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train_coda_small.py
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# -*- coding:utf-8 -*-
# author: Xinge
# @file: train_cylinder_asym.py
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from utils.metric_util import per_class_iu, fast_hist_crop
from dataloader.pc_dataset import get_SemKITTI_label_name
from builder import data_builder, model_builder, loss_builder
from config.config import load_config_data
from utils.load_save_util import load_checkpoint
import warnings
warnings.filterwarnings("ignore")
def main(args):
pytorch_device = torch.device('cuda:0')
config_path = args.config_path
configs = load_config_data(config_path)
dataset_config = configs['dataset_params']
train_dataloader_config = configs['train_data_loader']
val_dataloader_config = configs['val_data_loader']
val_batch_size = val_dataloader_config['batch_size']
train_batch_size = train_dataloader_config['batch_size']
model_config = configs['model_params']
train_hypers = configs['train_params']
grid_size = model_config['output_shape']
num_class = model_config['num_class']
ignore_label = dataset_config['ignore_label']
model_load_path = train_hypers['model_load_path']
model_save_path = train_hypers['model_save_path']
wd = train_hypers['weight_decay'] # weight decay
amp = train_hypers['mixed_fp16']
SemKITTI_label_name = get_SemKITTI_label_name(dataset_config["label_mapping"])
unique_label = np.asarray(sorted(list(SemKITTI_label_name.keys())))[1:] - 1
unique_label_str = [SemKITTI_label_name[x] for x in unique_label + 1]
my_model = model_builder.build(model_config)
if os.path.exists(model_load_path):
my_model = load_checkpoint(model_load_path, my_model)
my_model.to(pytorch_device)
# Mixed Precision
amp_scaler = torch.cuda.amp.GradScaler(enabled=amp)
# EPS is a fixed value to prevent MixedPrecision training errors.
optimizer = optim.AdamW(my_model.parameters(), lr=train_hypers["learning_rate"], eps=1e-4, weight_decay=wd)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, train_hypers['max_num_epochs'])
loss_func, lovasz_softmax = loss_builder.build(wce=True, lovasz=True,
num_class=num_class, ignore_label=ignore_label)
train_dataset_loader, val_dataset_loader = data_builder.build(dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=grid_size)
# training
epoch = 0
best_val_miou = 0
my_model.train()
global_iter = 0
check_iter = train_hypers['eval_every_n_steps']
while epoch < train_hypers['max_num_epochs']:
loss_list = []
pbar = tqdm(total=len(train_dataset_loader))
# time.sleep(10)
# lr_scheduler.step(epoch)
for i_iter, (_, train_vox_label, train_grid, _, train_pt_fea) in enumerate(train_dataset_loader):
if global_iter % check_iter == 0 and epoch >= 1:
# Evaluation set
my_model.eval()
hist_list = []
val_loss_list = []
with torch.no_grad():
for i_iter_val, (_, val_vox_label, val_grid, val_pt_labs, val_pt_fea) in enumerate(
val_dataset_loader):
val_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in
val_pt_fea]
val_grid_ten = [torch.from_numpy(i).to(pytorch_device) for i in val_grid]
val_label_tensor = val_vox_label.type(torch.LongTensor).to(pytorch_device)
predict_labels = my_model(val_pt_fea_ten, val_grid_ten, val_batch_size)
# aux_loss = loss_fun(aux_outputs, point_label_tensor)
loss = lovasz_softmax(torch.nn.functional.softmax(predict_labels).detach(), val_label_tensor,
ignore=0) + loss_func(predict_labels.detach(), val_label_tensor)
predict_labels = torch.argmax(predict_labels, dim=1)
predict_labels = predict_labels.cpu().detach().numpy()
for count, i_val_grid in enumerate(val_grid):
hist_list.append(fast_hist_crop(predict_labels[
count, val_grid[count][:, 0], val_grid[count][:, 1],
val_grid[count][:, 2]], val_pt_labs[count],
unique_label))
val_loss_list.append(loss.detach().cpu().numpy())
my_model.train()
iou = per_class_iu(sum(hist_list))
print('Validation per class iou: ')
for class_name, class_iou in zip(unique_label_str, iou):
print('%s : %.2f%%' % (class_name, class_iou * 100))
val_miou = np.nanmean(iou) * 100
del val_vox_label, val_grid, val_pt_fea, val_grid_ten
# save model if performance is improved
if best_val_miou < val_miou:
best_val_miou = val_miou
torch.save(my_model.state_dict(), model_save_path)
print('Current val miou is %.3f while the best val miou is %.3f' %
(val_miou, best_val_miou))
print('Current val loss is %.3f' %
(np.mean(val_loss_list)))
# Training set
train_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in train_pt_fea]
# train_grid_ten = [torch.from_numpy(i[:,:2]).to(pytorch_device) for i in train_grid]
train_vox_ten = [torch.from_numpy(i).to(pytorch_device) for i in train_grid]
point_label_tensor = train_vox_label.type(torch.LongTensor).to(pytorch_device)
# forward + backward + optimize
with torch.cuda.amp.autocast(enabled=amp):
outputs = my_model(train_pt_fea_ten, train_vox_ten, train_batch_size)
loss = lovasz_softmax(torch.nn.functional.softmax(outputs), point_label_tensor, ignore=0) + loss_func(
outputs, point_label_tensor)
amp_scaler.scale(loss).backward()
amp_scaler.step(optimizer)
amp_scaler.update()
# optimizer.step()
loss_list.append(loss.item())
if global_iter % 1000 == 0:
if len(loss_list) > 0:
print('epoch %d iter %5d, loss: %.3f\n' %
(epoch, i_iter, np.mean(loss_list)))
else:
print('loss error')
optimizer.zero_grad()
pbar.update(1)
global_iter += 1
if global_iter % check_iter == 0:
if len(loss_list) > 0:
print('epoch %d iter %5d, loss: %.3f\n' %
(epoch, i_iter, np.mean(loss_list)))
else:
print('loss error')
scheduler.step() # Updates cosine annealing
pbar.close()
epoch += 1
if __name__ == '__main__':
# Training settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('-y', '--config_path', default='config/coda_test.yaml')
args = parser.parse_args()
print(' '.join(sys.argv))
print(args)
main(args)