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classifier_train.py
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classifier_train.py
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import argparse
import os
import blobfile as bf
import torch as th
import torch.nn.functional as F
from torch.optim import AdamW
from guided_diffusion import logger
from guided_diffusion.fp16_util import MixedPrecisionTrainer
from guided_diffusion.image_datasets import load_data
from guided_diffusion.resample import create_named_schedule_sampler
from guided_diffusion.script_util import (
add_dict_to_argparser,
args_to_dict,
classifier_and_diffusion_defaults,
create_classifier_and_diffusion,
)
from guided_diffusion.train_util import parse_resume_step_from_filename, log_loss_dict
def main():
args = create_argparser().parse_args()
print(args)
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_classifier_and_diffusion(
**args_to_dict(args, classifier_and_diffusion_defaults().keys())
)
if args.noised:
schedule_sampler = create_named_schedule_sampler(
args.schedule_sampler, diffusion
)
resume_step = 0
if args.resume_checkpoint:
resume_step = parse_resume_step_from_filename(args.resume_checkpoint)
mp_trainer = MixedPrecisionTrainer(
model=model, use_fp16=args.classifier_use_fp16, initial_lg_loss_scale=16.0
)
logger.log("creating data loader...")
data = load_data(data_dir="/.../target2_SET_C_train_actives.csv",
batch_size = 8,
)
logger.log(f"creating optimizer...")
opt = AdamW(mp_trainer.master_params, lr=args.lr, weight_decay=args.weight_decay)
if args.resume_checkpoint:
opt_checkpoint = bf.join(
bf.dirname(args.resume_checkpoint), f"opt{resume_step:06}.pt"
)
logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
opt.load_state_dict(opt_checkpoint)
logger.log("training classifier model...")
def forward_backward_log(data_loader, prefix="train"):
batch, extra = next(data_loader)
batch_bf, batch_cp = batch.split(split_size=[3,5], dim=1)
labels = extra["y"]
if args.noised:
t, _ = schedule_sampler.sample(batch_cp.shape[0])
batch_cp = diffusion.q_sample(batch_cp, t)
else:
t = th.zeros(batch.shape[0], dtype=th.long)
batch = th.cat((batch_bf, batch_cp), dim=1)
print("Microbatching")
for i, (sub_batch, sub_labels, sub_t) in enumerate(
split_microbatches(args.microbatch, batch, labels, t)
):
print(i)
logits = model(sub_batch, gammas=sub_t)
loss = F.cross_entropy(logits, sub_labels, reduction="none")
losses = {}
losses[f"{prefix}_loss"] = loss.detach()
losses[f"{prefix}_acc@1"] = compute_top_k(
logits, sub_labels, k=1, reduction="none"
)
log_loss_dict(diffusion, sub_t, losses)
del losses
loss = loss.mean()
if loss.requires_grad:
if i == 0:
mp_trainer.zero_grad()
mp_trainer.backward(loss * len(sub_batch) / len(batch))
for step in range(args.iterations - resume_step):
logger.logkv("step", step + resume_step)
logger.logkv(
"samples",
(step + resume_step + 1) * args.batch_size,
)
if args.anneal_lr:
set_annealed_lr(opt, args.lr, (step + resume_step) / args.iterations)
print(step)
forward_backward_log(data_loader = data, prefix="train")
mp_trainer.optimize(opt)
if not step % args.log_interval:
logger.dumpkvs()
if (
step
and not (step + resume_step) % args.save_interval
):
logger.log("saving model...")
save_model(mp_trainer, opt, step + resume_step)
def set_annealed_lr(opt, base_lr, frac_done):
lr = base_lr * (1 - frac_done)
for param_group in opt.param_groups:
param_group["lr"] = lr
def save_model(mp_trainer, opt, step):
th.save(
mp_trainer.master_params_to_state_dict(mp_trainer.master_params),
os.path.join(logger.get_dir(), f"model{step:06d}.pt"),
)
th.save(opt.state_dict(), os.path.join(logger.get_dir(), f"opt{step:06d}.pt"))
def compute_top_k(logits, labels, k, reduction="mean"):
_, top_ks = th.topk(logits, k, dim=-1)
if reduction == "mean":
return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
elif reduction == "none":
return (top_ks == labels[:, None]).float().sum(dim=-1)
def split_microbatches(microbatch, *args):
bs = len(args[0])
if microbatch == -1 or microbatch >= bs:
yield tuple(args)
else:
for i in range(0, bs, microbatch):
yield tuple(x[i : i + microbatch] if x is not None else None for x in args)
def create_argparser():
defaults = dict(
data_dir="",
val_data_dir="",
noised=True,
iterations=150000,
lr=3e-4,
weight_decay=0.05,
anneal_lr=True,
image_size=512,
batch_size=8,
microbatch=1,
schedule_sampler="uniform",
resume_checkpoint="",
log_interval=10,
eval_interval=10,
save_interval=10,
)
defaults.update(classifier_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()