def __init__(self, args, device=DEVICE):
self.args = args
self.device = device
self.rderr = renderer.Renderer(renderer=args.renderer,
CANVAS_WIDTH=args.canvas_size,
canvas_color=args.canvas_color)
# define G
self.net_G = define_G(rdrr=self.rderr, netG=args.net_G).to(device)
# define some other vars to record the training states
self.x_ctt = None
self.x_color = None
self.x_alpha = None
self.G_pred_foreground = None
self.G_pred_alpha = None
self.G_final_pred_canvas = torch.zeros([1, 3, 128, 128]).to(device)
self.G_loss = torch.tensor(0.0)
self.step_id = 0
self.anchor_id = 0
self.renderer_checkpoint_dir = args.renderer_checkpoint_dir
self.output_dir = args.output_dir
self.lr = args.lr
# define the loss functions
self._pxl_loss = loss.PixelLoss(p=1)
self._sinkhorn_loss = loss.SinkhornLoss(epsilon=0.01,
niter=5,
normalize=False)
# some other vars to be initialized in child classes
self.input_aspect_ratio = None
self.img_path = None
self.img_batch = None
self.img_ = None
self.final_rendered_images = None
self.m_grid = None
self.m_strokes_per_block = None
if os.path.exists(self.output_dir) is False:
os.mkdir(self.output_dir)
if os.path.exists(args.vector_file):
npzfile = np.load(args.vector_file)
print(npzfile['x_ctt'].shape)
print(npzfile['x_color'].shape)
print(npzfile['x_alpha'].shape)
self.x_ctt = np.copy(npzfile['x_ctt'][:, ::-1, :])
self.x_color = np.copy(npzfile['x_color'][:, ::-1, :])
self.x_alpha = np.copy(npzfile['x_alpha'][:, ::-1, :])
self.m_grid = int(np.sqrt(self.x_ctt.shape[0]))
self.anchor_id = self.x_ctt.shape[1] - 1
def __init__(self, args):
self.args = args
self.rderr = renderer.Renderer(
renderer=args.renderer,
CANVAS_WIDTH=args.canvas_size,
canvas_color=args.canvas_color,
)
# define G
self.net_G = define_G(rdrr=self.rderr, netG=args.net_G).to(device)
# define some other vars to record the training states
self.x_ctt = None
self.x_color = None
self.x_alpha = None
self.G_pred_foreground = None
self.G_pred_alpha = None
self.G_final_pred_canvas = torch.zeros(
[1, 3, self.net_G.out_size, self.net_G.out_size]).to(device)
self.G_loss = torch.tensor(0.0)
self.step_id = 0
self.anchor_id = 0
self.renderer_checkpoint_dir = args.renderer_checkpoint_dir
self.output_dir = args.output_dir
self.lr = args.lr
# define the loss functions
self._pxl_loss = loss.PixelLoss(p=1)
self._sinkhorn_loss = loss.SinkhornLoss(epsilon=0.01,
niter=5,
normalize=False)
# some other vars to be initialized in child classes
self.input_aspect_ratio = None
self.img_path = None
self.img_batch = None
self.img_ = None
self.final_rendered_images = None
self.m_grid = None
self.m_strokes_per_block = None
if os.path.exists(self.output_dir) is False:
os.mkdir(self.output_dir)
