def __init__(self, channels, imsize, zdim):
super().__init__()
# Encoder
self.encoder = Encoder(channels, zdim)
# Decoder
self.decoder = Decoder(zdim, channels, imsize, hdim=256)
def __init__(self, channels, imsize, zdim):
super().__init__()
self.imsize = imsize
# Image size should be a power of 2
upsamples = math.log(imsize, 2)
assert upsamples.is_integer()
upsamples = int(upsamples)
# Encoder
self.encoder = Encoder(channels, zdim)
draw_layers = []
hdim = 128
for i in range(upsamples):
if i == 0:
draw_layers.append(DrawLayer(zdim, hdim, channels, imsize))
elif i == upsamples - 1:
draw_layers.append(DrawLayer(hdim, channels, channels, imsize))
else:
draw_layers.append(DrawLayer(hdim, hdim, channels, imsize))
self.draw_layers = nn.Sequential(*draw_layers)
mnist_train_dataset = CustomTensorDataset(torch.from_numpy(mnist_x_train).float(), torch.from_numpy(mnist_y_train).long(), transform=train_transform) kannada_train_dataset = CustomTensorDataset(torch.from_numpy(kannada_x_train).float(), torch.from_numpy(kannada_y_train).long(), transform=train_transform) kannada_val_dataset = CustomTensorDataset(torch.from_numpy(kannada_x_val).float(), torch.from_numpy(kannada_y_val).long(), transform=train_transform) train_loader_1 = DataLoader(mnist_train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) train_loader_2 = DataLoader(kannada_train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) val_loader = DataLoader(kannada_val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) num_batch = min(len(train_loader_1), len(train_loader_2)) dim_z = 512 # Modules shared_latent = GaussianVAE2D(dim_z, dim_z, kernel_size=1, stride=1).to(device) encoder_1 = Encoder().to(device) encoder_2 = Encoder().to(device) decoder_1 = Decoder().to(device) decoder_2 = Decoder().to(device) discriminator_1 = Discriminator().to(device) discriminator_2 = Discriminator().to(device) criterion_discr = nn.BCELoss(reduction='sum') criterion_class = nn.CrossEntropyLoss(reduction='sum') fixed_noise = torch.randn(args.batch_size, dim_z, 1, 1, device=device) real_label = 1 fake_label = 0 label_noise = 0.1 # setup optimizer
def main():
import pydevd_pycharm
pydevd_pycharm.settrace('172.26.3.54',
port=12345,
stdoutToServer=True,
stderrToServer=True)
parser = argparse.ArgumentParser(description="DFDGAN Showing G pic")
parser.add_argument("--config_file",
default="./configs/show_pic.yml",
help="path to config file",
type=str)
parser.add_argument("opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
datasets_dir = ''
for dataset_name in cfg.DATASETS.NAMES:
if datasets_dir != '':
datasets_dir += '-'
datasets_dir += dataset_name
output_dir = os.path.join(output_dir, datasets_dir)
time_string = 'show_pic[{}]'.format(
time.strftime('%Y-%m-%d-%X', time.localtime(time.time())))
output_dir = os.path.join(output_dir, time_string)
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
device = cfg.TEST.DEVICE
if device == "cuda":
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.TEST.DEVICE_ID
cudnn.benchmark = True
logger = setup_logger("DFDGAN", output_dir, 0)
logger.info("Running with config:\n{}".format(cfg))
data_loader, num_classes = make_dataloaders(cfg)
E = Encoder(num_classes, cfg.E.LAST_STRIDE, cfg.E.PRETRAIN_PATH,
cfg.E.NECK, cfg.TEST.NECK_FEAT, cfg.E.NAME,
cfg.E.PRETRAIN_CHOICE).to(device)
Ed = Encoder(num_classes, cfg.ED.LAST_STRIDE, cfg.ED.PRETRAIN_PATH,
cfg.ED.NECK, cfg.TEST.NECK_FEAT, cfg.ED.NAME,
cfg.ED.PRETRAIN_CHOICE).to(device)
G = DFDGenerator(cfg.G.PRETRAIN_PATH,
cfg.G.PRETRAIN_CHOICE,
noise_size=cfg.TRAIN.NOISE_SIZE).to(device)
for _, batch in enumerate(data_loader):
img_x1, img_x2, img_y1, img_y2, target_pid, target_setid = batch
img_x1, img_x2, img_y1, img_y2, target_pid, target_setid = img_x1.to(
device), img_x2.to(device), img_y1.to(device), img_y2.to(
device), target_pid.to(device), target_setid.to(device)
g_img = G(E(img_x1)[0], Ed(img_y1)[0])
img_x1_PIL = transforms.ToPILImage()(img_x1[0].cpu()).convert('RGB')
img_x1_PIL.save(os.path.join(output_dir, 'img_x1.jpg'))
img_y1_PIL = transforms.ToPILImage()(img_y1[0].cpu()).convert('RGB')
img_y1_PIL.save(os.path.join(output_dir, 'img_y1.jpg'))
g_img_PIL = transforms.ToPILImage()(g_img[0].cpu()).convert('RGB')
g_img_PIL.save(os.path.join(output_dir, 'g_img.jpg'))
break
torch.from_numpy(kannada_y_train).long(),
transform=train_transform)
mnist_loader = DataLoader(mnist_train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
kannada_loader = DataLoader(kannada_train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
# Modules
dim_z = 512
shared_latent = GaussianVAE2D(dim_z, dim_z, kernel_size=1, stride=1).to(device)
encoder_1 = Encoder().to(device)
encoder_2 = Encoder().to(device)
decoder_1 = Decoder().to(device)
decoder_2 = Decoder().to(device)
discriminator_1 = Discriminator().to(device)
discriminator_2 = Discriminator().to(device)
if os.path.isfile(args.model):
print("===> Loading Checkpoint to Evaluate '{}'".format(args.model))
checkpoint = torch.load(args.model)
shared_latent.load_state_dict(checkpoint['shared_latent'])
encoder_1.load_state_dict(checkpoint['encoder_1'])
encoder_2.load_state_dict(checkpoint['encoder_2'])
decoder_1.load_state_dict(checkpoint['decoder_1'])
decoder_2.load_state_dict(checkpoint['decoder_2'])
discriminator_1.load_state_dict(checkpoint['discriminator_1'])