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run_models_trsode.py
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run_models_trsode.py
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import os
import sys
from lib.non_graph_loader import ParseData
from tqdm import tqdm
import argparse
import numpy as np
from random import SystemRandom
import torch
import torch.optim as optim
import lib.utils as utils
from torch.distributions.normal import Normal
from lib.trsoden import ODENetwork
from torch.utils.tensorboard import SummaryWriter
from pathlib import Path
import pdb
def compute_loss_all_batches(model, encoder, graph, decoder, n_batches, device, function_type):
total = {}
total["loss"] = 0
total["mse"] = 0
total["mape"] = 0
total["forward_gt_mse"] = 0
total["reverse_f_mse"] = 0
n_test_batches = 0
model.eval()
print("Computing loss... ")
if function_type == 'hamiltonian':
for i in tqdm(range(n_batches)):
batch_dict_encoder = utils.get_next_batch(encoder, device)
batch_dict_decoder = utils.get_next_batch(decoder, device)
batch_dict_graph = utils.get_next_batch_new(graph, device)
results,_,_ = model.compute_loss(batch_dict_encoder, batch_dict_decoder, batch_dict_graph)
for key in total.keys():
if key in results:
var = results[key]
if isinstance(var, torch.Tensor):
var = var.detach().item()
total[key] += var
n_test_batches += 1
del batch_dict_encoder, batch_dict_decoder, results
else:
with torch.no_grad():
for i in tqdm(range(n_batches)):
batch_dict_encoder = utils.get_next_batch(encoder, device)
batch_dict_decoder = utils.get_next_batch(decoder, device)
batch_dict_graph = utils.get_next_batch_new(graph, device)
results,_,_ = model.compute_loss(batch_dict_encoder, batch_dict_decoder, batch_dict_graph)
for key in total.keys():
if key in results:
var = results[key]
if isinstance(var, torch.Tensor):
var = var.detach().item()
total[key] += var
n_test_batches += 1
del batch_dict_encoder, batch_dict_decoder, results
if n_test_batches > 0:
for key, value in total.items():
total[key] = total[key] / n_test_batches
return total
# Generative model for noisy data based on ODE
parser = argparse.ArgumentParser('Latent ODE')
parser.add_argument('--n-balls', type=int, default=2,
help='Number of objects in the dataset.')
parser.add_argument('--niters', type=int, default=500)
parser.add_argument('--lr', type=float, default=2e-4, help="Starting learning rate.")
parser.add_argument('-b', '--batch-size', type=int, default=256)
parser.add_argument('--save', type=str, default='experiments/', help="Path for save checkpoints")
parser.add_argument('--save-graph', type=str, default='plot/', help="Path for save checkpoints")
parser.add_argument('--load', type=str, default=None, help="name of ckpt. If None, run a new experiment.")
parser.add_argument('-r', '--random-seed', type=int, default=1991, help="Random_seed")
parser.add_argument('--data', type=str, default='Attractor', help="spring,charged,motion,spring_external")
parser.add_argument('--z0-encoder', type=str, default='GTrans', help="GTrans")
parser.add_argument('-l', '--latents', type=int, default=16, help="Size of the latent state")
parser.add_argument('--rec-dims', type=int, default=64, help="Dimensionality of the recognition model .")
parser.add_argument('--ode-dims', type=int, default=128, help="Dimensionality of the ODE func")
parser.add_argument('--rec-layers', type=int, default=2, help="Number of layers in recognition model ")
parser.add_argument('--n-heads', type=int, default=1, help="Number of heads in GTrans")
parser.add_argument('--gen-layers', type=int, default=1, help="Number of layers ODE func ")
parser.add_argument('--extrap', type=str, default="True",
help="Set extrapolation mode. If this flag is not set, run interpolation mode.")
parser.add_argument('--dropout', type=float, default=0.2, help='Dropout rate (1 - keep probability).')
parser.add_argument('--sample-percent-train', type=float, default=0.6, help='Percentage of training observtaion data')
parser.add_argument('--sample-percent-test', type=float, default=0.6, help='Percentage of testing observtaion data')
parser.add_argument('--augment_dim', type=int, default=0, help='augmented dimension')
parser.add_argument('--edge_types', type=int, default=2, help='edge number in NRI')
parser.add_argument('--odenet', type=str, default="NRI", help='NRI')
parser.add_argument('--solver', type=str, default="rk4", help='dopri5,rk4,euler')
parser.add_argument('--l2', type=float, default=1e-3, help='l2 regulazer')
parser.add_argument('--optimizer', type=str, default="AdamW", help='Adam, AdamW')
parser.add_argument('--clip', type=float, default=10, help='Gradient Norm Clipping')
parser.add_argument('--cutting_edge', type=bool, default=True, help='True/False')
parser.add_argument('--extrap_num', type=int, default=40, help='extrap num ')
parser.add_argument('--rec_attention', type=str, default="attention")
parser.add_argument('--alias', type=str, default="run")
parser.add_argument('--train_cut', type=int, default=2000, help='maximum number of train samples')
parser.add_argument('--test_cut', type=int, default=500, help='maximum number of test samples')
parser.add_argument('--total_ode_step', type=int, default=60, help='total number of ode steps')
parser.add_argument('--dataset', type=str, default='data', help='dataset directory')
parser.add_argument('--tensorboard_dir', type=str, default='tensorboards', help='tensorboard root directory')
parser.add_argument('--warmup_epoch', type=int, default=20,
help='number of warmup epoch to train with forward mse only')
parser.add_argument('--reverse_f_lambda', type=float, default=0, help='weight of reverse_f mse after warmup')
parser.add_argument('--reverse_gt_lambda', type=float, default=0, help='weight of reverse_gt mse after warmup')
parser.add_argument('--energy_lambda', type=float, default=0, help='weight of energy mse after warmup')
parser.add_argument('--device', type=int, default=0, help='running device')
parser.add_argument('--Tmax', type=float, default=2000, help='optimazor')
parser.add_argument('--eta_min', type=float, default=0, help='optimazor')
parser.add_argument('--visdata_dir', type=str, default='visdata', help='vis root directory')
parser.add_argument('--function_type', type=str, default='ode', help='function type for ode network')
parser.add_argument('--time_augment', action='store_true', default=False, help='time augment for ode network')
parser.add_argument('--nb_units', type=int, default=1000, help='nb_units for ode network')
parser.add_argument('--nb_layers', type=int, default=1, help='nb_layers for ode network')
parser.add_argument('--activation', type=str, default='tanh', help='activation for ode network')
parser.add_argument('--name', type=str, default="TRSODE", help="TRSODE")
parser.add_argument('--feature_dim', type=int, default=3, help="3d")
args = parser.parse_args()
assert (int(args.rec_dims % args.n_heads) == 0)
if args.data == "Attractor":
args.suffix = '_attractor2'
args.dataset='data/attractor'
if args.data == "simple_spring":
args.suffix = '_springs_simpled_long5'
args.dataset = 'data/simple_spring'
if args.data == "damped_spring":
args.suffix = '_springs_damped5'
args.dataset = 'data/damped_spring'
if args.data == "forced_spring":
args.suffix = '_forced_spring5'
args.dataset = 'data/forced_spring'
# if args.data ==xf "charged":
# args.suffix = '_charged5'
# args.dataset = 'data/charged'
if args.data == "pendulum":
args.suffix = '_pendulum3'
args.dataset = 'data/pendulum'
if args.data == "motion":
args.dataset = 'data/motion'
args.suffix = '_motion'
args.total_ode_step = 49
args.n_balls = 31
if args.data == "motion_dance":
args.dataset = 'data/motion_dance'
args.suffix = '_motion_dance'
args.total_ode_step = 49
args.n_balls = 31
task = 'extrapolation' if args.extrap == 'True' else 'intrapolation'
############ CPU AND GPU related, Mode related, Dataset Related
if torch.cuda.is_available():
print("Using GPU" + "-" * 80)
device = torch.device("cuda:%d" % args.device)
else:
print("Using CPU" + "-" * 80)
device = torch.device("cpu")
if args.extrap == "True":
print("Running extrap mode" + "-" * 80)
args.mode = "extrap"
elif args.extrap == "False":
print("Running interp mode" + "-" * 80)
args.mode = "interp"
#####################################################################################################
if __name__ == '__main__':
torch.manual_seed(args.random_seed)
np.random.seed(args.random_seed)
############ Saving Path and Preload.
file_name = os.path.basename(__file__)[:-3] # run_models
utils.makedirs(args.save)
utils.makedirs(args.save_graph)
experimentID = args.load
if experimentID is None:
# Make a new experiment ID
experimentID = int(SystemRandom().random() * 100000)
############ Loading Data
print("Loading dataset: " + args.dataset)
dataloader = ParseData(args.dataset, suffix=args.suffix, mode=args.mode, args=args)
test_encoder, test_decoder, test_graph, test_batch = dataloader.load_data(sample_percent=args.sample_percent_test,
batch_size=args.batch_size,
data_type="test",
cut_num=args.test_cut)
train_encoder, train_decoder, train_graph, train_batch = dataloader.load_data(
sample_percent=args.sample_percent_train, batch_size=args.batch_size, data_type="train",
cut_num=args.train_cut)
input_dim = dataloader.feature
############ Command Related
input_command = sys.argv
ind = [i for i in range(len(input_command)) if input_command[i] == "--load"]
if len(ind) == 1:
ind = ind[0]
input_command = input_command[:ind] + input_command[(ind + 2):]
input_command = " ".join(input_command)
############ Model Select
# Create the model
obsrv_std = 0.01
obsrv_std = torch.Tensor([obsrv_std]).to(device)
z0_prior = Normal(torch.Tensor([0.0]).to(device), torch.Tensor([1.]).to(device))
model = ODENetwork(nb_object=args.n_balls, lambda_trs=args.reverse_f_lambda,
learning_rate=args.lr, function_type=args.function_type,
time_augment=args.time_augment, nb_units=args.nb_units,
nb_layers=args.nb_layers, nb_coords=args.feature_dim, activation=args.activation, args=args,
device=device
).to(device)
##################################################################
# Load checkpoint and evaluate the model
if args.load is not None:
ckpt_path = os.path.join(args.save, args.load)
utils.get_ckpt_model(ckpt_path, model, device)
# exit()
##################################################################
# Training
# pdb.set_trace()
# log_dir = os.path.join("./home/zijiehuang/LGODE_logs/", '%s_%s'%(args.data, task))
log_dir = os.path.join("/home/zijiehuang", "PIGODE_logs", '%s_%d_%s' % (args.data, args.total_ode_step, args.name))
# args.alias + "_" + args.z0_encode./home/zijiehuang/LGODE_logsr + "_" + args.data + "_" + str(
# args.sample_percent_train) + "_" + args.mode + "_" + str(experimentID) + ".log"
Path(log_dir).mkdir(parents=True, exist_ok=True)
logname = 'niters%d_lr%f-%d-%f_warmup-epoch%d_reverse_f_lambda%.2f_reverse_gt_lambda%.2f_traincut%d_testcut%d_observ-ratio_train%.2f_test%.2f.log' % (
args.niters, args.lr, args.Tmax, args.eta_min,
args.warmup_epoch, args.reverse_f_lambda,
args.reverse_gt_lambda, args.train_cut, args.test_cut, args.sample_percent_train, args.sample_percent_test
)
logger = utils.get_logger(logpath=os.path.join(log_dir, logname), filepath=os.path.abspath(__file__))
logger.info(input_command)
logger.info(str(args))
logger.info(args.alias)
# Optimizer
if args.optimizer == "AdamW":
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.l2)
elif args.optimizer == "Adam":
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.l2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.Tmax, args.eta_min)
# pdb.set_trace()
# wait_until_kl_inc = 10
best_test_mse = np.inf
best_train_mse = np.inf
n_iters_to_viz = 1
# #weight of reverse:
# reverse_f_lambda=None
# reverse_gt_lambda=None
writer = SummaryWriter(log_dir=os.path.join(
args.tensorboard_dir,
'%s_%d_%s' % (args.data, args.total_ode_step, args.name),
'observe_ratio_train%.2f_test%.2f' % (args.sample_percent_train, args.sample_percent_test),
'niters%d_lr%f-%d-%f_warmup_epoch%d_reverse_f_lambda%.2f_reverse_gt_lambda%.2f_traincut%d_testcut%d' % (
args.niters, args.lr, args.Tmax, args.eta_min,
args.warmup_epoch, args.reverse_f_lambda,
args.reverse_gt_lambda, args.sample_percent_train, args.sample_percent_test)
))
def train_single_batch(model, batch_dict_encoder, batch_dict_decoder, batch_dict_graph=None):
optimizer.zero_grad()
train_res,_,_ = model.compute_loss(batch_dict_encoder, batch_dict_decoder, batch_dict_graph)
loss = train_res["loss"]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
loss_value = loss.data.item()
del loss
torch.cuda.empty_cache()
# train_res, loss
# return loss_value, train_res["mse"], train_res["likelihood"],train_res["energy_mse"],train_res["forward_gt_mse"],train_res["reverse_f_mse"],train_res["reverse_gt_mse"]
return loss_value, train_res["mse"], train_res["forward_gt_mse"], train_res["reverse_f_mse"]
def train_epoch(epo):
model.train()
loss_list = []
mse_list = []
forward_gt_mse_list = []
reverse_f_mse_list = []
reverse_gt_mse_list = []
likelihood_list = []
# energy_mse_list=[]
kl_first_p_list = []
std_first_p_list = []
torch.cuda.empty_cache()
for itr in tqdm(range(train_batch)):
batch_dict_encoder = utils.get_next_batch(train_encoder, device)
batch_dict_decoder = utils.get_next_batch(train_decoder, device)
batch_dict_graph = None
if args.function_type:
batch_dict_graph = utils.get_next_batch_new(train_graph, device)
loss, mse, forward_gt_mse, reverse_f_mse = train_single_batch(model, batch_dict_encoder, batch_dict_decoder,
batch_dict_graph)
loss_list.append(loss), mse_list.append(mse), forward_gt_mse_list.append(
forward_gt_mse), reverse_f_mse_list.append(reverse_f_mse)
del batch_dict_encoder, batch_dict_decoder
# train_res, loss
torch.cuda.empty_cache()
scheduler.step()
message_train = 'Epoch {:04d} | [Train seq (cond on sampled tp)] | Loss {:.6f} | Forward gt MSE {:.6f} | Reverse f MSE {:.6f}'.format(
epo,
np.mean(loss_list),
np.mean(forward_gt_mse_list), np.mean(reverse_f_mse_list))
# return message_train ,np.mean(energy_mse_list), np.mean(forward_gt_mse_list), np.mean(reverse_f_mse_list),np.mean(reverse_gt_mse_list)
return message_train, np.mean(forward_gt_mse_list), np.mean(reverse_f_mse_list)
for epo in range(1, args.niters + 1):
reverse_f_lambda = args.reverse_f_lambda
reverse_gt_lambda = args.reverse_gt_lambda
# message_train,train_energy_mse,train_forward_gt_mse,train_reverse_f_mse,train_reverse_gt_mse = train_epoch(epo)
message_train, train_forward_gt_mse, train_reverse_f_mse = train_epoch(epo)
if epo % n_iters_to_viz == 0:
model.eval()
test_res = compute_loss_all_batches(model, test_encoder, test_graph, test_decoder,
n_batches=test_batch, device=device,
function_type=args.function_type)
message_test = 'Epoch {:04d} [Test seq (cond on sampled tp)] | r_f_lambda {:.4f} | Loss {:.6f} | Forward gt MSE {:.6f} | Forward gt MAPE {:.6f} |Reverse f MSE {:.6f}'.format(
epo, reverse_f_lambda,
test_res["loss"],
test_res["forward_gt_mse"],
test_res["mape"],
test_res["reverse_f_mse"])
logger.info("Experiment " + str(experimentID))
logger.info(message_train)
logger.info(message_test)
# logger.info(
# "KL coef: {}".format(kl_coef))
print(
"data: %s, encoder: %s, lr: %s, epoch: %s, train_sample: %s,test_sample: %s, mode: %s, reverse_f_lambda: %s" % (
args.data, args.z0_encoder, str(args.lr), str(args.niters), str(args.sample_percent_train),
str(args.sample_percent_test), args.mode, reverse_f_lambda))
if test_res["forward_gt_mse"] < best_test_mse:
best_test_mse = test_res["forward_gt_mse"]
best_test_mape = test_res["mape"]
message_test_best = 'Epoch {:04d} [Test seq (cond on sampled tp)] | Best forward gt mse {:.6f}| Best epoch mape {:.6f}'.format(
epo,
best_test_mse, test_res["mape"])
ckpt_path = os.path.join(args.save, "experiment_" + str(
experimentID) + "_" + args.data + "_" + str(
args.total_ode_step) + "_" + args.name + "_obratio_" + str(
args.sample_percent_train) + "_rflambda_" + str(args.reverse_f_lambda) + "_epoch_" + str(
epo) + "_mse_" + str(
best_test_mse) + "_mape_" + str(
best_test_mape) + '.ckpt')
torch.save({
'args': args,
'state_dict': model.state_dict(),
}, ckpt_path)
# groundtruth_dir = os.path.join(
# args.visdata_dir,
# '%s_%s' % (args.data, task),
# 'observe_ratio_train%.2f_test%.2f' % (args.sample_percent_train, args.sample_percent_test),
# 'train_cut%d_test_cut%d' % (args.train_cut, args.test_cut),
# 'reverse_f_lambda%.2f_reverse_gt_lambda%.2f' % (args.reverse_f_lambda, args.reverse_gt_lambda)
# )
#
# forward_dir = os.path.join(
# args.visdata_dir,
# '%s_%s' % (args.data, task),
# 'observe_ratio_train%.2f_test%.2f' % (args.sample_percent_train, args.sample_percent_test),
# 'train_cut%d_test_cut%d' % (args.train_cut, args.test_cut),
# 'reverse_f_lambda%.2f_reverse_gt_lambda%.2f_energy_lambda%.2f' % (args.reverse_f_lambda, args.reverse_gt_lambda,args.energy_lambda)
# )
#
# reverse_dir = os.path.join(
# args.visdata_dir,
# '%s_%s' % (args.data, task),
# 'observe_ratio_train%.2f_test%.2f' % (args.sample_percent_train, args.sample_percent_test),
# 'train_cut%d_test_cut%d' % (args.train_cut, args.test_cut),
# 'reverse_f_lambda%.2f_reverse_gt_lambda%.2f_energy_lambda%.2f' % (args.reverse_f_lambda, args.reverse_gt_lambda,args.energy_lambda)
# )
# # Create directories
# Path(groundtruth_dir).mkdir(parents=True, exist_ok=True)
# Path(forward_dir).mkdir(parents=True, exist_ok=True)
# Path(reverse_dir).mkdir(parents=True, exist_ok=True)
# # Save files
# np.save(os.path.join(groundtruth_dir, 'groundtruth_trajectory.npy'), gt.cpu().detach().numpy())
# np.save(os.path.join(forward_dir, 'forward_trajectory.npy'), f.cpu().detach().numpy())
# np.save(os.path.join(reverse_dir, 'reverse_trajectory.npy'), r.cpu().detach().numpy())
logger.info(message_test_best)
if train_forward_gt_mse < best_train_mse:
best_train_mse = train_forward_gt_mse
message_train_best = 'Epoch {:04d} [Train seq (cond on sampled tp)] | Best forward gt mse {:.6f}'.format(
epo, best_train_mse)
logger.info(message_train_best)
writer.add_scalar('train_MSE/train_forward_gt_mse', train_forward_gt_mse, epo)
writer.add_scalar('train_MSE/train_reverse_f_mse', train_reverse_f_mse, epo)
# writer.add_scalar('train_MSE/train_reverse_gt_mse', train_reverse_gt_mse, epo)
# writer.add_scalar('train_MSE/train_energy_mse', train_energy_mse, epo)
writer.add_scalar('test_MSE/test_forward_gt_mse', test_res["forward_gt_mse"], epo)
writer.add_scalar('test_MSE/test_reverse_f_mse', test_res["reverse_f_mse"], epo)
# writer.add_scalar('test_MSE/test_reverse_gt_mse', test_res["reverse_gt_mse"], epo)
# writer.add_scalar('test_MSE/test_energy_mse', test_res["energy_mse"], epo)
# writer.add_scalar('Weight/FGT_RF', test_res["forward_gt_mse"] / test_res["reverse_f_mse"], epo)
# writer.add_scalar('Weight/FGT_RGT', test_res["forward_gt_mse"] / test_res["reverse_gt_mse"], epo)
torch.cuda.empty_cache()
writer.close()