-
Notifications
You must be signed in to change notification settings - Fork 0
/
sfp_resnet50_2.m
512 lines (431 loc) · 16.2 KB
/
sfp_resnet50_2.m
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
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
%%%%%%%%%%%%%%%%%%%%%%%input(224*224*3)%%%%%%%%%%%%%%%%%%%%%%%
clear;
image_file = fopen('val.txt');
image_name = strings;
image_label = zeros(1,50000);
i = 1;
tline = fgetl(image_file);
while ischar(tline)
image_name(i) = tline(1:28);
image_label(i) = str2num(tline(30:end));
tline = fgetl(image_file);
i = i + 1;
end
fclose(image_file);
result_top1 = zeros(1,50000);
result_top5 = zeros(1,50000);
% for j = 1:50000
j = 34;
pic = imread('./imagenet/' + image_name(j));
pic = imresize(pic,[224 224]);
pic = single(pic);
image_size = size(pic);
%gray_to_three_channels
if length(image_size) == 2
image(:,:,1) = pic;
image(:,:,2) = pic;
image(:,:,3) = pic;
else
image = pic;
end
modelfile = 'resnet_50.h5';
lgraph = importKerasLayers(modelfile,'ImportWeights',true);
eps = 1.0010e-5;
%original image:float32_to_sfp
%conv1_zeropadding(230*230*3),(3,3,3,3)
image = zero_padding(image,3);
%conv1_bn merge
weights = lgraph.Layers(3,1).Weights; %7*7*3*64
bias = lgraph.Layers(3,1).Bias; %1*1*64
trained_mean = lgraph.Layers(4,1).TrainedMean;
trained_variance = lgraph.Layers(4,1).TrainedVariance;
beta = lgraph.Layers(4,1).Offset;
gamma = lgraph.Layers(4,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv1_conv
image = conv(weights,bias,2,image,0);
%conv1_relu
image = relu(image);
%pool1_pad
image = zero_padding(image,1);
%pool1_pool
image = maxpooling(image,2,3,0);
%conv2_block1
image = block1(image,14,16,1,0,8,9,1,0,11,12,1,15,17,1,0);
%conv2_block2
image = block(image,20,21,1,0,23,24,1,26,27,1,0);
%conv2_block3
image = block(image,30,31,1,0,33,34,1,36,37,1,0);
%conv3_block1
image = block1(image,46,48,2,0,40,41,2,0,43,44,1,47,49,1,0);
%conv3_block2
image = block(image,52,53,1,0,55,56,1,58,59,1,0);
%conv3_block3
image = block(image,62,63,1,0,65,66,1,68,69,1,0);
%conv3_block4
image = block(image,72,73,1,0,75,76,1,78,79,1,0);
%conv4_block1
image = block1(image,88,90,2,0,82,83,2,0,85,86,1,89,91,1,0);
%conv4_block2
image = block(image,94,95,1,0,97,98,1,100,101,1,0);
%conv4_block3
image = block(image,104,105,1,0,107,108,1,110,111,1,0);
%conv4_block4
image = block(image,114,115,1,0,117,118,1,120,121,1,0);
%conv4_block5
image = block(image,124,125,1,0,127,128,1,130,131,1,0);
%conv4_block6
image = block(image,134,135,1,0,137,138,1,140,141,1,0);
%conv5_block1
image = block1(image,150,152,2,0,144,145,2,0,147,148,1,151,153,1,0);
%conv5_block2
image = block(image,156,157,1,0,159,160,1,162,163,1,0);
%conv5_block3
image = block(image,166,167,1,0,169,170,1,172,173,1,0);
%avg_pool
image = global_average_pool(image); %[2048]
%full_connect
weights = lgraph.Layers(177,1).Weights;
bias = lgraph.Layers(177,1).Bias;
image = full_connect(weights,bias,image)
%softmax
predict = softmax_out(image);
%TOP1
[pred_acc,pred_label] = max(predict);
if pred_label - 1 == image_label(j)
result_top1(j) = 1;
end
%TOP5
[b,pred_label_top5]=sort(predict,'descend');
pred_label_top5 = pred_label_top5 -1;
if find(pred_label_top5(1:5) == image_label(j))
result_top5(j) = 1;
else
result_top5(j) = 0;
end
% end
%
% %TOP1_accuracy
% top1_acc = sum(result_top1) / 50000;
%
% %TOP5_accuracy
% top5_acc = sum(result_top5) / 50000;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%function%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%block
function [image] = block1(image,w0,tr0,strides0,padding0,w1,tr1,strides1,padding1,w2,tr2,strides2,w3,tr3,strides3,padding3)
%conv_block1_0_bn merge
modelfile = 'resnet_50.h5';
lgraph = importKerasLayers(modelfile,'ImportWeights',true);
image_sc = image;
weights = lgraph.Layers(w0,1).Weights;
bias = lgraph.Layers(w0,1).Bias;
trained_mean = lgraph.Layers(tr0,1).TrainedMean;
trained_variance = lgraph.Layers(tr0,1).TrainedVariance;
beta = lgraph.Layers(tr0,1).Offset;
gamma = lgraph.Layers(tr0,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_0_conv
image_sc = conv(weights,bias,strides0,image_sc,padding0);
%conv_block1_1_bn merge
weights = lgraph.Layers(w1,1).Weights;
bias = lgraph.Layers(w1,1).Bias;
trained_mean = lgraph.Layers(tr1,1).TrainedMean;
trained_variance = lgraph.Layers(tr1,1).TrainedVariance;
beta = lgraph.Layers(tr1,1).Offset;
gamma = lgraph.Layers(tr1,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_1_conv
image = conv(weights,bias,strides1,image,padding1);
%conv_block1_1_relu
image = relu(image);
%conv_block1_2_bn merge
weights = lgraph.Layers(w2,1).Weights;
bias = lgraph.Layers(w2,1).Bias;
trained_mean = lgraph.Layers(tr2,1).TrainedMean;
trained_variance = lgraph.Layers(tr2,1).TrainedVariance;
beta = lgraph.Layers(tr2,1).Offset;
gamma = lgraph.Layers(tr2,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_2_conv
image = conv_padding_same(weights,bias,strides2,image);
%conv_block1_2_relu
image = relu(image);
%conv_block1_3_bn merge
weights = lgraph.Layers(w3,1).Weights;
bias = lgraph.Layers(w3,1).Bias;
trained_mean = lgraph.Layers(tr3,1).TrainedMean;
trained_variance = lgraph.Layers(tr3,1).TrainedVariance;
beta = lgraph.Layers(tr3,1).Offset;
gamma = lgraph.Layers(tr3,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_3_conv
image = conv(weights,bias,strides3,image,padding3);
%conv_block1_add
image = image + image_sc;
%conv_block1_relu_out
image = relu(image);
end
%block
function [image] = block(image,w1,tr1,strides1,padding1,w2,tr2,strides2,w3,tr3,strides3,padding3)
%conv_block1_1_bn merge
modelfile = 'resnet_50.h5';
lgraph = importKerasLayers(modelfile,'ImportWeights',true);
image_sc = image;
weights = lgraph.Layers(w1,1).Weights;
bias = lgraph.Layers(w1,1).Bias;
trained_mean = lgraph.Layers(tr1,1).TrainedMean;
trained_variance = lgraph.Layers(tr1,1).TrainedVariance;
beta = lgraph.Layers(tr1,1).Offset;
gamma = lgraph.Layers(tr1,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_1_conv
image = conv(weights,bias,strides1,image,padding1);
%conv_block1_1_relu
image = relu(image);
%conv_block1_2_bn merge
weights = lgraph.Layers(w2,1).Weights;
bias = lgraph.Layers(w2,1).Bias;
trained_mean = lgraph.Layers(tr2,1).TrainedMean;
trained_variance = lgraph.Layers(tr2,1).TrainedVariance;
beta = lgraph.Layers(tr2,1).Offset;
gamma = lgraph.Layers(tr2,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_2_conv
image = conv_padding_same(weights,bias,strides2,image);
%conv_block1_2_relu
image = relu(image);
%conv_block1_3_bn merge
weights = lgraph.Layers(w3,1).Weights;
bias = lgraph.Layers(w3,1).Bias;
trained_mean = lgraph.Layers(tr3,1).TrainedMean;
trained_variance = lgraph.Layers(tr3,1).TrainedVariance;
beta = lgraph.Layers(tr3,1).Offset;
gamma = lgraph.Layers(tr3,1).Scale;
[weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma);
%conv_block1_3_conv
image = conv(weights,bias,strides3,image,padding3);
%conv_block1_add
image = image + image_sc;
%conv_block1_relu_out
image = relu(image);
end
%merge
function [weights,bias] = bn_merge(weights,bias,trained_mean,trained_variance,beta,gamma)
size_weight = size(weights); %[7 7 3 64]
image_channels = size_weight(3);
filters = size_weight(4); %64
kernels = size_weight(1); %7
parfor weight_filters = 1:filters
for weight_channels = 1:image_channels
for weight_row = 1:kernels
for weight_line = 1:kernels
weights(weight_row,weight_line,weight_channels,weight_filters) = weights(weight_row,weight_line,weight_channels,weight_filters) * gamma(:,:,weight_filters) / (sqrt(trained_variance(:,:,weight_filters) + eps));
end
end
end
bias(:,:,weight_filters) = gamma(:,:,weight_filters) * (bias(:,:,weight_filters) - trained_mean(:,:,weight_filters)) / (sqrt(trained_variance(:,:,weight_filters) + eps)) + beta(:,:,weight_filters);
end
end
%conv
function [image_conv_out] = conv(weights,bias,strides,image,padding_size)
size_weight = size(weights); %[7 7 3 64]
image_channels = size_weight(3);
filters = size_weight(4); %64
kernels = size_weight(1); %7
image = zero_padding(image,padding_size);
image_size = size(image);
image_size = image_size(1);
image_conv_row = (image_size - kernels + 1) / strides;
image_conv_line = ceil(image_conv_row);
image_conv_row = image_conv_line;
parfor filter_conv = 1:filters
for image_conv_channels = 1:image_channels
for image_conv_r = 1:image_conv_row
for image_conv_l = 1:image_conv_line
conv_mul = [];
count = 1;
pic_r = 1 + strides * (image_conv_r - 1) ;
for conv_r = 1:kernels
pic_l = 1 + strides * (image_conv_l - 1);
for conv_l = 1:kernels
conv_mul(count) = image(pic_r,pic_l,image_conv_channels) * weights(conv_r,conv_l,image_conv_channels,filter_conv);
if pic_l < image_size
pic_l = pic_l + 1;
count = count + 1;
end
end
pic_r = pic_r + 1;
end
image_conv(image_conv_r,image_conv_l,image_conv_channels,filter_conv) = sum(conv_mul);
end
end
end
end
image_conv_out = zeros(image_conv_row,image_conv_line,filters);
parfor filter_conv = 1:filters
for image_conv_r = 1:image_conv_row
for image_conv_l = 1:image_conv_line
for image_conv_channels = 1:image_channels
image_conv_out(image_conv_r,image_conv_l,filter_conv) = image_conv(image_conv_r,image_conv_l,image_conv_channels,filter_conv) + image_conv_out(image_conv_r,image_conv_l,filter_conv);
end
image_conv_out(image_conv_r,image_conv_l,filter_conv) = image_conv_out(image_conv_r,image_conv_l,filter_conv) + bias(:,:,filter_conv);
end
end
end
end
%conv_padding_same
function [image_conv_out] = conv_padding_same(weights,bias,strides,image)
size_weight = size(weights); %[7 7 3 64]
image_channels = size_weight(3);
filters = size_weight(4); %64
kernels = size_weight(1); %7
image = zero_padding_same(image,strides,kernels);
image_size = size(image);
image_size = image_size(1);
image_conv_row = (image_size - kernels + 1) / strides;
image_conv_line = ceil(image_conv_row);
image_conv_row = image_conv_line;
parfor filter_conv = 1:filters
for image_conv_channels = 1:image_channels
for image_conv_r = 1:image_conv_row
for image_conv_l = 1:image_conv_line
conv_mul = [];
count = 1;
pic_r = 1 + strides * (image_conv_r - 1) ;
for conv_r = 1:kernels
pic_l = 1 + strides * (image_conv_l - 1);
for conv_l = 1:kernels
conv_mul(count) = image(pic_r,pic_l,image_conv_channels) * weights(conv_r,conv_l,image_conv_channels,filter_conv);
if pic_l < image_size
pic_l = pic_l + 1;
count = count + 1;
end
end
pic_r = pic_r + 1;
end
image_conv(image_conv_r,image_conv_l,image_conv_channels,filter_conv) = sum(conv_mul);
end
end
end
end
image_conv_out = zeros(image_conv_row,image_conv_line,filters);
parfor filter_conv = 1:filters
for image_conv_r = 1:image_conv_row
for image_conv_l = 1:image_conv_line
for image_conv_channels = 1:image_channels
image_conv_out(image_conv_r,image_conv_l,filter_conv) = image_conv(image_conv_r,image_conv_l,image_conv_channels,filter_conv) + image_conv_out(image_conv_r,image_conv_l,filter_conv);
end
image_conv_out(image_conv_r,image_conv_l,filter_conv) = image_conv_out(image_conv_r,image_conv_l,filter_conv) + bias(:,:,filter_conv);
end
end
end
end
%full_connected_layer
function [full_out] = full_connect(weights,bias,image)
size_weight = size(weights); %[1000*2048]
kernels = size_weight(1); %1000
filters = size_weight(2); %2048
full_out = zeros(1,kernels);
for full_size = 1:kernels
for filter_full = 1:filters
full_out(1,full_size) = full_out(1,full_size) + image(1,filter_full) * weights(full_size,filter_full);
end
full_out(1,full_size) = full_out(1,full_size) + bias(full_size,1);
end
end
%ZeroPadding
function [image_padding_out] = zero_padding(image,padding_size)
image_filter = size(image);
filters = image_filter(3);
parfor filter_padding = 1:filters
image_padding = image(:,:,filter_padding);
image_padding = padarray(image_padding, [padding_size padding_size]);
image_padding_out(:,:,filter_padding) = image_padding;
end
end
%zeropadding_same
function [image_padding_out] = zero_padding_same(image,strides,kernels)
image_size = size(image);
filters = image_size(3);
image_size = image_size(1);
padding_height = (floor(image_size / strides) - 1) * strides + kernels - image_size;
padding_top = ceil(padding_height / 2);
padding_down = padding_height - padding_top;
padding_width = (floor(image_size / strides) - 1) * strides + kernels - image_size;
padding_left = ceil(padding_width / 2);
padding_right = padding_width - padding_left;
parfor filter_padding = 1:filters
image_padding = image(:,:,filter_padding);
image_padding = padarray(image_padding, [padding_top padding_left],'pre');
image_padding = padarray(image_padding, [padding_down padding_right],'post');
image_padding_out(:,:,filter_padding) = image_padding;
end
end
%relu
function [image_relu] = relu(image)
image_size = size(image);
filters = image_size(3);
image_row = image_size(1);
image_line = image_size(2);
image_relu = image;
parfor filter_relu = 1:filters
for image_relu_r = 1:image_row
for image_relu_l = 1:image_line
if image_relu(image_relu_r,image_relu_l,filter_relu) < 0
image_relu(image_relu_r,image_relu_l,filter_relu) = 0;
end
end
end
end
end
%MaxPooling
function [image_pool] = maxpooling(image,strides,pool_size,padding_size)
image = zero_padding(image,padding_size);
image_s = size(image);
filters = image_s(3);
image_size = image_s(1);
image_pool_row = (image_size - pool_size + 1) / strides;
image_pool_line = ceil(image_pool_row);
image_pool_row = image_pool_line;
image_pool = zeros(image_pool_row,image_pool_line,filters);
parfor filter_pool = 1:filters
for image_pool_r = 1:image_pool_row
for image_pool_l = 1:image_pool_line
image_r = 1 + strides * (image_pool_r - 1);
for pool_r = 1:pool_size
image_l = 1 + strides * (image_pool_l - 1);
for pool_l = 1:pool_size
if image_pool(image_pool_r,image_pool_l,filter_pool) < image(image_r,image_l,filter_pool)
image_pool(image_pool_r,image_pool_l,filter_pool) = image(image_r,image_l,filter_pool);
end
if image_r < image_size
image_l = image_l + 1;
end
end
if image_l <image_size
image_r = image_r + 1;
end
end
end
end
end
end
%global_average_pool
function [image_average_pool] = global_average_pool(image)
image_s = size(image);
filters = image_s(3);
image_average_pool = zeros(1,filters);
for filter_ave_pool = 1:filters
image_average_pool(1,filter_ave_pool) = mean(image(:,:,filter_ave_pool),'all');
end
end
%softmax
function [predict] = softmax_out(image)
image_size = size(image);
image_size = image_size(2); %1000
sum_exp = sum(exp(image));
predict = zeros(1,image_size);
for predict_n = 1:image_size
predict(1,predict_n) = exp(image(1,predict_n)) / sum_exp;
end
end