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use loss container

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This commit is contained in:
Ray Wong 2020-10-11 23:36:37 +08:00
parent 74a7cfb2d8
commit 2de00d0245
1 changed files with 13 additions and 6 deletions
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19
engine/U-GAT-IT.py
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@ -13,6 +13,10 @@ from model.image_translation.UGATIT import RhoClipper
from util.image import attention_colored_map
def pixel_loss(level):
return nn.L1Loss() if level == 1 else nn.MSELoss()
def mse_loss(x, target_flag):
return F.mse_loss(x, torch.ones_like(x) if target_flag else torch.zeros_like(x))
@ -28,10 +32,13 @@ class UGATITEngineKernel(EngineKernel):
gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
gan_loss_cfg.pop("weight")
self.gan_loss = GANLoss(**gan_loss_cfg).to(idist.device())
self.cycle_loss = LossContainer(config.loss.cycle.weight,
nn.L1Loss() if config.loss.cycle.level == 1 else nn.MSELoss())
self.cycle_loss = LossContainer(config.loss.cycle.weight, pixel_loss(config.loss.cycle.level))
self.mgc_loss = LossContainer(config.loss.mgc.weight, MyLoss())
self.id_loss = LossContainer(config.loss.id.weight, nn.L1Loss() if config.loss.id.level == 1 else nn.MSELoss())
self.id_loss = LossContainer(config.loss.id.weight, pixel_loss(config.loss.id.level))
self.bce_loss = LossContainer(self.config.loss.cam.weight, bce_loss)
self.mse_loss = LossContainer(self.config.loss.gan.weight, mse_loss)
self.rho_clipper = RhoClipper(0, 1)
self.train_generator_first = False
@ -86,10 +93,10 @@ class UGATITEngineKernel(EngineKernel):
generated_image = generated["images"]["a2b" if phase == "b" else "b2a"]
pred_fake, cam_pred = self.discriminators[dk + phase](generated_image)
loss[f"gan_{phase}_{dk}"] = self.config.loss.gan.weight * self.gan_loss(pred_fake, True)
loss[f"gan_cam_{phase}_{dk}"] = self.config.loss.gan.weight * mse_loss(cam_pred, True)
loss[f"gan_cam_{phase}_{dk}"] = self.mse_loss(cam_pred, True)
for t, f in [("a2b", "b2b"), ("b2a", "a2a")]:
loss[f"cam_{t[-1]}"] = self.config.loss.cam.weight * (
bce_loss(generated["cam_pred"][t], True) + bce_loss(generated["cam_pred"][f], False))
loss[f"cam_{t[-1]}"] = self.bce_loss(generated["cam_pred"][t], True) + \
self.bce_loss(generated["cam_pred"][f], False)
return loss
def criterion_discriminators(self, batch, generated) -> dict: