def load_model_kpn_1f(cfg):
if cfg.dynamic.frame_size == 128:
return KPN_1f(color=True,
kernel_size=[9],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
alpha=1.0)
else:
return KPN_1f_fs64(color=True,
kernel_size=[9],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
alpha=1.0)
def load_model_kpn(cfg):
# return KPN_1f_model(color=True,kernel_size=[cfg.kpn_frame_size],burst_length=cfg.input_N,blind_est=True),LossFunc(tensor_grad=~cfg.blind,alpha=1.0)
return KPN_model(color=True,
kernel_size=[cfg.kpn_frame_size],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
recon_l1=cfg.recon_l1)
def load_model_kpn(cfg, num_frames):
return KPN_model(color=True,
burst_length=num_frames,
blind_est=True,
kernel_size=[cfg.kpn_frame_size],
cascade=cfg.kpn_cascade_output), LossFunc(
tensor_grad=~cfg.blind, alpha=1.0)
def load_model_kpn_1f_cls(cfg):
if cfg.dynamic.frame_size in [64, 128]:
return KPN_1f_cls(color=True,
kernel_size=[cfg.kpn_1f_frame_size],
burst_length=cfg.input_N,
blind_est=True,
filter_thresh=cfg.kpn_filter_onehot,
cascade=cfg.kpn_cascade_output,
frame_size=cfg.dynamic.frame_size), LossFunc(
tensor_grad=~cfg.blind, alpha=1.0)
elif cfg.dynamic.frame_size == 32:
return KPN_1f_cls_fs32(color=True,
kernel_size=[cfg.kpn_1f_frame_size],
burst_length=cfg.input_N,
blind_est=True,
filter_thresh=cfg.kpn_filter_onehot,
cascade=cfg.kpn_cascade_output), LossFunc(
tensor_grad=~cfg.blind, alpha=1.0)
else:
raise KeyError(f"Uknown frame size [{cfg.dynamic.frame_size}]")
def load_model_kpn_1f(cfg):
if cfg.dynamic.frame_size == 128:
return KPN_1f(color=True,
kernel_size=[cfg.kpn_1f_frame_size],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
alpha=1.0)
elif cfg.dynamic.frame_size == 64:
return KPN_1f_fs64(color=True,
kernel_size=[cfg.kpn_1f_frame_size],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
alpha=1.0)
elif cfg.dynamic.frame_size == 32:
return KPN_1f_fs32(color=True,
kernel_size=[cfg.kpn_1f_frame_size],
burst_length=cfg.input_N,
blind_est=True), LossFunc(tensor_grad=~cfg.blind,
alpha=1.0)
else:
raise KeyError("Uknown frame size [{cfg.dynamic.frame_size}]")
def get_kpn_model(cfg):
model = KPN_model(color=True,
burst_length=cfg.nframes,
blind_est=True,
kernel_size=[5],
cascade=False)
model = model.to(cfg.gpuid, non_blocking=True)
loss_fxn_base = LossFunc(tensor_grad=True, alpha=1.0)
loss_fxn_base = loss_fxn_base.to(cfg.gpuid, non_blocking=True)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# -- create closure for loss --
def wrap_loss_fxn(denoised, gt_img, denoised_frames, step):
gt_img_nmlz = gt_img - 0.5 #gt_img.mean()
loss_basic, loss_anneal = loss_fxn_base(denoised_frames, denoised,
gt_img_nmlz, step)
return loss_basic + loss_anneal
loss_fxn = wrap_loss_fxn
# -- create empty scheduler --
def scheduler_fxn(epoch):
pass
# -- wrap call function for interface --
forward_fxn = model.forward
def wrap_forward(dyn_noisy, noise_info):
noisy = dyn_noisy - 0.5 #dyn_noisy.mean()
kpn_stack = rearrange(noisy, 't b c h w -> b t c h w')
kpn_cat = rearrange(noisy, 't b c h w -> b (t c) h w')
denoised, denoised_ave, filters = forward_fxn(kpn_cat, kpn_stack)
denoised_ave += 0.5
return denoised_ave, denoised
model.forward = wrap_forward
return model, loss_fxn, optimizer, scheduler_fxn
def load_model_kpn(cfg):
return KPN_model(color=True,
burst_length=cfg.input_N,
blind_est=True,
kernel_size=[cfg.kpn_frame_size]), LossFunc()
def load_model_stn(cfg):
img_shape = (cfg.dynamic.frame_size, cfg.dynamic.frame_size, 3)
return STNBurst(img_shape), LossFunc(tensor_grad=~cfg.blind)
def load_model_kpn(cfg):
return KPN_model(color=True, burst_length=cfg.input_N,
blind_est=True), LossFunc()