unet 256
UnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): ReLU(inplace=True)
(3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(128, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): Tanh()
)
)
unet 128
OrderedDict([('model', UnetSkipConnectionBlock(
(model): Sequential(
(0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
(3): UnetSkipConnectionBlock(
(model): Sequential(
(0): LeakyReLU(negative_slope=0.2, inplace=True)
(1): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(2): ReLU(inplace=True)
(3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(4): ReLU(inplace=True)
(5): ConvTranspose2d(128, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(6): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
)
)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(4): Tanh()
)
))])
unet-128 time
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 model.model.0 1 1280 2048 32 640 1024 544.0 80.00 671,088,640.0 356,515,840.0 10487936.0 83886080.0 2.58% 9.437402e+07
1 model.model.1.model.0 32 640 1024 32 640 1024 0.0 80.00 0.0 20,971,520.0 83886080.0 83886080.0 2.71% 1.677722e+08
2 model.model.1.model.1 32 640 1024 64 320 512 32832.0 40.00 10,737,418,240.0 5,379,194,880.0 84017408.0 41943040.0 5.83% 1.259604e+08
3 model.model.1.model.2 64 320 512 64 320 512 0.0 40.00 0.0 0.0 0.0 0.0 0.76% 0.000000e+00
4 model.model.1.model.3.model.0 64 320 512 64 320 512 0.0 40.00 0.0 10,485,760.0 41943040.0 41943040.0 2.79% 8.388608e+07
5 model.model.1.model.3.model.1 64 320 512 128 160 256 131200.0 20.00 10,737,418,240.0 5,373,952,000.0 42467840.0 20971520.0 3.74% 6.343936e+07
6 model.model.1.model.3.model.2 128 160 256 128 160 256 0.0 20.00 0.0 0.0 0.0 0.0 2.12% 0.000000e+00
7 model.model.1.model.3.model.3.model.0 128 160 256 128 160 256 0.0 20.00 0.0 5,242,880.0 20971520.0 20971520.0 1.93% 4.194304e+07
8 model.model.1.model.3.model.3.model.1 128 160 256 256 80 128 524544.0 10.00 10,737,418,240.0 5,371,330,560.0 23069696.0 10485760.0 3.04% 3.355546e+07
9 model.model.1.model.3.model.3.model.2 256 80 128 256 80 128 0.0 10.00 0.0 0.0 0.0 0.0 0.29% 0.000000e+00
10 model.model.1.model.3.model.3.model.3.model.0 256 80 128 256 80 128 0.0 10.00 0.0 2,621,440.0 10485760.0 10485760.0 0.69% 2.097152e+07
11 model.model.1.model.3.model.3.model.3.model.1 256 80 128 256 40 64 1048832.0 2.50 5,368,709,120.0 2,685,009,920.0 14681088.0 2621440.0 3.09% 1.730253e+07
12 model.model.1.model.3.model.3.model.3.model.2 256 40 64 256 40 64 0.0 2.50 0.0 0.0 0.0 0.0 0.52% 0.000000e+00
13 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 40 64 0.0 2.50 0.0 655,360.0 2621440.0 2621440.0 0.16% 5.242880e+06
14 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 20 32 1048832.0 0.62 1,342,177,280.0 671,252,480.0 6816768.0 655360.0 2.84% 7.472128e+06
15 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 0.0 0.0 0.0 0.05% 0.000000e+00
16 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 163,840.0 655360.0 655360.0 0.04% 1.310720e+06
17 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 10 16 1048832.0 0.16 335,544,320.0 167,813,120.0 4850688.0 163840.0 2.90% 5.014528e+06
18 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 10 16 0.0 0.16 40,960.0 40,960.0 163840.0 163840.0 0.01% 3.276800e+05
19 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 20 32 1048832.0 0.62 335,544,320.0 0.0 0.0 0.0 0.49% 0.000000e+00
20 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 0.0 0.0 0.0 0.09% 0.000000e+00
21 model.model.1.model.3.model.3.model.3.model.3.... 512 20 32 512 20 32 0.0 1.25 327,680.0 327,680.0 1310720.0 1310720.0 0.01% 2.621440e+06
22 model.model.1.model.3.model.3.model.3.model.3.... 512 20 32 256 40 64 2097408.0 2.50 2,684,354,560.0 0.0 0.0 0.0 2.11% 0.000000e+00
23 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 40 64 0.0 2.50 0.0 0.0 0.0 0.0 0.08% 0.000000e+00
24 model.model.1.model.3.model.3.model.3.model.4 512 40 64 512 40 64 0.0 5.00 1,310,720.0 1,310,720.0 5242880.0 5242880.0 0.63% 1.048576e+07
25 model.model.1.model.3.model.3.model.3.model.5 512 40 64 256 80 128 2097408.0 10.00 10,737,418,240.0 0.0 0.0 0.0 7.47% 0.000000e+00
26 model.model.1.model.3.model.3.model.3.model.6 256 80 128 256 80 128 0.0 10.00 0.0 0.0 0.0 0.0 0.30% 0.000000e+00
27 model.model.1.model.3.model.3.model.4 512 80 128 512 80 128 0.0 20.00 5,242,880.0 5,242,880.0 20971520.0 20971520.0 0.54% 4.194304e+07
28 model.model.1.model.3.model.3.model.5 512 80 128 128 160 256 1048704.0 20.00 21,474,836,480.0 0.0 0.0 0.0 9.57% 0.000000e+00
29 model.model.1.model.3.model.3.model.6 128 160 256 128 160 256 0.0 20.00 0.0 0.0 0.0 0.0 0.38% 0.000000e+00
30 model.model.1.model.3.model.4 256 160 256 256 160 256 0.0 40.00 10,485,760.0 10,485,760.0 41943040.0 41943040.0 0.19% 8.388608e+07
31 model.model.1.model.3.model.5 256 160 256 64 320 512 262208.0 40.00 21,474,836,480.0 0.0 0.0 0.0 14.56% 0.000000e+00
32 model.model.1.model.3.model.6 64 320 512 64 320 512 0.0 40.00 0.0 0.0 0.0 0.0 1.50% 0.000000e+00
33 model.model.1.model.4 128 320 512 128 320 512 0.0 80.00 20,971,520.0 20,971,520.0 83886080.0 83886080.0 0.38% 1.677722e+08
34 model.model.1.model.5 128 320 512 32 640 1024 65568.0 80.00 21,474,836,480.0 0.0 0.0 0.0 19.28% 0.000000e+00
35 model.model.1.model.6 32 640 1024 32 640 1024 0.0 80.00 0.0 0.0 0.0 0.0 2.15% 0.000000e+00
36 model.model.2 64 640 1024 64 640 1024 0.0 160.00 41,943,040.0 41,943,040.0 167772160.0 167772160.0 0.76% 3.355443e+08
37 model.model.3 64 640 1024 1 1280 2048 1025.0 10.00 1,342,177,280.0 0.0 0.0 0.0 3.10% 0.000000e+00
38 model.model.4 1 1280 2048 1 1280 2048 0.0 10.00 0.0 0.0 0.0 0.0 0.32% 0.000000e+00
total 10456769.0 1012.19 119,534,100,480.0 20,125,532,160.0 0.0 0.0 100.00% 1.310825e+09
======================================================================================================================================================================================================
Total params: 10,456,769
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 1012.19MB
Total MAdd: 119.53GMAdd
Total Flops: 20.13GFlops
Total MemR+W: 1.22GB
unet 256
module name input shape output shape params memory(MB) MAdd Flops MemRead(B) MemWrite(B) duration[%] MemR+W(B)
0 model.model.0 1 1280 2048 32 640 1024 544.0 80.00 671,088,640.0 356,515,840.0 10487936.0 83886080.0 5.41% 9.437402e+07
1 model.model.1.model.0 32 640 1024 32 640 1024 0.0 80.00 0.0 20,971,520.0 83886080.0 83886080.0 2.45% 1.677722e+08
2 model.model.1.model.1 32 640 1024 64 320 512 32832.0 40.00 10,737,418,240.0 5,379,194,880.0 84017408.0 41943040.0 5.17% 1.259604e+08
3 model.model.1.model.2 64 320 512 64 320 512 0.0 40.00 0.0 0.0 0.0 0.0 1.71% 0.000000e+00
4 model.model.1.model.3.model.0 64 320 512 64 320 512 0.0 40.00 0.0 10,485,760.0 41943040.0 41943040.0 3.34% 8.388608e+07
5 model.model.1.model.3.model.1 64 320 512 128 160 256 131200.0 20.00 10,737,418,240.0 5,373,952,000.0 42467840.0 20971520.0 3.67% 6.343936e+07
6 model.model.1.model.3.model.2 128 160 256 128 160 256 0.0 20.00 0.0 0.0 0.0 0.0 1.46% 0.000000e+00
7 model.model.1.model.3.model.3.model.0 128 160 256 128 160 256 0.0 20.00 0.0 5,242,880.0 20971520.0 20971520.0 1.16% 4.194304e+07
8 model.model.1.model.3.model.3.model.1 128 160 256 256 80 128 524544.0 10.00 10,737,418,240.0 5,371,330,560.0 23069696.0 10485760.0 4.97% 3.355546e+07
9 model.model.1.model.3.model.3.model.2 256 80 128 256 80 128 0.0 10.00 0.0 0.0 0.0 0.0 1.06% 0.000000e+00
10 model.model.1.model.3.model.3.model.3.model.0 256 80 128 256 80 128 0.0 10.00 0.0 2,621,440.0 10485760.0 10485760.0 0.54% 2.097152e+07
11 model.model.1.model.3.model.3.model.3.model.1 256 80 128 256 40 64 1048832.0 2.50 5,368,709,120.0 2,685,009,920.0 14681088.0 2621440.0 2.26% 1.730253e+07
12 model.model.1.model.3.model.3.model.3.model.2 256 40 64 256 40 64 0.0 2.50 0.0 0.0 0.0 0.0 0.09% 0.000000e+00
13 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 40 64 0.0 2.50 0.0 655,360.0 2621440.0 2621440.0 0.16% 5.242880e+06
14 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 20 32 1048832.0 0.62 1,342,177,280.0 671,252,480.0 6816768.0 655360.0 1.25% 7.472128e+06
15 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 0.0 0.0 0.0 0.06% 0.000000e+00
16 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 163,840.0 655360.0 655360.0 0.03% 1.310720e+06
17 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 10 16 1048832.0 0.16 335,544,320.0 167,813,120.0 4850688.0 163840.0 0.21% 5.014528e+06
18 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 10 16 0.0 0.16 0.0 0.0 0.0 0.0 0.04% 0.000000e+00
19 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 10 16 0.0 0.16 0.0 40,960.0 163840.0 163840.0 0.01% 3.276800e+05
20 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 5 8 1048832.0 0.04 83,886,080.0 41,953,280.0 4359168.0 40960.0 0.53% 4.400128e+06
21 model.model.1.model.3.model.3.model.3.model.3.... 256 5 8 256 5 8 0.0 0.04 10,240.0 10,240.0 40960.0 40960.0 0.00% 8.192000e+04
22 model.model.1.model.3.model.3.model.3.model.3.... 256 5 8 256 10 16 1048832.0 0.16 83,886,080.0 0.0 0.0 0.0 2.28% 0.000000e+00
23 model.model.1.model.3.model.3.model.3.model.3.... 256 10 16 256 10 16 0.0 0.16 0.0 0.0 0.0 0.0 0.04% 0.000000e+00
24 model.model.1.model.3.model.3.model.3.model.3.... 512 10 16 512 10 16 0.0 0.31 81,920.0 81,920.0 327680.0 327680.0 0.85% 6.553600e+05
25 model.model.1.model.3.model.3.model.3.model.3.... 512 10 16 256 20 32 2097408.0 0.62 671,088,640.0 0.0 0.0 0.0 0.34% 0.000000e+00
26 model.model.1.model.3.model.3.model.3.model.3.... 256 20 32 256 20 32 0.0 0.62 0.0 0.0 0.0 0.0 0.05% 0.000000e+00
27 model.model.1.model.3.model.3.model.3.model.3.... 512 20 32 512 20 32 0.0 1.25 327,680.0 327,680.0 1310720.0 1310720.0 0.01% 2.621440e+06
28 model.model.1.model.3.model.3.model.3.model.3.... 512 20 32 256 40 64 2097408.0 2.50 2,684,354,560.0 0.0 0.0 0.0 2.55% 0.000000e+00
29 model.model.1.model.3.model.3.model.3.model.3.... 256 40 64 256 40 64 0.0 2.50 0.0 0.0 0.0 0.0 0.64% 0.000000e+00
30 model.model.1.model.3.model.3.model.3.model.4 512 40 64 512 40 64 0.0 5.00 1,310,720.0 1,310,720.0 5242880.0 5242880.0 0.03% 1.048576e+07
31 model.model.1.model.3.model.3.model.3.model.5 512 40 64 256 80 128 2097408.0 10.00 10,737,418,240.0 0.0 0.0 0.0 6.23% 0.000000e+00
32 model.model.1.model.3.model.3.model.3.model.6 256 80 128 256 80 128 0.0 10.00 0.0 0.0 0.0 0.0 0.59% 0.000000e+00
33 model.model.1.model.3.model.3.model.4 512 80 128 512 80 128 0.0 20.00 5,242,880.0 5,242,880.0 20971520.0 20971520.0 0.49% 4.194304e+07
34 model.model.1.model.3.model.3.model.5 512 80 128 128 160 256 1048704.0 20.00 21,474,836,480.0 0.0 0.0 0.0 12.07% 0.000000e+00
35 model.model.1.model.3.model.3.model.6 128 160 256 128 160 256 0.0 20.00 0.0 0.0 0.0 0.0 0.97% 0.000000e+00
36 model.model.1.model.3.model.4 256 160 256 256 160 256 0.0 40.00 10,485,760.0 10,485,760.0 41943040.0 41943040.0 0.16% 8.388608e+07
37 model.model.1.model.3.model.5 256 160 256 64 320 512 262208.0 40.00 21,474,836,480.0 0.0 0.0 0.0 11.37% 0.000000e+00
38 model.model.1.model.3.model.6 64 320 512 64 320 512 0.0 40.00 0.0 0.0 0.0 0.0 1.15% 0.000000e+00
39 model.model.1.model.4 128 320 512 128 320 512 0.0 80.00 20,971,520.0 20,971,520.0 83886080.0 83886080.0 0.39% 1.677722e+08
40 model.model.1.model.5 128 320 512 32 640 1024 65568.0 80.00 21,474,836,480.0 0.0 0.0 0.0 18.57% 0.000000e+00
41 model.model.1.model.6 32 640 1024 32 640 1024 0.0 80.00 0.0 0.0 0.0 0.0 1.92% 0.000000e+00
42 model.model.2 64 640 1024 64 640 1024 0.0 160.00 41,943,040.0 41,943,040.0 167772160.0 167772160.0 1.09% 3.355443e+08
43 model.model.3 64 640 1024 1 1280 2048 1025.0 10.00 1,342,177,280.0 0.0 0.0 0.0 2.54% 0.000000e+00
44 model.model.4 1 1280 2048 1 1280 2048 0.0 10.00 0.0 0.0 0.0 0.0 0.09% 0.000000e+00
total 13603009.0 1013.05 120,037,468,160.0 20,167,577,600.0 0.0 0.0 100.00% 1.315963e+09
======================================================================================================================================================================================================
Total params: 13,603,009
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 1013.05MB
Total MAdd: 120.04GMAdd
Total Flops: 20.17GFlops
Total MemR+W: 1.23GB