138 lines
5.7 KiB
Python
138 lines
5.7 KiB
Python
from itertools import chain
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from omegaconf import OmegaConf
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import torch
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import torch.nn as nn
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import ignite.distributed as idist
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from ignite.engine import Events
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from omegaconf import read_write, OmegaConf
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from model.weight_init import generation_init_weights
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from loss.I2I.perceptual_loss import PerceptualLoss
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from loss.gan import GANLoss
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from engine.base.i2i import EngineKernel, run_kernel
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from engine.util.build import build_model
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class TAFGEngineKernel(EngineKernel):
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def __init__(self, config):
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super().__init__(config)
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perceptual_loss_cfg = OmegaConf.to_container(config.loss.perceptual)
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perceptual_loss_cfg.pop("weight")
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self.perceptual_loss = PerceptualLoss(**perceptual_loss_cfg).to(idist.device())
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gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
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gan_loss_cfg.pop("weight")
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self.gan_loss = GANLoss(**gan_loss_cfg).to(idist.device())
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self.fm_loss = nn.L1Loss() if config.loss.fm.level == 1 else nn.MSELoss()
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self.recon_loss = nn.L1Loss() if config.loss.recon.level == 1 else nn.MSELoss()
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self.style_recon_loss = nn.L1Loss() if config.loss.style_recon.level == 1 else nn.MSELoss()
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def _process_batch(self, batch, inference=False):
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# batch["b"] = batch["b"] if inference else batch["b"][0].expand(batch["a"].size())
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return batch
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def build_models(self) -> (dict, dict):
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generators = dict(
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main=build_model(self.config.model.generator)
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)
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discriminators = dict(
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a=build_model(self.config.model.discriminator),
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b=build_model(self.config.model.discriminator)
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)
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self.logger.debug(discriminators["a"])
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self.logger.debug(generators["main"])
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for m in chain(generators.values(), discriminators.values()):
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generation_init_weights(m)
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return generators, discriminators
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def setup_after_g(self):
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for discriminator in self.discriminators.values():
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discriminator.requires_grad_(True)
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def setup_before_g(self):
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for discriminator in self.discriminators.values():
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discriminator.requires_grad_(False)
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def forward(self, batch, inference=False) -> dict:
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generator = self.generators["main"]
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batch = self._process_batch(batch, inference)
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with torch.set_grad_enabled(not inference):
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fake = dict(
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a=generator(content_img=batch["edge_a"], style_img=batch["a"], which_decoder="a"),
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b=generator(content_img=batch["edge_a"], style_img=batch["b"], which_decoder="b"),
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)
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return fake
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def criterion_generators(self, batch, generated) -> dict:
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batch = self._process_batch(batch)
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loss = dict()
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loss_perceptual, _ = self.perceptual_loss(generated["b"], batch["a"])
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loss["perceptual"] = loss_perceptual * self.config.loss.perceptual.weight
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for phase in "ab":
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pred_fake = self.discriminators[phase](generated[phase])
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loss[f"gan_{phase}"] = 0
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for sub_pred_fake in pred_fake:
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# last output is actual prediction
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loss[f"gan_{phase}"] += self.gan_loss(sub_pred_fake[-1], True)
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if self.config.loss.fm.weight > 0 and phase == "b":
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pred_real = self.discriminators[phase](batch[phase])
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loss_fm = 0
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num_scale_discriminator = len(pred_fake)
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for i in range(num_scale_discriminator):
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# last output is the final prediction, so we exclude it
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num_intermediate_outputs = len(pred_fake[i]) - 1
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for j in range(num_intermediate_outputs):
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loss_fm += self.fm_loss(pred_fake[i][j], pred_real[i][j].detach()) / num_scale_discriminator
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loss[f"fm_{phase}"] = self.config.loss.fm.weight * loss_fm
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loss["recon"] = self.config.loss.recon.weight * self.recon_loss(generated["a"], batch["a"])
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# loss["style_recon"] = self.config.loss.style_recon.weight * self.style_recon_loss(
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# self.generators["main"].module.style_encoders["b"](batch["b"]),
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# self.generators["main"].module.style_encoders["b"](generated["b"])
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# )
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return loss
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def criterion_discriminators(self, batch, generated) -> dict:
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loss = dict()
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# batch = self._process_batch(batch)
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for phase in self.discriminators.keys():
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pred_real = self.discriminators[phase](batch[phase])
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pred_fake = self.discriminators[phase](generated[phase].detach())
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loss[f"gan_{phase}"] = 0
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for i in range(len(pred_fake)):
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loss[f"gan_{phase}"] += (self.gan_loss(pred_fake[i][-1], False, is_discriminator=True)
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+ self.gan_loss(pred_real[i][-1], True, is_discriminator=True)) / 2
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return loss
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def intermediate_images(self, batch, generated) -> dict:
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"""
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returned dict must be like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
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:param batch:
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:param generated: dict of images
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:return: dict like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
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"""
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batch = self._process_batch(batch)
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edge = batch["edge_a"][:, 0:1, :, :]
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return dict(
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a=[edge.expand(-1, 3, -1, -1).detach(), batch["a"].detach(), batch["b"].detach(), generated["a"].detach(),
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generated["b"].detach()]
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)
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def change_engine(self, config, trainer):
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@trainer.on(Events.ITERATION_STARTED(once=int(config.max_iteration / 3)))
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def change_config(engine):
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with read_write(config):
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config.loss.perceptual.weight = 5
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def run(task, config, _):
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kernel = TAFGEngineKernel(config)
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run_kernel(task, config, kernel)
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