def __init__(self):
opt = TestOptions().parse() # get test options
# init pygame
pygame.init()
self.size = (256, 256)
self.screen = pygame.display.set_mode(self.size)
self.font = pygame.font.SysFont(pygame.font.get_fonts()[0], 64)
self.time = pygame.time.get_ticks()
#self.surface_test = pygame.surfarray.make_surface()
self.screen.fill(pygame.Color(255, 255, 255))
pygame.display.flip()
self.model = CycleGANModel(opt)
self.model.setup(opt)
#norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
#net = ResnetGenerator(256, 256, 64, norm_layer=norm_layer, use_dropout=False, n_blocks=9)
#self.net = init_net(net, 'normal', 0.02, [])
impath = os.getcwd() + "/datasets/bird/testA/514.png"
image = pygame.image.load(impath)
def main():
opt = ArchTrainOptions().parse()
torch.cuda.manual_seed(12345)
opt.path_helper = set_log_dir(opt.checkpoints_dir, opt.name)
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
print('The number of training images = %d' % len(dataset))
cycle_gan = CycleGANModel(opt)
cycle_gan.setup(opt)
cycle_gan.set_arch(opt.arch, opt.n_resnet - 1)
writer_dict = {
"writer": SummaryWriter(opt.path_helper['log_path']),
'train_steps': 0
}
# for i, data in tqdm(enumerate(dataset)):
# cycle_gan.set_input(data)
# cycle_gan.forward()
# cycle_gan.compute_visuals()
# save_current_results(opt, cycle_gan.get_current_visuals(), i)
cyclgan_train(opt, cycle_gan, dataset, writer_dict)
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
parser.parse_known_args()
# modify model-related parser options
parser = CycleGANModel.modify_commandline_options(parser, self.isTrain)
parser.parse_known_args() # parse again with new defaults
# save and return the parser
self.parser = parser
return parser.parse_args()
def cyclgan_train(opt, cycle_gan: CycleGANModel, train_loader, writer_dict):
cycle_gan.train()
writer = writer_dict['writer']
total_iters = 0
t_data = 0.0
for epoch in trange(opt.epoch_count,
opt.n_epochs + opt.n_epochs_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
train_steps = writer_dict['train_steps']
for i, data in enumerate(train_loader):
iter_start_time = time.time()
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_iters += opt.batch_size
epoch_iter += opt.batch_size
cycle_gan.set_input(data)
cycle_gan.optimize_parameters()
if (i + 1) % opt.print_freq == 0:
losses = cycle_gan.get_current_losses()
t_comp = (time.time() - iter_start_time)
message = "GAN: [Ep: %d/%d]" % (epoch, opt.n_epochs +
opt.n_epochs_decay)
message += "[Batch: %d/%d][time: %.3f][data: %.3f]" % (
epoch_iter, len(train_loader), t_comp, t_data)
for k, v in losses.items():
message += '[%s: %.3f]' % (k, v)
tqdm.write(message)
if (total_iters + 1) % opt.display_freq == 0:
cycle_gan.compute_visuals()
save_current_results(opt, cycle_gan.get_current_visuals(),
train_steps)
if (total_iters + 1) % opt.save_latest_freq == 0:
tqdm.write(
'saving the latest model (epoch %d, total_iters %d)' %
(epoch, total_iters))
save_suffix = 'latest'
cycle_gan.save_networks(save_suffix)
iter_data_time = time.time()
if (epoch + 1) % opt.save_epoch_freq == 0:
cycle_gan.save_networks('latest')
cycle_gan.save_networks(epoch)
tqdm.write('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.n_epochs + opt.n_epochs_decay,
time.time() - epoch_start_time))
writer.add_scalars('Train/discriminator', {
"A": float(cycle_gan.loss_D_A),
"B": float(cycle_gan.loss_D_B),
}, train_steps)
writer.add_scalars('Train/generator', {
"A": float(cycle_gan.loss_G_A),
"B": float(cycle_gan.loss_G_B),
}, train_steps)
writer.add_scalars(
'Train/cycle', {
"A": float(cycle_gan.loss_cycle_A),
"B": float(cycle_gan.loss_cycle_B),
}, train_steps)
writer.add_scalars('Train/idt', {
"A": float(cycle_gan.loss_idt_A),
"B": float(cycle_gan.loss_idt_B),
}, train_steps)
writer_dict['train_steps'] += 1
cycle_gan.update_learning_rate()
class DrawingCanvas:
def __init__(self):
opt = TestOptions().parse() # get test options
# init pygame
pygame.init()
self.size = (256, 256)
self.screen = pygame.display.set_mode(self.size)
self.font = pygame.font.SysFont(pygame.font.get_fonts()[0], 64)
self.time = pygame.time.get_ticks()
#self.surface_test = pygame.surfarray.make_surface()
self.screen.fill(pygame.Color(255, 255, 255))
pygame.display.flip()
self.model = CycleGANModel(opt)
self.model.setup(opt)
#norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
#net = ResnetGenerator(256, 256, 64, norm_layer=norm_layer, use_dropout=False, n_blocks=9)
#self.net = init_net(net, 'normal', 0.02, [])
impath = os.getcwd() + "/datasets/bird/testA/514.png"
image = pygame.image.load(impath)
#self.screen.blit(image, (0, 0))
"""
Method 'game_loop' will be executed every frame to drive
the display and handling of events in the background.
In Processing this is done behind the screen. Don't
change this, unless you know what you are doing.
"""
def game_loop(self):
current_time = pygame.time.get_ticks()
delta_time = current_time - self.time
self.time = current_time
self.handle_events()
self.update_game(delta_time)
self.draw_components()
"""
Method 'update_game' is there to update the state of variables
and objects from frame to frame.
"""
def update_game(self, dt):
pass
"""
Method 'draw_components' is similar is meant to contain
everything that draws one frame. It is similar to method
void draw() in Processing. Put all draw calls here. Leave all
updates in method 'update'
"""
def draw_components(self):
#self.screen.fill([255, 255, 255])
#pygame.display.flip()
pass
def reset(self):
pass
"""
Method 'handle_event' loop over all the event types and
handles them accordingly.
In Processing this is done behind the screen. Don't
change this, unless you know what you are doing.
"""
def handle_events(self):
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
if event.type == pygame.KEYDOWN:
self.handle_key_down(event)
if event.type == pygame.KEYUP:
self.handle_key_up(event)
if event.type == pygame.MOUSEMOTION:
self.handle_mouse_motion(event)
if event.type == pygame.MOUSEBUTTONDOWN:
self.handle_mouse_pressed(event)
if event.type == pygame.MOUSEBUTTONUP:
self.handle_mouse_released(event)
"""
This method will store a currently pressed buttons
in list 'keyboard_handler.pressed'.
"""
def handle_key_down(self, event):
pass
"""
This method will remove a released button
from list 'keyboard_handler.pressed'.
"""
def handle_key_up(self, event):
pass
"""
Similar to void mouseMoved() in Processing
"""
def handle_mouse_motion(self, event):
#print("test: ",pygame.mouse.get_pressed()[0])
if pygame.mouse.get_pressed()[0]:
pos = pygame.mouse.get_pos()
pygame.display.update(
pygame.draw.ellipse(self.screen, (0, 0, 0), [pos, [5, 5]]))
#print(pos)
self.screen.blit(self.screen, (0, 0))
"""
Similar to void mousePressed() in Processing
"""
def handle_mouse_pressed(self, event):
pos = pygame.mouse.get_pos()
pygame.display.update(
pygame.draw.rect(self.screen, (0, 0, 0), [pos, [5, 5]]))
#(pos)
self.screen.blit(self.screen, (0, 0))
"""
Similar to void mouseReleased() in Processing
"""
def handle_mouse_released(self, event):
#pygame.display.flip()
test = pygame.surfarray.array3d(self.screen)
print(test.shape)
#test = test.T
test = test.transpose(1, 0, 2)
print(test.shape)
#string_image = pygame.image.tostring(self.screen, 'RGBA')
#temp_surf = pygame.image.fromstring(string_image, (512, 512), 'RGB')
#tmp_arr = pygame.surfarray.array2d(temp_surf)
compose = transforms.Compose([
transforms.ToPILImage(),
#transforms.Resize(256, interpolation=Image.CUBIC),
transforms.ToTensor()
])
test_tensor = compose(test).unsqueeze(0)
#plt.figure()
#plt.imshow(test)
#plt.show()
print(test_tensor.size())
#test = compose(test)
#self.net.set_input(test)
self.model.set_input(test_tensor)
result = self.model.forward()
result = self.model.get_generated()
print("Result", result)
resultT = result.squeeze(0)
resultT[resultT < 0] = 0
im = transforms.ToPILImage()(resultT).convert("RGB")
test = result.squeeze(0)
print(test.size())
result = result.detach().numpy()
result = np.squeeze(result, axis=0)
result = result.transpose(1, 2, 0)
print(result.shape)
results = result[:] * 255
#results[result < 0]
print(im)
print(im.size)
plt.imshow(im)
plt.show()
def lab2rgb(self, L, AB):
"""Convert an Lab tensor image to a RGB numpy output
Parameters:
L (1-channel tensor array): L channel images (range: [-1, 1], torch tensor array)
AB (2-channel tensor array): ab channel images (range: [-1, 1], torch tensor array)
Returns:
rgb (RGB numpy image): rgb output images (range: [0, 255], numpy array)
"""
AB2 = AB * 110.0
L2 = (L + 1.0) * 50.0
Lab = torch.cat([L2, AB2], dim=1)
Lab = Lab[0].data.cpu().float().numpy()
Lab = np.transpose(Lab.astype(np.float64), (1, 2, 0))
rgb = color.lab2rgb(Lab) * 255
return rgb
from models.cycle_gan_with_distillation import CycleGANModelWithDistillation
from models.cycle_gan_model import CycleGANModel
if __name__ == '__main__':
opt = TrainOptions().parse() # get test options
# hard-code some parameters for test
opt.num_threads = 0 # test code only supports num_threads = 1
opt.batch_size = 1 # test code only supports batch_size = 1
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
opt2 = Namespace(**vars(opt))
opt2.name = 'monet2photo_pretrained'
opt2.isTrain = False
teacher = CycleGANModel(opt2)
teacher.isTeacher = True
opt.continue_train = True
opt2.continue_train = True
teacher.setup(
opt2) # regular setup: load and print networks; create schedulers
opt.netG = 'resnet_3blocks'
opt.results_dir = 'results'
model = CycleGANModelWithDistillation(
opt, teacher) # create a model given opt.model and other options
model.setup(
opt) # regular setup: load and print networks; create schedulers
# create a website
web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(
opt.phase, opt.epoch)) # define the website directory
if opt.load_iter > 0: # load_iter is 0 by default
import numpy as np
from visdom import Visdom
viz = Visdom()
assert viz.check_connection()
viz.close()
opt = TrainOptions().parse()
save_opt(opt)
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
total_steps = 0
sparse_c_loss_points, sparse_c_loss_avr_points = [], []
win_sparse_C = viz.line(X=torch.zeros((1, )),
Y=torch.zeros((1, )),
name="win_sparse_C")
for epoch in range(1, opt.epoch + 1):
epoch_start_time = time.time()
epoch_iter = 0
from utilSet import html
from models.cycle_gan_model import CycleGANModel
from utilSet.visualizer import Visualizer
from config import TestOptions
from data.dataset import DataLoader
import ntpath
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
root_dir = os.path.join(opt.result_root_dir, opt.variable)
web_dir = os.path.join(root_dir, opt.variable_value, opt.phase)
webpage = html.HTML(web_dir,
'Experiment = GAN2C, Phase = test, Epoch = latest')
# test
for i, data in enumerate(dataset):
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
def main():
opt = SearchOptions().parse()
torch.cuda.manual_seed(12345)
_init_inception(MODEL_DIR)
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
start_search_iter = 0
cur_stage = 1
delta_grow_steps = [int(opt.grow_step ** i) for i in range(1, opt.max_skip_num)] + \
[int(opt.grow_step ** 3) for _ in range(1, opt.n_resnet - opt.max_skip_num + 1)]
opt.max_search_iter = sum(delta_grow_steps)
grow_steps = [
sum(delta_grow_steps[:i]) for i in range(len(delta_grow_steps))
][1:]
grow_ctrler = GrowCtrler(opt.grow_step, steps=grow_steps)
if opt.load_path:
print(f'=> resuming from {opt.load_path}')
assert os.path.exists(opt.load_path)
checkpoint_file = os.path.join(opt.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file,
map_location={'cuda:0': 'cpu'})
# set controller && its optimizer
cur_stage = checkpoint['cur_stage']
start_search_iter = checkpoint["search_iter"]
opt.path_helper = checkpoint['path_helper']
cycle_gan = CycleGANModel(opt)
cycle_gan.setup(opt)
cycle_controller = CycleControllerModel(opt, cur_stage=cur_stage)
cycle_controller.setup(opt)
cycle_controller.set(cycle_gan)
cycle_gan.load_from_state(checkpoint["cycle_gan"])
cycle_controller.load_from_state(checkpoint["cycle_controller"])
else:
opt.path_helper = set_log_dir(opt.checkpoints_dir, opt.name)
cycle_gan = CycleGANModel(opt)
cycle_gan.setup(opt)
cycle_controller = CycleControllerModel(opt, cur_stage=cur_stage)
cycle_controller.setup(opt)
cycle_controller.set(cycle_gan)
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
print('The number of training images = %d' % len(dataset))
writer_dict = {
"writer": SummaryWriter(opt.path_helper['log_path']),
'controller_steps': start_search_iter * opt.ctrl_step,
'train_steps': start_search_iter * opt.shared_epoch
}
g_loss_history = RunningStats(opt.dynamic_reset_window)
d_loss_history = RunningStats(opt.dynamic_reset_window)
dynamic_reset = None
for search_iter in tqdm(
range(int(start_search_iter), int(opt.max_search_iter))):
tqdm.write(f"<start search iteration {search_iter}>")
cycle_controller.reset()
if search_iter in grow_steps:
cur_stage = grow_ctrler.cur_stage(search_iter) + 1
tqdm.write(f'=> grow to stage {cur_stage}')
prev_archs_A, prev_hiddens_A = cycle_controller.get_topk_arch_hidden_A(
)
prev_archs_B, prev_hiddens_B = cycle_controller.get_topk_arch_hidden_B(
)
del cycle_controller
cycle_controller = CycleControllerModel(opt, cur_stage)
cycle_controller.setup(opt)
cycle_controller.set(cycle_gan, prev_hiddens_A, prev_hiddens_B,
prev_archs_A, prev_archs_B)
dynamic_reset = cyclgan_train(opt, cycle_gan, cycle_controller,
dataset, g_loss_history, d_loss_history,
writer_dict)
controller_train(opt, cycle_gan, cycle_controller, writer_dict)
if dynamic_reset:
tqdm.write('re-initialize share GAN')
del cycle_gan
cycle_gan = CycleGANModel(opt)
cycle_gan.setup(opt)
save_checkpoint(
{
'cur_stage':
cur_stage,
'search_iter':
search_iter + 1,
'cycle_gan':
cycle_gan.save_networks(epoch=search_iter),
'cycle_controller':
cycle_controller.save_networks(epoch=search_iter),
'path_helper':
opt.path_helper
}, False, opt.path_helper['ckpt_path'])
final_archs_A, _ = cycle_controller.get_topk_arch_hidden_A()
final_archs_B, _ = cycle_controller.get_topk_arch_hidden_B()
print(f"discovered archs: {final_archs_A}")
print(f"discovered archs: {final_archs_B}")
def cyclgan_train(opt, cycle_gan: CycleGANModel,
cycle_controller: CycleControllerModel, train_loader,
g_loss_history: RunningStats, d_loss_history: RunningStats,
writer_dict):
cycle_gan.train()
cycle_controller.eval()
dynamic_reset = False
writer = writer_dict['writer']
total_iters = 0
t_data = 0.0
for epoch in range(opt.shared_epoch):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
train_steps = writer_dict['train_steps']
for i, data in enumerate(train_loader):
iter_start_time = time.time()
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_iters += opt.batch_size
epoch_iter += opt.batch_size
cycle_controller.forward()
cycle_gan.set_input(data)
cycle_gan.optimize_parameters()
g_loss_history.push(cycle_gan.loss_G.item())
d_loss_history.push(cycle_gan.loss_D_A.item() +
cycle_gan.loss_D_B.item())
if (i + 1) % opt.print_freq == 0:
losses = cycle_gan.get_current_losses()
t_comp = (time.time() - iter_start_time)
message = "GAN: [Ep: %d/%d]" % (epoch, opt.shared_epoch)
message += "[Batch: %d/%d][time: %.3f][data: %.3f]" % (
epoch_iter, len(train_loader), t_comp, t_data)
for k, v in losses.items():
message += '[%s: %.3f]' % (k, v)
tqdm.write(message)
if (total_iters + 1) % opt.display_freq == 0:
cycle_gan.compute_visuals()
save_current_results(opt, cycle_gan.get_current_visuals(),
train_steps)
if g_loss_history.is_full():
if g_loss_history.get_var() < opt.dynamic_reset_threshold \
or d_loss_history.get_var() < opt.dynamic_reset_threshold:
dynamic_reset = True
tqdm.write("=> dynamic resetting triggered")
g_loss_history.clear()
d_loss_history.clear()
return dynamic_reset
if (
total_iters + 1
) % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
tqdm.write(
'saving the latest model (epoch %d, total_iters %d)' %
(epoch, total_iters))
save_suffix = 'latest'
# cycle_gan.save_networks(train_steps)
iter_data_time = time.time()
if (epoch + 1) % opt.save_epoch_freq == 0:
cycle_gan.save_networks('latest')
# cycle_gan.save_networks(train_steps)
tqdm.write('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.n_epochs + opt.n_epochs_decay,
time.time() - epoch_start_time))
writer.add_scalars('Train/discriminator', {
"A": float(cycle_gan.loss_D_A),
"B": float(cycle_gan.loss_D_B),
}, train_steps)
writer.add_scalars('Train/generator', {
"A": float(cycle_gan.loss_G_A),
"B": float(cycle_gan.loss_G_B),
}, train_steps)
writer.add_scalars(
'Train/cycle', {
"A": float(cycle_gan.loss_cycle_A),
"B": float(cycle_gan.loss_cycle_B),
}, train_steps)
writer.add_scalars('Train/idt', {
"A": float(cycle_gan.loss_idt_A),
"B": float(cycle_gan.loss_idt_B),
}, train_steps)
writer_dict['train_steps'] += 1
return dynamic_reset
def controller_train(opt, cycle_gan: CycleGANModel,
cycle_controller: CycleControllerModel, writer_dict):
writer = writer_dict['writer']
# train mode
cycle_controller.train()
# eval mode
cycle_gan.eval()
iter_start_time = time.time()
for i in range(0, opt.ctrl_step):
controller_step = writer_dict['controller_steps']
cycle_controller.step_A()
cycle_controller.step_B()
if (i + 1) % opt.print_freq_controller == 0:
losses = cycle_controller.get_current_losses()
t_comp = (time.time() - iter_start_time)
iter_start_time = time.time()
message = "Cont: [Ep: %d/%d]" % (
i, opt.ctrl_step) + "[{}][{}]".format(cycle_controller.arch_A,
cycle_controller.arch_B)
message += "[time: %.3f]" % (t_comp)
for k, v in losses.items():
message += '[%s: %.3f]' % (k, v)
tqdm.write(message)
# write
writer.add_scalars(
'Controller/loss', {
"A": cycle_controller.loss_A.item(),
"B": cycle_controller.loss_B.item()
}, controller_step)
writer.add_scalars(
'Controller/discriminator', {
"A": cycle_controller.loss_D_A.item(),
"B": cycle_controller.loss_D_B.item()
}, controller_step)
writer.add_scalars(
'Controller/inception_score', {
"A": cycle_controller.loss_IS_A.item(),
"B": cycle_controller.loss_IS_B.item()
}, controller_step)
writer.add_scalars('Controller/adv', {
"A": cycle_controller.loss_adv_A,
"B": cycle_controller.loss_adv_B
}, controller_step)
writer.add_scalars(
'Controller/entropy', {
"A": cycle_controller.loss_entropy_A,
"B": cycle_controller.loss_entropy_B
}, controller_step)
writer.add_scalars(
'Controller/reward', {
"A": cycle_controller.loss_reward_A,
"B": cycle_controller.loss_reward_B
}, controller_step)
writer_dict['controller_steps'] = controller_step + 1
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import time
from options.train_options import TrainOptions
from data import create_dataset
from models.cycle_gan_model import CycleGANModel
from util.util import create_log_txt, print_current_losses
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
model = CycleGANModel(
opt) # create a model given opt.model and other options
model.setup(
opt) # regular setup: load and print networks; create schedulers
log_file = create_log_txt(opt) # create a log file for training progress
total_iters = 0 # the total number of training iterations
for epoch in range(
opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1
): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
model.update_learning_rate(
) # update learning rates in the beginning of every epoch.
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time(
ngf=64,
no_dropout=True,
no_flip=True,
norm="instance",
ntest=float("inf"),
num_test=100,
num_threads=0,
output_nc=3,
phase="test",
preprocess="no_preprocessing",
results_dir="./results/",
serial_batches=True,
suffix="",
verbose=False,
)
model = CycleGANModel(opt).netG_A
model.load_state_dict(torch.load(model_fp))
preprocess = get_transform(opt)
class SingleImageDataset(torch.utils.data.Dataset):
def __init__(self, *args, **kwargs):
img = kwargs.pop("img")
super().__init__(*args, **kwargs)
img = preprocess(img)
self.img = img
def __getitem__(self, i):
return self.img
# -*- coding:utf-8 -*-
import time
from config import TrainOptions
from models.cycle_gan_model import CycleGANModel
from utilSet.visualizer import Visualizer, save_opt
from data.dataset import DataLoader
opt = TrainOptions().parse()
save_opt(opt)
data_loader = DataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
model = CycleGANModel()
model.initialize(opt)
visualizer = Visualizer(opt)
if __name__ == '__main__':
total_steps = 0
for epoch in range(1, opt.epoch + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
visualizer.reset()
total_steps += 1
epoch_iter += 1
model.set_input(data)
model.optimize_parameters()
from options.test_options import TestOptions
from data import create_dataset
from models.cycle_gan_model import CycleGANModel
from util.util import save_images, create_results_dir
from util import html
if __name__ == '__main__':
opt = TestOptions().parse() # get test options
# hard-code some parameters for test
opt.num_threads = 0 # test code only supports num_threads = 0
opt.batch_size = 1 # test code only supports batch_size = 1
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
model = CycleGANModel(
opt) # create a model given opt.model and other options
model.setup(
opt) # regular setup: load and print networks; create schedulers
# create results dir
image_dir = create_results_dir(opt)
# test with eval mode. This only affects layers like batchnorm and dropout.
# For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
if opt.eval:
model.eval()
for i, data in enumerate(dataset):
if i >= opt.num_test: # only apply our model to opt.num_test images.
break
model.set_input(data) # unpack data from data loader
model.test() # run inference
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
