course/application_example/cyclegan

• CycleGAN
• • Pretrained model
  • Dataset
  • Training Parameter description
  • Performance
  • Examples
  • • Train
    • infer

CycleGAN

---

Image-to-image translation is a visual and image problem. Its goal is to use paired images as a training set and (let the machine) learn the mapping from input images to output images. However, in many tasks, paired training data cannot be obtained. CycleGAN does not require the training data to be paired. It only needs to provide images of different domains to successfully train the image mapping between different domains. CycleGAN shares two generators, and then each has a discriminator.

Paper: Zhu J Y , Park T , Isola P , et al. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks[J]. 2017.

Pretrained model

Model trained by MindSpore, for one style, there are 4 ckpt files.

style	ckpt
horse2zebra	ckpt
apple2orrange	ckpt
cezanne2photo	ckpt
momet2photo	ckpt
vangogh2photo	ckpt
ukiyoe2photo	ckpt
summer2winter_yosemite	ckpt

---

Dataset

Dataset for training, there are 7 styles.

style	connection
horse2zebra	connection
apple2orrange	connection
cezanne2photo	connection
momet2photo	connection
vangogh2photo	connection
ukiyoe2photo	connection
summer2winter_yosemite	connection

After downloading dataset, place it in src/data, and unzip it. The structure of folder is as follows:

cyclegan/src/data/horse2zebra
├── trainA
├── trainB
├── testA
└── testB

Training Parameter description

Parameter	Default	Description
platform	GPU	run platform, only support GPU and Ascend
device_id	0	device id, default is 0
pool_size	50	the size of image buffer that stores previously generated images
lr_policy	linear	learning rate policy
image_size	256	input image_size
batch_size	1	Number of batch size
max_epoch	200	epoch size for training
ngf	64	generator model filter numbers
in_planes	3	input channels
gl_num	9	generator model residual block numbers
ndf	64	discriminator model filter numbers
dl_num	3	discriminator model residual block numbers
outputs_dir	./outputs	Path to save predicted images
outputs_log	./outputs/log	Path to save logs
outputs_ckpt	./outputs/ckpt	Path to save ckpts
outputs_imgs	./outputs/imgs	Path to save imgs
dataroot	./data/horse2zebra	path of dataset (should have subfolders trainA, trainB, testA, testB, etc)
load_ckpt	False	whether load pretrained ckpt
g_a_ckpt	./outputs/ckpt/g_a_200.ckpt	pretrained checkpoint file path of g_a
g_b_ckpt	./outputs/ckpt/g_b_200.ckpt	pretrained checkpoint file path of g_b
d_a_ckpt	./outputs/ckpt/d_a_200.ckpt	pretrained checkpoint file path of d_a
d_b_ckpt	./outputs/ckpt/d_b_200.ckpt	pretrained checkpoint file path of d_b

Performance

Dataset	Resource	Speed	Total time
horse2zebra	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 72.99 ms/step 1pc(GPU): 470 ms/step	1pc(Ascend): 5.5h 1pc(GPU): 35.9h
apple2orrange	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 71.68 ms/step 1pc(GPU): 481 ms/step	1pc(Ascend): 4h 1pc(GPU): 26.8h
cezanne2photo	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 71.61 ms/step 1pc(GPU): 499.57 ms/step	1pc(Ascend): 25h 1pc(GPU): 174.4h
momet2photo	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 70.25 ms/step 1pc(GPU): 504.43 ms/step	1pc(Ascend): 24.5h 1pc(GPU): 175.9h
vangogh2photo	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 71.43 ms/step 1pc(GPU): 502.85 ms/step	1pc(Ascend): 24.5h 1pc(GPU): 172.5h
ukiyoe2photo	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 69.93 ms/step 1pc(GPU): 532.99 ms/step	1pc(Ascend): 24.5h 1pc(GPU): 186.7h
summer2winter_yosemite	Ascend 910/NV SMX2 V100-32G	1pc(Ascend): 69.13 ms /step 1pc(GPU): 952.50 ms/step	1pc(Ascend): 4.8h 1pc(GPU): 65.1h

Examples

---

Train

• The following configuration uses 1 GPUs for training. We select horse2zebra.zip. The image input size is set to 256.

  python train.py --platform GPU --dataroot ./data/horse2zebra --outputs_log ./outputs/log --outputs_ckpt ./outputs/ckpt --outputs_imgs ./outputs/imgs
  

  output:

  Epoch[1] [100/1334] step cost: 323.63 ms, G_loss: 10.88, D_loss:0.67, loss_G_A: 0.34, loss_G_B: 0.54, loss_C_A: 1.65,loss_C_B: 5.06, loss_idt_A: 0.84, loss_idt_B：2.45
  Epoch[1] [200/1334] step cost: 131.31 ms, G_loss: 12.07, D_loss:0.56, loss_G_A: 0.27, loss_G_B: 0.18, loss_C_A: 2.07,loss_C_B: 5.78, loss_idt_A: 0.86, loss_idt_B：2.91
  Epoch[1] [300/1334] step cost: 130.52 ms, G_loss: 10.03, D_loss:0.56, loss_G_A: 0.37, loss_G_B: 0.51, loss_C_A: 3.41,loss_C_B: 2.74, loss_idt_A: 1.68, loss_idt_B：1.31
  Epoch[1] [400/1334] step cost: 129.36 ms, G_loss: 8.24, D_loss:0.59, loss_G_A: 0.26, loss_G_B: 0.45, loss_C_A: 2.60,loss_C_B: 2.48, loss_idt_A: 1.36, loss_idt_B：1.09
  Epoch[1] [500/1334] step cost: 129.56 ms, G_loss: 5.82, D_loss:0.52, loss_G_A: 0.32, loss_G_B: 0.36, loss_C_A: 1.83,loss_C_B: 1.75, loss_idt_A: 0.86, loss_idt_B：0.71
  ...
  

infer

• The following configuration for infer.

  python infer.py --platform GPU --dataroot ./data/horse2zebra --g_a_ckpt ./outputs/ckpt/g_a_200.ckpt --g_b_ckpt ./outputs/ckpt/g_b_200.ckpt --outputs_dir ./outputs
  

  output:

  ==========start predict A to B===============
  save fake_B at ./outputs/predict/fake_B/n02381460_9240.jpg
  total 120 imgs cost 18269.25 ms, per img cost 152.24
  ==========end predict A to B===============
  
  ==========start predict B to A===============
  save fake_A at ./outputs/predict/fake_A/n02391049_10100.jpg
  total 140 imgs cost 4170.92 ms, per img cost 29.79
  ==========end predict B to A===============
  
  

  result:

  

• There are other styles infer result:
  Art style:

  

  Weather and fruit: