120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
from itertools import chain
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import ignite.distributed as idist
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import torch
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import torch.nn as nn
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from omegaconf import OmegaConf
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from engine.base.i2i import EngineKernel, run_kernel, TestEngineKernel
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from engine.util.build import build_model
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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 model.weight_init import generation_init_weights
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class TSITEngineKernel(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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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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b=build_model(self.config.model.discriminator)
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)
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self.logger.debug(discriminators["b"])
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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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with torch.set_grad_enabled(not inference):
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fake = dict(
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b=self.generators["main"](content_img=batch["a"])
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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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loss = dict()
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loss["perceptual"] = self.perceptual_loss(generated["b"], batch["a"]) * self.config.loss.perceptual.weight
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for phase in "b":
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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.config.loss.gan.weight * self.gan_loss(sub_pred_fake[-1], True)
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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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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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return dict(
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b=[batch["a"].detach(), batch["b"].detach(), generated["b"].detach()]
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)
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class TSITTestEngineKernel(TestEngineKernel):
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def __init__(self, config):
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super().__init__(config)
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def build_generators(self) -> 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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return generators
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def to_load(self):
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return {f"generator_{k}": self.generators[k] for k in self.generators}
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def inference(self, batch):
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with torch.no_grad():
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fake = self.generators["main"](content_img=batch["a"][0], style_img=batch["b"][0])
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return {"a": fake.detach()}
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def run(task, config, _):
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if task == "train":
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kernel = TSITEngineKernel(config)
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run_kernel(task, config, kernel)
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elif task == "test":
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kernel = TSITTestEngineKernel(config)
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run_kernel(task, config, kernel)
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else:
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raise NotImplemented
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