CycleGAN - Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks(2017)
CycleGAN 실습
- 이인엽 작성
- dldlsduq94@korea.ac.kr
- 논문 이름: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks(2017)
Discriminator Class 생성
import torch
import torch.nn as nn
# 사용할 블록 만들기
class Block(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.conv = nn.Sequential(
# "reflect": helps to reduce artifact
nn.Conv2d(in_channels, out_channels, 4, stride, 1, bias=True, padding_mode="reflect"),
nn.InstanceNorm2d(out_channels),
nn.LeakyReLU(0.2), # for D / ReLu for G
)
def forward(self, x):
return self.conv(x)
# Discriminator 정의
class Discriminator(nn.Module):
def __init__(self, in_channels=3, features=[64, 128, 256, 512]):
super().__init__()
self.initial = nn.Sequential(
nn.Conv2d(in_channels,
features[0],
kernel_size=4,
stride = 2,
padding=1,
padding_mode="reflect"
),
nn.LeakyReLU(0.2),
)
layers = []
in_channels = features[0]
# [1:] skipping the first one becasue that was in self.initial already
for feature in features[1:]:
# if output feature's channel size is 512, stride's 1, otherwise it's 2
layers.append(Block(in_channels, feature, stride= 1 if feature==features[-2] else 2 ))
in_channels = feature
# output returns single value(scalar), which is between 0-1, so the out_channels = 1
layers.append(nn.Conv2d(in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode="reflect"))
# unwrapping 'layers' list to nn.Sequential
self.model = nn.Sequential(*layers)
# 즉, 채널은 깊어지고, w/h는 작아진다
def forward(self, x):
x = self.initial(x)
# 0-1
return torch.sigmoid(self.model(x))
# # test if I'm outputing the right shape
# def test():
# # in the paper, it needs to be 70X70 patch GAN!
# # meaning the output shape of preds.shape is 30X30
# # and each value in grid sees a 70X70 patch in the original image
# x = torch.randn((5,3,256,256))
# model = Discriminator(in_channels=3)
# preds = model(x)
# print(model)
# print(preds.shape)
# if __name__ == "__main__":
# test()
Generator Class 생성
class ConvBlock(nn.Module):
# use_act: if we use activation or not
# kwargs: keyword arguments, can accept any parameter that exists in super init()
def __init__(self, in_channels, out_channels, down=True, use_act=True, **kwargs):
super().__init__()
self.conv = nn.Sequential(
# ******** param 레벨에서 if / else 쓰는 법 체크 ! ********
nn.Conv2d(in_channels, out_channels, padding_mode="reflect", **kwargs)
if down
else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
nn.InstanceNorm2d(out_channels),
nn.ReLU(inplace=True) if use_act else nn.Identity(),
)
def forward(self, x):
return self.conv(x)
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block = nn.Sequential(
# 너비 높이 유지, (x +1*2 -3)/1 + 1 = x 니까
ConvBlock(channels, channels, kernel_size=3, padding=1),
# activation 안씀
ConvBlock(channels, channels, use_act=False, kernel_size=3, padding=1),
)
def forward(self, x):
# concatenating previous low-level info
return x + self.block(x)
class Generator(nn.Module):
# 256X256 이상의 shape이라면 num_residuals=9
# 미만의 shape이라면 num_residuals=6
def __init__(self, img_channels, num_features=64, num_residuals=9):
super().__init__()
# won't use intancenorm
self.initial = nn.Sequential(
nn.Conv2d(img_channels, num_features, kernel_size=7, stride=1, padding=3, padding_mode="reflect"),
nn.ReLU(inplace=True),
)
self.down_blocks = nn.ModuleList(
[
# down=True by default
ConvBlock(num_features,num_features*2, kernel_size=3, stride = 2, padding=1),
ConvBlock(num_features*2,num_features*4, kernel_size=3, stride = 2, padding=1),
]
)
# residual blocks don't change the input/num of channels
# nn.Sequential은 List를 언랩한 것을 받을 수 있음
self.residual_blocks = nn.Sequential(
# bunch of residuals ..
# '_' meaning there's no iterable object specified, just wanna iterate
*[ResidualBlock(num_features*4) for _ in range(num_residuals)]
)
self.up_blocks = nn.ModuleList(
[
# output_padding: additional padding after the convBlock
# it's not neccesary in the most case (following what the paper did)
ConvBlock(num_features*4, num_features*2, down=False, kernel_size=3, stride = 2, padding=1, output_padding = 1),
# 출력 channel: 64
ConvBlock(num_features*2, num_features, down=False, kernel_size=3, stride = 2, padding=1, output_padding = 1),
]
)
# 아직 channel이 RGB 즉, 3이 아니므로, Img_channels로 바꿔줌
self.last = nn.Conv2d(num_features, img_channels, kernel_size=7, stride=1, padding=3, padding_mode = "reflect")
def forward(self, x):
x = self.initial(x)
for layer in self.down_blocks:
x = layer(x)
x = self.residual_blocks(x)
for layer in self.up_blocks:
x = layer(x)
# -1 ~ 1 range
return torch.tanh(self.last(x))
# # test if I'm outputing the right shape
# def test():
# img_channels = 3
# img_size = 256
# x = torch.randn((2,img_channels,256,256))
# gen = Generator(img_channels,9)
# print(gen)
# print(gen(x).shape) # 2X3X256X256
# if __name__ == "__main__":
# test()
데이터 로드
- 데이터 셋 다운로드
# cloning repository
# 한번 다운로드 했으므로 이제 실행할 필요 없다
!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
Cloning into 'pytorch-CycleGAN-and-pix2pix'...
remote: Enumerating objects: 2447, done.[K
remote: Total 2447 (delta 0), reused 0 (delta 0), pack-reused 2447[K
Receiving objects: 100% (2447/2447), 8.18 MiB | 23.72 MiB/s, done.
Resolving deltas: 100% (1535/1535), done.
# mounting drive
from google.colab import drive
drive.mount('/content/drive/',force_remount=True)
Mounted at /content/drive/
import os
os.chdir('drive/MyDrive/pytorch-CycleGAN-and-pix2pix/')
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!pip install -r requirements.txt
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Datasets
Download one of the official datasets with:
-
bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos] -
Or use your own dataset by creating the appropriate folders and adding in the images.
- Create a dataset folder under
/datasetfor your dataset. -
Create subfolders
testA,testB,trainA, andtrainBunder your dataset’s folder. Place any images you want to transform from a to b (cat2dog) in thetestAfolder, images you want to transform from b to a (dog2cat) in thetestBfolder, and do the same for thetrainAandtrainBfolders. - 학습 속도 높이기위해 구글 드라이브 직접접근 막기
- 데이터 셋을 /content로 옮겨온다
import os
# Location of Zip File
drive_path = '/content/drive/MyDrive/pytorch-CycleGAN-and-pix2pix/horse2zebra-20220327T123200Z-001.zip'
# colab instance(local)의 path
local_path = '/content'
# Copy the zip file and move it up one level (AKA out of the drive folder)
!cp '{drive_path}' .
# Navigate to the copied file and unzip it quietly
os.chdir(local_path)
!unzip -q 'horse2zebra-20220327T123200Z-001.zip'
# # dataset download (horse2zebra only)
# # now downloaded permenantly at MyDrive, not neccessary
# !bash ./datasets/download_cyclegan_dataset.sh horse2zebra
Specified [horse2zebra]
WARNING: timestamping does nothing in combination with -O. See the manual
for details.
--2022-03-26 14:52:39-- http://efrosgans.eecs.berkeley.edu/cyclegan/datasets/horse2zebra.zip
Resolving efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)... 128.32.244.190
Connecting to efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)|128.32.244.190|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 116867962 (111M) [application/zip]
Saving to: ‘./datasets/horse2zebra.zip’
./datasets/horse2ze 100%[===================>] 111.45M 3.17MB/s in 49s
2022-03-26 14:53:28 (2.30 MB/s) - ‘./datasets/horse2zebra.zip’ saved [116867962/116867962]
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- albumentations 패키지 downloading
- colab에 없음(pycharm에서는 필요없을듯)
# downloading ToTensorV2
!pip install albumentations==0.4.6
Collecting albumentations==0.4.6
Downloading albumentations-0.4.6.tar.gz (117 kB)
[K |████████████████████████████████| 117 kB 34.0 MB/s
[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (1.21.5)
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Collecting imgaug>=0.4.0
Downloading imgaug-0.4.0-py2.py3-none-any.whl (948 kB)
[K |████████████████████████████████| 948 kB 31.5 MB/s
[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.4.6) (3.13)
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Building wheels for collected packages: albumentations
Building wheel for albumentations (setup.py) ... [?25l[?25hdone
Created wheel for albumentations: filename=albumentations-0.4.6-py3-none-any.whl size=65174 sha256=28c520643eedf70531b685c5e83fd59f116f2177af1ee84c726b3b62639d6cf6
Stored in directory: /root/.cache/pip/wheels/cf/34/0f/cb2a5f93561a181a4bcc84847ad6aaceea8b5a3127469616cc
Successfully built albumentations
Installing collected packages: imgaug, albumentations
Attempting uninstall: imgaug
Found existing installation: imgaug 0.2.9
Uninstalling imgaug-0.2.9:
Successfully uninstalled imgaug-0.2.9
Attempting uninstall: albumentations
Found existing installation: albumentations 0.1.12
Uninstalling albumentations-0.1.12:
Successfully uninstalled albumentations-0.1.12
Successfully installed albumentations-0.4.6 imgaug-0.4.0
# !pip list
# 두 버전 일치해야하고, 현재 4.1.2.30 버전 아니고 그 이후면 작동 albumentations 패키지 임포트 안됨
!pip list | grep opencv
opencv-contrib-python 4.1.2.30
opencv-python 4.1.2.30
- config 파일 생성(pycharm에서는 파일 생성으로!)
- colab이므로 따로 선언해둠
import torch
import albumentations as A
from albumentations.pytorch import ToTensorV2
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# DATA_DIR = "/content/horse2zebra"
DATA_DIR = "/content/drive/MyDrive/pytorch-CycleGAN-and-pix2pix/datasets/horse2zebra"
BATCH_SIZE = 1
LEARNING_RATE = 2e-4
LAMDA_IDENTITY = 0.0
LAMDA_CYCLE = 10
NUM_WORKERS = 4
NUM_EPOCHS = 200
LOAD_MODEL = True
SAVE_MODEL = True
CHECKPOINT_GEN_H = "genh.pth.tar"
CHECKPOINT_GEN_Z = "genz.pth.tar"
CHECKPOINT_CRITIC_H = "critich.pth.tar"
CHECKPOINT_CRITIC_Z = "criticz.pth.tar"
transforms = A.Compose(
[
A.Resize(width=256,height=256),
A.HorizontalFlip(p=0.5),
# mean for all 3 channels
A.Normalize(mean=[0.5,0.5,0.5], std = [0.5,0.5,0.5], max_pixel_value=255),
ToTensorV2(),
],
# horse : zebra
# Dict with keys - new target name, values - old target name. ex: {'image2': 'image'}
# the pipeline will augment those two images in the same way.
# it could be more than two, adding ", key:value" inside.
# 관련 parameter에 대한 참고내용 : https://albumentations.ai/docs/examples/example_multi_target/
additional_targets={"image0": "image"}
)
- utils file 생성(for saving and loading checkpoints)
import random, torch, os, numpy as np
import torch.nn as nn
# import config
import copy
def save_checkpoint(model, optimizer, filename="my_checkpoint.pth.tar"):
print("=> Saving checkpoint")
checkpoint = {
# state_dict()
# state_dict 는 PyTorch에서 모델을 저장하거나 불러오는 데 관심이 있다면 필수적인 항목
# state_dict 객체는 Python 사전이기 때문에 쉽게 저장, 업데이트, 변경 및 복원할 수 있으며, 이는 PyTorch 모델과 옵티마이저에 엄청난 모듈성(modularity)을 제공
# 이 때, 학습 가능한 매개변수를 갖는 계층(합성곱 계층, 선형 계층 등) 및 등록된 버퍼들(batchnorm의 running_mean)만 모델의 state_dict항목을 가진다는 점에 유의
# 옵티마이저 객체( torch.optim ) 또한 옵티마이저의 상태 뿐만 아니라 사용된 하이퍼 매개변수 (Hyperparameter) 정보가 포함된 state_dict 을 갖습니다.(epoch 등등)
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
}
torch.save(checkpoint, filename)
def load_checkpoint(checkpoint_file, model, optimizer, lr):
print("=> Loading checkpoint")
checkpoint = torch.load(checkpoint_file, map_location=DEVICE)
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
# If we don't do this then it will just have learning rate of old checkpoint
# and it will lead to many hours of debugging \:
for param_group in optimizer.param_groups:
param_group["lr"] = lr
def seed_everything(seed=42):
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
- Horse data set 로더 생성
# import torch
# to load images
from PIL import Image
import os
# # if you modularize all of these ipynb to python files later
# import config
from torch.utils.data import Dataset
import numpy as np
class HorseZebraDataset(Dataset):
def __init__(self, root_zebra, root_horse, transforms=None):
self.root_zebra = root_zebra
self.root_horse = root_horse
self.transform = transforms
# list all the image files inside the above root_zebra/_horse dir
self.zebra_images = os.listdir(root_zebra)
self.horse_images = os.listdir(root_horse)
# data's are not paired, so the lenght of the data sets are not exactly equal
# so we're checking what is the max length
self.length_dataset = max(len(self.zebra_images), len(self.horse_images)) #ex) 1000, 1500
self.zebra_len = len(self.zebra_images)
self.horse_len = len(self.horse_images)
def __len__(self):
#returning max len
return self.length_dataset
def __getitem__(self, index):
# getting 'index'th image from the list of zebra images files
# why modular %? index could be greater than the dataset that we have
# b/c we're taking the max out of two
# but, there could be flaws in that some images could be shown more often(b/c Dataset's arent paired)
zebra_img = self.zebra_images[index % self.zebra_len]
horse_img = self.horse_images[index % self.horse_len]
# so far, this is just JPEG file in the root dir
# to load img we have to have a PATH
# joining img path with the root dir
zebra_path = os.path.join(self.root_zebra, zebra_img)
horse_path = os.path.join(self.root_horse, horse_img)
# converting those right above to PIL images or numpy array
# numpy arrays in this case
# our purpose here is to *alubumentate / Albumentations is a Python library for image augmentation.
# so we're using numpy array of image.open
# .convert("RGB") could actually be unnecessary but, just in case it's grayscale
zebra_img = np.array(Image.open(zebra_path).convert("RGB"))
horse_img = np.array(Image.open(horse_path).convert("RGB"))
if self.transform:
# this way, we apply 'same' transformations to both of the images
# So it will apply the same set of transformations with the same parameters.
# b/c config 부분에서 additional_targets={"image0": "image"} 했기에 밑에 처럼 변형 적용
augmentation = self.transform(image=zebra_img, image0 = horse_img)
# image 다음 , image0이 같은 방식으로 augment되어 저장되어있음 augmentation(Dict) 안에
# 고로 각각을 Key로 인덱스 접근해서 zebra_img, horse_img에 replace
zebra_img = augmentation["image"]
horse_img = augmentation["image0"]
return zebra_img, horse_img
## 학습 진행하기
import torch
# from datset import HorseZebraDataset
import sys
# from utils import save_checkpoint, load_checkpoint
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
# import config
from tqdm import tqdm
from torchvision.utils import save_image
# from discriminator_model import Discriminator
# from generator_model import Generator
def train_fn(disc_H, disc_Z, gen_Z, gen_H, loader, opt_disc, opt_gen, L1, mse, d_scaler, g_scaler):
# getting nice progress bar
loop = tqdm(loader, leave=True)
# dataset 클래스에서 우리가 return 하는 게 zebra_img, horse_img니깐 (zebra, horse)로 loop돌린다
for idx, (zebra, horse) in enumerate(loop):
zebra = zebra.to(DEVICE)
horse = horse.to(DEVICE)
###########################
####### TRAIN DISC ########
###########################
#train disc_H, and disc_Z
with torch.cuda.amp.autocast(): #float16일 때 필수과정임
#####################
## TRAINING disc_H ##
#####################
fake_horse = gen_H(zebra)
D_H_real = disc_H(horse) # disc on real horse
# doing detach() b/c we'll use this fake_horse when we train gen_z, with detach() you don't have to retype in all these
D_H_fake = disc_H(fake_horse.detach()) # disc on fake horse(generated horse)
###################################
# disc_H의 loss function 정의 #
# torch.ones_like(tensor) : tesor shape과 동일한 1로 채워진 matrix 생성
D_H_real_loss = mse(D_H_real, torch.ones_like(D_H_real))
D_H_fake_loss = mse(D_H_fake, torch.zeros_like(D_H_fake))
# total D_H loss
D_H_loss = D_H_real_loss + D_H_fake_loss
#####################
## TRAINING disc_Z ##
#####################
fake_zebra = gen_Z(horse)
D_Z_real = disc_Z(zebra) # disc result on real zebra
# doing detach() b/c we'll use this fake_horse when we train gen_h, with detach() you don't have to retype in all these
D_Z_fake = disc_Z(fake_zebra.detach()) # disc on fake zebra(generated zebra)
###################################
# disc_Z의 loss function 정의 #
# torch.ones_like(tensor) : tesor shape과 동일한 1로 채워진 matrix 생성(labeling)
D_Z_real_loss = mse(D_Z_real, torch.ones_like(D_Z_real))
D_Z_fake_loss = mse(D_Z_fake, torch.zeros_like(D_Z_fake))
# total D_Z loss
D_Z_loss = D_Z_real_loss + D_Z_fake_loss
## put it together ##
D_loss = (D_H_loss + D_Z_loss) / 2 # as paper did
# grad 초기화 후
opt_disc.zero_grad()
# 역전파 수행해서 각 parameter 별 gradient구해서 저장해둠
d_scaler.scale(D_loss).backward()
d_scaler.step(opt_disc)
d_scaler.update()
###########################
####### TRAIN GEN ########
###########################
#train gen_H, and gen_Z
with torch.cuda.amp.autocast(): #float16일 때 필수과정임
####################
## TRAINING gen_H ##
####################
## adversarial loss ##
D_H_fake = disc_H(fake_horse) # disc result on fake horse
# doing detach() b/c we'll use this fake_horse when we train gen_z, with detach() you don't have to retype in all these
D_Z_fake = disc_Z(fake_zebra) # disc result on fake zebra
G_H_loss = mse(D_H_fake, torch.ones_like(D_H_fake))
G_Z_loss = mse(D_Z_fake, torch.ones_like(D_Z_fake))
## cycle loss ##
cycle_zebra = gen_Z(fake_horse)
cycle_horse = gen_H(fake_zebra)
cycle_zebra_loss = L1(zebra, cycle_zebra)
cycle_horse_loss = L1(horse, cycle_horse)
## identity loss ## (optional to preserve 'color preservation' when reconstructing input from the output)
## * 현재 LAMDA_IDENTITY = 0.0 인데, 추후에 쓰고 싶으면 상수만 변경해서 쓰면 된다.
# identity_zebra = gen_Z(zebra)
# identity_horse = gen_H(horse)
# identity_zebra_loss = L1(zebra, identity_zebra)
# identity_horse_loss = L1(horse, identity_horse)
## put it together ##
G_loss = (
G_H_loss
+ G_Z_loss
+ cycle_zebra_loss * LAMDA_CYCLE
+ cycle_horse_loss * LAMDA_CYCLE
# + identity_horse_loss * LAMDA_IDENTITY
# + identity_zebra_loss * LAMDA_IDENTITY
)
# grad 초기화 후
opt_gen.zero_grad()
# 역전파 수행해서 각 parameter 별 gradient구해서 저장해둠
g_scaler.scale(G_loss).backward()
g_scaler.step(opt_gen)
g_scaler.update()
if idx % 200 == 0:
# why *0.5 + 0.5...?
# inversing normalization we did in 'config' in A.Compose to augemnt the data
fake_horse = fake_horse*0.5+0.5
fake_zebra = fake_zebra*0.5+0.5
result_horse = torch.cat((zebra.data, fake_horse.data), -2)
result_zebra = torch.cat((horse.data, fake_zebra.data), -2)
save_image(result_horse, f"saved_images/horse_{idx}.png")
save_image(result_zebra, f"saved_images/zebra_{idx}.png")
def main():
#initailizing D
disc_H = Discriminator(in_channels=3).to(DEVICE) # discriminating horses
disc_Z = Discriminator(in_channels=3).to(DEVICE) # discriminating zebras
gen_H = Generator(img_channels=3, num_residuals=9).to(DEVICE) # generating horses
gen_Z = Generator(img_channels=3, num_residuals=9).to(DEVICE) # generating zebras
opt_disc = optim.Adam(
# parameters(): Returns an iterator over module parameters. This is typically passed to an optimizer.
# betas : coefficients used for computing running averages of gradient and its square
# '+' each list : concatenating
# why concat? to minimize the number of optimizer(4->2)
list(disc_H.parameters()) + list(disc_Z.parameters()),
lr = LEARNING_RATE,
# 0.5 for momentum term / 0.999 for beta2
betas = (0.5,0.999)
)
opt_gen = optim.Adam(
list(gen_Z.parameters()) + list(gen_H.parameters()),
lr = LEARNING_RATE,
betas = (0.5,0.999)
)
# for cycle-consistency loss / identity loss
L1 = nn.L1Loss()
# advaersarial loss(=GAN loss)
mse = nn.MSELoss()
if LOAD_MODEL:
load_checkpoint(
CHECKPOINT_GEN_H, gen_H, opt_gen, LEARNING_RATE,
)
load_checkpoint(
CHECKPOINT_GEN_Z, gen_Z, opt_gen, LEARNING_RATE,
)
load_checkpoint(
CHECKPOINT_CRITIC_H, disc_H, opt_disc, LEARNING_RATE,
)
load_checkpoint(
CHECKPOINT_CRITIC_Z, disc_Z, opt_disc, LEARNING_RATE,
)
dataset = HorseZebraDataset(
# trainA : horse / trainB : zebra
# 영상과는 약간 다르게 코딩함, original paper의 dataset으로 진행
root_horse=DATA_DIR+"/trainA", root_zebra=DATA_DIR+"/trainB", transforms=transforms
)
loader = DataLoader(
dataset,
batch_size = BATCH_SIZE,
shuffle = True,
num_workers=NUM_WORKERS,
pin_memory=True
)
# float16으로 진행한다면 실행 / float32으로 진행한다면 실행 안해도됨
# always nice to run in float16
g_scaler = torch.cuda.amp.GradScaler()
d_scaler = torch.cuda.amp.GradScaler()
for epoch in range(NUM_EPOCHS):
train_fn(disc_H, disc_Z, gen_Z, gen_H, loader, opt_disc, opt_gen, L1, mse, d_scaler, g_scaler)
if SAVE_MODEL:
save_checkpoint(gen_H, opt_gen, filename=CHECKPOINT_GEN_H)
save_checkpoint(gen_Z, opt_gen, filename=CHECKPOINT_GEN_Z)
save_checkpoint(disc_H, opt_disc, filename=CHECKPOINT_CRITIC_H)
save_checkpoint(disc_Z, opt_disc, filename=CHECKPOINT_CRITIC_Z)
if __name__ == "__main__":
main()
=> Loading checkpoint
=> Loading checkpoint
=> Loading checkpoint
=> Loading checkpoint
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
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---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-9-4017041713b1> in <module>()
218
219 if __name__ == "__main__":
--> 220 main()
221
222
<ipython-input-9-4017041713b1> in main()
208
209 for epoch in range(NUM_EPOCHS):
--> 210 train_fn(disc_H, disc_Z, gen_Z, gen_H, loader, opt_disc, opt_gen, L1, mse, d_scaler, g_scaler)
211
212 if SAVE_MODEL:
<ipython-input-9-4017041713b1> in train_fn(disc_H, disc_Z, gen_Z, gen_H, loader, opt_disc, opt_gen, L1, mse, d_scaler, g_scaler)
99 ## cycle loss ##
100
--> 101 cycle_zebra = gen_Z(fake_horse)
102 cycle_horse = gen_H(fake_zebra)
103 cycle_zebra_loss = L1(zebra, cycle_zebra)
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-2-4fe02f98518c> in forward(self, x)
86 x = layer(x)
87
---> 88 x = self.residual_blocks(x)
89
90 for layer in self.up_blocks:
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
139 def forward(self, input):
140 for module in self:
--> 141 input = module(input)
142 return input
143
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-2-4fe02f98518c> in forward(self, x)
31 def forward(self, x):
32 # concatenating previous low-level info
---> 33 return x + self.block(x)
34
35
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
139 def forward(self, input):
140 for module in self:
--> 141 input = module(input)
142 return input
143
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-2-4fe02f98518c> in forward(self, x)
15
16 def forward(self, x):
---> 17 return self.conv(x)
18
19
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
139 def forward(self, input):
140 for module in self:
--> 141 input = module(input)
142 return input
143
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/instancenorm.py in forward(self, input)
57 return F.instance_norm(
58 input, self.running_mean, self.running_var, self.weight, self.bias,
---> 59 self.training or not self.track_running_stats, self.momentum, self.eps)
60
61
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in instance_norm(input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps)
2326 _verify_spatial_size(input.size())
2327 return torch.instance_norm(
-> 2328 input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, torch.backends.cudnn.enabled
2329 )
2330
KeyboardInterrupt:
결과 출력
- Zebra -> Horse
- Horse -> Zebra
## 테스트 데이터 출력해보기
- 테스트 HORSE IMAGE 출력
from PIL import Image
import matplotlib.pyplot as plt
# for i in range (10):
# imgs = next(iter(test_dataloader))
# real_horse = imgs[]
image = Image.open('/content/drive/MyDrive/pytorch-CycleGAN-and-pix2pix/datasets/horse2zebra/testA/n02381460_1210.jpg')
print("image size: ", image.size)
plt.imshow(image)
plt.show()
image size: (256, 256)
- 학습한 모델 불러와서 IMAGE TRANSLATION 적용해보기
# # not done yet # #
# def generate_images(model, test_input):
# prediction = model(test_input)
# plt.figure(figsize=(12, 12))
# display_list = [test_input[0], prediction[0]]
# title = ['Input Image', 'Predicted Image']
# for i in range(2):
# plt.subplot(1, 2, i+1)
# plt.title(title[i])
# # getting the pixel values between [0, 1] to plot it.
# plt.imshow(display_list[i] * 0.5 + 0.5)
# plt.axis('off')
# plt.show()
# test_dataset = HorseZebraDataset(
# root_horse=DATA_DIR+"/testA", root_zebra=DATA_DIR+"/testB", transforms=transforms
# )
# test_dataloader = DataLoader(
# test_dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, pin_memory=True
# )
# zebra, horse = test_dataset.__getitem__(0)
# # PIL -> np.array = np.array( ) 또는 np.asarray( ) 함수를 이용하여 PIL Image를 NumPy array로 변환할 수 있다.
# # np.array -> PIL = PIL 패키지의 Image.fromarray() 를 사용하면 된다.
# # tesor(pytorch) -> PIL = from torchvision.transforms.functional import to_pil_image / to_pil_image
# # model initialize
# gen_H = Generator(img_channels=3, num_residuals=9).to(DEVICE) # generating horses
# gen_Z = Generator(img_channels=3, num_residuals=9).to(DEVICE) # generating zebras
# # model.load_state_dict(torch.load('model_weights.pth')
# checkpoint_gen_h = torch.load(CHECKPOINT_GEN_H)
# checkpoint_gen_z = torch.load(CHECKPOINT_GEN_Z)
# gen_H.load_state_dict(checkpoint_gen_h["state_dict"])
# gen_Z.load_state_dict(checkpoint_gen_z["state_dict"])
# gen_H.eval()
# gen_Z.eval()
# # CHECKPOINT_GEN_H = "genh.pth.tar"
# # CHECKPOINT_GEN_Z = "genz.pth.tar"
# # CHECKPOINT_CRITIC_H = "critich.pth.tar"
# # CHECKPOINT_CRITIC_Z = "criticz.pth.tar"
# fake_horse = gen_H(zebra)
# fake_zebra = gen_Z(horse)
# # Run the trained model on the test dataset
# for inp in test_horses.take(5):
# generate_images(generator_g, inp)
(256, 256)
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-73-6c2eb7e2fa33> in <module>()
45 # CHECKPOINT_CRITIC_Z = "criticz.pth.tar"
46
---> 47 fake_horse = gen_H(zebra)
48 fake_zebra = gen_Z(horse)
49
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-2-4fe02f98518c> in forward(self, x)
81 def forward(self, x):
82
---> 83 x = self.initial(x)
84
85 for layer in self.down_blocks:
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
139 def forward(self, input):
140 for module in self:
--> 141 input = module(input)
142 return input
143
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in forward(self, input)
444
445 def forward(self, input: Tensor) -> Tensor:
--> 446 return self._conv_forward(input, self.weight, self.bias)
447
448 class Conv3d(_ConvNd):
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
439 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
440 weight, bias, self.stride,
--> 441 _pair(0), self.dilation, self.groups)
442 return F.conv2d(input, weight, bias, self.stride,
443 self.padding, self.dilation, self.groups)
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 3-dimensional input of size [3, 262, 262] instead