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import sys, os, json
root = os.sep + os.sep.join(__file__.split(os.sep)[1:__file__.split(os.sep).index("Recurrent-Parameter-Generation")+1])
sys.path.append(root)
os.chdir(root)
with open("./workspace/config.json", "r") as f:
additional_config = json.load(f)
USE_WANDB = additional_config["use_wandb"]
# set global seed
import random
import numpy as np
import torch
seed = SEED = 999
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
np.random.seed(seed)
random.seed(seed)
# other
import math
import random
import warnings
from _thread import start_new_thread
warnings.filterwarnings("ignore", category=UserWarning)
if USE_WANDB: import wandb
# torch
import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
from torch.cuda.amp import autocast
# model
from model.pdiff import PDiff as Model
from model.pdiff import OneDimVAE as VAE
from model.diffusion import DDPMSampler, DDIMSampler
from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
from accelerate.utils import DistributedDataParallelKwargs
from accelerate.utils import AutocastKwargs
from accelerate import Accelerator
# dataset
from dataset import Cifar100_ResNet18BN as Dataset
from torch.utils.data import DataLoader
config = {
"seed": SEED,
# dataset setting
"dataset": Dataset,
"sequence_length": 'auto',
# train setting
"batch_size": 50,
"num_workers": 25,
"total_steps": 10000,
"vae_steps": 1000,
"learning_rate": 0.0001,
"vae_learning_rate": 0.00002,
"weight_decay": 0.01,
"save_every": 10000//1,
"print_every": 50,
"autocast": lambda i: True,
"checkpoint_save_path": "./checkpoint",
# test setting
"test_batch_size": 1, # fixed, don't change this
"generated_path": Dataset.generated_path,
"test_command": Dataset.test_command,
# to log
"model_config": {
# diffusion config
"layer_channels": [1, 64, 128, 256, 512, 256, 128, 64, 1],
"model_dim": 128,
"kernel_size": 7,
"sample_mode": DDPMSampler,
"beta": (0.0001, 0.02),
"T": 1000,
# vae config
"channels": [64, 256, 384, 512, 64],
},
"tag": "compare_pdiff_resnet18bn_vae",
}
# Data
divide_slice_length = int(2 ** len(config["model_config"]["channels"]))
print('==> Preparing data..')
train_set = config["dataset"](
dim_per_token=divide_slice_length,
granularity=0,
pe_granularity=0,
fill_value=0.
)
print("Dataset length:", train_set.real_length)
print("input shape:", train_set[0][0].flatten().shape)
if config["sequence_length"] == "auto":
config["sequence_length"] = train_set.sequence_length * divide_slice_length
print(f"sequence length: {config['sequence_length']}")
train_loader = DataLoader(
dataset=train_set,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
persistent_workers=True,
drop_last=True,
shuffle=True,
)
# Model
print('==> Building model..')
Model.config = config["model_config"]
model = Model(sequence_length=config["sequence_length"]) # model setting is in model
vae = VAE(d_model=config["model_config"]["channels"],
d_latent=config["model_config"]["model_dim"],
sequence_length=config["sequence_length"],
kernel_size=config["model_config"]["kernel_size"])
# Optimizer
print('==> Building optimizer..')
vae_optimizer = optim.AdamW(
params=vae.parameters(),
lr=config["vae_learning_rate"],
weight_decay=config["weight_decay"],
)
optimizer = optim.AdamW(
params=model.parameters(),
lr=config["learning_rate"],
weight_decay=config["weight_decay"],
)
vae_scheduler = CosineAnnealingLR(
optimizer=vae_optimizer,
T_max=config["vae_steps"],
)
scheduler = CosineAnnealingLR(
optimizer=optimizer,
T_max=config["total_steps"],
)
# accelerator
if __name__ == "__main__":
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[kwargs,])
vae, model, vae_optimizer, optimizer, train_loader = \
accelerator.prepare(vae, model, vae_optimizer, optimizer, train_loader)
# wandb
if __name__ == "__main__" and USE_WANDB and accelerator.is_main_process:
wandb.login(key=additional_config["wandb_api_key"])
wandb.init(project="Recurrent-Parameter-Generation", name=config['tag'], config=config,)
# Training
print('==> Defining training..')
def train_vae():
if not USE_WANDB:
train_loss = 0
this_steps = 0
print("==> Start training vae..")
vae.train()
for batch_idx, (param, _) in enumerate(train_loader):
vae_optimizer.zero_grad()
# train
# noinspection PyArgumentList
with accelerator.autocast(autocast_handler=AutocastKwargs(enabled=config["autocast"](batch_idx))):
param = param.flatten(start_dim=1)
loss = vae(x=param, use_var=False, manual_std=0.01, kld_weight=0.01)
accelerator.backward(loss)
vae_optimizer.step()
if accelerator.is_main_process:
vae_scheduler.step()
# to logging losses and print and save
if USE_WANDB and accelerator.is_main_process:
wandb.log({"vae_loss": loss.item()})
elif USE_WANDB:
pass # don't print
else: # not use wandb
train_loss += loss.item()
this_steps += 1
if this_steps % config["print_every"] == 0:
print('Loss: %.6f' % (train_loss/this_steps))
this_steps = 0
train_loss = 0
if batch_idx >= config["vae_steps"]:
break
def train():
if not USE_WANDB:
train_loss = 0
this_steps = 0
print("==> start training..")
model.train()
for batch_idx, (param, _) in enumerate(train_loader):
optimizer.zero_grad()
# train
# noinspection PyArgumentList
with accelerator.autocast(autocast_handler=AutocastKwargs(enabled=config["autocast"](batch_idx))):
param = param.flatten(start_dim=1)
with torch.no_grad():
mu, _ = vae.encode(param)
loss = model(x=mu)
accelerator.backward(loss)
optimizer.step()
if accelerator.is_main_process:
scheduler.step()
# to logging losses and print and save
if USE_WANDB and accelerator.is_main_process:
wandb.log({"train_loss": loss.item()})
elif USE_WANDB:
pass # don't print
else: # not use wandb
train_loss += loss.item()
this_steps += 1
if this_steps % config["print_every"] == 0:
print('Loss: %.6f' % (train_loss/this_steps))
this_steps = 0
train_loss = 0
if batch_idx % config["save_every"] == 0 and accelerator.is_main_process:
os.makedirs(config["checkpoint_save_path"], exist_ok=True)
state = {"diffusion": accelerator.unwrap_model(model).state_dict(), "vae": vae.state_dict()}
torch.save(state, os.path.join(config["checkpoint_save_path"], config["tag"]+".pth"))
generate(save_path=config["generated_path"], need_test=True)
if batch_idx >= config["total_steps"]:
break
def generate(save_path=config["generated_path"], need_test=True):
print("\n==> Generating..")
model.eval()
with torch.no_grad():
mu = model(sample=True)
prediction = vae.decode(mu)
generated_norm = prediction.abs().mean()
print("Generated_norm:", generated_norm.item())
if USE_WANDB:
wandb.log({"generated_norm": generated_norm.item()})
prediction = prediction.view(-1, divide_slice_length)
train_set.save_params(prediction, save_path=save_path)
if need_test:
start_new_thread(os.system, (config["test_command"],))
model.train()
return prediction
if __name__ == '__main__':
train_vae()
vae = accelerator.unwrap_model(vae)
train()
del train_loader # deal problems by dataloader
print("Finished Training!")
exit(0) |