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UGATIT version 0.1

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budui
2020-08-24 06:51:42 +08:00
parent 54b0799c48
commit 31aafb3470
4 changed files with 90 additions and 109 deletions
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138
engine/UGATIT.py
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@@ -4,7 +4,6 @@ from math import ceil
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import ignite.distributed as idist
from ignite.engine import Events, Engine
@@ -20,11 +19,28 @@ from loss.gan import GANLoss
from model.weight_init import generation_init_weights
from model.GAN.residual_generator import GANImageBuffer
from model.GAN.UGATIT import RhoClipper
from util.image import make_2d_grid, fuse_attention_map
from util.image import make_2d_grid, fuse_attention_map, attention_colored_map
from util.handler import setup_common_handlers, setup_tensorboard_handler
from util.build import build_model, build_optimizer
def build_lr_schedulers(optimizers, config):
g_milestones_values = [
(0, config.optimizers.generator.lr),
(int(config.data.train.scheduler.start_proportion * config.max_iteration), config.optimizers.generator.lr),
(config.max_iteration, config.data.train.scheduler.target_lr)
]
d_milestones_values = [
(0, config.optimizers.discriminator.lr),
(int(config.data.train.scheduler.start_proportion * config.max_iteration), config.optimizers.discriminator.lr),
(config.max_iteration, config.data.train.scheduler.target_lr)
]
return dict(
g=PiecewiseLinear(optimizers["g"], param_name="lr", milestones_values=g_milestones_values),
d=PiecewiseLinear(optimizers["d"], param_name="lr", milestones_values=d_milestones_values)
)
def get_trainer(config, logger):
generators = dict(
a2b=build_model(config.model.generator, config.distributed.model),
@@ -42,23 +58,14 @@ def get_trainer(config, logger):
logger.debug(discriminators["ga"])
logger.debug(generators["a2b"])
optimizer_g = build_optimizer(chain(*[m.parameters() for m in generators.values()]), config.optimizers.generator)
optimizer_d = build_optimizer(chain(*[m.parameters() for m in discriminators.values()]),
config.optimizers.discriminator)
optimizers = dict(
g=build_optimizer(chain(*[m.parameters() for m in generators.values()]), config.optimizers.generator),
d=build_optimizer(chain(*[m.parameters() for m in discriminators.values()]), config.optimizers.discriminator),
)
logger.info(f"build optimizers:\n{optimizers}")
milestones_values = [
(0, config.optimizers.generator.lr),
(int(config.data.train.scheduler.start_proportion * config.max_iteration), config.optimizers.generator.lr),
(config.max_iteration, config.data.train.scheduler.target_lr)
]
lr_scheduler_g = PiecewiseLinear(optimizer_g, param_name="lr", milestones_values=milestones_values)
milestones_values = [
(0, config.optimizers.discriminator.lr),
(int(config.data.train.scheduler.start_proportion * config.max_iteration), config.optimizers.discriminator.lr),
(config.max_iteration, config.data.train.scheduler.target_lr)
]
lr_scheduler_d = PiecewiseLinear(optimizer_d, param_name="lr", milestones_values=milestones_values)
lr_schedulers = build_lr_schedulers(optimizers, config)
logger.info(f"build lr_schedulers:\n{lr_schedulers}")
gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
gan_loss_cfg.pop("weight")
@@ -116,26 +123,26 @@ def get_trainer(config, logger):
identity["a"], cam_identity_pred["a"], heatmap["a2a"] = generators["b2a"](real["a"])
identity["b"], cam_identity_pred["b"], heatmap["b2b"] = generators["a2b"](real["b"])
optimizer_g.zero_grad()
optimizers["g"].zero_grad()
loss_g = dict()
for n in ["a", "b"]:
loss_g.update(criterion_generator(n, real[n], fake[n], rec[n], identity[n], cam_generator_pred[n],
cam_identity_pred[n], discriminators["l" + n], discriminators["g" + n]))
sum(loss_g.values()).backward()
optimizer_g.step()
optimizers["g"].step()
for generator in generators.values():
generator.apply(rho_clipper)
for discriminator in discriminators.values():
discriminator.requires_grad_(True)
optimizer_d.zero_grad()
optimizers["d"].zero_grad()
loss_d = dict()
for k in discriminators.keys():
n = k[-1] # "a" or "b"
loss_d.update(
criterion_discriminator(k, discriminators[k], real[n], image_buffers[k].query(fake[n].detach())))
sum(loss_d.values()).backward()
optimizer_d.step()
optimizers["d"].step()
for h in heatmap:
heatmap[h] = heatmap[h].detach()
@@ -157,19 +164,19 @@ def get_trainer(config, logger):
trainer = Engine(_step)
trainer.logger = logger
trainer.add_event_handler(Events.ITERATION_COMPLETED, lr_scheduler_g)
trainer.add_event_handler(Events.ITERATION_COMPLETED, lr_scheduler_d)
for lr_shd in lr_schedulers.values():
trainer.add_event_handler(Events.ITERATION_COMPLETED, lr_shd)
RunningAverage(output_transform=lambda x: sum(x["loss"]["g"].values())).attach(trainer, "loss_g")
RunningAverage(output_transform=lambda x: sum(x["loss"]["d"].values())).attach(trainer, "loss_d")
to_save = dict(optimizer_d=optimizer_d, optimizer_g=optimizer_g, trainer=trainer, lr_scheduler_d=lr_scheduler_d,
lr_scheduler_g=lr_scheduler_g)
to_save = dict(trainer=trainer)
to_save.update({f"lr_scheduler_{k}": lr_schedulers[k] for k in lr_schedulers})
to_save.update({f"optimizer_{k}": optimizers[k] for k in optimizers})
to_save.update({f"generator_{k}": generators[k] for k in generators})
to_save.update({f"discriminator_{k}": discriminators[k] for k in discriminators})
setup_common_handlers(trainer, config, to_save=to_save, metrics_to_print=["loss_g", "loss_d"],
clear_cuda_cache=False, end_event=Events.ITERATION_COMPLETED(once=config.max_iteration))
setup_common_handlers(trainer, config, to_save=to_save, clear_cuda_cache=True,
end_event=Events.ITERATION_COMPLETED(once=config.max_iteration))
def output_transform(output):
loss = dict()
@@ -185,46 +192,36 @@ def get_trainer(config, logger):
if tensorboard_handler is not None:
tensorboard_handler.attach(
trainer,
log_handler=OptimizerParamsHandler(optimizer_g, tag="optimizer_g"),
log_handler=OptimizerParamsHandler(optimizers["g"], tag="optimizer_g"),
event_name=Events.ITERATION_STARTED(every=config.interval.tensorboard.scalar)
)
@trainer.on(Events.ITERATION_COMPLETED(every=config.interval.tensorboard.image))
def show_images(engine):
output = engine.state.output
image_a_order = ["real_a", "fake_b", "rec_a", "id_a"]
image_b_order = ["real_b", "fake_a", "rec_b", "id_b"]
image_order = dict(
a=["real_a", "fake_b", "rec_a", "id_a"],
b=["real_b", "fake_a", "rec_b", "id_b"]
)
output["img"]["generated"]["real_a"] = fuse_attention_map(
output["img"]["generated"]["real_a"], output["img"]["heatmap"]["a2b"])
output["img"]["generated"]["real_b"] = fuse_attention_map(
output["img"]["generated"]["real_b"], output["img"]["heatmap"]["b2a"])
tensorboard_handler.writer.add_image(
"train/a",
make_2d_grid([output["img"]["generated"][o] for o in image_a_order]),
engine.state.iteration
)
tensorboard_handler.writer.add_image(
"train/b",
make_2d_grid([output["img"]["generated"][o] for o in image_b_order]),
engine.state.iteration
)
for k in "ab":
tensorboard_handler.writer.add_image(
f"train/{k}",
make_2d_grid([output["img"]["generated"][o] for o in image_order[k]]),
engine.state.iteration
)
with torch.no_grad():
g = torch.Generator()
g.manual_seed(config.misc.random_seed)
indices = torch.randperm(len(engine.state.test_dataset), generator=g).tolist()[:10]
empty_grid = torch.zeros(0, config.model.generator.in_channels, config.model.generator.img_size,
config.model.generator.img_size)
fake = dict(a=empty_grid.clone(), b=empty_grid.clone())
rec = dict(a=empty_grid.clone(), b=empty_grid.clone())
heatmap = dict(a2b=torch.zeros(0, 1, config.model.generator.img_size,
config.model.generator.img_size),
b2a=torch.zeros(0, 1, config.model.generator.img_size,
config.model.generator.img_size))
real = dict(a=empty_grid.clone(), b=empty_grid.clone())
test_images = dict(
a=[[], [], [], []],
b=[[], [], [], []]
)
for i in indices:
batch = convert_tensor(engine.state.test_dataset[i], idist.device())
@@ -234,27 +231,18 @@ def get_trainer(config, logger):
rec_a = generators["b2a"](fake_b)[0]
rec_b = generators["a2b"](fake_a)[0]
fake["a"] = torch.cat([fake["a"], fake_a.cpu()])
fake["b"] = torch.cat([fake["b"], fake_b.cpu()])
real["a"] = torch.cat([real["a"], real_a.cpu()])
real["b"] = torch.cat([real["b"], real_b.cpu()])
rec["a"] = torch.cat([rec["a"], rec_a.cpu()])
rec["b"] = torch.cat([rec["b"], rec_b.cpu()])
heatmap["a2b"] = torch.cat(
[heatmap["a2b"], torch.nn.functional.interpolate(heatmap_a2b, real_a.size()[-2:]).cpu()])
heatmap["b2a"] = torch.cat(
[heatmap["b2a"], torch.nn.functional.interpolate(heatmap_b2a, real_a.size()[-2:]).cpu()])
tensorboard_handler.writer.add_image(
"test/a",
make_2d_grid([heatmap["a2b"].expand_as(real["a"]), real["a"], fake["b"], rec["a"]]),
engine.state.iteration
)
tensorboard_handler.writer.add_image(
"test/b",
make_2d_grid([heatmap["b2a"].expand_as(real["a"]), real["b"], fake["a"], rec["b"]]),
engine.state.iteration
)
for idx, im in enumerate(
[attention_colored_map(heatmap_a2b, real_a.size()[-2:]), real_a, fake_b, rec_a]):
test_images["a"][idx].append(im.cpu())
for idx, im in enumerate(
[attention_colored_map(heatmap_b2a, real_b.size()[-2:]), real_b, fake_a, rec_b]):
test_images["b"][idx].append(im.cpu())
for n in "ab":
tensorboard_handler.writer.add_image(
f"test/{n}",
make_2d_grid([torch.cat(ti) for ti in test_images[n]]),
engine.state.iteration
)
return trainer