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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 
23 #include <stdint.h>
24 
25 #include "tensorflow/examples/android/jni/object_tracking/image-inl.h"
26 #include "tensorflow/examples/android/jni/object_tracking/image.h"
27 #include "tensorflow/examples/android/jni/object_tracking/image_utils.h"
28 #include "tensorflow/examples/android/jni/object_tracking/utils.h"
29 
30 namespace tf_tracking {
31 
32 // This function does the bulk of the work.
33 template <>
Downsample2x32ColumnsNeon(const uint8_t * const original,const int stride,const int orig_x)34 void Image<uint8_t>::Downsample2x32ColumnsNeon(const uint8_t* const original,
35                                                const int stride,
36                                                const int orig_x) {
37   // Divide input x offset by 2 to find output offset.
38   const int new_x = orig_x >> 1;
39 
40   // Initial offset into top row.
41   const uint8_t* offset = original + orig_x;
42 
43   // This points to the leftmost pixel of our 8 horizontally arranged
44   // pixels in the destination data.
45   uint8_t* ptr_dst = (*this)[0] + new_x;
46 
47   // Sum along vertical columns.
48   // Process 32x2 input pixels and 16x1 output pixels per iteration.
49   for (int new_y = 0; new_y < height_; ++new_y) {
50     uint16x8_t accum1 = vdupq_n_u16(0);
51     uint16x8_t accum2 = vdupq_n_u16(0);
52 
53     // Go top to bottom across the four rows of input pixels that make up
54     // this output row.
55     for (int row_num = 0; row_num < 2; ++row_num) {
56       // First 16 bytes.
57       {
58         // Load 16 bytes of data from current offset.
59         const uint8x16_t curr_data1 = vld1q_u8(offset);
60 
61         // Pairwise add and accumulate into accum vectors (16 bit to account
62         // for values above 255).
63         accum1 = vpadalq_u8(accum1, curr_data1);
64       }
65 
66       // Second 16 bytes.
67       {
68         // Load 16 bytes of data from current offset.
69         const uint8x16_t curr_data2 = vld1q_u8(offset + 16);
70 
71         // Pairwise add and accumulate into accum vectors (16 bit to account
72         // for values above 255).
73         accum2 = vpadalq_u8(accum2, curr_data2);
74       }
75 
76       // Move offset down one row.
77       offset += stride;
78     }
79 
80     // Divide by 4 (number of input pixels per output
81     // pixel) and narrow data from 16 bits per pixel to 8 bpp.
82     const uint8x8_t tmp_pix1 = vqshrn_n_u16(accum1, 2);
83     const uint8x8_t tmp_pix2 = vqshrn_n_u16(accum2, 2);
84 
85     // Concatenate 8x1 pixel strips into 16x1 pixel strip.
86     const uint8x16_t allpixels = vcombine_u8(tmp_pix1, tmp_pix2);
87 
88     // Copy all pixels from composite 16x1 vector into output strip.
89     vst1q_u8(ptr_dst, allpixels);
90 
91     ptr_dst += stride_;
92   }
93 }
94 
95 // This function does the bulk of the work.
96 template <>
Downsample4x32ColumnsNeon(const uint8_t * const original,const int stride,const int orig_x)97 void Image<uint8_t>::Downsample4x32ColumnsNeon(const uint8_t* const original,
98                                                const int stride,
99                                                const int orig_x) {
100   // Divide input x offset by 4 to find output offset.
101   const int new_x = orig_x >> 2;
102 
103   // Initial offset into top row.
104   const uint8_t* offset = original + orig_x;
105 
106   // This points to the leftmost pixel of our 8 horizontally arranged
107   // pixels in the destination data.
108   uint8_t* ptr_dst = (*this)[0] + new_x;
109 
110   // Sum along vertical columns.
111   // Process 32x4 input pixels and 8x1 output pixels per iteration.
112   for (int new_y = 0; new_y < height_; ++new_y) {
113     uint16x8_t accum1 = vdupq_n_u16(0);
114     uint16x8_t accum2 = vdupq_n_u16(0);
115 
116     // Go top to bottom across the four rows of input pixels that make up
117     // this output row.
118     for (int row_num = 0; row_num < 4; ++row_num) {
119       // First 16 bytes.
120       {
121         // Load 16 bytes of data from current offset.
122         const uint8x16_t curr_data1 = vld1q_u8(offset);
123 
124         // Pairwise add and accumulate into accum vectors (16 bit to account
125         // for values above 255).
126         accum1 = vpadalq_u8(accum1, curr_data1);
127       }
128 
129       // Second 16 bytes.
130       {
131         // Load 16 bytes of data from current offset.
132         const uint8x16_t curr_data2 = vld1q_u8(offset + 16);
133 
134         // Pairwise add and accumulate into accum vectors (16 bit to account
135         // for values above 255).
136         accum2 = vpadalq_u8(accum2, curr_data2);
137       }
138 
139       // Move offset down one row.
140       offset += stride;
141     }
142 
143     // Add and widen, then divide by 16 (number of input pixels per output
144     // pixel) and narrow data from 32 bits per pixel to 16 bpp.
145     const uint16x4_t tmp_pix1 = vqshrn_n_u32(vpaddlq_u16(accum1), 4);
146     const uint16x4_t tmp_pix2 = vqshrn_n_u32(vpaddlq_u16(accum2), 4);
147 
148     // Combine 4x1 pixel strips into 8x1 pixel strip and narrow from
149     // 16 bits to 8 bits per pixel.
150     const uint8x8_t allpixels = vmovn_u16(vcombine_u16(tmp_pix1, tmp_pix2));
151 
152     // Copy all pixels from composite 8x1 vector into output strip.
153     vst1_u8(ptr_dst, allpixels);
154 
155     ptr_dst += stride_;
156   }
157 }
158 
159 
160 // Hardware accelerated downsampling method for supported devices.
161 // Requires that image size be a multiple of 16 pixels in each dimension,
162 // and that downsampling be by a factor of 2 or 4.
163 template <>
DownsampleAveragedNeon(const uint8_t * const original,const int stride,const int factor)164 void Image<uint8_t>::DownsampleAveragedNeon(const uint8_t* const original,
165                                             const int stride,
166                                             const int factor) {
167   // TODO(andrewharp): stride is a bad approximation for the src image's width.
168   // Better to pass that in directly.
169   SCHECK(width_ * factor <= stride, "Uh oh!");
170   const int last_starting_index = width_ * factor - 32;
171 
172   // We process 32 input pixels lengthwise at a time.
173   // The output per pass of this loop is an 8 wide by downsampled height tall
174   // pixel strip.
175   int orig_x = 0;
176   for (; orig_x <= last_starting_index; orig_x += 32) {
177     if (factor == 2) {
178       Downsample2x32ColumnsNeon(original, stride, orig_x);
179     } else {
180       Downsample4x32ColumnsNeon(original, stride, orig_x);
181     }
182   }
183 
184   // If a last pass is required, push it to the left enough so that it never
185   // goes out of bounds. This will result in some extra computation on devices
186   // whose frame widths are multiples of 16 and not 32.
187   if (orig_x < last_starting_index + 32) {
188     if (factor == 2) {
189       Downsample2x32ColumnsNeon(original, stride, last_starting_index);
190     } else {
191       Downsample4x32ColumnsNeon(original, stride, last_starting_index);
192     }
193   }
194 }
195 
196 
197 // Puts the image gradient matrix about a pixel into the 2x2 float array G.
198 // vals_x should be an array of the window x gradient values, whose indices
199 // can be in any order but are parallel to the vals_y entries.
200 // 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)201 void CalculateGNeon(const float* const vals_x, const float* const vals_y,
202                     const int num_vals, float* const G) {
203   const float32_t* const arm_vals_x = (const float32_t*) vals_x;
204   const float32_t* const arm_vals_y = (const float32_t*) vals_y;
205 
206   // Running sums.
207   float32x4_t xx = vdupq_n_f32(0.0f);
208   float32x4_t xy = vdupq_n_f32(0.0f);
209   float32x4_t yy = vdupq_n_f32(0.0f);
210 
211   // Maximum index we can load 4 consecutive values from.
212   // e.g. if there are 81 values, our last full pass can be from index 77:
213   // 81-4=>77 (77, 78, 79, 80)
214   const int max_i = num_vals - 4;
215 
216   // Defined here because we want to keep track of how many values were
217   // processed by NEON, so that we can finish off the remainder the normal
218   // way.
219   int i = 0;
220 
221   // Process values 4 at a time, accumulating the sums of
222   // the pixel-wise x*x, x*y, and y*y values.
223   for (; i <= max_i; i += 4) {
224     // Load xs
225     float32x4_t x = vld1q_f32(arm_vals_x + i);
226 
227     // Multiply x*x and accumulate.
228     xx = vmlaq_f32(xx, x, x);
229 
230     // Load ys
231     float32x4_t y = vld1q_f32(arm_vals_y + i);
232 
233     // Multiply x*y and accumulate.
234     xy = vmlaq_f32(xy, x, y);
235 
236     // Multiply y*y and accumulate.
237     yy = vmlaq_f32(yy, y, y);
238   }
239 
240   static float32_t xx_vals[4];
241   static float32_t xy_vals[4];
242   static float32_t yy_vals[4];
243 
244   vst1q_f32(xx_vals, xx);
245   vst1q_f32(xy_vals, xy);
246   vst1q_f32(yy_vals, yy);
247 
248   // Accumulated values are store in sets of 4, we have to manually add
249   // the last bits together.
250   for (int j = 0; j < 4; ++j) {
251     G[0] += xx_vals[j];
252     G[1] += xy_vals[j];
253     G[3] += yy_vals[j];
254   }
255 
256   // Finishes off last few values (< 4) from above.
257   for (; i < num_vals; ++i) {
258     G[0] += Square(vals_x[i]);
259     G[1] += vals_x[i] * vals_y[i];
260     G[3] += Square(vals_y[i]);
261   }
262 
263   // The matrix is symmetric, so this is a given.
264   G[2] = G[1];
265 }
266 
267 }  // namespace tf_tracking
268 
269 #endif
270