base

engine/TAFG.py

@@ -0,0 +1,133 @@
+from itertools import chain
+from math import ceil
+
+from omegaconf import read_write, OmegaConf
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import ignite.distributed as idist
+
+import data
+from engine.base.i2i import get_trainer, EngineKernel, build_model
+from model.weight_init import generation_init_weights
+
+from loss.I2I.perceptual_loss import PerceptualLoss
+from loss.gan import GANLoss
+
+
+class TAFGEngineKernel(EngineKernel):
+    def __init__(self, config, logger):
+        super().__init__(config, logger)
+        perceptual_loss_cfg = OmegaConf.to_container(config.loss.perceptual)
+        perceptual_loss_cfg.pop("weight")
+        self.perceptual_loss = PerceptualLoss(**perceptual_loss_cfg).to(idist.device())
+
+        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.fm_loss = nn.L1Loss() if config.loss.fm.level == 1 else nn.MSELoss()
+        self.recon_loss = nn.L1Loss() if config.loss.recon.level == 1 else nn.MSELoss()
+
+    def build_models(self) -> (dict, dict):
+        generators = dict(
+            main=build_model(self.config.model.generator)
+        )
+        discriminators = dict(
+            a=build_model(self.config.model.discriminator),
+            b=build_model(self.config.model.discriminator)
+        )
+        self.logger.debug(discriminators["a"])
+        self.logger.debug(generators["main"])
+
+        for m in chain(generators.values(), discriminators.values()):
+            generation_init_weights(m)
+
+        return generators, discriminators
+
+    def setup_before_d(self):
+        for discriminator in self.discriminators.values():
+            discriminator.requires_grad_(True)
+
+    def setup_before_g(self):
+        for discriminator in self.discriminators.values():
+            discriminator.requires_grad_(False)
+
+    def forward(self, batch, inference=False) -> dict:
+        generator = self.generators["main"]
+        with torch.set_grad_enabled(not inference):
+            fake = dict(
+                a=generator(content_img=batch["edge_a"], style_img=batch["a"], which_decoder="a"),
+                b=generator(content_img=batch["edge_a"], style_img=batch["b"], which_decoder="b"),
+            )
+        return fake
+
+    def criterion_generators(self, batch, generated) -> dict:
+        loss = dict()
+        loss["perceptual"], _, = self.perceptual_loss(generated["b"], batch["b"]) * self.config.loss.perceptual.weight
+        for phase in "ab":
+            pred_fake = self.discriminators[phase](generated[phase])
+            for i, sub_pred_fake in enumerate(pred_fake):
+                # last output is actual prediction
+                loss[f"gan_{phase}_sub_{i}"] = self.gan_loss(sub_pred_fake[-1], True)
+
+            if self.config.loss.fm.weight > 0 and phase == "b":
+                pred_real = self.discriminators[phase](batch[phase])
+                loss_fm = 0
+                num_scale_discriminator = len(pred_fake)
+                for i in range(num_scale_discriminator):
+                    # last output is the final prediction, so we exclude it
+                    num_intermediate_outputs = len(pred_fake[i]) - 1
+                    for j in range(num_intermediate_outputs):
+                        loss_fm += self.fm_loss(pred_fake[i][j], pred_real[i][j].detach()) / num_scale_discriminator
+                loss[f"fm_{phase}"] = self.config.loss.fm.weight * loss_fm
+        loss["recon"] = self.recon_loss(generated["a"], batch["a"]) * self.config.loss.recon.weight
+        return loss
+
+    def criterion_discriminators(self, batch, generated) -> dict:
+        loss = dict()
+        for phase in self.discriminators.keys():
+            pred_real = self.discriminators[phase](batch[phase])
+            pred_fake = self.discriminators[phase](generated[phase].detach())
+            loss[f"gan_{phase}"] = 0
+            for i in range(len(pred_fake)):
+                loss[f"gan_{phase}"] += (self.gan_loss(pred_fake[i][-1], False, is_discriminator=True)
+                                         + self.gan_loss(pred_real[i][-1], True, is_discriminator=True)) / 2
+        return loss
+
+    def intermediate_images(self, batch, generated) -> dict:
+        """
+        returned dict must be like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
+        :param batch:
+        :param generated: dict of images
+        :return: dict like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
+        """
+        return dict(
+            a=[batch[f"edge_a"].expand(-1, 3, -1, -1).detach(), batch["a"].detach(), generated["a"].detach()],
+            b=[batch["b"].detach(), generated["b"].detach()]
+        )
+
+
+def run(task, config, logger):
+    assert torch.backends.cudnn.enabled
+    torch.backends.cudnn.benchmark = True
+    logger.info(f"start task {task}")
+    with read_write(config):
+        config.max_iteration = ceil(config.max_pairs / config.data.train.dataloader.batch_size)
+
+    if task == "train":
+        train_dataset = data.DATASET.build_with(config.data.train.dataset)
+        logger.info(f"train with dataset:\n{train_dataset}")
+        train_data_loader = idist.auto_dataloader(train_dataset, **config.data.train.dataloader)
+        trainer = get_trainer(config, TAFGEngineKernel(config, logger), len(train_data_loader))
+        if idist.get_rank() == 0:
+            test_dataset = data.DATASET.build_with(config.data.test.dataset)
+            trainer.state.test_dataset = test_dataset
+        try:
+            trainer.run(train_data_loader, max_epochs=ceil(config.max_iteration / len(train_data_loader)))
+        except Exception:
+            import traceback
+            print(traceback.format_exc())
+    else:
+        return NotImplemented(f"invalid task: {task}")