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main.py
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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import sys
import yaml
import time
import h5py
import logging
import argparse
import numpy as np
from attrdict import AttrDict
from tensorboardX import SummaryWriter
from collections import OrderedDict
from skimage.metrics import adapted_rand_error as adapted_rand_ref
from skimage.metrics import variation_of_information as voi_ref
import torch
import torch.nn as nn
from dataloader.data_provider_labeled import Provider
from dataloader.data_provider_unlabel import Provider as Provider_unlabel
from dataloader.provider_valid import Provider_valid
from loss.loss_unlabel import MSELoss_unlabel, BCELoss_unlabel
from utils.show import show_affs, show_affs_whole
from utils.consistency_aug import convert_consistency_scale, convert_consistency_flip, convert_consistency_rot
from model.unet3d_mala import UNet3D_MALA
from model.model_superhuman import UNet_PNI
from utils.utils import setup_seed, execute
from utils.post_waterz import post_waterz
from utils.post_lmc import post_lmc
def init_project(cfg):
def init_logging(path):
logging.basicConfig(
level = logging.INFO,
format = '%(message)s',
datefmt = '%m-%d %H:%M',
filename = path,
filemode = 'w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
# seeds
setup_seed(cfg.TRAIN.random_seed)
if cfg.TRAIN.if_cuda:
if torch.cuda.is_available() is False:
raise AttributeError('No GPU available')
prefix = cfg.time
if cfg.TRAIN.resume:
model_name = cfg.TRAIN.model_name
else:
model_name = prefix + '_' + cfg.NAME
cfg.cache_path = os.path.join(cfg.TRAIN.cache_path, model_name)
cfg.save_path = os.path.join(cfg.TRAIN.save_path, model_name)
cfg.record_path = os.path.join(cfg.save_path, model_name)
cfg.valid_path = os.path.join(cfg.save_path, 'valid')
if cfg.TRAIN.resume is False:
if not os.path.exists(cfg.cache_path):
os.makedirs(cfg.cache_path)
if not os.path.exists(cfg.save_path):
os.makedirs(cfg.save_path)
if not os.path.exists(cfg.record_path):
os.makedirs(cfg.record_path)
if not os.path.exists(cfg.valid_path):
os.makedirs(cfg.valid_path)
init_logging(os.path.join(cfg.record_path, prefix + '.log'))
logging.info(cfg)
writer = SummaryWriter(cfg.record_path)
writer.add_text('cfg', str(cfg))
return writer
def load_dataset(cfg):
print('Caching datasets ... ', flush=True)
t1 = time.time()
train_provider = Provider('train', cfg)
valid_provider = Provider_valid(cfg)
print('Done (time: %.2fs)' % (time.time() - t1))
return train_provider, valid_provider
def build_model(cfg, writer):
print('Building model on ', end='', flush=True)
t1 = time.time()
device = torch.device('cuda:0')
show_feature = False
if cfg.MODEL.model_type == 'mala':
print('load mala model!')
model = UNet3D_MALA(output_nc=cfg.MODEL.output_nc, if_sigmoid=cfg.MODEL.if_sigmoid, init_mode=cfg.MODEL.init_mode_mala).to(device)
else:
print('load superhuman model!')
model = UNet_PNI(in_planes=cfg.MODEL.input_nc,
out_planes=cfg.MODEL.output_nc,
filters=cfg.MODEL.filters,
upsample_mode=cfg.MODEL.upsample_mode,
decode_ratio=cfg.MODEL.decode_ratio,
merge_mode=cfg.MODEL.merge_mode,
pad_mode=cfg.MODEL.pad_mode,
bn_mode=cfg.MODEL.bn_mode,
relu_mode=cfg.MODEL.relu_mode,
init_mode=cfg.MODEL.init_mode,
show_feature=show_feature).to(device)
if cfg.MODEL.pre_train:
print('Load pre-trained model ...')
ckpt_path = os.path.join('./trained_model', \
cfg.MODEL.trained_model_name, \
cfg.MODEL.trained_model_id+'.ckpt')
checkpoint = torch.load(ckpt_path)
pretrained_dict = checkpoint['model_weights']
if cfg.MODEL.trained_gpus > 1:
pretained_model_dict = OrderedDict()
for k, v in pretrained_dict.items():
name = k[7:] # remove module.
# name = k
pretained_model_dict[name] = v
else:
pretained_model_dict = pretrained_dict
from utils.encoder_dict import ENCODER_DICT2, ENCODER_DECODER_DICT2
model_dict = model.state_dict()
encoder_dict = OrderedDict()
if cfg.MODEL.if_skip == 'True':
print('Load the parameters of encoder and decoder!')
encoder_dict = {k: v for k, v in pretained_model_dict.items() if k.split('.')[0] in ENCODER_DECODER_DICT2}
elif cfg.MODEL.if_skip == 'all':
print('Load the all parameters of model!')
encoder_dict = pretained_model_dict
else:
print('Load the parameters of encoder!')
encoder_dict = {k: v for k, v in pretained_model_dict.items() if k.split('.')[0] in ENCODER_DICT2}
model_dict.update(encoder_dict)
model.load_state_dict(model_dict)
cuda_count = torch.cuda.device_count()
if cuda_count > 1:
if cfg.TRAIN.batch_size % cuda_count == 0:
print('%d GPUs ... ' % cuda_count, end='', flush=True)
model = nn.DataParallel(model)
else:
raise AttributeError('Batch size (%d) cannot be equally divided by GPU number (%d)' % (cfg.TRAIN.batch_size, cuda_count))
else:
print('a single GPU ... ', end='', flush=True)
print('Done (time: %.2fs)' % (time.time() - t1))
return model
def resume_params(cfg, model, optimizer, resume):
if resume:
t1 = time.time()
model_path = os.path.join(cfg.save_path, 'model-%06d.ckpt' % cfg.TRAIN.model_id)
print('Resuming weights from %s ... ' % model_path, end='', flush=True)
if os.path.isfile(model_path):
checkpoint = torch.load(model_path)
model.load_state_dict(checkpoint['model_weights'])
else:
raise AttributeError('No checkpoint found at %s' % model_path)
print('Done (time: %.2fs)' % (time.time() - t1))
print('valid %d' % checkpoint['current_iter'])
return model, optimizer, checkpoint['current_iter']
else:
return model, optimizer, 0
def calculate_lr(iters):
if iters < cfg.TRAIN.warmup_iters:
current_lr = (cfg.TRAIN.base_lr - cfg.TRAIN.end_lr) * pow(float(iters) / cfg.TRAIN.warmup_iters, cfg.TRAIN.power) + cfg.TRAIN.end_lr
else:
if iters < cfg.TRAIN.decay_iters:
current_lr = (cfg.TRAIN.base_lr - cfg.TRAIN.end_lr) * pow(1 - float(iters - cfg.TRAIN.warmup_iters) / cfg.TRAIN.decay_iters, cfg.TRAIN.power) + cfg.TRAIN.end_lr
else:
current_lr = cfg.TRAIN.end_lr
return current_lr
def loop(cfg, train_provider, valid_provider, model, criterion, optimizer, iters, writer):
f_loss_txt = open(os.path.join(cfg.record_path, 'loss.txt'), 'a')
f_valid_txt = open(os.path.join(cfg.record_path, 'valid.txt'), 'a')
rcd_time = []
sum_time = 0
sum_loss = 0
sum_labeled_loss = 0
sum_unlabel_loss = 0
device = torch.device('cuda:0')
if cfg.TRAIN.loss_func == 'MSELoss':
criterion = nn.MSELoss()
elif cfg.TRAIN.loss_func == 'BCELoss':
criterion = nn.BCELoss()
else:
raise AttributeError("NO this criterion")
if cfg.TRAIN.loss_func_unlabel == 'MSELoss':
criterion_unlabel = MSELoss_unlabel()
elif cfg.TRAIN.loss_func_unlabel == 'BCELoss':
criterion_unlabel = BCELoss_unlabel()
else:
raise AttributeError("NO this criterion")
train_provider_unlabel = Provider_unlabel('train', cfg)
try:
valid_data = cfg.DATA.valid_dataset
except:
valid_data = cfg.DATA.dataset_name
if valid_data == 'snemi3d-ac3':
valid_mode = 'ac3'
elif valid_data == 'snemi3d-ac4':
valid_mode = 'ac4'
elif valid_data == 'cremi-C':
valid_mode = 'cremic'
elif valid_data == 'cremi-A':
valid_mode = 'cremia'
elif valid_data == 'cremi-B':
valid_mode = 'cremib'
elif valid_data == 'fib-25':
valid_mode = 'fib'
elif valid_data == 'cremi-all':
valid_mode = 'cremic'
else:
raise NotImplementedError
start_split = cfg.DATA.test_split
end_split = 0
test_split = start_split - end_split
seg_name = 'waterz_' + valid_mode + '_' + str(test_split) + '.txt'
f_seg_txt = open(os.path.join(cfg.record_path, seg_name), 'a')
while iters <= cfg.TRAIN.total_iters:
# train
model.train()
iters += 1
t1 = time.time()
inputs, target, _ = train_provider.next()
inputs_unlabel_aug, inputs_unlabel_gt, det_sizes, rules, rotnums = train_provider_unlabel.next()
# decay learning rate
if cfg.TRAIN.end_lr == cfg.TRAIN.base_lr:
current_lr = cfg.TRAIN.base_lr
else:
current_lr = calculate_lr(iters)
for param_group in optimizer.param_groups:
param_group['lr'] = current_lr
optimizer.zero_grad()
# cat labeled and unlabeled data
batchsize = inputs.shape[0]
inputs_all = torch.cat([inputs, inputs_unlabel_aug], dim=0)
pred_all = model(inputs_all)
pred = pred_all[0:batchsize, ...]
pred_unlabel = pred_all[batchsize:, ...]
with torch.no_grad():
pred_unlabel_gt = model(inputs_unlabel_gt)
# convert rotation
if cfg.DATA.if_rotation_aug_unlabel:
pred_unlabel_gt = convert_consistency_rot(pred_unlabel_gt, rotnums)
# convert flip
if cfg.DATA.if_filp_aug_unlabel:
pred_unlabel_gt = convert_consistency_flip(pred_unlabel_gt, rules)
# gen pseudo label and mask
if cfg.DATA.if_scale_aug_unlabel:
pred_unlabel_gt, masks = convert_consistency_scale(pred_unlabel_gt, det_sizes)
else:
masks = torch.ones_like(pred_unlabel_gt)
##############################
# LOSS
loss_labeled = criterion(pred, target)
loss_unlabel = cfg.TRAIN.weight_unlabel * criterion_unlabel(pred_unlabel, pred_unlabel_gt, masks)
loss = loss_labeled + loss_unlabel
loss.backward()
##############################
if cfg.TRAIN.weight_decay is not None:
for group in optimizer.param_groups:
for param in group['params']:
param.data = param.data.add(-cfg.TRAIN.weight_decay * group['lr'], param.data)
optimizer.step()
sum_loss += loss.item()
sum_labeled_loss += loss_labeled.item()
sum_unlabel_loss += loss_unlabel.item()
sum_time += time.time() - t1
# log train
if iters % cfg.TRAIN.display_freq == 0 or iters == 1:
rcd_time.append(sum_time)
if iters == 1:
logging.info('step %d, loss = %.6f, labeled_loss=%.6f, unlabel_loss=%.6f (wt: *1, lr: %.8f, et: %.2f sec, rd: %.2f min)'
% (iters, sum_loss, sum_labeled_loss, sum_unlabel_loss, current_lr, sum_time,
(cfg.TRAIN.total_iters - iters) / cfg.TRAIN.display_freq * np.mean(np.asarray(rcd_time)) / 60))
writer.add_scalar('loss', sum_loss * 1, iters)
else:
logging.info('step %d, loss = %.6f, labeled_loss=%.6f, unlabel_loss=%.6f (wt: *1, lr: %.8f, et: %.2f sec, rd: %.2f min)' \
% (iters, sum_loss / cfg.TRAIN.display_freq * 1, \
sum_labeled_loss / cfg.TRAIN.display_freq * 1, \
sum_unlabel_loss / cfg.TRAIN.display_freq * 1, current_lr, sum_time, \
(cfg.TRAIN.total_iters - iters) / cfg.TRAIN.display_freq * np.mean(np.asarray(rcd_time)) / 60))
writer.add_scalar('loss', sum_loss / cfg.TRAIN.display_freq * 1, iters)
f_loss_txt.write('step = %d, loss = %.6f, labeled_loss=%.6f, unlabel_loss=%.6f' \
% (iters, sum_loss / cfg.TRAIN.display_freq * 1, \
sum_labeled_loss / cfg.TRAIN.display_freq * 1, \
sum_unlabel_loss / cfg.TRAIN.display_freq * 1))
f_loss_txt.write('\n')
f_loss_txt.flush()
sys.stdout.flush()
sum_time = 0
sum_loss = 0
sum_labeled_loss = 0
sum_unlabel_loss = 0
# display
if iters % cfg.TRAIN.valid_freq == 0 or iters == 1:
# show_affs(iters, inputs, pred, target, cfg.cache_path, model_type=cfg.MODEL.model_type)
show_affs(iters, inputs_unlabel_aug, pred_unlabel, pred_unlabel_gt, cfg.cache_path, model_type=cfg.MODEL.model_type)
# valid
if cfg.TRAIN.if_valid:
if iters % cfg.TRAIN.save_freq == 0 and iters >= cfg.TRAIN.min_valid_iters:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.eval()
dataloader = torch.utils.data.DataLoader(valid_provider, batch_size=1, num_workers=0,
shuffle=False, drop_last=False, pin_memory=True)
losses_valid = []
for k, batch in enumerate(dataloader, 0):
inputs, target, _ = batch
inputs = inputs.cuda()
target = target.cuda()
with torch.no_grad():
pred = model(inputs)
tmp_loss = criterion(pred, target)
losses_valid.append(tmp_loss.item())
valid_provider.add_vol(np.squeeze(pred.data.cpu().numpy()))
epoch_loss = sum(losses_valid) / len(losses_valid)
out_affs = valid_provider.get_results()
gt_affs = valid_provider.get_gt_affs()
gt_seg = valid_provider.get_gt_lb()
valid_provider.reset_output()
show_affs_whole(iters, out_affs, gt_affs, cfg.valid_path)
# f_affs = h5py.File(os.path.join(cfg.record_path, 'affs-%s-%d.hdf' % (valid_mode, test_split)), 'w')
# f_affs.create_dataset('main', data=out_affs, dtype=np.float32, compression='gzip')
# f_affs.close()
# for post-processing
# for python3
try:
pred_seg = post_waterz(out_affs)
# pred_seg = post_lmc(out_affs)
arand = adapted_rand_ref(gt_seg, pred_seg, ignore_labels=(0))[0]
voi_split, voi_merge = voi_ref(gt_seg, pred_seg, ignore_labels=(0))
voi_sum = voi_split + voi_merge
except:
print('model-%d, segmentation failed!' % iters)
arand = 0.0
voi_split = 0.0
voi_merge = 0.0
voi_sum = 0.0
# MSE
whole_mse = np.sum(np.square(out_affs - gt_affs)) / np.size(gt_affs)
out_affs = np.clip(out_affs, 0.000001, 0.999999)
bce = -(gt_affs * np.log(out_affs) + (1 - gt_affs) * np.log(1 - out_affs))
whole_bce = np.sum(bce) / np.size(gt_affs)
print('model-%d, valid-loss=%.6f, MSE-loss=%.6f, BCE-loss=%.6f' % \
(iters, epoch_loss, whole_mse, whole_bce), flush=True)
writer.add_scalar('valid/epoch_loss', epoch_loss, iters)
writer.add_scalar('valid/mse_loss', whole_mse, iters)
writer.add_scalar('valid/bce_loss', whole_bce, iters)
f_valid_txt.write('model-%d, valid-loss=%.6f, MSE-loss=%.6f, BCE-loss=%.6f' % \
(iters, epoch_loss, whole_mse, whole_bce))
f_valid_txt.write('\n')
f_valid_txt.flush()
print('model-%d, voi_split=%.6f, voi_merge=%.6f, voi_sum=%.6f, arand=%.6f' % \
(iters, voi_split, voi_merge, voi_sum, arand))
f_seg_txt.write('model=%d, voi_split=%.6f, voi_merge=%.6f, voi_sum=%.6f, arand=%.6f' % \
(iters, voi_split, voi_merge, voi_sum, arand))
f_seg_txt.write('\n')
f_seg_txt.flush()
torch.cuda.empty_cache()
# for post-processing
# for python2
# try:
# seg_name1 = 'waterz_' + valid_mode + '_' + str(test_split)
# cmd_val = ['python2','evaluate_mala_models.py', '-in',cfg.record_path, '-gt',cfg.DATA.data_folder,
# '-id',str(iters), '-m',valid_mode, '-sn',seg_name1, '-ss',str(start_split),'-es',str(end_split)]
# for path in execute(cmd_val):
# print(path, end="")
# except:
# print('model-%d, segmentation failed!' % iters)
# try:
# seg_name2 = 'lmc_' + valid_mode + '_' + str(test_split)
# cmd_val = ['python','evaluate_lmc_models.py', '-in',cfg.record_path, '-gt',cfg.DATA.data_folder,
# '-id',str(iters), '-m',valid_mode, '-sn',seg_name2, '-ss',str(start_split),'-es',str(end_split)]
# for path in execute(cmd_val):
# print(path, end="")
# except:
# print('model-%d, segmentation failed!' % iters)
# save
if iters % cfg.TRAIN.save_freq == 0:
states = {'current_iter': iters, 'valid_result': None,
'model_weights': model.state_dict()}
torch.save(states, os.path.join(cfg.save_path, 'model-%06d.ckpt' % iters))
print('***************save modol, iters = %d.***************' % (iters), flush=True)
f_loss_txt.close()
f_valid_txt.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--cfg', type=str, default='seg_inpainting', help='path to config file')
parser.add_argument('-m', '--mode', type=str, default='train', help='path to config file')
args = parser.parse_args()
cfg_file = args.cfg + '.yaml'
print('cfg_file: ' + cfg_file)
print('mode: ' + args.mode)
with open('./config/' + cfg_file, 'r') as f:
cfg = AttrDict(yaml.load(f))
timeArray = time.localtime()
time_stamp = time.strftime('%Y-%m-%d--%H-%M-%S', timeArray)
print('time stamp:', time_stamp)
cfg.path = cfg_file
cfg.time = time_stamp
if args.mode == 'train':
writer = init_project(cfg)
train_provider, valid_provider = load_dataset(cfg)
model = build_model(cfg, writer)
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.TRAIN.base_lr, betas=(0.9, 0.999),
eps=0.01, weight_decay=1e-6, amsgrad=True)
model, optimizer, init_iters = resume_params(cfg, model, optimizer, cfg.TRAIN.resume)
loop(cfg, train_provider, valid_provider, model, nn.L1Loss(), optimizer, init_iters, writer)
writer.close()
else:
pass
print('***Done***')