CycleGAN

Dataset

Download dataset

The original CycleGAN GitHub repo provides shell scripts to download CycleGAN dataset: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/datasets/download_cyclegan_dataset.sh

You can download the horse2zebra dataset file using this direct link if you prefer: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip

In [13]:
%cd cgan/
/home/ubuntu/fastai/courses/dl2/cgan
In [16]:
!bash ./datasets/download_cyclegan_dataset.sh horse2zebra
WARNING: timestamping does nothing in combination with -O. See the manual
for details.

--2018-07-10 10:21:26--  https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip
Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.189.73
Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.189.73|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 116867962 (111M) [application/zip]
Saving to: ‘./datasets/horse2zebra.zip’

./datasets/horse2ze 100%[===================>] 111.45M  11.1MB/s    in 11s     

2018-07-10 10:21:38 (10.3 MB/s) - ‘./datasets/horse2zebra.zip’ saved [116867962/116867962]

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In [20]:
%cd ..
/home/ubuntu/fastai/courses/dl2

Initial setup

In [1]:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
In [2]:
from fastai.conv_learner import *
from fastai.dataset import *

Setup CycleGAN module, cgan.

In [3]:
from cgan.options.train_options import *

Modify the following code to suit your needs:

  • '--dataroot', 'cgan/datasets/horse2zebra': horse2zebra.zip file path
  • '-–nThreads', '8': lower the no. of threads if kernels die
  • '–-gpu_ids', '2': set '0' if you only have one GPU
In [4]:
opt = TrainOptions().parse(['--dataroot', 'cgan/datasets/horse2zebra', '--nThreads', '8', '--no_dropout',
                           '--niter', '100', '--niter_decay', '100', '--name', 'nodrop', '--gpu_ids', '0'])
------------ Options -------------
batchSize: 1
beta1: 0.5
checkpoints_dir: ./checkpoints
continue_train: False
dataroot: cgan/datasets/horse2zebra
dataset_mode: unaligned
display_freq: 100
display_id: 1
display_port: 8097
display_single_pane_ncols: 0
display_winsize: 256
epoch_count: 1
fineSize: 256
gpu_ids: [0]
init_type: normal
input_nc: 3
isTrain: True
lambda_A: 10.0
lambda_B: 10.0
lambda_identity: 0.5
loadSize: 286
lr: 0.0002
lr_decay_iters: 50
lr_policy: lambda
max_dataset_size: inf
model: cycle_gan
nThreads: 8
n_layers_D: 3
name: nodrop
ndf: 64
ngf: 64
niter: 100
niter_decay: 100
no_dropout: True
no_flip: False
no_html: False
no_lsgan: False
norm: instance
output_nc: 3
phase: train
pool_size: 50
print_freq: 100
resize_or_crop: resize_and_crop
save_epoch_freq: 5
save_latest_freq: 5000
serial_batches: False
update_html_freq: 1000
which_direction: AtoB
which_epoch: latest
which_model_netD: basic
which_model_netG: resnet_9blocks
-------------- End ----------------

Data loader

In [5]:
from cgan.data.data_loader import CreateDataLoader
from cgan.models.models import create_model
In [6]:
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
dataset_size
CustomDatasetDataLoader
dataset [UnalignedDataset] was created
/home/ubuntu/anaconda3/envs/fastai/lib/python3.6/site-packages/torchvision/transforms/transforms.py:188: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
  "please use transforms.Resize instead.")
Out[6]:
1334

Create a model

In [7]:
model = create_model(opt)
cycle_gan
initialization method [normal]
initialization method [normal]
initialization method [normal]
initialization method [normal]
---------- Networks initialized -------------
ResnetGenerator(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (3): ReLU(inplace)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (6): ReLU(inplace)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (9): ReLU(inplace)
    (10): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (11): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (12): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (13): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (14): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (15): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (16): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (17): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (18): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (21): ReLU(inplace)
    (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (24): ReLU(inplace)
    (25): ReflectionPad2d((3, 3, 3, 3))
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh()
  )
)
Total number of parameters: 11378179
ResnetGenerator(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (3): ReLU(inplace)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (6): ReLU(inplace)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (9): ReLU(inplace)
    (10): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (11): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (12): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (13): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (14): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (15): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (16): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (17): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (18): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
        (3): ReLU(inplace)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
      )
    )
    (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (21): ReLU(inplace)
    (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
    (24): ReLU(inplace)
    (25): ReflectionPad2d((3, 3, 3, 3))
    (26): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (27): Tanh()
  )
)
Total number of parameters: 11378179
NLayerDiscriminator(
  (model): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(0.2, inplace)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (4): LeakyReLU(0.2, inplace)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (7): LeakyReLU(0.2, inplace)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
    (10): LeakyReLU(0.2, inplace)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  )
)
Total number of parameters: 2764737
NLayerDiscriminator(
  (model): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(0.2, inplace)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
    (4): LeakyReLU(0.2, inplace)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False)
    (7): LeakyReLU(0.2, inplace)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
    (10): LeakyReLU(0.2, inplace)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  )
)
Total number of parameters: 2764737
-----------------------------------------------
model [CycleGANModel] was created

Train

In [ ]:
# opt.niter=9
# opt.niter_decay=1
In [8]:
def train(total_steps=0):
    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
        epoch_start_time = time.time()
        iter_data_time = time.time()
        epoch_iter = 0

        for i, data in tqdm(enumerate(dataset)):
            iter_start_time = time.time()
            if total_steps % opt.print_freq == 0: t_data = iter_start_time - iter_data_time
            total_steps += opt.batchSize
            epoch_iter += opt.batchSize
            model.set_input(data)
            model.optimize_parameters()

            if total_steps % opt.display_freq == 0:
                save_result = total_steps % opt.update_html_freq == 0

            if total_steps % opt.print_freq == 0:
                errors = model.get_current_errors()
                t = (time.time() - iter_start_time) / opt.batchSize

            if total_steps % opt.save_latest_freq == 0:
                print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
                model.save('latest')

            iter_data_time = time.time()
        if epoch % opt.save_epoch_freq == 0:
            print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
            model.save('latest')
            model.save(epoch)

        print('End of epoch %d / %d \t Time Taken: %d sec' %
              (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
        model.update_learning_rate()
In [9]:
# My addition
torch.backends.cudnn.benchmark = True
In [ ]:
%time train(0)
1334it [27:56,  1.26s/it]
End of epoch 1 / 200 	 Time Taken: 1676 sec
learning rate = 0.0002000
1334it [27:44,  1.25s/it]
End of epoch 2 / 200 	 Time Taken: 1665 sec
learning rate = 0.0002000
64it [01:20,  1.26s/it]

Test

In [19]:
def show_img(im, ax=None, figsize=None):
    if not ax:
        fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(im)
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    return ax
In [20]:
def get_one(data):
    model.set_input(data)
    model.test()
    return list(model.get_current_visuals().values())

Save our work.

In [13]:
model.save(201)

Load saved model weights

In [24]:
model.load('cycle_gan_400_epch')
In [31]:
test_ims = []
for i, o in enumerate(dataset):
    if i > 20:
        break
    test_ims.append(get_one(o))
In [26]:
def show_grid(ims):
    fig, axes = plt.subplots(2, 3, figsize=(9, 6))
    for i,ax in enumerate(axes.flat):
        show_img(ims[i], ax);
    fig.tight_layout()

Results after training for 4 epochs:

In [17]:
for i in range(8):
    show_grid(test_ims[i])