batch: Using default '1'
learning_rate: Using default '0.001000'
momentum: Using default '0.900000'
subdivisions: Using default '1'
policy: Using default 'constant'
max_batches: Using default '0'
layer filters size input output
0
conv 16 3 x 3 / 1 512 x 512 x 3 -> 512 x 512 x 16 0.226 BFLOPs
1 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs
2 max 2 x 2 / 2 512 x 512 x 16 -> 256 x 256 x 16
3 conv 32 3 x 3 / 1 256 x 256 x 16 -> 256 x 256 x 32 0.604 BFLOPs
4 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs
5 max 2 x 2 / 2 256 x 256 x 32 -> 128 x 128 x 32
6 conv 64 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.604 BFLOPs
7 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs
8 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64
9 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
10 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs
11 max 2 x 2 / 2 64 x 64 x 128 -> 32 x 32 x 128
12 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
13 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs
14 max 2 x 2 / 2 32 x 32 x 256 -> 16 x 16 x 256
15 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
16 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs
17 max 2 x 2 / 2 16 x 16 x 512 -> 8 x 8 x 512
18 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
19 conv 1024 3 x 3 / 1 8 x 8 x1024 -> 8 x 8 x1024 1.208 BFLOPs
20 upsample 2x 8 x 8 x1024 -> 16 x 16 x1024
21 route 16 20
22 conv 512 3 x 3 / 1 16 x 16 x1536 -> 16 x 16 x 512 3.624 BFLOPs
23 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs
24 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs
25 upsample 2x 16 x 16 x 512 -> 32 x 32 x 512
26 route 13 25
27 conv 256 3 x 3 / 1 32 x 32 x 768 -> 32 x 32 x 256 3.624 BFLOPs
28 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs
29 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs
30 upsample 2x 32 x 32 x 256 -> 64 x 64 x 256
31 route 10 30
32 conv 128 3 x 3 / 1 64 x 64 x 384 -> 64 x 64 x 128 3.624 BFLOPs
33 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs
34 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs
35 upsample 2x 64 x 64 x 128 -> 128 x 128 x 128
36 route 7 35
37 conv 64 3 x 3 / 1 128 x 128 x 192 -> 128 x 128 x 64 3.624 BFLOPs
38 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs
39 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs
40 upsample 2x 128 x 128 x 64 -> 256 x 256 x 64
41 route 4 40
42 conv 32 3 x 3 / 1 256 x 256 x 96 -> 256 x 256 x 32 3.624 BFLOPs
43 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs
44 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs
45 upsample 2x 256 x 256 x 32 -> 512 x 512 x 32
46 route 1 45
47 conv 16 3 x 3 / 1 512 x 512 x 48 -> 512 x 512 x 16 3.624 BFLOPs
48 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs
49 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs
50 conv 2 1 x 1 / 1 512 x 512 x 16 -> 512 x 512 x 2 0.017 BFLOPs
Loading weights from models/table.weights...Done!
到这就结束啦，这是什么问题
batch: Using default '1' learning_rate: Using default '0.001000' momentum: Using default '0.900000' subdivisions: Using default '1' policy: Using default 'constant' max_batches: Using default '0' layer filters size input output 0 conv 16 3 x 3 / 1 512 x 512 x 3 -> 512 x 512 x 16 0.226 BFLOPs 1 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs 2 max 2 x 2 / 2 512 x 512 x 16 -> 256 x 256 x 16 3 conv 32 3 x 3 / 1 256 x 256 x 16 -> 256 x 256 x 32 0.604 BFLOPs 4 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs 5 max 2 x 2 / 2 256 x 256 x 32 -> 128 x 128 x 32 6 conv 64 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.604 BFLOPs 7 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs 8 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64 9 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs 10 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs 11 max 2 x 2 / 2 64 x 64 x 128 -> 32 x 32 x 128 12 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs 13 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs 14 max 2 x 2 / 2 32 x 32 x 256 -> 16 x 16 x 256 15 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs 16 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs 17 max 2 x 2 / 2 16 x 16 x 512 -> 8 x 8 x 512 18 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs 19 conv 1024 3 x 3 / 1 8 x 8 x1024 -> 8 x 8 x1024 1.208 BFLOPs 20 upsample 2x 8 x 8 x1024 -> 16 x 16 x1024 21 route 16 20 22 conv 512 3 x 3 / 1 16 x 16 x1536 -> 16 x 16 x 512 3.624 BFLOPs 23 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs 24 conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 1.208 BFLOPs 25 upsample 2x 16 x 16 x 512 -> 32 x 32 x 512 26 route 13 25 27 conv 256 3 x 3 / 1 32 x 32 x 768 -> 32 x 32 x 256 3.624 BFLOPs 28 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs 29 conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 1.208 BFLOPs 30 upsample 2x 32 x 32 x 256 -> 64 x 64 x 256 31 route 10 30 32 conv 128 3 x 3 / 1 64 x 64 x 384 -> 64 x 64 x 128 3.624 BFLOPs 33 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs 34 conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 1.208 BFLOPs 35 upsample 2x 64 x 64 x 128 -> 128 x 128 x 128 36 route 7 35 37 conv 64 3 x 3 / 1 128 x 128 x 192 -> 128 x 128 x 64 3.624 BFLOPs 38 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs 39 conv 64 3 x 3 / 1 128 x 128 x 64 -> 128 x 128 x 64 1.208 BFLOPs 40 upsample 2x 128 x 128 x 64 -> 256 x 256 x 64 41 route 4 40 42 conv 32 3 x 3 / 1 256 x 256 x 96 -> 256 x 256 x 32 3.624 BFLOPs 43 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs 44 conv 32 3 x 3 / 1 256 x 256 x 32 -> 256 x 256 x 32 1.208 BFLOPs 45 upsample 2x 256 x 256 x 32 -> 512 x 512 x 32 46 route 1 45 47 conv 16 3 x 3 / 1 512 x 512 x 48 -> 512 x 512 x 16 3.624 BFLOPs 48 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs 49 conv 16 3 x 3 / 1 512 x 512 x 16 -> 512 x 512 x 16 1.208 BFLOPs 50 conv 2 1 x 1 / 1 512 x 512 x 16 -> 512 x 512 x 2 0.017 BFLOPs Loading weights from models/table.weights...Done! 到这就结束啦，这是什么问题