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train_temporal.py
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train_temporal.py
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import sys
import os
sys.path.append(['.','./../'])
os.environ['OMP_NUM_THREADS'] = '16'
import json
import time
import argparse
import torch
import numpy as np
import torch.nn as nn
from timeit import default_timer
from torch.optim.lr_scheduler import OneCycleLR, StepLR, LambdaLR, CosineAnnealingWarmRestarts, CyclicLR
from torch.utils.tensorboard import SummaryWriter
from utils.optimizer import Adam, Lamb
from utils.utilities import count_parameters, get_grid, load_model_from_checkpoint
from utils.criterion import SimpleLpLoss
from utils.griddataset import MixedTemporalDataset
from utils.make_master_file import DATASET_DICT
from models.fno import FNO2d
from models.dpot import DPOTNet
from models.dpot_res import CDPOTNet
import pickle
################################################################
# configs
################################################################
parser = argparse.ArgumentParser(description='Training or pretraining on multiple PDE datasets')
parser.add_argument('--model', type=str, default='DPOT')
parser.add_argument('--dataset',type=str, default='ns2d')
parser.add_argument('--train_paths',nargs='+', type=str, default=['ns2d_fno_1e-5','ns2d_pdb_M1_eta1e-1_zeta1e-1'])
parser.add_argument('--test_paths',nargs='+',type=str, default=['ns2d_pdb_M1_eta1e-1_zeta1e-1'])
parser.add_argument('--resume_path',type=str, default='')
parser.add_argument('--ntrain_list', nargs='+', type=int, default=[1000, 5000])
parser.add_argument('--data_weights',nargs='+',type=int, default=[1])
parser.add_argument('--use_writer', action='store_true',default=False)
parser.add_argument('--res', type=int, default=128)
parser.add_argument('--noise_scale',type=float, default=0.0)
# parser.add_argument('--n_channels',type=int,default=-1)
### shared params
parser.add_argument('--width', type=int, default=512)
parser.add_argument('--n_layers',type=int, default=4)
parser.add_argument('--act',type=str, default='gelu')
### FNO params
parser.add_argument('--modes', type=int, default=32)
parser.add_argument('--use_ln',type=int, default=0)
parser.add_argument('--normalize',type=int, default=0)
### DPOT
parser.add_argument('--patch_size',type=int, default=8)
parser.add_argument('--n_blocks',type=int, default=8)
parser.add_argument('--mlp_ratio',type=int, default=1)
parser.add_argument('--out_layer_dim', type=int, default=32)
parser.add_argument('--batch_size', type=int, default=20)
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--opt',type=str, default='adam', choices=['adam','lamb'])
parser.add_argument('--beta1',type=float,default=0.9)
parser.add_argument('--beta2',type=float,default=0.9)
parser.add_argument('--lr_method',type=str, default='cycle')
parser.add_argument('--grad_clip',type=float, default=10000.0)
parser.add_argument('--step_size', type=int, default=100)
parser.add_argument('--step_gamma', type=float, default=0.5)
parser.add_argument('--warmup_epochs',type=int, default=100)
parser.add_argument('--T_in', type=int, default=10)
parser.add_argument('--T_ar', type=int, default=1)
parser.add_argument('--T_bundle', type=int, default=1)
parser.add_argument('--gpu', type=str, default="5")
parser.add_argument('--comment',type=str, default="")
parser.add_argument('--log_path',type=str,default='')
args = parser.parse_args()
device = torch.device("cuda:{}".format(args.gpu))
print(f"Current working directory: {os.getcwd()}")
################################################################
# load data and dataloader
################################################################
train_paths = args.train_paths
test_paths = args.test_paths
args.data_weights = [1] * len(args.train_paths) if len(args.data_weights) == 1 else args.data_weights
print('args',args)
train_dataset = MixedTemporalDataset(args.train_paths, args.ntrain_list, res=args.res, t_in = args.T_in, t_ar = args.T_ar, normalize=False,train=True, data_weights=args.data_weights)
test_datasets = [MixedTemporalDataset(test_path, res=args.res, n_channels = train_dataset.n_channels,t_in = args.T_in, t_ar=-1, normalize=False, train=False) for i, test_path in enumerate(test_paths)]
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=8)
test_loaders = [torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False,num_workers=8) for test_dataset in test_datasets]
ntrain, ntests = len(train_dataset), [len(test_dataset) for test_dataset in test_datasets]
print('Train num {} test num {}'.format(train_dataset.n_sizes, ntests))
################################################################
# load model
################################################################
if args.model == "FNO":
model = FNO2d(args.modes, args.modes, args.width, img_size = args.res, patch_size=args.patch_size, in_timesteps = args.T_in, out_timesteps=1,normalize=args.normalize,n_layers = args.n_layers,use_ln = args.use_ln, n_channels=train_dataset.n_channels, n_cls=len(args.train_paths)).to(device)
elif args.model == 'DPOT':
model = DPOTNet(img_size=args.res, patch_size=args.patch_size, in_channels=train_dataset.n_channels, in_timesteps = args.T_in, out_timesteps = args.T_bundle, out_channels=train_dataset.n_channels, normalize=args.normalize, embed_dim=args.width, modes=args.modes, depth=args.n_layers, n_blocks = args.n_blocks, mlp_ratio=args.mlp_ratio, out_layer_dim=args.out_layer_dim, act=args.act, n_cls=len(args.train_paths)).to(device)
elif args.model == 'CDPOT':
model = CDPOTNet(img_size=args.res, patch_size=args.patch_size, in_channels=train_dataset.n_channels, in_timesteps = args.T_in, out_timesteps = args.T_bundle, out_channels=train_dataset.n_channels, normalize=args.normalize, embed_dim=args.width, modes=args.modes, depth=args.n_layers, n_blocks = args.n_blocks, mlp_ratio=args.mlp_ratio, out_layer_dim=args.out_layer_dim, act=args.act, n_cls=len(args.train_paths)).to(device)
else:
raise NotImplementedError
if args.resume_path:
print('Loading models and fine tune from {}'.format(args.resume_path))
args.resume_path = args.resume_path
load_model_from_checkpoint(model, torch.load(args.resume_path,map_location='cuda:{}'.format(args.gpu))['model'])
#### set optimizer
if args.opt == 'lamb':
optimizer = Lamb(model.parameters(), lr=args.lr, betas = (args.beta1, args.beta2), adam=True, debias=False,weight_decay=1e-4)
else:
optimizer = Adam(model.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), weight_decay=1e-6)
if args.lr_method == 'cycle':
print('Using cycle learning rate schedule')
scheduler = OneCycleLR(optimizer, max_lr=args.lr, div_factor=1e4, pct_start=(args.warmup_epochs / args.epochs), final_div_factor=1e4, steps_per_epoch=len(train_loader), epochs=args.epochs)
elif args.lr_method == 'step':
print('Using step learning rate schedule')
scheduler = StepLR(optimizer, step_size=args.step_size * len(train_loader), gamma=args.step_gamma)
elif args.lr_method == 'warmup':
print('Using warmup learning rate schedule')
scheduler = LambdaLR(optimizer, lambda steps: min((steps + 1) / (args.warmup_epochs * len(train_loader)), np.power(args.warmup_epochs * len(train_loader) / float(steps + 1), 0.5)))
elif args.lr_method == 'linear':
print('Using warmup learning rate schedule')
scheduler = LambdaLR(optimizer, lambda steps: (1 - steps / (args.epochs * len(train_loader))))
elif args.lr_method == 'restart':
print('Using cos anneal restart')
scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=len(train_loader) * args.lr_step_size, eta_min=0.)
elif args.lr_method == 'cyclic':
scheduler = CyclicLR(optimizer, base_lr=1e-5, max_lr=1e-3, step_size_up=args.lr_step_size * len(train_loader),mode='triangular2', cycle_momentum=False)
else:
raise NotImplementedError
comment = args.comment + '_{}_{}'.format(len(train_paths), ntrain)
log_path = './logs/' + time.strftime('%m%d_%H_%M_%S') + comment if len(args.log_path)==0 else os.path.join('./logs',args.log_path + comment)
model_path = log_path + '/model.pth'
if args.use_writer:
writer = SummaryWriter(log_dir=log_path)
fp = open(log_path + '/logs.txt', 'w+',buffering=1)
json.dump(vars(args), open(log_path + '/params.json', 'w'),indent=4)
sys.stdout = fp
else:
writer = None
print(model)
count_parameters(model)
################################################################
# Main function for pretraining
################################################################
myloss = SimpleLpLoss(size_average=False)
clsloss = torch.nn.CrossEntropyLoss(reduction='sum')
iter = 0
for ep in range(args.epochs):
model.train()
t1 = t_1 = default_timer()
t_load, t_train = 0., 0.
train_l2_step = 0
train_l2_full = 0
cls_total, cls_correct, cls_acc = 0, 0, 0.
loss_previous = np.inf
for xx, yy, msk, cls in train_loader:
t_load += default_timer() - t_1
t_1 = default_timer()
loss, cls_loss = 0. , 0.
xx = xx.to(device) ## B, n, n, T_in, C
yy = yy.to(device) ## B, n, n, T_ar, C
msk = msk.to(device)
cls = cls.to(device)
## auto-regressive training loop, support 1. noise injection, 2. long rollout backward, 3. temporal bundling prediction
for t in range(0, yy.shape[-2], args.T_bundle):
y = yy[..., t:t + args.T_bundle, :]
### auto-regressive training
xx = xx + args.noise_scale *torch.sum(xx**2, dim=(1,2,3),keepdim=True)**0.5 * torch.randn_like(xx)
im, cls_pred = model(xx)
loss += myloss(im, y, mask=msk)
### classification
pred_labels = torch.argmax(cls_pred,dim=1)
cls_loss += clsloss(cls_pred, cls.squeeze())
cls_correct += (pred_labels == cls.squeeze()).sum().item()
cls_total += cls.shape[0]
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), dim=-2)
xx = torch.cat((xx[..., args.T_bundle:, :], im), dim=-2)
train_l2_step += loss.item()
l2_full = myloss(pred, yy, mask=msk)
train_l2_full += l2_full.item()
optimizer.zero_grad()
total_loss = loss # + 1.0 * cls_loss
total_loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
scheduler.step()
train_l2_step_avg, train_l2_full_avg = train_l2_step / ntrain / (yy.shape[-2] / args.T_bundle), train_l2_full / ntrain
cls_acc = cls_correct / cls_total
iter +=1
if args.use_writer:
writer.add_scalar("train_loss_step", loss.item()/(xx.shape[0] * yy.shape[-2] / args.T_bundle), iter)
writer.add_scalar("train_loss_full", l2_full / xx.shape[0], iter)
## reset model
if loss.item() > 10 * loss_previous : # or (ep > 50 and l2_full / xx.shape[0] > 0.9):
print('loss explodes, loading model from previous epoch')
checkpoint = torch.load(model_path,map_location='cuda:{}'.format(args.gpu))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint["optimizer"])
loss_previous = loss.item()
t_train += default_timer() - t_1
t_1 = default_timer()
test_l2_fulls, test_l2_steps = [], []
with torch.no_grad():
model.eval()
for id, test_loader in enumerate(test_loaders):
test_l2_full, test_l2_step = 0, 0
for xx, yy, msk, _ in test_loader:
loss = 0
xx = xx.to(device)
yy = yy.to(device)
msk = msk.to(device)
for t in range(0, yy.shape[-2], args.T_bundle):
y = yy[..., t:t + args.T_bundle, :]
im, _ = model(xx)
loss += myloss(im, y, mask=msk)
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -2)
xx = torch.cat((xx[..., args.T_bundle:,:], im), dim=-2)
test_l2_step += loss.item()
test_l2_full += myloss(pred, yy, mask=msk)
test_l2_step_avg, test_l2_full_avg = test_l2_step / ntests[id] / (yy.shape[-2] / args.T_bundle), test_l2_full / ntests[id]
test_l2_steps.append(test_l2_step_avg)
test_l2_fulls.append(test_l2_full_avg)
if args.use_writer:
writer.add_scalar("test_loss_step_{}".format(test_paths[id]), test_l2_step_avg, ep)
writer.add_scalar("test_loss_full_{}".format(test_paths[id]), test_l2_full_avg, ep)
if args.use_writer:
torch.save({'args': args, 'model': model.state_dict(), 'optimizer': optimizer.state_dict()}, model_path)
t_test = default_timer() - t_1
t2 = t_1 = default_timer()
lr = optimizer.param_groups[0]['lr']
print('epoch {}, time {:.5f}, lr {:.2e}, train l2 step {:.5f} train l2 full {:.5f}, test l2 step {} test l2 full {}, time train avg {:.5f} load avg {:.5f} test {:.5f}'.format(ep, t2 - t1, lr,train_l2_step_avg, train_l2_full_avg,', '.join(['{:.5f}'.format(val) for val in test_l2_steps]),', '.join(['{:.5f}'.format(val) for val in test_l2_fulls]), t_train / len(train_loader), t_load / len(train_loader), t_test))