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ScoreSeg.py
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ScoreSeg.py
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import torch
import argparse
import logging
import torch.distributed as dist
import os
import numpy as np
from tensorboardX import SummaryWriter
import data as Data
import model as Model
import utils.logger as Logger
import utils.metrics as Metrics
from utils.wandb_logger import WandbLogger
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='config/potsdam_segmentation.json',
help='JSON file for configuration')
parser.add_argument('-p', '--phase', type=str, choices=['train', 'test'],
help='Run either train(training + validation) or testing', default='train')
parser.add_argument('-debug', '-d', action='store_true')
parser.add_argument('-enable_wandb', action='store_true')
# DDP initial
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
dist.init_process_group(backend='nccl')
torch.multiprocessing.set_sharing_strategy('file_system')
device = torch.device("cuda", local_rank)
# parse configs
args = parser.parse_args()
opt = Logger.parse(args)
# Convert to NoneDict, which return None for missing key.
opt = Logger.dict_to_nonedict(opt)
# DDP
opt['local_rank'] = local_rank
# logging
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
Logger.setup_logger(None, opt['path']['log'],
'train', level=logging.INFO, screen=True)
Logger.setup_logger('test', opt['path']['log'], 'test', level=logging.INFO)
logger = logging.getLogger('base')
logger.info(Logger.dict2str(opt))
tb_logger = SummaryWriter(log_dir=opt['path']['tb_logger'])
# Initialize WandbLogger
if opt['enable_wandb'] and local_rank == 0:
import wandb
print("Initializing wandblog.")
wandb_logger = WandbLogger(opt)
# Training log
wandb.define_metric('epoch')
wandb.define_metric('training/train_step')
wandb.define_metric("training/*", step_metric="train_step")
# Validation log
wandb.define_metric('validation/val_step')
wandb.define_metric("validation/*", step_metric="val_step")
# Initialization
train_step = 0
val_step = 0
else:
wandb_logger = None
# Loading datasets.
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train' and args.phase != 'test':
print("Creating [train] segmentation dataloader.")
train_set = Data.create_seg_dataset(dataset_opt, phase)
train_loader, train_sampler = Data.create_seg_dataloader(
train_set, dataset_opt, phase)
opt['len_train_dataloader'] = len(train_loader)
print("[train] dataloader length:{}".format(len(train_loader)))
elif phase == 'val' and args.phase != 'test':
print("Creating [val] segmentation dataloader.")
val_set = Data.create_seg_dataset(dataset_opt, phase)
val_loader, val_sampler = Data.create_seg_dataloader(
val_set, dataset_opt, phase)
opt['len_val_dataloader'] = len(val_loader)
print("[val] dataloader length:{}".format(len(val_loader)))
elif phase == 'test' and args.phase == 'test':
print("Creating [test] segmentation dataloader.")
print(phase)
test_set = Data.create_seg_dataset(dataset_opt, phase)
test_loader, test_sampler = Data.create_seg_dataloader(
test_set, dataset_opt, phase)
opt['len_test_dataloader'] = len(test_loader)
print("[test] dataloader length:{}".format(len(test_loader)))
# results = dict(img_id=img_id, img=img, gt_semantic_seg=mask)
logger.info('Initial Dataset Finished')
# Loading score-based model [also called Denoising Diffusion Probabilistic Models (DDPM)]
ScoreModel = Model.create_model(opt)
logger.info('Initial Diffusion Model Finished')
# Set noise schedule for the score-based model
ScoreModel.set_new_noise_schedule(
opt['model']['beta_schedule'][opt['phase']], schedule_phase=opt['phase'])
# Creating semantic segmentation model
segmentation_model = Model.create_seg_model(opt)
#################
# Training loop #
#################
n_epoch = opt['train']['n_epoch']
best_OA = 0.0
start_epoch = segmentation_model.begin_epoch
if opt['phase'] == 'train':
assert opt['train']['save_freq'] > 0, 'check configs for saving frequency'
for current_epoch in range(start_epoch, n_epoch):
train_sampler.set_epoch(current_epoch)
segmentation_model._clear_cache()
train_result_path = '{}/train/{}'.format(opt['path_Seg']
['results'], current_epoch)
os.makedirs(train_result_path, exist_ok=True)
################
### training ###
################
message = 'lr: %0.7f\n \n' % segmentation_model.optSeg.param_groups[0]['lr']
logger.info(message)
for current_step, train_data in enumerate(train_loader):
# Feeding RSIs to score-based model and get features
ScoreModel.feed_data(train_data)
f_img = []
for t in opt['model_Seg']['t']:
fe_img_t, fd_img_t = ScoreModel.get_feats(t=t)
if opt['model_Seg']['feat_type'] == "dec":
f_img.append(fd_img_t)
del fe_img_t
else:
f_img.append(fe_img_t)
del fd_img_t
# Feeding features from the score-based model to the Seg model
segmentation_model.feed_data(f_img, train_data)
segmentation_model.optimize_parameters()
segmentation_model._collect_running_batch_states()
# log running batch status
if current_step % opt['train']['train_print_freq'] == 0:
# message
logs = segmentation_model.get_current_log()
message = '[Training Seg]. epoch: [%d/%d]. Iter: [%d/%d], loss: %.5f, running_mf1: %.5f\n' % \
(current_epoch, n_epoch - 1, current_step, len(train_loader), logs['loss'],
logs['running_acc'])
logger.info(message)
# visualization
visuals = segmentation_model.get_current_visuals()
assert train_data['gt_semantic_seg'].shape == visuals['pred'].shape, 'mask shape: {} != pred ' \
'shape: {}'.format(
train_data['gt_semantic_seg'].shape, visuals['pred'].shape)
img = Metrics.tensor2img(train_data['img'], out_type=np.uint8, min_max=(-1, 1)) # uint8
label = Metrics.seg_mask2img(train_data['gt_semantic_seg'].unsqueeze(1), # b x 1 x h x w
out_type=np.uint8,
dataname=opt['datasets']['train']['name']) # uint8
pred = Metrics.seg_mask2img(visuals['pred'].unsqueeze(1),
out_type=np.uint8,
dataname=opt['datasets']['train']['name']) # uint8
# save imgs
Metrics.save_img(
img,
'{}/img_e{}_s{}_r{}.png'.format(train_result_path, current_epoch, current_step, local_rank))
Metrics.save_img(
pred,
'{}/pred_e{}_s{}_r{}.png'.format(train_result_path, current_epoch, current_step, local_rank))
Metrics.save_img(
label,
'{}/gt_e{}_s{}_r{}.png'.format(train_result_path, current_epoch, current_step, local_rank))
### log epoch status ###
segmentation_model._collect_epoch_states()
logs = segmentation_model.get_current_log()
message = '[Training Seg (epoch summary)]: epoch: [%d/%d]. epoch_mF1=%.5f \n' % \
(current_epoch, n_epoch - 1, logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
tb_logger.add_scalar(k, v, current_step)
message += '\n'
logger.info(message)
if wandb_logger:
wandb_logger.log_metrics({
'training/OA': logs['OA'],
'training/mF1': logs['epoch_acc'],
'training/mIoU': logs['miou'],
'training/kappa': logs['kappa'],
'training/train_step': current_epoch
})
segmentation_model._clear_cache()
segmentation_model._update_lr_schedulers()
##################
### validation ###
##################
if current_epoch % opt['train']['val_freq'] == 0:
val_sampler.set_epoch(current_epoch)
val_result_path = '{}/val/{}'.format(opt['path_Seg']
['results'], current_epoch)
os.makedirs(val_result_path, exist_ok=True)
for current_step, val_data in enumerate(val_loader):
# Feed data to score-based model
ScoreModel.feed_data(val_data)
f_img = []
for t in opt['model_Seg']['t']:
fe_img_t, fd_img_t = ScoreModel.get_feats(t=t)
if opt['model_Seg']['feat_type'] == "dec":
f_img.append(fd_img_t)
del fe_img_t
else:
f_img.append(fe_img_t)
del fd_img_t
# Feeding features from the score-based model to the Seg model
segmentation_model.feed_data(f_img, val_data)
segmentation_model.test()
segmentation_model._collect_running_batch_states()
# log running batch status for val data
if current_step % opt['train']['val_print_freq'] == 0:
# message
logs = segmentation_model.get_current_log()
message = '[Validation Seg]. epoch: [%d/%d]. Iter: [%d/%d], running_mf1: %.5f\n' % \
(current_epoch, n_epoch - 1, current_step, len(val_loader), logs['running_acc'])
logger.info(message)
# visuals
visuals = segmentation_model.get_current_visuals()
img = Metrics.tensor2img(val_data['img'], out_type=np.uint8, min_max=(-1, 1)) # uint8
label = Metrics.seg_mask2img(val_data['gt_semantic_seg'].unsqueeze(1),
out_type=np.uint8,
dataname=opt['datasets']['val']['name']) # uint8
pred = Metrics.seg_mask2img(visuals['pred'].unsqueeze(1),
out_type=np.uint8,
dataname=opt['datasets']['val']['name']) # uint8
# save imgs
Metrics.save_img(
img,
'{}/img_e{}_s{}_r{}.png'.format(val_result_path, current_epoch, current_step, local_rank))
Metrics.save_img(
pred,
'{}/pred_e{}_s{}_r{}.png'.format(val_result_path, current_epoch, current_step, local_rank))
Metrics.save_img(
label,
'{}/gt_e{}_s{}_r{}.png'.format(val_result_path, current_epoch, current_step, local_rank))
segmentation_model._collect_epoch_states()
logs = segmentation_model.get_current_log()
message = '[Validation Seg (epoch summary)]: epoch: [%d/%d]. epoch_mF1=%.5f \n' % \
(current_epoch, n_epoch - 1, logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
tb_logger.add_scalar(k, v, current_step)
message += '\n'
logger.info(message)
if wandb_logger:
wandb_logger.log_metrics({
'validation/OA': logs['OA'],
'validation/mF1': logs['epoch_acc'],
'validation/mIoU': logs['miou'],
'validation/kappa': logs['kappa'],
'validation/val_step': current_epoch
})
if logs['OA'] > best_OA:
is_best_model = True
best_OA = logs['OA']
logger.info(
'[Validation Seg] Best model updated. Saving the best model and training states.')
segmentation_model.save_network(current_epoch, is_best_model=is_best_model)
if current_epoch % opt['train']['save_freq'] == 0:
is_best_model = False
logger.info('[Validation Seg]Saving the current Seg model and training states.')
segmentation_model.save_network(current_epoch, is_best_model=is_best_model)
logger.info('--- Proceed To The Next Epoch ----\n \n')
segmentation_model._clear_cache()
if wandb_logger:
wandb_logger.log_metrics({'epoch': current_epoch - 1})
logger.info('End of training.')
else:
logger.info('Begin Model Evaluation (testing).')
test_result_path = '{}/test/'.format(opt['path_Seg']
['results'])
os.makedirs(test_result_path, exist_ok=True)
logger_test = logging.getLogger('test') # test logger
segmentation_model._clear_cache()
for current_step, test_data in enumerate(test_loader):
# Feed data to score-based model
ScoreModel.feed_data(test_data)
f_img = []
for t in opt['model_Seg']['t']:
fe_img_t, fd_img_t = ScoreModel.get_feats(t=t)
if opt['model_Seg']['feat_type'] == "dec":
f_img.append(fd_img_t)
del fe_img_t
else:
f_img.append(fe_img_t)
del fd_img_t
# Feeding features from the score-based model to the Seg model
segmentation_model.feed_data(f_img, test_data)
segmentation_model.test()
segmentation_model._collect_running_batch_states()
# Logs
logs = segmentation_model.get_current_log()
message = '[Testing Seg]. Iter: [%d/%d], running_mf1: %.5f\n' % \
(current_step, len(test_loader), logs['running_acc'])
logger_test.info(message)
# visuals
visuals = segmentation_model.get_current_visuals()
img = Metrics.tensor2img(test_data['img'], out_type=np.uint8, min_max=(-1, 1)) # uint8
label = Metrics.seg_mask2img(test_data['gt_semantic_seg'].unsqueeze(1),
out_type=np.uint8,
dataname=opt['datasets']['test']['name']) # uint8
pred = Metrics.seg_mask2img(visuals['pred'].unsqueeze(1),
out_type=np.uint8,
dataname=opt['datasets']['test']['name']) # uint8
# set batchsize == 1, use the image name to save prediction and groundtruth
if len(test_data['img_id']) == 1:
img_name = os.path.split(test_data['img_id'][0])[-1]
Metrics.save_img(
img, '{}/{}_img.png'.format(test_result_path, img_name))
Metrics.save_img(
pred, '{}/{}_pred.png'.format(test_result_path, img_name))
Metrics.save_img(
label, '{}/{}_gt.png'.format(test_result_path, img_name))
else:
Metrics.save_img(
img, '{}/img_s{}_r{}.png'.format(test_result_path, current_step, local_rank))
Metrics.save_img(
pred, '{}/pred_s{}_r{}.png'.format(test_result_path, current_step, local_rank))
Metrics.save_img(
label, '{}/gt_s{}_r{}.png'.format(test_result_path, current_step, local_rank))
segmentation_model._collect_epoch_states()
logs = segmentation_model.get_current_log()
message = '[Test Seg summary]: Test mF1=%.5f \n' % \
(logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
message += '\n'
logger_test.info(message)
if wandb_logger:
wandb_logger.log_metrics({
'test/OA': logs['OA'],
'test/mF1': logs['epoch_acc'],
'test/mIoU': logs['miou'],
'test/kappa': logs['kappa'],
})
logger.info('End of testing...')