include <arm_neon.h>
22 #include <stdint.h>
23
24 #include "tensorflow/tools/android/test/jni/object_tracking/image-inl.h"
25 #include "tensorflow/tools/android/test/jni/object_tracking/image.h"
26 #include "tensorflow/tools/android/test/jni/object_tracking/image_utils.h"
27 #include "tensorflow/tools/android/test/jni/object_tracking/utils.h"
28
29 namespace tf_tracking {
30
31 // This function does the bulk of the work.
32 template <>
Downsample2x32ColumnsNeon(const uint8_t * const original,const int stride,const int orig_x)33 void Image<uint8_t>::Downsample2x32ColumnsNeon(const uint8_t* const original,
34 const int stride,
35 const int orig_x) {
36 // Divide input x offset by 2 to find output offset.
37 const int new_x = orig_x >> 1;
38
39 // Initial offset into top row.
40 const uint8_t* offset = original + orig_x;
41
42 // This points to the leftmost pixel of our 8 horizontally arranged
43 // pixels in the destination data.
44 uint8_t* ptr_dst = (*this)[0] + new_x;
45
46 // Sum along vertical columns.
47 // Process 32x2 input pixels and 16x1 output pixels per iteration.
48 for (int new_y = 0; new_y < height_; ++new_y) {
49 uint16x8_t accum1 = vdupq_n_u16(0);
50 uint16x8_t accum2 = vdupq_n_u16(0);
51
52 // Go top to bottom across the four rows of input pixels that make up
53 // this output row.
54 for (int row_num = 0; row_num < 2; ++row_num) {
55 // First 16 bytes.
56 {
57 // Load 16 bytes of data from current offset.
58 const uint8x16_t curr_data1 = vld1q_u8(offset);
59
60 // Pairwise add and accumulate into accum vectors (16 bit to account
61 // for values above 255).
62 accum1 = vpadalq_u8(accum1, curr_data1);
63 }
64
65 // Second 16 bytes.
66 {
67 // Load 16 bytes of data from current offset.
68 const uint8x16_t curr_data2 = vld1q_u8(offset + 16);
69
70 // Pairwise add and accumulate into accum vectors (16 bit to account
71 // for values above 255).
72 accum2 = vpadalq_u8(accum2, curr_data2);
73 }
74
75 // Move offset down one row.
76 offset += stride;
77 }
78
79 // Divide by 4 (number of input pixels per output
80 // pixel) and narrow data from 16 bits per pixel to 8 bpp.
81 const uint8x8_t tmp_pix1 = vqshrn_n_u16(accum1, 2);
82 const uint8x8_t tmp_pix2 = vqshrn_n_u16(accum2, 2);
83
84 // Concatenate 8x1 pixel strips into 16x1 pixel strip.
85 const uint8x16_t allpixels = vcombine_u8(tmp_pix1, tmp_pix2);
86
87 // Copy all pixels from composite 16x1 vector into output strip.
88 vst1q_u8(ptr_dst, allpixels);
89
90 ptr_dst += stride_;
91 }
92 }
93
94 // This function does the bulk of the work.
95 template <>
Downsample4x32ColumnsNeon(const uint8_t * const original,const int stride,const int orig_x)96 void Image<uint8_t>::Downsample4x32ColumnsNeon(const uint8_t* const original,
97 const int stride,
98 const int orig_x) {
99 // Divide input x offset by 4 to find output offset.
100 const int new_x = orig_x >> 2;
101
102 // Initial offset into top row.
103 const uint8_t* offset = original + orig_x;
104
105 // This points to the leftmost pixel of our 8 horizontally arranged
106 // pixels in the destination data.
107 uint8_t* ptr_dst = (*this)[0] + new_x;
108
109 // Sum along vertical columns.
110 // Process 32x4 input pixels and 8x1 output pixels per iteration.
111 for (int new_y = 0; new_y < height_; ++new_y) {
112 uint16x8_t accum1 = vdupq_n_u16(0);
113 uint16x8_t accum2 = vdupq_n_u16(0);
114
115 // Go top to bottom across the four rows of input pixels that make up
116 // this output row.
117 for (int row_num = 0; row_num < 4; ++row_num) {
118 // First 16 bytes.
119 {
120 // Load 16 bytes of data from current offset.
121 const uint8x16_t curr_data1 = vld1q_u8(offset);
122
123 // Pairwise add and accumulate into accum vectors (16 bit to account
124 // for values above 255).
125 accum1 = vpadalq_u8(accum1, curr_data1);
126 }
127
128 // Second 16 bytes.
129 {
130 // Load 16 bytes of data from current offset.
131 const uint8x16_t curr_data2 = vld1q_u8(offset + 16);
132
133 // Pairwise add and accumulate into accum vectors (16 bit to account
134 // for values above 255).
135 accum2 = vpadalq_u8(accum2, curr_data2);
136 }
137
138 // Move offset down one row.
139 offset += stride;
140 }
141
142 // Add and widen, then divide by 16 (number of input pixels per output
143 // pixel) and narrow data from 32 bits per pixel to 16 bpp.
144 const uint16x4_t tmp_pix1 = vqshrn_n_u32(vpaddlq_u16(accum1), 4);
145 const uint16x4_t tmp_pix2 = vqshrn_n_u32(vpaddlq_u16(accum2), 4);
146
147 // Combine 4x1 pixel strips into 8x1 pixel strip and narrow from
148 // 16 bits to 8 bits per pixel.
149 const uint8x8_t allpixels = vmovn_u16(vcombine_u16(tmp_pix1, tmp_pix2));
150
151 // Copy all pixels from composite 8x1 vector into output strip.
152 vst1_u8(ptr_dst, allpixels);
153
154 ptr_dst += stride_;
155 }
156 }
157
158
159 // Hardware accelerated downsampling method for supported devices.
160 // Requires that image size be a multiple of 16 pixels in each dimension,
161 // and that downsampling be by a factor of 2 or 4.
162 template <>
DownsampleAveragedNeon(const uint8_t * const original,const int stride,const int factor)163 void Image<uint8_t>::DownsampleAveragedNeon(const uint8_t* const original,
164 const int stride,
165 const int factor) {
166 // TODO(andrewharp): stride is a bad approximation for the src image's width.
167 // Better to pass that in directly.
168 SCHECK(width_ * factor <= stride, "Uh oh!");
169 const int last_starting_index = width_ * factor - 32;
170
171 // We process 32 input pixels lengthwise at a time.
172 // The output per pass of this loop is an 8 wide by downsampled height tall
173 // pixel strip.
174 int orig_x = 0;
175 for (; orig_x <= last_starting_index; orig_x += 32) {
176 if (factor == 2) {
177 Downsample2x32ColumnsNeon(original, stride, orig_x);
178 } else {
179 Downsample4x32ColumnsNeon(original, stride, orig_x);
180 }
181 }
182
183 // If a last pass is required, push it to the left enough so that it never
184 // goes out of bounds. This will result in some extra computation on devices
185 // whose frame widths are multiples of 16 and not 32.
186 if (orig_x < last_starting_index + 32) {
187 if (factor == 2) {
188 Downsample2x32ColumnsNeon(original, stride, last_starting_index);
189 } else {
190 Downsample4x32ColumnsNeon(original, stride, last_starting_index);
191 }
192 }
193 }
194
195
196 // Puts the image gradient matrix about a pixel into the 2x2 float array G.
197 // vals_x should be an array of the window x gradient values, whose indices
198 // can be in any order but are parallel to the vals_y entries.
199 // See http://robots.stanford.edu/cs223b04/algo_tracking.pdf for more details.
CalculateGNeon(const float * const vals_x,const float * const vals_y,const int num_vals,float * const G)200 void CalculateGNeon(const float* const vals_x, const float* const vals_y,
201 const int num_vals, float* const G) {
202 const float32_t* const arm_vals_x = (const float32_t*) vals_x;
203 const float32_t* const arm_vals_y = (const float32_t*) vals_y;
204
205 // Running sums.
206 float32x4_t xx = vdupq_n_f32(0.0f);
207 float32x4_t xy = vdupq_n_f32(0.0f);
208 float32x4_t yy = vdupq_n_f32(0.0f);
209
210 // Maximum index we can load 4 consecutive values from.
211 // e.g. if there are 81 values, our last full pass can be from index 77:
212 // 81-4=>77 (77, 78, 79, 80)
213 const int max_i = num_vals - 4;
214
215 // Defined here because we want to keep track of how many values were
216 // processed by NEON, so that we can finish off the remainder the normal
217 // way.
218 int i = 0;
219
220 // Process values 4 at a time, accumulating the sums of
221 // the pixel-wise x*x, x*y, and y*y values.
222 for (; i <= max_i; i += 4) {
223 // Load xs
224 float32x4_t x = vld1q_f32(arm_vals_x + i);
225
226 // Multiply x*x and accumulate.
227 xx = vmlaq_f32(xx, x, x);
228
229 // Load ys
230 float32x4_t y = vld1q_f32(arm_vals_y + i);
231
232 // Multiply x*y and accumulate.
233 xy = vmlaq_f32(xy, x, y);
234
235 // Multiply y*y and accumulate.
236 yy = vmlaq_f32(yy, y, y);
237 }
238
239 static float32_t xx_vals[4];
240 static float32_t xy_vals[4];
241 static float32_t yy_vals[4];
242
243 vst1q_f32(xx_vals, xx);
244 vst1q_f32(xy_vals, xy);
245 vst1q_f32(yy_vals, yy);
246
247 // Accumulated values are store in sets of 4, we have to manually add
248 // the last bits together.
249 for (int j = 0; j < 4; ++j) {
250 G[0] += xx_vals[j];
251 G[1] += xy_vals[j];
252 G[3] += yy_vals[j];
253 }
254
255 // Finishes off last few values (< 4) from above.
256 for (; i < num_vals; ++i) {
257 G[0] += Square(vals_x[i]);
258 G[1] += vals_x[i] * vals_y[i];
259 G[3] += Square(vals_y[i]);
260 }
261
262 // The matrix is symmetric, so this is a given.
263 G[2] = G[1];
264 }
265
266 } // namespace tf_tracking
267
268 #endif
269