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train.py
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train.py
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import argparse
import os
import time
import torch
import numpy as np
from torch.utils.data import DataLoader
import torch.optim
from torch.nn.utils import clip_grad_norm_
from data import TrainStation
from log_utils import log_summary
from utils import save_ckpt, load_ckpt, print_scalor
from common import *
import parse
from tensorboardX import SummaryWriter
from scalor import SCALOR
def main(args):
args.color_t = torch.rand(700, 3)
if not os.path.exists(args.ckpt_dir):
os.mkdir(args.ckpt_dir)
if not os.path.exists(args.summary_dir):
os.mkdir(args.summary_dir)
device = torch.device(
"cuda" if not args.nocuda and torch.cuda.is_available() else "cpu")
train_data = TrainStation(args=args, train=True)
train_loader = DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, drop_last=True)
num_train = len(train_data)
model = SCALOR(args)
model.to(device)
model.train()
optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr)
global_step = 0
if args.last_ckpt:
global_step, args.start_epoch = \
load_ckpt(model, optimizer, args.last_ckpt, device)
writer = SummaryWriter(args.summary_dir)
args.global_step = global_step
log_tau_gamma = np.log(args.tau_end) / args.tau_ep
for epoch in range(int(args.start_epoch), args.epochs):
local_count = 0
last_count = 0
end_time = time.time()
for batch_idx, (sample, counting_gt) in enumerate(train_loader):
tau = np.exp(global_step * log_tau_gamma)
tau = max(tau, args.tau_end)
args.tau = tau
global_step += 1
log_phase = global_step % args.print_freq == 0 or global_step == 1
args.global_step = global_step
args.log_phase = log_phase
imgs = sample.to(device)
y_seq, log_like, kl_z_what, kl_z_where, kl_z_depth, \
kl_z_pres, kl_z_bg, log_imp, counting, \
log_disc_list, log_prop_list, scalor_log_list = model(imgs)
log_like = log_like.mean(dim=0)
kl_z_what = kl_z_what.mean(dim=0)
kl_z_where = kl_z_where.mean(dim=0)
kl_z_depth = kl_z_depth.mean(dim=0)
kl_z_pres = kl_z_pres.mean(dim=0)
kl_z_bg = kl_z_bg.mean(0)
total_loss = - (log_like - kl_z_what - kl_z_where - kl_z_depth - kl_z_pres - kl_z_bg)
optimizer.zero_grad()
total_loss.backward()
clip_grad_norm_(model.parameters(), args.cp)
optimizer.step()
local_count += imgs.data.shape[0]
if log_phase:
time_inter = time.time() - end_time
end_time = time.time()
count_inter = local_count - last_count
print_scalor(global_step, epoch, local_count, count_inter,
num_train, total_loss, log_like, kl_z_what, kl_z_where,
kl_z_pres, kl_z_depth, time_inter)
writer.add_scalar('train/total_loss', total_loss.item(), global_step=global_step)
writer.add_scalar('train/log_like', log_like.item(), global_step=global_step)
writer.add_scalar('train/What_KL', kl_z_what.item(), global_step=global_step)
writer.add_scalar('train/Where_KL', kl_z_where.item(), global_step=global_step)
writer.add_scalar('train/Pres_KL', kl_z_pres.item(), global_step=global_step)
writer.add_scalar('train/Depth_KL', kl_z_depth.item(), global_step=global_step)
writer.add_scalar('train/Bg_KL', kl_z_bg.item(), global_step=global_step)
# writer.add_scalar('train/Bg_alpha_KL', kl_z_bg_mask.item(), global_step=global_step)
writer.add_scalar('train/tau', tau, global_step=global_step)
log_summary(args, writer, imgs, y_seq, global_step, log_disc_list,
log_prop_list, scalor_log_list, prefix='train')
last_count = local_count
if global_step % args.generate_freq == 0:
####################################### do generation ####################################
model.eval()
with torch.no_grad():
args.phase_generate = True
y_seq, log_like, kl_z_what, kl_z_where, kl_z_depth, \
kl_z_pres, kl_z_bg, log_imp, counting, \
log_disc_list, log_prop_list, scalor_log_list = model(imgs)
args.phase_generate = False
log_summary(args, writer, imgs, y_seq, global_step, log_disc_list,
log_prop_list, scalor_log_list, prefix='generate')
model.train()
####################################### end generation ####################################
if global_step % args.save_epoch_freq == 0 or global_step == 1:
save_ckpt(args.ckpt_dir, model, optimizer, global_step, epoch,
local_count, args.batch_size, num_train)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='SCALOR')
args = parse.parse(parser)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
main(args)