97 lines
4.1 KiB
Python
97 lines
4.1 KiB
Python
from itertools import chain
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import torch
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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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from engine.util.container import GANImageBuffer, LossContainer
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from engine.util.loss import pixel_loss, gan_loss
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from loss.I2I.minimal_geometry_distortion_constraint_loss import MGCLoss
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from model.weight_init import generation_init_weights
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class CycleGANEngineKernel(EngineKernel):
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def __init__(self, config):
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super().__init__(config)
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self.gan_loss = gan_loss(config.loss.gan)
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self.cycle_loss = LossContainer(config.loss.cycle.weight, pixel_loss(config.loss.cycle.level))
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self.id_loss = LossContainer(config.loss.id.weight, pixel_loss(config.loss.id.level))
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self.mgc_loss = LossContainer(config.loss.mgc.weight, MGCLoss())
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self.image_buffers = {k: GANImageBuffer(config.data.train.buffer_size or 50) for k in
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self.discriminators.keys()}
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def build_models(self) -> (dict, dict):
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generators = dict(
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a2b=build_model(self.config.model.generator),
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b2a=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["a2b"])
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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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images = dict()
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with torch.set_grad_enabled(not inference):
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images["a2b"] = self.generators["a2b"](batch["a"])
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images["b2a"] = self.generators["b2a"](batch["b"])
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images["a2b2a"] = self.generators["b2a"](images["a2b"])
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images["b2a2b"] = self.generators["a2b"](images["b2a"])
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if self.id_loss.weight > 0:
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images["a2a"] = self.generators["b2a"](batch["a"])
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images["b2b"] = self.generators["a2b"](batch["b"])
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return images
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def criterion_generators(self, batch, generated) -> dict:
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loss = dict()
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for ph in "ab":
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loss[f"cycle_{ph}"] = self.cycle_loss(generated["a2b2a" if ph == "a" else "b2a2b"], batch[ph])
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loss[f"id_{ph}"] = self.id_loss(generated[f"{ph}2{ph}"], batch[ph])
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loss[f"mgc_{ph}"] = self.mgc_loss(generated["a2b" if ph == "a" else "b2a"], batch[ph])
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loss[f"gan_{ph}"] = self.config.loss.gan.weight * self.gan_loss(
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self.discriminators[ph](generated["a2b" if ph == "b" else "b2a"]), 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 "ab":
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generated_image = self.image_buffers[phase].query(generated["b2a" if phase == "a" else "a2b"].detach())
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loss[f"gan_{phase}"] = (self.gan_loss(self.discriminators[phase](generated_image), False,
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is_discriminator=True) +
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self.gan_loss(self.discriminators[phase](batch[phase]), True,
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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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a=[batch["a"].detach(), generated["a2b"].detach(), generated["a2b2a"].detach()],
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b=[batch["b"].detach(), generated["b2a"].detach(), generated["b2a2b"].detach()],
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)
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def run(task, config, _):
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kernel = CycleGANEngineKernel(config)
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run_kernel(task, config, kernel)
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